CMP Associates Regulation Blog: United States Inflation, Rules, Discretionary Authorities and Slow Productivity Growth, Twenty-One Million in Insured Unemployment, Collapse of United States Dynamism of Income Growth and Employment Creation in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide Followed by the Probable Global Recession in the Lockdown of Economic Activity in the COVID-19 Event, Probable Global Recession, World Cyclical Slow Growth, and Government Intervention in Globalization: Part II

United States Inflation, Rules, Discretionary Authorities and Slow Productivity Growth, Twenty-One Million in Insured Unemployment, Collapse of United States Dynamism of Income Growth and Employment Creation in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide Followed by the Probable Global Recession in the Lockdown of Economic Activity in the COVID-19 Event, Probable Global Recession, World Cyclical Slow Growth, and Government Intervention in Globalization

Carlos M. Pelaez

IC United States Inflation

IC Long-term US Inflation

ID Current US Inflation

II Rules, Discretionary Authorities and Slow Productivity Growth

II IB Collapse of United States Dynamism of Income Growth and Employment Creation in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide Followed by the Probable Global Recession in the Lockdown of Economic Activity in the COVID-19 Event

III World Financial Turbulence

IV Global Inflation

V World Economic Slowdown

VA United States

VB Japan

VC China

VD Euro Area

VE Germany

VF France

VG Italy

VH United Kingdom

VI Valuation of Risk Financial Assets

VII Economic Indicators

VIII Interest Rates

IX Conclusion

References

Appendixes

Appendix I The Great Inflation

IIIB Appendix on Safe Haven Currencies

IIIC Appendix on Fiscal Compact

IIID Appendix on European Central Bank Large Scale Lender of Last Resort

IIIG Appendix on Deficit Financing of Growth and the Debt Crisis

II IB Collapse of United States Dynamism of Income Growth and Employment Creation in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide. There are four major approaches to the analysis of the depth of the financial crisis and global recession from IVQ2007 (Dec) to IIQ2009 (Jun) and the subpar recovery from IIIQ2009 (Jul) to the present:

(1) Deeper contraction and slower recovery in recessions with financial crises

(2) Counterfactual of avoiding deeper contraction by fiscal and monetary policies

(3) Theory and Reality of Secular Stagnation

(4) Counterfactual that the financial crises and global recession would have been avoided had economic policies been different

(5) Evidence that growth rates are higher after deeper recessions with financial crises.

A counterfactual consists of theory and measurements of what would have occurred otherwise if economic policies or institutional arrangements had been different. This task is quite difficult because economic data are observed with all effects as they actually occurred while the counterfactual attempts to evaluate how data would differ had policies and institutional arrangements been different (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008b), 125, 136; Pelaez 1979, 26-8). Counterfactual data are unobserved and must be calculated using theory and measurement methods. The measurement of costs and benefits of projects or applied welfare economics (Harberger 1971, 1997) specifies and attempts to measure projects such as what would be economic welfare with or without a bridge or whether markets would be more or less competitive in the absence of antitrust and regulation laws (Winston 2006). The “new economic history” of the United States used counterfactuals to measure the economy with or without railroads (Fishlow 1965, Fogel 1964) and in analyzing slavery (Fogel and Engerman 1974). A critical counterfactual in economic history is how Britain surged ahead of France (North and Weingast 1989). There is similarly path-breaking research on railroads in Latin America by Coastworth (1981) and Summerhill (1997, 1998, 2003). Coastworth (2006, 176) argues that: “We already have so many history books that tells us so much about what really occurred in the past, that what we need now are books about what did not happen—but might have, or perhaps even should have happened: Counterfactual History, that is, history that is contrary to fact.”

These four approaches are discussed below in turn followed with comparison of the two recessions of the 1980s from IQ1980 (Jan) to IIIQ1980 (Jul) and from IIIQ1981 (Jul) to IVQ1982 (Nov) with the recession from IVQ2007 (Dec) to IIQ2009 (Jun) as dated by the National Bureau of Economic Research (NBER http://www.nber.org/cycles.html). These comparisons are not idle exercises, defining the interpretation of history and even possibly critical policies and institutional arrangements. There is active debate on these issues (Bordo 2012Oct 21 http://www.bloomberg.com/news/2012-10-21/why-this-u-s-recovery-is-weaker.html Reinhart and Rogoff, 2012Oct14 http://www.economics.harvard.edu/faculty/rogoff/files/Is_US_Different_RR_3.pdf Taylor 2012Oct 25 http://www.johnbtaylorsblog.blogspot.co.uk/2012/10/an-unusually-weak-recovery-as-usually.html, Wolf 2012Oct23 http://www.ft.com/intl/cms/s/0/791fc13a-1c57-11e2-a63b-00144feabdc0.html#axzz2AotsUk1q).

(1) Lower Growth Rates in Recoveries from Recessions with Financial Crises. A monumental effort of data gathering, calculation and analysis by Professors Carmen M. Reinhart and Kenneth Rogoff at Harvard University is highly relevant to banking crises, financial crash, debt crises and economic growth (Reinhart 2010CB; Reinhart and Rogoff 2011AF, 2011Jul14, 2011EJ, 2011CEPR, 2010FCDC, 2010GTD, 2009TD, 2009AFC, 2008TDPV; see also Reinhart and Reinhart 2011Feb, 2010AF and Reinhart and Sbrancia 2011). See http://cmpassocregulationblog.blogspot.com/2011/07/debt-and-financial-risk-aversion-and.html. The dataset of Reinhart and Rogoff (2010GTD, 1) is quite unique in breadth of countries and over time periods:

“Our results incorporate data on 44 countries spanning about 200 years. Taken together, the data incorporate over 3,700 annual observations covering a wide range of political systems, institutions, exchange rate and monetary arrangements and historic circumstances. We also employ more recent data on external debt, including debt owed by government and by private entities.”

Reinhart and Rogoff (2010GTD, 2011CEPR) classify the dataset of 2317 observations into 20 advanced economies and 24 emerging market economies. In each of the advanced and emerging categories, the data for countries is divided into buckets according to the ratio of gross central government debt to GDP: below 30, 30 to 60, 60 to 90 and higher than 90 (Reinhart and Rogoff 2010GTD, Table 1, 4). Median and average yearly percentage growth rates of GDP are calculated for each of the buckets for advanced economies. There does not appear to be any relation for debt/GDP ratios below 90. The highest growth rates are for debt/GDP ratios below 30: 3.7 percent for the average and 3.9 percent for the median. Growth is significantly lower for debt/GDP ratios above 90: 1.7 percent for the average and 1.9 percent for the median. GDP growth rates for the intermediate buckets are in a range around 3 percent: the highest 3.4 percent average is for the bucket 60 to 90 and 3.1 percent median for 30 to 60. There is even sharper contrast for the United States: 4.0 percent growth for debt/GDP ratio below 30; 3.4 percent growth for debt/GDP ratio of 30 to 60; 3.3 percent growth for debt/GDP ratio of 60 to 90; and minus 1.8 percent, contraction, of GDP for debt/GDP ratio above 90.

For the five countries with systemic financial crises—Iceland, Ireland, UK, Spain and the US—real average debt levels have increased by 75 percent between 2007 and 2009 (Reinhart and Rogoff 2010GTD, Figure 1). The cumulative increase in public debt in the three years after systemic banking crisis in a group of episodes after World War II is 86 percent (Reinhart and Rogoff 2011CEPR, Figure 2, 10).

An important concept is “this time is different syndrome,” which “is rooted in the firmly-held belief that financial crises are something that happens to other people in other countries at other times; crises do not happen here and now to us” (Reinhart and Rogoff 2010FCDC, 9). There is both an arrogance and ignorance in “this time is different” syndrome, as explained by Reinhart and Rogoff (2010FCDC, 34):

“The ignorance, of course, stems from the belief that financial crises happen to other people at other time in other places. Outside a small number of experts, few people fully appreciate the universality of financial crises. The arrogance is of those who believe they have figured out how to do things better and smarter so that the boom can long continue without a crisis.”

There is sober warning by Reinhart and Rogoff (2011CEPR, 42) based on the momentous effort of their scholarly data gathering, calculation and analysis:

“Despite considerable deleveraging by the private financial sector, total debt remains near its historic high in 2008. Total public sector debt during the first quarter of 2010 is 117 percent of GDP. It has only been higher during a one-year stint at 119 percent in 1945. Perhaps soaring US debt levels will not prove to be a drag on growth in the decades to come. However, if history is any guide, that is a risky proposition and over-reliance on US exceptionalism may only be one more example of the ‘This Time is Different’ syndrome.”

As both sides of the Atlantic economy maneuver around defaults, the experience on debt and growth deserves significant emphasis in research and policy. The world economy is slowing with high levels of unemployment in advanced economies. Countries do not grow themselves out of unsustainable debts but rather through de facto defaults by means of financial repression and in some cases through inflation. The conclusion is that this time is not different.

Professor Alan M. Taylor (2012) at the University of Virginia analyzes own and collaborative research on 140 years of history with data from 14 advanced economies in the effort to elucidate experience preceding, during and after financial crises. The conclusion is (Allan M. Taylor 2012, 8):

“Recessions might be painful, but they tend to be even more painful when combined with financial crises or (worse) global crises, and we already know that post-2008 experience will not overturn this conclusion. The impact on credit is also very strong: financial crises lead to strong setbacks in the rate of growth of loans as compared to what happens in normal recessions, and this effect is strong for global crises. Finally, inflation generally falls in recessions, but the downdraft is stronger in financial crisis times.”

Alan M. Taylor (2012) also finds that advanced economies entered the global recession with the largest financial sector in history. There was doubling after 1980 of the ratio of loans to GDP and tripling of the size of bank balance sheets. In contrast, in the period from 1950 to 1970 there was high investment, savings and growth in advanced economies with firm regulation of finance and controls of foreign capital flows.

(2) Counterfactual of the Global Recession. There is a difficult decision on when to withdraw the fiscal stimulus that could have adverse consequences on current growth and employment analyzed by Krugman (2011Jun18). CBO (2011JunLTBO, Chapter 2) considers the timing of withdrawal as well as the equally tough problems that result from not taking prompt action to prevent a possible debt crisis in the future. Krugman (2011Jun18) refers to Eggertsson and Krugman (2010) on the possible contractive effects of debt. The world does not become poorer as a result of debt because an individual’s asset is another’s liability. Past levels of credit may become unacceptable by credit tightening, such as during a financial crisis. Debtors are forced into deleveraging, which results in expenditure reduction, but there may not be compensatory effects by creditors who may not be in need of increasing expenditures. The economy could be pushed toward the lower bound of zero interest rates, or liquidity trap, remaining in that threshold of deflation and high unemployment.

Analysis of debt can lead to the solution of the timing of when to cease stimulus by fiscal spending (Krugman 2011Jun18). Excessive debt caused the financial crisis and global recession and it is difficult to understand how more debt can recover the economy. Krugman (2011Jun18) argues that the level of debt is not important because one individual’s asset is another individual’s liability. The distribution of debt is important when economic agents with high debt levels are encountering different constraints than economic agents with low debt levels. The opportunity for recovery may exist in borrowing by some agents that can adjust the adverse effects of past excessive borrowing by other agents. As Krugman (2011Jun18, 20) states:

“Suppose, in particular, that the government can borrow for a while, using the borrowed money to buy useful things like infrastructure. The true social cost of these things will be very low, because the spending will be putting resources that would otherwise be unemployed to work. And government spending will also make it easier for highly indebted players to pay down their debt; if the spending is sufficiently sustained, it can bring the debtors to the point where they’re no longer so severely balance-sheet constrained, and further deficit spending is no longer required to achieve full employment. Yes, private debt will in part have been replaced by public debt – but the point is that debt will have been shifted away from severely balance-sheet-constrained players, so that the economy’s problems will have been reduced even if the overall level of debt hasn’t fallen. The bottom line, then, is that the plausible-sounding argument that debt can’t cure debt is just wrong. On the contrary, it can – and the alternative is a prolonged period of economic weakness that actually makes the debt problem harder to resolve.”

Besides operational issues, the consideration of this argument would require specifying and measuring two types of gains and losses from this policy: (1) the benefits in terms of growth and employment currently; and (2) the costs of postponing the adjustment such as in the exercise by CBO (2011JunLTO, 28-31) in Table 11. It may be easier to analyze the costs and benefits than actual measurement.

An analytical and empirical approach is followed by Blinder and Zandi (2010), using the Moody’s Analytics model of the US economy with four different scenarios: (1) baseline with all policies used; (2) counterfactual including all fiscal stimulus policies but excluding financial stimulus policies; (3) counterfactual including all financial stimulus policies but excluding fiscal stimulus; and (4) a scenario excluding all policies. The scenario excluding all policies is an important reference or the counterfactual of what would have happened if the government had been entirely inactive. A salient feature of the work by Blinder and Zandi (2010) is the consideration of both fiscal and financial policies. There was probably more activity with financial policies than with fiscal policies. Financial policies included the Fed balance sheet, 11 facilities of direct credit to illiquid segments of financial markets, interest rate policy, the Financial Stability Plan including stress tests of banks, the Troubled Asset Relief Program (TARP) and others (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009b), 157-67; Regulation of Banks and Finance (2009a), 224-7).

Blinder and Zandi (2010, 4) find that:

“In the scenario that excludes all the extraordinary policies, the downturn continues into 2011. Real GDP falls a stunning 7.4% in 2009 and another 3.7% in 2010 (see Table 3). The peak-to-trough decline in GDP is therefore close to 12%, compared to an actual decline of about 4%. By the time employment hits bottom, some 16.6 million jobs are lost in this scenario—about twice as many as actually were lost. The unemployment rate peaks at 16.5%, and although not determined in this analysis, it would not be surprising if the underemployment rate approached one-fourth of the labor force. The federal budget deficit surges to over $2 trillion in fiscal year 2010, $2.6 trillion in fiscal year 2011, and $2.25 trillion in FY 2012. Remember, this is with no policy response. With outright deflation in prices and wages in 2009-2011, this dark scenario constitutes a 1930s-like depression.”

The conclusion by Blinder and Zandi (2010) is that if the US had not taken massive fiscal and financial measures the economy could have suffered far more during a prolonged period. There are still a multitude of questions that cloud understanding of the impact of the recession and what would have happened without massive policy impulses. Some effects are quite difficult to measure. An important argument by Blinder and Zandi (2010) is that this evaluation of counterfactuals is relevant to the need of stimulus if economic conditions worsened again.

(3) Theory and Reality of Cyclical Stagnation Not Secular Stagnation. There is current interest in past theories of “secular stagnation.” Alvin H. Hansen (1939, 4, 7; see Hansen 1938, 1941; for an early critique see Simons 1942) argues:

“Not until the problem of full employment of our productive resources from the long-run, secular standpoint was upon us, were we compelled to give serious consideration to those factors and forces in our economy which tend to make business recoveries weak and anaemic (sic) and which tend to prolong and deepen the course of depressions. This is the essence of secular stagnation-sick recoveries which die in their infancy and depressions which feed on themselves and leave a hard and seemingly immovable core of unemployment. Now the rate of population growth must necessarily play an important role in determining the character of the output; in other words, the composition of the flow of final goods. Thus a rapidly growing population will demand a much larger per capita volume of new residential building construction than will a stationary population. A stationary population with its larger proportion of old people may perhaps demand more personal services; and the composition of consumer demand will have an important influence on the quantity of capital required. The demand for housing calls for large capital outlays, while the demand for personal services can be met without making large investment expenditures. It is therefore not unlikely that a shift from a rapidly growing population to a stationary or declining one may so alter the composition of the final flow of consumption goods that the ratio of capital to output as a whole will tend to decline.”

In the analysis of Hansen (1939, 3) of secular stagnation, economic progress consists of growth of real income per person driven by growth of productivity. The “constituent elements” of economic progress are “(a) inventions, (b) the discovery and development of new territory and new resources, and (c) the growth of population” (Hansen 1939, 3). Secular stagnation originates in decline of population growth and discouragement of inventions. According to Hansen (1939, 2), US population grew by 16 million in the 1920s but grew by one half or about 8 million in the 1930s with forecasts at the time of Hansen’s writing in 1938 of growth of around 5.3 million in the 1940s. Hansen (1939, 2) characterized demography in the US as “a drastic decline in the rate of population growth. Hansen’s plea was to adapt economic policy to stagnation of population in ensuring full employment. In the analysis of Hansen (1939, 8), population caused half of the growth of US GDP per year. Growth of output per person in the US and Europe was caused by “changes in techniques and to the exploitation of new natural resources.” In this analysis, population caused 60 percent of the growth of capital formation in the US. Declining population growth would reduce growth of capital formation. Residential construction provided an important share of growth of capital formation. Hansen (1939, 12) argues that market power of imperfect competition discourages innovation with prolonged use of obsolete capital equipment. Trade unions would oppose labor-savings innovations. The combination of stagnating and aging population with reduced innovation caused secular stagnation. Hansen (1939, 12) concludes that there is role for public investments to compensate for lack of dynamism of private investment but with tough tax/debt issues.

Table SE1 provides contributions to growth of GDP in the 1930s. These data were not available until much more recently. Residential investment (RSI) contributed 1.03 percentage points to growth of GDP of 8.0 percent in 1939, which is a high percentage of the contribution of gross private domestic investment of 2.39 percentage points. Residential investment contributed 0.42 percentage points to GDP growth of 8.8 percent in 1940 with gross private domestic investment contributing 3.99 percentage points.

Table SE1, US, Contributions to Growth of GDP

	GDP ∆%	PCE PP	GDI PP	NRI PP	RSI PP	Net Trade PP	GOVT PP
1930	-8.5	-3.96	-5.18	-1.84	-1.50	-0.31	0.94
1931	-6.4	-2.37	-4.28	-3.32	-0.40	-0.22	0.48
1932	-12.9	-7.00	-5.28	-2.78	-1.02	-0.20	-0.42
1933	-1.3	-1.79	1.16	-0.44	-0.24	-0.11	-0.52
1934	10.8	5.71	2.83	1.31	0.38	0.33	1.91
1935	8.9	4.69	4.54	1.41	0.56	-0.83	0.50
1936	12.9	7.68	2.58	2.10	0.47	0.24	2.44
1937	5.1	2.72	2.57	1.42	0.17	0.45	-0.64
1938	-3.3	-1.15	-4.13	-2.13	0.01	0.88	1.09
1939	8.0	4.11	2.39	0.71	1.03	0.07	1.41
1940	8.8	3.72	3.99	1.60	0.42	0.52	0.57

GDP ∆%: Annual Growth of GDP; PCE PP: Percentage Points Contributed by Personal Consumption Expenditures (PCE); GDI PP: Percentage Points Contributed by Gross Private Domestic Investment (GDI); NRI PP: Percentage Points Contributed by Nonresidential Investment (NRI); RSI: Percentage Points Contributed by Residential Investment; Net Trade PP: Percentage Points Contributed by Net Exports less Imports of Goods and Services; GOVT PP: Percentage Points Contributed by Government Consumption and Gross Investment

Source: Bureau of Economic Analysis

http://www.bea.gov/iTable/index_nipa.cfm

Table ES2 provides percentage shares of GDP in 1929, 1939, 1940, 2006 and 2015. The share of residential investment was 3.9 percent in 1929, 3.4 percent in 1939 and 6.0 percent in 2006 at the peak of the real estate boom. The share of residential investment in GDP has not been very high historically.

Table ES2, Percentage Shares in GDP

	1929	1939	1940	2006	2015
GDP	100.00	100.00	100.00	100.00	100.00
PCE	74.0	71.9	69.2	67.1	68.4
GDI	16.4	10.9	14.2	19.3	16.8
NRI	11.1	7.3	8.3	12.8	12.8
RSI	3.9	3.4	3.5	6.0	3.4
Net Trade	0.4	0.9	1.4	-5.6	-3.0
GOVT	9.2	16.3	15.2	19.1	17.8

PCE: Personal Consumption Expenditures; GDI: Gross Domestic Investment; NRI: Nonresidential Investment; RSI: Residential Investment; Net Trade: Net Exports less Imports of Goods and Services; GOVT: Government Consumption and Gross Investment

Source: Bureau of Economic Analysis

PCE: Personal Consumption Expenditures; GDI: Gross Private Domestic Investment; NRI: Nonresidential Investment; RSI: Residential Investment; Net Trade: Net Exports less Imports of Goods and Services; GOVT: Government Consumption and Gross Investment

Source: Bureau of Economic Analysis

http://www.bea.gov/iTable/index_nipa.cfm

An interpretation of the New Deal is that fiscal stimulus must be massive in recovering growth and employment and that it should not be withdrawn prematurely to avoid a sharp second contraction as it occurred in 1937 (Christina Romer 2009). Proposals for another higher dose of stimulus explain the current weakness by insufficient fiscal expansion and warn that failure to spend more can cause another contraction as in 1937. According to a different interpretation, private hours worked declined by 25 percent by 1939 compared with the level in 1929, suggesting that the economy fell to a lower path of expansion than in 1929 (works by Harold Cole and Lee Ohanian (1999) (cited in Pelaez and Pelaez, Regulation of Banks and Finance, 215-7). Major real variables of output and employment were below trend by 1939: -26.8 percent for GNP, -25.4 percent for consumption, -51 percent for investment and -25.6 percent for hours worked. Surprisingly, total factor productivity increased by 3.1 percent and real wages by 21.8 percent (Cole and Ohanian 1999). The policies of the Roosevelt administration encouraged increasing unionization to maintain high wages with lower hours worked and high prices by lax enforcement of antitrust law to encourage cartels or collusive agreements among producers. The encouragement by the government of labor bargaining by unions and higher prices by collusion depressed output and employment throughout the 1930s until Roosevelt abandoned the policies during World War II after which the economy recovered full employment (Cole and Ohanian 1999). The fortunate ones who worked during the New Deal received higher real wages at the expense of many who never worked again. In a way, the administration behaved like the father of the unionized workers and the uncle of the collusive rich, neglecting the majority in the middle. Inflation-adjusted GDP increased by 10.8 percent in 1934, 8.9 percent in 1935, 12.9 percent in 1936 but only 5.1 percent in 1937, contracting by -3.3 percent in 1938 (US Bureau of Economic Analysis cited in Pelaez and Pelaez, Financial Regulation after the Global Recession, 151, Globalization and the State, Vol. II, 206). The competing explanation is that the economy did not decline from 1937 to 1938 because of lower government spending in 1937 but rather because of the expansion of unions promoted by the New Deal and increases in tax rates (Thomas Cooley and Lee Ohanian 2010). Government spending adjusted for inflation fell only 0.7 percent in 1936 and 1937 and could not explain the decline of GDP by 3.4 percent in 1938. In 1936, the administration imposed a tax on retained profits not distributed to shareholders according to a sliding scale of 7 percent for retaining 1 percent of total net income up to 27 percent for retaining 70 percent of total net income, increasing costs of investment that were mostly financed in that period with retained earnings (Cooley and Ohanian 2010). The tax rate on dividends jumped from 10.1 percent in 1929 to 15.9 percent in 1932 and doubled by 1936. A recent study finds that “tax rates on dividends rose dramatically during the 1930s and imply significant declines in investment and equity values and nontrivial declines in GDP and hours of work” (Ellen McGrattan 2010), explaining a significant part of the decline of 26 percent in business fixed investment in 1937-1938. The National Labor Relations Act of 1935 caused an increase in union membership from 12 percent in 1934 to 25 percent in 1938. The alternative lesson from the 1930s is that capital income taxes and higher unionization caused increases in business costs that perpetuated job losses of the recession with current risks of repeating the 1930s (Cooley and Ohanian 1999).

The current application of Hansen’s (1938, 1939, 1941) proposition argues that secular stagnation occurs because full employment equilibrium can be attained only with negative real interest rates between minus 2 and minus 3 percent. Professor Lawrence H. Summers (2013Nov8) finds that “a set of older ideas that went under the phrase secular stagnation are not profoundly important in understanding Japan’s experience in the 1990s and may not be without relevance to America’s experience today” (emphasis added). Summers (2013Nov8) argues there could be an explanation in “that the short-term real interest rate that was consistent with full employment had fallen to -2% or -3% sometime in the middle of the last decade. Then, even with artificial stimulus to demand coming from all this financial imprudence, you wouldn’t see any excess demand. And even with a relative resumption of normal credit conditions, you’d have a lot of difficulty getting back to full employment.” The US economy could be in a situation where negative real rates of interest with fed funds rates close to zero as determined by the Federal Open Market Committee (FOMC) do not move the economy to full employment or full utilization of productive resources. Summers (2013Oct8) finds need of new thinking on “how we manage an economy in which the zero nominal interest rates is a chronic and systemic inhibitor of economy activity holding our economies back to their potential.”

Former US Treasury Secretary Robert Rubin (2014Jan8) finds three major risks in prolonged unconventional monetary policy of zero interest rates and quantitative easing: (1) incentive of delaying action by political leaders; (2) “financial moral hazard” in inducing excessive exposures pursuing higher yields of risker credit classes; and (3) major risks in exiting unconventional policy. Rubin (2014Jan8) proposes reduction of deficits by structural reforms that could promote recovery by improving confidence of business attained with sound fiscal discipline.

Professor John B. Taylor (2014Jan01, 2014Jan3) provides clear thought on the lack of relevance of Hansen’s contention of secular stagnation to current economic conditions. The application of secular stagnation argues that the economy of the US has attained full-employment equilibrium since around 2000 only with negative real rates of interest of minus 2 to minus 3 percent. At low levels of inflation, the so-called full-employment equilibrium of negative interest rates of minus 2 to minus 3 percent cannot be attained and the economy stagnates. Taylor (2014Jan01) analyzes multiple contradictions with current reality in this application of the theory of secular stagnation:

Secular stagnation would predict idle capacity, in particular in residential investment when fed fund rates were fixed at 1 percent from Jun 2003 to Jun 2004. Taylor (2014Jan01) finds unemployment at 4.4 percent with house prices jumping 7 percent from 2002 to 2003 and 14 percent from 2004 to 2005 before dropping from 2006 to 2007. GDP prices doubled from 1.7 percent to 3.4 percent when interest rates were low from 2003 to 2005.
Taylor (2014Jan01, 2014Jan3) finds another contradiction in the application of secular stagnation based on low interest rates because of savings glut and lack of investment opportunities. Taylor (2009) shows that there was no savings glut. The savings rate of the US in the past decade is significantly lower than in the 1980s.
Taylor (2014Jan01, 2014Jan3) finds another contradiction in the low ratio of investment to GDP currently and reduced investment and hiring by US business firms.
Taylor (2014Jan01, 2014Jan3) argues that the financial crisis and global recession were caused by weak implementation of existing regulation and departure from rules-based policies.
Taylor (2014Jan01, 2014Jan3) argues that the recovery from the global recession was constrained by a change in the regime of regulation and fiscal/monetary policies.

In revealing research, Edward P. Lazear and James R. Spletzer (2012JHJul22) use the wealth of data in the valuable database and resources of the Bureau of Labor Statistics (https://www.bls.gov/data/) in providing clear thought on the nature of the current labor market of the United States. The critical issue of analysis and policy currently is whether unemployment is structural or cyclical. Structural unemployment could occur because of (1) industrial and demographic shifts and (2) mismatches of skills and job vacancies in industries and locations. Consider the aggregate unemployment rate, Y, expressed in terms of share s_i of a demographic group in an industry i and unemployment rate y_i of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):

Y = ∑_is_iy_i (1)

This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):

∆Y = ∑_i∆s_iy*_i+ ∑_i∆y_is*_i (2)

The first term in (2) captures changes in the demographic and industrial composition of the economy ∆s_i multiplied by the average rate of unemployment y*_i, or structural factors. The second term in (2) captures changes in the unemployment rate specific to a group, or ∆y_i, multiplied by the average share of the group s*_i,or cyclical factors. There are also mismatches in skills and locations relative to available job vacancies. A simple observation by Lazear and Spletzer (2012JHJul22) casts intuitive doubt on structural factors: the rate of unemployment jumped from 4.4 percent in the spring of 2007 to 10 percent in October 2009. By nature, structural factors should be permanent or occur over relative long periods. The revealing result of the exhaustive research of Lazear and Spletzer (2012JHJul22) is:

“The analysis in this paper and in others that we review do not provide any compelling evidence that there have been changes in the structure of the labor market that are capable of explaining the pattern of persistently high unemployment rates. The evidence points to primarily cyclic factors.”

Table I-4b and Chart I-12-b provide the US labor force participation rate or percentage of the labor force in population. It is not likely that simple demographic trends caused the sharp decline during the global recession and failure to recover earlier levels. The civilian labor force participation rate dropped from the peak of 66.9 percent in Jul 2006 to 62.6 percent in Dec 2013, 62.5 percent in Dec 2014, 62.4 percent in Dec 2015 and 62.4 in Dec 2016. The civilian labor force participation rate reached 62.4 in Dec 2017, and 63.1 percent in 2019. The civilian labor force participation rate was at 62.9 percent in Nov 2018 and 62.8 percent in Dec 2018. The civilian labor force participation was 63.0 in Dec 2019. The civilian labor force participation rate was 60.0 in Apr 2020. The civilian labor force participation rate was 63.7 percent on an annual basis in 1979 and 63.4 percent in Dec 1980 and Dec 1981, reaching even 62.9 percent in both Apr and May 1979. The civilian labor force participation rate jumped with the recovery to 64.8 percent on an annual basis in 1985 and 65.9 percent in Jul 1985. Structural factors cannot explain these sudden changes vividly shown visually in the final segment of Chart I-12b. Seniors would like to delay their retiring especially because of the adversities of financial repression on their savings. Labor force statistics are capturing the disillusion of potential workers with their chances in finding a job in what Lazear and Spletzer (2012JHJul22) characterize as accentuated cyclical factors. The argument that anemic population growth causes “secular stagnation” in the US (Hansen 1938, 1939, 1941) is as misplaced currently as in the late 1930s (for early dissent see Simons 1942). There is currently population growth in the ages of 16 to 24 years but not enough job creation and discouragement of job searches for all ages (https://cmpassocregulationblog.blogspot.com/2020/04/united-states-imbalances-of-internal.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/sharp-contraction-of-valuations-of-risk.html). “Secular stagnation” would be a process over many years and not from one year to another. This is merely another case of theory without reality with dubious policy proposals.

Table I-4b, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1979-2020

Year	Jan	Feb	Mar	Apr	Nov	Dec	Annual
1979	62.9	63.0	63.2	62.9	63.8	63.8	63.7
1980	63.3	63.2	63.2	63.2	63.7	63.4	63.8
1981	63.2	63.2	63.5	63.6	63.8	63.4	63.9
1982	63.0	63.2	63.4	63.3	64.1	63.8	64.0
1983	63.3	63.2	63.3	63.2	64.1	63.8	64.0
1984	63.3	63.4	63.6	63.7	64.4	64.3	64.4
1985	64.0	64.0	64.4	64.3	64.9	64.6	64.8
1986	64.2	64.4	64.6	64.6	65.4	65.0	65.3
1987	64.7	64.8	65.0	64.9	65.7	65.5	65.6
1988	65.1	65.2	65.2	65.3	66.2	65.9	65.9
1989	65.8	65.6	65.7	65.9	66.7	66.3	66.5
1990	66.0	66.0	66.2	66.1	66.3	66.1	66.5
1991	65.5	65.7	65.9	66.0	66.0	65.8	66.2
1992	65.7	65.8	66.0	66.0	66.2	66.1	66.4
1993	65.6	65.8	65.8	65.6	66.3	66.2	66.3
1994	66.0	66.2	66.1	66.0	66.7	66.5	66.6
1995	66.1	66.2	66.4	66.4	66.5	66.2	66.6
1996	65.8	66.1	66.4	66.2	67.0	66.7	66.8
1997	66.4	66.5	66.9	66.7	67.1	67.0	67.1
1998	66.6	66.7	67.0	66.6	67.1	67.0	67.1
1999	66.7	66.8	66.9	66.7	67.0	67.0	67.1
2000	66.8	67.0	67.1	67.0	66.9	67.0	67.1
2001	66.8	66.8	67.0	66.7	66.6	66.6	66.8
2002	66.2	66.6	66.6	66.4	66.3	66.2	66.6
2003	66.1	66.2	66.2	66.2	66.1	65.8	66.2
2004	65.7	65.7	65.8	65.7	66.1	65.8	66.0
2005	65.4	65.6	65.6	65.8	66.1	65.9	66.0
2006	65.5	65.7	65.8	65.8	66.4	66.3	66.2
2007	65.9	65.8	65.9	65.7	66.1	65.9	66.0
2008	65.7	65.5	65.7	65.7	65.8	65.7	66.0
2009	65.4	65.5	65.4	65.4	64.9	64.4	65.4
2010	64.6	64.6	64.8	64.9	64.4	64.1	64.7
2011	63.9	63.9	64.0	63.9	63.9	63.8	64.1
2012	63.4	63.6	63.6	63.4	63.5	63.4	63.7
2013	63.3	63.2	63.1	63.1	62.9	62.6	63.2
2014	62.5	62.7	62.9	62.6	62.8	62.5	62.9
2015	62.5	62.5	62.5	62.6	62.5	62.4	62.7
2016	62.3	62.7	62.8	62.7	62.6	62.4	62.8
2017	62.5	62.7	62.9	62.8	62.7	62.4	62.9
2018	62.3	62.9	62.8	62.7	62.9	62.8	62.9
2019	62.8	63.0	63.0	62.7	63.2	63.0	63.1
2020	63.0	63.3	62.6	60.0

Source: US Bureau of Labor Statistics

https://www.bls.gov/cps/

Chart I-12b, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1979-2020

Source: Bureau of Labor Statistics

https://www.bls.gov/data/

The magnitude of the stress in US labor markets is magnified by the increase in the civilian noninstitutional population of the United States from 231.958 million in Jul 2007 to 259.758 million in Mar 2020 or by 27.800 million (https://www.bls.gov/data/). The number with full-time jobs in Mar 2020 is 127.981 million, which is higher by 4.762 million relative to the peak of 123.219 million in Jul 2007. The ratio of full-time jobs of 123.219 million in Jul 2007 to civilian noninstitutional population of 231.958 million was 53.1 percent. If that ratio had remained the same, there would be 137.932 million full-time jobs with population of 259.758 million in Mar 2020 (0.531 x 259.758) or 9.951 million fewer full-time jobs relative to actual 127.981 million. There appear to be around 10 million fewer full-time jobs in the US than before the global recession while population increased around 20 million. Mediocre GDP growth is the main culprit of the fractured US labor market.

There is current interest in past theories of “secular stagnation.” Alvin H. Hansen (1939, 4, 7; see Hansen 1938, 1941; for an early critique see Simons 1942) argues:

“Not until the problem of full employment of our productive resources from the long-run, secular standpoint was upon us, were we compelled to give serious consideration to those factors and forces in our economy which tend to make business recoveries weak and anaemic (sic) and which tend to prolong and deepen the course of depressions. This is the essence of secular stagnation-sick recoveries which die in their infancy and depressions which feed on them-selves and leave a hard and seemingly immovable core of unemployment. Now the rate of population growth must necessarily play an important role in determining the character of the output; in other words, the com-position of the flow of final goods. Thus a rapidly growing population will demand a much larger per capita volume of new residential building construction than will a stationary population. A stationary population with its larger proportion of old people may perhaps demand more personal services; and the composition of consumer demand will have an important influence on the quantity of capital required. The demand for housing calls for large capital outlays, while the demand for personal services can be met without making large investment expenditures. It is therefore not unlikely that a shift from a rapidly growing population to a stationary or declining one may so alter the composition of the final flow of consumption goods that the ratio of capital to output as a whole will tend to decline.”

The argument that anemic population growth causes “secular stagnation” in the US (Hansen 1938, 1939, 1941) is as misplaced currently as in the late 1930s (for early dissent see Simons 1942). This is merely another case of theory without reality with dubious policy proposals.

Inferior performance of the US economy and labor markets, during cyclical slow growth not secular stagnation, is the critical current issue of analysis and policy design.

Chart I-20, US, Full-time Employed, Thousands, NSA, 2001-2020

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Chart I-20A provides the noninstitutional civilian population of the United States from 2001 to 2020. There is clear trend of increase of the population while the number of full-time jobs collapsed after 2008 with insufficient recovery as shown in the preceding Chart I-20.

Chart I-20A, US, Noninstitutional Civilian Population, Thousands, 2001-2020

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Chart I-20B provides number of full-time jobs in the US from 1968 to 2020. There were multiple recessions followed by expansions without contraction of full-time jobs and without recovery as during the period after 2008. The problem is specific of the current cycle and not secular.

Chart I-20B, US, Full-time Employed, Thousands, NSA, 1968-2020

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Chart I-20C provides the noninstitutional civilian population of the United States from 1968 to 2019. Population expanded at a relatively constant rate of increase with the assurance of creation of full-time jobs that has been broken since 2008.

Chart I-20C, US, Noninstitutional Civilian Population, Thousands, 1968-2020

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Table EMP provides the comparison between the labor market in the current whole cycle from 2007 to 2019 and the whole cycle from 1979 to 1989. In the entire cycle from 2007 to 2019, the number employed increased 11.491 million, full-time employed increased 9.506 million, part-time for economic reasons increased 0.006 million and population increased 27.308 million. The number employed increased 7.9 percent, full-time employed increased 7.9 percent, part-time for economic reasons increased 0.1 percent and population increased 11.8 percent. There is sharp contrast with the contractions of the 1980s and with most economic history of the United States. In the whole cycle from 1979 to 1989, the number employed increased 18.518 million, full-time employed increased 14.715 million, part-time for economic reasons increased 1.317 million and population increased 21.530 million. In the entire cycle from 1979 to 1989, the number employed increased 18.7 percent, full-time employed increased 17.8 percent, part-time for economic reasons increased 36.8 percent and population increased 13.1 percent. The difference between the 1980s and the current cycle after 2007 is in the high rate of growth after the contraction that maintained trend growth around 3.0 percent for the entire cycle and per capital growth at 2.0 percent. The evident fact is that current weakness in labor markets originates in cyclical slow growth and not in imaginary secular stagnation.

Table EMP, US, Annual Level of Employed, Full-Time Employed, Employed Part-Time for Economic Reasons and Noninstitutional Civilian Population, Millions

	Employed	Full-Time Employed	Part Time Economic Reasons	Noninstitutional Civilian Population
2000s
2000	136.891	113.846	3.227	212.577
2001	136.933	113.573	3.715	215.092
2002	136.485	112.700	4.213	217.570
2003	137.736	113.324	4.701	221.168
2004	139.252	114.518	4.567	223.357
2005	141.730	117.016	4.350	226.082
2006	144.427	119.688	4.162	228.815
2007	146.047	121.091	4.401	231.867
2008	145.362	120.030	5.875	233.788
2009	139.877	112.634	8.913	235.801
2010	139.064	111.714	8.874	237.830
2011	139.869	112.556	8.560	239.618
2012	142.469	114.809	8.122	243.284
2013	143.929	116.314	7.935	245.679
2014	146.305	118.718	7.213	247.947
2015	148.834	121.492	6.371	250.801
2016	151.436	123.761	5.943	253.538
2017	153.337	125.967	5.250	255.079
2018	155.761	128.572	4.778	257.791
2019	157.538	130.597	4.407	259.175
∆2007-2019	11.491	9.506	0.006	27.308
∆% 2007-2019	7.9	7.9	0.1	11.8
1980s
1979	98.824	82.654	3.577	164.863
1980	99.303	82.562	4.321	167.745
1981	100.397	83.243	4.768	170.130
1982	99.526	81.421	6.170	172.271
1983	100.834	82.322	6.266	174.215
1984	105.005	86.544	5.744	176.383
1985	107.150	88.534	5.590	178.206
1986	109.597	90.529	5.588	180.587
1987	112.440	92.957	5.401	182.753
1988	114.968	95.214	5.206	184.613
1989	117.342	97.369	4.894	186.393
∆1979-1989	18.518	14.715	1.317	21.530
∆% 1979-1989	18.7	17.8	36.8	13.1

Source: Bureau of Labor Statistics

https://www.bls.gov/

Table Summary Total, US, Total Noninstitutional Civilian Population, Full-time Employment, Employment, Civilian Labor Force, Civilian Labor Force Participation Rate, Employment Population Ratio, Unemployment, NSA, Millions and Percent

	ICP	FTE	EMP	CLF	CLFP	EPOP	UNE
2006	228.8	119.7	144.4	151.4	66.2	63.1	7.0
2009	235.8	112.6	139.9	154.1	65.4	59.3	14.3
2012	243.3	114.8	142.5	155.0	63.7	58.6	12.5
2013	245.7	116.3	143.9	155.4	63.2	58.6	11.5
2014	247.9	118.7	146.3	155.9	62.9	59.0	9.6
2015	250.8	121.5	148.8	157.1	62.7	59.3	8.3
2016	253.5	123.8	151.4	159.2	62.8	59.7	7.8
2017	255.1	126.0	153.3	160.3	62.9	60.1	7.0
2018	257.8	128.6	155.8	162.1	62.9	60.4	6.3
2019	259.2	130.6	157.5	163.5	63.1	60.8	6.0
12/07	233.2	121.0	146.3	153.7	65.9	62.8	7.4
9/09	236.3	112.0	139.1	153.6	65.0	59.7	14.5
3/20	259.8	128.0	155.2	162.5	62.6	60.9	7.4

ICP: Total Noninstitutional Civilian Population; FTE: Full-time Employment Level, EMP: Total Employment Level; CLF: Civilian Labor Force; CLFP: Civilian Labor Force Participation Rate; EPOP: Employment Population Ratio; UNE: Unemployment

Source: Bureau of Labor Statistics

https://www.bls.gov/

The same situation is present in the labor market for young people in ages 16 to 24 years with data in Table Summary Youth. The youth noninstitutional civilian population (ICP) continued to increase during and after the global recession. There is the same disastrous labor market with decline for young people in employment (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP) and employment population ratio (EPOP). There are only increases for unemployment of young people (UNE) and youth unemployment rate (UNER). If aging were a factor of secular stagnation, growth of population of young people would attract a premium in remuneration in labor markets. The sad fact is that young people are also facing tough labor markets. The application of the theory of secular stagnation to the US economy and labor markets is void of reality in the form of key facts, which are best explained by accentuated cyclic factors analyzed by Lazear and Spletzer (2012JHJul22).

