Oscillation of Valuations of Risk Financial Assets, World Inflation Waves, Recovery without Hiring, Ten Million Fewer Full-Time Jobs, United States Industrial Production, Squeeze of Economic Activity by Carry Trades Induced by Zero Interest Rates, 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, World Financial Turbulence, World Cyclical Slow Growth, Government Intervention in Globalization, and Global Recession Risk: Part II

Oscillation of Valuations of Risk Financial Assets, World Inflation Waves, Recovery without Hiring, Ten Million Fewer Full-Time Jobs, United States Industrial Production, Squeeze of Economic Activity by Carry Trades Induced by Zero Interest Rates, 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, World Financial Turbulence, World Cyclical Slow Growth, Government Intervention in Globalization, and Global Recession Risk

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018

I World Inflation Waves

IA Appendix: Transmission of Unconventional Monetary Policy

IB1 Theory

IB2 Policy

IB3 Evidence

IB4 Unwinding Strategy

IC United States Inflation

IC Long-term US Inflation

ID Current US Inflation

IE Theory and Reality of Economic History, Cyclical Slow Growth Not Secular Stagnation and Monetary Policy Based on Fear of Deflation

I Recovery without Hiring

IA1 Hiring Collapse

IA2 Labor Underutilization

ICA3 Ten Million Fewer Full-time Jobs

IA4 Theory and Reality of Cyclical Slow Growth Not Secular Stagnation: Youth and Middle-Age Unemployment

II United States Industrial Production

IIB Squeeze of Economic Activity by Carry Trades Induced by Zero Interest Rates

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

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

IE Theory and Reality of Economic History, Cyclical Slow Growth Not Secular Stagnation and Monetary Policy Based on Fear of Deflation. Fear of deflation as had occurred during the Great Depression and in Japan was used as an argument for the first round of unconventional monetary policy with 1 percent interest rates from Jun 2003 to Jun 2004 and quantitative easing in the form of withdrawal of supply of 30-year securities by suspension of the auction of 30-year Treasury bonds with the intention of reducing mortgage rates (for fear of deflation see Pelaez and Pelaez, International Financial Architecture (2005), 18-28, and Pelaez and Pelaez, The Global Recession Risk (2007), 83-95). The financial crisis and global recession were caused by interest rate and housing subsidies and affordability policies that encouraged high leverage and risks, low liquidity and unsound credit (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 157-66, Regulation of Banks and Finance (2009b), 217-27, International Financial Architecture (2005), 15-18, The Global Recession Risk (2007), 221-5, Globalization and the State Vol. II (2008b), 197-213, Government Intervention in Globalization (2008c), 182-4). Several past comments of this blog elaborate on these arguments, among which: http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html http://cmpassocregulationblog.blogspot.com/2011/01/professor-mckinnons-bubble-economy.html http://cmpassocregulationblog.blogspot.com/2011/01/world-inflation-quantitative-easing.html http://cmpassocregulationblog.blogspot.com/2011/01/treasury-yields-valuation-of-risk.html http://cmpassocregulationblog.blogspot.com/2010/11/quantitative-easing-theory-evidence-and.html http://cmpassocregulationblog.blogspot.com/2010/12/is-fed-printing-money-what-are.html

If the forecast of the central bank is of recession and low inflation with controlled inflationary expectations, monetary policy should consist of lowering the short-term policy rate of the central bank, which in the US is the fed funds rate. The intended effect is to lower the real rate of interest (Svensson 2003LT, 146-7). The real rate of interest, r, is defined as the nominal rate, i, adjusted by expectations of inflation, π*, with all variables defined as proportions: (1+r) = (1+i)/(1+π*) (Fisher 1930). If i, the fed funds rate, is lowered by the Fed, the numerator of the right-hand side is lower such that if inflationary expectations, π*, remain unchanged, the left-hand (1+r) decreases, that is, the real rate of interest, r, declines. Expectations of lowering short-term real rates of interest by policy of the Federal Open Market Committee (FOMC) fixing a lower fed funds rate would lower long-term real rates of interest, inducing with a lag investment and consumption, or aggregate demand, that can lift the economy out of recession. Inflation also increases with a lag by higher aggregate demand and inflation expectations (Fisher 1933). This reasoning explains why the FOMC lowered the fed funds rate in Dec 2008 to 0 to 0.25 percent and left it unchanged.

The fear of the Fed is expected deflation or negative π*. In that case, (1+ π*) < 1, and (1+r) would increase because the right-hand side of the equation would be divided by a fraction. A simple numerical example explains the effect of deflation on the real rate of interest. Suppose that the nominal rate of interest or fed funds rate, i, is 0.25 percent, or in proportion 0.25/100 = 0.0025, such that (1+i) = 1.0025. Assume now that economic agents believe that inflation will remain at 1 percent for a long period, which means that π* = 1 percent, or in proportion 1/100 =0.01. The real rate of interest, using the equation, is (1+0.0025)/(1+0.01) = (1+r) = 0.99257, such that r = 0.99257 - 1 = -0.00743, which is a proportion equivalent to –(0.00743)100 = -0.743 percent. That is, Fed policy has created a negative real rate of interest of 0.743 percent with the objective of inducing aggregate demand by higher investment and consumption. This is true if expected inflation, π*, remains at 1 percent. Suppose now that expectations of deflation become generalized such that π* becomes -1 percent, that is, the public believes prices will fall at the rate of 1 percent in the foreseeable future. Then the real rate of interest becomes (1+0.0025) divided by (1-0.01) equal to (1.0025)/(0.99) = (1+r) = 1.01263, or r = (1.01263-1) = 0.01263, which results in positive real rate of interest of (0.01263)100 = 1.263 percent.

Irving Fisher also identified the impact of deflation on debts as an important cause of deepening contraction of income and employment during the Great Depression illustrated by an actual example (Fisher 1933, 346):

“By March, 1933, liquidation had reduced the debts about 20 percent, but had increased the dollar about 75 percent, so that the real debt, that is the debt measured in terms of commodities, was increased about 40 percent [100%-20%)X(100%+75%) =140%]. Unless some counteracting cause comes along to prevent the fall in the price level, such a depression as that of 1929-1933 (namely when the more the debtors pay the more they owe) tends to continue, going deeper, in a vicious spiral, for many years. There is then no tendency of the boat to stop tipping until it has capsized”

The nominal rate of interest must always be nonnegative, that is, i ≥ 0 (Hicks 1937, 154-5):

“If the costs of holding money can be neglected, it will always be profitable to hold money rather than lend it out, if the rate of interest is not greater than zero. Consequently the rate of interest must always be positive. In an extreme case, the shortest short-term rate may perhaps be nearly zero. But if so, the long-term rate must lie above it, for the long rate has to allow for the risk that the short rate may rise during the currency of the loan, and it should be observed that the short rate can only rise, it cannot fall”

The interpretation by Hicks of the General Theory of Keynes is the special case in which at interest rates close to zero liquidity preference is infinitely or perfectly elastic, that is, the public holds infinitely large cash balances at that near zero interest rate because there is no opportunity cost of foregone interest. Increases in the money supply by the central bank would not decrease interest rates below their near zero level, which is called the liquidity trap. The only alternative public policy would consist of fiscal policy that would act similarly to an increase in investment, increasing employment without raising the interest rate. There are negative nominal interest rates fixed by central banks in Europe and Japan.

An influential view on the policy required to steer the economy away from the liquidity trap is provided by Paul Krugman (1998). Suppose the central bank faces an increase in inflation. An important ingredient of the control of inflation is the central bank communicating to the public that it will maintain a sustained effort by all available policy measures and required doses until inflation is subdued and price stability is attained. If the public believes that the central bank will control inflation only until it declines to a more benign level but not sufficiently low level, current expectations will develop that inflation will be higher once the central bank abandons harsh measures. During deflation and recession the central bank has to convince the public that it will maintain zero interest rates and other required measures until the rate of inflation returns convincingly to a level consistent with expansion of the economy and stable prices. Krugman (1998, 161) summarizes the argument as:

“The ineffectuality of monetary policy in a liquidity trap is really the result of a looking-glass version of the standard credibility problem: monetary policy does not work because the public expects that whatever the central bank may do now, given the chance, it will revert to type and stabilize prices near their current level. If the central bank can credibly promise to be irresponsible—that is, convince the market that it will in fact allow prices to rise sufficiently—it can bootstrap the economy out of the trap”

This view is consistent with results of research by Christina Romer that “the rapid rates of growth of real output in the mid- and late 1930s were largely due to conventional aggregate demand stimulus, primarily in the form of monetary expansion. My calculations suggest that in the absence of these stimuli the economy would have remained depressed far longer and far more deeply than it actually did” (Romer 1992, 757-8, cited in Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 210-2). The average growth rate of the money supply in 1933-1937 was 10 percent per year and increased in the early 1940s. Romer calculates that GDP would have been much lower without this monetary expansion. The growth of “the money supply was primarily due to a gold inflow, which was in turn due to the devaluation in 1933 and to capital flight from Europe because of political instability after 1934” (Romer 1992, 759). Gold inflow coincided with the decline in real interest rates in 1933 that remained negative through the latter part of the 1930s, suggesting that they could have caused increases in spending that was sensitive to declines in interest rates. Bernanke finds dollar devaluation against gold to have been important in preventing further deflation in the 1930s (Bernanke 2002):

“There have been times when exchange rate policy has been an effective weapon against deflation. A striking example from US history is Franklin Roosevelt’s 40 percent devaluation of the dollar against gold in 1933-34, enforced by a program of gold purchases and domestic money creation. The devaluation and the rapid increase in money supply it permitted ended the US deflation remarkably quickly. Indeed, consumer price inflation in the United States, year on year, went from -10.3 percent in 1932 to -5.1 percent in 1933 to 3.4 percent in 1934. The economy grew strongly, and by the way, 1934 was one of the best years of the century for the stock market”

Fed policy is seeking what Irving Fisher proposed “that great depressions are curable and preventable through reflation and stabilization” (Fisher 1933, 350).

The President of the Federal Reserve Bank of Chicago argues that (Charles Evans 2010):

“I believe the US economy is best described as being in a bona fide liquidity trap. Highly plausible projections are 1 percent for core Personal Consumption Expenditures (PCE) inflation at the end of 2012 and 8 percent for the unemployment rate. For me, the Fed’s dual mandate misses are too large to shrug off, and there is currently no policy conflict between improving employment and inflation outcomes”

There are two types of monetary policies that could be used in this situation. First, the Fed could announce a price-level target to be attained within a reasonable time frame (Evans 2010):

“For example, if the slope of the price path is 2 percent and inflation has been underunning the path for some time, monetary policy would strive to catch up to the path. Inflation would be higher than 2 percent for a time until the path was reattained”

Optimum monetary policy with interest rates near zero could consist of “bringing the price level back up to a level even higher than would have prevailed had the disturbance never occurred” (Gauti Eggertsson and Michael Woodford 2003, 207). Bernanke (2003JPY) explains as follows:

“Failure by the central bank to meet its target in a given period leads to expectations of (and public demands for) increased effort in subsequent periods—greater quantities of assets purchased on the open market for example. So even if the central bank is reluctant to provide a time frame for meetings its objective, the structure of the price-level objective provides a means for the bank to commit to increasing its anti-deflationary efforts when its earlier efforts prove unsuccessful. As Eggertsson and Woodford show, the expectations that an increasing price level gap will give rise to intensified effort by the central bank should lead the public to believe that ultimately inflation will replace deflation, a belief that supports the central bank’s own objectives by lowering the current real rate of interest”

Second, the Fed could use its balance sheet to increase purchases of long-term securities together with credible commitment to maintain the policy until the dual mandates of maximum employment and price stability are attained. Policy continues with reinvestment of principal in securities.

In the restatement of the liquidity trap and large-scale policies of monetary/fiscal stimulus, Krugman (1998, 162) finds:

“In the traditional open economy IS-LM model developed by Robert Mundell [1963] and Marcus Fleming [1962], and also in large-scale econometric models, monetary expansion unambiguously leads to currency depreciation. But there are two offsetting effects on the current account balance. On one side, the currency depreciation tends to increase net exports; on the other side, the expansion of the domestic economy tends to increase imports. For what it is worth, policy experiments on such models seem to suggest that these effects very nearly cancel each other out.

Krugman (1998) uses a different dynamic model with expectations that leads to similar conclusions.

The central bank could also be pursuing competitive devaluation of the national currency in the belief that it could increase inflation to a higher level and promote domestic growth and employment at the expense of growth and unemployment in the rest of the world. An essay by Chairman Bernanke in 1999 on Japanese monetary policy received attention in the press, stating that (Bernanke 2000, 165):

“Roosevelt’s specific policy actions were, I think, less important than his willingness to be aggressive and experiment—in short, to do whatever it took to get the country moving again. Many of his policies did not work as intended, but in the end FDR deserves great credit for having the courage to abandon failed paradigms and to do what needed to be done”

Quantitative easing has never been proposed by Chairman Bernanke or other economists as certain science without adverse effects. What has not been mentioned in the press is another suggestion to the Bank of Japan (BOJ) by Chairman Bernanke in the same essay that is very relevant to current events and the contentious issue of ongoing devaluation wars (Bernanke 2000, 161):

“Because the BOJ has a legal mandate to pursue price stability, it certainly could make a good argument that, with interest rates at zero, depreciation of the yen is the best available tool for achieving its mandated objective. The economic validity of the beggar-thy-neighbor thesis is doubtful, as depreciation creates trade—by raising home country income—as well as diverting it. Perhaps not all those who cite the beggar-thy-neighbor thesis are aware that it had its origins in the Great Depression, when it was used as an argument against the very devaluations that ultimately proved crucial to world economic recovery. A yen trading at 100 to the dollar is in no one’s interest”

Chairman Bernanke is referring to the argument by Joan Robinson based on the experience of the Great Depression that: “in times of general unemployment a game of beggar-my-neighbour is played between the nations, each one endeavouring to throw a larger share of the burden upon the others” (Robinson 1947, 156). Devaluation is one of the tools used in these policies (Robinson 1947, 157). Banking crises dominated the experience of the United States, but countries that recovered were those devaluing early such that competitive devaluations rescued many countries from a recession as strong as that in the US (see references to Ehsan Choudhri, Levis Kochin and Barry Eichengreen in Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 205-9; for the case of Brazil that devalued early in the Great Depression recovering with an increasing trade balance see Pelaez, 1968, 1968b, 1972; Brazil devalued and abandoned the gold standard during crises in the historical period as shown by Pelaez 1976, Pelaez and Suzigan 1981). Beggar-my-neighbor policies did work for individual countries but the criticism of Joan Robinson was that it was not optimal for the world as a whole.

Chairman Bernanke (2013Mar 25) reinterprets devaluation and recovery from the Great Depression:

“The uncoordinated abandonment of the gold standard in the early 1930s gave rise to the idea of "beggar-thy-neighbor" policies. According to this analysis, as put forth by important contemporary economists like Joan Robinson, exchange rate depreciations helped the economy whose currency had weakened by making the country more competitive internationally. Indeed, the decline in the value of the pound after 1931 was associated with a relatively early recovery from the Depression by the United Kingdom, in part because of some rebound in exports. However, according to this view, the gains to the depreciating country were equaled or exceeded by the losses to its trading partners, which became less internationally competitive--hence, ‘beggar thy neighbor.’ Economists still agree that Smoot-Hawley and the ensuing tariff wars were highly counterproductive and contributed to the depth and length of the global Depression. However, modern research on the Depression, beginning with the seminal 1985 paper by Barry Eichengreen and Jeffrey Sachs, has changed our view of the effects of the abandonment of the gold standard. Although it is true that leaving the gold standard and the resulting currency depreciation conferred a temporary competitive advantage in some cases, modern research shows that the primary benefit of leaving gold was that it freed countries to use appropriately expansionary monetary policies. By 1935 or 1936, when essentially all major countries had left the gold standard and exchange rates were market-determined, the net trade effects of the changes in currency values were certainly small. Yet the global economy as a whole was much stronger than it had been in 1931. The reason was that, in shedding the strait jacket of the gold standard, each country became free to use monetary policy in a way that was more commensurate with achieving full employment at home.”

Nurkse (1944) raised concern on the contraction of trade by competitive devaluations during the 1930s. Haberler (1937) dwelled on the issue of flexible exchange rates. Bordo and James (2001) provide perceptive exegesis of the views of Haberler (1937) and Nurkse (1944) together with the evolution of thought by Haberler. Policy coordination among sovereigns may be quite difficult in practice even if there were sufficient knowledge and sound forecasts. Friedman (1953) provided strong case in favor of a system of flexible exchange rates.

Eichengreen and Sachs (1985) argue theoretically with measurements using a two-sector model that it is possible for series of devaluations to improve the welfare of all countries. There were adverse effects of depreciation on other countries but depreciation by many countries could be beneficial for all. The important counterfactual is if depreciations by many countries would have promoted faster recovery from the Great Depression. Depreciation in the model of Eichengreen and Sachs (1985) affected domestic and foreign economies through real wages, profitability, international competitiveness and world interest rates. Depreciation causes increase in the money supply that lowers world interest rates, promoting growth of world output. Lower world interest rates could compensate contraction of output from the shift of demand away from home goods originating in neighbor’s exchange depreciation. Eichengreen and Sachs (1985, 946) conclude:

“This much, however, is clear. We do not present a blanket endorsement of the competitive devaluations of the 1930s. Though it is indisputable that currency depreciation conferred macroeconomic benefits on the initiating country, because of accompanying policies the depreciations of the 1930s had beggar-thy-neighbor effects. Though it is likely that currency depreciation (had it been even more widely adopted) would have worked to the benefit of the world as a whole, the sporadic and uncoordinated approach taken to exchange-rate policy in the 1930s tended, other things being equal, to reduce the magnitude of the benefits.”

There could major difference in the current world economy. The initiating impulse for depreciation originates in zero interest rates on the fed funds rate. The dollar is the world’s reserve currency. Risk aversion intermittently channels capital flight to the safe haven of the dollar and US Treasury securities. In the absence of risk aversion, zero interest rates induce carry trades of short positions in dollars and US debt (borrowing) together with long leveraged exposures in risk financial assets such as stocks, emerging stocks, commodities and high-yield bonds. Without risk aversion, the dollar depreciates against every currency in the world. The dollar depreciated against the euro by 39.3 percent from USD 1.1423/EUR con Jun 26, 2003 to USD 1.5914/EUR on Jun 14, 2008 during unconventional monetary policy before the global recession (Table VI-1). Unconventional monetary policy causes devaluation of the dollar relative to other currencies, which can increases net exports of the US that increase aggregate economic activity (Yellen 2011AS). The country issuing the world’s reserve currency appropriates the advantage from initiating devaluation that in policy intends to generate net exports that increase domestic output.

The Swiss franc rate relative to the euro (CHF/EUR) appreciated 18.7 percent on Jan 15, 2015. The Swiss franc rate relative to the dollar (CHF/USD) appreciated 17.7 percent. Central banks are taking measures in anticipation of the quantitative easing by the European Central Bank. On Jan 22, 2015, the European Central Bank (ECB) decided to implement an “expanded asset purchase program” with combined asset purchases of €60 billion per month “until at least Sep 2016 (http://www.ecb.europa.eu/press/pr/date/2015/html/pr150122_1.en.html). The DAX index of German equities increased 1.3 percent on Jan 22, 2015 and 2.1 percent on Jan 23, 2015. The euro depreciated from EUR 1.1611/USD (EUR 0.8613/USD) on Wed Jan 21, 2015, to EUR 1.1206/USD (EUR 0.8924/USD) on Fri Jan 23, 2015, or 3.6 percent. Yellen (2011AS, 6) admits that Fed monetary policy results in dollar devaluation with the objective of increasing net exports, which was the policy that Joan Robinson (1947) labeled as “beggar-my-neighbor” remedies for unemployment. Risk aversion erodes devaluation of the dollar.

Pelaez and Pelaez (Regulation of Banks and Finance (2009b), 208-209) summarize the experience of Brazil as follows:

“During 1927–9, Brazil accumulated ￡30 million of foreign exchange of which ￡20 million were deposited at its stabilization fund (Pelaez 1968, 43–4). After the decline in coffee prices and the first impact of the Great Depression in Brazil a hot money movement wiped out foreign exchange reserves. In addition, capital inflows stopped entirely. The deterioration of the terms of trade further complicated matters, as the value of exports in foreign currency declined abruptly. Because of this exchange crisis, the service of the foreign debt of Brazil became impossible. In August 1931, the federal government was forced to cancel the payment of principal on certain foreign loans. The balance of trade in 1931 was expected to yield ￡20 million whereas the service of the foreign debt alone amounted to ￡22.6 million. Part of the solution given to these problems was typical of the 1930s. In September 1931, the government of Brazil required that all foreign transactions were to be conducted through the Bank of Brazil. This monopoly of foreign exchange was exercised by the Bank of Brazil for the following three years. Export permits were granted only after the exchange derived from sales abroad was officially sold to the Bank, which in turn allocated it in accordance with the needs of the economy. An active black market in foreign exchange developed. Brazil was in the first group of countries that abandoned early the gold standard, in 1931, and suffered comparatively less from the Great Depression. The Brazilian federal government, advised by the BOE, increased taxes and reduced expenditures in 1931 to compensate a decline in custom receipts (Pelaez 1968, 40). Expenditures caused by a revolution in 1932 in the state of Sao Paulo and a drought in the northeast explain the deficit. During 1932–6, the federal government engaged in strong efforts to stabilize the budget. Apart from the deliberate efforts to balance the budget during the 1930s, the recovery in economic activity itself may have induced a large part of the reduction of the deficit (Ibid, 41). Brazil’s experience is similar to that of the United States in that fiscal policy did not promote recovery from the Great Depression.”

Is depreciation of the dollar the best available tool currently for achieving the dual mandate of higher inflation and lower unemployment? Bernanke (2002) finds dollar devaluation against gold to have been important in preventing further deflation in the 1930s (http://www.federalreserve.gov/boarddocs/speeches/2002/20021121/default.htm):

“Although a policy of intervening to affect the exchange value of the dollar is nowhere on the horizon today, it's worth noting that there have been times when exchange rate policy has been an effective weapon against deflation. A striking example from U.S. history is Franklin Roosevelt's 40 percent devaluation of the dollar against gold in 1933-34, enforced by a program of gold purchases and domestic money creation. The devaluation and the rapid increase in money supply it permitted ended the U.S. deflation remarkably quickly. Indeed, consumer price inflation in the United States, year on year, went from -10.3 percent in 1932 to -5.1 percent in 1933 to 3.4 percent in 1934.¹⁷ The economy grew strongly, and by the way, 1934 was one of the best years of the century for the stock market. If nothing else, the episode illustrates that monetary actions can have powerful effects on the economy, even when the nominal interest rate is at or near zero, as was the case at the time of Roosevelt's devaluation.”

Should the US devalue following Roosevelt? Alternatively, has monetary policy intended devaluation? Fed policy is seeking, deliberately or as a side effect, what Irving Fisher proposed “that great depressions are curable and preventable through reflation and stabilization” (Fisher, 1933, 350). The Fed has created not only high volatility of assets but also what many countries are regarding as a competitive devaluation similar to those criticized by Nurkse (1944). Yellen (2011AS, 6) admits that Fed monetary policy results in dollar devaluation with the objective of increasing net exports, which was the policy that Joan Robinson (1947) labeled as “beggar-my-neighbor” remedies for unemployment.

Unconventional monetary policy of zero interest rates and large-scale purchases of long-term securities for the balance sheet of the central bank is proposed to prevent deflation. The data of CPI inflation of all goods and CPI inflation excluding food and energy for the past six decades does not show even one negative change, as shown in Table CPIEX.

Table CPIEX, Annual Percentage Changes of the CPI All Items Excluding Food and Energy

Year	Annual ∆%
1958	2.4
1959	2.0
1960	1.3
1961	1.3
1962	1.3
1963	1.3
1964	1.6
1965	1.2
1966	2.4
1967	3.6
1968	4.6
1969	5.8
1970	6.3
1971	4.7
1972	3.0
1973	3.6
1974	8.3
1975	9.1
1976	6.5
1977	6.3
1978	7.4
1979	9.8
1980	12.4
1981	10.4
1982	7.4
1983	4.0
1984	5.0
1985	4.3
1986	4.0
1987	4.1
1988	4.4
1989	4.5
1990	5.0
1991	4.9
1992	3.7
1993	3.3
1994	2.8
1995	3.0
1996	2.7
1997	2.4
1998	2.3
1999	2.1
2000	2.4
2001	2.6
2002	2.4
2003	1.4
2004	1.8
2005	2.2
2006	2.5
2007	2.3
2008	2.3
2009	1.7
2010	1.0
2011	1.7
2012	2.1
2013	1.8
2014	1.7
2015	1.8
2016	2.2
2017	1.8

Source: Bureau of Labor Statistics

http://www.bls.gov/cpi/

The history of producer price inflation in the past five decades does not provide evidence of deflation. The finished core PPI does not register even one single year of decline, as shown in Table PPIEX.

Table PPIEX, Annual Percentage Changes of the PPI Finished Goods Excluding Food and Energy

Year	Annual ∆%
1974	11.4
1975	11.4
1976	5.7
1977	6.0
1978	7.5
1979	8.9
1980	11.2
1981	8.6
1982	5.7
1983	3.0
1984	2.4
1985	2.5
1986	2.3
1987	2.4
1988	3.3
1989	4.4
1990	3.7
1991	3.6
1992	2.4
1993	1.2
1994	1.0
1995	2.1
1996	1.4
1997	0.3
1998	0.9
1999	1.7
2000	1.3
2001	1.4
2002	0.1
2003	0.2
2004	1.5
2005	2.4
2006	1.5
2007	1.9
2008	3.4
2009	2.6
2010	1.2
2011	2.4
2012	2.6
2013	1.5
2014	1.9
2015	2.0
2016	1.6
2017	1.8

Source: Bureau of Labor Statistics

http://www.bls.gov/ppi/

The producer price index of the US from 1947 to 2018 in Chart I-6 shows various periods of more rapid or less rapid inflation but no bumps. The major event is the decline in 2008 when risk aversion because of the global recession caused the collapse of oil prices from $148/barrel to less than $80/barrel with most other commodity prices also collapsing. The event had nothing in common with explanations of deflation but rather with the concentration of risk exposures in commodities after the decline of stock market indexes. Eventually, there was a flight to government securities because of the fears of insolvency of banks caused by statements supporting proposals for withdrawal of toxic assets from bank balance sheets in the Troubled Asset Relief Program (TARP), as explained by Cochrane and Zingales (2009). The bump in 2008 with decline in 2009 is consistent with the view that zero interest rates with subdued risk aversion induce carry trades into commodity futures.

Chart I-6, US, Producer Price Index, Finished Goods, NSA, 1947-2018

Source: US Bureau of Labor Statistics

http://www.bls.gov/ppi/

Chart I-7 provides 12-month percentage changes of the producer price index from 1948 to 2018. The distinguishing even in Chart I-7 is the Great Inflation of the 1970s. The shape of the two-hump Bactrian camel of the 1970’s resembles the double hump from 2007 to 2018.

Chart I-7, US, Producer Price Index, Finished Goods, 12-Month Percentage Change, NSA, 1948-2018

Source: US Bureau of Labor Statistics

http://www.bls.gov/ppi/

Annual percentage changes of the producer price index from 1948 to 2017 are shown in Table I-1A. The producer price index fell 2.8 percent in 1949 following the adjustment to World War II and fell 0.6 percent in 1952 and 1.0 percent in 1953 around the Korean War. There are two other mild declines of 0.3 percent in 1959 and 0.3 percent in 1963. There are only few subsequent and isolated declines of the producer price index of 1.4 percent in 1986, 0.8 percent in 1998, 1.3 percent in 2002 and 2.6 percent in 2009. The decline of 2009 was caused by unwinding of carry trades in 2008 that had lifted oil prices to $140/barrel during deep global recession because of the panic of probable toxic assets in banks that would be removed with the Troubled Asset Relief Program (TARP) (Cochrane and Zingales 2009). Producer prices fell 3.2 percent in 2015 and declined 1.0 percent in 2016 during collapse of commodity prices form high prices induced by zero interest rates. Producer prices increased 3.2 percent in 2017. There is no evidence in this history of 66 years of the US producer price index suggesting that there is frequent and persistent deflation shock requiring aggressive unconventional monetary policy. The design of such anti-deflation policy could provoke price and financial instability because of lags in effect of monetary policy, model errors, inaccurate forecasts and misleading analysis of current economic conditions.

Table I-1A, US, Annual PPI Inflation ∆% 1948-2017

Year	Annual ∆%
1948	8.0
1949	-2.8
1950	1.8
1951	9.2
1952	-0.6
1953	-1.0
1954	0.3
1955	0.3
1956	2.6
1957	3.8
1958	2.2
1959	-0.3
1960	0.9
1961	0.0
1962	0.3
1963	-0.3
1964	0.3
1965	1.8
1966	3.2
1967	1.1
1968	2.8
1969	3.8
1970	3.4
1971	3.1
1972	3.2
1973	9.1
1974	15.4
1975	10.6
1976	4.5
1977	6.4
1978	7.9
1979	11.2
1980	13.4
1981	9.2
1982	4.1
1983	1.6
1984	2.1
1985	1.0
1986	-1.4
1987	2.1
1988	2.5
1989	5.2
1990	4.9
1991	2.1
1992	1.2
1993	1.2
1994	0.6
1995	1.9
1996	2.7
1997	0.4
1998	-0.8
1999	1.8
2000	3.8
2001	2.0
2002	-1.3
2003	3.2
2004	3.6
2005	4.8
2006	3.0
2007	3.9
2008	6.3
2009	-2.6
2010	4.2
2011	6.1
2012	1.9
2013	1.2
2014	1.9
2015	-3.2
2016	-1.0
2017	3.2

Source: US Bureau of Labor Statistics

http://www.bls.gov/ppi/

Chart I-12 provides the consumer price index NSA from 1913 to 2018. The dominating characteristic is the increase in slope during the Great Inflation from the middle of the 1960s through the 1970s. There is long-term inflation in the US and no evidence of deflation risks.

Chart I-12, US, Consumer Price Index, NSA, 1913-2018

Source: US Bureau of Labor Statistics http://www.bls.gov/cpi/data.htm

Chart I-13 provides 12-month percentage changes of the consumer price index from 1914 to 2018. The only episode of deflation after 1950 is in 2009, which is explained by the reversal of speculative commodity futures carry trades that were induced by interest rates driven to zero in a shock of monetary policy in 2008. The only persistent case of deflation is from 1930 to 1933, which has little if any relevance to the contemporary United States economy. There are actually three waves of inflation in the second half of the 1960s, in the mid-1970s and again in the late 1970s. Inflation rates then stabilized in a range with only two episodes above 5 percent.

Chart I-13, US, Consumer Price Index, All Items, 12- Month Percentage Change 1914-2018

Source: US Bureau of Labor Statistics http://www.bls.gov/cpi/data.htm

Table I-2 provides annual percentage changes of United States consumer price inflation from 1914 to 2017. There have been only cases of annual declines of the CPI after wars:

• World War I minus 10.5 percent in 1921 and minus 6.1 percent in 1922 following cumulative increases of 83.5 percent in four years from 1917 to 1920 at the average of 16.4 percent per year
• World War II: minus 1.2 percent in 1949 following cumulative 33.9 percent in three years from 1946 to 1948 at average 10.2 percent per year
• Minus 0.4 percent in 1955 two years after the end of the Korean War
• Minus 0.4 percent in 2009.
• The decline of 0.4 percent in 2009 followed increase of 3.8 percent in 2008 and is explained by the reversal of speculative carry trades into commodity futures that were created in 2008 as monetary policy rates were driven to zero. The reversal occurred after misleading statement on toxic assets in banks in the proposal for TARP (Cochrane and Zingales 2009).

There were declines of 1.7 percent in both 1927 and 1928 during the episode of revival of rules of the gold standard. The only persistent deflationary period since 1914 was during the Great Depression in the years from 1930 to 1933 and again in 1938-1939. Consumer prices increased only 0.1 percent in 2015 because of the collapse of commodity prices from artificially high levels induced by zero interest rates. Consumer prices increased 1.3 percent in 2016, increasing at 2.1 percent in 2017. Fear of deflation based on that experience does not justify unconventional monetary policy of zero interest rates that has failed to stop deflation in Japan. Financial repression causes far more adverse effects on allocation of resources by distorting the calculus of risk/returns than alleged employment-creating effects or there would not be current recovery without jobs and hiring after zero interest rates since Dec 2008 and intended now forever in a self-imposed forecast growth and employment mandate of monetary policy. Unconventional monetary policy drives wide swings in allocations of positions into risk financial assets that generate instability instead of intended pursuit of prosperity without inflation. There is insufficient knowledge and imperfect tools to maintain the gap of actual relative to potential output constantly at zero while restraining inflation in an open interval of (1.99, 2.0). Symmetric targets appear to have been abandoned in favor of a self-imposed single jobs mandate of easing monetary policy even with the economy growing at or close to potential output that is actually a target of growth forecast. The impact on the overall economy and the financial system of errors of policy are magnified by large-scale policy doses of trillions of dollars of quantitative easing and zero interest rates. The US economy has been experiencing financial repression as a result of negative real rates of interest during nearly a decade and programmed in monetary policy statements until 2015 or, for practical purposes, forever. The essential calculus of risk/return in capital budgeting and financial allocations has been distorted. If economic perspectives are doomed until 2015 such as to warrant zero interest rates and open-ended bond-buying by “printing” digital bank reserves (http://cmpassocregulationblog.blogspot.com/2010/12/is-fed-printing-money-what-are.html; see Shultz et al 2012), rational investors and consumers will not invest and consume until just before interest rates are likely to increase. Monetary policy statements on intentions of zero interest rates for another three years or now virtually forever discourage investment and consumption or aggregate demand that can increase economic growth and generate more hiring and opportunities to increase wages and salaries. The doom scenario used to justify monetary policy accentuates adverse expectations on discounted future cash flows of potential economic projects that can revive the economy and create jobs. If it were possible to project the future with the central tendency of the monetary policy scenario and monetary policy tools do exist to reverse this adversity, why the tools have not worked before and even prevented the financial crisis? If there is such thing as “monetary policy science”, why it has such poor record and current inability to reverse production and employment adversity? There is no excuse of arguing that additional fiscal measures are needed because they were deployed simultaneously with similar ineffectiveness. Jon Hilsenrath, writing on “New view into Fed’s response to crisis,” on Feb 21, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303775504579396803024281322?mod=WSJ_hp_LEFTWhatsNewsCollection), analyzes 1865 pages of transcripts of eight formal and six emergency policy meetings at the Fed in 2008 (http://www.federalreserve.gov/monetarypolicy/fomchistorical2008.htm). If there were an infallible science of central banking, models and forecasts would provide accurate information to policymakers on the future course of the economy in advance. Such forewarning is essential to central bank science because of the long lag between the actual impulse of monetary policy and the actual full effects on income and prices many months and even years ahead (Romer and Romer 2004, Friedman 1961, 1953, Culbertson 1960, 1961, Batini and Nelson 2002). Jon Hilsenrath, writing on “New view into Fed’s response to crisis,” on Feb 21, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303775504579396803024281322?mod=WSJ_hp_LEFTWhatsNewsCollection), analyzed 1865 pages of transcripts of eight formal and six emergency policy meetings at the Fed in 2008 (http://www.federalreserve.gov/monetarypolicy/fomchistorical2008.htm). Jon Hilsenrath demonstrates that Fed policymakers frequently did not understand the current state of the US economy in 2008 and much less the direction of income and prices. The conclusion of Friedman (1953) that monetary impulses increase financial and economic instability because of lags in anticipating needs of policy, taking policy decisions and effects of decisions. This a fortiori true when untested unconventional monetary policy in gargantuan doses shocks the economy and financial markets.

Table I-2, US, Annual CPI Inflation ∆% 1914-2017

Year	Annual ∆%
1914	1.0
1915	1.0
1916	7.9
1917	17.4
1918	18.0
1919	14.6
1920	15.6
1921	-10.5
1922	-6.1
1923	1.8
1924	0.0
1925	2.3
1926	1.1
1927	-1.7
1928	-1.7
1929	0.0
1930	-2.3
1931	-9.0
1932	-9.9
1933	-5.1
1934	3.1
1935	2.2
1936	1.5
1937	3.6
1938	-2.1
1939	-1.4
1940	0.7
1941	5.0
1942	10.9
1943	6.1
1944	1.7
1945	2.3
1946	8.3
1947	14.4
1948	8.1
1949	-1.2
1950	1.3
1951	7.9
1952	1.9
1953	0.8
1954	0.7
1955	-0.4
1956	1.5
1957	3.3
1958	2.8
1959	0.7
1960	1.7
1961	1.0
1962	1.0
1963	1.3
1964	1.3
1965	1.6
1966	2.9
1967	3.1
1968	4.2
1969	5.5
1970	5.7
1971	4.4
1972	3.2
1973	6.2
1974	11.0
1975	9.1
1976	5.8
1977	6.5
1978	7.6
1979	11.3
1980	13.5
1981	10.3
1982	6.2
1983	3.2
1984	4.3
1985	3.6
1986	1.9
1987	3.6
1988	4.1
1989	4.8
1990	5.4
1991	4.2
1992	3.0
1993	3.0
1994	2.6
1995	2.8
1996	3.0
1997	2.3
1998	1.6
1999	2.2
2000	3.4
2001	2.8
2002	1.6
2003	2.3
2004	2.7
2005	3.4
2006	3.2
2007	2.8
2008	3.8
2009	-0.4
2010	1.6
2011	3.2
2012	2.1
2013	1.5
2014	1.6
2015	0.1
2016	1.3
2017	2.1

Source: US Bureau of Labor Statistics http://www.bls.gov/cpi/data.htm

Friedman (1969) finds that the optimal rule for the quantity of money is deflation at a rate that results in a zero nominal interest rate (see Ireland 2003 and Cole and Kocherlakota 1998). Atkeson and Kehoe (2004) argue that central bankers are not inclined to implement policies that could result in deflation because of the interpretation of the Great Depression as closely related to deflation. They use panel data on inflation and growth of real output for 17 countries over more than 100 years. The time-series data for each individual country are broken into five-year events with deflation measured as average negative inflation and depression as average negative growth rate of real output. Atkeson and Kehoe (2004) find that the Great Depression from 1929 to 1934 is the only case of association between deflation and depression without any evidence whatsoever of such relation in any other period. Their conclusion is (Atkeson and Kehoe 2004, 99): “Our finding thus suggests that policymakers’ fear of anticipated policy-induced deflation that would result from following, say, the Friedman rule is greatly overblown.” Their conclusion on the experience of Japan is (Atkeson and Kehoe 2004, 99):

“Since 1960, Japan’s average growth rates have basically fallen monotonically, and since 1970, its average inflation rates have too. Attributing this 40-year slowdown to monetary forces is a stretch. More reasonable, we think, is that much of the slowdown is the natural pattern for a country that was far behind the world leaders and had begun to catch up.”

In the sample of Atkeson and Kehoe (2004), there are only eight five-year periods besides the Great Depression with both inflation and depression. Deflation and depression is shown in 65 cases with 21 of depression without deflation. There is no depression in 65 of 73 five-year periods and there is no deflation in 29 episodes of depression. There is a remarkable result of no depression in 90 percent of deflation episodes. Excluding the Great Depression, there is virtually no relation of deflation and depression. Atkeson and Kehoe (2004, 102) find that the average growth rate of Japan of 1.41 percent in the 1990s is “dismal” when compared with 3.20 percent in the United States but is not “dismal” when compared with 1.61 percent for Italy and 1.84 percent for France, which are also catch-up countries in modern economic growth (see Atkeson and Kehoe 1998). The conclusion of Atkeson and Kehoe (2004), without use of controls, is that there is no association of deflation and depression in their dataset.

Benhabib and Spiegel (2009) use a dataset similar to that of Atkeson and Kehoe (2004) but allowing for nonlinearity and inflation volatility. They conclude that in cases of low and negative inflation an increase of average inflation of 1 percent is associated with an increase of 0.31 percent of average annual growth. The analysis of Benhabib and Spiegel (2009) leads to the significantly different conclusion that inflation and economic performance are strongly associated for low and negative inflation. There is no claim of causality by Atkeson and Kehoe (2004) and Benhabib and Spiegel (2009).

Delfim Netto (1959) partly reprinted in Pelaez (1973) conducted two classical nonparametric tests (Mann 1945, Wallis and Moore 1941; see Kendall and Stuart 1968) with coffee-price data in the period of free markets from 1857 to 1906 with the following conclusions (Pelaez, 1976a, 280):

“First, the null hypothesis of no trend was accepted with high confidence; secondly, the null hypothesis of no oscillation was rejected also with high confidence. Consequently, in the nineteenth century international prices of coffee fluctuated but without long-run trend. This statistical fact refutes the extreme argument of structural weakness of the coffee trade.”