Table Summary Youth, US, Youth, Ages 16 to 24 Years, Noninstitutional Civilian Population, Full-time Employment, Employment, Civilian Labor Force, Civilian Labor Force Participation Rate, Employment Population Ratio, Unemployment, NSA, Millions and Percent

	ICP	EMP	CLF	CLFP	EPOP	UNE	UNER
2006	36.9	20.0	22.4	60.6	54.2	2.4	10.5
2009	37.6	17.6	21.4	56.9	46.9	3.8	17.6
2012	38.8	17.8	21.3	54.9	46.0	3.5	16.2
2013	38.8	18.1	21.4	55.0	46.5	3.3	15.5
2014	38.7	18.4	21.3	55.0	47.6	2.9	13.4
2015	38.6	18.8	21.2	55.0	48.6	2.5	11.6
2016	38.4	19.0	21.2	55.2	49.4	2.2	10.4
2017	38.2	19.2	21.2	55.5	50.3	2.0	9.2
2018	38.0	19.2	21.0	55.2	50.5	1.8	8.6
2019	37.7	19.3	21.1	55.9	51.2	1.8	8.4
12/07	37.5	19.4	21.7	57.8	51.6	2.3	10.7
9/09	37.6	17.0	20.7	55.2	45.1	3.8	18.2
3/20	37.5	18.1	20.1	53.5	48.2	2.0	10.0

ICP: Youth Noninstitutional Civilian Population; EMP: Youth Employment Level; CLF: Youth Civilian Labor Force; CLFP: Youth Civilian Labor Force Participation Rate; EPOP: Youth Employment Population Ratio; UNE: Unemployment; UNER: Youth Unemployment Rate

Source: Bureau of Labor Statistics

https://www.bls.gov/

Table I-10, US, Employment Level 16-24 Years, Thousands, NSA

Year	Jan	Feb	Mar	Nov	Dec	Annual
2001	19678	19745	19800	19675	19547	20088
2002	18653	19074	19091	19397	19394	19683
2003	18811	18880	18709	19163	19136	19351
2004	18852	18841	18752	19615	19619	19630
2005	18858	18670	18989	19750	19733	19770
2006	19003	19182	19291	19903	20129	20041
2007	19407	19415	19538	19660	19361	19875
2008	18724	18546	18745	18454	18378	19202
2009	17467	17606	17564	16689	16615	17601
2010	16166	16412	16587	16946	16727	17077
2011	16512	16638	16898	17402	17234	17362
2012	16944	17150	17301	17877	17604	17834
2013	17183	17257	17271	18104	18106	18057
2014	17372	17357	17939	18576	18347	18442
2015	17912	18222	18076	18485	18720	18756
2016	18159	18456	18580	18801	18830	18992
2017	18311	18604	18914	18631	18610	19206
2018	18503	18802	19047	18840	18775	19177
2019	18296	18476	18812	19092	18977	19322
2020	18646	19082	18060

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Chart I-21 provides the level of employment for ages 16 to 24 years. There was much sharper decline in employment levels of youth in the global recession than in the recession of 2001 to 2002. There has not been full recovery of the employment levels of youth before the global recession after 2007.

Chart I-21, US, Employment Level 16-24 Years, Thousands SA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data

Chart I-21A provides the US civilian noninstitutional population ages 16 to 24 years not seasonally adjusted from 2001 to 2019. The youth civilian noninstitutional population increased by 1.292 million from 37.443 million in Jul 2007 to 38.735 million in Jul 2014 while the number of youth jobs fell 1.632 million. The youth civilian noninstitutional population increased from 37.445 million in Aug 2007 to 38.706 million in Aug 2014 or 1.251 million while the number of youth jobs fell 1.441 million. The youth civilian noninstitutional population increased 1.652 million from 37.027 million in Sep 2006 to 38.679 million in Sep 2014 while the number of youth jobs fell 1.500 million. The youth civilian noninstitutional population increased from 37.047 million in Oct 2006 to 38.650 million in Oct 2014 or 1.603 million while the number of youth jobs fell 1.072 million. The youth civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.628 million in Nov 2014 or 1.552 million while the number of youth jobs fell 1.327 million. The civilian noninstitutional population increased from 37.100 million in Dec 2006 to 38.606 million in Dec 2014 or 1.506 million while the number of youth jobs fell 1.782 million. The civilian noninstitutional population increased 1.971 million from 36.761 million in Jan 2006 to 38.732 million in Jan 2015 while the number of youth jobs fell 1.091 million. The civilian noninstitutional population increased 1.914 million from 36.791 million in Feb 2006 to 38.705 million in Feb 2015 while the number of youth jobs fell 0.960 million. The civilian noninstitutional population increased 1.858 million from 36.821 million in Mar 2006 to 38.679 million in Mar 2015 while the number of youth jobs fell 1.215 million. The youth civilian noninstitutional population increased 1.800 million from 36.854 million in Apr 2006 to 38.654 million in Apr 2015 while the number of youth jobs fell 1.165 million. The youth civilian noninstitutional population increased 1,733 million from 36.897 million in May 2006 to 38.630 million in May 2015 while the number of youth jobs fell 1.060 million. The youth civilian noninstitutional population increased 1.666 million from 36.943 million in Jun 2006 to 38.609 million in Jun 2015 while the number of youth jobs fell 1.479 million. The youth civilian noninstitutional population increased 1.600 million from 36.989 million in Jul 2006 to 38.589 million in Jul 2015 while the number of youth jobs fell 1.581 million. The youth civilian noninstitutional population increased 1.548 million from 37.008 million in Aug 2006 to 38.556 million in Aug 2015 while the number of youth jobs fell 1.590 million. The youth civilian noninstitutional population increased 1,498 million from 37.027 million in Sep 2006 to 38.525 million in Sep 2015 while the number of youth jobs fell 1.249 million. The youth civilian noninstitutional population increased 1.444 million from 37.047 million in Oct 2006 to 38.491 million in Oct 2015 while the number of youth jobs fell 1.199 million. The youth civilian noninstitutional population increased 1.392 million from 37.076 million in Nov 2006 to 38.468 million in Nov 2015 while the number of youth jobs fell 1.418 million. The youth civilian noninstitutional population increased 1.341 million from 37.100 million in Dec 2006 to 38.441 million in Dec 2015 while the level of youth jobs 1.409 million. The youth civilian noninstitutional population increased 1.734 million from 36.761 million in Jan 2006 to 38.495 million in Jan 2016 while the level of youth jobs fell 0.844 million. The youth civilian noninstitutional population increased 1.698 million from 36.791 million in Feb 2006 to 38.489 million in Feb 2016 while the number of youth jobs fell 0.726 million. The youth civilian noninstitutional population increased 1,662 million from 36.821 million in Mar 2006 to 38.483 million in Mar 2016 while the number of youth jobs fell 0.711 million. The youth civilian noninstitutional population increased 1.626 million from 36.854 million in Apr 2006 to 38.480 million in Apr 2016 while the number of youth jobs fell 0.895 million. The youth civilian noninstitutional population increased 1.571 million from 36.897 million in May 2006 to 38.468 million in May 2016 while the number of youth jobs fell 0.894 million. The youth civilian noninstitutional population increased 1.516 million from 36.943 million in Jun 2006 to 38.459 million in Jun 2016 while the number of youth jobs fell 1.301 million. The youth civilian noninstitutional population increased 1.461 million from 36.989 million in Jul 2006 to 38.450 million in Jul 2016 while the number of youth jobs fell 1.458 million. The youth civilian noninstitutional population increased 1.414 million from 37.008 million in Aug 2006 to 38.422 million in Aug 2016 while the number of youth jobs fell 1.291 million. The youth civilian noninstitutional population increased 1.368 million from 37.027 million in Sep 2006 to 38.395 million in Sep 2016 while the number of youth jobs fell 0.911 million. The youth civilian noninstitutional population increased 1.320 million from 37.047 million in Oct 2006 to 38.367 million in Oct 2016 while the number of youth jobs fell 1.158 million. The youth civilian noninstitutional population increased 1.283 million from 37.076 million in Nov 2006 to 38.359 million in Nov 2016 while the number of youth jobs fell 1.102 million. The youth civilian noninstitutional population increased 1.248 million from 37.100 million in Dec 2006 to 38.348 million in Dec 2016 while the number of youth jobs fell 1.299 million. The youth civilian noninstitutional population increased 1.488 million from 36.761 million in Jan 2006 to 38.249 million in Jan 2017 while the number of youth jobs decreased 0.692 million. The youth civilian noninstitutional population increased 1.440 million from 36.791 million in Feb 2006 to 38.231 million in Feb 2017 while the number of youth jobs decreased 0.578 million. The youth civilian noninstitutional population increased 1.393 million from 36.821 million in Mar 2006 to 38.214 million in Mar 2017 while the number of youth jobs decreased 0.377 million. The youth civilian noninstitutional population increased 1.343 million from 36.854 million in Apr 2006 to 38.197 million in Apr 2017 while the number of youth jobs decreased 0.458 million. The youth civilian noninstitutional population increased 1.284 million from 36.897 million in May 2006 to 38.181 million in May 2017 while the number of youth jobs decreased 0.699 million. The youth civilian noninstitutional population increased 1.223 million from 36.943 million in Jun 2006 to 38.166 million in Jun 2017 while the number of youth jobs decreased 0.938 million. The youth civilian noninstitutional population increased 1.163 million from 36.989 million in Jul 2006 to 38.152 million in Jul 2017 while the number of youth jobs decreased 1.024 million. The youth civilian noninstitutional population increased 1.163 million from 36.989 million in Jul 2006 to 38.152 million in Jul 2017 while the number of youth jobs decreased 1.024 million. The youth civilian noninstitutional population increased 1.120 million from 37.008 million in Aug 2006 to 38.128 million in Aug 2017 while the number of youth jobs decreased 1.129 million. The youth civilian noninstitutional population increased 1.076 million from 37.027 million in Sep 2006 to 38.103 million in Sep 2017 while the number of youth jobs decreased 0.485 million. The youth civilian noninstitutional population increased 1.032 million from 37.047 million in Oct 2006 to 38.079 million in Oct 2017 while the number of youth jobs decreased 0.852 million. The youth civilian noninstitutional population increased 0.984 million from 37.076 million in Nov 2006 to 38.060 million in Nov 2017 while the number of youth jobs decreased 1.272 million. The youth civilian noninstitutional population increased 0.938 million from 37.100 million in Dec 2006 to 38.038 million in Dec 2017 while the number of youth jobs decreased 1.519 million. The youth civilian noninstitutional population increased 1.304 million from 36.761 million in Jan 2006 to 38.065 million in Jan 2018 while the number of youth jobs decreased 0.500 million. The youth civilian noninstitutional population increased 1.264 million from 36.791 million in Feb 2006 to 38.055 million in Feb 2018 while the number of youth jobs decreased 0.380 million. The youth civilian noninstitutional population increased 1.225 million from 36.821 million in Mar 2006 to 38.046 million in Mar 2018 while the number of youth jobs decreased 0.244 million. The youth civilian noninstitutional population increased 1.185 million from 36.854 million in Apr 2006 to 38.039 million in Apr 2018 while the number of youth jobs decreased 0.533 million. The youth civilian noninstitutional population increased 1.126 million from 36.897 million in May 2006 to 38.023 million in May 2018 while the number of youth jobs decreased 0.785 million. The youth civilian noninstitutional population increased 1.066 million from 36.943 million in Jun 2006 to 38.009 million in Jun 2018 while the number of youth jobs decreased 0.936 million. The youth civilian noninstitutional population increased 1.008 million from 36.989 million in Jul 2006 to 37.997 million in Jul 2018 while the number of youth jobs decreased 1.017 million. The youth civilian noninstitutional population increased 0.977 million from 37.008 million in Aug 2006 to 37.985 million in Aug 2018 while the number of youth jobs decreased 1.795 million. The youth civilian noninstitutional population increased 0.947 million from 37.027 million in Sep 2006 to 37.974 million in Sep 2018 while the number of youth jobs decreased 0.818 million. The youth civilian noninstitutional population increased 0.915 million from 37.047 million in Oct 2006 to 37.962 million in Oct 2018 while the number of youth jobs decreased 0.943 million. The youth civilian noninstitutional population increased 0.876 million from 37.076 million in Nov 2006 to 37.952 million in Nov 2018 while the number of youth jobs decreased 1.063 million. The youth civilian noninstitutional population increased 0.876 million from 37.076 million in Nov 2006 to 37.952 million in Nov 2018 while the number of youth jobs decreased 1.063 million. The youth civilian noninstitutional population increased 0.840 million from 37.100 million in Dec 2006 to 37.940 million in Dec 2018 while the number of youth jobs decreased 1.354 million. The youth civilian noninstitutional population increased 1.038 million from 36.761 million in Jan 2006 to 37.799 million in Jan 2019 while the number of youth jobs decreased 0.707 million. The youth civilian noninstitutional population increased 0.996 million from 36.791 million in Feb 2006 to 37.787 million in Feb 2019 while the number of youth jobs decreased 0.706 million. The youth civilian noninstitutional population increased 0.952 million from 36.821 million in Mar 2006 to 37.773 million in Mar 2019 while the number of youth jobs decreased 0.479 million. The youth civilian noninstitutional population increased 0.908 million from 36.854 million in Apr 2006 to 37.762 million in Apr 2019 while the number of youth jobs decreased 0.620 million. The youth civilian noninstitutional population increased 0.853 million from 36.897 million in May 2006 to 37.750 million in May 2019 while the number of youth jobs decreased 0.592 million. The youth civilian noninstitutional population increased 0.795 million from 36.943 million in Jun 2006 to 37.738 million in Jun 2019 while the number of youth jobs decreased 0.629 million. The youth civilian noninstitutional population increased 0.740 million from 36.989 million in Jul 2006 to 37.729 million in Jul 2019 while the number of youth jobs decreased 0.718 million. This hardship does not originate in low growth of population but in underperformance of the economy in the expansion from the business cycle. The youth civilian noninstitutional population increased 0.722 million from 37.008 million in Aug 2006 to 37.730 million in Aug 2019 while the number of youth jobs decreased 1.269 million. The youth civilian noninstitutional population increased 0.705 million from 37.027 million in Sep 2006 to 37.732 million in Sep 2019 while the number of youth jobs decreased 0.475 million. The youth civilian noninstitutional population increased 0.687 million from 37.047 million in Oct 2006 to 37.734 million in Oct 2019 while the number of youth jobs decreased 0.466 million. The youth civilian noninstitutional population increased 0.650 million from 37.076 million in Nov 2006 to 37.726 million in Nov 2019 while the number of youth jobs decreased 0.811 million. The youth civilian noninstitutional population increased 0.617 million from 37.100 million in Dec 2006 to 37.717 million in Dec 2019 while the number of youth jobs decreased 1.152 million. The youth civilian noninstitutional population increased 0.758 million from 36.761 million in Jan 2006 to 37.519 million in Jan 2020 while the number of youth jobs decreased 0.357 million. The youth civilian noninstitutional population increased 0.721 million from 36.791 million in Feb 2006 to 37.512 million in Feb 2020 while the number of youth jobs decreased 0.100 million. The youth civilian noninstitutional population increased 0.683 million from 36.821 million in Mar 2006 to 37.504 million in Mar 2020 while the number of youth jobs decreased 1.231 million. This hardship does not originate in low growth of population but in underperformance of the economy in the expansion from the business cycle. This hardship does not originate in low growth of population but in underperformance of the economy in the expansion from the business cycle. There are two hardships behind these data. First, young people cannot find employment after finishing high school and college, reducing prospects for achievement in older age. Second, students with more modest means cannot find employment to keep them in college.

Chart I-21A, US, Civilian Noninstitutional Population Ages 16 to 24 Years, Thousands NSA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

Chart I-21B provides the civilian labor force of the US ages 16 to 24 years NSA from 2001 to 2018. The US civilian labor force ages 16 to 24 years fell from 24.339 million in Jul 2007 to 23.506 million in Jul 2013, by 0.833 million or decline of 3.4 percent, while the civilian noninstitutional population NSA increased from 37.443 million in Jul 2007 to 38.861 million in Jul 2013, by 1.418 million or 3.8 percent. The US civilian labor force ages 16 to 24 fell from 22.801 million in Aug 2007 to 22.089 million in Aug 2013, by 0.712 million or 3.1 percent, while the noninstitutional population for ages 16 to 24 years increased from 37.455 million in Aug 2007 to 38.841 million in Aug 2013, by 1.386 million or 3.7 percent. The US civilian labor force ages 16 to 24 years fell from 21.917 million in Sep 2007 to 21.183 million in Sep 2013, by 0.734 million or 3.3 percent while the civilian noninstitutional youth population increased from 37.467 million in Sep 2007 to 38.822 million in Sep 2013 by 1.355 million or 3.6 percent. The US civilian labor force fell from 21.821 million in Oct 2007 to 21.003 million in Oct 2013, by 0.818 million or 3.7 percent while the noninstitutional youth population increased from 37.480 million in Oct 2007 to 38.804 million in Oct 2013, by 1.324 million or 3.5 percent. The US youth civilian labor force fell from 21.909 million in Nov 2007 to 20.825 million in Nov 2013, by 1.084 million or 4.9 percent while the civilian noninstitutional youth population increased from 37.076 million in Nov 2006 to 38.798 million in Nov 2013 or by 1.722 million. The US youth civilian labor force fell from 21.684 million in Dec 2007 to 20.642 million in Dec 2013, by 1.042 million or 4.8 percent, while the civilian noninstitutional population increased from 37.518 million in Dec 2007 to 38.790 million in Dec 2013, by 1.272 million or 3.4 percent. The youth civilian labor force of the US fell from 21.770 million in Jan 2007 to 20.423 million in Jan 2014, by 1.347 million or 6.2 percent while the youth civilian noninstitutional population increased 37.282 million in Jan 2007 to 38.770 million in Jan 2014, by 1.488 million or 4.0 percent. The youth civilian labor force of the US fell 1.255 million from 21.645 million in Feb 2007 to 20.390 million in Feb 2014 while the youth civilian noninstitutional population increased 1.464 million from 37.302 million in Feb 2007 to 38.766 million in Feb 2014. The youth civilian labor force of the US fell 0.693 million from 21.634 million in Mar 2007 to 20.941 million in Mar 2014 or 3.2 person while the youth noninstitutional civilian population 1.437 million from 37.324 million in Mar 2007 to 38.761 million in Mar 2014 or 3.9 percent. The US youth civilian labor force fell 981 thousand from 21.442 million in Apr 2007 to 20.461 million in Apr 2014 while the youth civilian noninstitutional population increased from 37.349 million in Apr 2007 to 38.759 million in Apr 2014 by 1.410 thousand or 3.8 percent. The youth civilian labor force decreased from 21.659 million in May 2007 to 21.160 million in May 2014 by 499 thousand or 2.3 percent while the youth civilian noninstitutional population increased 1.370 million from 37.739 million in May 2007 to 38.749 million in May 2007 or by 2.7 percent. The youth civilian labor force decreased from 24.128 million in Jun 2006 to 22.851 million in Jun 2014 by 1.277 million or 5.3 percent while the civilian noninstitutional population increased from 36.943 million in Jun 2006 to 38.740 million in Jun 2014 by 1.797 million or 4.9 percent. The youth civilian labor force fell from 24.664 million in Jul 2006 to 23.437 million in Jul 2014 while the civilian noninstitutional population increased from 36.989 million in Jul 2006 to 38.735 million in Jul 2014. The youth civilian labor force fell 1.818 million from 23.634 million in Aug 2006 to 21.816 million in Aug 2014 while the civilian noninstitutional population increased from 37.008 million in Aug 2006 to 38.706 million in Aug 2914 or 1.698 million. The youth civilian labor force fell 0.942 million from 21.901 million in Sep 2006 to 20.959 million in Sep 2014 while the noninstitutional population increased 1.652 million from 37.027 million in Sep 2006 to 38.679 million in Sep 2014. The youth civilian labor force decreased 0.702 million from 22.105 million in Oct 2006 to 21.403 million in Oct 2014 while the youth civilian noninstitutional population increased from 37.047 million in Oct 2006 to 38.650 million in Oct 2014 or 1.603 million. The youth civilian labor force decreased 1.111 million from 22.145 million in Nov 2006 to 21.034 million in Nov 2014 while the youth civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.628 million in Nov 2014 or 1.552 million. The youth civilian labor force decreased 1.472 million from 22.136 million in Dec 2006 to 20.664 million in Dec 2014 while the youth civilian noninstitutional population increased from 37.100 million in Dec 2006 to 38.606 million in Dec 2014 or 1.506 million. The youth civilian labor force decreased 0.831 million from 21.368 million in Jan 2006 to 20.555 million in Jan 2015 while the youth noninstitutional population increased from 36.761 million in Jan 2006 to 38.732 million in Jan 2015 or 1.971 million. The youth civilian labor force decreased 0.864 million from 21.615 million in Feb 2006 to 20.751 million in Feb 2015 while the youth noninstitutional population increased 1.914 million from 36.791 million in Feb 2006 to 38.705 million in Feb 2015. The youth civilian labor force decreased 0.907 million from 21.507 million in Mar 2006 to 20.600 million in Mar 2015 while the civilian noninstitutional population increased 1.858 million from 36.821 million in Mar 2006 to 38.679 million in Mar 2015. The youth civilian labor force decreased 1.082 million from 21.498 million in Apr 2006 to 20.416 million in Apr 2015 while the youth civilian noninstitutional population increased 1.800 million from 36.854 million in Apr 2006 to 38.654 million in Apr 2015. The youth civilian labor force decreased 0.681 million from 22.023 million in May 2006 to 21.342 million in May 2015 while the youth civilian noninstitutional population increased 1,733 million from 36.897 million in May 2006 to 38.630 million in May 2015. The youth civilian labor force decreased 1.202 million from 24.128 million in Jun 2006 to 22.926 million in Jun 2015 while the youth civilian noninstitutional population increased 1.666 million from 36.943 million in Jun 2006 to 38.609 million in Jun 2015. The youth civilian labor force decreased 1.502 million from 24.664 million in Jul 2007 to 23.162 million in Jul 2015 while the youth civilian noninstitutional population increased 1.600 million from 36.989 million in Jul 2006 to 38.589 million in Jul 2015. The youth civilian labor force decreased 1.667 million from 23.634 million in Aug 2006 to 21.967 million in Aug 2015 while the youth civilian noninstitutional population increased 1.548 million from 37.008 in Aug 2006 to 38.556 million in Aug 2015. The youth civilian labor force decreased 1.290 million from 21.901 million in Sep 2006 to 20.611 in Sep 2015 while the youth civilian noninstitutional population increased 1.498 million from 37.027 million in Sep 2006 to 38.525 million in Sep 2015. The youth civilian labor force decreased 1.228 million from 22.105 million in Oct 2006 to 20.877 million in Oct 2015 while the youth civilian noninstitutional population increased 1.444 million from 37.047 million in Oct 2006 to 38.491 million in Oct 2015. The youth civilian labor force decreased 1.513 million from 22.145 million in Nov 2006 to 20.632 million in Nov 2015 while the youth civilian noninstitutional population increased 1.392 million from 37.076 million in Nov 2006 to 38.468 million in Nov 2015. The youth civilian labor force decreased 1.301 million from 22.136 million in Dec 2006 to 20.835 million in Dec 2015 while the youth civilian noninstitutional population increased 1.341 million from 37.100 million in Dec 2006 to 38.441 million in Dec 2015. The youth civilian labor force decreased 1.004 million from 21.368 million in Jan 2006 to 20.364 million in Jan 2016 while the youth civilian noninstitutional population increased 1.734 million from 36.761 million in Jan 2006 to 38.495 million in Jan 2016. The youth civilian labor force decreased 0.930 million from 21.615 million in Feb 2006 to 20.685 million in Feb 2016 while the youth civilian noninstitutional population increased 1.698 million from 36.791 million in Feb 2006 to 38.489 million in Feb 2016. The youth civilian labor force decreased 0.767 million from 21.507 million in Mar 2006 to 20.740 million in Mar 2016 while the youth civilian noninstitutional population increased 1.662 million from 36.821 million in Mar 2006 to 38.483 million in Mar 2016. The youth civilian labor force decreased 0.950 million from 21.498 million in Apr 2006 to 20.548 million in Apr 2016 while the youth civilian noninstitutional population increased 1.626 million from 36.854 million in Apr 2006 to 38.480 million in Apr 2016. The youth civilian labor force decreased 0.921 million from 22.023 million in May 2006 to 21.102 million in May 2016 while the youth civilian noninstitutional population increased 1.571 million from 36.897 million in May 2006 to 38.468 million in May 2016. The youth civilian labor force decreased 1.373 million from 24.128 million in Jun 2006 to 22.755 million in Jun 2016 while the youth civilian noninstitutional population increased 1.516 million from 36.943 million in Jun 2006 to 38.459 million in Jun 2016. The youth civilian labor force decreased 1.560 million from 24.664 million in Jul 2006 to 23.104 million in Jul 2016 while the youth civilian noninstitutional population increased 1,461 million from 36.989 million in Jul 2006 to 38.450 million in Jul 2016. The youth civilian labor force decreased 1.536 million from 23.634 million in Aug 2006 to 22.098 million in Aug 2016 while the youth civilian noninstitutional population increased 1.414 million from 37.008 million in Aug 2006 to 38.422 million in Aug 2016. The youth civilian labor force decreased 1.082 million from 21.901 million in Sep 2006 to 20.891 million in Sep 2016 while the youth civilian noninstitutional population increased 1.368 million from 37.027 million in Sep 2006 to 38.395 million in Sep 2016. The youth civilian labor force decreased 1.315 million from 22.105 million in Oct 2006 to 20.790 million in Oct 2016 while the youth civilian noninstitutional population increased 1.320 million from 37.047 million in Oct 2006 to 38.367 million in Oct 2016. The youth civilian labor force decreased 1.410 million from 22.145 million in Nov 2006 to 20.735 million in Nov 2016 while the youth civilian noninstitutional population increased 1.283 million from 37.076 million in Nov 2006 to 38.359 million in Nov 2016. The youth civilian labor force decreased 1.447 million from 22.136 million in Dec 2006 to 20.689 million in Dec 2016 while the youth civilian noninstitutional population increased 1.248 million from 37.100 million in Dec 2006 to 38.348 million in Dec 2016. The youth civilian labor force decreased 0.861 million from 21.368 million in Jan 2006 to 20.507 million in Jan 2017 while the youth civilian noninstitutional population increased 1.488 million from 36.761 million in Jan 2006 to 38.249 million in Jan 2017. The youth civilian labor force decreased 0.918 million from 21.615 million in Feb 2006 to 20.697 million in Feb 2017 while the youth civilian noninstitutional population increased 1.440 million from 36.791 million in Feb 2006 to 38.231 million in Feb 2017. The youth civilian labor force decreased 0.751 million from 21.507 million in Mar 2006 to 20.756 million in Mar 2017 while the youth civilian noninstitutional population increased 1.393 million from 36.821 million in Mar 2006 to 38.214 million in Mar 2017. The youth civilian labor force decreased 0.790 million from 21.498 million in Apr 2006 to 20.708 million in Apr 2017 while the youth civilian noninstitutional population increased 1.343 million from 36.854 million in Apr 2006 to 38.197 million in Apr 2017. The youth civilian labor force decreased 1.124 million from 22.023 million in May 2006 to 20.899 million in May 2017 while the youth civilian noninstitutional population increased 1.284 million from 36.897 million in May 2006 to 38.181 million in May 2017. The youth civilian labor force decreased 1.408 million from 24.128 million in Jun 2006 to 22.720 million in Jun 2017 while the youth civilian noninstitutional population increased 1.223 million from 36.943 million in Jun 2006 to 38.166 million in Jun 2017. The youth civilian labor force decreased 1.557 million from 24.664 million in Jul 2006 to 23.107 million in Jul 2017 while the youth civilian noninstitutional population increased 1.163 million from 36.989 million in Jul 2006 to 38.152 million in Jul 2017. The youth civilian labor force decreased 1.666 million from 23.634 million in Aug 2006 to 21.968 million in Aug 2017 while the youth civilian noninstitutional population increased 1.120 million from 37.008 million in Aug 2006 to 38.128 million in Aug 2017. The youth civilian labor force decreased 0.904 million from 21.901 million in Sep 2006 to 20.997 million in Sep 2017 while the youth civilian noninstitutional population increased 1.076 million from 37.027 million in Sep 2006 to 38.103 million in Sep 2017. The youth civilian labor force decreased 1.284 million from 22.105 million in Oct 2006 to 20.821 million in Oct 2017 while the youth civilian noninstitutional population increased 1.032 million from 37.047 million in Oct 2006 to 38.079 million in Oct 2017. The youth civilian labor force decreased 1.652 million from 22.145 million in Nov 2006 to 20.493 million in Nov 2017 while the youth civilian noninstitutional population increased 0.984 million from 37.076 million in Nov 2006 to 38.060 million in Nov 2017. The youth civilian labor force decreased 1.885 million from 22.136 million in Dec 2006 to 20.251 million in Dec 2017 while the youth civilian noninstitutional population increased 0.938 million from 37.100 million in Dec 2006 to 38.038 million in Dec 2017. The youth civilian labor force decreased 0.835 million from 21.368 million in Jan 2006 to 20.533 million in Jan 2018 while the youth civilian noninstitutional population increased 1.304 million from 36.761 million in Jan 2006 to 38.065 million in Jan 2018. The youth civilian labor force decreased 0.835 million from 21.368 million in Jan 2006 to 20.533 million in Jan 2018 while the youth civilian noninstitutional population increased 1.304 million from 36.761 million in Jan 2006 to 38.065 million in Jan 2018. The youth civilian labor force decreased 0.907 million from 21.615 million in Feb 2006 to 20.708 million in Jan 2018 while the youth civilian noninstitutional population increased 1.264 million from 36.791 million in Feb 2006 to 38.055 million in Feb 2018. The youth civilian labor force decreased 0.755 million from 21.507 million in Mar 2006 to 20.752 million in Mar 2018 while the youth civilian noninstitutional population increased 1.225 million from 36.821 million in Mar 2006 to 38.046 million in Mar 2018. The youth civilian labor force decreased 1.073 million from 21.498 million in Apr 2006 to 20.425 million in Apr 2018 while the youth civilian noninstitutional population increased 1.185 million from 36.854 million in Apr 2006 to 38.039 million in Apr 2018. The youth civilian labor force decreased 1.244 million from 22.023 million in May 2006 to 20.779 million in May 2018 while the youth civilian noninstitutional population increased 1.126 million from 36.897 million in Apr 2006 to 38.023 million in Apr 2018. The youth civilian labor force decreased 1.244 million from 22.023 million in May 2006 to 20.779 million in May 2018 while the youth civilian noninstitutional population increased 1.126 million from 36.897 million in May 2006 to 38.023 million in May 2018. The youth civilian labor force decreased 1.488 million from 24.128 million in Jun 2006 to 22.640 million in Jun 2018 while the youth civilian noninstitutional population increased 1.066 million from 36.943 million in Jun 2006 to 38.009 million in Jun 2018. The youth civilian labor force decreased 1.648 million from 24.664 million in Jul 2006 to 23.016 million in Jul 2018 while the youth civilian noninstitutional population increased 1.008 million from 36.989 million in Jul 2006 to 37.997 million in Jul 2018. The youth civilian labor force decreased 2.542 million from 23.634 million in Aug 2006 to 21.092 million in Aug 2018 while the youth civilian noninstitutional population increased 0.977 million from 37.027 million in Aug 2006 to 37.985 million in Aug 2018. The youth civilian labor force decreased 1.400 million from 21.901 million in Sep 2006 to 20.501 million in Sep 2018 while the youth civilian noninstitutional population increased 0.947 million from 37.027 million in Sep 2006 to 37.974 million in Sep 2018. The youth civilian labor force decreased 1.544 million from 22.105 million in Oct 2006 to 20.561 million in Oct 2018 while the youth civilian noninstitutional population increased 0.915 million from 37.047 million in Oct 2006 to 37.962 million in Oct 2018. The youth civilian labor force decreased 1.743 million from 22.145 million in Nov 2006 to 20.402 million in Nov 2018 while the youth civilian noninstitutional population increased 0.876 million from 37.076 million in Nov 2006 to 37.952 million in Nov 2018. The youth civilian labor force decreased 1.735 million from 22.136 million in Dec 2006 to 20.401 million in Dec 2018 while the youth civilian noninstitutional population increased 0.840 million from 37.100 million in Dec 2006 to 37.940 million in Dec 2018. The youth civilian labor force decreased 1.045 million from 21.368 million in Jan 2006 to 20.323 million in Jan 2019 while the youth civilian noninstitutional population increased 1.038 million from 36.761 million in Jan 2006 to 37.799 million in Jan 2019. The youth civilian labor force decreased 1.252 million from 21.615 million in Feb 2006 to 20.363 million in Feb 2019 while the youth civilian noninstitutional population increased 0.996 million from 36.791 million in Feb 2006 to 37.787 million in Feb 2019. The youth civilian labor force decreased 0.951 million from 21.507 million in Mar 2006 to 20.556 million in Mar 2019 while the youth civilian noninstitutional population increased 0.952 million from 36.821 million in Mar 2006 to 37.773 million in Mar 2019. The youth civilian labor force decreased 1.212 million from 21.498 million in Apr 2006 to 20.286 million in Apr 2019 while the youth civilian noninstitutional population increased 0.908 million from 36.854 million in Apr 2006 to 37.762 million in Apr 2019. The youth civilian labor force decreased 1.055 million from 22.023 million in May 2006 to 20.968 million in May 2019 while the youth civilian noninstitutional population increased 0.853 million from 36.897 million in May 2006 to 37.750 million in May 2019. The youth civilian labor force decreased 1.377 million from 24.128 million in Jun 2006 to 22.751 million in Jun 2019 while the youth civilian noninstitutional population increased 0.795 million from 36.943 million in Jun 2006 to 37.738 million in Jun 2019. The youth civilian labor force decreased 1.353 million from 24.664 million in Jul 2006 to 23.311 million in Jul 2019 while the youth civilian noninstitutional population increased 0.740 million from 36.989 million in Jul 2006 to 37.729 million in Jul 2019. The youth civilian labor force decreased 1.931 million from 23.634 million in Aug 2006 to 21.703 million in Aug 2019 while the youth civilian noninstitutional population increased 0.722 million from 37.008 million in Aug 2006 to 37.730 million in Aug 2019. The youth civilian labor force decreased 1.148 million from 21.901 million in Sep 2006 to 20.753 million in Sep 2019 while the youth civilian noninstitutional population increased 0.705 million from 37.027 million in Sep 2006 to 37.732 million in Sep 2019. The youth civilian labor force decreased 1.137 million from 22.105 million in Oct 2006 to 20.968 million in Oct 2019 while the youth civilian noninstitutional population increased 0.687 million from 37.047 million in Oct 2006 to 37.734 million in Oct 2019. The youth civilian labor force decreased 1.513 million from 22.145 million in Nov 2006 to 20.632 million in Nov 2019 while the youth civilian noninstitutional population increased 0.650 million from 37.076 million in Nov 2006 to 37.726 million in Nov 2019. The youth civilian labor force decreased 1.643 million from 22.136 million in Dec 2006 to 20.493 million in Dec 2019 while the youth civilian noninstitutional population increased 0.617 million from 37.100 million in Dec 2006 to 37.717 million in Dec 2019. The youth civilian labor force decreased 0.854 million from 21.368 million in Jan 2006 to 20.514 million in Jan 2020 while the youth civilian noninstitutional population increased 0.758 million from 36.761 million in Jan 2006 to 37.519 million in Jan 2020. The youth civilian labor force decreased 0.879 million from 21.615 million in Feb 2006 to 20.736 million in Feb 2020 while the youth civilian noninstitutional population increased 0.721 million from 36.791 million in Feb 2006 to 37.512 million in Feb 2020. The youth civilian labor force decreased 1.440 million from 21.507 million in Mar 2006 to 20.067 million in Mar 2020 while the youth civilian noninstitutional population increased 0.683 million from 36.821 million in Mar 2006 to 37.504 million in Mar 2020. Youth in the US abandoned their participation in the labor force because of the frustration that there are no jobs available for them.

Chart I-21B, US, Civilian Labor Force Ages 16 to 24 Years, Thousands NSA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

Chart I-21C provides the ratio of labor force to noninstitutional population or labor force participation of ages 16 to 24 years not seasonally adjusted. The US labor force participation rates for ages 16 to 24 years fell from 66.7 in Jul 2006 to 60.5 in Jul 2013 because of the frustration of young people who believe there may not be jobs available for them. The US labor force participation rate of young people fell from 63.9 in Aug 2006 to 56.9 in Aug 2013. The US labor force participation rate of young people fell from 59.1 percent in Sep 2006 to 54.6 percent in Sep 2013. The US labor force participation rate of young people fell from 59.7 percent in Oct 2006 to 54.1 in Oct 2013. The US labor force participation rate of young people fell from 59.7 percent in Nov 2006 to 53.7 percent in Nov 2013. The US labor force participation rate fell from 57.8 in Dec 2007 to 53.2 in Dec 2013. The youth labor force participation rate fell from 58.4 in Jan 2007 to 52.7 in Jan 2014. The US youth labor force participation rate fell from 58.0 percent in Feb 2007 to 52.6 percent in Feb 2013. The labor force participation rate of ages 16 to 24 years fell from 58.0 in Mar 2007 to 54.0 in Mar 2014. The labor force participation rate of ages 16 to 24 years fell from 57.4 in Apr 2007 to 52.8 in Apr 2014. The labor force participation rate of ages 16 to 24 years fell from 57.9 in May 2007 to 54.6 in May 2014. The labor force participation rate of ages 16 to 24 years fell from 65.3 in Jun 2006 to 59.0 in Jun 2014. The labor force participation rate ages 16 to 24 years fell from 66.7 in Jul 2006 to 60.5 in Jul 2014. The labor force participation rate ages 16 to 24 years fell from 63.9 in Aug 2006 to 56.4 in Aug 2014. The labor force participation rate ages 16 to 24 years fell from 59.1 in Sep 2006 to 54.2 in Sep 2014. The labor force participation rate ages 16 to 24 years fell from 59.7 in Oct 2006 to 55.4 in Oct 2014. The labor force participation rate ages 16 to 24 years fell from 59.7 in Nov 2006 to 54.5 in Nov 2014. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 53.5 in Dec 2014. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 53.1 in Jan 2015. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 53.6 in Feb 2015. The labor force participation rate ages 16 to 64 fell from 58.4 in Mar 2006 to 53.3 in Mar 2015. The labor force participation rate ages 16 to 64 fell from 58.7 in Apr 2005 to 52.8 in Apr 2006. The labor force participation rate ages 16 to 64 fell from 59.7 in May 2006 to 55.2 in May 2015. The labor force participation rate ages 16 to 64 fell from 65.3 in Jun 2006 to 59.4 in Jun 2015. The labor force participation rate ages 16 to 24 fell from 66.7 in Jul 2006 to 60.0 in Jul 2014. The labor force participation rate ages 16 to 24 fell from 63.9 in Aug 2006 to 57.0 in Aug 2015. The labor force participation rate ages 16 to 24 fell from 59.1 in Sep 2006 to 53.5 in Sep 2015. The labor force participation rate ages 16 to 24 fell from 59.7 in Oct 2006 to 54.2 in Oct 2015. The labor force participation rate ages 16 to 24 fell from 59.7 in Nov 2006 to 53.6 in Nov 2015. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 54.2 in Dec 2015. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 52.9 in Jan 2016. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 53.7 in Feb 2016. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 53.9 in Mar 2016. The labor force participation rate ages 16 to 24 fell from 58.3 in Apr 2006 to 53.4 in Apr 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in May 2006 to 54.9 in May 2016. The labor force participation rate ages 16 to 24 fell from 65.3 in Jun 2006 to 59.2 in Jun 2016. The labor force participation rate ages 16 to 24 fell from 66.7 in Jul 2006 to 60.1 in Jul 2016. The labor force participation rate ages 16 to 24 fell from 63.9 in Aug 2006 to 57.5 in Aug 2016. The labor force participation rate ages 16 to 24 fell from 59.1 in Sep 2006 to 54.2 in Sep 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in Oct 2006 to 54.2 in Oct 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in Nov 2006 to 54.1 in Nov 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 54.0 in Dec 2016. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 53.6 in Jan 2017. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 54.1 in Feb 2017. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 54.3 in Mar 2017. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 54.3 in Mar 2017. The labor force participation rate ages 16 to 24 fell from 58.3 in Apr 2006 to 54.2 in Apr 2017. The labor force participation rate ages 16 to 24 fell from 59.7 in May 2006 to 54.7 in May 2017. The labor force participation rate ages 16 to 24 fell from 65.3 in Jun 2006 to 59.5 in Jun 2017. The labor force participation rate ages 16 to 24 fell from 66.7 in Jul 2006 to 60.6 in Jul 2017. The labor force participation rate ages 16 to 24 fell from 63.9 in Aug 2006 to 57.6 in Aug 2017. The labor force participation rate ages 16 to 24 fell from 59.1 in Sep 2006 to 55.1 in Sep 2017. The labor force participation rate ages 16 to 24 fell from 59.7 in Oct 2006 to 54.7 in Oct 2017. The labor force participation rate ages 16 to 24 fell from 59.7 in Nov 2006 to 53.8 in Nov 2017. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 53.2 in Dec 2017. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 53.9 in Jan 2018. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 54.4 in Feb 2018. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 54.5 in Mar 2018. The labor force participation rate ages 16 to 24 fell from 58.3 in Apr 2006 to 53.7 in Apr 2018. The labor force participation rate ages 16 to 24 fell from 59.7 in May 2006 to 54.6 in May 2018. The labor force participation rate ages 16 to 24 fell from 65.3 in Jun 2006 to 59.6 in Jun 2018. The labor force participation rate ages 16 to 24 fell from 66.7 in Jul 2006 to 60.6 in Jul 2018. The labor force participation rate ages 16 to 24 fell from 63.9 in Aug 2006 to 55.5 in Aug 2018. The labor force participation rate ages 16 to 24 fell from 59.1 in Sep 2006 to 54.0 in Sep 2018. The labor force participation rate ages 16 to 24 fell from 59.7 in Oct 2006 to 54.2 in Oct 2018. The labor force participation rate ages 16 to 24 fell from 59.7 in Nov 2006 to 53.8 in Nov 2018. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 53.8 in Dec 2018. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 53.8 in Jan 2019. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 53.9 in Feb 2019. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 54.4 in Mar 2019. The labor force participation rate ages 16 to 24 fell from 58.3 in Apr 2006 to 53.7 in Apr 2019. The labor force participation rate ages 16 to 24 fell from 59.7 in May 2006 to 55.5 in May 2019. The labor force participation rate ages 16 to 24 fell from 65.3 in Jun 2006 to 60.3 in Jun 2019. The labor force participation rate ages 16 to 24 fell from 66.7 in Jul 2006 to 61.8 in Jul 2019. The labor force participation rate ages 16 to 24 fell from 63.9 in Aug 2006 to 57.5 in Aug 2019. The labor force participation rate ages 16 to 24 fell from 59.1 in Sep 2006 to 55.0 in Sep 2019. The labor force participation rate ages 16 to 24 fell from 59.7 in Oct 2006 to 55.6 in Oct 2019. The labor force participation rate ages 16 to 24 fell from 59.7 in Nov 2006 to 54.7 in Nov 2019. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 54.3 in Dec 2019. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 54.7 in Jan 2020. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 55.3 in Feb 2020. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 53.5 in Mar 2020. Many young people abandoned searches for employment, dropping from the labor force.

Chart I-21C, US, Labor Force Participation Rate Ages 16 to 24 Years, NSA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

An important measure of the job market is the number of people with jobs relative to population available for work (civilian noninstitutional population) or employment/population ratio. Chart I-21D provides the employment population ratio for ages 16 to 24 years. The US employment/population ratio NSA for ages 16 to 24 years collapsed from 59.2 in Jul 2006 to 50.7 in Jul 2013. The employment population ratio for ages 16 to 24 years dropped from 57.2 in Aug 2006 to 48.0 in Aug 2013. The employment population ratio for ages to 16 to 24 years declined from 52.9 in Sep 2006 to 46.5 in Sep 2013. The employment population ratio for ages 16 to 24 years fell from 53.6 in Oct 2006 to 46.3 in Oct 2013. The employment population ratio for ages 16 to 24 years fell from 53.7 in Nov 2007 to 46.7 in Nov 2013. The US employment population ratio for ages 16 to 24 years fell from 51.6 in Dec 2007 to 46.7 in Dec 2013. The US employment population ratio fell from 52.1 in Jan 2007 to 44.8 in Jan 2014. The US employment population ratio for ages 16 to 24 fell from 52.0 in Feb 2007 to 44.8 in Feb 2014. The US employment population ratio for ages 16 to 24 years fell from 52.3 in Mar 2007 to 46.3 in Mar 2014. The US employment population ratio for ages 16 to 24 years fell from 51.9 in Apr 2007 to 46.5 in Apr 2014. The US employment population ratio for ages 16 to 24 years fell from 52.1 in May 2007 to 47.3 in May 2014. The US employment population ratio for ages 16 to 24 years fell from 57.6 in Jun 2006 to 50.1 in Jun 2014. The US employment population ratio for ages 16 to 24 years fell from 59.2 in Jul 2006 to 50.1 in Jul 2014. The employment population ratio for ages 16 to 24 years fell from 57.2 in Aug 2006 to 49.0 in Aug 2014. The employment population ratio for ages 16 to 24 fell from 52.9 in Sep 2006 to 46.8 in Sep 2014. The employment population ratio for ages 16 to 24 fell from 53.6 in Oct 2006 to 48.6 in Oct 2014. The employment population ratio for ages 16 to 24 fell from 53.7 in Nov 2006 to 48.1 in Nov 2014. The employment population ration for ages 16 to 24 fell from 54.3 in Dec 2006 to 47.5 in Dec 2014. The employment population ration for ages 16 to 24 years fell from 51.7 in Jan 2006 to 46.2 in Jan 2015. The employment population ratio for ages 16 to 24 fell from 52.1 in Feb 2006 to 47.1 in Feb 2015. The employment population ratio for ages 16 to 24 years fell from 52.4 in Mar 2006 to 46.7 in Mar 2015. The employment population ratio for ages 16 to 24 years fell from 52.7 in Apr 2006 to 47.2 in Apr 2015. The employment population ratio for ages 16 to 24 fell from 53.6 in May 206 to 48.4 in May 2015. The employment population ratio for ages 16 to 24 fell from 57.6 in Jun 2006 to 51.3 in Jun 2015. The employment population ratio for ages 16 to 24 fell from 59.2 in Jul 2006 to 52.7 in Jul 2015. The employment population ratio for ages 16 to 24 fell from 57.2 in Aug 2006 to 50.8 in Aug 2015. The employment population ratio for ages 16 to 24 years fell from 52.9 in Sep 2006 to 47.6 in Sep 2015. The employment population ratio for ages 16 to 24 years fell from 53.6 in Oct 2006 to 48.5 in Oct 2015. The employment population ratio for ages 16 to 24 years fell from 53.7 in Nov 2006 to 48.1 in Nov 2015. The employment population ratio for ages 16 to 24 years fell from 54.3 in Dec 2006 to 48.7 in Dec 2015. The employment population ratio for ages 16 to 24 years fell from 51.7 in Jan 2006 to 47.2 in Jan 2016. The employment population ration for ages 16 to 24 years fell from 52.1 in Feb 2006 to 48.0 in Feb 2016. The employment population ratio for ages 16 to 24 fell from 52.4 in Mar 2006 to 48.3 in Mar 2016. The employment population ratio for ages 16 to 24 fell from 52.7 in Apr 2006 to 48.1 in Apr 2016. The employment population ratio for ages 16 to 24 fell from 53.6 in May 2006 to 49.1 in May 2016. The employment population ratio for ages 16 to 24 fell from 57.6 in Jun 2006 to 51.9 in Jun 2016. The employment population ratio for ages 16 to 24 fell from 59.2 in Jul 2006 to 53.2 in Jul 2016. The employment population ratio for ages 16 to 24 fell from 57.2 in Aug 2006 to 51.7 in Aug 2016. The employment population ratio for ages 16 to 24 fell from 52.9 in Sep 2006 to 48.7 in Sep 2016. The employment population ratio for ages 16 to 24 fell from 53.6 in Oct 2006 to 48.7 in Oct 2016. The employment population ratio for ages 16 to 24 fell from 53.7 in Nov 2006 to 49.0 in Nov 2016. The employment population ratio for ages 16 to 24 fell from 54.3 in Dec 2006 to 49.1 in Dec 2016. The employment population ratio for ages 16 to 24 fell from 51.7 in Jan 2006 to 47.9 in Jan 2017. The employment population ratio for ages 16 to 24 fell from 52.1 in Feb 2006 to 48.7 in Feb 2017. The employment population ratio for ages 16 to 24 fell from 52.4 in Mar 2006 to 49.5 in Mar 2017. The employment population ratio for ages 16 to 24 fell from 52.7 in Apr 2006 to 49.6 in Apr 2017. The employment population ratio for ages 16 to 24 fell from 53.6 in May 2006 to 49.9 in May 2017. The employment population ratio for ages 16 to 24 fell from 57.6 in Jun 2006 to 53.3 in Jun 2017. The employment population ratio for ages 16 to 24 fell from 59.2 in Jul 2006 to 54.8 in Jul 2017. The employment population ratio for ages 16 to 24 fell from 57.2 in Aug 2006 to 52.6 in Aug 2017. The employment population ratio for ages 16 to 24 fell from 52.9 in Sep 2006 to 50.2 in Sep 2017. The employment population ratio for ages 16 to 24 fell from 53.6 in Oct 2006 to 49.9 in Oct 2017. The employment population ratio for ages 16 to 24 fell from 53.7 in Nov 2006 to 49.0 in Nov 2017. The employment population ratio for ages 16 to 24 fell from 54.3 in Dec 2006 to 48.9 in Dec 2017. The employment population ratio for ages 16 to 24 fell from 51.7 in Jan 2006 to 48.6 in Jan 2018. The employment population ratio for ages 16 to 24 fell from 52.1 in Feb 2006 to 49.4 in Feb 2018. The employment population ratio for ages 16 to 24 fell from 52.4 in Mar 2006 to 50.1 in Mar 2018. The employment population ratio for ages 16 to 24 fell from 52.7 in Apr 2006 to 49.6 in Apr 2018. The employment population ratio for ages 16 to 24 fell from 53.6 in May 2006 to 49.9 in May 2018. The employment population ratio for ages 16 to 24 fell from 57.6 in Jun 2006 to 53.5 in Jun 2018. The employment population ratio for ages 16 to 24 fell from 59.2 in Jul 2006 to 55.0 in Jul 2018. The employment population ratio for ages 16 to 24 fell from 57.2 in Aug 2006 to 51.0 in Aug 2018. The employment population ratio for ages 16 to 24 fell from 52.9 in Sep 2006 to 49.5 in Sep 2018. The employment population ratio for ages 16 to 24 fell from 53.6 in Oct 2006 to 49.8 in Oct 2018. The employment population ratio for ages 16 to 24 fell from 53.7 in Nov 2006 to 49.6 in Nov 2018. The employment population ratio for ages 16 to 24 fell from 54.3 in Dec 2006 to 49.5 in Dec 2018. The employment population ratio for ages 16 to 24 fell from 51.7 in Jan 2006 to 48.4 in Jan 2019. The employment population ratio for ages 16 to 24 fell from 52.1 in Feb 2006 to 48.9 in Feb 2019. The employment population ratio for ages 16 to 24 fell from 52.4 in Mar 2006 to 49.8 in Mar 2019. The employment population ratio for ages 16 to 24 fell from 52.7 in Apr 2006 to 49.7 in Apr 2019. The employment population ratio for ages 16 to 24 fell from 53.6 in May 2006 to 50.8 in May 2019. The employment population ratio for ages 16 to 24 fell from 57.6 in Jun 2006 to 54.7 in Jun 2019. The employment population ratio for ages 16 to 24 fell from 59.2 in Jul 2006 to 56.2 in Jul 2019. The employment population ratio for ages 16 to 24 fell from 57.2 in Aug 2006 to 52.7 in Aug 2019. The employment population ratio for ages 16 to 24 fell from 52.9 in Sep 2006 to 50.7 in Sep 2019. The employment population ratio for ages 16 to 24 fell from 53.6 in Oct 2006 to 51.4 in Oct 2019. The employment population ratio for ages 16 to 24 fell from 53.7 in Nov 2006 to 50.6 in Nov 2019. The employment population ratio for ages 16 to 24 fell from 54.3 in Dec 2006 to 50.3 in Dec 2019. The employment population ratio for ages 16 to 24 fell from 51.7 in Jan 2006 to 49.7 in Jan 2020. The employment population ratio for ages 16 to 24 fell from 52.1 in Feb 2006 to 50.9 in Feb 2020. The employment population ratio for ages 16 to 24 fell from 52.4 in Mar 2006 to 48.2 in Mar 2020. Chart I-21D shows vertical drop during the global recession without recovery.

Chart I-21C, US, Labor Force Participation Rate Ages 16 to 24 Years, NSA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

Table I-11 provides US unemployment level ages 16 to 24 years. The number unemployed ages 16 to 24 years increased from 2342 thousand in 2007 to 3634 thousand in 2011 or by 1.292 million and 3451 thousand in 2012 or by 1.109 million. The unemployment level ages 16 to 24 years increased from 2342 in 2007 to 3324 thousand in 2013 or by 0.982 million. The unemployment level ages 16 to 24 years increased from 2342 thousand in 2007 to 2853 thousand in 2014 or by 0.511 million. The unemployment level for ages 16 to 24 increased from 2342 thousand in 2007 to 2467 thousand in 2015, decreasing to 2.211 million in 2016. The unemployment level decreased to 1.955 million in 2017. The unemployment level decreased to 1.807 million in 2018. The unemployment level decreased to 1.770 million in 2019. The unemployment level ages 16 to 24 years decreased from 2.216 million in Mar 2006 to 2.007 million in Mar 2020 or decrease by 0.209 million. This situation may persist for many years.

Chart I-21D, US, Employment Population Ratio Ages 16 to 24 Years, Thousands NSA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

Table I-12 provides the rate of unemployment of young peoples in ages 16 to 24 years. The annual rate jumped from 10.5 percent in 2007 to 18.4 percent in 2010, 17.3 percent in 2011 and 16.2 percent in 2012. The rate of youth unemployment fell marginally to 15.5 percent in 2013, declining to 13.4 percent in Dec 2014. The rate of youth unemployment fell to 11.6 percent in 2015, 10.4 percent in 2016 and 9.2 percent in 2017. During the seasonal peak in Jul, the rate of youth unemployed was 18.1 percent in Jul 2011, 17.1 percent in Jul 2012 and 16.3 percent in Jul 2013 compared with 10.8 percent in Jul 2007. The rate of youth unemployment rose from 11.2 percent in Jul 2006 to 16.3 percent in Jul 2013 and likely higher if adding those who ceased searching for a job in frustration none may be available. The rate of youth unemployment rose from 10.8 in Jul 2007 to 14.3 in Jul 2014. The rate of youth unemployment increased from 9.1 percent in Dec 2006 to 12.3 percent in Dec 2013. The rate of youth unemployment increased from 10.9 percent in Jan 2007 to 14.9 percent in Jan and Feb 2014. The rate of youth unemployment increased from 9.7 percent in Mar 2007 to 14.3 percent in Mar 2014. The rate of youth unemployment increased from 9.7 percent in Apr 2007 to 11.9 percent in Apr 2014. The rate of youth unemployment increased from 10.2 percent in May 2007 to 13.4 percent in May 2014. The rate of youth unemployment increased from 12.0 percent in Jun 2007 to 15.0 percent in Jun 2014. The rate of youth unemployment increased from 10.8 in Jul 2007 to 14.3 in Jul 2014. The rate of youth unemployment increased from 10.5 in Aug 2007 to 13.0 in Aug 2014. The rate of youth unemployment increased from 11.0 in Sep 2007 to 13.6 in Sep 2014. The rate of youth unemployment increased from 10.3 in Oct 2007 to 12.2 in Oct 2014. The rate of youth unemployment increased from 10.3 in Nov 2007 to 11.7 in Nov 2014. The rate of youth unemployment increased from 10.7 in Dec 2007 to 11.2 in Dec 2014. The rate of youth unemployment increased from 10.9 in Jan 2007 to 12.9 in Jan 2015. The rate of youth unemployment increased from 10.3 percent in Feb 2007 to 12.2 percent in Feb 2015. The rate of youth unemployment increased from 9.7 in Mar 2007 to 12.3 in Mar 2015. The rate of youth unemployment increased from 9.7 in Apr 2007 to 10.7 in Apr 2015. The rate of youth unemployment increased from 10.2 in May 2007 to 12.3 in May 2015. The rate of youth unemployment increased from 11.9 in Jun 2006 to 13.7 in Jun 2015. The rate of youth unemployment increased from 10.8 in Jul 2007 to 12.2 in Jul 2015. The rate of youth unemployment increased from 10.5 in Aug 2007 to 10.9 in Aug 2015. The rate of youth unemployment decreased from 11.0 in Sep 2007 to 10.9 in Sep 2015. The rate of youth unemployment increased from 10.3 in Oct 2007 to 10.6 in Oct 2015. The rate of youth unemployment increased from 10.3 in Nov 2007 to 10.4 in Nov 2015. The rate of youth unemployment decreased from 10.7 in Dec 2007 to 10.1 in Dec 2015. The rate of youth unemployment decreased from 10.9 in Jan 2007 to 10.8 in Jan 2016. The rate of youth unemployment increased from 10.3 in Feb 2007 to 10.8 in Feb 2016. The rate of youth unemployment increased from 9.7 in Mar 2007 to 10.4 in Mar 2016. The rate of youth unemployment increased from 9.7 in Apr 2007 to 9.9 in Apr 2016. The rate of youth unemployment increased from 10.2 in May 2007 to 10.6 in May 2016. The rate of youth unemployment increased from 12.0 in Jun 2007 to 12.3 in Jun 2016. The rate of youth unemployment increased from 10.8 in Jul 2007 to 11.5 in Jul 2016. The rate of youth unemployment fell from 10.5 in Aug 2007 to 10.1 in Aug 2016. The rate of youth unemployment fell from 11.0 in Sep 2007 to 10.2 in Sep 2016. The rate of youth unemployment fell from 10.3 in Oct 2007 to 10.1 in Oct 2016. The rate of youth unemployment fell from 10.3 in Nov 2007 to 9.3 in Nov 2016. The rate of youth unemployment fell from 10.7 in Dec 2007 to 9.0 in Dec 2016. The rate of youth unemployment fell from 10.9 in Jan 2007 to 10.7 in Jan 2017. The rate of youth unemployment fell from 10.3 in Feb 2007 to 10.1 in Feb 2017. The rate of youth unemployment fell from 9.7 in Mar 2007 to 8.9 in Mar 2017. The rate of youth unemployment fell from 9.7 in Apr 2007 to 8.5 in Apr 2017. The rate of youth unemployment fell from 10.2 in May 2007 to 8.8 in May 2017. The rate of youth unemployment fell from 12.0 in Jun 2007 to 10.5 in Jun 2017. The rate of youth unemployment fell from 10.8 in Jul 2007 to 9.6 in Jul 2017. The rate of youth unemployment fell from 10.5 in Aug 2007 to 8.8 in Aug 2017. The rate of youth unemployment fell from 10.8 in Jul 2007 to 9.6 in Jul 2017. The rate of youth unemployment fell from 10.5 in Aug 2007 to 8.8 in Aug 2017. The rate of youth unemployment fell from 11.0 in Sep 2007 to 8.9 in Sep 2017. The rate of youth unemployment fell from 10.3 in Oct 2007 to 8.7 in Oct 2017. The rate of youth unemployment fell from 10.3 in Nov 2007 to 9.1 in Nov 2017. The rate of youth unemployment fell from 10.7 in Dec 2007 to 8.1 in Dec 2017. The rate of youth unemployment fell from 10.9 in Jan 2007 to 9.9 in Jan 2018, decreasing to 9.2 percent in Feb 2018. The rate of youth unemployment fell to 8.2 in Mar 2018, decreasing to 7.6 in Apr 2018. The rate of youth unemployment fell to 8.2 in Mar 2018, decreasing to 7.6 in Apr 2018. The rate of youth unemployment increased to 8.6 in May 2018, increasing to 10.2 in Jun 2018. The rate of youth unemployment decreased to 9.2 percent in Jul 2018, decreasing to 8.2 percent in Aug 2018. The rate of youth unemployment increased to 8.4 percent in Sep 2018, decreasing to 8.0 in Oct 2018. The rate of youth unemployment decreased to 7.7 percent in Nov 2018, increasing to 8.0 percent in Dec 2018. The rate of youth unemployment increased to 10.0 percent in Jan 2019, decreasing to 9.3 percent in Feb 2019. The rate of youth unemployment decreased to 8.5 in Mar 2019, decreasing to 7.4 percent in Apr 2019. The rate of youth unemployment increased to 8.5 in May 2019, increasing to 9.3 in Jun 2019. The rate of youth unemployment decreased to 9.1 in Jul 2019, decreasing to 8.3 in Aug 2019. The rate of youth unemployment decreased to 7.8 in Sep 2019, decreasing to 7.5 in Oct 2019 and stabilizing to 7.5 in Nov 2019. The rate of youth unemployment decreased to 7.4 in Dec 2019, increasing to 9.1 in Jan 2020. The rate of youth unemployment decreased to 8.0 in Feb 2020. The actual rate is higher because of the difficulty in counting those dropping from the labor force because they believe there are no jobs available for them.

Table I-11, US, Unemployment Level 16-24 Years, NSA, Thousands

Year	Jan	Feb	Mar	Dec	Annual
2001	2250	2258	2253	2412	2371
2002	2754	2731	2822	2374	2683
2003	2748	2740	2601	2248	2746
2004	2767	2631	2588	2294	2638
2005	2661	2787	2520	2055	2521
2006	2366	2433	2216	2007	2353
2007	2363	2230	2096	2323	2342
2008	2633	2480	2347	2928	2830
2009	3278	3457	3371	3532	3760
2010	3983	3888	3748	3352	3857
2011	3851	3696	3520	3161	3634
2012	3416	3507	3294	3153	3451
2013	3674	3449	3261	2536	3324
2014	3051	3033	3002	2317	2853
2015	2644	2529	2524	2114	2467
2016	2205	2229	2160	1859	2211
2017	2195	2093	1842	1640	1955
2018	2029	1906	1705	1626	1807
2019	2027	1887	1744	1516	1770
2020	1868	1655	2007

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Chart I-22 provides the unemployment level for ages 16 to 24 from 2001 to 2020. The level rose sharply from 2007 to 2010 with tepid improvement into 2012 and deterioration into 2013-2014 with recent marginal improvement in 2015-2020 alternating with deterioration.

Chart I-22, US, Unemployment Level 16-24 Years, Thousands SA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

Table I-12 provides the rate of unemployment of young peoples in ages 16 to 24 years. The annual rate jumped from 10.5 percent in 2007 to 18.4 percent in 2010, 17.3 percent in 2011 and 16.2 percent in 2012. The rate of youth unemployment fell marginally to 15.5 percent in 2013, declining to 13.4 percent in Dec 2014. The rate of youth unemployment fell to 11.6 percent in 2015, 10.4 percent in 2016 and 9.2 percent in 2017. During the seasonal peak in Jul, the rate of youth unemployed was 18.1 percent in Jul 2011, 17.1 percent in Jul 2012 and 16.3 percent in Jul 2013 compared with 10.8 percent in Jul 2007. The rate of youth unemployment rose from 11.2 percent in Jul 2006 to 16.3 percent in Jul 2013 and likely higher if adding those who ceased searching for a job in frustration none may be available. The rate of youth unemployment rose from 10.8 in Jul 2007 to 14.3 in Jul 2014. The rate of youth unemployment increased from 9.1 percent in Dec 2006 to 12.3 percent in Dec 2013. The rate of youth unemployment increased from 10.9 percent in Jan 2007 to 14.9 percent in Jan and Feb 2014. The rate of youth unemployment increased from 9.7 percent in Mar 2007 to 14.3 percent in Mar 2014. The rate of youth unemployment increased from 9.7 percent in Apr 2007 to 11.9 percent in Apr 2014. The rate of youth unemployment increased from 10.2 percent in May 2007 to 13.4 percent in May 2014. The rate of youth unemployment increased from 12.0 percent in Jun 2007 to 15.0 percent in Jun 2014. The rate of youth unemployment increased from 10.8 in Jul 2007 to 14.3 in Jul 2014. The rate of youth unemployment increased from 10.5 in Aug 2007 to 13.0 in Aug 2014. The rate of youth unemployment increased from 11.0 in Sep 2007 to 13.6 in Sep 2014. The rate of youth unemployment increased from 10.3 in Oct 2007 to 12.2 in Oct 2014. The rate of youth unemployment increased from 10.3 in Nov 2007 to 11.7 in Nov 2014. The rate of youth unemployment increased from 10.7 in Dec 2007 to 11.2 in Dec 2014. The rate of youth unemployment increased from 10.9 in Jan 2007 to 12.9 in Jan 2015. The rate of youth unemployment increased from 10.3 percent in Feb 2007 to 12.2 percent in Feb 2015. The rate of youth unemployment increased from 9.7 in Mar 2007 to 12.3 in Mar 2015. The rate of youth unemployment increased from 9.7 in Apr 2007 to 10.7 in Apr 2015. The rate of youth unemployment increased from 10.2 in May 2007 to 12.3 in May 2015. The rate of youth unemployment increased from 11.9 in Jun 2006 to 13.7 in Jun 2015. The rate of youth unemployment increased from 10.8 in Jul 2007 to 12.2 in Jul 2015. The rate of youth unemployment increased from 10.5 in Aug 2007 to 10.9 in Aug 2015. The rate of youth unemployment decreased from 11.0 in Sep 2007 to 10.9 in Sep 2015. The rate of youth unemployment increased from 10.3 in Oct 2007 to 10.6 in Oct 2015. The rate of youth unemployment increased from 10.3 in Nov 2007 to 10.4 in Nov 2015. The rate of youth unemployment decreased from 10.7 in Dec 2007 to 10.1 in Dec 2015. The rate of youth unemployment decreased from 10.9 in Jan 2007 to 10.8 in Jan 2016. The rate of youth unemployment increased from 10.3 in Feb 2007 to 10.8 in Feb 2016. The rate of youth unemployment increased from 9.7 in Mar 2007 to 10.4 in Mar 2016. The rate of youth unemployment increased from 9.7 in Apr 2007 to 9.9 in Apr 2016. The rate of youth unemployment increased from 10.2 in May 2007 to 10.6 in May 2016. The rate of youth unemployment increased from 12.0 in Jun 2007 to 12.3 in Jun 2016. The rate of youth unemployment increased from 10.8 in Jul 2007 to 11.5 in Jul 2016. The rate of youth unemployment fell from 10.5 in Aug 2007 to 10.1 in Aug 2016. The rate of youth unemployment fell from 11.0 in Sep 2007 to 10.2 in Sep 2016. The rate of youth unemployment fell from 10.3 in Oct 2007 to 10.1 in Oct 2016. The rate of youth unemployment fell from 10.3 in Nov 2007 to 9.3 in Nov 2016. The rate of youth unemployment fell from 10.7 in Dec 2007 to 9.0 in Dec 2016. The rate of youth unemployment fell from 10.9 in Jan 2007 to 10.7 in Jan 2017. The rate of youth unemployment fell from 10.3 in Feb 2007 to 10.1 in Feb 2017. The rate of youth unemployment fell from 9.7 in Mar 2007 to 8.9 in Mar 2017. The rate of youth unemployment fell from 9.7 in Apr 2007 to 8.5 in Apr 2017. The rate of youth unemployment fell from 10.2 in May 2007 to 8.8 in May 2017. The rate of youth unemployment fell from 12.0 in Jun 2007 to 10.5 in Jun 2017. The rate of youth unemployment fell from 10.8 in Jul 2007 to 9.6 in Jul 2017. The rate of youth unemployment fell from 10.5 in Aug 2007 to 8.8 in Aug 2017. The rate of youth unemployment fell from 10.8 in Jul 2007 to 9.6 in Jul 2017. The rate of youth unemployment fell from 10.5 in Aug 2007 to 8.8 in Aug 2017. The rate of youth unemployment fell from 11.0 in Sep 2007 to 8.9 in Sep 2017. The rate of youth unemployment fell from 10.3 in Oct 2007 to 8.7 in Oct 2017. The rate of youth unemployment fell from 10.3 in Nov 2007 to 9.1 in Nov 2017. The rate of youth unemployment fell from 10.7 in Dec 2007 to 8.1 in Dec 2017. The rate of youth unemployment fell from 10.9 in Jan 2007 to 9.9 in Jan 2018, decreasing to 9.2 percent in Feb 2018. The rate of youth unemployment fell to 8.2 in Mar 2018, decreasing to 7.6 in Apr 2018. The rate of youth unemployment fell to 8.2 in Mar 2018, decreasing to 7.6 in Apr 2018. The rate of youth unemployment increased to 8.6 in May 2018, increasing to 10.2 in Jun 2018. The rate of youth unemployment decreased to 9.2 percent in Jul 2018, decreasing to 8.2 percent in Aug 2018. The rate of youth unemployment increased to 8.4 percent in Sep 2018, decreasing to 8.0 in Oct 2018. The rate of youth unemployment decreased to 7.7 percent in Nov 2018, increasing to 8.0 percent in Dec 2018. The rate of youth unemployment increased to 10.0 percent in Jan 2019, decreasing to 9.3 percent in Feb 2019. The rate of youth unemployment decreased to 8.5 in Mar 2019, decreasing to 7.4 percent in Apr 2019. The rate of youth unemployment increased to 8.5 in May 2019, increasing to 9.3 in Jun 2019. The rate of youth unemployment decreased to 9.1 in Jul 2019, decreasing to 8.3 in Aug 2019. The rate of youth unemployment decreased to 7.8 in Sep 2019, decreasing to 7.5 in Oct 2019 and stabilizing to 7.5 in Nov 2019. The rate of youth unemployment decreased to 7.4 in Dec 2019, increasing to 9.1 in Jan 2020. The rate of youth unemployment decreased to 8.0 in Feb 2020. The rate of youth unemployment increased to 10.0 in Mar 2020. The actual rate is higher because of the difficulty in counting those dropping from the labor force because they believe there are no jobs available for them.

Table I-12, US, Unemployment Rate 16-24 Years, Thousands, NSA

Year	Jan	Feb	Mar	Nov	Dec	Annual
2001	10.3	10.3	10.2	11.2	11.0	10.6
2002	12.9	12.5	12.9	11.7	10.9	12.0
2003	12.7	12.7	12.2	11.6	10.5	12.4
2004	12.8	12.3	12.1	11.1	10.5	11.8
2005	12.4	13.0	11.7	10.7	9.4	11.3
2006	11.1	11.3	10.3	10.1	9.1	10.5
2007	10.9	10.3	9.7	10.3	10.7	10.5
2008	12.3	11.8	11.1	13.3	13.7	12.8
2009	15.8	16.4	16.1	18.1	17.5	17.6
2010	19.8	19.2	18.4	17.4	16.7	18.4
2011	18.9	18.2	17.2	15.9	15.5	17.3
2012	16.8	17.0	16.0	14.8	15.2	16.2
2013	17.6	16.7	15.9	13.1	12.3	15.5
2014	14.9	14.9	14.3	11.7	11.2	13.4
2015	12.9	12.2	12.3	10.4	10.1	11.6
2016	10.8	10.8	10.4	9.3	9.0	10.4
2017	10.7	10.1	8.9	9.1	8.1	9.2
2018	9.9	9.2	8.2	7.7	8.0	8.6
2019	10.0	9.3	8.5	7.5	7.4	8.4
2020	9.1	8.0	10.0

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Chart I-23 provides the BLS estimate of the not-seasonally-adjusted rate of youth unemployment for ages 16 to 24 years from 2001 to 2020. The rate of youth unemployment increased sharply during the global recession of 2008 and 2009 but failed to drop faster to earlier lower levels because of cyclically slower growth of GDP. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E. Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent.

Chart I-23, US, Unemployment Rate 16-24 Years, Percent, NSA, 2001-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

Chart I-24 provides longer perspective with the rate of youth unemployment in ages 16 to 24 years from 1948 to 2020. The rate of youth unemployment rose to 20 percent during the contractions of the early 1980s and also during the contraction of the global recession in 2008 and 2009. The data illustrate again the argument in this blog that the contractions of the early 1980s are the valid framework for comparison with the global recession of 2008 and 2009 instead of misleading comparisons with the 1930s. During the initial phase of recovery, the rate of youth unemployment 16 to 24 years NSA fell from 18.9 percent in Jun 1983 to 14.5 percent in Jun 1984. In contrast, the rate of youth unemployment 16 to 24 years was nearly the same during the expansion after IIIQ2009: 17.5 percent in Dec 2009, 16.7 percent in Dec 2010, 15.5 percent in Dec 2011, 15.2 percent in Dec 2012, 17.6 percent in Jan 2013, 16.7 percent in Feb 2013, 15.9 percent in Mar 2013, 15.1 percent in Apr 2013. The rate of youth unemployment was 16.4 percent in May 2013, 18.0 percent in Jun 2013, 16.3 percent in Jul 2013 and 15.6 percent in Aug 2013. In Sep 2006, the rate of youth unemployment was 10.5 percent, increasing to 14.8 percent in Sep 2013. The rate of youth unemployment was 10.3 in Oct 2007, increasing to 14.4 percent in Oct 2013. The rate of youth unemployment was 10.3 percent in Nov 2007, increasing to 13.1 percent in Nov 2013. The rate of youth unemployment was 10.7 percent in Dec 2013, increasing to 12.3 percent in Dec 2013. The rate of youth unemployment was 10.9 percent in Jan 2007, increasing to 14.9 percent in Jan 2014. The rate of youth unemployment was 10.3 percent in Feb 2007, increasing to 14.9 percent in Feb 2014. The rate of youth unemployment was 9.7 percent in Mar 2007, increasing to 14.3 percent in Mar 2014. The rate of youth unemployment was 9.7 percent in Apr 2007, increasing to 11.9 percent in Apr 2014. The rate of youth unemployment was 10.2 percent in May 2007, increasing to 13.4 percent in May 2014. The rate of youth unemployment was 12.0 percent in Jun 2007, increasing to 15.0 percent in Jun 2014. The rate of youth unemployment was 10.8 percent in Jul 2007, increasing to 14.3 percent in Jul 2014. The rate of youth unemployment was 10.5 percent in Aug 2007, increasing to 13.0 percent in Aug 2014. The rate of youth unemployment was 11.0 percent in Sep 2007, increasing to 13.6 percent in Sep 2014. The rate of youth unemployment increased from 10.3 in Oct 2007 to 12.2 in Oct 2014. The rate of youth unemployment increased from 10.3 percent in Nov 2007 to 11.7 percent in Nov 2014. The rate of youth unemployment increased from 10.7 in Dec 2007 to 11.2 in Dec 2014. The rate of youth unemployment increased from 9.7 in Mar 2007 to 12.3 in Mar 2015. The rate of youth unemployment increased from 9.7 in Apr 2007 to 10.7 in Apr 2015. The rate of youth unemployment increased from 10.2 in May 2007 to 12.3 in May 2015. The rate of youth unemployment increased from 12.0 in Jun 2007 to 13.7 in Jun 2015. The rate of youth unemployment increased from 10.8 in Jul 2007 to 12.2 in Jul 2015. The rate of youth unemployment increased from 10.5 in Aug 2007 to 10.9 in Aug 2015. The rate of youth unemployment decreased from 11.0 in Sep 2007 to 10.9 in Sep 2015. The rate of youth unemployment increased from 10.3 in Oct 2007 to 10.6 in Oct 2015, decreasing to 10.4 in Nov 2015. The rate of youth unemployment decreased to 10.1 in Dec 2015. The rate of youth unemployment stood at 10.8 in Jan 2016, 10.8 in Feb 2016, 10.4 in Mar 2016 and 9.9 in Apr 2016. The rate of youth unemployment increased to 10.6 in May 2016 and 12.3 in Jun 2016. The rate of youth unemployment fell to 11.5 in Jul 2016, decreasing to 10.1 in Aug 2016. The rate of youth unemployment increased to 10.2 in Sep 2016, decreasing to 10.1 in Oct 2016 and 9.3 in Nov 2016. The rate of youth unemployment decreased to 9.0 in Dec 2016, increasing to 10.7 in Jan 2017. The rate of youth unemployment fell to 10.1 in Feb 2017, decreasing to 8.9 in Mar 2017. The rate of youth unemployment fell to 8.5 in Apr 2017, increasing to 8.8 in May 2017. The rate of youth unemployment increased to 10.5 percent in Jun 2017, decreasing to 9.6 in Jul 2017. The rate of youth unemployment decreased to 8.8 percent in Aug 2017, increasing to 8.9 percent in Sep 2017. The rate of youth unemployment fell to 8.7 percent in Oct 2017, increasing to 9.1 percent in Nov 2017. The rate of youth unemployment fell to 8.1 percent in Dec 2017, increasing to 9.9 percent in Jan 2018. The rate of youth unemployment decreased to 9.2 percent in Feb 2018, decreasing to 8.2 percent in Mar 2018. The rate of youth unemployment decreased to 7.6 percent in Apr 2018. The rate of youth unemployment increased to 8.6 percent in May 2018 and increased to 10.2 percent in Jun 2018. The rate of youth unemployment decreased to 9.2 percent in Jul 2018, decreasing to 8.2 percent in Aug 2018. The rate of youth unemployment increased to 8.4 percent in Sep 2018, decreasing to 8.0 percent in Oct 2018. The rate of youth unemployment decreased to 7.7 percent in Nov 2018, increasing to 8.0 percent in Dec 2018. The rate of youth unemployment increased to 10.0 percent in Jan 2019, decreasing to 9.3 percent in Feb 2019. The rate of youth unemployment decreased to 8.5 percent in Mar 2018, decreasing to 7.4 percent in Apr 2019. The rate of youth unemployment increased to 8.5 percent in May 2018, increasing to 9.3 percent in Jun 2019. The rate of youth unemployment decreased to 9.1 percent in Jul 2019, decreasing to 8.3 percent in Aug 2019. The rate of youth unemployment decreased to 7.8 percent in Sep 2019, decreasing to 7.5 percent in Oct 2019 and stabilizing to 7.5 percent in Nov 2019. The rate of youth unemployment decreased to 7.4 percent in Dec 2019, increasing to 9.1 percent in Jan 2020. The rate of youth unemployment decreased to 8.0 percent in Feb 2020. The rate of youth unemployment increased to 10.0 in Mar 2020. The actual rate is higher because of the difficulty in counting those dropping from the labor force because they believe there are no jobs available for them. The difference originates in the vigorous seasonally adjusted annual equivalent average rate of GDP growth of 5.9 percent during the recovery from IQ1983 to IVQ1985 and 3.6 percent from IQ1983 to IIQ1993 compared with 2.3 percent on average during the first 42 quarters of expansion from IIIQ2009 to IVQ2019. US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 42 quarters from IIIQ2009 to IVQ2019. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IVQ2019 (https://www.bea.gov/system/files/2020-03/gdp4q19_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html and earlier https://cmpassocregulationblog.blogspot.com/2020/02/sharp-worldwide-contraction-of.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ2019, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html and earlier https://cmpassocregulationblog.blogspot.com/2020/02/sharp-worldwide-contraction-of.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IVQ2019 would have accumulated to 42.6 percent. GDP in IVQ2019 would be $22,476.6 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $3254.6 billion than actual $19,222.0 billion. There are more than three trillion dollars of GDP less than at trend, explaining the 24.1 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/stress-of-world-financial-markets-fomc.html). Unemployment is increasing sharply while employment is declining rapidly because of the lockdown of economic activity in the probable global recession resulting from the COVID-19 event (https://www.bls.gov/cps/employment-situation-covid19-faq-march-2020.pdf). US GDP in IVQ2019 is 14.5 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $19,222.0 billion in IVQ2019 or 22.0 percent at the average annual equivalent rate of 1.7 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Mar 1919 to Mar 2020. Growth at 3.1 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 157.4135 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511 which is 37.4 percent below trend. The deterioration of manufacturing in Mar 2020 originates in the lockdown of economic activity in the COVID-19 event. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 161.1952 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511, which is 38.9 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and Mar 2020. Using trend growth of 1.7 percent per year, the index would increase to 133.1389 in Mar 2020. The output of manufacturing at 98.5511 in Mar 2020 is 26.0 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index increased from 86.3800 in Apr 2009 to 99.9350 in Mar 2020 or 15.7 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 106.6777 in Dec 2007 to 162.5897 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 38.5 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 106.6777 in Dec 2007 to 131.1461 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 23.8 percent below trend under this alternative calculation.

Chart I-24, US, Unemployment Rate 16-24 Years, Percent NSA, 1948-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

It is more difficult to move to other jobs after a certain age because of fewer available opportunities for mature individuals than for new entrants into the labor force. Middle-aged unemployed are less likely to find another job. Table I-13 provides the unemployment level ages 45 years and over. The number unemployed ages 45 years and over rose from 1.607 million in Oct 2006 to 4.576 million in Oct 2010 or by 184.8 percent. The number of unemployed ages 45 years and over declined to 3.800 million in Oct 2012 that is still higher by 136.5 percent than in Oct 2006. The number unemployed age 45 and over increased from 1.704 million in Nov 2006 to 3.861 million in Nov 2012, or 126.6 percent. The number unemployed age 45 and over is still higher by 98.5 percent at 3.383 million in Nov 2013 than 1.704 million in Nov 2006. The number unemployed age 45 and over jumped from 1.794 million in Dec 2006 to 4.762 million in Dec 2010 or 165.4 percent. At 3.927 million in Dec 2012, mature unemployment is higher by 2.133 million or 118.9 percent higher than 1.794 million in Dec 2006. The level of unemployment of those aged 45 year or more of 3.632 million in Oct 2013 is higher by 2.025 million than 1.607 million in Oct 2006 or higher by 126.0 percent. The number of unemployed 45 years and over increased from 1.794 million in Dec 2006 to 3.378 million in Nov 2013 or 88.3 percent. The annual number of unemployed 45 years and over increased from 1.848 million in 2006 to 3.719 million in 2013 or 101.2 percent. The number of unemployed 45 years and over increased from 2.126 million in Jan 2006 to 4.394 million in Jan 2013, by 2.618 million or 106.7 percent. The number of unemployed 45 years and over rose from 2.126 million in Jan 2006 to 3.508 million in Jan 2014, by 1.382 million or 65.0 percent. The level of unemployed 45 years or older increased 2.051 million or 99.8 percent from 2.056 million in Feb 2006 to 4.107 million in Feb 2013 and at 3.490 million in Feb 2014 is higher by 69.7 percent than in Feb 2006. The number of unemployed 45 years and over increased 2.048 million or 108.9 percent from 1.881 million in Mar 2006 to 3.929 million in Mar 2013 and at 3.394 million in Mar 2014 is higher by 80.4 percent than in Mar 2006. The number of unemployed 45 years and over increased 1.846 million or 100.2 percent from 1.843 million in Apr 2006 to 3.689 million in Apr 2013 and at 3.006 million in Apr 2014 is higher by 1.163 million or 63.1 percent. The number of unemployed ages 45 years and over increased 102.1 percent from 1.784 million in May 2006 to 3.605 million in May 2014 and at 2.913 million in May 2014 is higher by 63.3 percent than in May 2007.