In his classic work on the theory of international trade, Jacob Viner (1937, 563) analyzed the “index of total gains from trade,” or “amount of gain per unit of trade,” denoted as T:

T= (∆P^e/∆Pⁱ)∆Q

Where ∆P^e is the change in export prices, ∆Pⁱ is the change in import prices and ∆Q is the change in export volume. Dorrance (1948, 52) restates “Viner’s index of total gain from trade” as:

“What should be done is to calculate an index of the value (quantity multiplied by price) of exports and the price of imports for any country whose foreign accounts are to be analysed. Then the export value index should be divided by the import price index. The result would be an index which would reflect, for the country concerned, changes in the volume of imports obtainable from its export income (i.e. changes in its "real" export income, measured in import terms). The present writer would suggest that this index be referred to as the ‘income terms of trade’ index to differentiate it from the other indexes at present used by economists.”

What really matters for an export activity especially during modernization is the purchasing value of goods that it exports in terms of prices of imports. For a primary producing country, the purchasing power of exports in acquiring new technology from the country providing imports is the critical measurement. The barter terms of trade of Brazil improved from 1857 to 1906 because international coffee prices oscillated without trend (Delfim Netto 1959) while import prices from the United Kingdom declined at the rate of 0.5 percent per year (Imlah 1958). The accurate measurement of the opportunity afforded by the coffee exporting economy was incomparably greater when considering the purchasing power in British prices of the value of coffee exports, or Dorrance’s (1948) income terms of trade.

The conventional theory that the terms of trade of Brazil deteriorated over the long term is without reality (Pelaez 1976a, 280-281):

“Moreover, physical exports of coffee by Brazil increased at the high average rate of 3.5 per cent per year. Brazil's exchange receipts from coffee-exporting in sterling increased at the average rate of 3.5 per cent per year and receipts in domestic currency at 4.5 per cent per year. Great Britain supplied nearly all the imports of the coffee economy. In the period of the free coffee market, British export prices declined at the rate of 0.5 per cent per year. Thus, the income terms of trade of the coffee economy improved at the relatively satisfactory average rate of 4.0 per cent per year. This is only a lower bound of the rate of improvement of the terms of trade. While the quality of coffee remained relatively constant, the quality of manufactured products improved significantly during the fifty-year period considered. The trade data and the non-parametric tests refute conclusively the long-run hypothesis. The valid historical fact is that the tropical export economy of Brazil experienced an opportunity of absorbing rapidly increasing quantities of manufactures from the "workshop" countries. Therefore, the coffee trade constituted a golden opportunity for modernization in nineteenth-century Brazil.”

Imlah (1958) provides decline of British export prices at 0.5 percent in the nineteenth century and there were no lost decades, depressions or unconventional monetary policies in the highly dynamic economy of England that drove the world’s growth impulse. Inflation in the United Kingdom between 1857 and 1906 is measured by the composite price index of O’Donoghue and Goulding (2004) at minus 7.0 percent or average rate of decline of 0.2 percent per year.

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.”

Cameron (1961) analyzes the mechanism by which the Industrial Revolution in Great Britain spread throughout Europe and Cameron (1967) analyzes the financing by banks of the Industrial Revolution in Great Britain. O’Donoghue and Goulding (2004) provide consumer price inflation in England since 1750 and MacFarlane and Mortimer-Lee (1994) analyze inflation in England over 300 years. Lucas (2004) estimates world population and production since the year 1000 with sustained growth of per capita incomes beginning to accelerate for the first time in English-speaking countries and in particular in the Industrial Revolution in Great Britain. The conventional theory is unequal distribution of the gains from trade and technical progress between the industrialized countries and developing economies (Singer 1950, 478):

“Dismissing, then, changes in productivity as a governing factor in changing terms of trade, the following explanation presents itself: the fruits of technical progress may be distributed either to producers (in the form of rising incomes) or to consumers (in the form of lower prices). In the case of manufactured commodities produced in more developed countries, the former method, i.e., distribution to producers through higher incomes, was much more important relatively to the second method, while the second method prevailed more in the case of food and raw material production in the underdeveloped countries. Generalizing, we may say -that technical progress in manufacturing industries showed in a rise in incomes while technical progress in the production of food and raw materials in underdeveloped countries showed in a fall in prices”

Temin (1997, 79) uses a Ricardian trade model to discriminate between two views on the Industrial Revolution with an older view arguing broad-based increases in productivity and a new view concentration of productivity gains in cotton manufactures and iron:

“Productivity advances in British manufacturing should have lowered their prices relative to imports. They did. Albert Imlah [1958] correctly recognized this ‘severe deterioration’ in the net barter terms of trade as a signal of British success, not distress. It is no surprise that the price of cotton manufactures fell rapidly in response to productivity growth. But even the price of woolen manufactures, which were declining as a share of British exports, fell almost as rapidly as the price of exports as a whole. It follows, therefore, that the traditional ‘old-hat’ view of the Industrial Revolution is more accurate than the new, restricted image. Other British manufactures were not inefficient and stagnant, or at least, they were not all so backward. The spirit that motivated cotton manufactures extended also to activities as varied as hardware and haberdashery, arms, and apparel.”

Phyllis Deane (1968, 96) estimates growth of United Kingdom gross national product (GNP) at around 2 percent per year for several decades in the nineteenth century. The facts that the terms of trade of Great Britain deteriorated during the period of epochal innovation and high rates of economic growth while the income terms of trade of the coffee economy of nineteenth-century Brazil improved at the average yearly rate of 4.0 percent from 1857 to 1906 disprove the hypothesis of weakness of trade as an explanation of relatively lower income and wealth. As Temin (1997) concludes, Britain did pass on lower prices and higher quality the benefits of technical innovation. Explanation of late modernization must focus on laborious historical research on institutions and economic regimes together with economic theory, data gathering and measurement instead of grand generalizations of weakness of trade and alleged neocolonial dependence (Stein and Stein 1970, 134-5):

“Great Britain, technologically and industrially advanced, became as important to the Latin American economy as to the cotton-exporting southern United States. [After Independence in the nineteenth century] Latin America fell back upon traditional export activities, utilizing the cheapest available factor of production, the land, and the dependent labor force.”

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.

The experience of the United Kingdom with deflation and economic growth is relevant and rich. Table IE-1 uses yearly percentage changes of the composite index of prices of the United Kingdom of O’Donoghue and Goulding (2004). There are 73 declines of inflation in the 145 years from 1751 to 1896. Prices declined in 50.3 percent of 145 years. Some price declines were quite sharp and many occurred over several years. Table IE-1 also provides yearly percentage changes of the UK composite price index of O’Donoghue and Goulding (2004) from 1929 to 1934. Deflation was much sharper in continuous years in earlier periods than during the Great Depression. The United Kingdom could not have led the world in modern economic growth if there were meaningful causality from deflation to depression.

Table IE-1, United Kingdom, Negative Percentage Changes of Composite Price Index, 1751-1896, 1929-1934, Yearly ∆%

Year	∆%	Year	∆%	Year	∆%	Year	∆%
1751	-2.7	1797	-10.0	1834	-7.8	1877	-0.7
1753	-2.7	1798	-2.2	1841	-2.3	1878	-2.2
1755	-6.0	1802	-23.0	1842	-7.6	1879	-4.4
1758	-0.3	1803	-5.9	1843	-11.3	1881	-1.1
1759	-7.9	1806	-4.4	1844	-0.1	1883	-0.5
1760	-4.5	1807	-1.9	1848	-12.1	1884	-2.7
1761	-4.5	1811	-2.9	1849	-6.3	1885	-3.0
1768	-1.1	1814	-12.7	1850	-6.4	1886	-1.6
1769	-8.2	1815	-10.7	1851	-3.0	1887	-0.5
1770	-0.4	1816	-8.4	1857	-5.6	1893	-0.7
1773	-0.3	1819	-2.5	1858	-8.4	1894	-2.0
1775	-5.6	1820	-9.3	1859	-1.8	1895	-1.0
1776	-2.2	1821	-12.0	1862	-2.6	1896	-0.3
1777	-0.4	1822	-13.5	1863	-3.6	1929	-0.9
1779	-8.5	1826	-5.5	1864	-0.9	1930	-2.8
1780	-3.4	1827	-6.5	1868	-1.7	1931	-4.3
1785	-4.0	1828	-2.9	1869	-5.0	1932	-2.6
1787	-0.6	1830	-6.1	1874	-3.3	1933	-2.1
1789	-1.3	1832	-7.4	1875	-1.9	1934	0.0
1791	-0.1	1833	-6.1	1876	-0.3

Source:

O’Donoghue, Jim and Louise Goulding, 2004. Consumer Price Inflation since 1750. UK Office for National Statistics Economic Trends 604, Mar 2004, 38-46.

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).

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.

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.

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 (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 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. The civilian labor force participation rate was at 62.7 percent in Aug 2018 and 62.7 percent in Sep 2018. 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/2018/09/recovery-without-hiring-in-lost.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/dollar-revaluation-recovery-without.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, 1979-2018

Year	May	Jun	Jul	Aug	Sep	Dec	Annual
1979	62.9	64.5	64.9	64.5	63.8	63.8	63.7
1980	63.5	64.6	65.1	64.5	63.6	63.4	63.8
1981	63.9	64.6	65.0	64.6	63.5	63.4	63.9
1982	63.9	64.8	65.3	64.9	64.0	63.8	64.0
1983	63.4	65.1	65.4	65.1	64.3	63.8	64.0
1984	64.3	65.5	65.9	65.2	64.4	64.3	64.4
1985	64.6	65.5	65.9	65.4	64.9	64.6	64.8
1986	65.0	66.3	66.6	66.1	65.3	65.0	65.3
1987	65.6	66.3	66.8	66.5	65.5	65.5	65.6
1988	65.5	66.7	67.1	66.8	65.9	65.9	65.9
1989	66.2	67.4	67.7	67.2	66.3	66.3	66.5
1990	66.5	67.4	67.7	67.1	66.4	66.1	66.5
1991	66.0	67.2	67.3	66.6	66.1	65.8	66.2
1992	66.4	67.6	67.9	67.2	66.3	66.1	66.4
1993	66.3	67.3	67.5	67.0	66.1	66.2	66.3
1994	66.5	67.2	67.5	67.2	66.5	66.5	66.6
1995	66.4	67.2	67.7	67.1	66.5	66.2	66.6
1996	66.7	67.4	67.9	67.2	66.8	66.7	66.8
1997	67.0	67.8	68.1	67.6	67.0	67.0	67.1
1998	67.0	67.7	67.9	67.3	67.0	67.0	67.1
1999	67.0	67.7	67.9	67.3	66.8	67.0	67.1
2000	67.0	67.7	67.6	67.2	66.7	67.0	67.1
2001	66.6	67.2	67.4	66.8	66.6	66.6	66.8
2002	66.5	67.1	67.2	66.8	66.6	66.2	66.6
2003	66.2	67.0	66.8	66.3	65.9	65.8	66.2
2004	65.8	66.5	66.8	66.2	65.7	65.8	66.0
2005	66.0	66.5	66.8	66.5	66.1	65.9	66.0
2006	66.0	66.7	66.9	66.5	66.1	66.3	66.2
2007	65.8	66.6	66.8	66.1	66.0	65.9	66.0
2008	66.0	66.6	66.8	66.4	65.9	65.7	66.0
2009	65.5	66.2	66.2	65.6	65.0	64.4	65.4
2010	64.8	65.1	65.3	65.0	64.6	64.1	64.7
2011	64.1	64.5	64.6	64.3	64.2	63.8	64.1
2012	63.8	64.3	64.3	63.7	63.6	63.4	63.7
2013	63.5	64.0	64.0	63.4	63.2	62.6	63.2
2014	62.9	63.4	63.5	63.0	62.8	62.5	62.9
2015	63.0	63.1	63.2	62.7	62.3	62.4	62.7
2016	62.7	63.2	63.4	62.9	62.8	62.4	62.8
2017	62.8	63.3	63.5	63.0	63.0	62.4	62.9
2018	62.8	63.4	63.5	62.7	62.7

Source: Bureau of Labor Statistics http://www.bls.gov/cps/

Chart I-12b, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1979-2018

Source: Bureau of Labor Statistics

http://www.bls.gov/cps/

Broader perspective is in Chart I-12c of the US Bureau of Labor Statistics. The United States civilian noninstitutional population has increased along a consistent trend since 1948 that continued through earlier recessions and the global recession from IVQ2007 to IIQ2009 and the cyclical expansion after IIIQ2009.

Chart I-12c, US, Civilian Noninstitutional Population, Thousands, NSA, 1948-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

The labor force of the United States in Chart I-12d has increased along a trend similar to that of the civilian noninstitutional population in Chart I-12c. There is an evident stagnation of the civilian labor force in the final segment of Chart I-12d during the current economic cycle. This stagnation is explained by cyclical factors similar to those analyzed by Lazear and Spletzer (2012JHJul22) that motivated an increasing population to drop out of the labor force instead of structural factors. Large segments of the potential labor force are not observed, constituting unobserved unemployment and of more permanent nature because those afflicted have been seriously discouraged from working by the lack of opportunities.

Chart I-12d, US, Labor Force, Thousands, NSA, 1948-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

The rate of labor force participation in the US is in Chart I-12E from 1948 to 2018. There is sudden decline during the global recession after 2007 without recovery explained by cyclical factors (Lazear and Spletzer2012JHJul22) as may many potential workers stopped their searches disillusioned that there could be an opportunity for them in sharply contracted markets.

Chart I-12E, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1948-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20 provides the level of full-time jobs from 2001 to 2018. 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 258.290 million in Sep 2018 or by 26.332 million (http://www.bls.gov/data/). The number with full-time jobs in Sep 2018 is 129.466 million, which is higher by 6.247 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 137.152 million full-time jobs with population of 258.290 million in Sep 2018 (0.531 x 258.290) or 7.686 million fewer full-time jobs relative to actual 129.466 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, 2001-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20A provides the noninstitutional civilian population of the United States from 2001 to 2018. There is clear trend of increase of the population while the number of full-time jobs collapsed after 2008 without sufficient recovery as shown in the preceding Chart I-20.

Chart I-20A, US, Noninstitutional Civilian Population, Thousands, 2001-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20B provides number of full-time jobs in the US from 1968 to 2018. 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, 1968-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20C provides the noninstitutional civilian population of the United States from 1968 to 2018. 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, 1968-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Table EMP provides the comparison between the labor market in the current whole cycle from 2007 to 2017 and the whole cycle from 1979 to 1989. In the entire cycle from 2007 to 2017, the number employed increased 7.290 million, full-time employed increased 4.876 million, part-time for economic reasons increased 0.849 million and population increased 23.212 million. The number employed increased 5.0 percent, full-time employed increased 4.0 percent, part-time for economic reasons increased 19.3 percent and population increased 10.0 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
∆2007-2017	7.290	4.876	0.849	23.212
∆% 2007-2016	5.0	4.0	19.3	10.0
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

http://www.bls.gov/

The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. There are accentuated cyclic factors for both the entire population and the young population of ages 16 to 24 years. Table Summary Total provides the total noninstitutional population (ICP) of the US, full-time employment level (FTE), employment level (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP), employment/population ratio (EPOP) and unemployment level (UNE). Secular stagnation would spread over long periods instead of immediately. All indicators of the labor market weakened sharply during the contraction and did not recover. Population continued to grow but all other variables collapsed and did not recover. 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), 11-16). Hansen (1938, 1939) finds secular stagnation in lower growth of an aging population. In the current US economy, Table Summary shows that population is dynamic while the labor market is fractured. There is key explanation in the behavior of the civilian labor force participation rate (CLFP) and the employment population ratio (EPOP) that collapsed during the global recession with inadequate recovery. Abandoning job searches are difficult to capture in labor statistics but likely explain the decline in the participation of the population in the labor force. Allowing for abandoning job searches, the total number of people unemployed or underemployed is 20.7 million or 12.1 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html).

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
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	58.9	14.5
9/18	258.3	129.5	156.2	162.0	62.7	60.5	5.8

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

http://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
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
8/18	38.0	19.4	21.1	55.5	51.0	1.7	8.2

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

http://www.bls.gov/

The eminent economist and historian Professor Rondo E. Cameron (1989, 3) searches for the answer of “why are some nations rich and others poor?” by analyzing economic history since Paleolithic times. Cameron (1989, 4) argues that:

“Policymakers and their staffs of experts, faced with the responsibility of proposing and implementing policies for development, frequently shrug off the potential contributions of historical analysis to the solution of their problems with the observation that the contemporary situation is unique and therefore history is irrelevant to their concerns. Such an attitude contains a double fallacy. In the first place, those who are ignorant of the past are not qualified to generalize about it. Second, it implicitly denies the uniformity of nature, including human behavior and the behavior of social institutions—an assumption on which all scientific inquiry is founded. Such attitudes reveal how easy it is, without historical perspective, to mistake the symptoms of a problem for its causes.”

Scholars detached from practical issues of economic policy are more likely to discover sound knowledge (Cohen and Nagel 1934). There is troublesome sacrifice of rigorous scientific objectivity in cutting the economic past by a procrustean bed fitting favored current economic policies.

Nicholas Georgescu-Rogen (1960, 1) reprinted in Pelaez (1973) argues that “the agrarian economy has to this day remained a reality without theory.” The economic history of Latin America shares with the relation of deflation and unconventional monetary policy and secular stagnation when the event is cyclical slow growth a more frustrating intellectual misfortune: theory without reality. MacFarlane and Mortimer-Lee (1994, 159) quote in a different context a phrase by Thomas Henry Huxley in the President’s Address to the British Association for the Advancement of Science on Sep 14, 1870 that is appropriate to these issues: “The great tragedy of science—the slaying of a beautiful hypothesis by an ugly fact.” There may be current relevance in another quote from Thomas Henry Huxley: “The deepest sin against the human mind is to believe things without evidence.”

I Recovery without Hiring. Professor Edward P. Lazear (2012Jan19) at Stanford University finds that recovery of hiring in the US to peaks attained in 2007 requires an increase of hiring by 30 percent while hiring levels increased by only 4 percent from Jan 2009 to Jan 2012. The high level of unemployment with low level of hiring reduces the statistical probability that the unemployed will find a job. According to Lazear (2012Jan19), the probability of finding a new job in early 2012 is about one third of the probability of finding a job in 2007. Improvements in labor markets have not increased the probability of finding a new job. Lazear (2012Jan19) quotes an essay coauthored with James R. Spletzer in the American Economic Review (Lazear and Spletzer 2012Mar, 2012May) on the concept of churn. A dynamic labor market occurs when a similar number of workers is hired as those who are separated. This replacement of separated workers is called churn, which explains about two-thirds of total hiring. Typically, wage increases received in a new job are higher by 8 percent. Lazear (2012Jan19) argues that churn has declined 35 percent from the level before the recession in IVQ2007. Because of the collapse of churn, there are no opportunities in escaping falling real wages by moving to another job. As this blog argues, there are meager chances of escaping unemployment because of the collapse of hiring and those employed cannot escape falling real wages by moving to another job (Section II and earlier https://cmpassocregulationblog.blogspot.com/2018/09/recovery-without-hiring-in-lost.html). Lazear and Spletzer (2012Mar, 1) argue that reductions of churn reduce the operational effectiveness of labor markets. Churn is part of the allocation of resources or in this case labor to occupations of higher marginal returns. The decline in churn can harm static and dynamic economic efficiency. Losses from decline of churn during recessions can affect an economy over the long-term by preventing optimal growth trajectories because resources are not used in the occupations where they provide highest marginal returns. Lazear and Spletzer (2012Mar 7-8) conclude that: “under a number of assumptions, we estimate that the loss in output during the recession [of 2007 to 2009] and its aftermath resulting from reduced churn equaled $208 billion. On an annual basis, this amounts to about .4% of GDP for a period of 3½ years.”

There are two additional facts discussed below: (1) there are about ten million fewer full-time jobs currently than before the recession of 2008 and 2009; and (2) the extremely high and rigid rate of youth unemployment is denying an early start to young people ages 16 to 24 years while unemployment of ages 45 years or over has swelled. There are four subsections. IA1 Hiring Collapse provides the data and analysis on the weakness of hiring in the United States economy. IA2 Labor Underutilization provides the measures of labor underutilization of the Bureau of Labor Statistics (BLS). Statistics on the decline of full-time employment are in IA3 Ten Million Fewer Full-time Jobs. IA4 Theory and Reality of Cyclical Slow Growth Not Secular Stagnation: Youth and Middle-Age Unemployment provides the data on high unemployment of ages 16 to 24 years and of ages 45 years or over.