The number of unemployed ages 45 years and over increased 102.1 percent from 1.805 million in Jun 2007 to 3.648 million in Jun 2013 and at 2.832 million in Jun 2014 is higher by 56.9 percent than in Jun 2007. The number of unemployed ages 45 years and over increased 81.5 percent from 2.053 million in Jul 2007 to 3.727 million in Jul 2013 and at 3.083 million in Jul 2014 is higher by 50.2 percent than in Jul 2007. The level unemployed ages 45 years and over increased 84.4 percent from 1.956 million in Aug 2007 to 3.607 million in Aug 2013 and at 3.037 million in Aug 2014 is 55.2 percent higher than in Aug 2007. The level unemployed ages 45 years and over increased 90.7 percent from 1.854 million in Sep 2007 to 3.535 million in Sep 2013 and at 2.640 million in Sep 2014 is 42.4 percent higher than in Sep 2007. The level unemployed ages 45 years and over increased 1.747 million from 1.885 million in Oct 2007 to 3.632 million in Oct 2013 and at 2.606 million in Oct 2014 is 38.2 percent higher than in Oct 2007. The level unemployed ages 45 years and over increased 1.458 million from 1.925 million in Nov 2007 to 3.383 million in Nov 2013 and at 2.829 million in Nov 2014 is 47.0 percent higher than in Nov 2007. The level of unemployed ages 45 years and over increased 1.258 million from Dec 2007 to Dec 2013 and at 2.667 million in Dec 2014 is 25.8 higher than in Dec 2007. The level unemployed ages 45 years and over increased 1.353 million from Jan 2007 to Jan 2015 and at 3.077 million in Jan 2015 is 42.8 percent higher than in Jan 2007. The level unemployed ages 45 years and over increased 1.352 million from 2.138 million in Feb 2007 to 3.490 million in Feb 2014 and at 2.991 million in Feb 2015 is 39.9 percent higher than in Feb 2007. The level of unemployed ages 45 years and over increased 1.363 million from 2.031 million in Mar 2007 to 3.394 million in Mar 2014 and at 2.724 million in Mar 2015 is 34.1 percent higher than in Mar 2007. The level of unemployed ages 45 years and over increased from 1.871 million in Apr 2007 to 3.006 million in Apr 2014 and at 2.579 million in Apr 2015 is 37.8 higher than in Apr 2007. The level of unemployed ages 45 years and over increased from 1.803 million in May 2007 to 2.913 million in Jun 2014 and at 2.457 million in May 2015 is 36.3 percent higher than in May 2007. The level of unemployed ages 45 years and over increased from 1.805 million in Jun 2007 to 2.832 million in Jun 2014 and at 2.359 million in Jun 2015 is 30.7 percent higher than in Jun 2007. The level of unemployed ages 45 years and over increased from 2.053 million in Jul 2007 to 3.083 million in Jul 2014 and at 2.666 million in Jul 2015 is 30.0 percent higher than in Jul 2007. The level of unemployed ages 45 years and over increased from 1.956 million in Aug 2007 to 3.037 million in Aug 2014 and at 2.693 million in Aug 2015 is 37.7 higher than in Aug 2007. The level of unemployed ages 45 years and over increased from 1.854 million in Sep 2007 to 2.640 million in Sep 2015 and at 2.388 million in Sep 2015 is 28.8 percent higher than in Sep 2007. The level of unemployment ages 45 years and over increased from 1.885 million in Oct 2007 to 2.606 million in Oct 2014 and at 2.290 million in Oct 2015 is 21.5 percent higher than in Oct 2007. The level of unemployment ages 45 years and over increased from 1.925 million in Nov 2007 to 2.829 million in Nov 2014 and at 2.349 million in Nov 2015 is 22.0 percent higher than in Nov 2007. The level of unemployment ages 45 years and over increased from 2.120 million in Dec 2007 to 2.667 million in Dec 2014 and at 2.317 million in Dec 2015 is 9.3 percent higher than in Dec 2007. The level of unemployment ages 45 and over increased from 2.155 million in Jan 2007 to 3.077 million in Jan 2015 and at 2.736 million in Jan 2016 is 27.0 percent higher than in Jan 2007. The level of unemployment ages 45 and over increased from 2.138 million in Feb 2007 to 2.991 million in Feb 2015 and at 2.744 million in Feb 2016 is 28.3 percent higher than in Feb 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.724 million in Mar 2015 and at 2.747 million in Mar 2016 is 35.3 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 1.871 million in Apr 2007 to 2.579 million in Apr 2015 and at 2.410 million in Apr 2016 is 28.8 percent higher than in Apr 2007. The level of unemployment ages 45 and over increased from 1.803 million in May 2007 to 2.457 million in May 2015 and at 2.190 million in May 2016 is 21.5 percent higher than in May 2007. The level of unemployment ages 45 and over increased from 1.805 million in Jun 2007 to 2.359 million in Jun 2015 and at 2.345 million in Jun 2016 is 29.9 percent higher than in Jun 2007. The level of unemployment ages 45 and over increased from 2.053 million in Jul 2007 to 2.666 million in Jul 2015 and at 2.619 million in Jul 2016 is 27.6 percent higher than in Jul 2007. The level of unemployment ages 45 and over increased from 1.956 million in Aug 2007 to 2.693 million in Aug 2015 and at 2.565 million in Aug 2016 is 31.1 percent higher than in Aug 2007. The level of unemployment ages 45 and over increased from 1.854 million in Sep 2007 to 2.388 million in Sep 2015 and at 2.414 million in Sep 2016 is 30.2 percent higher than in Sep 2007. The level of unemployment ages 45 and over increased from 1.885 million in Oct 2007 to 2.290 million in Oct 2015 and at 2.337 million in Oct 2016 is 24.0 percent higher than in Oct 2007. The level of unemployment ages 45 and over increased from 1.925 million in Nov 2007 to 2.349 million in Nov 2015 and at 2.355 million in Nov 2016 is 22.3 percent higher than in Nov 2007. The level of unemployment ages 45 and over increased from 2.120 million in Dec 2007 to 2.317 million in Dec 2015 and at 2.360 million in Dec 2016 is 11.3 percent higher than in Dec 2007. The level of unemployment ages 45 and over increased from 2.155 million in Jan 2007 to 2.736 million in Jan 2016 and at 2.585 million in Jan 2017 is 20.0 percent higher than in Jan 2007. The level of unemployment ages 45 and over increased from 2.138 million in Feb 2007 to 2.744 million in Feb 2016 and at 2.493 million in Feb 2017 is 16.6 percent higher than in Feb 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.747 million in Mar 2016 and at 2.413 million in Mar 2017 is 18.8 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.747 million in Mar 2016 and at 2.413 million in Mar 2017 is 18.8 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 1.871 million in Apr 2007 to 2.410 million in Apr 2016 and at 2.202 million in Apr 2017 is 17.7 percent higher than in Apr 2007. The level of unemployment ages 45 and over increased from 1.803 million in May 2007 to 2.190 million in May 2016 and at 2.052 million in May 2017 is 13.8 percent higher than in May 2007. The level of unemployment ages 45 and over increased from 1.805 million in Jun 2007 to 2.345 million in Jun 2016 and at 2.256 million in Jun 2017 is 25.0 percent higher than in Jun 2007. The level of unemployment ages 45 and over increased from 2.053 million in Jul 2007 to 2.619 million in Jul 2016 and at 2.335 million in Jul 2017 is 13.7 percent higher than in Jul 2007. The level of unemployment ages 45 and over increased from 1.956 million in Aug 2007 to 2.565 million in Aug 2016 and at 2.444 million in Aug 2017 is 24.9 percent higher than in Aug 2007. The level of unemployment ages 45 and over increased from 1.854 million in Sep 2007 to 2.414 million in Sep 2016 and at 2.140 million in Sep 2017 is 15.4 percent higher than in Sep 2007. The level of unemployment ages 45 and over increased from 1.885 million in Oct 2007 to 2.337 million in Oct 2016 and at 1.899 million in Oct 2017 is 0.7 percent higher than in Oct 2007. The level of unemployment ages 45 and over increased from 1.925 million in Nov 2007 to 2.355 million in Nov 2016 and at 1.958 million in Nov 2017 is 1.7 percent higher than in Nov 2007. The level of unemployment ages 45 and over increased from 2.120 million in Dec 2007 to 2.360 million in Dec 2016 and at 2.079 million in Dec 2017 is 1.9 percent lower than in Dec 2007. The level of unemployment ages 45 and over increased from 2.155 million in Jan 2007 to 2.585 million in Jan 2017 and at 2.300 million in Jan 2018 is 6.7 percent higher than in Jan 2007. The level of unemployment ages 45 and over increased from 2.138 million in Feb 2007 to 2.493 million in Feb 2017 and at 2.354 million in Feb 2018 is 10.1 percent higher than in Feb 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.413 million in Mar 2017 and at 2.266 million in Mar 2018 is 11.6 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 1.871 million in Apr 2007 to 2.202 million in Apr 2017 and at 1.937 million in Apr 2018 is 3.5 percent higher than in Apr 2007. The level of unemployment ages 45 and over increased from 1.803 million in May 2007 to 2.052 million in May 2017 and at 1.774 million in May 2018 is 1.6 percent lower than in May 2007. The level of unemployment ages 45 and over increased from 1.805 million in Jun 2007 to 2.256 million in Jun 2017 and at 2.102 million in Jun 2018 is 16.5 percent higher than in Jun 2007. The level of unemployment ages 45 and over increased from 2.053 million in Jul 2007 to 2.335 million in Jul 2017 and at 2.235 million in Jul 2018 is 8.9 percent higher than in Jul 2007. The level of unemployment ages 45 and over increased from 1.956 million in Aug 2007 to 2.444 million in Aug 2017 and at 2.096 million in Aug 2018 is 7.2 percent higher than in Aug 2007. The level of unemployment ages 45 and over increased from 1.854 million in Sep 2007 to 2.140 million in Sep 2017 and at 1.872 million in Sep 2018 is 1.0 percent higher than in Sep 2007. The level of unemployment ages 45 and over increased from 1.885 million in Oct 2007 to 1.889 million in Oct 2017 and at 1.833 million in Oct 2018 is 2.8 percent lower than in Oct 2007. The level of unemployment ages 45 and over increased from 1.925 million in Nov 2007 to 1.958 million in Nov 2017 and at 1.850 million in Nov 2018 is 3.9 percent lower than in Nov 2007. The level of unemployment ages 45 and over decreased from 2.120 million in Dec 2007 to 2.079 million in Dec 2017 and at 2.026 million in Dec 2018 is 4.4 percent lower than in Dec 2007. The level of unemployment ages 45 and over increased from 2.155 million in Jan 2007 to 2.300 million in Jan 2018 and at 2.393 million in Jan 2019 is 11.0 percent higher than in Jan 2007. The level of unemployment ages 45 and over increased from 2.138 million in Feb 2007 to 2.354 million in Feb 2018 and at 2.149 million in Feb 2019 is 0.5 percent higher than in Feb 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.266 million in Mar 2018 and at 2.091 million in Mar 2019 is 3.0 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 1.871 million in Apr 2007 to 1.937 million in Apr 2018 and at 1.707 million in Apr 2019 is 8.8 percent lower than in Apr 2007. The level of unemployment ages 45 and over decreased from 1.803 million in May 2007 to 1.774 million in May 2018 and at 1.712 million in May 2019 is 5.0 percent lower than in May 2007. The level of unemployment ages 45 and over increased from 1.805 million in Jun 2007 to 2.102 million in Jun 2018 and at 1.976 million in Jun 2019 is 9.5 percent higher than in Jun 2007. The level of unemployment ages 45 and over increased from 2.053 million in Jul 2007 to 2.235 million in Jul 2018 and at 2.053 million in Jul 2019 is 0.0 percent higher than in Jul 2007. The level of unemployment ages 45 and over increased from 1.956 million in Aug 2007 to 2.096 million in Aug 2018 and at 2.018 million in Aug 2019 is 3.2 percent higher than in Aug 2007. The level of unemployment ages 45 and over increased from 1.854 million in Sep 2007 to 1.872 million in Sep 2018 and at 1.755 million in Sep 2019 is 5.3 percent lower than in Sep 2007. The level of unemployment ages 45 and over decreased from 1.885 million in Oct 2007 to 1.833 million in Oct 2018 and at 1.703 million in Oct 2019 is 9.7 percent lower than in Oct 2007. The level of unemployment ages 45 and over decreased from 1.925 million in Nov 2007 to 1.850 million in Nov 2018 and at 1.732 million in Nov 2019 is 10.0 percent lower than in Nov 2007. The level of unemployment ages 45 and over decreased from 2.120 million in Dec 2007 to 2.026 million in Dec 2018 and at 1.713 million in Dec 2019 is 19.2 percent lower than in Dec 2007. The level of unemployment ages 45 and over increased from 2.155 million in Jan 2007 to 2.393 million in Jan 2019 and at 1.990 million in Jan 2020 is 7.7 percent lower than in Jan 2007. The level of unemployment ages 45 and over increased from 2.138 million in Feb 2007 to 2.149 million in Feb 2019 and at 2.000 million in Feb 2020 is 6.5 percent lower than in Feb 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.091 million in Mar 2019 and at 2.462 million in Mar 2020 is 21.2 percent higher than in Mar 2007. The actual number unemployed is likely much higher because many are not accounted who abandoned job searches in frustration there may not be a job for them. Recent improvements may be illusory. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 42 quarters from IIIQ2009 to IVQ2019. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IVQ2019 (https://www.bea.gov/system/files/2020-03/gdp4q19_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html and earlier https://cmpassocregulationblog.blogspot.com/2020/02/sharp-worldwide-contraction-of.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ2019, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html and earlier https://cmpassocregulationblog.blogspot.com/2020/02/sharp-worldwide-contraction-of.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IVQ2019 would have accumulated to 42.6 percent. GDP in IVQ2019 would be $22,476.6 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $3254.6 billion than actual $19,222.0 billion. There are more than three trillion dollars of GDP less than at trend, explaining the 24.1 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/stress-of-world-financial-markets-fomc.html). Unemployment is increasing sharply while employment is declining rapidly because of the lockdown of economic activity in the probable global recession resulting from the COVID-19 event (https://www.bls.gov/cps/employment-situation-covid19-faq-march-2020.pdf). US GDP in IVQ2019 is 14.5 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $19,222.0 billion in IVQ2019 or 22.0 percent at the average annual equivalent rate of 1.7 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Mar 1919 to Mar 2020. Growth at 3.1 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 157.4135 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511 which is 37.4 percent below trend. The deterioration of manufacturing in Mar 2020 originates in the lockdown of economic activity in the COVID-19 event. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 161.1952 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511, which is 38.9 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and Mar 2020. Using trend growth of 1.7 percent per year, the index would increase to 133.1389 in Mar 2020. The output of manufacturing at 98.5511 in Mar 2020 is 26.0 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index increased from 86.3800 in Apr 2009 to 99.9350 in Mar 2020 or 15.7 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 106.6777 in Dec 2007 to 162.5897 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 38.5 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 106.6777 in Dec 2007 to 131.1461 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 23.8 percent below trend under this alternative calculation.

Table I-13, US, Unemployment Level 45 Years and Over, NSA

Year	Jan	Feb	Mar	Dec	Annual
2000	1498	1392	1291	1217	1249
2001	1572	1587	1533	1901	1576
2002	2235	2280	2138	2210	2114
2003	2495	2415	2485	2130	2253
2004	2453	2397	2354	2086	2149
2005	2286	2286	2126	1963	2009
2006	2126	2056	1881	1794	1848
2007	2155	2138	2031	2120	1966
2008	2336	2336	2326	3485	2540
2009	4138	4380	4518	4960	4500
2010	5314	5307	5194	4762	4879
2011	5027	4837	4748	4182	4537
2012	4458	4472	4390	3927	4133
2013	4394	4107	3929	3378	3719
2014	3508	3490	3394	2667	3000
2015	3077	2991	2724	2317	2574
2016	2736	2744	2747	2360	2485
2017	2585	2493	2413	2079	2238
2018	2300	2354	2266	2026	2054
2019	2393	2149	2091	1713	1917
2020	1990	2000	2462

Sources: US Bureau of Labor Statistics

https://www.bls.gov/data/

Chart I-25 provides the level unemployed ages 45 years and over. There was an increase in the recessions of the 1980s, 1991 and 2001 followed by declines to earlier levels. The current expansion of the economy after IIIQ2009 has not been sufficiently vigorous to reduce significantly middle-age unemployment. Recent improvements could be illusory because many abandoned job searches in frustration that there may not be jobs for them and are not counted as unemployed.

Chart I-25, US, Unemployment Level Ages 45 Years and Over, Thousands, NSA, 1976-2020

Source: US Bureau of Labor Statistics https://www.bls.gov/data/

In the analysis of Hansen (1939, 3) of secular stagnation, economic progress consists of growth of real income per person driven by growth of productivity. The “constituent elements” of economic progress are “(a) inventions, (b) the discovery and development of new territory and new resources, and (c) the growth of population” (Hansen 1939, 3). Secular stagnation originates in decline of population growth and discouragement of inventions. According to Hansen (1939, 2), US population grew by 16 million in the 1920s but grew by one half or about 8 million in the 1930s with forecasts at the time of Hansen’s writing in 1938 of growth of around 5.3 million in the 1940s. Hansen (1939, 2) characterized demography in the US as “a drastic decline in the rate of population growth.” Hansen’s plea was to adapt economic policy to stagnation of population in ensuring full employment. In the analysis of Hansen (1939, 8), population caused half of the growth of US GDP per year. Growth of output per person in the US and Europe was caused by “changes in techniques and to the exploitation of new natural resources.” In this analysis, population caused 60 percent of the growth of capital formation in the US. Declining population growth would reduce growth of capital formation. Residential construction provided an important share of growth of capital formation. Hansen (1939, 12) argues that market power of imperfect competition discourages innovation with prolonged use of obsolete capital equipment. Trade unions would oppose labor-savings innovations. The combination of stagnating and aging population with reduced innovation caused secular stagnation. Hansen (1939, 12) concludes that there is role for public investments to compensate for lack of dynamism of private investment but with tough tax/debt issues.

Secular stagnation would predict idle capacity, in particular in residential investment when fed fund rates were fixed at 1 percent from Jun 2003 to Jun 2004. Taylor (2014Jan01) finds unemployment at 4.4 percent with house prices jumping 7 percent from 2002 to 2003 and 14 percent from 2004 to 2005 before dropping from 2006 to 2007. GDP prices doubled from 1.7 percent to 3.4 percent when interest rates were low from 2003 to 2005.
Taylor (2014Jan01, 2014Jan3) finds another contradiction in the application of secular stagnation based on low interest rates because of savings glut and lack of investment opportunities. Taylor (2009) shows that there was no savings glut. The savings rate of the US in the past decade is significantly lower than in the 1980s.
Taylor (2014Jan01, 2014Jan3) finds another contradiction in the low ratio of investment to GDP currently and reduced investment and hiring by US business firms.
Taylor (2014Jan01, 2014Jan3) argues that the financial crisis and global recession were caused by weak implementation of existing regulation and departure from rules-based policies.

Taylor (2014Jan01, 2014Jan3) argues that the recovery from the global recession was constrained by a change in the regime of regulation and fiscal/monetary policies.

The analysis by Kydland (https://www.nobelprize.org/prizes/economic-sciences/2004/kydland/biographical/) and Prescott (https://www.nobelprize.org/prizes/economic-sciences/2004/prescott/biographical/) (1977, 447-80, equation 5) uses the “expectation augmented” Phillips curve with the natural rate of unemployment of Friedman (1968) and Phelps (1968), which in the notation of Barro and Gordon (1983, 592, equation 1) is:

U_t = Uⁿ_t – α(π_t – π^e) α > 0 (1)

Where U_t is the rate of unemployment at current time t, Uⁿ_t is the natural rate of unemployment, π_t is the current rate of inflation and π^e is the expected rate of inflation by economic agents based on current information. Equation (1) expresses unemployment net of the natural rate of unemployment as a decreasing function of the gap between actual and expected rates of inflation. The system is completed by a social objective function, W, depending on inflation, π, and unemployment, U:

W = W(π_t, U_t) (2)

The policymaker maximizes the preferences of the public, (2), subject to the constraint of the tradeoff of inflation and unemployment, (1). The total differential of W set equal to zero provides an indifference map in the Cartesian plane with ordered pairs (π_t, U_t- Uⁿ) such that the consistent equilibrium is found at the tangency of an indifference curve and the Phillips curve in (1). The indifference curves are concave to the origin. The consistent policy is not optimal. Policymakers without discretionary powers following a rule of price stability would attain equilibrium with unemployment not higher than with the consistent policy. The optimal outcome is obtained by the rule of price stability, or zero inflation, and no more unemployment than under the consistent policy with nonzero inflation and the same unemployment. Taylor (1998LB) attributes the sustained boom of the US economy after the stagflation of the 1970s to following a monetary policy rule instead of discretion (see Taylor 1993, 1999). Professor John B. Taylor (2014Jul15, 2014Jun26) building on advanced research (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB, 2015, 2012 Oct 25; 2013Oct28, 2014 Jan01, 2014Jan3, 2014Jun26, 2014Jul15, 2015, 2016Dec7, 2016Dec20 http://www.johnbtaylor.com/) finds that a monetary policy rule would function best in promoting an environment of low inflation and strong economic growth with stability of financial markets. There is strong case for using rules instead of discretionary authorities in monetary policy (http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). It is not uncommon for effects of regulation differing from those intended by policy. Professors Edward C. Prescott and Lee E. Ohanian (2014Feb), writing on “US productivity growth has taken a dive,” on Feb 3, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303942404579362462611843696?KEYWORDS=Prescott), argue that impressive productivity growth over the long-term constructed US prosperity and wellbeing. Prescott and Ohanian (2014Feb) measure US productivity growth at 2.5 percent per year since 1948. Average US productivity growth has been only 1.1 percent since 2011. Prescott and Ohanian (2014Feb) argue that living standards in the US increased at 28 percent in a decade but with current slow growth of productivity will only increase 12 percent by 2024. There may be collateral effects on productivity growth from policy design similar to those in Kydland and Prescott (1977). Professor Edward P. Lazear (2017Feb27), writing in the Wall Street Journal, on Feb 27, 2017 (https://www.wsj.com/articles/how-trump-can-hit-3-growthmaybe-1488239746), finds that productivity growth was 7 percent between 2009 and 2016 at annual equivalent 1 percent. Lazear measures productivity growth at 2.3 percent per year from 2001 to 2008. Herkenhoff, Ohanian and Prescott (2017) and Ohanian and Prescott (2017Dec) analyze how restriction of land use by states in the United States have been depressing economic activity. Professor Edmund S. Phelps (https://www.nobelprize.org/prizes/economic-sciences/2006/phelps/auto-biography/) argues that there is failed analysis that fiscal stimulus in the form of higher government expenditures and tax reductions caused the recovery of the economy to normal levels by 2017 (Phelps, Edmund S. 2018. The fantasy of fiscal stimulus. The Wall Street Journal Oct 29, 2018 https://www.wsj.com/articles/the-fantasy-of-fiscal-stimulus-1540852299?mod=searchresults&page=1&pos=2). The evidence analyzed by Phelps leads to the conclusion that countries with disorderly government finance grew less rapidly than those with sounder fiscal performance. Phelps concludes convincingly that “there is a strong relationship between the speed of recovery and a proxy of its dynamism—the long-term growth rate of total factor productivity from 1990 to 2007. Some countries have preexisting social institutions and cultural capital that enables them to bounce back from an economic downturn. Much credit of the U.S.’s relatively speedy recovery is owed to this country’s endemic culture of innovation and enterprise.” Professor Edward P. Lazear, writing on “Mind the productivity gap to reduce inequality,” published in the Wall Street Journal on May 6, 2019 (https://www.wsj.com/articles/mind-the-productivity-gap-to-reduce-inequality-11557181134?mod=searchresults&page=1&pos=1), analyzes the causes of the growing differential of wages between the income of the 90^th percentile and the 50^th percentile in terms of technological change. The improvement of the lower half of wage earners would consist of increasing their skills. The Bureau of Labor Statistics important report on productivity and costs released Mar 5, 2020 (https://www.bls.gov/lpc/) supports the argument of decline of productivity growth in the US analyzed by Prescott and Ohanian (2014Feb), Lazear (2017Feb27) and Phelps (2018). Table II-2 provides the annual percentage changes of productivity, real hourly compensation and unit labor costs for the entire economic cycle from 2007 to 2019. The estimates incorporate the yearly revision of the US national accounts (https://www.bea.gov/information-updates-national-income-and-product-accounts) and the comprehensive revisions since 1929 (https://apps.bea.gov/national/pdf/2018-ComprehensiveUpdate-Results.pdf). The data confirm the argument of Prescott and Ohanian (2014Feb) and Lazear (2017Feb27): productivity increased cumulatively 8.8 percent from 2011 to 2019 at the average annual rate of 0.9 percent. Average productivity growth for the entire economic cycle from 2007 to 2019 is only 1.4 percent. The argument by Prescott and Ohanian (2014Feb) is proper in choosing the tail of the business cycle because the increase in productivity in 2009 of 3.6 percent and 3.4 percent in 2010 consisted of reducing labor hours

In revealing research, Edward P. Lazear and James R. Spletzer (2012JHJul22) use the wealth of data in the valuable database and resources of the Bureau of Labor Statistics (http://www.bls.gov/data/) in providing clear thought on the nature of the current labor market of the United States. The critical issue of analysis and policy currently is whether unemployment is structural or cyclical. Structural unemployment could occur because of (1) industrial and demographic shifts and (2) mismatches of skills and job vacancies in industries and locations. Consider the aggregate unemployment rate, Y, expressed in terms of share s_i of a demographic group in an industry i and unemployment rate y_i of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):

Y = ∑_is_iy_i (1)

This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):

∆Y = ∑_i∆s_iy*_i+ ∑_i∆y_is*_i (2)

The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. The theory of secular stagnation departs from an aggregate production function in which output grows with the use of labor, capital and technology (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), 11-6). Simon Kuznets (1971) analyzes modern economic growth in his Lecture in Memory of Alfred Nobel:

“The major breakthroughs in the advance of human knowledge, those that constituted dominant sources of sustained growth over long periods and spread to a substantial part of the world, may be termed epochal innovations. And the changing course of economic history can perhaps be subdivided into economic epochs, each identified by the epochal innovation with the distinctive characteristics of growth that it generated.Without considering the feasibility of identifying and dating such economic epochs, we may proceed on the working assumption that modern economic growth represents such a distinct epoch - growth dating back to the late eighteenth century and limited (except in significant partial effects) to economically developed countries. These countries, so classified because they have managed to take adequate advantage of the potential of modern technology, include most of Europe, the overseas offshoots of Western Europe, and Japan—barely one quarter of world population.”

Chart II-7 provides nonfarm-business labor productivity, measured by output per hour, from 1947 to 2019. The rate of productivity increase continued in the early part of the 2000s but then softened and fell during the global recession. The interruption of productivity increases occurred exclusively in the current business cycle. Lazear and Spletzer (2012JHJul22) find “primarily cyclic” factors in explaining the frustration of currently depressed labor markets in the United States. Stagnation of productivity is another cyclic event and not secular trend. The theory and application of secular stagnation to current US economic conditions is void of reality.

Chart II-7, US, Nonfarm Business Labor Productivity, Output per Hour, 1947-2020, Index 2012=100

Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/

Table II-6 expands Table II-2 providing more complete measurements of the Productivity and Cost research of the Bureau of Labor Statistics. The proper emphasis of Prescott and Ohanian (2014Feb) is on the low productivity increases from 2011 to 2019. Labor productivity increased 3.4 percent in 2010 and 3.6 percent in 2009. There is much stronger yet not sustained performance in 2010 with productivity growing 3.4 percent because of growth of output of 3.3 percent with decline of hours worked of 0.1 percent. Productivity growth of 3.6 percent in 2009 consists of decline of output by 3.9 percent while hours worked collapsed 7.2 percent, which is not a desirable route to progress. The expansion phase of the economic cycle concentrated in one year, 2010, with underperformance in the remainder of the expansion from 2011 to 2019 of productivity growth at average 0.9 percent per year.

Table II-6, US, Productivity and Costs, Annual Percentage Changes 2007-2019

	2017	2018	2019
Productivity	1.3	1.4	19
Output	2.9	3.5	2.7
Hours Worked	1.5	2.0	0.7
Employment	1.6	1.9	1.2
Average Weekly Hours Worked	0.0	0.1	-0.5
Unit Labor Costs	2.1	1.8	1.7
Hourly Compensation	3.5	3.3	3.7
Consumer Price Inflation	2.1	2.4	1.8
Real Hourly Compensation	1.3	0.8	1.9
Non-labor Payments	3.9	6.5	3.7
Output per Job	1.3	1.6	1.4
	2016	2015	2014	2013	2012
Productivity	0.3	1.3	0.9	0.5	0.9
Output	1.7	3.5	3.2	2.2	3.1
Hours Worked	1.5	2.1	2.3	1.7	2.3
Employment	1.8	2.2	2.0	1.8	2.0
Average Weekly Hours Worked	-0.3	-0.1	0.2	-0.1	0.3
Unit Labor Costs	0.8	1.7	1.9	0.8	1.8
Hourly Compensation	1.1	3.1	2.8	1.3	2.7
Consumer Price Inflation	1.3	0.1	1.6	1.5	2.1
Real Hourly Compensation	-0.2	2.9	1.1	-0.2	0.5
Non-labor Payments	2.9	3.1	4.8	4.6	5.2
Output per Job	-0.1	1.2	1.1	0.4	1.1

	2011	2010	2009	2008	2007
Productivity	0.0	3.4	3.6	1.1	1.7
Output	2.0	3.3	-3.9	-1.0	2.4
Hours Worked	2.0	-0.1	-7.2	-2.1	0.7
Employment	1.6	-1.2	-5.7	-1.4	0.9
Average Weekly Hours Worked	0.4	1.1	-1.6	-0.7	-0.2
Unit Labor Costs	2.2	-1.5	-2.5	1.7	2.6
Hourly Compensation	2.2	1.9	0.9	2.9	4.3
Consumer Price Inflation	3.2	1.6	-0.4	3.8	2.8
Real Hourly Compensation	-0.9	0.2	1.3	-0.9	1.5
Non-labor Payments	3.6	7.8	1.0	0.3	3.6
Output per Job	0.4	4.5	1.9	0.4	1.5

Source: US Bureau of Labor Statistics https://www.bls.gov/lpc/

Productivity growth can bring about prosperity while productivity regression can jeopardize progress. Cobet and Wilson (2002) provide estimates of output per hour and unit labor costs in national currency and US dollars for the US, Japan and Germany from 1950 to 2000 (see Pelaez and Pelaez, The Global Recession Risk (2007), 137-44). The average yearly rate of productivity change from 1950 to 2000 was 2.9 percent in the US, 6.3 percent for Japan and 4.7 percent for Germany while unit labor costs in USD increased at 2.6 percent in the US, 4.7 percent in Japan and 4.3 percent in Germany. From 1995 to 2000, output per hour increased at the average yearly rate of 4.6 percent in the US, 3.9 percent in Japan and 2.6 percent in Germany while unit labor costs in USD fell at minus 0.7 percent in the US, 4.3 percent in Japan and 7.5 percent in Germany. There was increase in productivity growth in Japan and France within the G7 in the second half of the 1990s but significantly lower than the acceleration of 1.3 percentage points per year in the US. Table II-7 provides average growth rates of indicators in the research of productivity and growth of the US Bureau of Labor Statistics. There is dramatic decline of productivity growth from 2.1 percent per year on average from 1947 to 2019 to 1.4 percent per year on average in the whole cycle from 2007 to 2019. Productivity increased at the average rate of 2.3 percent from 1947 to 2007. There is profound drop in the average rate of output growth from 3.4 percent on average from 1947 to 2019 to 1.9 percent from 2007 to 2019. Output grew at 3.7 percent per year on average from 1947 to 2007. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 43 quarters from IIIQ2009 to IQ2020. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the first estimate of GDP for IQ2020 (https://www.bea.gov/system/files/2020-04/gdp1q20_adv.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ2019, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2020 and the lockdown of economic activity in COVID-19 would have accumulated to 43.6 percent. GDP in IQ2020 would be $22,634.2 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $3646.3 billion than actual $18,987.9 billion. There are more than three trillion dollars of GDP less than at trend, explaining the 51.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 30.0 percent of the effective labor force with the largest part originating in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html). Unemployment is increasing sharply while employment is declining rapidly because of the lockdown of economic activity in the probable global recession resulting from the COVID-19 event (https://www.bls.gov/cps/employment-situation-covid19-faq-april-2020.pdf). US GDP in IQ2020 is 16.1 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,987.9 billion in IQ2020 or 20.5 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Mar 1919 to Mar 2020. Growth at 3.1 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 157.4135 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511 which is 37.4 percent below trend. The deterioration of manufacturing in Mar 2020 originates in the lockdown of economic activity in the COVID-19 event. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 161.1952 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511, which is 38.9 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and Mar 2020. Using trend growth of 1.7 percent per year, the index would increase to 133.1389 in Mar 2020. The output of manufacturing at 98.5511 in Mar 2020 is 26.0 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index increased from 86.3800 in Apr 2009 to 99.9350 in Mar 2020 or 15.7 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 106.6777 in Dec 2007 to 162.5897 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 38.5 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 106.6777 in Dec 2007 to 131.1461 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 23.8 percent below trend under this alternative calculation.

Table II-7, US, Productivity and Costs, Average Annual Percentage Changes 2007-2019, 1947-2007 and 1947-2019

	Average Annual Percentage Rate 2007-2019	Average Annual Percentage Rate 1947-2007	Average Annual Percentage Rate 1947-2019
Productivity	1.4	2.3	2.1
Output	1.9	3.7	3.4
Hours	0.5	1.4	1.2
Employment	0.6	1.6	1.5
Average Weekly Hours	-1.2*	-14.4*	-15.5*
Hourly Compensation	2.4	5.4	4.9
Consumer Price Inflation	1.8	3.8	3.4
Real Hourly Compensation	0.6	1.7	1.5
Unit Labor Costs	1.0	3.0	2.7
Unit Non-Labor Payments	2.0	3.5	3.2
Output per Job	1.3	2.0	1.9

* Percentage Change

Source: US Bureau of Labor Statistics https://www.bls.gov/lpc/

Unit labor costs increased sharply during the Great Inflation from the late 1960s to 1981 as shown by sharper slope in Chart II-8. Unit labor costs continued to increase but at a lower rate because of cyclic factors and not because of imaginary secular stagnation.

Chart II-8, US, Nonfarm Business, Unit Labor Costs, 1947-2020, Index 2012=100

Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/

Real hourly compensation increased at relatively high rates after 1947 to the early 1970s but reached a plateau that lasted until the early 1990s, as shown in Chart II-9. There were rapid increases until the global recession. Cyclic factors and not alleged secular stagnation explain the interruption of increases in real hourly compensation.

Chart II-9, US, Nonfarm Business, Real Hourly Compensation, 1947-2020, Index 2012=100

Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/

There are collateral effects of unconventional monetary policy. Chart VIII-1 of the Board of Governors of the Federal Reserve System provides the rate on the overnight fed funds rate and the yields of the 10-year constant maturity Treasury and the Baa seasoned corporate bond. Table VIII-3 provides the data for selected points in Chart VIII-1. There are two important economic and financial events, illustrating the ease of inducing carry trade with extremely low interest rates and the resulting financial crash and recession of abandoning extremely low interest rates.

The Federal Open Market Committee (FOMC) lowered the target of the fed funds rate from 7.03 percent on Jul 3, 2000, to 1.00 percent on Jun 22, 2004, in pursuit of non-existing deflation (Pelaez and Pelaez, International Financial Architecture (2005), 18-28, The Global Recession Risk (2007), 83-85). Central bank commitment to maintain the fed funds rate at 1.00 percent induced adjustable-rate mortgages (ARMS) linked to the fed funds rate. Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment. The exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at interest rates close to zero, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV). The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper with the objective of purchasing default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever-increasing house prices. The suspension of auctions of 30-year Treasuries was designed to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity by the penalty in the form of low interest rates and unsound credit decisions. The put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4). The FOMC implemented increments of 25 basis points of the fed funds target from Jun 2004 to Jun 2006, raising the fed funds rate to 5.25 percent on Jul 3, 2006, as shown in Chart VIII-1. The gradual exit from the first round of unconventional monetary policy from 1.00 percent in Jun 2004 (http://www.federalreserve.gov/boarddocs/press/monetary/2004/20040630/default.htm) to 5.25 percent in Jun 2006 (http://www.federalreserve.gov/newsevents/press/monetary/20060629a.htm) caused the financial crisis and global recession.
On Dec 16, 2008, the policy determining committee of the Fed decided (http://www.federalreserve.gov/newsevents/press/monetary/20081216b.htm): “The Federal Open Market Committee decided today to establish a target range for the federal funds rate of 0 to 1/4 percent.” Policymakers emphasize frequently that there are tools to exit unconventional monetary policy at the right time. At the confirmation hearing on nomination for Chair of the Board of Governors of the Federal Reserve System, Vice Chair Yellen (2013Nov14 http://www.federalreserve.gov/newsevents/testimony/yellen20131114a.htm), states that: “The Federal Reserve is using its monetary policy tools to promote a more robust recovery. A strong recovery will ultimately enable the Fed to reduce its monetary accommodation and reliance on unconventional policy tools such as asset purchases. I believe that supporting the recovery today is the surest path to returning to a more normal approach to monetary policy.” Perception of withdrawal of $2671 billion, or $2.7 trillion, of bank reserves (http://www.federalreserve.gov/releases/h41/current/h41.htm#h41tab1), would cause Himalayan increase in interest rates that would provoke another recession. There is no painless gradual or sudden exit from zero interest rates because reversal of exposures created on the commitment of zero interest rates forever.

In his classic restatement of the Keynesian demand function in terms of “liquidity preference as behavior toward risk,” James Tobin (http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1981/tobin-bio.html) identifies the risks of low interest rates in terms of portfolio allocation (Tobin 1958, 86):

“The assumption that investors expect on balance no change in the rate of interest has been adopted for the theoretical reasons explained in section 2.6 rather than for reasons of realism. Clearly investors do form expectations of changes in interest rates and differ from each other in their expectations. For the purposes of dynamic theory and of analysis of specific market situations, the theories of sections 2 and 3 are complementary rather than competitive. The formal apparatus of section 3 will serve just as well for a non-zero expected capital gain or loss as for a zero expected value of g. Stickiness of interest rate expectations would mean that the expected value of g is a function of the rate of interest r, going down when r goes down and rising when r goes up. In addition to the rotation of the opportunity locus due to a change in r itself, there would be a further rotation in the same direction due to the accompanying change in the expected capital gain or loss. At low interest rates expectation of capital loss may push the opportunity locus into the negative quadrant, so that the optimal position is clearly no consols, all cash. At the other extreme, expectation of capital gain at high interest rates would increase sharply the slope of the opportunity locus and the frequency of no cash, all consols positions, like that of Figure 3.3. The stickier the investor's expectations, the more sensitive his demand for cash will be to changes in the rate of interest (emphasis added).”

Tobin (1969) provides more elegant, complete analysis of portfolio allocation in a general equilibrium model. The major point is equally clear in a portfolio consisting of only cash balances and a perpetuity or consol. Let g be the capital gain, r the rate of interest on the consol and r^e the expected rate of interest. The rates are expressed as proportions. The price of the consol is the inverse of the interest rate, (1+r^e). Thus, g = [(r/r^e) – 1]. The critical analysis of Tobin is that at extremely low interest rates there is only expectation of interest rate increases, that is, dr^e>0, such that there is expectation of capital losses on the consol, dg<0. Investors move into positions combining only cash and no consols. Valuations of risk financial assets would collapse in reversal of long positions in carry trades with short exposures in a flight to cash. There is no exit from a central bank created liquidity trap without risks of financial crash and another global recession. The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Friedman 1957). According to a subsequent statement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:

W = Y/r (1)

Equation (1) shows that as r goes to zero, r→0, W grows without bound, W→∞. Unconventional monetary policy lowers interest rates to increase the present value of cash flows derived from projects of firms, creating the impression of long-term increase in net worth. An attempt to reverse unconventional monetary policy necessarily causes increases in interest rates, creating the opposite perception of declining net worth. As r→∞, W = Y/r →0. There is no exit from unconventional monetary policy without increasing interest rates with resulting pain of financial crisis and adverse effects on production, investment and employment.

Dan Strumpf and Pedro Nicolaci da Costa, writing on “Fed’s Yellen: Stock Valuations ‘Generally are Quite High,’” on May 6, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/feds-yellen-cites-progress-on-bank-regulation-1430918155?tesla=y ), quote Chair Yellen at open conversation with Christine Lagarde, Managing Director of the IMF, finding “equity-market valuations” as “quite high” with “potential dangers” in bond valuations. The DJIA fell 0.5 percent on May 6, 2015, after the comments and then increased 0.5 percent on May 7, 2015 and 1.5 percent on May 8, 2015.

Fri May 1

Mon 4

Tue 5

Wed 6

Thu 7

Fri 8

DJIA

18024.06

-0.3%

1.0%

18070.40

0.3%

17928.20

-0.5%

-0.8%

17841.98

-1.0%

-0.5%

17924.06

-0.6%

0.5%

18191.11

0.9%

1.5%

There are two approaches in theory considered by Bordo (2012Nov20) and Bordo and Lane (2013). The first approach is in the classical works of Milton Friedman and Anna Jacobson Schwartz (1963a, 1987) and Karl Brunner and Allan H. Meltzer (1973). There is a similar approach in Tobin (1969). Friedman and Schwartz (1963a, 66) trace the effects of expansionary monetary policy into increasing initially financial asset prices: “It seems plausible that both nonbank and bank holders of redundant balances will turn first to securities comparable to those they have sold, say, fixed-interest coupon, low-risk obligations. But as they seek to purchase these they will tend to bid up the prices of those issues. Hence they, and also other holders not involved in the initial central bank open-market transactions, will look farther afield: the banks, to their loans; the nonbank holders, to other categories of securities-higher risk fixed-coupon obligations, equities, real property, and so forth.”

The second approach is by the Austrian School arguing that increases in asset prices can become bubbles if monetary policy allows their financing with bank credit. Professor Michael D. Bordo provides clear thought and empirical evidence on the role of “expansionary monetary policy” in inflating asset prices (Bordo2012Nov20, Bordo and Lane 2013). Bordo and Lane (2013) provide revealing narrative of historical episodes of expansionary monetary policy. Bordo and Lane (2013) conclude that policies of depressing interest rates below the target rate or growth of money above the target influences higher asset prices, using a panel of 18 OECD countries from 1920 to 2011. Bordo (2012Nov20) concludes: “that expansionary money is a significant trigger” and “central banks should follow stable monetary policies…based on well understood and credible monetary rules.” Taylor (2007, 2009) explains the housing boom and financial crisis in terms of expansionary monetary policy. Professor Martin Feldstein (2016), at Harvard University, writing on “A Federal Reserve oblivious to its effects on financial markets,” on Jan 13, 2016, published in the Wall Street Journal (http://www.wsj.com/articles/a-federal-reserve-oblivious-to-its-effect-on-financial-markets-1452729166), analyzes how unconventional monetary policy drove values of risk financial assets to high levels. Quantitative easing and zero interest rates distorted calculation of risks with resulting vulnerabilities in financial markets.