IA1 Hiring Collapse. An important characteristic of the current fractured labor market of the US is the closing of the avenue for exiting unemployment and underemployment normally available through dynamic hiring. Another avenue that is closed is the opportunity for advancement in moving to new jobs that pay better salaries and benefits again because of the collapse of hiring in the United States. Those who are unemployed or underemployed cannot find a new job even accepting lower wages and no benefits. The employed cannot escape declining inflation-adjusted earnings because there is no hiring. The objective of this section is to analyze hiring and labor underutilization in the United States.

Blanchard and Katz (1997, 53 consider an appropriate measure of job stress:

“The right measure of the state of the labor market is the exit rate from unemployment, defined as the number of hires divided by the number unemployed, rather than the unemployment rate itself. What matters to the unemployed is not how many of them there are, but how many of them there are in relation to the number of hires by firms.”

The natural rate of unemployment and the similar NAIRU are quite difficult to estimate in practice (Ibid; see Ball and Mankiw 2002).

The Bureau of Labor Statistics (BLS) created the Job Openings and Labor Turnover Survey (JOLTS) with the purpose that (http://www.bls.gov/jlt/jltover.htm#purpose):

“These data serve as demand-side indicators of labor shortages at the national level. Prior to JOLTS, there was no economic indicator of the unmet demand for labor with which to assess the presence or extent of labor shortages in the United States. The availability of unfilled jobs—the jobs opening rate—is an important measure of tightness of job markets, parallel to existing measures of unemployment.”

The BLS collects data from about 16,000 US business establishments in nonagricultural industries through the 50 states and DC. The data are released monthly and constitute an important complement to other data provided by the BLS (see also Lazear and Spletzer 2012Mar, 6-7).

There is socio-economic stress in the combination of adverse events and cyclical performance:

• Mediocre economic growth below potential and long-term trend, resulting in idle productive resources with GDP two trillion dollars below trend (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html). US GDP grew at the average rate of 3.2 percent per year from 1929 to 2017, with similar performance in whole cycles of contractions and expansions, but only at 1.5 percent per year on average from 2007 to 2017. GDP in IIQ2018 is 13.9 percent lower than what it would have been had it grown at trend of 3.0 percent
• Private fixed investment stagnating initially followed by cumulative increase of 26.3 percent in the entire cycle from IVQ2007 to IIQ2018 (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html).
• Twenty-one million or 12.1 percent of the effective labor force unemployed or underemployed in involuntary part-time jobs with stagnating or declining real wages (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/fomc-policy-rate-unchanged-competitive.html and earlier https://cmpassocregulationblog.blogspot.com/2018/07/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/05/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/04/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/twenty-three-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/02/twenty-four-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/01/twenty-three-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/11/unchanged-fomc-policy-rate-gradual.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/09/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/data-dependent-monetary-policy-with.html and earlier https://cmpassocregulationblog.blogspot.com/2017/07/rising-yields-twenty-two-million.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/increasing-interest-rates-twenty-four.html and earlier https://cmpassocregulationblog.blogspot.com/2017/02/twenty-six-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html and earlier http://cmpassocregulationblog.blogspot.com/2016/10/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/interest-rates-and-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/08/global-competitive-easing-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/fluctuating-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/financial-turbulence-twenty-four.html and earlier http://cmpassocregulationblog.blogspot.com/2016/05/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/proceeding-cautiously-in-monetary.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/twenty-five-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/02/fluctuating-risk-financial-assets-in.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/weakening-equities-with-exchange-rate.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/12/liftoff-of-fed-funds-rate-followed-by.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/live-possibility-of-interest-rates.html and earlier http://cmpassocregulationblog.blogspot.com/2015/10/labor-market-uncertainty-and-interest.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-policy-dependent-on-what.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/fluctuating-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html)
• Stagnating real disposable income per person or income per person after inflation and taxes (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/revision-of-united-states-national.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/fomc-policy-rate-unchanged-competitive.html and earlier https://cmpassocregulationblog.blogspot.com/2018/07/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/stronger-dollar-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2018/05/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/04/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/04/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/twenty-three-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/02/twenty-four-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/dollar-devaluation-cyclically.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/11/unchanged-fomc-policy-rate-gradual.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/09/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/data-dependent-monetary-policy-with.html and earlier https://cmpassocregulationblog.blogspot.com/2017/07/rising-yields-twenty-two-million.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/rising-valuations-of-risk-financial.html and earlier https://cmpassocregulationblog.blogspot.com/2017/02/twenty-six-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html and earlier http://cmpassocregulationblog.blogspot.com/2016/10/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/interest-rates-and-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/08/global-competitive-easing-or.html and earlier (http://cmpassocregulationblog.blogspot.com/2016/07/financial-asset-values-rebound-from.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/financial-turbulence-twenty-four.html and earlier http://cmpassocregulationblog.blogspot.com/2016/05/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/proceeding-cautiously-in-monetary.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/twenty-five-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/twenty-five-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/02/fluctuating-risk-financial-assets-in.html and earlier http://cmpassocregulationblog.blogspot.com/2015/12/dollar-revaluation-and-decreasing.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/dollar-revaluation-constraining.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/dollar-revaluation-constraining.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/live-possibility-of-interest-rates.html and earlier http://cmpassocregulationblog.blogspot.com/2015/10/labor-market-uncertainty-and-interest.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-policy-dependent-on-what.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/fluctuating-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html and earlier http://cmpassocregulationblog.blogspot.com/2015/05/dollar-devaluation-and-carry-trade.html and earlier http://cmpassocregulationblog.blogspot.com/2015/04/volatility-of-valuations-of-financial.html)

• Depressed hiring that does not afford an opportunity for reducing unemployment/underemployment and moving to better-paid jobs (Section II and earlier https://cmpassocregulationblog.blogspot.com/2018/09/recovery-without-hiring-in-lost.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/dollar-revaluation-recovery-without.html and earlier https://cmpassocregulationblog.blogspot.com/2018/07/recovery-without-hiring-ten-million.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/05/recovery-without-hiring-ten-million.html and earlier https://cmpassocregulationblog.blogspot.com/2018/04/rising-yields-world-inflation-waves.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/decreasing-valuations-of-risk-financial.html and earlier https://cmpassocregulationblog.blogspot.com/2018/02/collateral-effects-of-unwinding.html and earlier https://cmpassocregulationblog.blogspot.com/2018/01/dollar-devaluation-and-rising.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/fomc-increases-interest-rates-with.html and earlier https://cmpassocregulationblog.blogspot.com/2017/11/recovery-without-hiring-ten-million.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/increasing-valuations-of-risk-financial.html and earlier https://cmpassocregulationblog.blogspot.com/2017/09/dollar-devaluation-world-inflation.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/recovery-without-hiring-ten-million_40.html and earlier https://cmpassocregulationblog.blogspot.com/2017/07/dollar-devaluation-and-valuation-of.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/flattening-us-treasury-yield-curve.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/recovery-without-hiring-ten-million_14.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/recovery-without-hiring-ten-million.html and earlier https://cmpassocregulationblog.blogspot.com/2017/02/recovery-without-hiring-ten-million.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/unconventional-monetary-policy-and.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-values-of-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/dollar-revaluation-and-valuations-of.html and earlier http://cmpassocregulationblog.blogspot.com/2016/10/imf-view-of-world-economy-and-finance.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/interest-rate-uncertainty-and-valuation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/08/rising-valuations-of-risk-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/oscillating-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/considerable-uncertainty-about-economic.html and earlier http://cmpassocregulationblog.blogspot.com/2016/05/recovery-without-hiring-ten-million.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/proceeding-cautiously-in-reducing.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/contraction-of-united-states-corporate.html and earlier http://cmpassocregulationblog.blogspot.com/2016/02/subdued-foreign-growth-and-dollar.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/unconventional-monetary-policy-and.html and earlier http://cmpassocregulationblog.blogspot.com/2015/12/liftoff-of-interest-rates-with-volatile_17.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/interest-rate-policy-conundrum-recovery.html and earlier http://cmpassocregulationblog.blogspot.com/2015/10/impact-of-monetary-policy-on-exchange.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-policy-dependent-on-what_13.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/exchange-rate-and-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/07/oscillating-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/volatility-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/volatility-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/05/fluctuating-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/04/dollar-revaluation-recovery-without.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/global-exchange-rate-struggle-recovery.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/02/g20-monetary-policy-recovery-without.html)
• Productivity growth fell from 2.1 percent per year on average from 1947 to 2017 and average 2.3 percent per year from 1947 to 2007 to 1.3 percent per year on average from 2007 to 2017, deteriorating future growth and prosperity (https://cmpassocregulationblog.blogspot.com/2018/09/recovery-without-hiring-in-lost.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/revision-of-united-states-national.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/fomc-increases-interest-rates-with.html and earlier (https://cmpassocregulationblog.blogspot.com/2018/05/recovery-without-hiring-ten-million.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/united-states-inflation-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/02/collateral-effects-of-unwinding.html and earlier (https://cmpassocregulationblog.blogspot.com/2017/12/fomc-increases-interest-rates-with.html and earlier (https://cmpassocregulationblog.blogspot.com/2017/11/recovery-without-hiring-ten-million.html and earlier https://cmpassocregulationblog.blogspot.com/2017/09/ii-rules-discretionary-authorities-and.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/recovery-without-hiring-ten-million_40.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/flattening-us-treasury-yield-curve.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/recovery-without-hiring-ten-million_14.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/increasing-interest-rates-twenty-four.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-values-of-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/interest-rates-and-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/08/rising-valuations-of-risk-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/considerable-uncertainty-about-economic.html and earlier http://cmpassocregulationblog.blogspot.com/2016/05/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/twenty-five-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/closely-monitoring-global-economic-and.html and earlier http://cmpassocregulationblog.blogspot.com/2015/12/liftoff-of-fed-funds-rate-followed-by.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/live-possibility-of-interest-rates.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-policy-dependent-on-what.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/exchange-rate-and-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html and earlier http://cmpassocregulationblog.blogspot.com/2015/05/quite-high-equity-valuations-and.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/financial-risks-twenty-six-million.html)

• Output of manufacturing in Sep 2018 at 29.4 percent below long-term trend since 1919 and at 21.6 percent below trend since 1986 (Section II and earlier https://cmpassocregulationblog.blogspot.com/2018/09/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/world-inflation-waves-lost-economic.html and earlier https://cmpassocregulationblog.blogspot.com/2018/07/continuing-gradual-increases-in-fed.html and earlier (https://cmpassocregulationblog.blogspot.com/2018/06/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/05/dollar-revaluation-united-states_24.html and earlier (https://cmpassocregulationblog.blogspot.com/2018/04/rising-yields-world-inflation-waves.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/united-states-inflation-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/02/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/01/dollar-devaluation-and-increasing.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/mediocre-cyclical-united-states_23.html and earlier https://cmpassocregulationblog.blogspot.com/2017/11/the-lost-economic-cycle-of-global_25.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/world-inflation-waves-long-term-and.html and earlier) (https://cmpassocregulationblog.blogspot.com/2017/09/monetary-policy-of-reducing-central.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/fluctuating-valuations-of-risk.html and earlier (https://cmpassocregulationblog.blogspot.com/2017/07/rising-valuations-of-risk-financial.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/fomc-interest-rate-increase-planned.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/dollar-devaluation-world-inflation.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/united-states-commercial-banks-assets.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/fomc-increases-interest-rates-world.html and earlier https://cmpassocregulationblog.blogspot.com/2017/02/world-inflation-waves-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/world-inflation-waves-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/of-course-economic-outlook-is-highly.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/interest-rate-increase-could-well.html and earlier http://cmpassocregulationblog.blogspot.com/2016/10/dollar-revaluation-world-inflation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/interest-rates-and-volatility-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/08/interest-rate-policy-uncertainty-and.html and earlier (http://cmpassocregulationblog.blogspot.com/2016/07/unresolved-us-balance-of-payments.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/fomc-projections-world-inflation-waves.html and earlier (http://cmpassocregulationblog.blogspot.com/2016/05/most-fomc-participants-judged-that-if.html and earlier (http://cmpassocregulationblog.blogspot.com/2016/04/contracting-united-states-industrial.html and earlier (http://cmpassocregulationblog.blogspot.com/2016/03/monetary-policy-and-competitive.html and earlier http://cmpassocregulationblog.blogspot.com/2016/02/squeeze-of-economic-activity-by-carry.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/unconventional-monetary-policy-and.html and earlier http://cmpassocregulationblog.blogspot.com/2015/12/liftoff-of-interest-rates-with-monetary.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/interest-rate-liftoff-followed-by.html http://cmpassocregulationblog.blogspot.com/2015/10/interest-rate-policy-quagmire-world.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-increase-on-hold-because.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/exchange-rate-and-financial-asset.html

and earlier http://cmpassocregulationblog.blogspot.com/2015/07/fluctuating-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/fluctuating-financial-asset-valuations.html and earlier http://cmpassocregulationblog.blogspot.com/2015/05/fluctuating-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/04/global-portfolio-reallocations-squeeze.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/impatience-with-monetary-policy-of.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/02/world-financial-turbulence-squeeze-of.html and earlier http://cmpassocregulationblog.blogspot.com/2015/01/exchange-rate-conflicts-squeeze-of.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/patience-on-interest-rate-increases.html and earlier http://cmpassocregulationblog.blogspot.com/2014/11/squeeze-of-economic-activity-by-carry.html and earlier http://cmpassocregulationblog.blogspot.com/2014/10/imf-view-squeeze-of-economic-activity.html and earlier http://cmpassocregulationblog.blogspot.com/2014/09/world-inflation-waves-squeeze-of.html)

• Unsustainable government deficit/debt and balance of payments deficit (https://cmpassocregulationblog.blogspot.com/2018/10/global-contraction-of-valuations-of.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/unresolved-us-balance-of-payments.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/proceeding-cautiously-in-reducing.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/weakening-equities-and-dollar.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/monetary-policy-designed-on-measurable.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/fluctuating-financial-asset-valuations.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/impatience-with-monetary-policy-of.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/patience-on-interest-rate-increases.html http://cmpassocregulationblog.blogspot.com/2014/09/world-inflation-waves-squeeze-of.html http://cmpassocregulationblog.blogspot.com/2014/08/monetary-policy-world-inflation-waves.html http://cmpassocregulationblog.blogspot.com/2014/06/valuation-risks-world-inflation-waves.html http://cmpassocregulationblog.blogspot.com/2014/02/theory-and-reality-of-cyclical-slow.html http://cmpassocregulationblog.blogspot.com/2014/03/interest-rate-risks-world-inflation.html http://cmpassocregulationblog.blogspot.com/2013/12/tapering-quantitative-easing-mediocre.html and earlier http://cmpassocregulationblog.blogspot.com/2013/09/duration-dumping-and-peaking-valuations.html)
• Worldwide waves of inflation (Section I and earlier https://cmpassocregulationblog.blogspot.com/2018/09/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/world-inflation-waves-lost-economic.html and earlier https://cmpassocregulationblog.blogspot.com/2018/07/continuing-gradual-increases-in-fed.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/05/dollar-strengthening-world-inflation.htm and earlier https://cmpassocregulationblog.blogspot.com/2018/04/rising-yields-world-inflation-waves.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/decreasing-valuations-of-risk-financial.html and earlier https://cmpassocregulationblog.blogspot.com/2018/02/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/01/dollar-devaluation-and-increasing.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/fomc-increases-interest-rates-with.html and earlier https://cmpassocregulationblog.blogspot.com/2017/11/dollar-devaluation-and-decline-of.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/world-inflation-waves-long-term-and.html and earlier https://cmpassocregulationblog.blogspot.com/2017/09/dollar-devaluation-world-inflation.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/fluctuating-valuations-of-risk.html and earlier https://cmpassocregulationblog.blogspot.com/2017/07/dollar-devaluation-and-valuation-of.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/fomc-interest-rate-increase-planned.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/dollar-devaluation-world-inflation.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/fomc-increases-interest-rates-world.html and earlier https://cmpassocregulationblog.blogspot.com/2017/02/world-inflation-waves-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/world-inflation-waves-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/of-course-economic-outlook-is-highly.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/interest-rate-increase-could-well.html and earlier http://cmpassocregulationblog.blogspot.com/2016/10/dollar-revaluation-world-inflation.html and earlier (http://cmpassocregulationblog.blogspot.com/2016/09/interest-rates-and-volatility-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/08/interest-rate-policy-uncertainty-and.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/oscillating-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/fomc-projections-world-inflation-waves.html and earlier http://cmpassocregulationblog.blogspot.com/2016/05/most-fomc-participants-judged-that-if.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/contracting-united-states-industrial.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/monetary-policy-and-competitive.html and earlier http://cmpassocregulationblog.blogspot.com/2016/02/squeeze-of-economic-activity-by-carry.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/uncertainty-of-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/12/liftoff-of-interest-rates-with-monetary.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/interest-rate-liftoff-followed-by.html and earlier http://cmpassocregulationblog.blogspot.com/2015/10/interest-rate-policy-quagmire-world.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-increase-on-hold-because.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/global-decline-of-values-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/07/fluctuating-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/fluctuating-financial-asset-valuations.html and earlier http://cmpassocregulationblog.blogspot.com/2015/05/interest-rate-policy-and-dollar.html and earlier http://cmpassocregulationblog.blogspot.com/2015/04/global-portfolio-reallocations-squeeze.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2015/01/competitive-currency-conflicts-world.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/patience-on-interest-rate-increases.html and earlier (http://cmpassocregulationblog.blogspot.com/2014/11/squeeze-of-economic-activity-by-carry.html and earlier http://cmpassocregulationblog.blogspot.com/2014/10/financial-oscillations-world-inflation.html http://cmpassocregulationblog.blogspot.com/2014/09/world-inflation-waves-squeeze-of.html and earlier http://cmpassocregulationblog.blogspot.com/2014/08/monetary-policy-world-inflation-waves.html http://cmpassocregulationblog.blogspot.com/2014/07/world-inflation-waves-united-states.html)
• Deteriorating terms of trade and net revenue margins of production across countries in squeeze of economic activity by carry trades induced by zero interest rates (Section II and earlier https://cmpassocregulationblog.blogspot.com/2018/09/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/world-inflation-waves-lost-economic.html and earlier https://cmpassocregulationblog.blogspot.com/2018/07/continuing-gradual-increases-in-fed.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/05/dollar-revaluation-united-states_24.html and earlier https://cmpassocregulationblog.blogspot.com/2018/04/rising-yields-world-inflation-waves.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/decreasing-valuations-of-risk-financial.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/united-states-inflation-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/02/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/01/dollar-devaluation-and-increasing.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/mediocre-cyclical-united-states_23.html and earlier https://cmpassocregulationblog.blogspot.com/2017/11/the-lost-economic-cycle-of-global_25.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/world-inflation-waves-long-term-and.html and earlier https://cmpassocregulationblog.blogspot.com/2017/09/monetary-policy-of-reducing-central.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/fluctuating-valuations-of-risk.html and earlier https://cmpassocregulationblog.blogspot.com/2017/07/dollar-devaluation-and-valuation-of.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/fomc-interest-rate-increase-planned.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/dollar-devaluation-world-inflation.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/united-states-commercial-banks-assets.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/fomc-increases-interest-rates-world.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/world-inflation-waves-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/of-course-economic-outlook-is-highly.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/interest-rate-increase-could-well.html and earlier http://cmpassocregulationblog.blogspot.com/2016/10/dollar-revaluation-world-inflation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/interest-rates-and-volatility-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/unresolved-us-balance-of-payments.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/fomc-projections-world-inflation-waves.html and earlier http://cmpassocregulationblog.blogspot.com/2016/05/most-fomc-participants-judged-that-if.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/imf-view-of-world-economy-and-finance.html and earlier) (http://cmpassocregulationblog.blogspot.com/2016/03/monetary-policy-and-competitive.html and earlier http://cmpassocregulationblog.blogspot.com/2016/02/squeeze-of-economic-activity-by-carry.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/uncertainty-of-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/12/liftoff-of-interest-rates-with-monetary.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/interest-rate-liftoff-followed-by.html http://cmpassocregulationblog.blogspot.com/2015/10/interest-rate-policy-quagmire-world.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-increase-on-hold-because.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/global-decline-of-values-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/07/fluctuating-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/fluctuating-financial-asset-valuations.html and earlier http://cmpassocregulationblog.blogspot.com/2015/04/global-portfolio-reallocations-squeeze.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/impatience-with-monetary-policy-of.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/world-financial-turbulence-squeeze-of.html http://cmpassocregulationblog.blogspot.com/2015/01/exchange-rate-conflicts-squeeze-of.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/patience-on-interest-rate-increases.html and earlier http://cmpassocregulationblog.blogspot.com/2014/11/squeeze-of-economic-activity-by-carry.html and earlier http://cmpassocregulationblog.blogspot.com/2014/10/imf-view-squeeze-of-economic-activity.html and earlier http://cmpassocregulationblog.blogspot.com/2014/09/world-inflation-waves-squeeze-of.html
• Financial repression of interest rates and credit affecting the most people without means and access to sophisticated financial investments with likely adverse effects on income distribution and wealth disparity (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/08/revision-of-united-states-national.html and earlier https://cmpassocregulationblog.blogspot.com/2018/07/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/stronger-dollar-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2018/05/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/04/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/04/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/twenty-three-million-unemployed-or.html and earlier (https://cmpassocregulationblog.blogspot.com/2018/02/twenty-four-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/dollar-devaluation-cyclically.html and earlier (https://cmpassocregulationblog.blogspot.com/2017/12/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/11/unchanged-fomc-policy-rate-gradual.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/09/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/08/data-dependent-monetary-policy-with.html and earlier https://cmpassocregulationblog.blogspot.com/2017/07/rising-yields-twenty-two-million.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier (https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/rising-valuations-of-risk-financial.html and earlier https://cmpassocregulationblog.blogspot.com/2017/02/twenty-six-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html and earlier http://cmpassocregulationblog.blogspot.com/2016/10/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/interest-rates-and-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2016/08/global-competitive-easing-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/financial-asset-values-rebound-from.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/financial-turbulence-twenty-four.html and earlier http://cmpassocregulationblog.blogspot.com/2016/05/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/proceeding-cautiously-in-monetary.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/twenty-five-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/twenty-five-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/closely-monitoring-global-economic-and.html and earlier http://cmpassocregulationblog.blogspot.com/2015/12/dollar-revaluation-and-decreasing.html and earlier http://cmpassocregulationblog.blogspot.com/2015/11/dollar-revaluation-constraining.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/11/live-possibility-of-interest-rates.html and earlier http://cmpassocregulationblog.blogspot.com/2015/10/labor-market-uncertainty-and-interest.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/interest-rate-policy-dependent-on-what.html and earlier http://cmpassocregulationblog.blogspot.com/2015/08/fluctuating-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html and earlier http://cmpassocregulationblog.blogspot.com/2015/05/dollar-devaluation-and-carry-trade.html and earlier http://cmpassocregulationblog.blogspot.com/2015/04/volatility-of-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html and earlier (http://cmpassocregulationblog.blogspot.com/2014/12/valuations-of-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2014/11/valuations-of-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2014/11/growth-uncertainties-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2014/10/world-financial-turbulence-twenty-seven.html)
• 43 million in poverty and 29 million without health insurance with family income adjusted for inflation regressing to 1999 levels (http://cmpassocregulationblog.blogspot.com/2016/09/the-economic-outlook-is-inherently.html and earlier http://cmpassocregulationblog.blogspot.com/2015/10/interest-rate-policy-uncertainty-imf.html and earlier http://cmpassocregulationblog.blogspot.com/2014/09/financial-volatility-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2013/09/duration-dumping-and-peaking-valuations.html)
• Net worth of households and nonprofits organizations increasing by 30.3 percent after adjusting for inflation in the entire cycle from IVQ2007 to IIQ2018 when it would have grown over 39.2 percent at trend of 3.2 percent per year in real terms from IVQ1945 to IIQ2018 (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/06/world-inflation-waves-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2018/03/mediocre-cyclical-united-states_31.html and earlier https://cmpassocregulationblog.blogspot.com/2017/12/dollar-devaluation-cyclically.html and earlier https://cmpassocregulationblog.blogspot.com/2017/10/destruction-of-household-nonfinancial.html and earlier https://cmpassocregulationblog.blogspot.com/2017/06/united-states-commercial-banks-united.html and earlier (https://cmpassocregulationblog.blogspot.com/2017/03/recovery-without-hiring-ten-million.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2016/09/the-economic-outlook-is-inherently.html and earlier http://cmpassocregulationblog.blogspot.com/2016/06/of-course-considerable-uncertainty.html and earlier http://cmpassocregulationblog.blogspot.com/2016/03/monetary-policy-and-fluctuations-of_13.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/weakening-equities-and-dollar.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/monetary-policy-designed-on-measurable.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/fluctuating-financial-asset-valuations.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/valuations-of-risk-financial-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2014/09/financial-volatility-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2014/06/financial-indecision-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2014/03/global-financial-risks-recovery-without.html and earlier http://cmpassocregulationblog.blogspot.com/2013/12/collapse-of-united-states-dynamism-of.html). Financial assets increased $32.9 trillion while nonfinancial assets increased $6.9 trillion with likely concentration of wealth in those with access to sophisticated financial investments. Real estate assets adjusted for inflation increased 3.6 percent.