Another hurdle of exit from zero interest rates is “competitive easing” that Professor Raghuram Rajan, governor of the Reserve Bank of India, characterizes as disguised “competitive devaluation” (http://www.centralbanking.com/central-banking-journal/interview/2358995/raghuram-rajan-on-the-dangers-of-asset-prices-policy-spillovers-and-finance-in-india). The fed has been considering increasing interest rates. The European Central Bank (ECB) announced, on Mar 5, 2015, the beginning on Mar 9, 2015 of its quantitative easing program denominated as Public Sector Purchase Program (PSPP), consisting of “combined monthly purchases of EUR 60 bn [billion] in public and private sector securities” (http://www.ecb.europa.eu/mopo/liq/html/pspp.en.html). Expectation of increasing interest rates in the US together with euro rates close to zero or negative cause revaluation of the dollar (or devaluation of the euro and of most currencies worldwide). US corporations suffer currency translation losses of their foreign transactions and investments (http://www.fasb.org/jsp/FASB/Pronouncement_C/SummaryPage&cid=900000010318) while the US becomes less competitive in world trade (Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), Government Intervention in Globalization (2008c)). The DJIA fell 1.5 percent on Mar 6, 2015 and the dollar revalued 2.2 percent from Mar 5 to Mar 6, 2015. The euro has devalued 47.0 percent relative to the dollar from the high on Jul 15, 2008 to May 15, 2020.

Fri 27 Feb

Mon 3/2

Tue 3/3

Wed 3/4

Thu 3/5

Fri 3/6

USD/ EUR

1.1197

1.6%

0.0%

1.1185

0.1%

1.1176

0.2%

0.1%

1.1081

1.0%

0.9%

1.1030

1.5%

0.5%

1.0843

3.2%

1.7%

Chair Yellen explained the removal of the word “patience” from the advanced guidance at the press conference following the FOMC meeting on Mar 18, 2015 (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20150318.pdf):

“In other words, just because we removed the word “patient” from the statement doesn’t mean we are going to be impatient. Moreover, even after the initial increase in the target funds rate, our policy is likely to remain highly accommodative to support continued progress toward our objectives of maximum employment and 2 percent inflation.”

Exchange rate volatility is increasing in response of “impatience” in financial markets with monetary policy guidance and measures:

Fri Mar 6

Mon 9

Tue 10

Wed 11

Thu 12

Fri 13

USD/ EUR

1.0843

3.2%

1.7%

1.0853

-0.1%

1.0700

1.3%

1.4%

1.0548

2.7%

1.4%

1.0637

1.9%

-0.8%

1.0497

3.2%

1.3%

Fri Mar 13

Mon 16

Tue 17

Wed 18

Thu 19

Fri 20

USD/ EUR

1.0497

3.2%

1.3%

1.0570

-0.7%

1.0598

-1.0%

-0.3%

1.0864

-3.5%

-2.5%

1.0661

-1.6%

1.9%

1.0821

-3.1%

-1.5%

Fri Apr 24

Mon 27

Tue 28

Wed 29

Thu 30

May Fri 1

USD/ EUR

1.0874

-0.6%

-0.4%

1.0891

-0.2%

1.0983

-1.0%

-0.8%

1.1130

-2.4%

-1.3%

1.1223

-3.2%

-0.8%

1.1199

-3.0%

0.2%

In a speech at Brown University on May 22, 2015, Chair Yellen stated (http://www.federalreserve.gov/newsevents/speech/yellen20150522a.htm):

“For this reason, if the economy continues to improve as I expect, I think it will be appropriate at some point this year to take the initial step to raise the federal funds rate target and begin the process of normalizing monetary policy. To support taking this step, however, I will need to see continued improvement in labor market conditions, and I will need to be reasonably confident that inflation will move back to 2 percent over the medium term. After we begin raising the federal funds rate, I anticipate that the pace of normalization is likely to be gradual. The various headwinds that are still restraining the economy, as I said, will likely take some time to fully abate, and the pace of that improvement is highly uncertain.”

The US dollar appreciated 3.8 percent relative to the euro in the week of May 22, 2015:

Fri May 15

Mon 18

Tue 19

Wed 20

Thu 21

Fri 22

USD/ EUR

1.1449

-2.2%

-0.3%

1.1317

1.2%

1.1150

2.6%

1.5%

1.1096

3.1%

0.5%

1.1113

2.9%

-0.2%

1.1015

3.8%

0.9%

The Managing Director of the International Monetary Fund (IMF), Christine Lagarde, warned on Jun 4, 2015, that: (http://blog-imfdirect.imf.org/2015/06/04/u-s-economy-returning-to-growth-but-pockets-of-vulnerability/):

“The Fed’s first rate increase in almost 9 years is being carefully prepared and telegraphed. Nevertheless, regardless of the timing, higher US policy rates could still result in significant market volatility with financial stability consequences that go well beyond US borders. I weighing these risks, we think there is a case for waiting to raise rates until there are more tangible signs of wage or price inflation than are currently evident. Even after the first rate increase, a gradual rise in the federal fund rates will likely be appropriate.”

The President of the European Central Bank (ECB), Mario Draghi, warned on Jun 3, 2015 that (http://www.ecb.europa.eu/press/pressconf/2015/html/is150603.en.html):

“But certainly one lesson is that we should get used to periods of higher volatility. At very low levels of interest rates, asset prices tend to show higher volatility…the Governing Council was unanimous in its assessment that we should look through these developments and maintain a steady monetary policy stance.”

The Chair of the Board of Governors of the Federal Reserve System, Janet L. Yellen, stated on Jul 10, 2015 that (http://www.federalreserve.gov/newsevents/speech/yellen20150710a.htm):

“Based on my outlook, I expect that it will be appropriate at some point later this year to take the first step to raise the federal funds rate and thus begin normalizing monetary policy. But I want to emphasize that the course of the economy and inflation remains highly uncertain, and unanticipated developments could delay or accelerate this first step. I currently anticipate that the appropriate pace of normalization will be gradual, and that monetary policy will need to be highly supportive of economic activity for quite some time. The projections of most of my FOMC colleagues indicate that they have similar expectations for the likely path of the federal funds rate. But, again, both the course of the economy and inflation are uncertain. If progress toward our employment and inflation goals is more rapid than expected, it may be appropriate to remove monetary policy accommodation more quickly. However, if progress toward our goals is slower than anticipated, then the Committee may move more slowly in normalizing policy.”

There is essentially the same view in the Testimony of Chair Yellen in delivering the Semiannual Monetary Policy Report to the Congress on Jul 15, 2015 (http://www.federalreserve.gov/newsevents/testimony/yellen20150715a.htm).

At the press conference after the meeting of the FOMC on Sep 17, 2015, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20150917.pdf 4):

“The outlook abroad appears to have become more uncertain of late, and heightened concerns about growth in China and other emerging market economies have led to notable volatility in financial markets. Developments since our July meeting, including the drop in equity prices, the further appreciation of the dollar, and a widening in risk spreads, have tightened overall financial conditions to some extent. These developments may restrain U.S. economic activity somewhat and are likely to put further downward pressure on inflation in the near term. Given the significant economic and financial interconnections between the United States and the rest of the world, the situation abroad bears close watching.”

Some equity markets fell on Fri Sep 18, 2015:

Fri Sep 11

Mon 14

Tue 15

Wed 16

Thu 17

Fri 18

DJIA

16433.09

2.1%

0.6%

16370.96

-0.4%

16599.85

1.0%

1.4%

16739.95

1.9%

0.8%

16674.74

1.5%

-0.4%

16384.58

-0.3%

-1.7%

Nikkei 225

18264.22

2.7%

-0.2%

17965.70

-1.6%

18026.48

-1.3%

0.3%

18171.60

-0.5%

0.8%

18432.27

0.9%

1.4%

18070.21

-1.1%

-2.0%

DAX

10123.56

0.9%

-0.9%

10131.74

0.1%

10188.13

0.6%

10227.21

1.0%

0.4%

10229.58

1.0%

0.0%

9916.16

-2.0%

-3.1%

Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. Chair Yellen, in a lecture on “Inflation dynamics and monetary policy,” on Sep 24, 2015 (http://www.federalreserve.gov/newsevents/speech/yellen20150924a.htm), states that (emphasis added):

· “The economic outlook, of course, is highly uncertain”

· “Considerable uncertainties also surround the outlook for economic activity”

· “Given the highly uncertain nature of the outlook…”

Is there a “science” or even “art” of central banking under this extreme uncertainty in which policy does not generate higher volatility of money, income, prices and values of financial assets?

Lingling Wei, writing on Oct 23, 2015, on China’s central bank moves to spur economic growth,” published in the Wall Street Journal (http://www.wsj.com/articles/chinas-central-bank-cuts-rates-1445601495), analyzes the reduction by the People’s Bank of China (http://www.pbc.gov.cn/ http://www.pbc.gov.cn/english/130437/index.html) of borrowing and lending rates of banks by 50 basis points and reserve requirements of banks by 50 basis points. Paul Vigna, writing on Oct 23, 2015, on “Stocks rally out of correction territory on latest central bank boost,” published in the Wall Street Journal (http://blogs.wsj.com/moneybeat/2015/10/23/stocks-rally-out-of-correction-territory-on-latest-central-bank-boost/), analyzes the rally in financial markets following the statement on Oct 22, 2015, by the President of the European Central Bank (ECB) Mario Draghi of consideration of new quantitative measures in Dec 2015 (https://www.youtube.com/watch?v=0814riKW25k&rel=0) and the reduction of bank lending/deposit rates and reserve requirements of banks by the People’s Bank of China on Oct 23, 2015. The dollar revalued 2.8 percent from Oct 21 to Oct 23, 2015, following the intended easing of the European Central Bank. The DJIA rose 2.8 percent from Oct 21 to Oct 23 and the DAX index of German equities rose 5.4 percent from Oct 21 to Oct 23, 2015.

Fri Oct 16

Mon 19

Tue 20

Wed 21

Thu 22

Fri 23

USD/ EUR

1.1350

0.1%

0.3%

1.1327

0.2%

1.1348

0.0%

-0.2%

1.1340

0.1%

1.1110

2.1%

2.0%

1.1018

2.9%

0.8%

DJIA

17215.97

0.8%

0.4%

17230.54

0.1%

17217.11

0.0%

-0.1%

17168.61

-0.3%

17489.16

1.6%

1.9%

17646.70

2.5%

0.9%

Dow Global

2421.58

0.3%

0.6%

2414.33

-0.3%

2411.03

-0.4%

-0.1%

2411.27

-0.4%

0.0%

2434.79

0.5%

1.0%

2458.13

1.5%

1.0%

DJ Asia Pacific

1402.31

1.1%

0.3%

1398.80

-0.3%

1395.06

-0.5%

-0.3%

1402.68

0.0%

0.5%

1396.03

-0.4%

-0.5%

1415.50

0.9%

1.4%

Nikkei 225

18291.80

-0.8%

1.1%

18131.23

-0.9%

18207.15

-0.5%

0.4%

18554.28

1.4%

1.9%

18435.87

0.8%

-0.6%

18825.30

2.9%

2.1%

Shanghai

3391.35

6.5%

1.6%

3386.70

-0.1%

3425.33

1.0%

1.1%

3320.68

-2.1%

-3.1%

3368.74

-0.7%

1.4%

3412.43

0.6%

1.3%

DAX

10104.43

0.1%

0.4%

10164.31

0.6%

10147.68

0.4%

-0.2%

10238.10

1.3%

0.9%

10491.97

3.8%

2.5%

10794.54

6.8%

2.9%

Ben Leubsdorf, writing on “Fed’s Yellen: December is “Live Possibility” for First Rate Increase,” on Nov 4, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/feds-yellen-december-is-live-possibility-for-first-rate-increase-1446654282) quotes Chair Yellen that a rate increase in “December would be a live possibility.” The remark of Chair Yellen was during a hearing on supervision and regulation before the Committee on Financial Services, US House of Representatives (http://www.federalreserve.gov/newsevents/testimony/yellen20151104a.htm) and a day before the release of the employment situation report for Oct 2015 (Section I). The dollar revalued 2.4 percent during the week. The euro has devalued 47.0 percent relative to the dollar from the high on Jul 15, 2008 to May 15, 2020.

Fri Oct 30

Mon 2

Tue 3

Wed 4

Thu 5

Fri 6

USD/ EUR

1.1007

0.1%

-0.3%

1.1016

-0.1%

1.0965

0.4%

0.5%

1.0867

1.3%

0.9%

1.0884

1.1%

-0.2%

1.0742

2.4%

1.3%

The release on Nov 18, 2015 of the minutes of the FOMC (Federal Open Market Committee) meeting held on Oct 28, 2015 (http://www.federalreserve.gov/monetarypolicy/fomcminutes20151028.htm) states:

“Most participants anticipated that, based on their assessment of the current economic situation and their outlook for economic activity, the labor market, and inflation, these conditions [for interest rate increase] could well be met by the time of the next meeting. Nonetheless, they emphasized that the actual decision would depend on the implications for the medium-term economic outlook of the data received over the upcoming intermeeting period… It was noted that beginning the normalization process relatively soon would make it more likely that the policy trajectory after liftoff could be shallow.”

Markets could have interpreted a symbolic increase in the fed funds rate at the meeting of the FOMC on Dec 15-16, 2015 (http://www.federalreserve.gov/monetarypolicy/fomccalendars.htm) followed by “shallow” increases, explaining the sharp increase in stock market values and appreciation of the dollar after the release of the minutes on Nov 18, 2015:

Fri Nov 13

Mon 16

Tue 17

Wed 18

Thu 19

Fri 20

USD/ EUR

1.0774

-0.3%

0.4%

1.0686

0.8%

1.0644

1.2%

0.4%

1.0660

1.1%

-0.2%

1.0735

0.4%

-0.7%

1.0647

1.2%

0.8%

DJIA

17245.24

-3.7%

-1.2%

17483.01

1.4%

17489.50

1.4%

0.0%

17737.16

2.9%

1.4%

17732.75

2.8%

0.0%

17823.81

3.4%

0.5%

DAX

10708.40

-2.5%

-0.7%

10713.23

0.0%

10971.04

2.5%

2.4%

10959.95

2.3%

-0.1%

11085.44

3.5%

1.1%

11119.83

3.8%

0.3%

In testimony before The Joint Economic Committee of Congress on Dec 3, 2015 (http://www.federalreserve.gov/newsevents/testimony/yellen20151203a.htm), Chair Yellen reiterated that the FOMC (Federal Open Market Committee) “anticipates that even after employment and inflation are near mandate-consistent levels, economic condition may, for some time, warrant keeping the target federal funds rate below the Committee views as normal in the longer run.” Todd Buell and Katy Burne, writing on “Draghi says ECB could step up stimulus efforts if necessary,” on Dec 4, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/draghi-says-ecb-could-step-up-stimulus-efforts-if-necessary-1449252934), analyze that the President of the European Central Bank (ECB), Mario Draghi, reassured financial markets that the ECB will increase stimulus if required to raise inflation the euro area to targets. The USD depreciated 3.1 percent on Thu Dec 3, 2015 after weaker than expected measures by the European Central Bank. DJIA fell 1.4 percent on Dec 3 and increased 2.1 percent on Dec 4. DAX fell 3.6 percent on Dec 3.

Fri Nov 27

Mon 30

Tue 1

Wed 2

Thu 3

Fri 4

USD/ EUR

1.0594

0.5%

0.2%

1.0565

0.3%

1.0634

-0.4%

-0.7%

1.0616

-0.2%

0.2%

1.0941

-3.3%

-3.1%

1.0885

-2.7%

0.5%

DJIA

17798.49

-0.1%

17719.92

-0.4%

17888.35

0.5%

1.0%

17729.68

-0.4%

-0.9%

17477.67

-1.8%

-1.4%

17847.63

0.3%

2.1%

DAX

11293.76

1.6%

-0.2%

11382.23

0.8%

11261.24

-0.3%

-1.1%

11190.02

-0.9%

-0.6%

10789.24

-4.5%

-3.6%

10752.10

-4.8%

-0.3%

At the press conference following the meeting of the FOMC on Dec 16, 2015, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20151216.pdf page 8):

“And we recognize that monetary policy operates with lags. We would like to be able to move in a prudent, and as we've emphasized, gradual manner. It's been a long time since the Federal Reserve has raised interest rates, and I think it's prudent to be able to watch what the impact is on financial conditions and spending in the economy and moving in a timely fashion enables us to do this.”

The implication of this statement is that the state of the art is not accurate in analyzing the effects of monetary policy on financial markets and economic activity. The US dollar appreciated and equities fluctuated:

Fri Dec 11

Mon 14

Tue 15

Wed 16

Thu 17

Fri 18

USD/ EUR

1.0991

-1.0%

-0.4%

1.0993

0.0%

1.0932

0.5%

0.6%

1.0913

0.7%

0.2%

1.0827

1.5%

0.8%

1.0868

1.1%

-0.4%

DJIA

17265.21

-3.3%

-1.8%

17368.50

0.6%

17524.91

1.5%

0.9%

17749.09

2.8%

1.3%

17495.84

1.3%

-1.4%

17128.55

-0.8%

-2.1%

DAX

10340.06

-3.8%

-2.4%

10139.34

-1.9%

10450.38

-1.1%

3.1%

10469.26

1.2%

0.2%

10738.12

3.8%

2.6%

10608.19

2.6%

-1.2%

On January 29, 2016, the Policy Board of the Bank of Japan introduced a new policy to attain the “price stability target of 2 percent at the earliest possible time” (https://www.boj.or.jp/en/announcements/release_2016/k160129a.pdf). The new framework consists of three dimensions: quantity, quality and interest rate. The interest rate dimension consists of rates paid to current accounts that financial institutions hold at the Bank of Japan of three tiers zero, positive and minus 0.1 percent. The quantitative dimension consists of increasing the monetary base at the annual rate of 80 trillion yen. The qualitative dimension consists of purchases by the Bank of Japan of Japanese government bonds (JGBs), exchange traded funds (ETFs) and Japan real estate investment trusts (J-REITS). The yen devalued sharply relative to the dollar and world equity markets soared after the new policy announced on Jan 29, 2016:

Fri 22

Mon 25

Tue 26

Wed 27

Thu 28

Fri 29

JPY/ USD

118.77

-1.5%

-0.9%

118.30

0.4%

118.42

0.3%

-0.1%

118.68

0.1%

-0.2%

118.82

0.0%

-0.1%

121.13

-2.0%

-1.9%

DJIA

16093.51

0.7%

1.3%

15885.22

-1.3%

16167.23

0.5%

1.8%

15944.46

-0.9%

-1.4%

16069.64

-0.1%

0.8%

16466.30

2.3%

2.5%

Nikkei

16958.53

-1.1%

5.9%

17110.91

0.9%

16708.90

-1.5%

-2.3%

17163.92

1.2%

2.7%

17041.45

0.5%

-0.7%

17518.30

3.3%

2.8%

Shanghai

2916.56

0.5%

1.3

2938.51

0.8%

2749.79

-5.7%

-6.4%

2735.56

-6.2%

-0.5%

2655.66

-8.9%

-2.9%

2737.60

-6.1%

3.1%

DAX

9764.88

2.3%

2.0%

9736.15

-0.3%

9822.75

0.6%

0.9%

9880.82

1.2%

0.6%

9639.59

-1.3%

-2.4%

9798.11

0.3%

1.6%

In testimony on the Semiannual Monetary Policy Report to the Congress on Feb 10-11, 2016, Chair Yellen (http://www.federalreserve.gov/newsevents/testimony/yellen20160210a.htm) states: “U.S. real gross domestic product is estimated to have increased about 1-3/4 percent in 2015. Over the course of the year, subdued foreign growth and the appreciation of the dollar restrained net exports. In the fourth quarter of last year, growth in the gross domestic product is reported to have slowed more sharply, to an annual rate of just 3/4 percent; again, growth was held back by weak net exports as well as by a negative contribution from inventory investment.”

Jon Hilsenrath, writing on “Yellen Says Fed Should Be Prepared to Use Negative Rates if Needed,” on Feb 11, 2016, published in the Wall Street Journal (http://www.wsj.com/articles/yellen-reiterates-concerns-about-risks-to-economy-in-senate-testimony-1455203865), analyzes the statement of Chair Yellen in Congress that the FOMC (Federal Open Market Committee) is considering negative interest rates on bank reserves. The Wall Street Journal provides yields of two and ten-year sovereign bonds with negative interest rates on shorter maturities where central banks pay negative interest rates on excess bank reserves:

Sovereign Yields 2/12/16	Japan	Germany	USA
2 Year	-0.168	-0.498	0.694
10 Year	0.076	0.262	1.744

On Mar 10, 2016, the European Central Bank (ECB) announced (1) reduction of the refinancing rate by 5 basis points to 0.00 percent; decrease the marginal lending rate to 0.25 percent; reduction of the deposit facility rate to 0,40 percent; increase of the monthly purchase of assets to €80 billion; include nonbank corporate bonds in assets eligible for purchases; and new long-term refinancing operations (https://www.ecb.europa.eu/press/pr/date/2016/html/pr160310.en.html). The President of the ECB, Mario Draghi, stated in the press conference (https://www.ecb.europa.eu/press/pressconf/2016/html/is160310.en.html): “How low can we go? Let me say that rates will stay low, very low, for a long period of time, and well past the horizon of our purchases…We don’t anticipate that it will be necessary to reduce rates further. Of course, new facts can change the situation and the outlook.”

The dollar devalued relative to the euro and open stock markets traded lower after the announcement on Mar 10, 2016, but stocks rebounded on Mar 11:

Fri 4

Mon 7

Tue 8

Wed 9

Thu10

Fri 11

USD/ EUR

1.1006

-0.7%

-0.4%

1.1012

-0.1%

1.1013

-0.1%

0.0%

1.0999

0.1%

1.1182

-1.6%

-1.7%

1.1151

-1.3%

0.3%

DJIA

17006.77

2.2%

0.4%

17073.95

0.4%

16964.10

-0.3%

-0.6%

17000.36

0.0%

0.2%

16995.13

-0.1%

0.0%

17213.31

1.2%

1.3%

DAX

9824.17

3.3%

0.7%

9778.93

-0.5%

0.5%

9692.82

-1.3%

-0.9%

9723.09

-1.0%

0.3%

9498.15

-3.3%

-2.3%

9831.13

0.1%

3.5%

At the press conference after the FOMC meeting on Sep 21, 2016, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20160921.pdf ): “However, the economic outlook is inherently uncertain.” In the address to the Jackson Hole symposium on Aug 26, 2016, Chair Yellen states: “I believe the case for an increase in in federal funds rate has strengthened in recent months…And, as ever, the economic outlook is uncertain, and so monetary policy is not on a preset course” (http://www.federalreserve.gov/newsevents/speech/yellen20160826a.htm). In a speech at the World Affairs Council of Philadelphia, on Jun 6, 2016 (http://www.federalreserve.gov/newsevents/speech/yellen20160606a.htm), Chair Yellen finds that “there is considerable uncertainty about the economic outlook.” There are fifteen references to this uncertainty in the text of 18 pages double-spaced. In the Semiannual Monetary Policy Report to the Congress on Jun 21, 2016, Chair Yellen states (http://www.federalreserve.gov/newsevents/testimony/yellen20160621a.htm), “Of course, considerable uncertainty about the economic outlook remains.” Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. Is there a “science” or even “art” of central banking under this extreme uncertainty in which policy does not generate higher volatility of money, income, prices and values of financial assets?

What is truly important is the fixing of the overnight fed funds at 0 to ¼ percent (https://www.federalreserve.gov/newsevents/pressreleases/monetary20200429a.htm): “The ongoing public health crisis will weigh heavily on economic activity, employment, and inflation in the near term, and poses considerable risks to the economic outlook over the medium term. In light of these developments, the Committee decided to maintain the target range for the federal funds rate at 0 to 1/4 percent. The Committee expects to maintain this target range until it is confident that the economy has weathered recent events and is on track to achieve its maximum employment and price stability goals.” There are multiple new policy measures, including purchases of Treasury securities and mortgage-backed securities for the balance sheet of the Fed (https://www.federalreserve.gov/newsevents/pressreleases/monetary20200315a.htm): “The Federal Reserve is prepared to use its full range of tools to support the flow of credit to households and businesses and thereby promote its maximum employment and price stability goals. To support the smooth functioning of markets for Treasury securities and agency mortgage-backed securities that are central to the flow of credit to households and businesses, over coming months the Committee will increase its holdings of Treasury securities by at least $500 billion and its holdings of agency mortgage-backed securities by at least $200 billion. The Committee will also reinvest all principal payments from the Federal Reserve's holdings of agency debt and agency mortgage-backed securities in agency mortgage-backed securities. In addition, the Open Market Desk has recently expanded its overnight and term repurchase agreement operations. The Committee will continue to closely monitor market conditions and is prepared to adjust its plans as appropriate.”

In the Opening Remarks to the Press Conference on Oct 30, 2019, the Chairman of the Federal Reserve Board, Jerome H. Powell, stated (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20191030.pdf): “We see the current stance of monetary policy as likely to remain appropriate as long as incoming information about the economy remains broadly consistent with our outlook of moderate economic growth, a strong labor market, and inflation near our symmetric 2 percent objective. We believe monetary policy is in a good place to achieve these outcomes. Looking ahead, we will be monitoring the effects of our policy actions, along with other information bearing on the outlook, as we assess the appropriate path of the target range for the fed funds rate. Of course, if developments emerge that cause a material reassessment of our outlook, we would respond accordingly. Policy is not on a preset course.” In the Opening Remarks to the Press Conference on Jan 30, 2019, the Chairman of the Federal Reserve Board, Jerome H. Powell, stated (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20190130.pdf): “Today, the FOMC decided that the cumulative effects of those developments over the last several months warrant a patient, wait-and-see approach regarding future policy changes. In particular, our statement today says, “In light of global economic and financial developments and muted inflation pressures, the Committee will be patient as it determines what future adjustments to the target range for the federal funds rate may be appropriate.” This change was not driven by a major shift in the baseline outlook for the economy. Like many forecasters, we still see “sustained expansion of economic activity, strong labor market conditions, and inflation near … 2 percent” as the likeliest case. But the cross-currents I mentioned suggest the risk of a less-favorable outlook. In addition, the case for raising rates has weakened somewhat. The traditional case for rate increases is to protect the economy from risks that arise when rates are too low for too long, particularly the risk of too-high inflation. Over the past few months, that risk appears to have diminished. Inflation readings have been muted, and the recent drop in oil prices is likely to Page 3 of 5 push headline inflation lower still in coming months. Further, as we noted in our post-meeting statement, while survey-based measures of inflation expectations have been stable, financial market measures of inflation compensation have moved lower. Similarly, the risk of financial imbalances appears to have receded, as a number of indicators that showed elevated levels of financial risk appetite last fall have moved closer to historical norms. In this environment, we believe we can best support the economy by being patient in evaluating the outlook before making any future adjustment to policy.” The FOMC is initiating the “normalization” or reduction of the balance sheet of securities held outright for monetary policy (https://www.federalreserve.gov/newsevents/pressreleases/monetary20190130c.htm) with significant changes (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20190320.pdf). In the opening remarks to the Mar 20, 2019, the Chairman of the Federal Reserve Board, Jerome H. Powell, stated (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20190320.pdf): “In discussing the Committee’s projections, it is useful to note what those projections are, as well as what they are not. The SEP includes participants’ individual projections of the most likely economic scenario along with their views of the appropriate path of the federal funds rate in that scenario. Views about the most likely scenario form one input into our policy discussions. We also discuss other plausible scenarios, including the risk of more worrisome outcomes. These and other scenarios and many other considerations go into policy, but are not reflected in projections of the most likely case. Thus, we always emphasize that the interest rate projections in the SEP are not a Committee decision. They are not a Committee plan. As Chair Yellen noted some years ago, the FOMC statement, rather than the dot plot, is the device that the Committee uses to express its opinions about the likely path of rates.”

In the Introductory Statement on Jul 25, 2019, in Frankfurt am Main, the President of the European Central Bank, Mario Draghi, stated (https://www.ecb.europa.eu/press/pressconf/2019/html/ecb.is190725~547f29c369.en.html): “Based on our regular economic and monetary analyses, we decided to keep the key ECB interest rates unchanged. We expect them to remain at their present or lower levels at least through the first half of 2020, and in any case for as long as necessary to ensure the continued sustained convergence of inflation to our aim over the medium term.

We intend to continue reinvesting, in full, the principal payments from maturing securities purchased under the asset purchase programme for an extended period of time past the date when we start raising the key ECB interest rates, and in any case for as long as necessary to maintain favourable liquidity conditions and an ample degree of monetary accommodation.” At its meeting on September 12, 2019, the Governing Council of the ECB (European Central Bank), decided to (https://www.ecb.europa.eu/press/pr/date/2019/html/ecb.mp190912~08de50b4d2.en.html): (1) decrease the deposit facility by 10 basis points to minus 0.50 percent while maintaining at 0.00 the main refinancing operations rate and at 0.25 percent the marginal lending facility rate; (2) restart net purchases of securities at the monthly rate of €20 billion beginning on Nov 1, 2019; (3) reinvest principal payments from maturing securities; (4) adapt long-term refinancing operations to maintain “favorable bank lending conditions;” and (5) exempt part of the “negative deposit facility rate” on bank excess liquidity.

The Federal Open Market Committee (FOMC) decided to lower the target range of the federal funds rate by 0.50 percent to 1.0 to 1¼ percent on Mar 3, 2020 in a decision outside the calendar meetings (https://www.federalreserve.gov/newsevents/pressreleases/monetary20200303a.htm):

March 03, 2020

Federal Reserve issues FOMC statement

For release at 10:00 a.m. EST

The fundamentals of the U.S. economy remain strong. However, the coronavirus poses evolving risks to economic activity. In light of these risks and in support of achieving its maximum employment and price stability goals, the Federal Open Market Committee decided today to lower the target range for the federal funds rate by 1/2 percentage point, to 1 to 1‑1/4 percent. The Committee is closely monitoring developments and their implications for the economic outlook and will use its tools and act as appropriate to support the economy.

Voting for the monetary policy action were Jerome H. Powell, Chair; John C. Williams, Vice Chair; Michelle W. Bowman; Lael Brainard; Richard H. Clarida; Patrick Harker; Robert S. Kaplan; Neel Kashkari; Loretta J. Mester; and Randal K. Quarles.

For media inquiries, call 202-452-2955.

Implementation Note issued March 3, 2020

The decisions of the FOMC (Federal Open Market Committee) depend on incoming data. There are unexpected swings in valuations of risk financial assets by “carry trades” from interest rates below inflation to exposures in stocks, commodities and their derivatives. Another issue is the unexpected “data surprises” such as the sharp decline in 12 months rates of increase of real disposable income, or what is left after taxes and inflation, and the price indicator of the FOMC, prices of personal consumption expenditures (PCE) excluding food and energy. There is no science or art of monetary policy that can deal with this uncertainty.

Real Disposable Personal Income	Real Personal Consumption Expenditures	Prices of Personal Consumption Expenditures	PCE Prices Excluding Food and Energy
∆%12M	∆%12M	∆%12M	∆%12M
6/2017	6/2017	6/2017	6/2017
1.2	2.4	1.4	1.5

In presenting the Semiannual Monetary Policy Report to Congress on Jul 17, 2018, the Chairman of the Board of Governors of the Federal Reserve System, Jerome H. Powell, stated (https://www.federalreserve.gov/newsevents/testimony/powell20180717a.htm): “With a strong job market, inflation close to our objective, and the risks to the outlook roughly balanced, the FOMC believes that--for now--the best way forward is to keep gradually raising the federal funds rate. We are aware that, on the one hand, raising interest rates too slowly may lead to high inflation or financial market excesses. On the other hand, if we raise rates too rapidly, the economy could weaken and inflation could run persistently below our objective. The Committee will continue to weigh a wide range of relevant information when deciding what monetary policy will be appropriate. As always, our actions will depend on the economic outlook, which may change as we receive new data.”

At an address to The Clearing House and The Bank Policy Institute Annual Conference (https://www.federalreserve.gov/newsevents/speech/clarida20181127a.htm), in New York City, on Nov 27, 2018, the Vice Chairman of the Fed, Richard H. Clarida, analyzes the data dependence of monetary policy. An important hurdle is critical unobserved parameters of monetary policy (https://www.federalreserve.gov/newsevents/speech/clarida20181127a.htm): “But what if key parameters that describe the long-run destination of the economy are unknown? This is indeed the relevant case that the FOMC and other monetary policymakers face in practice. The two most important unknown parameters needed to conduct‑‑and communicate‑‑monetary policy are the rate of unemployment consistent with maximum employment, u*, and the riskless real rate of interest consistent with price stability, r*. As a result, in the real world, monetary policy should, I believe, be data dependent in a second sense: that incoming data can reveal at each FOMC meeting signals that will enable it to update its estimates of r* and u* in order to obtain its best estimate of where the economy is heading.” Current robust economic growth, employment creation and inflation close to the Fed’s 2 percent objective suggest continuing “gradual policy normalization.” Incoming data can be used to update u^* and r^* in designing monetary policy that attains price stability and maximum employment. Clarida also finds that the current expansion will be the longest in history if it continues into 2019. In an address at The Economic Club of New York, New York City, Nov 28, 2018 (https://www.federalreserve.gov/newsevents/speech/powell20181128a.htm), the Chairman of the Fed, Jerome H. Powell, stated (https://www.federalreserve.gov/newsevents/speech/powell20181128a.htm): “For seven years during the crisis and its painful aftermath, the Federal Open Market Committee (FOMC) kept our policy interest rate unprecedentedly low--in fact, near zero--to support the economy as it struggled to recover. The health of the economy gradually but steadily improved, and about three years ago the FOMC judged that the interests of households and businesses, of savers and borrowers, were no longer best served by such extraordinarily low rates. We therefore began to raise our policy rate gradually toward levels that are more normal in a healthy economy. Interest rates are still low by historical standards, and they remain just below the broad range of estimates of the level that would be neutral for the economy‑‑that is, neither speeding up nor slowing down growth. My FOMC colleagues and I, as well as many private-sector economists, are forecasting continued solid growth, low unemployment, and inflation near 2 percent.” The market focused on policy rates “just below the broad range of estimates of the level that would be neutral for the economy—that is, neither speeding up nor slowing down growth.” There was a relief rally in the stock market of the United States:

Fri 23

Mon 26

Tue 27

Wed 28

Thu 29

Fri 30

USD/EUR

1.1339

0.7%

0.6%

1.1328

0.1%

1.1293

0.4%

0.3%

1.1368

-0.3%

-0.7%

1.1394

-0.5%

-0.2%

1.1320

0.2%

0.6%

DJIA

24285.95

-4.4%

-0.7%

24640.24

1.5%

24748.73

1.9%

0.4%

25366.43

4.4%

2.5%

25338.84

4.3%

-0.1%

25538.46

5.2%

0.8%

At a meeting of the American Economic Association in Atlanta on Friday, January 4, 2019, the Chairman of the Fed, Jerome H. Powell, stated that the Fed would be “patient” with interest rate increases, adjusting policy “quickly and flexibly” if required (https://www.aeaweb.org/webcasts/2019/us-federal-reserve-joint-interview). Treasury yields declined and stocks jumped.

Fri 28	Mon 31	Tue 1	Wed 2	Thu 3	Fri 4
10Y Note 2.736	2.683	2.683	2.663	2.560	2.658
2Y Note 2.528	2.500	2.500	2.488	2.387	2.480
DJIA 23062.40 2.7% -0.3%	23327.46 1.1% 1.1%	23327.46 1.1% 0.0%	23346.24 1.2% 0.1%	22686.22 -1.6% -2.8%	23433.16 1.6% 3.3%
Dow Global 2718.19 1.3% 0.8%	2734.40 0.6% 0.6%	2734.40 0.6% 0.0%	2729.74 0.4% -0.2%	2707.29 -0.4% -0.8%	2773.12 2.0% 2.4%

Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. The FOMC statement on Jun 19, 2019 analyzes uncertainty in the outlook (https://www.federalreserve.gov/newsevents/pressreleases/monetary20190619a.htm): “The Committee continues to view sustained expansion of economic activity, strong labor market conditions, and inflation near the Committee's symmetric 2 percent objective as the most likely outcomes, but uncertainties about this outlook have increased. In light of these uncertainties and muted inflation pressures, the Committee will closely monitor the implications of incoming information for the economic outlook and will act as appropriate to sustain the expansion, with a strong labor market and inflation near its symmetric 2 percent objective.” In the Semiannual Monetary Policy Report to the Congress, on Jul 10, 2019, Chair Jerome H. Powell states (https://www.federalreserve.gov/newsevents/testimony/powell20190710a.htm): “Since our May meeting, however, these crosscurrents have reemerged, creating greater uncertainty. Apparent progress on trade turned to greater uncertainty, and our contacts in business and agriculture report heightened concerns over trade developments. Growth indicators from around the world have disappointed on net, raising concerns that weakness in the global economy will continue to affect the U.S. economy. These concerns may have contributed to the drop in business confidence in some recent surveys and may have started to show through to incoming data.