The Bureau of Labor Statistics (BLS) revised on Mar 17, 2016 “With the release of January 2016 data on March 17, job openings, hires, and separations data have been revised from December 2000 forward to incorporate annual updates to the Current Employment Statistics employment estimates and the Job Openings and Labor Turnover Survey (JOLTS) seasonal adjustment factors. In addition, all data series are now available on a seasonally adjusted basis. Tables showing the revisions from 2000 through 2015 can be found using this link: http://www.bls.gov/jlt/revisiontables.htm.” (https://www.bls.gov/jlt/). The Bureau of Labor Statistics (BLS) revised on Mar 16, 2017: “With the release of January 2017 data on March 16, job openings, hires, and separations data have been revised to incorporate annual updates to the Current Employment Statistics employment estimates and the Job Openings and Labor Turnover Survey (JOLTS) seasonal adjustment factors” (https://www.bls.gov/jlt/revisiontables.htm) (https://www.bls.gov/jlt/). The Bureau of Labor Statistics (BLS) revised on Mar 16, 2018 “With the release of January 2018 data on March 16, job openings, hires, and separations data have been revised to incorporate annual updates to the Current Employment Statistics employment estimates and the Job Openings and Labor Turnover Survey (JOLTS) seasonal adjustment factors. Tables showing the revisions from 2013 through 2017 can be found using this link: https://www.bls.gov/jlt/revisiontables.htm (https://www.bls.gov/jlt/).“ The Bureau of Labor Statistics incorporated corrections on Sep 11, 2018: “This news release contains corrections to previously released data in tables A, 1-3, 5, 7-9, and 11. More information on these corrections as well as a complete list of corrections in this news release and in the JOLTS database can be found at www.bls.gov/bls/errata/jolts-errata-06122018.htm.” (https://www.bls.gov/news.release/pdf/jolts.pdf). Hiring in the nonfarm sector (HNF) has increased from 63.491 million in 2006 to 65.144 million in 2017 or by 1.653 million while hiring in the private sector (HP) has increased from 59.206 million in 2006 to 61.122 million in 2017 or by 1.916 million, as shown in Table I-1. The ratio of nonfarm hiring to employment (RNF) has fallen from 47.1 in 2005 to 44.4 in 2017 and in the private sector (RHP) from 52.8 in 2005 to 49.2 in 2017. Hiring has not recovered as in previous cyclical expansions because of the low rate of economic growth in the current cyclical expansion. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 255.079 million in 2017 or by 26.264 million. Hiring has not recovered prerecession levels while needs of hiring multiplied because of growth of population by more than 26 million. Private hiring of 59.206 million in 2006 was equivalent to 25.9 percent of the civilian noninstitutional population of 228.815, or those in condition of working, increasing to 61.122 million in 2017 or 24.0 percent of the civilian noninstitutional population of 255.079 million in 2017. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 66.065 million of hiring in 2017 (0.259x255.079), which would be 4.943 million higher than actual 61.122 million in 2017. 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 2.3 percent on average in the cyclical expansion in the 36 quarters from IIIQ2009 to IIQ2018. 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 IIQ2018 (https://www.bea.gov/system/files/2018-09/gdp2q18_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/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.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 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://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 (http://www.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 IIQ2018 would have accumulated to 36.4 percent. GDP in IIQ2018 would be $21,499.4 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $2987.8 billion than actual $18,511.6 billion. There are about two trillion dollars of GDP less than at trend, explaining the 20.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 12.1 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html). US GDP in IIQ2018 is 13.9 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,511.6 billion in IIQ2018 or 17.4 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 Sep 1919 to Sep 2018. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.3221 in Dec 2007 to 150.3999 in Sep 2018. The actual index NSA in Sep 2018 is 106.1908, which is 29.4 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Sep 2018. Using trend growth of 2.1 percent per year, the index would increase to 135.4390 in Sep 2018. The output of manufacturing at 106.1908 in Sep 2018 is 21.6 percent below trend under this alternative calculation.

Table I-1, US, Annual Total Nonfarm Hiring (HNF) and Total Private Hiring (HP) in the US in Thousands and Percentage of Total Employment

	HNF	Rate RNF	HP	Rate HP
2001	62,727	47.5	58,616	52.8
2002	58,416	44.7	54,592	50.0
2003	56,919	43.7	53,529	49.2
2004	60,236	45.7	56,567	51.3
2005	63,089	47.1	59,298	52.8
2006	63,491	46.5	59,206	51.7
2007	62,239	45.1	57,816	49.9
2008	54,764	39.9	51,260	44.7
2009	46,190	35.2	42,882	39.4
2010	48,659	37.3	44,831	41.6
2011	50,220	38.1	47,166	42.9
2012	52,294	39.0	48,898	43.6
2013	54,212	39.8	50,866	44.4
2014	58,556	42.1	54,985	47.0
2015	61891	43.6	57,899	48.3
2016	63,116	43.7	58,936	48.3
2017	65,144	44.4	61,122	49.2

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-1 shows the annual level of total nonfarm hiring (HNF) that collapsed during the global recession after 2007 in contrast with milder decline in the shallow recession of 2001. Nonfarm hiring has not recovered, remaining at a depressed level. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 255.079 million in 2017 or by 26.264 million. Hiring has not recovered precession levels while needs of hiring multiplied because of growth of population by more than 26 million.

Chart I-1, US, Level Total Nonfarm Hiring (HNF), Annual, 2001-2017

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-2 shows the ratio or rate of nonfarm hiring to employment (RNF) that also fell much more in the recession of 2007 to 2009 than in the shallow recession of 2001. Recovery is weak in the current environment of cyclical slow growth.

Chart I-2, US, Rate Total Nonfarm Hiring (HNF), Annual, 2001-2017

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Yearly percentage changes of total nonfarm hiring (HNF) are provided in Table I-2. There were much milder declines in 2002 of 6.9 percent and 2.6 percent in 2003 followed by strong rebounds of 5.8 percent in 2004 and 4.7 percent in 2005. In contrast, the contractions of nonfarm hiring in the recession after 2007 were much sharper in percentage points: 2.0 in 2007, 12.0 in 2008 and 15.7 percent in 2009. On a yearly basis, nonfarm hiring grew 5.3 percent in 2010 relative to 2009, 3.2 percent in 2011, 4.1 percent in 2012 and 3.7 percent in 2013. Nonfarm hiring grew 8.0 percent in 2014 and increased 5.7 percent in 2015. Nonfarm hiring grew 2.0 percent in 2016, increasing 3.2 percent in 2017. The relatively large length of 36 quarters of the current expansion reduces the likelihood of significant recovery of hiring levels in the United States because lower rates of growth and hiring in the final phase of expansions.

Table I-2, US, Annual Total Nonfarm Hiring (HNF), Annual Percentage Change, 2002-2017

Year	Annual ∆%
2002	-6.9
2003	-2.6
2004	5.8
2005	4.7
2006	0.6
2007	-2.0
2008	-12.0
2009	-15.7
2010	5.3
2011	3.2
2012	4.1
2013	3.7
2014	8.0
2015	5.7
2016	2.0
2017	3.2

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Total private hiring (HP) annual data are in Chart I-5. There has been sharp contraction of total private hiring in the US and only milder recovery from 2010 to 2017.

Chart I-5, US, Total Private Hiring, Annual, 2001-2017

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-5A shows the ratio or rate of total private hiring to employment (RHP) that also fell much more in the recession of 2007 to 2009 than in the shallow recession of 2001. Recovery is weak in the current environment of cyclical slow growth.

Chart I-5A, US, Rate Total Private Hiring, Annual, 2001-2017

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Total nonfarm hiring (HNF), total private hiring (HP) and their respective rates are in Table I-3 for the month of Aug in the years from 2001 to 2018. Hiring numbers are in thousands. There is recovery in HNF from 4185 thousand in Aug 2009 to 4317 thousand in Aug 2010, 4600 thousand in Aug 2011, 4907 thousand in Aug 2012, 5247 thousand in Aug 2013, 5270 thousand in Aug 2014, 5698 thousand in Aug 2015, 5885 thousand in Aug 2016, 6116 thousand in Aug 2017 and 6440 thousand in Aug 2018 for cumulative gain of 53.9 percent at the average yearly rate of 4.9 percent. HP rose from 3702 thousand in Aug 2009 to 3888 thousand in Aug 2010, 4189 thousand in Aug 2011, 4390 thousand in Aug 2012, 4719 thousand in Aug 2013, 4803 thousand in Aug 2014, 5056 thousand in Aug 2015, 5259 thousand in Aug 2016, 5504 thousand in Aug 2017 and 5805 thousand in Aug 2018 for cumulative gain of 56.8 percent at the average yearly rate of 5.1 percent. HNF has increased from 5772 thousand in Aug 2006 to 6440 thousand in Aug 2018 or by 11.6 percent. HP has increased from 5142 thousand in Aug 2006 to 5805 thousand in Aug 2018 or by 12.9 percent. The civilian noninstitutional population of the US, or those in condition of working, rose from 229.167 million in Aug 2006 to 258.066 million in Aug 2018, by 28.899 million or 12.6 percent. There is often ignored ugly fact that hiring increased by around 12.9 percent while population available for working increased around 12.6 percent. Private hiring of 59.206 million in 2006 was equivalent to 25.9 percent of the civilian noninstitutional population of 228.815, or those in condition of working, increasing to 61.122 million in 2017 or 24.0 percent of the civilian noninstitutional population of 255.079 million in 2017. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 66.065 million of hiring in 2016 (0.259x255.079), which would be 4.943 million higher than actual 61.122 million in 2017. 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. Cyclical slow growth over the entire business cycle from IVQ2007 to the present in comparison with earlier cycles and long-term trend (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html) explains the fact that there are many million fewer hires in the US than before the global recession. The labor market continues to be fractured, failing to provide an opportunity to exit from unemployment/underemployment or to find an opportunity for advancement away from declining inflation-adjusted earnings.

Table I-3, US, Total Nonfarm Hiring (HNF) and Total Private Hiring (HP) in the US in

Thousands and in Percentage of Total Employment Not Seasonally Adjusted

	HNF	Rate RNF	HP	Rate HP
2001 Aug	5421	4.1	4867	4.4
2002 Aug	5238	4.0	4696	4.3
2003 Aug	4985	3.8	4621	4.2
2004 Aug	5540	4.2	5057	4.5
2005 Aug	5903	4.4	5394	4.7
2006 Aug	5772	4.2	5142	4.4
2007 Aug	5662	4.1	5011	4.3
2008 Aug	4964	3.6	4504	3.9
2009 Aug	4185	3.2	3702	3.4
2010 Aug	4317	3.3	3888	3.6
2011 Aug	4600	3.5	4189	3.8
2012 Aug	4907	3.7	4390	3.9
2013 Aug	5247	3.8	4719	4.1
2014 Aug	5270	3.8	4803	4.1
2015 Aug	5698	4.0	5056	4.2
2016 Aug	5885	4.1	5259	4.3
2017 Aug	6116	4.2	5504	4.4
2018 Aug	6440	4.3	5805	4.5

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-6 provides total nonfarm hiring monthly from 2001 to 2018. Nonfarm hiring rebounded in early 2010 but then fell and stabilized at a lower level than the early peak not-seasonally adjusted (NSA) of 4815 in May 2010 until it surpassed it with 5004 in Jun 2011 but declined to 3087 in Dec 2012. Nonfarm hiring fell to 2991 in Dec 2011 from 3808 in Nov 2011 and to revised 3624 in Feb 2012, increasing to 4177 in Mar 2012, 3087 in Dec 2012 and 4269 in Jan 2013 and declining to 3669 in Feb 2013. Nonfarm hires not seasonally adjusted increased to 4210 in Nov 2013 and 3262 in Dec 2013. Nonfarm hires reached 3732 in Dec 2014, 4055 in Dec 2015 and 3882 in Dec 2016. Nonfarm hires reached 4103 in Dec 2017. Chart I-6 provides seasonally adjusted (SA) monthly data. The number of seasonally-adjusted hires in Oct 2011 was 4236 thousand, increasing to revised 4416 thousand in Feb 2012, or 4.2 percent, moving to 4350 in Dec 2012 for cumulative increase of 2.9 percent from 4228 in Dec 2011 and 4537 in Dec 2013 for increase of 4.3 percent relative to 4350 in Dec 2012. The number of hires not seasonally adjusted was 5004 in Jun 2011, falling to 2991 in Dec 2011 but increasing to 4139 in Jan 2012 and declining to 3087 in Dec 2012. The number of nonfarm hiring not seasonally adjusted fell by 40.2 percent from 5004 in Jun 2011 to 2991 in Dec 2011 and fell 40.0 percent from 5148 in Jun 2012 to 3087 in Dec 2012 in a yearly-repeated seasonal pattern. The number of nonfarm hires not seasonally adjusted fell from 5069 in Jun 2013 to 3262 in Dec 2013, or decline of 35.6 percent, showing strong seasonality. The number of nonfarm hires not seasonally adjusted fell from 5504 in Jun 2014 to 3732 in Dec 2014 or 32.2 percent. The level of nonfarm hires fell from 5849 in Jun 2015 to 4055 in Dec 2015 or 30.7percent. The level of nonfarm hires not seasonally adjusted fell from 6000 in Jun 2016 to 3882 in Dec 2016 or 35.3 percent. The level of nonfarm hires not seasonally adjusted fell from 6265 in Jun 2017 to 4103 in Dec 2017 or 34.5 percent.

Chart I-6, US, Total Nonfarm Hiring (HNF), 2001-2018 Month SA

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Similar behavior occurs in the rate of nonfarm hiring in Chart I-7. Recovery in early 2010 was followed by decline and stabilization at a lower level but with stability in monthly SA estimates of 3.2 in Aug 2011 to 3.2 in Jan 2012, increasing to 3.3 in May 2012 and stabilizing to 3.3 in Jun 2012. The rate stabilized at 3.2 in Jul 2012, increasing to 3.3 in Aug 2012 but falling to 3.2 in Dec 2012 and 3.3 in Dec 2013. The rate not seasonally adjusted fell from 3.8 in Jun 2011 to 2.2 in Dec 2011, climbing to 3.8 in Jun 2012 but falling to 2.3 in Dec 2012. The rate of nonfarm hires not seasonally adjusted fell from 3.7 in Jun 2013 to 2.4 in Dec 2013. The NSA rate of nonfarm hiring fell from 3.9 in Jun 2014 to 2.6 in Dec 2014. The NSA rate fell from 4.1 in Jun 2015 to 2.8 in Dec 2015. The NSA rate fell from 4.1 in Jun 2016 to 2.7 in Dec 2016. The NSA rate fell from 4.1 in Jun 2016 to 2.7 in Dec 2016. The NSA rate fell from 4.2 in Jun 2017 to 2.8 in Dec 2017. Rates of nonfarm hiring NSA were in the range of 2.7 (Dec) to 4.4 (Jun) in 2006. The rate of nonfarm hiring SA stood at 3.9 in Aug 2018 and at 4.3 NSA.

Chart I-7, US, Rate Total Nonfarm Hiring, Month SA 2001-2018

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

There is only milder improvement in total private hiring shown in Chart I-8. Hiring private (HP) rose in 2010 with stability and renewed increase in 2011 followed by almost stationary series in 2012. The number of private hiring seasonally adjusted fell from 4043 thousand in Sep 2011 to 3933 in Dec 2011 or by 2.7 percent, decreasing to 4014 in Jan 2012 or decline by 0.7 percent relative to the level in Sep 2011. Private hiring fell to 3959 in Sep 2012 or lower by 2.1 percent relative to Sep 2011, moving to 4063 in Dec 2012 for increase of 1.2 percent relative to 4014 in Jan 2012. The number of private hiring not seasonally adjusted fell from 4626 in Jun 2011 to 2817 in Dec 2011 or by 39.1 percent, reaching 3885 in Jan 2012 or decline of 16.0 percent relative to Jun 2011 and moving to 2918 in Dec 2012 or 38.5 percent lower relative to 4745 in Jun 2012. Hires not seasonally adjusted fell from 4727 in Jun 2013 to 3103 in Dec 2013, or 34.4 percent. The level of private hiring NSA fell from 5102 in Jun 2014 to 3539 in Dec 2014 or 30.6 percent. The level of private hiring fell from 5437 in Jun 2015 to 3841 in Dec 2015 or 29.4 percent. The level of private hiring not seasonally adjusted fell from 5570 in Jun 2016 to 3697 in Dec 2016 or 33.6 percent. The level of private hiring not seasonally adjusted fell from 5841 in Jun 2017 to 3899 in Dec 2017 or 33.2 percent. Companies reduce hiring in the latter part of the year that explains the high seasonality in year-end employment data. For example, NSA private hiring fell from 5614 in Jun 2006 to 3579 in Dec 2006 or by 36.2 percent. Private hiring NSA data are useful in showing the huge declines from the period before the global recession. Hiring in the nonfarm sector (HNF) has increased from 63.491 million in 2006 to 65.259 million in 2017 or by 1.768 million while hiring in the private sector (HP) has increased from 59.206 million in 2006 to 61.122 million in 2017 or by 1.916 million, as shown in Table I-1. The ratio of nonfarm hiring to employment (RNF) has fallen from 47.1 in 2005 to 44.5 in 2017 and in the private sector (RHP) from 52.8 in 2005 to 49.2 in 2017. Hiring has not recovered as in previous cyclical expansions because of the low rate of economic growth in the current cyclical expansion. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 255.079 million in 2017 or by 26.264 million. Hiring has not recovered prerecession levels while needs of hiring multiplied because of growth of population by more than 26 million. Private hiring of 59.206 million in 2006 was equivalent to 25.9 percent of the civilian noninstitutional population of 228.815, or those in condition of working, increasing to 61.122 million in 2017 or 24.0 percent of the civilian noninstitutional population of 255.079 million in 2016. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 66.065 million of hiring in 2017 (0.259x255.079), which would be 4.943 million higher than actual 61.122 million in 2017.

Chart I-8, US, Total Private Hiring Month SA 2001-2018

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-9 shows similar behavior in the rate of private hiring. The rate in 2011 in monthly SA data did not rise significantly above the peak in 2010. The rate seasonally adjusted fell from 3.7 in Sep 2011 to 3.5 in Dec 2011 and reached 3.6 in Dec 2012 and 3.7 in Dec 2013. The rate not seasonally adjusted (NSA) fell from 3.7 in Sep 2011 to 2.5 in Dec 2011, increasing to 3.8 in Oct 2012 but falling to 2.6 in Dec 2012 and 3.4 in Mar 2013. The NSA rate of private hiring fell from 4.8 in Jul 2006 to 3.4 in Aug 2009 but recovery was insufficient to only 3.9 in Aug 2012, 2.6 in Dec 2012 and 2.7 in Dec 2013. The NSA rate increased to 3.2 in Dec 2015 and 3.0 in Dec

2016. The rate NSA reached 3.1 in Dec 2017. The rate NSA stood at 4.5 in Aug 2018.

Chart I-9, US, Rate Total Private Hiring Month SA 2001-2018

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

The JOLTS report of the Bureau of Labor Statistics also provides total nonfarm job openings (TNF JOB), TNF JOB rate and TNF LD (layoffs and discharges) shown in Table I-4 for the month of Aug from 2001 to 2018. The final column provides annual TNF LD for the years from 2001 to 2017. Nonfarm job openings (TNF JOB) increased from 4586 in Aug 2007 to 7291 in Aug 2018 or by 59.0 percent while the rate increased from 3.2 to 4.7. This was mediocre performance because the civilian noninstitutional population of the US, or those in condition of working rose from 232.211 million in Aug 2007 to 258.066 million in Aug 2018, by 25.855 million or 11.1 percent. Nonfarm layoffs and discharges (TNF LD) increased from 1742 in Aug 2006 to 2265 in Jul 2009 or 30.0 percent. The annual data show layoffs and discharges rising from 20.9 million in 2006 to 26.6 million in 2009 or by 27.3 percent. Business pruned payroll jobs to survive the global recession but there has not been hiring because of the low rate of GDP growth. 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.

Table I-4, US, Total Nonfarm Job Openings and Total Nonfarm Layoffs and Discharges, Thousands NSA

	TNF JOB	TNF JOB Rate	TNF LD	TNF LD Annual
Aug 2001	4143	3.0	1976	24271
Aug 2002	3504	2.6	1939	22719
Aug 2003	3293	2.5	2121	23420
Aug 2004	3637	2.7	2085	22584
Aug 2005	4096	3.0	2044	22151
Aug 2006	4587	3.3	1742	20856
Aug 2007	4586	3.2	1960	21997
Aug 2008	3699	2.6	2238	23969
Aug 2009	2356	1.8	2265	26557
Aug 2010	2961	2.2	1935	21703
Aug 2011	3244	2.4	1882	20740
Aug 2012	3739	2.7	2004	20937
Aug 2013	4027	2.9	1913	19859
Aug 2014	5067	3.5	1844	20375
Aug 2015	5494	3.7	1896	20927
Aug 2016	5641	3.8	1899	20114
Aug 2017	6164	4.0	2060	20642
Aug 2018	7291	4.7	2038

Notes: TNF JOB: Total Nonfarm Job Openings; LD: Layoffs and Discharges

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-10 shows monthly job openings rising from the trough in 2009 to a high in the beginning

of 2010. Job openings then stabilized into 2011 but have surpassed the peak of 3220 seasonally adjusted in Apr 2010 with 3570 seasonally adjusted in Dec 2012, which is higher by 10.9 percent relative to Apr 2010 but higher by 0.8 percent relative to 3542 in Nov 2012 and lower by 6.8 percent than 3830 in Mar 2012. Nonfarm job openings increased from 3570 in Dec 2012 to 3782 in Dec 2013 or by 5.9 percent and to 4819 in Dec 2014 or 27.4 percent relative to Dec 2013. The high of job openings not seasonally adjusted was 3408 in Apr 2010 that was surpassed by 3648 in Jul 2011, increasing to 3906 in Oct 2012 but declining to 3213 in Dec 2012 and increasing to 3406 in Dec 2013. The level of job opening NSA increased to 4973 in Dec 2015. The level of job opening NSA increased to 5048 in Dec 2016, reaching 5224 in Dec 2017. The level of job openings not seasonally adjusted fell to 3213 in Dec 2012 or by 17.4 percent relative to 3891 in Apr 2012. There is here again the strong seasonality of year-end labor data. Job openings fell from 4208 in Apr 2013 to 3406 in Dec 2013 and from 4852 in Apr 2014 to 4412 in Dec 2014, showing strong seasonal effects. The level of nonfarm job openings decreased from 5956 in Apr 2015 to 4973 in Dec 2015 or by 16.5 percent. The level of nonfarm job openings decreased from 6285 in Apr 2016 to 5048 in Dec 2016 or by 19.7 percent. The level of nonfarm job openings decreased from 6504 in Apr 2017 to 5224 in Dec 2017 or by 19.7 percent. Nonfarm job openings (TNF JOB) increased from 4586 in Aug 2007 to 7291 in Aug 2018 or by 59.0 percent while the rate increased from 3.2 to 4.7. This was mediocre performance because the civilian noninstitutional population of the US, or those in condition of working rose from 232.211 million in Aug 2007 to 258.066 million in Aug 2018, by 25.855 million or 11.1 percent. Nonfarm layoffs and discharges (TNF LD) increased from 1742 in Aug 2006 to 2265 in Jul 2009 or 30.0 percent. The annual data show layoffs and discharges rising from 20.9 million in 2006 to 26.6 million in 2009 or by 27.3 percent. Business pruned payroll jobs to survive the global recession but there has not been hiring because of the low rate of GDP growth. 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. 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 2.3 percent on average in the cyclical expansion in the 36 quarters from IIIQ2009 to IIQ2018. 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 IIQ2018 (https://www.bea.gov/system/files/2018-09/gdp2q18_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/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.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 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://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 (http://www.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 IIQ2018 would have accumulated to 36.4 percent. GDP in IIQ2018 would be $21,499.4 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $2987.8 billion than actual $18,511.6 billion. There are about two trillion dollars of GDP less than at trend, explaining the 20.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 12.1 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html). US GDP in IIQ2018 is 13.9 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,511.6 billion in IIQ2018 or 17.4 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 Sep 1919 to Sep 2018. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.3221 in Dec 2007 to 150.3999 in Sep 2018. The actual index NSA in Sep 2018 is 106.1908, which is 29.4 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Sep 2018. Using trend growth of 2.1 percent per year, the index would increase to 135.4390 in Sep 2018. The output of manufacturing at 106.1908 in Sep 2018 is 21.6 percent below trend under this alternative calculation.

Chart I-10, US Job Openings, Thousands NSA, 2001-2018

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

The rate of job openings in Chart I-11 shows similar behavior. The rate seasonally adjusted increased from 2.2 in Jan 2011 to 2.5 in Dec 2011, 2.6 in Dec 2012, 2.7 in Dec 2013 and 3.3 in Dec 2014. The rate seasonally adjusted stood at 3.6 in Dec 2015 and 3.6 in Dec 2016. The rate seasonally adjusted reached 3.7 in Dec 2017. The rate SA stood at 4.6 in Aug 2018. The rate not seasonally adjusted rose from the high of 2.6 in Apr 2010 to 3.0 in Apr 2013, easing to 2.4 in Dec 2013. The rate of job openings NSA fell from 3.3 in Jul 2007 to 1.6 in Nov-Dec 2009, recovering to 3.3 in Dec 2015. The rate of job opening NSA stood at 3.3 in Dec 2016, reaching 3.4 in Dec 2017. The rate of job openings NSA was 4.7 in Aug 2018.

Chart I-11, US, Rate of Job Openings, NSA, 2001-2018

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Total separations are in Chart I-12. Separations are lower in 2012-18 than before the global recession but hiring has not recovered.

Chart I-12, US, Total Nonfarm Separations, Month Thousands SA, 2001-2018

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-13 provides annual total separations. Separations fell sharply during the global recession but hiring has not recovered relative to population growth.

Chart I-13, US, Total Separations, Annual, Thousands, 2001-2017

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Table I-5 provides total nonfarm total separations from 2001 to 2017. Separations fell from 61.3 million in 2006 to 47.6 million in 2010 or by 13.6 million and 48.2 million in 2011 or by 13.1 million. Total separations increased from 48.2 million in 2011 to 51.9 million in 2013 or by 3.7 million and to 55.6 million in 2014 or by 7.4 million relative to 2011. Total separations increased to 59.3 million in 2015 or by 11.1 million relative to 2011. Total separations increased to 60.858 million in 2016 or 12.7 million relative to 2011. Total separations increased to 62.997 million in 2017 or 14.8 million relative to 2011.

Table I-5, US, Total Nonfarm Total Separations, Thousands, 2001-2017

Year	Annual
2001	64560
2002	58942
2003	56961
2004	58224
2005	60633
2006	61284
2007	60984
2008	58209
2009	51358
2010	47649
2011	48198
2012	50126
2013	51907
2014	55569
2015	59260
2016	60858
2017	62997

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Total separations are in Chart I-12. Separations are lower in 2012-18 than before the global recession but hiring has not recovered.

Chart I-12, US, Total Nonfarm Separations, Month Thousands SA, 2001-2018

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-13 provides annual total separations. Separations fell sharply during the global recession but hiring has not recovered relative to population growth.

Chart I-13, US, Total Separations, Annual, Thousands, 2001-2017

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Monthly data of layoffs and discharges reach a peak in early 2009, as shown in Chart I-14. Layoffs and discharges dropped sharply with the recovery of the economy in 2010 and 2011 once employers reduced their job count to what was required for cost reductions and loss of business. 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. Growth rates have been unusually low in the expansion of the current economic cycle.

Chart I-14, US, Total Nonfarm Layoffs and Discharges, Monthly Thousands SA, 2001-2018

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Layoffs and discharges in Chart I-15 rose sharply to a peak in 2009. There was pronounced drop into 2010 and 2011 with mild increase into 2012 and renewed decline into 2013. There is mild increase into 2014-2015 followed by decline in 2016 and increase in 2017.

Chart I-15, US, Total Nonfarm Layoffs and Discharges, Annual, 2001-2017

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Annual layoff and discharges are in Table I-6. Layoffs and discharges increased sharply from 20.856 million in 2006 to 26.557 million in 2009 or 27.3 percent. Layoff and discharges fell to 19.859 million in 2013 or 25.2 percent relative to 2009 and increased to 20.375 million in 2014 or 2.6 percent relative to 2013. Layoffs and discharges increased to 20.927 million in 2015 or 2.7 percent relative to 2014. Layoffs and discharges fell to 20.114 in 2016 or 3.9 percent relative to 2015. Layoffs and discharges increased to 20.642 million in 2017 or 2.6 percent relative to 2016.

Table I-6, US, Total Nonfarm Layoffs and Discharges, Thousands, 2001-2017

Year	Annual
2001	24271
2002	22719
2003	23420
2004	22584
2005	22151
2006	20856
2007	21997
2008	23969
2009	26557
2010	21703
2011	20740
2012	20937
2013	19859
2014	20375
2015	20927
2016	20114
2017	20642

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

IA2 Labor Underutilization. The Bureau of Labor Statistics also provides alternative measures of labor underutilization shown in Table I-7. The most comprehensive measure is U6 that consists of total unemployed plus total employed part time for economic reasons plus all marginally attached workers as percent of the labor force. U6 not seasonally adjusted has moved from 8.2 percent in 2006 to 7.1 percent in Aug 2018.

Table I-7, US, Alternative Measures of Labor Underutilization NSA %

	U1	U2	U3	U4	U5	U6
2018
Sep	1.3	1.5	3.6	3.8	4.5	7.1
Aug	1.3	1.8	3.9	4.2	4.8	7.4
Jul	1.4	1.9	4.1	4.4	5.0	7.9
Jun	1.3	1.9	4.2	4.4	5.0	8.1
May	1.4	1.5	3.6	3.8	4.4	7.3
Apr	1.5	1.7	3.7	3.9	4.5	7.4
Mar	1.5	2.1	4.1	4.4	5.0	8.1
Feb	1.6	2.3	4.4	4.6	5.3	8.6
Jan	1.5	2.4	4.5	4.8	5.5	8.9
2017
Dec	1.4	2.1	3.9	4.2	4.9	8.0
Nov	1.5	1.8	3.9	4.2	4.8	7.7
Oct	1.5	1.8	3.9	4.2	4.8	7.6
Sep	1.6	1.9	4.1	4.3	5.0	8.0
Aug	1.7	2.2	4.5	4.8	5.4	8.6
Jul	1.6	2.2	4.6	4.9	5.5	8.9
Jun	1.5	2.1	4.5	4.8	5.4	8.9
May	1.8	1.9	4.1	4.3	5.0	8.1
Apr	1.8	2.1	4.1	4.4	5.0	8.1
Mar	1.9	2.4	4.6	4.8	5.5	8.9
Feb	1.9	2.6	4.9	5.3	6.0	9.5
Jan	2.0	2.7	5.1	5.5	6.2	10.1
2016
Dec	1.9	2.3	4.5	4.8	5.5	9.1
Nov	1.8	2.1	4.4	4.8	5.6	9.0
Oct	1.9	2.1	4.7	5.0	5.7	9.2
Sep	1.9	2.2	4.8	5.1	5.9	9.3
Aug	1.8	2.4	5.0	5.3	6.0	9.7
Jul	1.9	2.4	5.1	5.5	6.3	10.1
Jun	1.9	2.3	5.1	5.4	6.1	9.9
May	2.0	2.1	4.5	4.9	5.6	9.4
Apr	2.2	2.3	4.7	5.0	5.7	9.3
Mar	2.3	2.6	5.1	5.5	6.1	9.9
Feb	2.2	2.7	5.2	5.6	6.3	10.1
Jan	2.1	2.7	5.3	5.7	6.5	10.5
2015
Dec	2.1	2.4	4.8	5.2	5.9	9.8
Nov	2.1	2.3	4.8	5.2	5.8	9.6
Oct	2.1	2.3	4.8	5.2	6.0	9.5
Sep	2.0	2.2	4.9	5.3	6.0	9.6
Aug	2.1	2.5	5.2	5.6	6.3	10.3
Jul	2.0	2.7	5.6	6.0	6.7	10.7
Jun	2.1	2.5	5.5	5.8	6.6	10.8
May	2.4	2.5	5.3	5.6	6.4	10.4
Apr	2.4	2.5	5.1	5.5	6.4	10.4
Mar	2.6	2.9	5.6	6.0	6.8	11.0
Feb	2.7	3.0	5.8	6.3	7.1	11.4
Jan	2.7	3.1	6.1	6.5	7.4	12.0
2014
Dec	2.5	2.8	5.4	5.8	6.7	11.1
Nov	2.7	2.7	5.5	5.9	6.8	11.0
Oct	2.7	2.6	5.5	6.0	6.8	11.1
Sep	2.7	2.7	5.7	6.2	7.1	11.3
Aug	2.8	3.0	6.3	6.7	7.5	12.0
Jul	2.8	3.1	6.5	7.0	7.8	12.6
Jun	2.8	3.0	6.3	6.7	7.5	12.4
May	3.1	3.0	6.1	6.5	7.3	11.7
Apr	3.3	3.2	5.9	6.3	7.2	11.8
Mar	3.7	3.7	6.8	7.2	8.1	12.8
Feb	3.6	3.9	7.0	7.5	8.4	13.1
Jan	3.5	4.0	7.0	7.5	8.6	13.5
2013
Dec	3.5	3.5	6.5	7.0	7.9	13.0
Nov	3.7	3.5	6.6	7.1	7.9	12.7
Oct	3.7	3.6	7.0	7.4	8.3	13.2
Sep	3.7	3.5	7.0	7.5	8.4	13.1
Aug	3.7	3.8	7.3	7.9	8.7	13.6
Jul	3.7	3.8	7.7	8.3	9.1	14.3
Jun	3.9	3.8	7.8	8.4	9.3	14.6
May	4.1	3.7	7.3	7.7	8.5	13.4
Apr	4.3	3.9	7.1	7.6	8.5	13.4
Mar	4.3	4.3	7.6	8.1	9.0	13.9
Feb	4.3	4.6	8.1	8.6	9.6	14.9
Jan	4.3	4.9	8.5	9.0	9.9	15.4
2012
Dec	4.2	4.3	7.6	8.3	9.2	14.4
Nov	4.2	3.9	7.4	7.9	8.8	13.9
Oct	4.3	3.9	7.5	8.0	9.0	13.9
Sep	4.2	4.0	7.6	8.0	9.0	14.2
Aug	4.3	4.4	8.2	8.7	9.7	14.6
Jul	4.3	4.6	8.6	9.1	10.0	15.2
Jun	4.5	4.4	8.4	8.9	9.9	15.1
May	4.7	4.3	7.9	8.4	9.3	14.3
Apr	4.8	4.3	7.7	8.3	9.1	14.1
Mar	4.9	4.8	8.4	8.9	9.7	14.8
Feb	4.9	5.1	8.7	9.3	10.2	15.6
Jan	4.9	5.4	8.8	9.4	10.5	16.2
2011
Dec	4.8	5.0	8.3	8.8	9.8	15.2
Nov	4.9	4.7	8.2	8.9	9.7	15.0
Oct	5.0	4.8	8.5	9.1	10.0	15.3
Sep	5.2	5.0	8.8	9.4	10.2	15.7
Aug	5.2	5.1	9.1	9.6	10.6	16.1
Jul	5.2	5.2	9.3	10.0	10.9	16.3
Jun	5.1	5.1	9.3	9.9	10.9	16.4
May	5.5	5.1	8.7	9.2	10.0	15.4
Apr	5.5	5.2	8.7	9.2	10.1	15.5
Mar	5.7	5.8	9.2	9.7	10.6	16.2
Feb	5.6	6.0	9.5	10.1	11.1	16.7
Jan	5.6	6.2	9.8	10.4	11.4	17.3
Dec  2010	5.4	5.9	9.1	9.9	10.7	16.6
Annual
2017	1.7	2.1	4.4	4.6	5.3	8.5
2016	2.0	2.3	4.9	5.2	5.9	9.6
2015	2.3	2.6	5.3	5.7	6.4	10.4
2014	3.0	3.1	6.2	6.6	7.5	12.0
2013	3.9	3.9	7.4	7.9	8.8	13.8
2012	4.5	4.4	8.1	8.6	9.5	14.7
2011	5.3	5.3	8.9	9.5	10.4	15.9
2010	5.7	6.0	9.6	10.3	11.1	16.7
2009	4.7	5.9	9.3	9.7	10.5	16.2
2008	2.1	3.1	5.8	6.1	6.8	10.5
2007	1.5	2.3	4.6	4.9	5.5	8.3
2006	1.5	2.2	4.6	4.9	5.5	8.2
2005	1.8	2.5	5.1	5.4	6.1	8.9
2004	2.1	2.8	5.5	5.8	6.5	9.6
2003	2.3	3.3	6.0	6.3	7.0	10.1
2002	2.0	3.2	5.8	6.0	6.7	9.6
2001	1.2	2.4	4.7	4.9	5.6	8.1
2000	0.9	1.8	4.0	4.2	4.8	7.0

Note: LF: labor force; U1, persons unemployed 15 weeks % LF; U2, job losers and persons who completed temporary jobs %LF; U3, total unemployed % LF; U4, total unemployed plus discouraged workers, plus all other marginally attached workers; % LF plus discouraged workers; U5, total unemployed, plus discouraged workers, plus all other marginally attached workers % LF plus all marginally attached workers; U6, total unemployed, plus all marginally attached workers, plus total employed part time for economic reasons % LF plus all marginally attached workers

Source: US Bureau of Labor Statistics

http://www.bls.gov/

Monthly seasonally adjusted measures of labor underutilization are provided in Table I-8. U6 climbed from 16.1 percent in Aug 2011 to 16.4 percent in Sep 2011 and then fell to 14.5 percent in Mar 2012, reaching 7.5 percent in Sep 2018. Unemployment is an incomplete measure of the stress in US job markets. A different calculation in this blog is provided by using the participation rate in the labor force before the global recession. This calculation shows 20.7 million in job stress of unemployment/underemployment in Sep 2018, not seasonally adjusted, corresponding to 12.1 percent of the labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html).