”(emphasis added). European Central Bank President, Mario Draghi, stated at a meeting on “Twenty Years of the ECB’s Monetary Policy,” in Sintra, Portugal, on Jun 18, 2019, that (https://www.ecb.europa.eu/press/key/date/2019/html/ecb.sp190618~ec4cd2443b.en.html): “In this environment, what matters is that monetary policy remains committed to its objective and does not resign itself to too-low inflation. And, as I emphasised at our last monetary policy meeting, we are committed, and are not resigned to having a low rate of inflation forever or even for now. In the absence of improvement, such that the sustained return of inflation to our aim is threatened, additional stimulus will be required. In our recent deliberations, the members of the Governing Council expressed their conviction in pursuing our aim of inflation close to 2% in a symmetric fashion. Just as our policy framework has evolved in the past to counter new challenges, so it can again. In the coming weeks, the Governing Council will deliberate how our instruments can be adapted commensurate to the severity of the risk to price stability.” At its meeting on September 12, 2019, the Governing Council of the ECB (European Central Bank), decided to (https://www.ecb.europa.eu/press/pr/date/2019/html/ecb.mp190912~08de50b4d2.en.html): (1) decrease the deposit facility by 10 basis points to minus 0.50 percent while maintaining at 0.00 the main refinancing operations rate and at 0.25 percent the marginal lending facility rate; (2) restart net purchases of securities at the monthly rate of €20 billion beginning on Nov 1, 2019; (3) reinvest principal payments from maturing securities; (4) adapt long-term refinancing operations to maintain “favorable bank lending conditions;” and (5) exempt part of the “negative deposit facility rate” on bank excess liquidity. The harmonized index of consumer prices of the euro zone increased 1.2 percent in the 12 months ending in May 2019 and the PCE inflation excluding food and energy increased 1.6 percent in the 12 months ending in Apr 2019. Inflation below 2 percent with symmetric targets in both the United States and the euro zone together with apparently weakening economic activity could lead to interest rate cuts. Stock markets jumped worldwide in renewed risk appetite during the week of Jun 19, 2019 in part because of anticipation of major central bank rate cuts and also because of domestic factors:

Fri 14

Mon 17

Tue 18

Wed 19

Thu 20

Fri 21

DJIA

26089.61

0.4%

-0.1%

26112.53

0.1%

26465.54

1.4%

26504.00

1.6%

0.1%

26753.17

2.5%

0.9%

26719.13

2.4%

-0.1%

Dow Global

2998.79

0.2%

-0.4%

2999.93

0.0%

3034.59

1.2%

3050.80

1.7%

0.5%

3077.81

2.6%

0.9%

3081.62

2.8%

0.1%

DJ Asia Pacific

Nikkei

21116.89

1.1%

0.4%

21124.00

0.0%

20972.71

-0.7%

21333.87

1.0%

1.7%

21462.86

1.6%

0.6%

21258.64

0.7%

-1.0%

Shanghai

2881.97

1.9%

-1.0%

2887.62

0.2%

2890.16

0.3%

0.1%

2917.80

1.2%

1.0%

2987.12

3.6%

2.4%

3001.98

4.2%

0.5%

DAX

12096.40

0.4%

-0.6%

12085.82

-0.1%

12331.75

1.9%

2.0%

12308.53

1.8%

-0.2%

12355.39

2.1%

0.4%

12339.92

2.0%

-0.1%

BOVESPA

98040.06

0.2%

-0.7%

97623.25

-0.4%

99404.39

1.4%

1.8%

100303.41

2.3%

0.9%

100303.41

2.3%

0.0%

102012.64

4.1%

1.7%

Chart VIII-1, Fed Funds Rate and Yields of Ten-year Treasury Constant Maturity and Baa Seasoned Corporate Bond, Jan 2, 2001 to Oct 6, 2016

Source: Board of Governors of the Federal Reserve System

http://www.federalreserve.gov/releases/h15/

Chart VIII-1A, Fed Funds Rate and Yield of Ten-year Treasury Constant Maturity, Jan 2, 2001 to May 14, 2020

Source: Board of Governors of the Federal Reserve System

http://www.federalreserve.gov/releases/h15/

Table VIII-3, Selected Data Points in Chart VIII-1, % per Year

	Fed Funds Overnight Rate	10-Year Treasury Constant Maturity	Seasoned Baa Corporate Bond
1/2/2001	6.67	4.92	7.91
10/1/2002	1.85	3.72	7.46
7/3/2003	0.96	3.67	6.39
6/22/2004	1.00	4.72	6.77
6/28/2006	5.06	5.25	6.94
9/17/2008	2.80	3.41	7.25
10/26/2008	0.09	2.16	8.00
10/31/2008	0.22	4.01	9.54
4/6/2009	0.14	2.95	8.63
4/5/2010	0.20	4.01	6.44
2/4/2011	0.17	3.68	6.25
7/25/2012	0.15	1.43	4.73
5/1/13	0.14	1.66	4.48
9/5/13	0.089	2.98	5.53
11/21/2013	0.09	2.79	5.44
11/26/13	0.09	2.74	5.34 (11/26/13)
12/5/13	0.09	2.88	5.47
12/11/13	0.09	2.89	5.42
12/18/13	0.09	2.94	5.36
12/26/13	0.08	3.00	5.37
1/1/2014	0.08	3.00	5.34
1/8/2014	0.07	2.97	5.28
1/15/2014	0.07	2.86	5.18
1/22/2014	0.07	2.79	5.11
1/30/2014	0.07	2.72	5.08
2/6/2014	0.07	2.73	5.13
2/13/2014	0.06	2.73	5.12
2/20/14	0.07	2.76	5.15
2/27/14	0.07	2.65	5.01
3/6/14	0.08	2.74	5.11
3/13/14	0.08	2.66	5.05
3/20/14	0.08	2.79	5.13
3/27/14	0.08	2.69	4.95
4/3/14	0.08	2.80	5.04
4/10/14	0.08	2.65	4.89
4/17/14	0.09	2.73	4.89
4/24/14	0.10	2.70	4.84
5/1/14	0.09	2.63	4.77
5/8/14	0.08	2.61	4.79
5/15/14	0.09	2.50	4.72
5/22/14	0.09	2.56	4.81
5/29/14	0.09	2.45	4.69
6/05/14	0.09	2.59	4.83
6/12/14	0.09	2.58	4.79
6/19/14	0.10	2.64	4.83
6/26/14	0.10	2.53	4.71
7/2/14	0.10	2.64	4.84
7/10/14	0.09	2.55	4.75
7/17/14	0.09	2.47	4.69
7/24/14	0.09	2.52	4.72
7/31/14	0.08	2.58	4.75
8/7/14	0.09	2.43	4.71
8/14/14	0.09	2.40	4.69
8/21/14	0.09	2.41	4.69
8/28/14	0.09	2.34	4.57
9/04/14	0.09	2.45	4.70
9/11/14	0.09	2.54	4.79
9/18/14	0.09	2.63	4.91
9/25/14	0.09	2.52	4.79
10/02/14	0.09	2.44	4.76
10/09/14	0.08	2.34	4.68
10/16/14	0.09	2.17	4.64
10/23/14	0.09	2.29	4.71
11/13/14	0.09	2.35	4.82
11/20/14	0.10	2.34	4.86
11/26/14	0.10	2.24	4.73
12/04/14	0.12	2.25	4.78
12/11/14	0.12	2.19	4.72
12/18/14	0.13	2.22	4.78
12/23/14	0.13	2.26	4.79
12/30/14	0.06	2.20	4.69
1/8/15	0.12	2.03	4.57
1/15/15	0.12	1.77	4.42
1/22/15	0.12	1.90	4.49
1/29/15	0.11	1.77	4.35
2/05/15	0.12	1.83	4.43
2/12/15	0.12	1.99	4.53
2/19/15	0.12	2.11	4.64
2/26/15	0.11	2.03	4.47
3/5/215	0.11	2.11	4.58
3/12/15	0.11	2.10	4.56
3/19/15	0.12	1.98	4.48
3/26/15	0.11	2.01	4.56
4/03/15	0.12	1.92	4.47
4/9/15	0.12	1.97	4.50
4/16/15	0.13	1.90	4.45
4/23/15	0.13	1.96	4.50
5/1/15	0.08	2.05	4.65
5/7/15	0.13	2.18	4.82
5/14/15	0.13	2.23	4.97
5/21/15	0.12	2.19	4.94
5/28/15	0.12	2.13	4.88
6/04/15	0.13	2.31	5.03
6/11/15	0.13	2.39	5.10
6/18/15	0.14	2.35	5.17
6/25/15	0.13	2.40	5.20
7/1/15	0.13	2.43	5.26
7/9/15	0.13	2.32	5.20
7/16/15	0.14	2.36	5.24
7/23/15	0.13	2.28	5.13
7/30/15	0.14	2.28	5.16
8/06/15	0.14	2.23	5.15
8/20/15	0.15	2.09	5.13
8/27/15	0.14	2.18	5.33
9/03/15	0.14	2.18	5.35
9/10/15	0.14	2.23	5.35
9/17/15	0.14	2.21	5.39
9/25/15	0.14	2.13	5.29
10/01/15	0.13	2.05	5.36
10/08/15	0.13	2.12	5.40
10/15/15	0.13	2.04	5.33
10/22/15	0.12	2.04	5.30
10/29/15	0.12	2.19	5.40
11/05/15	0.12	2.26	5.44
11/12/15	0.12	2.32	5.51
11/19/15	0.12	2.24	5.44
11/25/15	0.12	2.23	5.44
12/03/15	0.13	2.33	5.51
12/10/15	0.14	2.24	5.43
12/17/15	0.37	2.24	5.45
12/23/15	0.36	2.27	5.53
12/30/15	0.35	2.31	5.54
1/07/2016	0.36	2.16	5.44
01/14/16	0.36	2.10	5.46
01/20/16	0.37	2.01	5.41
01/29/16	0.38	2.00	5.48
02/04/16	0.38	1.87	5.40
02/11/16	0.38	1.63	5.26
02/18/16	0.38	1.75	5.37
02/25/16	0.37	1.71	5.27
03/03/16	0.37	1.83	5.30
03/10/16	0.36	1.93	5.23
03/17/16	0.37	1.91	5.11
03/24/16	0.37	1.91	4.97
03/31/16	0.25	1.78	4.90
04/07/16	0.37	1.70	4.76
04/14/16	0.37	1.80	4.79
04/21/16	0.37	1.88	4.79
04/28/16	0.37	1.84	4.73
05/05/16	0.37	1.76	4.62
05/12/16	0.37	1.75	4.66
05/19/16	0.37	1.85	4.70
05/26/16	0.37	1.83	4.69
06/02/16	0.37	1.81	4.64
06/09/16	0.37	1.68	4.53
06/16/16	0.38	1.57	4.47
06/23/16	0.39	1.74	4.60
06/30/16	0.36	1.49	4.41
07/07/16	0.40	1.40	4.19
07/14/16	0.40	1.53	4.23
07/21/16	0.40	1.57	4.25
07/28/16	0.40	1.52	4.20
08/04/16	0.40	1.51	4.27
08/11/16	0.40	1.57	4.27
08/18/16	0.40	1.53	4.23
08/25/16	0.40	1.58	4.21
09/01/16	0.40	1.57	4.19
09/08/16	0.40	1.61	4.28
09/15/16	0.40	1.71	4.43
09/22/16	0.40	1.63	4.32
09/29/16	0.40	1.56	4.23
10/06/16	0.40	1.75	4.36
10/13/16	0.40	1.75	NA*
10/20/16	0.41	1.76	NA*
10/27/16	0.41	1.85	NA*
11/03/16	0.41	1.82	NA*
11/09/16	0.41	2.07	NA*
11/17/16	0.41	2.29	NA*
11/23/16	0.40	2.36	NA*
12/01/16	0.40	2.45	NA*
12/08/16	0.41	2.40	NA*
12/15/16	0.66	2.60	NA*
12/22/16	0.66	2.55	NA*
12/29/16	0.66	2.49	NA*
01/05/17	0.66	2.37	NA*
01/12/17	0.66	2.36	NA*
01/19/17	0.66	2.42	NA*
01/26/17	0.66	2.51	NA*
02/02/17	0.66	2.48	NA*
02/09/17	0.66	2.40	NA*
02/16/17	0.66	2.45	NA*
02/23/17	0.66	2.38	NA*
03/02/17	0.66	2.49	NA*
03/09/17	0.66	2.60	NA*
03/16/17	0.91	2.53	NA*
03/23/17	0.91	2.41	NA*
03/30/17	0.91	2.42	NA*
04/06/17	0.91	2.34	NA*
04/13/17	0.91	2.24	NA*
04/21/17	0.91	2.24	NA*
04/27/17	0.91	2.30	NA*
05/04/17	0.91	2.36	NA*
05/11/17	0.91	2.39	NA*
05/18/17	0.91	2.23	NA*
05/25/17	0.91	2.25	NA*
06/01/17	0.90	2.21	NA*
06/08/17	0.91	2.19	NA*
06/15/17	1.16	2.16	NA*
06/22/17	1.16	2.15	NA*
06/29/17	1.16	2.27	NA*
07/06/17	1.16	2.37	NA*
07/13/17	1.16	2.35	NA*
07/20/17	1.16	2.27	NA*
07/27/17	1.16	2.32	NA*
08/03/17	1.16	2.24	NA*
08/10/17	1.16	2.20	NA*
08/17/17	1.16	2.19	NA*
08/24/17	1.16	2.19	NA*
08/31/17	1.07	2.12	NA*
09/07/17	1.16	2.05	NA*
09/14/17	1.16	2.20	NA*
09/21/17	1.16	2.27	NA*
09/28/17	1.16	2.31	NA*
10/05/17	1.16	2.35	NA*
10/12/17	1.16	2.33	NA*
10/19/17	1.16	2.33	NA*
10/26/17	1.16	2.46	NA*
11/02/17	1.16	2.35	NA*
11/09/17	1.16	2.32	NA*
11/16/17	1.16	2.37	NA*
11/22/17	1.16	2.32	NA*
11/30/17	1.16	2.42	NA*
12/07/17	1.16	2.37	NA*
12/14/17	1.41	2.35	NA*
12/21/17	1.42	2.48	NA*
12/28/17	1.42	2.43	NA*
01/04/18	1.42	2.46	NA*
01/11/18	1.42	2.54	NA*
01/18/18	1.42	2.62	NA*
01/25/18	1.42	2.63	NA*
02/01/18	1.42	2.78	NA*
02/08/18	1.42	2.85	NA*
02/15/18	1.42	2.90	NA*
02/22/18	1.42	2.92	NA*
03/01/18	1.42	2.81	NA*
03/08/18	1.42	2.86	NA*
03/15/18	1.43	2.82	NA*
03/22/18	1.68	2.83	NA*
03/29/18	1.68	2.74	NA*
04/05/18	1.69	2.83	NA*
04/12/18	1.69	2.83	NA*
04/19/18	1.69	2.92	NA*
04/26/18	1.70	3.00	NA*
05/03/18	1.70	2.94	NA*
05/10/18	1.70	2.97	NA*
05/17/18	1.70	3.11	NA*
05/24/18	1.70	2.98	NA*
05/31/18	1.70	2.83	NA*
06/07/18	1.70	2.93	NA*
06/14/18	1.90	2.94	NA*
06/21/18	1.92	2.90	NA*
06/28/18	1.91	2.84	NA*
07/05/18	1.91	2.84	NA*
07/12/18	1.91	2.85	NA*
07/19/18	1.91	2.84	NA*
07/26/18	1.91	2.98	NA*
08/02/18	1.91	2.98	NA*
08/09/18	1.91	2.93	NA*
08/16/18	1.92	2.87	NA*
08/23/18	1.92	2.82	NA*
08/30/18	1.92	2.86	NA*
09/06/18	1.92	2.88	NA*
09/13/18	1.92	2.97	NA*
09/20/18	1.92	3.07	NA*
09/27/18	2.18	3.06	NA*
10/04/18	2.18	3.19	NA*
10/11/18	2.18	3.14	NA*
10/18/18	2.19	3.17	NA*
10/25/18	2.20	3.14	NA*
11/01/18	2.20	3.14	NA*
11/08/18	2.20	3.24	NA*
11/15/18	2.20	3.11	NA*
11/21/18	2.20	3.06	NA*
11/29/18	2.20	3.03	NA*
12/06/18	2.20	2.87	NA*
12/13/18	2.19	2.91	NA*
12/20/18	2.40	2.79	NA*
12/27/18	2.40	2.77	NA*
01/03/19	2.40	2.56	NA*
01/10/19	2.40	2.74	NA*
01/17/19	2.40	2.75	NA*
01/24/19	2.40	2.72	NA*
01/31/19	2.40	2.63	NA*
02/07/19	2.40	2.63	NA*
02/14/19	2.40	2.66	NA*
02/21/19	2.40	2.69	NA*
02/28/19	2.40	2.73	NA*
03/07/19	2.40	2.64	NA*
03/14/19	2.40	2.63	NA*
03/21/19	2.41	2.54	NA*
03/28/19	2.41	2.39	NA*
04/04/19	2.41	2.51	NA*
04/11/19	2.41	2.51	NA*
04/18/19	2.43	2.57	NA*
04/25/19	2.44	2.54	NA*
05/02/19	2.41	2.55	NA*
05/09/19	2.38	2.45	NA*
05/16/19	2.39	2.40	NA*
05/23/19	2.38	2.31	NA*
05/30/19	2.39	2.22	NA*
06/06/19	2.37	2.12	NA*
06/13/19	2.37	2.10	NA*
06/20/19	2.37	2.01	NA*
06/27/19	2.38	2.01	NA*
07/03/19	2.41	1.96	NA
07/11/19	2.40	1.85	NA*
07/18/19	2.41	2.04	NA*
07/25/19	2.40	2.08	NA*
08/01/19	2.14	1.90	NA*
08/08/19	2.12	1.72	NA*
08/15/19	2.13	1.52	NA*
08/22/19	2.12	1.62	NA*
08/29/19	2.12	1.50	NA*
09/05/19	2.13	1.57	NA*
09/12/19	2.13	1.79	NA*
09/19/19	1.90	1.79	NA*
09/26/19	1.85	1.70	NA*
10/03/19	1.83	1.54	NA*
10/10/19	1.83	1.67	NA*
10/17/19	1.85	1.76	NA*
10/24/19	1.85	1.77	NA*
10/31/19	1.58	1.69	NA*
11/07/19	1.55	1.92	NA*
11/14/19	1.55	1.82	NA*
11/21/19	1.55	1.77	NA*
11/27/19	1.55	1.77	NA*
12/05/19	1.55	1.80	NA*
12/12/19	1.55	1.90	NA*
12/19/19	1.55	1.92	NA*
12/26/19	1.55	1.90	NA*
01/02/20	1.55	1.88	NA*
01/09/20	1.55	1.85	NA*
01/16/20	1.54	1.81	NA*
01/23/20	1.55	1.74	NA*
01/30/20	1.60	1.57	NA*
02/06/20	1.59	1.65	NA*
02/13/20	1.59	1.61	NA*
02/20/20	1.59	1.59	NA*
02/27/20	1.58	1.30	NA*
03/05/20	1.09	0.92	NA*
03/12/20	1.10	0.88	NA*
03/19/20	0.20	1.12	NA*
03/26/20	0.10	0.83	NA*
04/02/20	0.05	0.63	NA*
04/09/20	0.05	0.73	NA*
04/16/20	0.05	0.61	NA*
04/23/20	0.04	0.61	NA*
04/30/20	0.05	0.64	NA*
05/07/20	0.05	0.63	NA*
05/14/20	0.05	0.63	NA*

*Note: The Board of Governors of the Federal Reserve System discontinued the publication of the BAA bond yield.

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15/

Chart VIII-2 of the Board of Governors of the Federal Reserve System provides the rate of US dollars (USD) per euro (EUR), USD/EUR. The rate appreciated from USD 1.1228/EUR on May 8, 2019 to USD 1.0854/EUR on May 8, 2020 or 3.3 percent. The euro has devalued 47.0 percent relative to the dollar from the high on Jul 15, 2008 to May 15, 2020. US corporations with foreign transactions and net worth experience losses in their balance sheets in converting revenues from depreciated currencies to the dollar. Corporate profits decreased at $18.6 billion in IVQ2018. Corporate profits decreased at $78.7 billion in IQ2019. Corporate profits increased at $75.8 billion in IIQ2019. Corporate profits decreased at $4.7 billion in IIIQ2019. Profits after tax with IVA and CCA decreased at $6.8 billion in IVQ2018. Profits after tax with IVA and CCA decreased at $75.7 billion in IQ2019. Profits after tax with IVA and CCA increased at $66.1 billion in IIQ2019. Profits after tax with IVA and CCA increased at $11.2 billion in IIIQ2019. Net dividends increased at $32.8 billion in IVQ2018. Net dividends decreased at $37.9 billion in IQ2019. Net dividends increased at $22.3 billion in IIQ2019. Net dividends decreased at $7.3 billion in IIIQ2019. Undistributed corporate profits decreased at $39.6 billion in IVQ2018. Undistributed corporate profits decreased at $37.8 billion in IQ2019. Undistributed corporate profits increased at $43.9 billion in IIQ2019. Undistributed corporate profits increased at $18.4 billion in IIIQ2019. Undistributed corporate profits swelled 287.3 percent from $138.0 billion in IQ2007 to $534.5 billion in IIIQ2019 and changed signs from minus $4.3 billion in current dollars in IVQ2007. The investment decision of United States corporations has been fractured in the current economic cycle in preference of cash. There is increase in corporate profits from devaluing the dollar with unconventional monetary policy of zero interest rates and decrease of corporate profits in revaluing the dollar with attempts at “normalization” or increases in interest rates. Conflicts arise while other central banks differ in their adjustment process. The current account deficit seasonally adjusted at 2.1 percent in IIQ2018 increases to 2.4 percent in IIIQ2018. The current account deficit increases to 2.8 percent in IVQ2018. The current account deficit decreases to 2.6 percent in IQ2019. The current account deficit decreases to 2.4 percent in IIQ2019. The absolute value of the net international investment position increases from minus $8.9 trillion in IIQ2018 to minus $9.7 trillion in IIIQ2018. The absolute value of the net international investment position stabilizes to $9.6 trillion in IIIQ2018. The absolute value of the net international investment position increases at $10.1 trillion in IQ2019. The absolute value of the net international investment position stabilizes to $10.1 trillion in IIQ2019. The BEA explains as follows (https://www.bea.gov/system/files/2019-09/intinv219.pdf):

“The U.S. net international investment position, the difference between U.S. residents’ foreign financial assets and liabilities, was –$10.56 trillion at the end of the second quarter of 2019, according to statistics released by the U.S. Bureau of Economic Analysis (BEA). Assets totaled $28.01 trillion and liabilities were $38.56 trillion.

At the end of the first quarter, the net investment position was –$10.16 trillion (Table1.”

The BEA explains further (https://www.bea.gov/system/files/2019-09/intinv219.pdf):

“U.S. assets increased by $952.7 billion, to a total of $28.01 trillion, at the end of the second quarter, reflecting increases in all major categories of assets, particularly in portfolio investment and direct investment assets. Portfolio investment assets increased by $366.4 billion, to $12.68 trillion, and direct investment assets increased by $232.8 billion, to $8.39 trillion. These increases were driven mainly by foreign stock price increases that raised the value of these assets.

U.S. liabilities increased by $1.35 trillion, to a total of $38.56 trillion, at the end of the second quarter, reflecting increases in all major categories of liabilities, particularly in portfolio investment liabilities. Portfolio investment liabilities increased by $665.3 billion, to $20.62 trillion, driven mainly by U.S. stock and bond price increases that raised the value of these liabilities.”

Chart VIII-2, Exchange Rate of US Dollars (USD) per Euro (EUR), May 8, 2019 to May 8, 2020

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VIII-3 of the Board of Governors of the Federal Reserve System provides the yield of the 10-year Treasury constant maturity note from 1.59 percent on Feb 14, 2020 to 0.63 percent on May 14, 2020. There is turbulence in financial markets originating in a combination of intentions of decreasing the US policy fed funds rate, quantitative easing in the United States, Europe and Japan and increasing perception of financial/economic risks.

Chart VIII-3, Yield of Ten-year Constant Maturity Treasury, Feb 14, 2020 to May 14, 2020

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15

(4) Counterfactual of Policies Causing the Financial Crisis and Global Recession. The counterfactual of avoidance of deeper and more prolonged contraction by fiscal and monetary policies is not the critical issue. The counterfactual of avoidance of deeper and more prolonged contraction by fiscal and monetary policies is not the critical issue. As Professor John B. Taylor (2012Oct25) argues, the critically important counterfactual is that the financial crisis and global recession would have not occurred in the first place if different economic policies had been followed. The counterfactual intends to verify that a combination of housing policies and discretionary monetary policies instead of rules (Taylor 1993) caused, deepened and prolonged the financial crisis (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB, 2015, 2012 Oct 25; 2013Oct28, 2014 Jan01, 2014Jan3, 2014Jun26, 2014Jul15, 2015, 2016Dec7, 2016Dec20 http://www.johnbtaylor.com/; see http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). The experience resembles that of the Great Inflation of the 1960s and 1970s with stop-and-go growth/inflation that coined the term stagflation (http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html http://cmpassocregulationblog.blogspot.com/2011/04/new-economics-of-rose-garden-turned.html http://cmpassocregulationblog.blogspot.com/2011/03/is-there-second-act-of-us-great.html and Appendix I).

The explanation of the sharp contraction of United States housing can probably be found in the origins of the financial crisis and global recession. Let V(T) represent the value of the firm’s equity at time T and B stand for the promised debt of the firm to bondholders and assume that corporate management, elected by equity owners, is acting on the interests of equity owners. Robert C. Merton (1974, 453) states:

“On the maturity date T, the firm must either pay the promised payment of B to the debtholders or else the current equity will be valueless. Clearly, if at time T, V(T) > B, the firm should pay the bondholders because the value of equity will be V(T) – B > 0 whereas if they do not, the value of equity would be zero. If V(T) ≤ B, then the firm will not make the payment and default the firm to the bondholders because otherwise the equity holders would have to pay in additional money and the (formal) value of equity prior to such payments would be (V(T)- B) < 0.”

Pelaez and Pelaez (The Global Recession Risk (2007), 208-9) apply this analysis to the US housing market in 2005-2006 concluding:

“The house market [in 2006] is probably operating with low historical levels of individual equity. There is an application of structural models [Duffie and Singleton 2003] to the individual decisions on whether or not to continue paying a mortgage. The costs of sale would include realtor and legal fees. There could be a point where the expected net sale value of the real estate may be just lower than the value of the mortgage. At that point, there would be an incentive to default. The default vulnerability of securitization is unknown.”

There are multiple important determinants of the interest rate: “aggregate wealth, the distribution of wealth among investors, expected rate of return on physical investment, taxes, government policy and inflation” (Ingersoll 1987, 405). Aggregate wealth is a major driver of interest rates (Ingersoll 1987, 406). Unconventional monetary policy, with zero fed funds rates and flattening of long-term yields by quantitative easing, causes uncontrollable effects on risk taking that can have profound undesirable effects on financial stability. Excessively aggressive and exotic monetary policy is the main culprit and not the inadequacy of financial management and risk controls.

The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Ibid). According to a subsequent restatement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption decisions is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:

W = Y/r (1)

Equation (1) shows that as r goes to zero, r →0, W grows without bound, W→∞.

Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment but the exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at close to zero interest rates, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV).

Fri May 1

Mon 4

Tue 5

Wed 6

Thu 7

Fri 8

DJIA

18024.06

-0.3%

1.0%

18070.40

0.3%

17928.20

-0.5%

-0.8%

17841.98

-1.0%

-0.5%

17924.06

-0.6%

0.5%

18191.11

0.9%

1.5%

Fri 27 Feb

Mon 3/2

Tue 3/3

Wed 3/4

Thu 3/5

Fri 3/6

USD/ EUR

1.1197

1.6%

0.0%

1.1185

0.1%

1.1176

0.2%

0.1%

1.1081

1.0%

0.9%

1.1030

1.5%

0.5%

1.0843

3.2%

1.7%

Exchange rate volatility is increasing in response of “impatience” in financial markets with monetary policy guidance and measures:

Fri Mar 6

Mon 9

Tue 10

Wed 11

Thu 12

Fri 13

USD/ EUR

1.0843

3.2%

1.7%

1.0853

-0.1%

1.0700

1.3%

1.4%

1.0548

2.7%

1.4%

1.0637

1.9%

-0.8%

1.0497

3.2%

1.3%

Fri Mar 13

Mon 16

Tue 17

Wed 18

Thu 19

Fri 20

USD/ EUR

1.0497

3.2%

1.3%

1.0570

-0.7%

1.0598

-1.0%

-0.3%

1.0864

-3.5%

-2.5%

1.0661

-1.6%

1.9%

1.0821

-3.1%

-1.5%

Fri Apr 24

Mon 27

Tue 28

Wed 29

Thu 30

May Fri 1

USD/ EUR

1.0874

-0.6%

-0.4%

1.0891

-0.2%

1.0983

-1.0%

-0.8%

1.1130

-2.4%

-1.3%

1.1223

-3.2%

-0.8%

1.1199

-3.0%

0.2%

In a speech at Brown University on May 22, 2015, Chair Yellen stated (http://www.federalreserve.gov/newsevents/speech/yellen20150522a.htm):

The US dollar appreciated 3.8 percent relative to the euro in the week of May 22, 2015:

Fri May 15

Mon 18

Tue 19

Wed 20

Thu 21

Fri 22

USD/ EUR

1.1449

-2.2%

-0.3%

1.1317

1.2%

1.1150

2.6%

1.5%

1.1096

3.1%

0.5%

1.1113

2.9%

-0.2%

1.1015

3.8%

0.9%

The President of the European Central Bank (ECB), Mario Draghi, warned on Jun 3, 2015 that (http://www.ecb.europa.eu/press/pressconf/2015/html/is150603.en.html):

The Chair of the Board of Governors of the Federal Reserve System, Janet L. Yellen, stated on Jul 10, 2015 that (http://www.federalreserve.gov/newsevents/speech/yellen20150710a.htm):

At the press conference after the meeting of the FOMC on Sep 17, 2015, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20150917.pdf 4):

Some equity markets fell on Fri Sep 18, 2015:

Fri Sep 11

Mon 14

Tue 15

Wed 16

Thu 17

Fri 18

DJIA

16433.09

2.1%

0.6%

16370.96

-0.4%

16599.85

1.0%

1.4%

16739.95

1.9%

0.8%

16674.74

1.5%

-0.4%

16384.58

-0.3%

-1.7%

Nikkei 225

18264.22

2.7%

-0.2%

17965.70

-1.6%

18026.48

-1.3%

0.3%

18171.60

-0.5%

0.8%

18432.27

0.9%

1.4%

18070.21

-1.1%

-2.0%

DAX

10123.56

0.9%

-0.9%

10131.74

0.1%

10188.13

0.6%

10227.21

1.0%

0.4%

10229.58

1.0%

0.0%

9916.16

-2.0%

-3.1%

· “The economic outlook, of course, is highly uncertain”

· “Considerable uncertainties also surround the outlook for economic activity”

· “Given the highly uncertain nature of the outlook…”

Fri Oct 16

Mon 19

Tue 20

Wed 21

Thu 22

Fri 23

USD/ EUR

1.1350

0.1%

0.3%

1.1327

0.2%

1.1348

0.0%

-0.2%

1.1340

0.1%

1.1110

2.1%

2.0%

1.1018

2.9%

0.8%

DJIA

17215.97

0.8%

0.4%

17230.54

0.1%

17217.11

0.0%

-0.1%

17168.61

-0.3%

17489.16

1.6%

1.9%

17646.70

2.5%

0.9%

Dow Global

2421.58

0.3%

0.6%

2414.33

-0.3%

2411.03

-0.4%

-0.1%

2411.27

-0.4%

0.0%

2434.79

0.5%

1.0%

2458.13

1.5%

1.0%

DJ Asia Pacific

1402.31

1.1%

0.3%

1398.80

-0.3%

1395.06

-0.5%

-0.3%

1402.68

0.0%

0.5%

1396.03

-0.4%

-0.5%

1415.50

0.9%

1.4%

Nikkei 225

18291.80

-0.8%

1.1%

18131.23

-0.9%

18207.15

-0.5%

0.4%

18554.28

1.4%

1.9%

18435.87

0.8%

-0.6%

18825.30

2.9%

2.1%

Shanghai

3391.35

6.5%

1.6%

3386.70

-0.1%

3425.33

1.0%

1.1%

3320.68

-2.1%

-3.1%

3368.74

-0.7%

1.4%

3412.43

0.6%

1.3%

DAX

10104.43

0.1%

0.4%

10164.31

0.6%

10147.68

0.4%

-0.2%

10238.10

1.3%

0.9%

10491.97

3.8%

2.5%

10794.54

6.8%

2.9%

Fri Oct 30

Mon 2

Tue 3

Wed 4

Thu 5

Fri 6

USD/ EUR

1.1007

0.1%

-0.3%

1.1016

-0.1%

1.0965

0.4%

0.5%

1.0867

1.3%

0.9%

1.0884

1.1%

-0.2%

1.0742

2.4%

1.3%

The release on Nov 18, 2015 of the minutes of the FOMC (Federal Open Market Committee) meeting held on Oct 28, 2015 (http://www.federalreserve.gov/monetarypolicy/fomcminutes20151028.htm) states:

Fri Nov 13

Mon 16

Tue 17

Wed 18

Thu 19

Fri 20

USD/ EUR

1.0774

-0.3%

0.4%

1.0686

0.8%

1.0644

1.2%

0.4%

1.0660

1.1%

-0.2%

1.0735

0.4%

-0.7%

1.0647

1.2%

0.8%

DJIA

17245.24

-3.7%

-1.2%

17483.01

1.4%

17489.50

1.4%

0.0%

17737.16

2.9%

1.4%

17732.75

2.8%

0.0%

17823.81

3.4%

0.5%

DAX

10708.40

-2.5%

-0.7%

10713.23

0.0%

10971.04

2.5%

2.4%

10959.95

2.3%

-0.1%

11085.44

3.5%

1.1%

11119.83

3.8%

0.3%

Fri Nov 27

Mon 30

Tue 1

Wed 2

Thu 3

Fri 4

USD/ EUR

1.0594

0.5%

0.2%

1.0565

0.3%

1.0634

-0.4%

-0.7%

1.0616

-0.2%

0.2%

1.0941

-3.3%

-3.1%

1.0885

-2.7%

0.5%

DJIA

17798.49

-0.1%

17719.92

-0.4%

17888.35

0.5%

1.0%

17729.68

-0.4%

-0.9%

17477.67

-1.8%

-1.4%

17847.63

0.3%

2.1%

DAX

11293.76

1.6%

-0.2%

11382.23

0.8%

11261.24

-0.3%

-1.1%

11190.02

-0.9%

-0.6%

10789.24

-4.5%

-3.6%

10752.10

-4.8%

-0.3%

At the press conference following the meeting of the FOMC on Dec 16, 2015, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20151216.pdf page 8):

Fri Dec 11

Mon 14

Tue 15

Wed 16

Thu 17

Fri 18

USD/ EUR

1.0991

-1.0%

-0.4%

1.0993

0.0%

1.0932

0.5%

0.6%

1.0913

0.7%

0.2%

1.0827

1.5%

0.8%

1.0868

1.1%

-0.4%

DJIA

17265.21

-3.3%

-1.8%

17368.50

0.6%

17524.91

1.5%

0.9%

17749.09

2.8%

1.3%

17495.84

1.3%

-1.4%

17128.55

-0.8%

-2.1%

DAX

10340.06

-3.8%

-2.4%

10139.34

-1.9%

10450.38

-1.1%

3.1%

10469.26

1.2%

0.2%

10738.12

3.8%

2.6%

10608.19

2.6%

-1.2%

Fri 22

Mon 25

Tue 26

Wed 27

Thu 28

Fri 29

JPY/ USD

118.77

-1.5%

-0.9%

118.30

0.4%

118.42

0.3%

-0.1%

118.68

0.1%

-0.2%

118.82

0.0%

-0.1%

121.13

-2.0%

-1.9%

DJIA

16093.51

0.7%

1.3%

15885.22

-1.3%

16167.23

0.5%

1.8%

15944.46

-0.9%

-1.4%

16069.64

-0.1%

0.8%

16466.30

2.3%

2.5%

Nikkei

16958.53

-1.1%

5.9%

17110.91

0.9%

16708.90

-1.5%

-2.3%

17163.92

1.2%

2.7%

17041.45

0.5%

-0.7%

17518.30

3.3%

2.8%

Shanghai

2916.56

0.5%

1.3

2938.51

0.8%

2749.79

-5.7%

-6.4%

2735.56

-6.2%

-0.5%

2655.66

-8.9%

-2.9%

2737.60

-6.1%

3.1%

DAX

9764.88

2.3%

2.0%

9736.15

-0.3%

9822.75

0.6%

0.9%

9880.82

1.2%

0.6%

9639.59

-1.3%

-2.4%

9798.11

0.3%

1.6%

Sovereign Yields 2/12/16	Japan	Germany	USA
2 Year	-0.168	-0.498	0.694
10 Year	0.076	0.262	1.744

The dollar devalued relative to the euro and open stock markets traded lower after the announcement on Mar 10, 2016, but stocks rebounded on Mar 11:

Fri 4

Mon 7

Tue 8

Wed 9

Thu10

Fri 11

USD/ EUR

1.1006

-0.7%

-0.4%

1.1012

-0.1%

1.1013

-0.1%

0.0%

1.0999

0.1%

1.1182

-1.6%

-1.7%

1.1151

-1.3%

0.3%

DJIA

17006.77

2.2%

0.4%

17073.95

0.4%

16964.10

-0.3%

-0.6%

17000.36

0.0%

0.2%

16995.13

-0.1%

0.0%

17213.31

1.2%

1.3%

DAX

9824.17

3.3%

0.7%

9778.93

-0.5%

0.5%

9692.82

-1.3%

-0.9%

9723.09

-1.0%

0.3%

9498.15

-3.3%

-2.3%

9831.13

0.1%

3.5%

In a speech at the World Affairs Council of Philadelphia, on Jun 6, 2016 (http://www.federalreserve.gov/newsevents/speech/yellen20160606a.htm), Chair Yellen finds that “there is considerable uncertainty about the economic outlook.” There are fifteen references to this uncertainty in the text of 18 pages double-spaced. In the Semiannual Monetary Policy Report to the Congress on Jun 21, 2016, Chair Yellen states (http://www.federalreserve.gov/newsevents/testimony/yellen20160621a.htm), “Of course, considerable uncertainty about the economic outlook remains.” Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. Is there a “science” or even “art” of central banking under this extreme uncertainty in which policy does not generate higher volatility of money, income, prices and values of financial assets?

Focus is shifting from tapering quantitative easing by the Federal Open Market Committee (FOMC). There is sharp distinction between the two measures of unconventional monetary policy: (1) fixing of the overnight rate of fed funds now currently at 1¾ to 2 percent and (2) outright purchase of Treasury and agency securities and mortgage-backed securities for the balance sheet of the Federal Reserve. Markets overreacted to the so-called “paring” of outright purchases to $25 billion of securities per month for the balance sheet of the Fed. What is truly important is the fixing of the overnight fed funds at 1¾ to 2 percent with all measures depending on “a wide range of information, including measures of labor market conditions, indicators of inflation pressures and inflation expectations, and readings on financial and international developments” (https://www.federalreserve.gov/newsevents/pressreleases/monetary20190918a.htm): “In determining the timing and size of future adjustments to the target range for the federal funds rate, the Committee will assess realized and expected economic conditions relative to its maximum employment objective and its symmetric 2 percent inflation objective. This assessment will take into account a wide range of information, including measures of labor market conditions, indicators of inflation pressures and inflation expectations, and readings on financial and international developments” (emphasis added). The “outlook is uncertain”: “Consistent with its statutory mandate, the Committee seeks to foster maximum employment and price stability. In light of the implications of global developments for the economic outlook as well as muted inflation pressures, the Committee decided to lower the target range for the federal funds rate to 1-3/4 to 2 percent. This action supports the Committee's view that sustained expansion of economic activity, strong labor market conditions, and inflation near the Committee's symmetric 2 percent objective are the most likely outcomes, but uncertainties about this outlook remain” (https://www.federalreserve.gov/newsevents/pressreleases/monetary20190918a.htm) In the Opening Remarks to the Press Conference on Jan 30, 2019, the Chairman of the Federal Reserve Board, Jerome H. Powell, stated (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20190130.pdf): “Today, the FOMC decided that the cumulative effects of those developments over the last several months warrant a patient, wait-and-see approach regarding future policy changes. In particular, our statement today says, “In light of global economic and financial developments and muted inflation pressures, the Committee will be patient as it determines what future adjustments to the target range for the federal funds rate may be appropriate.” This change was not driven by a major shift in the baseline outlook for the economy. Like many forecasters, we still see “sustained expansion of economic activity, strong labor market conditions, and inflation near … 2 percent” as the likeliest case. But the cross-currents I mentioned suggest the risk of a less-favorable outlook. In addition, the case for raising rates has weakened somewhat. The traditional case for rate increases is to protect the economy from risks that arise when rates are too low for too long, particularly the risk of too-high inflation. Over the past few months, that risk appears to have diminished. Inflation readings have been muted, and the recent drop in oil prices is likely to Page 3 of 5 push headline inflation lower still in coming months. Further, as we noted in our post-meeting statement, while survey-based measures of inflation expectations have been stable, financial market measures of inflation compensation have moved lower. Similarly, the risk of financial imbalances appears to have receded, as a number of indicators that showed elevated levels of financial risk appetite last fall have moved closer to historical norms. In this environment, we believe we can best support the economy by being patient in evaluating the outlook before making any future adjustment to policy.” The FOMC is initiating the “normalization” or reduction of the balance sheet of securities held outright for monetary policy (https://www.federalreserve.gov/newsevents/pressreleases/monetary20190130c.htm) with significant changes (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20190320.pdf). In the opening remarks to the Mar 20, 2019, the Chairman of the Federal Reserve Board, Jerome H. Powell, stated (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20190320.pdf): “In discussing the Committee’s projections, it is useful to note what those projections are, as well as what they are not. The SEP includes participants’ individual projections of the most likely economic scenario along with their views of the appropriate path of the federal funds rate in that scenario. Views about the most likely scenario form one input into our policy discussions. We also discuss other plausible scenarios, including the risk of more worrisome outcomes. These and other scenarios and many other considerations go into policy, but are not reflected in projections of the most likely case. Thus, we always emphasize that the interest rate projections in the SEP are not a Committee decision. They are not a Committee plan. As Chair Yellen noted some years ago, the FOMC statement, rather than the dot plot, is the device that the Committee uses to express its opinions about the likely path of rates.”

Real Disposable Personal Income	Real Personal Consumption Expenditures	Prices of Personal Consumption Expenditures	PCE Prices Excluding Food and Energy
∆%12M	∆%12M	∆%12M	∆%12M
6/2017	6/2017	6/2017	6/2017
1.2	2.4	1.4	1.5

At an address to The Clearing House and The Bank Policy Institute Annual Conference (https://www.federalreserve.gov/newsevents/speech/clarida20181127a.htm), in New York City, on Nov 27, 2018, the Vice Chairman of the Fed, Richard H. Clarida, analyzes the data dependence of monetary policy. An important hurdle is critical unobserved parameters of monetary policy (https://www.federalreserve.gov/newsevents/speech/clarida20181127a.htm): “But what if key parameters that describe the long-run destination of the economy are unknown? This is indeed the relevant case that the FOMC and other monetary policymakers face in practice. The two most important unknown parameters needed to conduct‑‑and communicate‑‑monetary policy are the rate of unemployment consistent with maximum employment, u*, and the riskless real rate of interest consistent with price stability, r*. As a result, in the real world, monetary policy should, I believe, be data dependent in a second sense: that incoming data can reveal at each FOMC meeting signals that will enable it to update its estimates of r* and u* in order to obtain its best estimate of where the economy is heading.” Current robust economic growth, employment creation and inflation close to the Fed’s 2 percent objective suggest continuing “gradual policy normalization.” Incoming data can be used to update u^* and r^* in designing monetary policy that attains price stability and maximum employment. Clarida also finds that the current expansion will be the longest in history if it continues into 2019. In an address at The Economic Club of New York, New York City, Nov 28, 2018 (https://www.federalreserve.gov/newsevents/speech/powell20181128a.htm), the Chairman of the Fed, Jerome H. Powell, stated (https://www.federalreserve.gov/newsevents/speech/powell20181128a.htm): “For seven years during the crisis and its painful aftermath, the Federal Open Market Committee (FOMC) kept our policy interest rate unprecedentedly low--in fact, near zero--to support the economy as it struggled to recover. The health of the economy gradually but steadily improved, and about three years ago the FOMC judged that the interests of households and businesses, of savers and borrowers, were no longer best served by such extraordinarily low rates. We therefore began to raise our policy rate gradually toward levels that are more normal in a healthy economy. Interest rates are still low by historical standards, and they remain just below the broad range of estimates of the level that would be neutral for the economy‑‑that is, neither speeding up nor slowing down growth. My FOMC colleagues and I, as well as many private-sector economists, are forecasting continued solid growth, low unemployment, and inflation near 2 percent.” The market focused on policy rates “just below the broad range of estimates of the level that would be neutral for the economy—that is, neither speeding up nor slowing down growth.” There was a relief rally in the stock market of the United States:

Fri 23

Mon 26

Tue 27

Wed 28

Thu 29

Fri 30

USD/EUR

1.1339

0.7%

0.6%

1.1328

0.1%

1.1293

0.4%

0.3%

1.1368

-0.3%

-0.7%

1.1394

-0.5%

-0.2%

1.1320

0.2%

0.6%

DJIA

24285.95

-4.4%

-0.7%

24640.24

1.5%

24748.73

1.9%

0.4%

25366.43

4.4%

2.5%

25338.84

4.3%

-0.1%

25538.46

5.2%

0.8%

Table III-I, Weekly Financial Risk Assets Dec 31, 2018 to Jan 4, 2019

Fri 28	Mon 31	Tue 1	Wed 2	Thu 3	Fri 4
10Y Note 2.736	2.683	2.683	2.663	2.560	2.658
2Y Note 2.528	2.500	2.500	2.488	2.387	2.480
DJIA 23062.40 2.7% -0.3%	23327.46 1.1% 1.1%	23327.46 1.1% 0.0%	23346.24 1.2% 0.1%	22686.22 -1.6% -2.8%	23433.16 1.6% 3.3%
Dow Global 2718.19 1.3% 0.8%	2734.40 0.6% 0.6%	2734.40 0.6% 0.0%	2729.74 0.4% -0.2%	2707.29 -0.4% -0.8%	2773.12 2.0% 2.4%

In the Opening Remarks to the Press Conference on Jan 30, 2019, the Chairman of the Federal Reserve Board, Jerome H. Powell, stated (https://www.federalreserve.gov/mediacenter/files/FOMCpresconf20190130.pdf): “Today, the FOMC decided that the cumulative effects of those developments over the last several months warrant a patient, wait-and-see approach regarding future policy changes. In particular, our statement today says, “In light of global economic and financial developments and muted inflation pressures, the Committee will be patient as it determines what future adjustments to the target range for the federal funds rate may be appropriate.” This change was not driven by a major shift in the baseline outlook for the economy. Like many forecasters, we still see “sustained expansion of economic activity, strong labor market conditions, and inflation near … 2 percent” as the likeliest case. But the cross-currents I mentioned suggest the risk of a less-favorable outlook. In addition, the case for raising rates has weakened somewhat. The traditional case for rate increases is to protect the economy from risks that arise when rates are too low for too long, particularly the risk of too-high inflation. Over the past few months, that risk appears to have diminished. Inflation readings have been muted, and the recent drop in oil prices is likely to Page 3 of 5 push headline inflation lower still in coming months. Further, as we noted in our post-meeting statement, while survey-based measures of inflation expectations have been stable, financial market measures of inflation compensation have moved lower. Similarly, the risk of financial imbalances appears to have receded, as a number of indicators that showed elevated levels of financial risk appetite last fall have moved closer to historical norms. In this environment, we believe we can best support the economy by being patient in evaluating the outlook before making any future adjustment to policy.” The FOMC is initiating the “normalization” or reduction of the balance sheet of securities held outright for monetary policy (https://www.federalreserve.gov/newsevents/pressreleases/monetary20190130c.htm).