Table I-8, US, Alternative Measures of Labor Underutilization SA %

	U1	U2	U3	U4	U5	U6
Sep 2018	1.4	1.7	3.7	3.9	4.6	7.5
Aug	1.4	1.8	3.9	4.1	4.7	7.4
Jul	1.5	1.9	3.9	4.2	4.8	7.5
Jun	1.4	1.9	4.0	4.3	4.9	7.8
May	1.3	1.8	3.8	4.0	4.6	7.6
Apr	1.4	1.8	3.9	4.2	4.7	7.8
Mar	1.4	1.9	4.1	4.3	4.9	8.0
Feb	1.4	2.0	4.1	4.4	5.1	8.2
Jan	1.5	2.0	4.1	4.4	5.1	8.2
Dec 2017	1.5	2.0	4.1	4.4	5.1	8.1
Nov	1.6	2.0	4.1	4.4	5.0	8.0
Oct	1.6	2.0	4.1	4.4	5.0	8.0
Sep	1.7	2.1	4.2	4.4	5.1	8.3
Aug	1.7	2.2	4.4	4.7	5.4	8.6
Jul	1.7	2.1	4.3	4.7	5.3	8.5
Jun	1.7	2.2	4.3	4.7	5.3	8.5
May	1.7	2.1	4.3	4.5	5.2	8.4
Apr	1.7	2.2	4.4	4.7	5.3	8.6
Mar	1.7	2.2	4.5	4.7	5.4	8.8
Feb	1.8	2.3	4.7	5.0	5.7	9.2
Jan	1.9	2.3	4.8	5.1	5.8	9.4
Dec 2016	1.9	2.3	4.7	5.0	5.7	9.1
Nov	1.8	2.2	4.6	5.0	5.8	9.3
Oct	2.0	2.4	4.9	5.1	5.9	9.6
Sep	2.0	2.5	5.0	5.3	6.0	9.7
Aug	1.9	2.4	4.9	5.2	5.9	9.6
Jul	2.0	2.3	4.9	5.2	6.0	9.7
Jun	2.0	2.4	4.9	5.2	6.0	9.5
May	1.9	2.3	4.7	5.0	5.7	9.8
Apr	2.1	2.4	5.0	5.3	6.0	9.8
Mar	2.1	2.4	5.0	5.4	6.1	9.8
Feb	2.1	2.4	4.9	5.3	6.0	9.7
Jan	2.0	2.3	4.9	5.3	6.2	9.9
Dec 2015	2.1	2.4	5.0	5.4	6.1	9.9
Nov	2.1	2.5	5.0	5.4	6.1	9.9
Oct	2.1	2.5	5.0	5.4	6.2	9.8
Sep	2.1	2.4	5.0	5.4	6.2	10.0
Aug	2.2	2.5	5.1	5.5	6.2	10.2
Jul	2.2	2.6	5.2	5.6	6.4	10.3
Jun	2.3	2.6	5.3	5.7	6.4	10.4
May	2.4	2.8	5.5	5.8	6.6	10.8
Apr	2.3	2.6	5.4	5.9	6.7	10.9
Mar	2.4	2.7	5.5	5.9	6.7	10.9
Feb	2.5	2.7	5.5	5.9	6.8	11.0
Jan	2.7	2.7	5.7	6.1	7.0	11.3
Dec 2014	2.6	2.8	5.6	6.0	6.9	11.2
Nov	2.7	2.9	5.8	6.2	7.0	11.4
Oct	2.7	2.8	5.7	6.2	7.0	11.5
Sep	2.8	2.9	5.9	6.4	7.3	11.7
Aug	2.9	3.0	6.2	6.6	7.4	11.9
July	3.0	3.1	6.2	6.6	7.5	12.1
Jun	3.0	3.1	6.1	6.5	7.3	12.0
May	3.1	3.2	6.3	6.7	7.5	12.1
Apr	3.2	3.3	6.3	6.7	7.5	12.3
Mar	3.5	3.5	6.7	7.1	8.0	12.7
Feb	3.5	3.5	6.7	7.1	8.0	12.7
Jan	3.4	3.5	6.6	7.1	8.1	12.7
Dec 2013	3.6	3.5	6.7	7.3	8.1	13.1
Nov	3.7	3.7	6.9	7.4	8.2	13.1
Oct	3.7	4.0	7.2	7.7	8.5	13.6
Sep	3.8	3.8	7.2	7.7	8.6	13.5
Aug	3.8	3.8	7.2	7.8	8.6	13.6
Jul	3.9	3.8	7.3	7.9	8.7	13.8
Jun	4.0	3.9	7.5	8.2	9.1	14.2
May	4.1	3.9	7.5	8.0	8.8	13.8
Apr	4.1	4.1	7.6	8.1	8.9	14.0
Mar	4.1	4.0	7.5	8.0	8.9	13.8
Feb	4.2	4.2	7.7	8.2	9.2	14.4
Jan	4.2	4.3	8.0	8.5	9.4	14.6
Dec 2012	4.3	4.2	7.9	8.5	9.4	14.4
Nov	4.2	4.2	7.7	8.3	9.2	14.4
Oct	4.4	4.2	7.8	8.3	9.2	14.4
Sep	4.4	4.2	7.8	8.3	9.3	14.8
Aug	4.5	4.4	8.1	8.6	9.6	14.6
Jul	4.5	4.6	8.2	8.7	9.6	14.8
Jun	4.7	4.6	8.2	8.7	9.6	14.8
May	4.6	4.5	8.2	8.7	9.6	14.7
Apr	4.6	4.4	8.2	8.8	9.6	14.6
Mar	4.6	4.5	8.2	8.7	9.6	14.5
Feb	4.7	4.6	8.3	8.9	9.8	15.0
Jan	4.8	4.7	8.3	8.9	9.9	15.2
Dec 2011	4.9	4.9	8.5	9.1	10.0	15.2
Nov	5.0	5.0	8.6	9.3	10.1	15.5
Oct	5.1	5.1	8.8	9.4	10.3	15.8
Sep	5.4	5.2	9.0	9.7	10.5	16.4
Aug	5.4	5.2	9.0	9.6	10.5	16.1
Jul	5.3	5.3	9.0	9.6	10.6	15.9
Jun	5.3	5.3	9.1	9.7	10.7	16.1
May	5.3	5.4	9.0	9.5	10.3	15.8
Apr	5.2	5.4	9.1	9.7	10.5	16.1
Mar	5.3	5.4	9.0	9.5	10.4	15.9
Feb	5.3	5.5	9.0	9.6	10.6	16.0
Jan	5.5	5.5	9.1	9.7	10.8	16.2

Note: LF: labor force; U1, persons unemployed 15 weeks % LF; U2, job losers and persons who completed temporary jobs %LF; U3, total unemployed % LF; U4, total unemployed plus discouraged workers, plus all other marginally attached workers; % LF plus discouraged workers; U5, total unemployed, plus discouraged workers, plus all other marginally attached workers % LF plus all marginally attached workers; U6, total unemployed, plus all marginally attached workers, plus total employed part time for economic reasons % LF plus all marginally attached workers

Source: US Bureau of Labor Statistics

http://www.bls.gov/

Chart I-16 provides U6 monthly from 2001 to 2018. There was a steep climb from 2007 into 2009 and then this measure of unemployment and underemployment stabilized at that high level but declined into 2012. The low of U6 SA was 8.0 percent in Mar 2007 and the peak was 17.1 percent in Apr 2010. The low NSA was 7.6 percent in Oct 2006 and the peak was 18.0 percent in Jan 2010.

Chart I-16, US, U6, total unemployed, plus all marginally attached workers, plus total employed Part-Time for Economic Reasons, Month, SA, 2001-2018

Source: US Bureau of Labor Statistics

Chart I-17 provides the number employed part-time for economic reasons or who cannot find full-time employment. There are sharp declines at the end of 2009, 2010 and 2011 but an increase in 2012 followed by relative decline into 2018.

Chart I-17, US, Working Part-time for Economic Reasons

Thousands, Month SA 2001-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/

ICA3 Ten Million Fewer Full-time Jobs. There is strong seasonality in US labor markets around the end of the year.

• Seasonally adjusted part-time for economic reasons. The number employed part-time for economic reasons because they could not find full-time employment fell from 9.166 million in Sep 2011 to 7.775 million in Mar 2012, seasonally adjusted, or decline of 1.391 million in six months, as shown in Table I-9. The number employed part-time for economic reasons rebounded to 8.671 million in Sep 2012 for increase of 697,000 in one month from Aug to Sep 2012. The number employed part-time for economic reasons declined to 8.203 million in Oct 2012 or by 468,000 again in one month, further declining to 8.166 million in Nov 2012 for another major one-month decline of 37,000 and 7.943 million in Dec 2012 or fewer 223,000 in just one month. The number employed part-time for economic reasons increased to 8.151 million in Jan 2013 or 208,000 more than in Dec 2012 and to 8.178 million in Feb 2013, declining to 7.937 million in May 2013 but increasing to 8.103 million in Jun 2013. The number employed part-time for economic reasons fell to 7.816 million in Aug 2013 for decline of 283,000 in one month from 8.099 million in Jul 2013. The number employed part-time for economic reasons decreased 52,000 from 7.816 million in Aug 2013 to 7.764 million in Sep 2013. The number part-time for economic reasons rose to 7.936 million in Oct 2013, falling by 218,000 to 7.718 million in Nov 2013. The number part-time for economic reasons increased to 7.827 million in Dec 2013, decreasing to 7.302 million in Jan 2014. The number employed part-time for economic reasons increased from 7.302 million in Jan 2014 to 7.304 million in Feb 2014. The number employed part-time for economic reasons increased to 7.451 million in Mar 2014 and 7.516 million in Apr 2014. The number employed part-time for economic reasons fell to 7.260 million in May 2014, increasing to 7.425 million in Jun 2014. The level employed part-time for economic reasons fell to 7.400 million in Jul 2014 and 7.169 million in Aug 2014. The level employed part-time for economic reasons fell to 7.007 million in Sep 2014, 7.031 million in Oct 2014 and 6.885 million in Nov 2014. The level employed part-time for economic reasons fell to 6.817 million in Dec 2014, increasing to 6.820 million in Jan 2015. The level employed part-time for economic reasons fell to 6.693 million in Feb 2015, increasing to 6.653 million in Mar 2015. The level of employed part-time for economic reasons fell to 6.622 million in Apr 2015, increasing to 6.643 million in May 2015. The level employed part-time for economic reasons fell to 6.386 million in Jun 2015 and 6.234 million in Jul 2015. The level employed part-time for economic reasons increased to 6.411 million in Aug 2015, declining to 6.025 million in Sep 2015. The level employed part-time for economic reasons fell to 5.807 million in Oct 2015, increasing to 6.159 million in Nov 2015. The level of part-time for economic reasons fell to 6.027 million in Dec 2015, decreasing to 5.960 million in Jan 2016. The level employed part-time for economic reasons decreased to 6.021 million in Feb 2016 and increased to 6.099 million in Mar 2016. The level employed part-time for economic reasons fell to 6.027 million in Apr 2016 and increased to 6.491 million in May 2016. The level of part-time for economic reasons fell to 5.751 million in Jun 2016, increasing to 5.898 million in Jul 2016. The level of part-time for economic reasons increased to 5.977 million in Aug 2016, decreasing to 5.893 million in Sep 2016. The level of part-time for economic reasons reached 5.955 million in Oct 2016, decreasing to 5.719 million in Nov 2016 and 5.554 million in Dec 2016. The level of part-time for economic reasons increased to 5.776 million in Jan 2017, decreasing to 5.670 million in Feb 2017. The level of part-time for economic reasons fell to 5.500 million in Mar 2017 and fell to 5.309 million in Apr 2017, decreasing to 5.268 million in May 2017. The level of part-time for economic reasons increased to 5.264 million in Jun 2017, decreasing to 5.236 million in Jul 2017 and 5.209 million in Aug 2017. The level of part-time for economic reasons fell to 5.148 million in Sep 2017, decreasing to 4.880 million in Oct 2017 and decreasing to 4.851 million in Nov 2017. The level of part-time for economic reasons increased to 4.915 million in Dec 2017, increasing to 4.989 million in Jan 2018 and increasing to 5.160 million in Feb 2018. The level of part-time for economic reasons fell to 5.019 million in Mar 2018, decreasing to 4.985 million in Apr 2018. The level of part-time for economic reasons eased to 4.948 million in May 2018 and to 4.743 million in Jun 2018. The level of part-time for economic reasons decreased to 4.567 million in Jul 2018, decreasing to 4.379 million in Aug 2018. The level of part-time for economic reasons increased to 4.642 million in Sep 2018.
• Seasonally adjusted full-time. The number employed full-time increased from 112.923 million in Oct 2011 to 115.024 million in Mar 2012 or 2.101 million but then fell to 114.233 million in May 2012 or 0.791 million fewer full-time employed than in Mar 2012. The number employed full-time increased from 114.736 million in Aug 2012 to 115.570 million in Oct 2012 or increase of 0.834 million full-time jobs in two months and further to 115.563 million in Jan 2013 or increase of 0.827 million more full-time jobs in five months from Aug 2012 to Jan 2013. The number of full time jobs decreased slightly to 115.695 million in Feb 2013, increasing to 116.240 million in May 2013 and 116.166 million in Jun 2013. Then number of full-time jobs increased to 116.201 million in Jul 2013, 116.417 million in Aug 2013 and 116.846 million in Sep 2013. The number of full-time jobs fell to 116.352 million in Oct 2013 and increased to 117.078 in Nov 2013. The level of full-time jobs increased to 117.394 million in Dec 2013, increasing to 117.746 million in Jan 2014 and 117.774 million in Feb 2014. The level of employment full-time increased to 117.953 million in Mar 2014 and 118.433 million in Apr 2014. The level of full-time employment reached 118.800 million in May 2014, decreasing to 118.248 million in Jun 2014. The level of full-time jobs increased to 118.436 million in Jul 2014 and 118.701 million in Aug 2014. The level of full-time jobs increased to 119.298 million in Sep 2014, 119.762 million in Oct 2014 and 119.672 million in Nov 2014. The level of full-time jobs increased to 120.115 million in Dec 2014 and 120.477 million in Jan 2015. The level of full-time jobs increased to 120.750 million in Feb 2015 and 120.951 million in Mar 2015. The level of full-time jobs decreased to 120.781 million in Apr 2015, increasing to 121.518 million in May 2015 and decreasing to 121.035 million in Jun 2015. The level of full-time jobs increased to 121.662 million in Jul 2015 and increased to 122.014 million in Aug 2015, decreasing to 121.773 million in Sep 2015. The level of full-time jobs increased to 122.069 million in Oct 2015 and increased to 122.153 million in Nov 2015. The level of full-time jobs increased to 122.740 million in Dec 2015 and 123.020 million in Jan 2016. The level of full-time jobs increased to 123.200 million in Feb 2016 and increased to 123.509 million in Mar 2016. The level of full-time jobs decreased to 123.154 million in Apr 2016 and 123.224 million in May 2016. The level of full-time jobs increased to 123.648 million in Jun 2016, increasing to 123.995 million in Jul 2016. The level of full-time jobs increased to 124.453 million in Aug 2016, decreasing to 124.185 million in Sep 2016 and 124.185 million in Oct 2016. The level of full-time jobs increased to 124.239 million in Nov 2016 and 124.301 million in Dec 2016. The level of full-time jobs increased to 124.625 million in Jan 2017, increasing to 125.071 million in Feb 2017. The level of full-time jobs increased to 125.563 million in Mar 2017 and increased to 125.963 million in Apr 2017, decreasing to 125.621 million in May 2017. The level of full-time jobs increased to 126.050 million in Jun 2017, decreasing to 126.002 million in Jul 2017 and 125.882 million in Aug 2017. The level of full-time jobs increased to 126.676 million in Sep 2017, decreasing to 126.636 million in Oct 2017. The level of full-time jobs increased to 126.758 million in Nov 2017, decreasing to 126.723 million in Dec 2017. The level of full-time jobs increased to 127.016 million in Jan 2018, increasing to 127.745 million in Feb 2018. The level of full-time jobs decreased to 127.434 million in Mar 2018, increasing to 127.753 million in Apr 2018. The level of full-time jobs increased to 128.657 million in May 2018, decreasing to 128.568 million in Jun 2018. The level of full-time jobs increased to 129.021 million in Jul 2018, decreasing to 128.557 million in Aug 2018. The level of full-time jobs increased to 128.894 million in Sep 2018. Adjustments of benchmark and seasonality-factors at the turn of every year could affect comparability of labor market indicators (http://cmpassocregulationblog.blogspot.com/2016/02/fluctuating-risk-financial-assets-in.html http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html http://cmpassocregulationblog.blogspot.com/2014/02/financial-instability-rules.html http://cmpassocregulationblog.blogspot.com/2013/02/thirty-one-million-unemployed-or.html).
• Not seasonally adjusted part-time for economic reasons. The number of employed part-time for economic reasons actually increased without seasonal adjustment from 8.271 million in Nov 2011 to 8.428 million in Dec 2011 or by 157,000 and then to 8.918 million in Jan 2012 or by an additional 490,000 for cumulative increase from Nov 2011 to Jan 2012 of 647,000. The level of employed part-time for economic reasons then fell from 8.918 million in Jan 2012 to 7.867 million in Mar 2012 or by 1.051 million and to 7.694 million in Apr 2012 or 1.224 million fewer relative to Jan 2012. In Aug 2012, the number employed part-time for economic reasons reached 7.842 million NSA or 148,000 more than in Apr 2012. The number employed part-time for economic reasons increased from 7.842 million in Aug 2012 to 8.110 million in Sep 2012 or by 3.4 percent. The number part-time for economic reasons fell from 8.110 million in Sep 2012 to 7.870 million in Oct 2012 or by 240.000 in one month. The number employed part-time for economic reasons NSA increased to 8.628 million in Jan 2013 or 758,000 more than in Oct 2012. The number employed part-time for economic reasons fell to 8.298 million in Feb 2013, which is lower by 330,000 relative to 8.628 million in Jan 2013 but higher by 428,000 relative to 7.870 million in Oct 2012. The number employed part time for economic reasons fell to 7.734 million in Mar 2013 or 564,000 fewer than in Feb 2013 and fell to 7.709 million in Apr 2013. The number employed part-time for economic reasons reached 7.618 million in May 2013. The number employed part-time for economic reasons jumped from 7.618 million in May 2013 to 8.440 million in Jun 2013 or 822,000 in one month. The number employed part-time for economic reasons fell to 8.324 million in Jul 2013 and 7.690 million in Aug 2013. The number employed part-time for economic reasons NSA fell to 7.522 million in Sep 2013, increasing to 7.700 million in Oct 2013. The number employed part-time for economic reasons fell to 7.563 million in Nov 2013 and increased to 7.990 million in Dec 2013. The number employed part-time for economic reasons fell to 7.771 million in Jan 2014 and 7.397 million in Feb 2014. The level of part-time for economic reasons increased to 7.455 million in Mar 2014 and fell to 7.243 million in Apr 2014. The number of part-time for economic reasons fell to 6.960 million in May 2014, increasing to 7.805 million in Jun 2014. The level of part-time for economic reasons fell to 7.665 million in Jul 2014 and 7.083 million in Aug 2014. The level of part-time for economic reasons fell to 6.711 million in Sep 2014 and increased to 6.787 million in Oct 2014. The level of part-time for economic reasons reached 6.713 million in Nov 2014 and 6.970 million in Dec 2014, increasing to 7.269 million in Jan 2015. The level of part-time for economic reasons fell to 6.772 million in Feb 2015 and 6.672 million in Mar 2015, falling to 6.356 million in Apr 2015. The level of part-time for economic reasons increased to 6.363 million in May 2015 and to 6.776 million in Jun 2015, decreasing to 6.511 million in Jul 2015. The level of part-time for economic reasons fell to 6.361 million in Aug 2015 and 5.693 million in Sep 2015. The level of part-time for economic reasons fell to 5.536 million in Oct 2015, increasing to 5.967 million in Nov 2015. The level of part-time for economic reasons increased to 6.179 million in Dec 2015, increasing to 6.406 million in Jan 2016. The level of part-time for economic reasons decreased to 6.106 million in Feb 2016 and increased to 6.138 million in Mar 2016. The level of part-time for economic reasons decreased to 5.771 million in Apr 2016 and increased to 6.238 million in May 2016. The level of part-time for economic reasons decreased to 6.119 million in Jun 2016, increasing to 6.157 million in Jul 2016. The level of part-time for economic reasons fell to 5.963 million in Aug 2016, decreasing to 5.550 million in Sep 2016. The level of part-time for economic reasons increased to 5.648 million in Oct 2016, decreasing to 5.518 million in Nov 2016 and increasing to 5.707 million in Dec 2016. The level of part-time for economic reasons increased to 6.226 million in Jan 2017, decreasing to 5.773 million in Feb 2017. The level of part-time for economic reasons fell to 5.552 million in Mar 2017, decreasing to 5.058 million in Apr 2017. The level of part-time for economic reasons fell to 5.038 million in May 2017, increasing to 5.602 million in Jun 2017. The level of part-time for economic reasons fell to 5.475 million in Jul 2017, 5.204 million in Aug 2017 and 4.818 million in Sep 2017. The level of part-time for economic reasons decreased to 4.553 million in Oct 2017, decreasing to 4.462 million in Nov 2017. The level of part-time for economic reasons increased to 5.060 million in Dec 2017, increasing to 5.474 million in Jan 2018 and decreasing to 5.331 million in Feb 2018. The level of part-time for economic reasons decreased to 5.080 million in Mar 2018, decreasing to 4.734 million in Apr 2018. The level of part-time for economic reasons increased to 4.739 million in May 2018, increasing to 5.042 million in Jun 2018. The level of part-time for economic reasons decreased to 4.766 million in Jul 2018, decreasing to 4.319 million in Aug 2018. The level of part-time for economic reasons decreased to 4.306 million in Sep 2018.
• Not seasonally adjusted full-time. The number employed full time without seasonal adjustment fell from 113.138 million in Nov 2011 to 113.050 million in Dec 2011 or by 88,000 and fell further to 111.879 in Jan 2012 for cumulative decrease of 1.259 million. The number employed full-time not seasonally adjusted fell from 113.138 million in Nov 2011 to 112.587 million in Feb 2012 or by 551.000 but increased to 116.214 million in Aug 2012 or 3.076 million more full-time jobs than in Nov 2011. The number employed full-time not seasonally adjusted decreased from 116.214 million in Aug 2012 to 115.678 million in Sep 2012 for loss of 536,000 full-time jobs and rose to 116.045 million in Oct 2012 or by 367,000 full-time jobs in one month relative to Sep 2012. The number employed full-time NSA fell from 116.045 million in Oct 2012 to 115.515 million in Nov 2012 or decline of 530.000 in one month. The number employed full-time fell from 115.515 in Nov 2012 to 115.079 million in Dec 2012 or decline by 436,000 in one month. The number employed full time fell from 115.079 million in Dec 2012 to 113.868 million in Jan 2013 or decline of 1.211 million in one month. The number of full-time jobs increased to 114.191 in Feb 2012 or by 323,000 in one month and increased to 114.796 million in Mar 2013 for cumulative increase from Jan by 928,000 full-time jobs but decrease of 283,000 from Dec 2012. The number employed full time reached 117.400 million in Jun 2013 and increased to 117.688 in Jul 2013 or by 288,000. The number employed full-time reached 117.868 million in Aug 2013 for increase of 180,000 in one month relative to Jul 2013. The number employed full-time fell to 117.308 million in Sep 2013 or by 560,000. The number employed full-time fell to 116.798 million in Oct 2013 or decline of 510.000 in one month. The number employed full-time rose to 116.875 million in Nov 2013, falling to 116.661 million in Dec 2013. The number employed full-time fell to 115.744 million in Jan 2014 but increased to 116.323 million in Feb 2014. The level of full-time jobs increased to 116.985 in Mar 2014 and 118.073 million in Apr 2014. The number of full-time jobs increased to 119.179 million in May 2014, increasing to 119.472 million in Jun 2014. The level of full-time jobs increased to 119.900 million in Jul 2014. Comparisons over long periods require use of NSA data. The number with full-time jobs fell from a high of 123.219 million in Jul 2007 to 108.777 million in Jan 2010 or by 14.442 million. The number with full-time jobs in Sep 2018 is 129.466 million, which is higher by 6.247 million relative to the peak of 123.219 million in Jul 2007.
• Loss of full-time jobs. 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 258.290 million in Sep 2018 or by 26.332 million (http://www.bls.gov/data/). The number with full-time jobs in Sep 2018 is 129.466 million, which is higher by 6.247 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 137.152 million full-time jobs with population of 258.290 million in Sep 2018 (0.531 x 258.290) or 7.686 million fewer full-time jobs relative to actual 129.466 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. 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. US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 36 quarters from IIIQ2009 to IIQ2018. 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 IIQ2018 (https://www.bea.gov/system/files/2018-09/gdp2q18_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/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.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 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://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 (http://www.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 IIQ2018 would have accumulated to 36.4 percent. GDP in IIQ2018 would be $21,499.4 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $2987.8 billion than actual $18,511.6 billion. There are about two trillion dollars of GDP less than at trend, explaining the 20.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 12.1 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html). US GDP in IIQ2018 is 13.9 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,511.6 billion in IIQ2018 or 17.4 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 Sep 1919 to Sep 2018. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.3221 in Dec 2007 to 150.3999 in Sep 2018. The actual index NSA in Sep 2018 is 106.1908, which is 29.4 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Sep 2018. Using trend growth of 2.1 percent per year, the index would increase to 135.4390 in Sep 2018. The output of manufacturing at 106.1908 in Sep 2018 is 21.6 percent below trend under this alternative calculation.