Fri 25

Mon 28

Tue 29

Wed 30

Thu 31

Fri 1

DJIA

24737.20

0.1%

0.7%

24528.22

-0.8%

24579.96

-0.6%

0.2%

25014.86

1.1%

1.8%

24999.67

1.1%

-0.1%

25063.89

1.3%

0.3%

Dow Global

2917.27

0.5%

1.0%

2899.74

-0.6%

2905.29

-0.4%

0.2%

2927.10

0.3%

0.8%

2945.73

1.0%

0.6%

2947.87

1.0%

0.1%

DJ Asia Pacific

Nikkei

20773.56

0.5%

1.0%

20649.00

-0.6%

20664.64

-0.5%

0.1%

20556.54

-1.0%

-0.5%

20773.49

0.0%

1.1%

20788.39

0.1%

Shanghai

2601.72

0.2%

0.4%

2596.98

-0.2%

2594.25

-0.3%

-0.1%

2575.58

-1.0%

-0.7%

2584.57

-0.7%

0.3%

2618.23

0.6%

1.3%

DAX

11281.79

0.7%

1.4%

11210.31

-0.6%

11218.83

-0.6%

0.1%

11181.66

-0.9%

-0.3%

11173.10

-1.0%

-0.1%

11180.66

-0.9%

0.1%

BOVESPA

97677.19

1.6%

0.0%

95443.88

-2.3%

95639.33

-2.1%

0.2%

96996.21

-0.7%

1.4%

97393.75

-0.3%

0.4%

97861.27

0.2%

0.5%

Fri 14

Mon 17

Tue 18

Wed 19

Thu 20

Fri 21

DJIA

26089.61

0.4%

-0.1%

26112.53

0.1%

26465.54

1.4%

26504.00

1.6%

0.1%

26753.17

2.5%

0.9%

26719.13

2.4%

-0.1%

Dow Global

2998.79

0.2%

-0.4%

2999.93

0.0%

3034.59

1.2%

3050.80

1.7%

0.5%

3077.81

2.6%

0.9%

3081.62

2.8%

0.1%

DJ Asia Pacific

Nikkei

21116.89

1.1%

0.4%

21124.00

0.0%

20972.71

-0.7%

21333.87

1.0%

1.7%

21462.86

1.6%

0.6%

21258.64

0.7%

-1.0%

Shanghai

2881.97

1.9%

-1.0%

2887.62

0.2%

2890.16

0.3%

0.1%

2917.80

1.2%

1.0%

2987.12

3.6%

2.4%

3001.98

4.2%

0.5%

DAX

12096.40

0.4%

-0.6%

12085.82

-0.1%

12331.75

1.9%

2.0%

12308.53

1.8%

-0.2%

12355.39

2.1%

0.4%

12339.92

2.0%

-0.1%

BOVESPA

98040.06

0.2%

-0.7%

97623.25

-0.4%

99404.39

1.4%

1.8%

100303.41

2.3%

0.9%

100303.41

2.3%

0.0%

102012.64

4.1%

1.7%

The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper used in purchasing default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever-increasing house prices. The suspension of auctions of 30-year Treasuries intended to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity because of the penalty in the form of low interest rates and unsound credit decisions because the put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4).

There are significant elements of the theory of bank financial fragility of Diamond and Dybvig (1983) and Diamond and Rajan (2000, 2001a, 2001b) that help to explain the financial fragility of banks during the credit/dollar crisis (see also Diamond 2007). The theory of Diamond and Dybvig (1983) as exposed by Diamond (2007) is that banks funding with demand deposits have a mismatch of liquidity (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 58-66). A run occurs when too many depositors attempt to withdraw cash at the same time. All that is needed is an expectation of failure of the bank. Three important functions of banks are providing evaluation, monitoring and liquidity transformation. Banks invest in human capital to evaluate projects of borrowers in deciding if they merit credit. The evaluation function reduces adverse selection or financing projects with low present value. Banks also provide important monitoring services of following the implementation of projects, avoiding moral hazard that funds be used for, say, real estate speculation instead of the original project of factory construction. The transformation function of banks involves both assets and liabilities of bank balance sheets. Banks convert an illiquid asset or loan for a project with cash flows in the distant future into a liquid liability in the form of demand deposits that can be withdrawn immediately.

In the theory of banking of Diamond and Rajan (2000, 2001a, 2001b), the bank creates liquidity by tying human assets to capital. The collection of skills of the relationship banker converts an illiquid project of an entrepreneur into liquid demand deposits that are immediately available for withdrawal. The deposit/capital structure is fragile because of the threat of bank runs. In these days of online banking, the run on Washington Mutual was through withdrawals online. A bank run can be triggered by the decline of the value of bank assets below the value of demand deposits.

Pelaez and Pelaez (Regulation of Banks and Finance 2009b, 60, 64-5) find immediate application of the theories of banking of Diamond, Dybvig and Rajan to the credit/dollar crisis after 2007. It is a credit crisis because the main issue was the deterioration of the credit portfolios of securitized banks caused by default of subprime mortgages. It is a dollar crisis because of the weakening dollar resulting from relatively low interest rate policies of the US. It caused systemic effects that converted into a global recession not only because of the huge weight of the US economy in the world economy but also because the credit crisis transferred to the UK and Europe. Management skills or human capital of banks are illustrated by financial engineering of complex products. The increasing importance of human relative to inanimate capital (Rajan and Zingales 2000) is revolutionizing the theory of the firm (Zingales 2000) and corporate governance (Rajan and Zingales 2001). Finance is one of the most important examples of this transformation. Bank charters were the source of profits in the original banking institution. Pricing and structuring financial instruments was revolutionized with option pricing formulas developed by Black and Scholes (1973) and Merton (1973, 1974, 1998) that permitted the development of complex products with fair pricing. The successful financial company must attract and retain finance professionals who have invested in human capital, which is a sunk cost to them and not of the institution where they work.

The complex financial products created for securitized banking with high investments in human capital are based on houses, which are as illiquid as the projects of entrepreneurs in the theory of banking. The liquidity fragility of the securitized bank is equivalent to that of the commercial bank in the theory of banking (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65). Banks created off-balance sheet structured investment vehicles (SIV) that issued commercial paper receiving AAA rating because of letters of liquidity guarantee by the banks. The commercial paper was converted into liquidity by its use as collateral in SRPs at the lowest rates and minimal haircuts because of the AAA rating of the guarantor bank. In the theory of banking, default can be triggered when the value of assets is perceived as lower than the value of the deposits. Commercial paper issued by SIVs, securitized mortgages and derivatives all obtained SRP liquidity based on illiquid home mortgage loans at the bottom of the pyramid. The run on the securitized bank had a clear origin (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65):

“The increasing default of mortgages resulted in an increase in counterparty risk. Banks were hit by the liquidity demands of their counterparties. The liquidity shock extended to many segments of the financial markets—interbank loans, asset-backed commercial paper (ABCP), high-yield bonds and many others—when counterparties preferred lower returns of highly liquid safe havens, such as Treasury securities, than the risk of having to sell the collateral in SRPs at deep discounts or holding an illiquid asset. The price of an illiquid asset is near zero.”

Gorton and Metrick (2010H, 507) provide a revealing quote to the work in 1908 of Edwin R. A. Seligman, professor of political economy at Columbia University, founding member of the American Economic Association and one of its presidents and successful advocate of progressive income taxation. The intention of the quote is to bring forth the important argument that financial crises are explained in terms of “confidence” but as Professor Seligman states in reference to historical banking crises in the US, the important task is to explain what caused the lack of confidence. It is instructive to repeat the more extended quote of Seligman (1908, xi) on the explanations of banking crises:

“The current explanations may be divided into two categories. Of these the first includes what might be termed the superficial theories. Thus it is commonly stated that the outbreak of a crisis is due to lack of confidence,--as if the lack of confidence was not in itself the very thing which needs to be explained. Of still slighter value is the attempt to associate a crisis with some particular governmental policy, or with some action of a country’s executive. Such puerile interpretations have commonly been confined to countries like the United States, where the political passions of democracy have had the fullest way. Thus the crisis of 1893 was ascribed by the Republicans to the impending Democratic tariff of 1894; and the crisis of 1907 has by some been termed the ‘[Theodore] Roosevelt panic,” utterly oblivious of the fact that from the time of President Jackson, who was held responsible for the troubles of 1837, every successive crisis had had its presidential scapegoat, and has been followed by a political revulsion. Opposed to these popular, but wholly unfounded interpretations, is the second class of explanations, which seek to burrow beneath the surface and to discover the more occult and fundamental causes of the periodicity of crises.”

Scholars ignore superficial explanations in the effort to seek good and truth. The problem of economic analysis of the credit/dollar crisis is the lack of a structural model with which to attempt empirical determination of causes (Gorton and Metrick 2010SB). There would still be doubts even with a well-specified structural model because samples of economic events do not typically permit separating causes and effects. There is also confusion is separating the why of the crisis and how it started and propagated, all of which are extremely important.

In true heritage of the principles of Seligman (1908), Gorton (2009EFM) discovers a prime causal driver of the credit/dollar crisis. The objective of subprime and Alt-A mortgages was to facilitate loans to populations with modest means so that they could acquire a home. These borrowers would not receive credit because of (1) lack of funds for down payments; (2) low credit rating and information; (3) lack of information on income; and (4) errors or lack of other information. Subprime mortgage “engineering” was based on the belief that both lender and borrower could benefit from increases in house prices over the short run. The initial mortgage would be refinanced in two or three years depending on the increase of the price of the house. According to Gorton (2009EFM, 13, 16):

“The outstanding amounts of Subprime and Alt-A [mortgages] combined amounted to about one quarter of the $6 trillion mortgage market in 2004-2007Q1. Over the period 2000-2007, the outstanding amount of agency mortgages doubled, but subprime grew 800%! Issuance in 2005 and 2006 of Subprime and Alt-A mortgages was almost 30% of the mortgage market. Since 2000 the Subprime and Alt-A segments of the market grew at the expense of the Agency (i.e., the government sponsored entities of Fannie Mae and Freddie Mac) share, which fell from almost 80% (by outstanding or issuance) to about half by issuance and 67% by outstanding amount. The lender’s option to rollover the mortgage after an initial period is implicit in the subprime mortgage. The key design features of a subprime mortgage are: (1) it is short term, making refinancing important; (2) there is a step-up mortgage rate that applies at the end of the first period, creating a strong incentive to refinance; and (3) there is a prepayment penalty, creating an incentive not to refinance early.”

The prime objective of successive administrations in the US during the past 20 years and actually since the times of Roosevelt in the 1930s has been to provide “affordable” financing for the “American dream” of home ownership. The US housing finance system is mixed with public, public/private and purely private entities. Congress established the Federal Home Loan Bank (FHLB) system in 1932 that also created the Federal Housing Administration in 1934 with the objective of insuring homes against default. In 1938, the government created the Federal National Mortgage Association, or Fannie Mae, to foster a market for FHA-insured mortgages. Government-insured mortgages were transferred from Fannie Mae to the Government National Mortgage Association, or Ginnie Mae, to permit Fannie Mae to become a publicly owned company. Securitization of mortgages began in 1970 with the government charter to the Federal Home Loan Mortgage Corporation, or Freddie Mac, with the objective of bundling mortgages created by thrift institutions that would be marketed as bonds with guarantees by Freddie Mac (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 42-8). In the third quarter of 2008, total mortgages in the US were $12,057 billion of which 43.5 percent, or $5423 billion, were retained or guaranteed by Fannie Mae and Freddie Mac (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 45). In 1990, Fannie Mae and Freddie Mac had a share of only 25.4 percent of total mortgages in the US. Mortgages in the US increased from $6922 billion in 2002 to $12,088 billion in 2007, or by 74.6 percent, while the retained or guaranteed portfolio of Fannie and Freddie rose from $3180 billion in 2002 to $4934 billion in 2007, or by 55.2 percent.

According to Pinto (2008) in testimony to Congress:

“There are approximately 25 million subprime and Alt-A loans outstanding, with an unpaid principal amount of over $4.5 trillion, about half of them held or guaranteed by Fannie and Freddie. Their high risk activities were allowed to operate at 75:1 leverage ratio. While they may deny it, there can be no doubt that Fannie and Freddie now own or guarantee $1.6 trillion in subprime, Alt-A and other default prone loans and securities. This comprises over 1/3 of their risk portfolios and amounts to 34% of all the subprime loans and 60% of all Alt-A loans outstanding. These 10.5 million unsustainable, nonprime loans are experiencing a default rate 8 times the level of the GSEs’ 20 million traditional quality loans. The GSEs will be responsible for a large percentage of an estimated 8.8 million foreclosures expected over the next 4 years, accounting for the failure of about 1 in 6 home mortgages. Fannie and Freddie have subprimed America.”

In perceptive analysis of growth and macroeconomics in the past six decades, Rajan (2012FA) argues that “the West can’t borrow and spend its way to recovery.” The Keynesian paradigm is not applicable in current conditions. Advanced economies in the West could be divided into those that reformed regulatory structures to encourage productivity and others that retained older structures. In the period from 1950 to 2000, Cobet and Wilson (2002) find that US productivity, measured as output/hour, grew at the average yearly rate of 2.9 percent while Japan grew at 6.3 percent and Germany at 4.7 percent (see Pelaez and Pelaez, The Global Recession Risk (2007), 135-44). In the period from 1995 to 2000, output/hour grew at the average yearly rate of 4.6 percent in the US but at lower rates of 3.9 percent in Japan and 2.6 percent in Germany. Rajan (2012FA) argues that the differential in productivity growth was accomplished by deregulation in the US at the end of the 1970s and during the 1980s. In contrast, Europe did not engage in reform with the exception of Germany in the early 2000s that empowered the German economy with significant productivity advantage. At the same time, technology and globalization increased relative remunerations in highly skilled, educated workers relative to those without skills for the new economy. It was then politically appealing to improve the fortunes of those left behind by the technological revolution by means of increasing cheap credit. As Rajan (2012FA) argues:

“In 1992, Congress passed the Federal Housing Enterprises Financial Safety and Soundness Act, partly to gain more control over Fannie Mae and Freddie Mac, the giant private mortgage agencies, and partly to promote affordable homeownership for low-income groups. Such policies helped money flow to lower-middle-class households and raised their spending—so much so that consumption inequality rose much less than income inequality in the years before the crisis. These policies were also politically popular. Unlike when it came to an expansion in government welfare transfers, few groups opposed expanding credit to the lower-middle class—not the politicians who wanted more growth and happy constituents, not the bankers and brokers who profited from the mortgage fees, not the borrowers who could now buy their dream houses with virtually no money down, and not the laissez-faire bank regulators who thought they could pick up the pieces if the housing market collapsed. The Federal Reserve abetted these shortsighted policies. In 2001, in response to the dot-com bust, the Fed cut short-term interest rates to the bone. Even though the overstretched corporations that were meant to be stimulated were not interested in investing, artificially low interest rates acted as a tremendous subsidy to the parts of the economy that relied on debt, such as housing and finance. This led to an expansion in housing construction (and related services, such as real estate brokerage and mortgage lending), which created jobs, especially for the unskilled. Progressive economists applauded this process, arguing that the housing boom would lift the economy out of the doldrums. But the Fed-supported bubble proved unsustainable. Many construction workers have lost their jobs and are now in deeper trouble than before, having also borrowed to buy unaffordable houses. Bankers obviously deserve a large share of the blame for the crisis. Some of the financial sector’s activities were clearly predatory, if not outright criminal. But the role that the politically induced expansion of credit played cannot be ignored; it is the main reason the usual checks and balances on financial risk taking broke down.”

In fact, Raghuram G. Rajan (2005) anticipated low liquidity in financial markets resulting from low interest rates before the financial crisis that caused distortions of risk/return decisions provoking the credit/dollar crisis and global recession from IVQ2007 to IIQ2009. Near zero interest rates of unconventional monetary policy induced excessive risks and low liquidity in financial decisions that were critical as a cause of the credit/dollar crisis after 2007. Rajan (2012FA) argues that it is not feasible to return to the employment and income levels before the credit/dollar crisis because of the bloated construction sector, financial system and government budgets.

(5) Historically Sharper Recoveries from Deeper Contractions and Financial Crises. Professor Michael D. Bordo (2012Sep27), at Rutgers University, is providing clear thought on the correct comparison of the current business cycles in the United States with those in United States history. There are two issues raised by Professor Bordo: (1) lumping together countries with different institutions, economic policies and financial systems; and (2) the conclusion that growth is mediocre after financial crises and deep recessions, which is repeated daily in the media, but that Bordo and Haubrich (2012DR) persuasively demonstrate to be inconsistent with United States experience.

Depriving economic history of institutions is perilous as is illustrated by the economic history of Brazil. Douglass C. North (1994) emphasized the key role of institutions in explaining economic history. Rondo E. Cameron (1961, 1967, 1972) applied institutional analysis to banking history. Friedman and Schwartz (1963) analyzed the relation of money, income and prices in the business cycle and related the monetary policy of an important institution, the Federal Reserve System, to the Great Depression. Bordo, Choudhri and Schwartz (1995) analyze the counterfactual of what would have been economic performance if the Fed had used during the Great Depression the Friedman (1960) monetary policy rule of constant growth of money (for analysis of the Great Depression see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 198-217). Alan Meltzer (2004, 2010a,b) analyzed the Federal Reserve System over its history. The reader would be intrigued by Figure 5 in Reinhart and Rogoff (2010FCDC, 15) in which Brazil is classified in external default for seven years between 1828 and 1834 but not again until 64 years later in 1989, above the 50 years of incidence for “serial default”. William R. Summerhill, Jr. (2007SC, 2007IR) has filled this void in scholarly research on nineteenth-century Brazil. There are important conclusions by Summerhill on the exceptional sample of institutional change or actually lack of change, public finance and financial repression in Brazil between 1822 and 1899, combining tools of economics, political science and history. During seven continuous decades, Brazil did not miss a single interest payment with government borrowing without repudiation of debt or default. What is surprising is that Brazil borrowed by means of long-term bonds and, even more surprising, interest rates fell over time. The external debt of Brazil in 1870 was ₤41,275,961 and the domestic debt in the internal market was ₤25,708,711, or 62.3 percent of the total (Summerhill 2007IR, 73).

The experience of Brazil differed from that of Latin America (Summerhill 2007IR). During the six decades when Brazil borrowed without difficulty, Latin American countries becoming independent after 1820 engaged in total defaults, suffering hardship in borrowing abroad. The countries that borrowed again fell again in default during the nineteenth century. Venezuela defaulted in four occasions. Mexico defaulted in 1827, rescheduling its debt eight different times and servicing the debt sporadically. About 44 percent of Latin America’s sovereign debt was in default in 1855 and approximately 86 percent of total government loans defaulted in London originated in Spanish American borrowing countries.

External economies of commitment to secure private rights in sovereign credit would encourage development of private financial institutions, as postulated in classic work by North and Weingast (1989), Summerhill (2007IR, 22). This is how banking institutions critical to the Industrial Revolution were developed in England (Cameron 1967). The obstacle in Brazil found by Summerhill (2007IR) is that sovereign debt credibility was combined with financial repression. There was a break in Brazil of the chain of effects from protecting public borrowing, as in North and Weingast (1989), to development of private financial institutions. Summerhill (2015, 2008IR) finds compelling evidence that sovereign credibility is insufficient to develop financial intermediation required for economic growth in the presence of inadequate political institutions.

Summerhill (2015) contributes momentous solid facts and analysis with an ideal method combining economic theory, econometrics, international comparisons, data reconstruction and exhaustive archival research. Summerhill (2015) finds that Brazil committed to service of sovereign foreign and internal debt. Contrary to conventional wisdom, Brazil generated primary fiscal surpluses during most of the Empire until 1889 (Summerhill 2015, 37-8, Figure 2.1). Econometric tests by Summerhill (2015, 19-44) show that Brazil’s sovereign debt was sustainable. Sovereign credibility in the North-Weingast (1989) sense spread to financial development that provided the capital for modernization in England and parts of Europe (see Cameron 1961, 1967). Summerhill (2015, 3, 194-6, Figure 7.1) finds that “Brazil’s annual cost of capital in London fell from a peak of 13.9 percent in 1829 to only 5.12 percent in 1889. Average rates on secured loans in the private sector in Rio, however, remained well above 12 percent through 1850.” Financial development would have financed diversification of economic activities, increasing productivity and wages and ensuring economic growth. Brazil restricted creation of limited liability enterprises (Summerhill 2015, 151-82) that prevented raising capital with issue of stocks and corporate bonds. Cameron (1961) analyzed how the industrial revolution in England spread to France and then to the rest of Europe. The Société Générale de Crédit Mobilier of Émile and Isaac Péreire provided the “mobilization of credit” for the new economic activities (Cameron 1961). Summerhill (2015, 151-9) provides facts and analysis demonstrating that regulation prevented the creation of a similar vehicle for financing modernization by Irineu Evangelista de Souza, the legendary Visconde de Mauá. Regulation also prevented the use of negotiable bearing notes of the Caisse Générale of Jacques Lafitte (Cameron 1961, 118-9). The government also restricted establishment and independent operation of banks (Summerhill 2015, 183-214). Summerhill (2015, 198-9) measures concentration in banking that provided economic rents or a social loss. The facts and analysis of Summerhill (2015) provide convincing evidence in support of the economic theory of regulation, which postulates that regulated entities capture the process of regulation to promote their self-interest. There appears to be a case that excessively centralized government can result in regulation favoring private instead of public interests with adverse effects on economic activity. The contribution of Summerhill (2015) explains why Brazil did not benefit from trade as an engine of growth—as did regions of recent settlement in the vision of nineteenth-century trade and development of Ragnar Nurkse (1959)—partly because of restrictions on financing and incorporation. Professor Rondo E. Cameron, in his memorable A Concise Economic History of the World (Cameron 1989, 307-8), finds that “from a broad spectrum of possible forms of interaction between the financial sector and other sectors of the economy that requires its services, one can isolate three type-cases: (1) that in which the financial sector plays a positive, growth-inducing role; (2) that in which the financial sector is essentially neutral or merely permissive; and (3) that in which inadequate finance restricts or hinders industrial and commercial development.” Summerhill (2015) proves exhaustively that Brazil failed to modernize earlier because of the restrictions of an inadequate institutional financial arrangement plagued by regulatory capture for self-interest.

Professor Stephen Haber (2011, 115) analyzes research in various fields of inquiry that lead to seminal conclusions full of implications for current social and economy policy and institutional organization:

“This chapter has looked at the political and economic histories of three New World economies in order to assess how the distribution of power across society shaped the institutions that governed entry into banking. The results are broadly consistent with the view that the distribution of human capital and the ability to project power exert an effect on an economy’s economic institutions. One clear pattern that emerges from these case studies is that representative institutions alone—such as Brazil’s parliament in the nineteenth century—are necessary but not sufficient conditions to generate economic institutions that give rise to broadly based financial development. Financial incumbents can either capture the representative institutions or form coalitions with their members; effective suffrage is necessary in order to align the incentives of political elites with the end users of credit.

Are these results generalizable? Obviously, more detailed case studies beyond the three studied here are necessary before any firm conclusions should be drawn, but the available evidence from large- N studies is broadly consistent with the patterns we find in Mexico, Brazil, and the United States.”

Banking was important in facilitating economic growth in historical periods (Cameron 1961, 1967, 1972; Cameron et al. 1992). Banking is also important currently because small- and medium-size business may have no other form of financing than banks in contrast with many options for larger and more mature companies that have access to capital markets. Calomiris and Haber (2014) find that broad voting rights and institutions restricting coalitions of bankers and populists ensure stable banking systems and access to credit. Barth, Caprio, and Levine (2006) analyze a cross section of sixty-five countries in 2003 and find that democratic political institutions are associated with greater ease in obtaining a bank charter and fewer restrictions on the operation of banks. They also find that the tight regulatory restrictions on banks created by autocratic political institutions are associated with lower credit market development and less bank stability, as well as with more corruption in lending. Bordo and Rousseau (2006) analyze a panel of seventeen countries over the period 1880 to 1997, and produce similar results: “there is a strong, independent effect of proportional representation, frequent elections, female suffrage, and political stability on the size of the financial sector.”

The first sample of Barth, Caprio and Levine (2006) includes 200 regulatory and supervisory practices in 100 countries. The second sample of Barth, Caprio and Levine (2006) increases coverage for 50 more countries and 100 new queries. The conclusions are quite powerful in favor of the private interest view, which explains regulation on motivation of promoting self-interest, in contrast with the public interest view, explaining regulation on the motive of improving public interest. Barth, Caprio and Levine (2006) conclude that disclosure of information would promote sound bank governance by empowering investors in enforcing such governance. Powerful government regulation does not ameliorate bank fragility or promote bank efficiency. The contrast of the private interest view and the public interest view is an important foundation of analysis of bank and financial regulation (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), Regulation of Banks and Finance (2008b)).

Nicia Vilela Luz and Carlos Manuel Peláez (1972, 276) find that:

“The lack of interest on historical moments by economists may explain their emphasis on secular trends in their research on the past instead of changes in the historical process. This may be the origin of why they fill gaps in documentation with their extrapolations.”

Vilela Luz (1960) provides classic research on the struggle for industrialization of Brazil from 1808 to 1930. According to Pelaez 1976, 283) following Cameron:

“The banking law of 1860 placed severe restrictions on two basic modern economic institutions—the corporation and the commercial bank. The growth of the volume of bank credit was one of the most significant factors of financial intermediation and economic growth in the major trading countries of the gold standard group. But Brazil placed strong restrictions on the development of banking and intermediation functions, preventing the channeling of coffee savings into domestic industry at an earlier date.”

Brazil actually abandoned the gold standard during multiple financial crises in the nineteenth century, as it should have to protect domestic economic activity. Pelaez (1975, 447) finds similar experience in the first half of nineteenth-century Brazil:

“Brazil’s experience is particularly interesting in that in the period 1808-1851 there were three types of monetary systems. Between 1808 and 1829, there was only one government-related Bank of Brazil, enjoying a perfect monopoly of banking services. No new banks were established in the 1830s after the liquidation of the Bank of Brazil in 1829. During the coffee boom in the late 1830s and 1840s, a system of banks of issue, patterned after similar institutions in the industrial countries [Cameron 1967], supplied the financial services required in the first stage of modernization of the export economy.”

Financial crises in the advanced economies transmitted to nineteenth-century Brazil by the arrival of a ship (Pelaez and Suzigan 1981). The explanation of those crises and the economy of Brazil requires knowledge and roles of institutions, economic policies and the financial system chosen by Brazil, in agreement with Bordo (2012Sep27).

The departing theoretical framework of Bordo and Haubrich (2012DR) is the plucking model of Friedman (1964, 1988). Friedman (1988, 1) recalls, “I was led to the model in the course of investigating the direction of influence between money and income. Did the common cyclical fluctuation in money and income reflect primarily the influence of money on income or of income on money?” Friedman (1964, 1988) finds useful for this purpose to analyze the relation between expansions and contractions. Analyzing the business cycle in the United States between 1870 and 1961, Friedman (1964, 15) found that “a large contraction in output tends to be followed on the average by a large business expansion; a mild contraction, by a mild expansion.” The depth of the contraction opens up more room in the movement toward full employment (Friedman 1964, 17):

“Output is viewed as bumping along the ceiling of maximum feasible output except that every now and then it is plucked down by a cyclical contraction. Given institutional rigidities and prices, the contraction takes in considerable measure the form of a decline in output. Since there is no physical limit to the decline short of zero output, the size of the decline in output can vary widely. When subsequent recovery sets in, it tends to return output to the ceiling; it cannot go beyond, so there is an upper limit to output and the amplitude of the expansion tends to be correlated with the amplitude of the contraction.”

Kim and Nelson (1999) test the asymmetric plucking model of Friedman (1964, 1988) relative to a symmetric model using reference cycles of the NBER and find evidence supporting the Friedman model. Bordo and Haubrich (2012DR) analyze 27 cycles beginning in 1872, using various measures of financial crises while considering different regulatory and monetary regimes. The revealing conclusion of Bordo and Haubrich (2012DR, 2) is that:

“Our analysis of the data shows that steep expansions tend to follow deep contractions, though this depends heavily on when the recovery is measured. In contrast to much conventional wisdom, the stylized fact that deep contractions breed strong recoveries is particularly true when there is a financial crisis. In fact, on average, it is cycles without a financial crisis that show the weakest relation between contraction depth and recovery strength. For many configurations, the evidence for a robust bounce-back is stronger for cycles with financial crises than those without.”

The average rate of growth of real GDP in expansions after recessions with financial crises was 8 percent but only 6.9 percent on average for recessions without financial crises (Bordo 2012Sep27). Real GDP declined 12 percent in the Panic of 1907 and increased 13 percent in the recovery, consistent with the plucking model of Friedman (Bordo 2012Sep27). Bordo (2012Sep27) finds two probable explanations for the weak recovery during the current economic cycle: (1) collapse of United States housing; and (2) uncertainty originating in fiscal policy, regulation and structural changes. There are serious doubts if monetary policy is adequate to recover the economy under these conditions.

Lucas (2011May) estimates US economic growth in the long-term at 3 percent per year and about 2 percent per year in per capita terms. There are displacements from this trend caused by events such as wars and recessions but the economy grows much faster during the expansion, compensating for the contraction and maintaining trend growth over the entire cycle. Historical US GDP data exhibit remarkable growth: Lucas (2011May) estimates an increase of US real income per person by a factor of 12 in the period from 1870 to 2010. The explanation by Lucas (2011May) of this remarkable growth experience is that government provided stability and education while elements of “free-market capitalism” were an important driver of long-term growth and prosperity. Lucas sharpens this analysis by comparison with the long-term growth experience of G7 countries (US, UK, France, Germany, Canada, Italy and Japan) and Spain from 1870 to 2010. Countries benefitted from “common civilization” and “technology” to “catch up” with the early growth leaders of the US and UK, eventually growing at a faster rate. Significant part of this catch up occurred after World War II. Lucas (2011May) finds that the catch up stalled in the 1970s. The analysis of Lucas (2011May) is that the 20-40 percent gap that developed originated in differences in relative taxation and regulation that discouraged savings and work incentives in comparison with the US. A larger welfare and regulatory state, according to Lucas (2011May), could be the cause of the 20-40 percent gap. Cobet and Wilson (2002) provide estimates of output per hour and unit labor costs in national currency and US dollars for the US, Japan and Germany from 1950 to 2000 (see Pelaez and Pelaez, The Global Recession Risk (2007), 137-44). The average yearly rate of productivity change from 1950 to 2000 was 2.9 percent in the US, 6.3 percent for Japan and 4.7 percent for Germany while unit labor costs in USD increased at 2.6 percent in the US, 4.7 percent in Japan and 4.3 percent in Germany. From 1995 to 2000, output per hour increased at the average yearly rate of 4.6 percent in the US, 3.9 percent in Japan and 2.6 percent in Germany while unit labor costs in USD fell at minus 0.7 percent in the US, 4.3 percent in Japan and 7.5 percent in Germany. There was increase in productivity growth in Japan and France within the G7 in the second half of the 1990s but significantly lower than the acceleration of 1.3 percentage points per year in the US. The key indicator of growth of real income per capita, which is what a person earns after inflation, measures long-term economic growth and prosperity. A refined concept would include real disposable income per capita, which is what a person earns after inflation and taxes.

Table IB-1 provides the data required for broader comparison of long-term and cyclical performance of the United States economy. Revisions and enhancements of United States GDP and personal income accounts by the Bureau of Economic Analysis (BEA) (https://bea.gov/iTable/index_nipa.cfm) provide valuable information on long-term growth and cyclical behavior. First, Long-term performance. Using annual data, US GDP grew at the average rate of 3.2 percent per year from 1929 to 2019 and at 3.2 percent per year from 1947 to 2019. Real disposable income grew at the average yearly rate of 3.2 percent from 1929 to 2019 and at 3.7 percent from 1947 to 1999. Real disposable income per capita grew at the average yearly rate of 2.0 percent from 1929 to 2019 and at 2.3 percent from 1947 to 1999. US economic growth was much faster during expansions, compensating contractions in maintaining trend growth for whole cycles. Using annual data, US real disposable income grew at the average yearly rate of 3.5 percent from 1980 to 1989 and real disposable income per capita at 2.6 percent. The US economy has lost its dynamism in the current cycle: real disposable income grew at the yearly average rate of 2.2 percent from 2006 to 2019 and real disposable income per capita at 1.5 percent. Real disposable income grew at the average rate of 2.2 percent from 2007 to 2019 and real disposable income per capita at 1.5 percent. Table IB-1 illustrates the contradiction of long-term growth with the proposition of secular stagnation (Hansen 1938, 1938, 1941 with early critique by Simons (1942). Secular stagnation would occur over long periods. Table IB-1 also provides the corresponding rates of population growth that is only marginally lower at 0.8 to 0.9 percent recently from 1.1 percent over the long-term. GDP growth fell abruptly from 2.6 percent on average from 2000 to 2006 to 1.7 percent from 2006 to 2019 and 1.7 percent from 2007 to 2019 and real disposable income growth fell from 2.9 percent on average from 2000 to 2006 to 2.2 percent from 2006 to 2019 and 2.2 percent from 2007 to 2019. The decline of growth of real per capita disposable income is even sharper from average 1.9 percent from 2000 to 2006 to 1.5 percent from 2006 to 2019 and 1.5 percent from 2007 to 2019 while population growth was 0.8 percent on average. Lazear and Spletzer (2012JHJul122) provide theory and measurements showing that cyclic factors explain currently depressed labor markets. This is also the case of the overall economy. Second, first four quarters of expansion. Growth in the first four quarters of expansion is critical in recovering loss of output and employment occurring during the contraction. In the first four quarters of expansion from IQ1983 to IVQ1983: GDP increased 7.9 percent, real disposable personal income 5.5 percent and real disposable income per capita 4.5 percent. In the first four quarters of expansion from IIIQ2009 to IIQ2010: GDP increased 2.8 percent, real disposable personal income 1.0 percent and real disposable income per capita 0.2 percent. Third, first 43 quarters of expansion. In the expansion from IQ1983 to IIIQ1993: GDP grew 46.4 percent at the annual equivalent rate of 3.6 percent; real disposable income grew 40.6 percent at the annual equivalent rate of 3.2 percent; and real disposable income per capita grew 25.7 percent at the annual equivalent rate of 2.2 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm).

In the expansion from IIIQ2009 to IQ2020: GDP grew 25.5 percent at the annual equivalent rate of 2.1 percent; real disposable income grew 29.1 percent at the annual equivalent rate of 2.4 percent; and real disposable personal income per capita grew 20.2 percent at the annual equivalent rate of 1.7 percent. Fourth, entire quarterly cycle. In the entire cycle combining contraction and expansion from IQ1980 to IIIQ1993: GDP grew 46.1 percent at the annual equivalent rate of 2.7 percent; real disposable personal income grew 49.0 percent at the annual equivalent rate of 2.9 percent; and real disposable personal income per capita 29.3 percent at the annual equivalent rate of 1.9 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm). In the entire cycle combining contraction and expansion from IVQ2007 to IQ2020: GDP grew 20.5 percent at the annual equivalent rate of 1.5 percent; real disposable personal income increased 31.0 percent at the annual equivalent rate of 2.2 percent; and real disposable personal income per capita grew 20.4 percent at the annual equivalent rate of 1.5 percent. The United States grew during its history at high rates of per capita income that made its economy the largest in the world. That dynamism is disappearing. Bordo (2012 Sep27) and Bordo and Haubrich (2012DR) provide convincing evidence that recoveries have been faster after deeper recessions and recessions with financial crises, casting serious doubts on the conventional explanation of weak growth during the current expansion allegedly because of the depth of the contraction of 4.0 percent from IVQ2007 to IIQ2009 and the financial crisis. There was sharp contraction of US GDP at the SAAR of 4.8 percent in IQ2020 in the probable global recession of the lockdown of economic activity of the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html). The proposition of secular stagnation should explain a long-term process of decay and not the actual abrupt collapse of the economy and labor markets currently.

Table IB-1, US, GDP, Real Disposable Personal Income, Real Disposable Income per Capita and Population Long-term and in 1983-92 and 2007-2019, %

Long-term Average ∆% per Year	GDP	Population
1929-2019	3.2	1.1
1947-2019	3.2	1.2
1947-1999	3.6	1.3
1980-1989	3.5	0.9
2000-2019	2.0	0.8
2000-2006	2.6	0.9
2006-2019	1.7	0.7
2007-2019	1.7	0.7
Long-term Average ∆% per Year	Real Disposable Income	Real Disposable Income per Capita	Population
1929-2019	3.2	2.0	1.1
1947-1999	3.7	2.3	1.3
2000-2019	2.4	1.6	0.8
2000-2006	2.9	1.9	0.9
2006-2019	2.2	1.5	0.7
2007-2019	2.2	1.5	0.7
Whole Cycles Average ∆% per Year
1980-1989	3.5	2.6	0.9
2006-2019	2.2	1.5	0.7
2007-2019	2.2	1.5	0.7
Comparison of Cycles	# Quarters	∆%	∆% Annual Equivalent
GDP
I83 to IV83 I83 to IQ87 I83 to II87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 I83 to IV88 I83 to I89 I83 to II89 I83 to III89 I83 to IV89 I83 to I90 I83 to II90 I83 to III90 I83 to IV90 I83 to I91 I83 to II91 I83 to III91 I83 to IV91 I83 to I92 I83 to II92 I83 to III92 I83 to IV92 I83 to I93 I83 to II93 I83 to III93	4 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43	7.9 23.0 24.4 25.4 27.6 28.3 29.9 30.7 32.5 33.8 34.8 35.8 36.1 37.6 38.1 38.2 36.9 36.3 37.3 38.0 38.5 40.2 41.7 43.1 44.6 44.8 45.7 46.4	7.9 5.0 5.0 4.9 5.0 4.9 4.9 4.8 4.8 4.8 4.7 4.6 4.5 4.5 4.4 4.3 4.0 3.8 3.8 3.8 3.7 3.7 3.7 3.7 3.8 3.7 3.6 3.6
RDPI
I83 to IV83 I83 to I87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 I83 to IV88 I83 to I89 I83 to II89 I83 to III89 I83 to IV89 I83 to I90 I83 to II90 I83 to III90 I83 to IV90 I83 to I91 I83 to II91 I83 to III91 I83 to IV91 I83 to I92 I83 to II92 I83 to III92 I83 to IV92 I83 to I93 I83 to II93 I83 to III93	4 17 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43	5.5 19.4 20.4 22.1 23.9 25.1 26.3 27.5 29.0 28.6 29.5 30.5 31.5 32.5 32.4 31.2 31.6 32.6 33.1 34.2 36.9 38.3 38.9 39.6 40.1 40.5 40.6	5.5 4.3 4.0 4.1 4.2 4.2 4.1 4.1 4.2 3.9 3.9 3.9 3.9 3.8 3.7 3.5 3.4 3.4 3.3 3.3 3.5 3.5 3.4 3.4 3.3 3.3 3.2
RDPI Per Capita
I83 to IV83 I83 to I87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 I83 to IV88 I83 to I89 I83 to II89 I83 to III89 I83 to IV89 I83 to I90 I83 to II90 I83 to III90 I83 to IV90 I83 to I91 I83 to II91 I83 to III91 I83 to IV91 I83 to I92 I83 to II92 I83 to III92 I83 to IV92 I83 to I93 I83 to II93 I83 to III93	4 17 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43	4.5 15.0 15.5 16.7 18.2 19.2 20.0 20.8 22.0 21.3 21.8 22.5 23.2 23.6 23.2 21.7 21.6 22.1 22.2 22.8 24.9 25.7 25.8 25.9 26.0 26.0 25.7	4.5 3.3 3.1 3.1 3.2 3.2 3.2 3.2 3.2 3.0 3.0 2.9 2.9 2.9 2.7 2.5 2.4 2.4 2.3 2.3 2.4 2.4 2.4 2.3 2.3 2.2 2.2
Whole Cycle IQ1980 to IIIQ1993
GDP	56	46.1	2.7
RDPI	56	49.0	2.9
RDPI per Capita	56	29.3	1.9
Population	56	15.3	1.0
GDP
III09 to II10 III09 to I20	4 43	2.8 25.5	2.8 2.1
RDPI
III09 to II10 III09 to IQ20	4 43	1.0 29.1	1.0 2.4
RDPI per Capita
III09 to II10 III09 to IQ20	4 43	0.2 20.2	0.2 1.7
Population
III09 to II10 III09 to IQ20	4 43	0.8 7.4	0.8 0.7
IVQ2007 to IQ2020	49
GDP	49	20.5	1.5
RDPI	49	31.0	2.2
RDPI per Capita	49	20.4	1.5
Population	49	8.8	0.7

RDPI: Real Disposable Personal Income

Source: US Bureau of Economic Analysis https://apps.bea.gov/iTable/index_nipa.cfm

There are seven basic facts illustrating the current economic disaster of the United States:

GDP maintained trend growth in the entire business cycle from IQ1980 to IQ1993, including contractions and expansions. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm). GDP is well below trend in the entire business cycle from IVQ2007 to IQ2020, including contractions and expansions. GDP contracted the SAAR of 4.8 percent in IQ2020 in the probable global recession in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html).
Per capita real disposable income exceeded trend growth in the 1980s but is substantially below trend in IQ2020
Level of employed persons increased in the 1980s but declined/stagnated cyclically into IQ2020 with recent recovery and sharp contraction in the probable global recession in the lockdown of economic activity in the COVID-19 event.
Level of full-time employed persons increased in the 1980s but declined/stagnated cyclically into IQ2020 with recent recovery and sharp contraction in the probable global recession in the lockdown of economic activity in the COVID-19 event.
Level unemployed, unemployment rate and employed part-time for economic reasons fell in the recovery from the recessions in the 1980s but not substantially in relative cyclical terms in the recovery since IIQ209 with recent recovery frustrated by the probable global recession in the lockdown of economic activity in the COVID-19 event.
Wealth of households and nonprofit organizations soared in the 1980s but stagnated in historically relative real terms into IVQ2019
Gross private domestic investment increased sharply from IQ1980 to IIQ1993, but gross private domestic investment stagnated, and private fixed investment stagnated in relative cyclical terms from IVQ2007 into IQ2020 with recent recovery frustrated by the probable global recession in the lockdown of economic activity in the COVID-19 event.

There are references to adverse periods as “lost decades.” There is a more prolonged and adverse period in Table V-3A: the lost economic cycle of the Global Recession with economic growth underperforming below trend worldwide. Economic contractions were relatively high but not comparable to the decline of GDP during the Great Depression. In fact, during the Great Depression in the four years of 1930 to 1933, US GDP in constant dollars fell 26.3 percent cumulatively and fell 45.3 percent in current dollars (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 150-2, Pelaez and Pelaez, Globalization and the State, Vol. II (2009b), 205-7 and revisions in https://bea.gov/iTable/index_nipa.cfm). Data are available for the 1930s only on a yearly basis. The contraction of GDP in the current cycle of the Global Recession was much lower, 4.0 percent (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). Contractions were deeper in Japan, 8.6 percent, the euro area (19 members), 5.7 percent, Germany 7.0 percent and the UK 6.0 percent. The contraction in France was 3.9 percent. There is adversity in low rates of growth during the expansion that did not compensate for the contraction such that for the whole cycle performance is disappointingly low. As a result, GDP is substantially below what it would have been in trend growth in all countries and regions in the world. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 43 quarters from IIIQ2009 to IQ2020. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the first estimate of GDP for IQ2020 (https://www.bea.gov/system/files/2020-04/gdp1q20_adv.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ2019, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2020 and the lockdown of economic activity in COVID-19 would have accumulated to 43.6 percent. GDP in IQ2020 would be $22,634.2 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $3646.3 billion than actual $18,987.9 billion. There are more than three trillion dollars of GDP less than at trend, explaining the 51.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 30.0 percent of the effective labor force with the largest part originating in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html). Unemployment is increasing sharply while employment is declining rapidly because of the lockdown of economic activity in the probable global recession resulting from the COVID-19 event (https://www.bls.gov/cps/employment-situation-covid19-faq-april-2020.pdf). US GDP in IQ2020 is 16.1 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,987.9 billion in IQ2020 or 20.5 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Mar 1919 to Mar 2020. Growth at 3.1 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 157.4135 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511 which is 37.4 percent below trend. The deterioration of manufacturing in Mar 2020 originates in the lockdown of economic activity in the COVID-19 event. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 161.1952 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511, which is 38.9 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and Mar 2020. Using trend growth of 1.7 percent per year, the index would increase to 133.1389 in Mar 2020. The output of manufacturing at 98.5511 in Mar 2020 is 26.0 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index increased from 86.3800 in Apr 2009 to 99.9350 in Mar 2020 or 15.7 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 106.6777 in Dec 2007 to 162.5897 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 38.5 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 106.6777 in Dec 2007 to 131.1461 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 23.8 percent below trend under this alternative calculation.

Table V-3A, Cycle 2007-2020, Percentage Contraction, Average Growth Rate in Expansion, Average Growth Rate in Whole Cycle and GDP Percent Below Trend

	Contraction ∆%	Expansion Average ∆%	Whole Cycle Average ∆%	Below Trend Percent
USA	4.0	2.1	1.5	16.1
Japan	8.6	1.3	0.4	NA
Euro Area 19	5.7	1.4	0.7	16.7
France	3.9	1.3	0.8	10.5
Germany	7.0	1.9	1.1	NA
UK	6.0	1.6	0.9	20.1

Note: AV: Average. Expansion and Whole Cycle AV ∆% calculated with quarterly growth, seasonally adjusted and quarterly adjusted when applicable, rates and converted into annual equivalent.

Data reported periodically in this blog.

Source: Country Statistical Agencies https://www.bls.gov/bls/other.htm https://www.census.gov/programs-surveys/international-programs/about/related-sites.html

There is a critical issue of the United States economy will be able in the future to attain again the level of activity and prosperity of projected trend growth. Growth at trend during the entire business cycles built the largest economy in the world but there may be an adverse, permanent weakness in United States economic performance and prosperity. Table IB-2 provides data for analysis of these seven basic facts. The seven blocks of Table IB-2 are separated initially after individual discussion of each one followed by the full Table IB-2.

1. Trend Growth.

i. As shown in Table IB-2, actual GDP grew cumulatively 45.6 percent from IQ1980 to IIIQ1993, which is relatively close to what trend growth would have been at 51.3 percent. Real GDP grew 44.5 percent from IVQ1979 to IIQ1993. Rapid growth at the average annual rate of 3.6 percent per quarter during the expansion from IQ1983 to IIIQ1993 erased the loss of GDP of 4.7 percent during the contractions and maintained relatively close trend growth at 2.7 percent for GDP and 2.9 percent for real disposable personal income over the entire cycle. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://www.bea.gov/iTable/index_nipa.cfm).

ii. In contrast, cumulative growth from IVQ2007 to IQ2020 was 20.5 percent while trend growth would have been 43.6 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2020 and the lockdown of economic activity in COVID-19 would have accumulated to 43.6 percent. GDP in IQ2020 would be $22,634.2 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $3646.3 billion than actual $18,987.9 billion. There are more than three trillion dollars of GDP less than at trend, explaining the 51.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 30.0 percent of the effective labor force with the largest part originating in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html). Unemployment is increasing sharply while employment is declining rapidly because of the lockdown of economic activity in the probable global recession resulting from the COVID-19 event (https://www.bls.gov/cps/employment-situation-covid19-faq-april-2020.pdf). US GDP in IQ2020 is 16.1 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,987.9 billion in IQ2020 or 20.5 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Mar 1919 to Mar 2020. Growth at 3.1 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 157.4135 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511 which is 37.4 percent below trend. The deterioration of manufacturing in Mar 2020 originates in the lockdown of economic activity in the COVID-19 event. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 161.1952 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511, which is 38.9 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and Mar 2020. Using trend growth of 1.7 percent per year, the index would increase to 133.1389 in Mar 2020. The output of manufacturing at 98.5511 in Mar 2020 is 26.0 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index increased from 86.3800 in Apr 2009 to 99.9350 in Mar 2020 or 15.7 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 106.6777 in Dec 2007 to 162.5897 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 38.5 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 106.6777 in Dec 2007 to 131.1461 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 23.8 percent below trend under this alternative calculation.

Period IQ1980 to IIIQ1993
GDP SAAR USD Billions
IQ1980	6,837.6
IIIQ1993	9,955.6
∆% IQ1980 to IIIQ1993 (46.1 percent from IVQ1979 $6816.2 billion)	45.6
∆% Trend Growth IQ1980 to IIIQ1993	51.3

Period IVQ2007 to IQ2020
GDP SAAR USD Billions
IVQ2007	15,762.0
IQ2020	18,987.9
∆% IVQ2007 to IQ2020	20.5
∆% IVQ2007 to IQ2020 Trend Growth	43.6

2. Real Disposable Income

i. In the entire business cycle from IQ1980 to IIIQ1993, as shown in Table IB-2, per capita real disposable income, or what is left per person after inflation and taxes, grew cumulatively 29.2 percent, which is close to what would have been trend growth of 31.9 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm).

ii. In contrast, in the entire business cycle from IVQ2007 to IQ2020, per capita real disposable income increased 20.4 percent while trend growth would have been 27.5 percent. Income available after inflation and taxes is cyclically lower relative to the level before the contraction after 43 consecutive quarters of GDP growth at mediocre rates relative to those prevailing during historical cyclical expansions. Growth of personal income during the expansion has been tepid even with the new revisions. In IVQ2014, personal income grew at 5.3 percent in nominal terms while nominal disposable income grew at 4.9 percent in nominal terms and at 5.4 percent in real terms (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IQ2015, nominal personal income grew at 4.3 percent while nominal disposable income grew at 2.8 percent and at 4.6 percent in real terms (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IIQ2015, nominal personal income grew at 5.4 percent while nominal disposable income grew at 5.1 percent and real disposable income grew at 3.0 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IIIQ2015, nominal personal income grew at 3.8 percent while nominal disposable income grew at 4.1 percent and real disposable income grew at 3.0 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IVQ2015, nominal personal income grew at 1.2 percent while nominal disposable income grew at 0.9 percent and real disposable income at 1.3 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IQ2016, personal income grew at 1.6 percent and fell at 2.1 percent excluding transfer receipts while nominal disposable income grew at 2.9 percent and real disposable income grew at 2.7 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IIQ2016, personal income grew at 2.3 percent and at 2.2 percent excluding transfer receipts while nominal disposable income grew at 2.0 percent and real disposable income fell at 0.4 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IIIQ2016, personal income grew at 3.7 percent and at 1.3 percent excluding transfer receipts while nominal disposable income grew at 3.5 percent and real disposable income grew at 1.8 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IVQ2016, nominal personal income grew at 4.2 percent, decreasing at 2.6 percent excluding current transfers while disposable income grew at 4.3 percent and real disposable income increased at 2.4 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IQ2017, nominal personal income grew at 6.6 percent and 4.7 percent excluding transfer receipts while nominal disposable income grew at 7.1 percent and real disposable income at 4.9 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IIQ2017, nominal personal income grew at 3.6 percent and 2.4 percent excluding transfer receipts while nominal disposable income grew at 3.6 percent and real disposable income at 2.7 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IIIQ2017, nominal personal income grew at 4.4 percent and at 2.5 percent excluding transfer receipts while nominal disposable income grew at 4.1 percent and real disposable personal income grew at 2.3 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IVQ2017, nominal personal income grew at 7.3 percent and at 2.9 percent real excluding transfer receipts while nominal disposable income grew at 6.5 percent and real disposable personal income grew at 3.7 percent (Table 14 at https://www.bea.gov/system/files/2019-07/pi0619.pdf). In IQ2018, nominal personal income grew at 7.4 percent and at 4.6 percent real excluding transfer receipts while nominal disposable income grew at 9.6 percent and real disposable income grew at 6.9 percent (Table 6 at https://www.bea.gov/system/files/2019-09/pi0819.pdf). In IIQ2018, nominal personal income grew at 4.3 percent and at 2.1 percent real excluding transfer receipts while nominal disposable income grew at 4.9 percent and real disposable income grew at 2.7 percent (Table 6 at https://www.bea.gov/system/files/2019-12/pi1119.pdf). In IIIQ2018, nominal personal income grew at 4.7 percent and at 3.4 percent real excluding transfer receipts while nominal disposable income grew at 4.9 percent and real disposable income grew at 3.3 percent (Table 6 at https://www.bea.gov/system/files/2020-03/pi0220_1.pdf). In IVQ2018, nominal personal income grew at 3.5 percent and at 2.3 percent real excluding transfer receipts while nominal disposable income grew at 4.2 percent and real disposable income grew at 2.8 percent (Table 6 at https://www.bea.gov/system/files/2020-03/pi0220_1.pdf). In IQ2019, nominal personal income grew at 6.2 percent and at 4.0 real percent excluding transfer receipts while nominal disposable income grew at 4.9 percent and real disposable income grew at 4.5 percent (Table 6 at https://www.bea.gov/system/files/2020-03/pi0220_1.pdf). In IIQ2019, nominal personal income grew at 4.4 percent and at 1.7 real percent excluding transfer receipts while nominal disposable income grew at 3.9 percent and real disposable income grew at 1.5 percent (Table 6 at https://www.bea.gov/system/files/2020-03/pi0220_1.pdf). In IIIQ2019, nominal personal income grew at 2.6 percent and at 0.7 real percent excluding transfer receipts while nominal disposable income grew at 3.6 percent and real disposable income grew at 2.1 percent (Table 6 at https://www.bea.gov/system/files/2020-03/pi0220_1.pdf). In IVQ2019, nominal personal income grew at 3.1 percent and at 1.7 percent real excluding current transfers while nominal disposable income grew at 3.0 percent and real disposable income grew at 1.6 percent (Table 6 at https://www.bea.gov/system/files/2020-03/pi0220_1.pdf).

Period IQ1980 to IIIQ1993
Real Disposable Personal Income per Capita IQ1980 Chained 2012 USD	21,579
Real Disposable Personal Income per Capita IIIQ1993 Chained 2012 USD	27,875
∆% IQ1980 to IIIQ1993 (29.3 percent from IVQ1979 $21,565)	29.2
∆% Trend Growth	31.9

Period IVQ2007 to IQ2020
Real Disposable Personal Income per Capita IVQ2007 Chained 2012 USD	38,037
Real Disposable Personal Income per Capita IQ2020 Chained 2012 USD	45,800
∆% IVQ2007 to IQ2020	20.4
∆% Trend Growth	27.5

3. Number of Employed Persons

i. As shown in Table IB-2, the number of employed persons increased over the entire business cycle from 98.527 million not seasonally adjusted (NSA) in IQ1980 to 120.673 million NSA in IIIQ1993 or by 22.5 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm).

ii. In contrast, during the entire business cycle the number employed nearly stagnated from 146.334 million in IVQ2007 to 155.167 million in IQI2020 or by 6.0 percent higher. There are 51.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 30.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html) caused by the lockdown of economic activity in the probable global recession in the COVID-19 event. The number employed in Apr 2020 was 133.326 million (NSA) or 13,989 million fewer people with jobs relative to the peak of 147.315 million in Aug 2007 while the civilian noninstitutional population of ages 16 years and over increased from 231.958 million in Jul 2007 to 259.896 million in Apr 2020 or by 27.938 million. The number employed decreased 9.5 percent from Jul 2007 to Apr 2020 while the noninstitutional civilian population of ages of 16 years and over, or those available for work, increased 12.0 percent. The ratio of employment to population in Jul 2007 was 63.5 percent (147.315 million employed as percent of population of 231.958 million). The same ratio in Apr 2020 would result in 165.034 million jobs (0.635 multiplied by noninstitutional civilian population of 259,896 million). There are effectively 31.708 million fewer jobs in Apr 2020 than in Jul 2007, or 165.034 million minus 133.326 million. There is actually not sufficient job creation in merely absorbing new entrants in the labor force because of those dropping from job searches, worsening the stock of unemployed or underemployed in involuntary part-time jobs.

Period IQ1980 to IIIQ1993
Employed Millions IQ1980 NSA End of Quarter	98.527
Employed Millions IIIQ1993 NSA End of Quarter	120.673
∆% Employed IQ1980 to IIIQ1993	22.5

Period IVQ2007 to IQ2020
Employed Millions IVQ2007 NSA End of Quarter	146.334
Employed Millions IQ2020 NSA End of Quarter	155.167
∆% Employed IVQ2007 to IQ2020	6.0

4. Number of Full-Time Employed Persons

i. As shown in Table IB-2, during the entire business cycle in the 1980s, including contractions and expansion, the number of employed full-time rose from 81.280 million NSA in IQ1980 to 99.599 million NSA in IIIQ1993 or 22.5 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm).

ii. In contrast, during the entire current business cycle, including contraction and expansion, the number of persons employed full-time increased from 121.042 million in IVQ2007 to 117.981 million in IQ2020 or 8.3 percent. The magnitude of the stress in US labor markets is magnified by the increase in the civilian noninstitutional population of the United States from 231.958 million in Jul 2007 to 259.758 million in Mar 2020 or by 27.800 million (https://www.bls.gov/data/). The number with full-time jobs in Mar 2020 is 127.981 million, which is higher by 4.762 million relative to the peak of 123.219 million in Jul 2007. The ratio of full-time jobs of 123.219 million in Jul 2007 to civilian noninstitutional population of 231.958 million was 53.1 percent. If that ratio had remained the same, there would be 137.932 million full-time jobs with population of 259.758 million in Mar 2020 (0.531 x 259.758) or 9.951 million fewer full-time jobs relative to actual 127.981 million. There appear to be around 10 million fewer full-time jobs in the US than before the global recession while population increased around 20 million. Mediocre GDP growth is the main culprit of the fractured US labor market.

Period IQ1980 to IIIQ1993
Employed Full-time Millions IQ1980 NSA End of Quarter	81.280
Employed Full-time Millions IIIQ1993 NSA End of Quarter	99.599
∆% Full-time Employed IQ1980 to IIIQ1993	22.5

Period IVQ2007 to IQ2020
Employed Full-time Millions IVQ2007 NSA End of Quarter	121.042
Employed Full-time Millions IQ2020 NSA End of Quarter	127.981
∆% Full-time Employed IVQ2007 to IQ2020	5.7

5. Unemployed, Unemployment Rate and Employed Part-time for Economic Reasons.

i. As shown in Table IB-2 and in the following block, in the cycle from IQ1980 to IIIQ1993: (a) The rate of unemployment was lower at 6.4 percent in IIIQ1993 relative to 6.6 percent in IQ1980. (b) The number unemployed increased from 6.983 million in IQ1980 to 8.302 million in IIIQ1993 or 18.9 percent. (c) The number employed part-time for economic reasons increased 70.0 percent from 3.624 million in IQ1980 to 6.159 million in IIIQ1993. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm).

ii. In contrast, in the economic cycle from IVQ2007 to IQ2020: (a) The rate of unemployment decreased from 4.8 percent in IVQ2007 to 4.5 percent in IQ2020. (b) The number unemployed changed 0.0 percent from 7.371 million in IVQ2007 to 7.370 million in IQ2020. (c) The number employed part-time for economic reasons because they could not find any other job increased 23.8 percent from 4.750 million in IVQ2007 to 5.879 million in IQ2020. (d) U6 Total Unemployed plus all marginally attached workers plus total employed part time for economic reasons as percent of all civilian labor force plus all marginally attached workers NSA increased from 8.7 percent in IVQ2007 to 8.9 percent in IQ2020. The probable global recession originating in the lockdown of economic activity in the COVID-19 event is deteriorating sharply labor markets.

Period IQ1980 to IIIQ1993
Unemployment Rate IQ1980 NSA End of Quarter	6.6
Unemployment Rate IIIQ1993 NSA End of Quarter	6.4
Unemployed IQ1980 Millions NSA End of Quarter	6.983
Unemployed IIIQ1993 Millions NSA End of Quarter	8.302
∆%	18.9
Employed Part-time Economic Reasons IQ1980 Millions NSA End of Quarter	3.624
Employed Part-time Economic Reasons Millions IIIQ1993 NSA End of Quarter	6.159
∆%	70.0

Period IVQ2007 to IQ2020
Unemployment Rate IVQ2007 NSA End of Quarter	4.8
Unemployment Rate IQ2020 NSA End of Quarter	4.5
Unemployed IVQ2007 Millions NSA End of Quarter	7.371
Unemployed IQ2020 Millions NSA End of Quarter	7.370
∆%	0.0
Employed Part-time Economic Reasons IVQ2007 Millions NSA End of Quarter	4.750
Employed Part-time Economic Reasons Millions IQ2020 NSA End of Quarter	5.879
∆%	23.8
U6 Total Unemployed plus all marginally attached workers plus total employed part time for economic reasons as percent of all civilian labor force plus all marginally attached workers NSA
IVQ2007	8.7
IQ2020	8.9

6. Wealth of Households and Nonprofit Organizations.

The comparison of net worth of households and nonprofit organizations in the entire economic cycle from IQ1980 (and from IVQ1979) to IIQ1993 and from IVQ2007 to IVQ2019 is in Table IIA-5. The data reveal the following facts for the cycles in the 1980s:

IVQ1979 to IIQ1993. Net worth increased 183.6 percent from IVQ1979 to IIQ1993, the all items CPI index increased 88.3 percent from 76.7 in Dec 1979 to 144.4 in Jun 1993 and real net worth increased 50.6 percent.
IQ1980 to IVQ1985. Net worth increased 66.5 percent, the all items CPI index increased 36.5 percent from 80.1 in Mar 1980 to 109.3 in Dec 1985 and real net worth increased 22.0 percent.
IVQ1979 to IVQ1985. Net worth increased 70.1 percent, the all items CPI index increased 42.5 percent from 76.7 in Dec 1979 to 109.3 in Dec 1985 and real net worth increased 19.3 percent.
IQ1980 to IQ1989. Net worth increased 121.5 percent, the all items CPI index increased 52.7 percent from 80.1 in Mar 1980 to 122.3 in Mar 1989 and real net worth increased 45.1 percent.
IQ1980 to IIQ1989. Net worth increased 126.1 percent, the all items CPI index increased 54.9 percent from 80.1 in Mar 1980 to 124.1 in Jun 1989 and real net worth increased 45.9 percent.
IQ1980 to IIIQ1989. Net worth increased 131.9 percent, the all items CPI index increased 56.1 percent from 80.1 in Mar 1980 to 125.0 in Sep 1989 and real net worth increased 48.6 percent.
IQ1980 to IVQ1989. Net worth increased 136.2 percent, the all items CPI index increased 57.4 from 80.1 in Mar 1980 to 126.1 in Dec 1989 and real net worth increased 50.1 percent.
IQ1980 to IQ1990. Net worth increased 137.6 percent, the all items CPI index increased 60.7 percent from 80.1 in Mar 1980 to 128.7 in Mar 1990 and real net worth increased 47.9 percent.
IQ1980 to IIQ1990. Net worth increased 140.2 percent, the all items CPI index increased 62.2 percent from 80.1 in Mar 1980 to 129.9 in Jun 1990 and real net worth increased 48.1 percent
IQ1980 to IIIQ1990. Net worth increased 138.4 percent, the all items CPI index increased 65.7 percent from 80.1 in Mar 1980 to 132.7 in Jun 1990 and real net worth increased 43.9 percent.
IQ1980 to IVQ1990. Net worth increased 143.1 percent, the all items CPI index increased 67.0 percent from 80.1 in Mar 1980 to 133.8 in Dec 1990 and real net worth increased 45.5 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). This new cyclical contraction explains the contraction of net worth in IIIQ1990
IQ1980 to IQ1991. Net worth increased 149.3 percent, the all items CPI index increased 68.5 percent from 80.1 in Mar 1980 to 135.0 in Mar 1991 and real net worth increased 47.9 percent.
IQ1980 to IIQ1991. Net worth increased 149.9 percent, the all items CPI index increased 69.8 percent from 80.1 in Mar 1980 to 136.0 in Jun 1991 and real net worth increased 47.2 percent.
IQ1980 to IIIQ1991. Net worth increased 152.9 percent, the all items CPI index increased 71.3 percent from 80.1 in Mar 1980 to 137.2 in Sep 1991 and real net worth increased 47.6 percent.
IQ1980 to IVQ1991. Net worth increased 159.2 percent, the all items CPI index increased 72.2 percent from 80.1 in Mar 1980 to 137.9 in Dec 1991 and real net worth increased 50.6 percent.
IQ1980 to IQ1992. Net worth increased 160.2 percent, the all items CPI index increased 73.9 percent from 80.1 in Mar 1980 to 139.3 in Mar 1992 and real net worth increased 49.6 percent.
IQ1980 to IIQ1992. Net worth increased 161.1 percent, the all items CPI index increased 75.0 percent from 80.1 in Mar 1980 to 140.2 in Jun 1992 and real net worth increased 49.2 percent.
IQ1980 to IIIQ1992. Net worth increased 164.9 percent, the all items CPI index increased 76.4 percent from 80.1 in Mar 1980 to 141.3 in Sep 1992 and real net worth increased 50.1 percent.
IQ1980 to IVQ1992. Net worth increased 171.3, the all items CPI index increased 77.2 percent from 80.1 in Mar 1980 to 141.9 in Dec 1992 and real net worth increased 53.2 percent.
IQ1980 to IQ1993. Net worth increased 174.9 percent, the all items CPI increased 79.3 percent from 80.1 in Mar 1980 to 143.6 in Mar 1993 and real net worth increased 53.3 percent.
IQ1980 to IIQ1993. Net worth increased 177.7 percent, the all items CPI increased 80.3 percent from 80.1 in Jun 1980 to 144.4 in Jun 1993 and real net worth increased 54.0 percent.

There is comparatively weaker performance in the current economic cycle:

IVQ2007 to IVQ2019. Net worth increased 67.6 percent, the all items CPI increased 22.3 percent from 210.036 in Dec 2007 to 256.974 in Dec 2019 and real or inflation adjusted net worth increased 37.0 percent. Real estate assets adjusted for inflation increased 4.5 percent. Growth of real net worth at the long-term average of 3.2 percent per year from IVQ1945 to IVQ2019 would have accumulated to 45.9 percent in the entire cycle from IVQ2007 to IVQ2019, much higher than actual 37.0 percent.

The explanation is partly in the sharp decline of wealth of households and nonprofit organizations and partly in the mediocre growth rates of the cyclical expansion beginning in IIIQ2009. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 43 quarters from IIIQ2009 to IQ2020. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the first estimate of GDP for IQ2020 (https://www.bea.gov/system/files/2020-04/gdp1q20_adv.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ2019, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2020 and the lockdown of economic activity in COVID-19 would have accumulated to 43.6 percent. GDP in IQ2020 would be $22,634.2 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $3646.3 billion than actual $18,987.9 billion. There are more than three trillion dollars of GDP less than at trend, explaining the 51.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 30.0 percent of the effective labor force with the largest part originating in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html). Unemployment is increasing sharply while employment is declining rapidly because of the lockdown of economic activity in the probable global recession resulting from the COVID-19 event (https://www.bls.gov/cps/employment-situation-covid19-faq-april-2020.pdf). US GDP in IQ2020 is 16.1 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,987.9 billion in IQ2020 or 20.5 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Mar 1919 to Mar 2020. Growth at 3.1 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 157.4135 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511 which is 37.4 percent below trend. The deterioration of manufacturing in Mar 2020 originates in the lockdown of economic activity in the COVID-19 event. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 161.1952 in Mar 2020. The actual index NSA in Mar 2020 is 98.5511, which is 38.9 percent below trend. Manufacturing output grew at average 1.7 percent between Dec 1986 and Mar 2020. Using trend growth of 1.7 percent per year, the index would increase to 133.1389 in Mar 2020. The output of manufacturing at 98.5511 in Mar 2020 is 26.0 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index increased from 86.3800 in Apr 2009 to 99.9350 in Mar 2020 or 15.7 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 106.6777 in Dec 2007 to 162.5897 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 38.5 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 106.6777 in Dec 2007 to 131.1461 in Mar 2020. The NAICS index at 99.9350 in Mar 2020 is 23.8 percent below trend under this alternative calculation.

Table IIA-5, Net Worth of Households and Nonprofit Organizations in Billions of Dollars, IVQ1979 to IIQ1993 and IVQ2007 to IVQ2019

Period IQ1980 to IIQ1993
Net Worth of Households and Nonprofit Organizations USD Millions
IVQ1979 IQ1980	9,097.9 9,290.5
IVQ1985 IIIQ1986 IVQ1986 IQ1987 IIQ1987 IIIQ1987 IVQ1987 IQ1988 IIQ1988 IIIQ1988 IVQ1988 IQ1989 IIQ1989 IIIQ1989 IVQ1989 IQ1990 IIQ1990	15,471.3 16,526.5 17,096.6 17,720.6 18,012.4 18,424.3 18,369.6 18,879.0 19,317.2 19,638.8 20,146.9 20,579.8 21,002.2 21,544.8 21,947.4 22,076.3 22,314.7
III1990	22,146.8
IV1990	22,581.0
I1991	23,165.8
IIQ1991	23,214.1
IIIQ1991	23,493.8
IVQ1991	24,080.6
IQ1992	24,170.9
IIQ1992	24,256.5
IIIQ1992	24,607.2
IVQ1992	25,206.6
IQ1993	25,536.0
IIQ1993	25,798.5
∆ USD IVQ1979 to IVQ1985 IVQ1979 to IIQ1993 IQ1980-IVQ1985 IQ1980-IIIQ1986 IQ1980-IVQ1986 IQ1980-IQ1987 IQ1980-IIQ1987 IQ1980-IIIQ1987 IQ1980-IVQ1987 IQ1980-IQ1988 IQ1980-IIQ1988 IQ1980-IIIQ1988 IQ1980-IVQ1988 IQ1980-IQ1989 IQ1980-IIQ1989 IQ1980-IIIQ1989 IQ1980-IVQ1989 IQ1980-IQ1990 IQ1980-IIQ1990	+6,373.4 ∆%70.1 R∆19.3 +16,700.6 ∆%183.6R∆%50.6 +6,180.8∆%66.5 R∆%22.0 +7,236.0 ∆%77.9 R∆%29.3 +7,806.1 ∆%84.0 R∆%33.4 +8,430.1 ∆%90.7 R∆%36.3 +8,721.9 ∆%93.9 R∆%36.8 +9,133.8 ∆%98.3 R∆%38.1 +9079.1 ∆%97.7 R∆%37.2 +9588.5 ∆%103.2 R∆%39.7 10,0026.7 ∆%107.9 R∆%41.1 +10,348.3 ∆%111.4 R∆%41.3 +10,856.4 ∆%116.9 R∆%44.2 +11289.3 ∆%121.5 R∆%45.1 +11,711.7 ∆%126.1 R∆% 45.9 +12,254.3 ∆%131.9 R∆% 48.6 +12,656.9 ∆%136.2 R∆%50.1 +12,785.8 ∆%137.6 R∆%47.9 +13,024.2 ∆%140.2 R∆%48.1
IQ1980-IIIQ1990	+12,586.3∆%138.4 R∆%43.9
IQ1980-IVQ1990	+13,290.5 ∆%143.1 R∆%45.5
IQ1980-IQ1991	+13,875.3 ∆%149.3 R∆%47.9
IQ1980-IIQ1991	+13,923.6 ∆%149.9 R∆%47.2
IQ1980-IIIQ1991	+14,203.3 ∆%152.9 R∆%47.6
IQ1980-IVQ1991	+14,790.1 ∆%159.2 R∆%50.6
IQ1980-IQ1992	+14,880.4 ∆%160.2 R∆%49.6
IQ1980-IIQ1992	+14,966.0 ∆%161.1 R∆%49.2
IQ1980-IIIQ1992	+15,316.7 ∆%164.9 R∆%50.1
IQ1980-IVQ1992	+15,916.1 ∆%171.3 R∆%53.2
IQ1980-IQ1993	+16,245.5 ∆%174.9 R∆%53.3
IQ1980-IIQ1993	+16,508.0 ∆%177.7 R∆% 54.0
Period IVQ2007 to IVQ2019
Net Worth of Households and Nonprofit Organizations USD Millions
IVQ2007	70,642.3
IVQ2019	118,368.2
∆ USD Billions	+47,725.9 ∆%67.6 R∆%37.0

Net Worth = Assets – Liabilities. R∆% real percentage change or adjusted for CPI percentage change.

Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities

Source: Board of Governors of the Federal Reserve System. 2020. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2019. Washington, DC, Federal Reserve System, Mar 12. https://www.federalreserve.gov/releases/z1/current/default.htm

7. Gross Private Domestic Investment.

i. The comparison of gross private domestic investment in the entire economic cycles from IQ1980 to IIIQ1993 and from IVQ2007 to IQ2020 is in the following block and in Table IB-2. Gross private domestic investment increased from $933.1 billion in IQ1980 to $1,303.6 billion in IIIQ1993 or by 39.7 percent. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://bea.gov/iTable/index_nipa.cfm).

ii In the current cycle, gross private domestic investment increased from $2,653.1 billion in IVQ2007 to $3,314.9 billion in IQ2020, or 24.9 percent. Private fixed investment edged from $2,630.0 billion in IVQ2007 to $3,304.4 billion in IQ2020 or increase by 25.6 percent. Private fixed investment contracted at the SAAR of minus 2.6 percent in IQ2020 in the probable global recession in the lockdown of economic activity of the COVID-19 event.

Period IQ1980-IIIQ1993
Gross Private Domestic Investment USD 2009 Billions
IQ1980	933.1
IIIQ1993	1303.6
∆%	39.7

Period IVQ2007 to IQ2020
Gross Private Domestic Investment USD 2012 Billions
IVQ2007	2,653.1
IQ2020	3,314.9
∆%	24.9
Private Fixed Investment USD 2012 Billions
IVQ2007	2,630.0
IQ2020	3,304.4
∆%	25.6

Table IB-2, US, GDP and Real Disposable Personal Income Per capita Actual and Trend Growth and Employment, 1980-1992 and 2007-2019, SAAR USD Billions, Millions of Persons and ∆%


Period IQ1980 to IIIQ1993
GDP SAAR USD Billions
IQ1980	6,837.6
IIIQ1993	9,955.6
∆% IQ1980 to IIIQ1993 (46.1 percent from IVQ1979 $6816.2 billion)	45.6
∆% Trend Growth IQ1980 to IIIQ1993	51.3
Real Disposable Personal Income per Capita IQ1980 Chained 2012 USD	21,579
Real Disposable Personal Income per Capita IIIQ1993 Chained 2012 USD	27,875
∆% IQ1980 to IIIQ1993 (29.3 percent from IVQ1979 $21,565 billion)	29.2
∆% Trend Growth	31.9
Employed Millions IQ1980 NSA End of Quarter	98.527
Employed Millions IIIQ1993 NSA End of Quarter	120.673
∆% Employed IQ1980 to IIIQ1993	22.5
Employed Full-time Millions IQ1980 NSA End of Quarter	81.280
Employed Full-time Millions IIIQ1993 NSA End of Quarter	99.599
∆% Full-time Employed IQ1980 to IIIQ1993	22.5
Unemployment Rate IQ1980 NSA End of Quarter	6.6
Unemployment Rate IIIQ1993 NSA End of Quarter	6.4
Unemployed IQ1980 Millions NSA End of Quarter	6.983
Unemployed IIIQ993 Millions NSA End of Quarter	8.302
∆%	18.9
Employed Part-time Economic Reasons IQ1980 Millions NSA End of Quarter	3.624
Employed Part-time Economic Reasons Millions IIIQ1993 NSA End of Quarter	6.159
∆%	70.0
Net Worth of Households and Nonprofit Organizations USD Billions
IVQ1979	9,097.9
IIQ1993	25,798.5
∆ USD Billions	+16,700.6
∆% CPI Adjusted	50.6
Gross Private Domestic Investment USD 2012 Billions
IQ1980	933.1
IIIQ1993	1303.6
∆%	39.7
Period IVQ2007 to IQ2020
GDP SAAR USD Billions
IVQ2007	15,762.0
IQ2020	18,987.9
∆% IVQ2007 to IQ2020	20.5
∆% IVQ2007 to IQ2020 Trend Growth	43.6
Real Disposable Personal Income per Capita IVQ2007 Chained 2012 USD	38,037
Real Disposable Personal Income per Capita IQ2020 Chained 2012 USD	45,800
∆% IVQ2007 to IQ2020	20.4
∆% Trend Growth	27.5
Employed Millions IVQ2007 NSA End of Quarter	146.334
Employed Millions IQ2020 NSA End of Quarter	155.167
∆% Employed IVQ2007 to IQ2020	6.0
Employed Full-time Millions IVQ2007 NSA End of Quarter	121.042
Employed Full-time Millions IQ2020 NSA End of Quarter	127.981
∆% Full-time Employed IVQ2007 to IQ2020	5.7
Unemployment Rate IVQ2007 NSA End of Quarter	4.8
Unemployment Rate IQ2020 NSA End of Quarter	4.5
Unemployed IVQ2007 Millions NSA End of Quarter	7.371
Unemployed IQ2020 Millions NSA End of Quarter	7.370
∆%	0.0
Employed Part-time Economic Reasons IVQ2007 Millions NSA End of Quarter	4.750
Employed Part-time Economic Reasons Millions IQ2020 NSA End of Quarter	5.879
∆%	23.8
U6 Total Unemployed plus all marginally attached workers plus total employed part time for economic reasons as percent of all civilian labor force plus all marginally attached workers NSA
IVQ2007	8.7
IQ2020	8.9
Net Worth of Households and Nonprofit Organizations USD Billions
IVQ2007	70,642.3
IVQ2019	118,368.2
∆ USD Billions	+47,725.9 ∆%67.6 R∆%37.0
Gross Private Domestic Investment USD Billions
IVQ2007	2,653.1
IQ2020	3,314.9
∆%	24.9
Private Fixed Investment USD 2009 Billions
IQ2007	2,630.0
IQ2020	3,304.4
∆%	25.6

Note: GDP trend growth used is 3.0 percent per year and GDP per capita is 2.0 percent per year as estimated by Lucas (2011May) on data from 1870 to 2010.

Source: US Bureau of Economic Analysis https://apps.bea.gov/iTable/index_nipa.cfm Source:

Board of Governors of the Federal Reserve System. 2019. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2019. Washington, DC, Federal Reserve System, Dec 2019. https://www.federalreserve.gov/releases/z1/current/default.htm

The Congressional Budget Office estimates potential GDP, potential labor force and potential labor productivity provided in Table IB-3. The CBO estimates average rate of growth of potential GDP from 1950 to 2017 at 3.2 percent per year. The projected path is significantly lower at 1.4 percent per year from 2018 to 2028. The legacy of the economic cycle expansion from IIIQ2009 to IQ2020 at 2.1 percent on average is in contrast with 3.6 percent on average in the expansion from IQ1983 to IIIQ1993 (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). Subpar economic growth may perpetuate unemployment and underemployment estimated at 51.6 million or 30.0 percent of the effective labor force in Apr 2020 (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html) with much lower hiring than in the period before the current cycle (https://cmpassocregulationblog.blogspot.com/2020/04/united-states-imbalances-of-internal.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/sharp-contraction-of-valuations-of-risk.html).

Table IB-3, US, Congressional Budget Office History and Projections of Potential GDP of US Overall Economy, ∆%

	Potential GDP	Potential Labor Force	Potential Labor Productivity*
Average Annual ∆%
1950-1973	4.0	1.6	2.4
1974-1981	3.2	2.5	0.7
1982-1990	3.4	1.7	1.7
1991-2001	3.2	1.2	2.0
2002-2007	2.5	1.0	1.5
2008-2017	1.5	0.5	0.9
Total 1950-2017	3.2	1.4	1.7
Projected Average Annual ∆%
2018-2022	2.0	0.6	1.4
2023-2028	1.8	0.4	1.4
2018-2028	1.9	0.5	1.4

*Ratio of potential GDP to potential labor force

Source: CBO, The budget and economic outlook: 2018-2028. Washington, DC, Apr 9, 2018 https://www.cbo.gov/publication/53651 CBO (2014BEOFeb4), CBO, Key assumptions in projecting potential GDP—February 2014 baseline. Washington, DC, Congressional Budget Office, Feb 4, 2014. CBO, The budget and economic outlook: 2015 to 2025. Washington, DC, Congressional Budget Office, Jan 26, 2015. Aug 2016

Chart IB1-BEO2818 of the Congressional Budget Office provides historical and projected annual growth of United States potential GDP. The projection is of faster growth of real potential GDP.

Chart IB1-BEO2818, CBO Economic Forecast

Source: CBO, The budget and economic outlook: 2018-2028. Washington, DC, Apr 9, 2018 https://www.cbo.gov/publication/53651 CBO (2014BEOFeb4).

Chart IB1-A1 of the Congressional Budget Office provides historical and projected annual growth of United States potential GDP. There is sharp decline of growth of United States potential GDP.

Chart IB-1A1, Congressional Budget Office, Projections of Annual Growth of United States Potential GDP

Source: CBO, The budget and economic outlook: 2017-2027. Washington, DC, Jan 24, 2017 https://www.cbo.gov/publication/52370

https://www.cbo.gov/about/products/budget-economic-data#6

Chart IB-1A of the Congressional Budget Office provides historical and projected potential and actual US GDP. The gap between actual and potential output closes by 2017. Potential output expands at a lower rate than historically. Growth is even weaker relative to trend.

Chart IB-1A, Congressional Budget Office, Estimate of Potential GDP and Gap

Source: Congressional Budget Office

https://www.cbo.gov/publication/49890

Chart IB-1 of the Congressional Budget Office (CBO 2013BEOFeb5) provides actual and potential GDP of the United States from 2000 to 2011 and projected to 2024. Lucas (2011May) estimates trend of United States real GDP of 3.0 percent from 1870 to 2010 and 2.2 percent for per capita GDP. The United States successfully returned to trend growth of GDP by higher rates of growth during cyclical expansion as analyzed by Bordo (2012Sep27, 2012Oct21) and Bordo and Haubrich (2012DR). Growth in expansions following deeper contractions and financial crises was much higher in agreement with the plucking model of Friedman (1964, 1988). The Congressional Budget Office estimates potential GDP, potential labor force and potential labor productivity provided in Table IB-3. The CBO estimates average rate of growth of potential GDP from 1950 to 2017 at 3.2 percent per year. The projected path is significantly lower at 1.4 percent per year from 2018 to 2028. The legacy of the economic cycle expansion from IIIQ2009 to IQ2020 at 2.1 percent on average is in contrast with 3.6 percent on average in the expansion from IQ1983 to IIIQ1993 (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). Subpar economic growth may perpetuate unemployment and underemployment estimated at 51.6 million or 30.0 percent of the effective labor force in Apr 2020 (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html) with much lower hiring than in the period before the current cycle (https://cmpassocregulationblog.blogspot.com/2020/04/united-states-imbalances-of-internal.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/sharp-contraction-of-valuations-of-risk.html). The US economy and labor markets collapsed without recovery. Abrupt collapse of economic conditions can be explained only with cyclic factors (Lazear and Spletzer 2012Jul22) and not by secular stagnation (Hansen 1938, 1939, 1941 with early dissent by Simons 1942).

Chart IB-1, US, Congressional Budget Office, Actual and Projections of Potential GDP, 2000-2024, Trillions of Dollars

Source: Congressional Budget Office, CBO (2013BEOFeb5). The last year in common in both projections is 2017. The revision lowers potential output in 2017 by 7.3 percent relative to the projection in 2007.

Chart IB-2 provides differences in the projections of potential output by the CBO in 2007 and more recently on Feb 4, 2014, which the CBO explains in CBO (2014Feb28).

Chart IB-2, Congressional Budget Office, Revisions of Potential GDP

Source: Congressional Budget Office, 2014Feb 28. Revisions to CBO’s Projection of Potential Output since 2007. Washington, DC, CBO, Feb 28, 2014.

Chart IB-3 provides actual and projected potential GDP from 2000 to 2024. The gap between actual and potential GDP disappears at the end of 2017 (CBO2014Feb4). GDP increases in the projection at 2.5 percent per year.

Chart IB-3, Congressional Budget Office, GDP and Potential GDP

Source: CBO (2013BEOFeb5), CBO, Key assumptions in projecting potential GDP—February 2014 baseline. Washington, DC, Congressional Budget Office, Feb 4, 2014.

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