Table I-9, US, Employed Part-time for Economic Reasons, Thousands, and Full-time, Millions

	Part-time Thousands	Full-time Millions
Seasonally Adjusted
Sep 2018	4,642	128.894
Aug 2018	4,379	128.577
Jul 2018	4,567	129.021
Jun 2018	4,743	128.568
May 2018	4,948	128.657
Apr 2018	4,985	127.753
Mar 2018	5,019	127.434
Feb 2018	5,160	127.745
Jan 2018	4,989	127.016
Dec 2017	4,915	126.723
Nov 2017	4,851	126.758
Oct 2017	4,880	126.636
Sep 2017	5,148	126.676
Aug 2017	5,209	125.882
Jul 2017	5,236	126.002
Jun 2017	5,264	126.050
May 2017	5,268	125.621
Apr 2017	5,309	125.963
Mar 2017	5,500	125.563
Feb 2017	5,670	125.071
Jan 2017	5,776	124.625
Dec 2016	5,554	124.301
Nov 2016	5,719	124.239
Oct 2016	5,955	124.185
Sep 2016	5,893	124.185
Aug 2016	5,977	124.453
Jul 2016	5,898	123.995
Jun 2016	5,751	123.648
May 2016	6,491	123.224
Apr 2016	6,027	123.154
Mar 2016	6,099	123.509
Feb 2016	6,021	123.200
Jan 2016	5,960	123.020
Dec 2015	6,027	122.740
Nov 2015	6,159	122.153
Oct 2015	5,807	122.069
Sep 2015	6,025	121.773
Aug 2015	6,411	122.014
Jul 2015	6,234	121.662
Jun 2015	6,386	121.035
May 2015	6,643	121.518
Apr 2015	6,622	120.781
Mar 2015	6,653	120.951
Feb 2015	6,693	120.750
Jan 2015	6,820	120.477
Dec 2014	6,817	120.115
Nov 2014	6,885	119.672
Oct 2014	7,031	119.762
Sep 2014	7,007	119.298
Aug 2014	7,169	118.701
Jul 2014	7,400	118.436
Jun 2014	7,425	118.248
May 2014	7,260	118.800
Apr 2014	7,516	118.433
Mar 2014	7,451	117.953
Feb 2014	7,304	117.774
Jan 2014	7,302	117.476
Dec 2013	7,827	117.394
Nov 2013	7,718	117.078
Oct 2013	7,936	116.352
Sep 2013	7,764	116.846
Aug 2013	7,816	116.417
Jul 2013	8,099	116.201
Jun 2013	8,103	116.166
May 2013	7,937	116.240
Apr 2013	7,964	116.014
Mar 2013	7,722	115.808
Feb 2013	8,178	115.695
Jan 2013	8,151	115.653
Dec 2012	7,943	115.791
Nov 2012	8,166	115.655
Oct 2012	8,203	115.570
Sep 2012	8,671	115.252
Aug 2012	7,974	114.736
Jul 2012	8,082	114.575
Jun 2012	8,072	114.749
May 2012	8,101	114.233
Apr 2012	7,913	114.371
Mar 2012	7,775	115.024
Feb 2012	8,238	114.141
Jan 2012	8,305	113.755
Dec 2011	8,171	113.774
Nov 2011	8,447	113.213
Oct 2011	8,657	112.923
Sep 2011	9,166	112.544
Aug 2011	8,788	112.723
Jul 2011	8,281	112.193
Not Seasonally Adjusted
Sep 2018	4,306	129.466
Aug 2018	4,319	129.975
Jul 2018	4,766	130.644
Jun 2018	5,042	129.937
May 2018	4,739	129.014
Apr 2018	4,734	127.340
Mar 2018	5,080	126.424
Feb 2018	5,331	126.401
Jan 2018	5,474	125.435
Dec 2017	5,060	125.985
Nov 2017	4,462	126.468
Oct 2017	4,553	127.055
Sep 2017	4,818	127.235
Aug 2017	5,204	127.353
Jul 2017	5,475	127.542
Jun 2017	5,602	127.337
May 2017	5,038	125.911
Apr 2017	5,058	125.532
Mar 2017	5,552	124.566
Feb 2017	5,773	123.610
Jan 2017	6,226	123.015
Dec 2016	5,707	123.570
Nov 2016	5,518	123.960
Oct 2016	5,648	124.588
Sep 2016	5,550	124.728
Aug 2016	5,963	125.892
Jul 2016	6,157	125.507
Jun 2016	6,119	124.903
May 2016	6,238	123.548
Apr 2016	5,771	122.742
Mar 2016	6,138	122.522
Feb 2016	6,106	121.757
Jan 2016	6,406	121.411
Dec 2015	6,179	122.013
Nov 2015	5,967	121.897
Oct 2015	5,536	122.466
Sep 2015	5,693	122.303
Aug 2015	6,361	123.420
Jul 2015	6,511	123.142
Jun 2015	6,776	122.268
May 2015	6,363	121.863
Apr 2015	6,356	120.402
Mar 2015	6,672	119.981
Feb 2015	6,772	119.313
Jan 2015	7,269	118.840
Dec 2014	6,970	119.394
Nov 2014	6,713	119.441
Oct 2014	6,787	120.176
Sep 2014	6,711	119.791
Aug 2014	7,083	120.110
Jul 2014	7,665	119.900
Jun 2014	7,805	119.472
May 2014	6,960	119.179
Apr 2014	7,243	118.073
Mar 2014	7,455	116.985
Feb 2014	7,397	116.323
Jan 2014	7,771	115.774
Dec 2013	7,990	116.661
Nov 2013	7,563	116.875
Oct 2013	7,700	116.798
Sep 2013	7,522	117.308
Aug 2013	7,690	117.868
Jul 2013	8,324	117.688
Jun 2013	8,440	117.400
May 2013	7,618	116.643
Apr 2013	7,709	115.674
Mar 2013	7,734	114.796
Feb 2013	8,298	114.191
Jan 2013	8,628	113.868
Dec 2012	8,166	115.079
Nov 2012	7,994	115.515
Oct 2012	7,870	116.045
Sep 2012	8,110	115.678
Aug 2012	7,842	116.214
Jul 2012	8,316	116.131
Jun 2012	8,394	116.024
May 2012	7,837	114.634
Apr 2012	7,694	113.999
Mar 2012	7,867	113.916
Feb 2012	8,455	112.587
Jan 2012	8,918	111.879
Dec 2011	8,428	113.050
Nov 2011	8,271	113.138
Oct 2011	8,258	113.456
Sep 2011	8,541	112.980
Aug 2011	8,604	114.286
Jul 2011	8,514	113.759
Jun 2011	8,738	113.255
May 2011	8,270	112.618
Apr 2011	8,425	111.844
Mar 2011	8,737	111.186
Feb 2011	8,749	110.731
Jan 2011	9,187	110.373
Dec 2010	9,205	111.207
Nov 2010	8,670	111.348
Oct 2010	8,408	112.342
Sep 2010	8,628	112.385
Aug 2010	8,628	113.508
Jul 2010	8,737	113.974
Jun 2010	8,867	113.856
May 2010	8,513	112.809
Apr 2010	8,921	111.391
Mar 2010	9,343	109.877
Feb 2010	9,282	109.100
Jan 2010	9,290	108.777 (low)
Dec 2009	9,354 (high)	109.875
Nov 2009	8,894	111.274
Oct 2009	8,474	111.599
Sep 2009	8,255	111.991
Aug 2009	8,835	113.863
Jul 2009	9,103	114.184
Jun 2009	9,301	114.014
May 2009	8,785	113.083
Apr 2009	8,648	112.746
Mar 2009	9,305	112.215
Feb 2009	9,170	112.947
Jan 2009	8,829	113.815
Dec 2008	8,250	116.422
Nov 2008	7,135	118.432
Oct 2008	6,267	120.020
Sep 2008	5,701	120.213
Aug 2008	5,736	121.556
Jul 2008	6,054	122.378
Jun 2008	5,697	121.845
May 2008	5,096	120.809
Apr 2008	5,071	120.027
Mar 2008	5,038	119.875
Feb 2008	5,114	119.452
Jan 2008	5,340	119.332
Dec 2007	4,750	121.042
Nov 2007	4,374	121.846
Oct 2007	4,028	122.006
Sep 2007	4,137	121.728
Aug 2007	4,494	122.870
Jul 2007	4,516	123.219 (high)
Jun 2007	4,469	122.150
May 2007	4,315	120.846
Apr 2007	4,205	119.609
Mar 2007	4,384	119.640
Feb 2007	4,417	119.041
Jan 2007	4,726	119.094
Dec 2006	4,281	120.371
Nov 2006	4,054	120.507
Oct 2006	4,010	121.199
Sep 2006	3,735 (low)	120.780
Aug 2006	4,104	121.979
Jul 2006	4,450	121.951
Jun 2006	4,456	121.070
May 2006	3,968	118.925
Apr 2006	3,787	118.559
Mar 2006	4,097	117.693
Feb 2006	4,403	116.823
Jan 2006	4,597	116.395

Source: US Bureau of Labor Statistics

http://www.bls.gov/

Chart I-20 provides the level of full-time jobs from 2001 to 2018. 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 258.290 million in Sep 2018 or by 26.332 million (http://www.bls.gov/data/). The number with full-time jobs in Sep 2018 is 129.466 million, which is higher by 6.247 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 137.152 million full-time jobs with population of 258.290 million in Sep 2018 (0.531 x 258.290) or 7.686 million fewer full-time jobs relative to actual 129.466 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, 2001-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20A provides the noninstitutional civilian population of the United States from 2001 to 2018. There is clear trend of increase of the population while the number of full-time jobs collapsed after 2008 without sufficient recovery as shown in the preceding Chart I-20.

Chart I-20A, US, Noninstitutional Civilian Population, Thousands, 2001-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20B provides number of full-time jobs in the US from 1968 to 2018. 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, 1968-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20C provides the noninstitutional civilian population of the United States from 1968 to 2018. 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, 1968-2018

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Table I-9A provides percentage change of real GDP in the United States in the 1930s, 1980s and 2000s. The recession in 1981-1982 is quite similar on its own to the 2007-2009 recession. In contrast, during the Great Depression in the four years of 1930 to 1933, 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 http://bea.gov/iTable/index_nipa.cfm). Data are available for the 1930s only on a yearly basis. US GDP fell 4.8 percent in the two recessions (1) from IQ1980 to IIIQ1980 and (2) from III1981 to IVQ1982 and 4.0 percent cumulatively in the recession from IVQ2007 to IIQ2009. It is instructive to compare the first years of the expansions in the 1980s and the current expansion. GDP grew at 4.6 percent in 1983, 7.2 percent in 1984, 4.2 percent in 1985, 3.5 percent in 1986, 3.5 percent in 1987, 4.2 percent in 1988 and 3.7 percent in 1989. In contrast, GDP grew 2.6 percent in 2010, 1.6 percent in 2011, 2.2 percent in 2012, 1.8 percent in 2013, 2.5 percent in 2014 and 2.9 percent in 2015. GDP grew 1.6 percent in 2016 and 2.2 percent in 2017. Actual annual equivalent GDP growth in the twenty-six quarters from IQ2012 to IIQ2018 is 2.3 percent and 2.9 percent in the four quarters ending in IIQ2018. GDP grew at 4.2 percent in 1985, 3.5 percent in 1986, 3.5 percent in 1987, 4.2 percent in 1988 and 3.7 percent in 1989. The forecasts of the central tendency of participants of the Federal Open Market Committee (FOMC) are in the range of 3.0 to 3.2 percent in 2018 (https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20180926.pdf) with less reliable forecast of 2.4 to 2.7 percent in 2019 (https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20180926.pdf). Growth of GDP in the expansion from IIIQ2009 to IIQ2018 has been at average 2.3 percent in annual equivalent.

Table I-9A, US, Percentage Change of GDP in the 1930s, 1980s and 2000s, ∆%

Year	GDP ∆%	Year	GDP ∆%	Year	GDP ∆%
1930	-8.5	1980	-0.3	2000	4.1
1931	-6.4	1981	2.5	2001	1.0
1932	-12.9	1982	-1.8	2002	1.7
1933	-1.2	1983	4.6	2003	2.9
1934	10.8	1984	7.2	2004	3.8
1935	8.9	1985	4.2	2005	3.5
1936	12.9	1986	3.5	2006	2.9
1937	5.1	1987	3.5	2007	1.9
1938	-3.3	1988	4.2	2008	-0.1
1939	8.0	1989	3.7	2009	-2.5
1940	8.8	1990	1.9	2010	2.6
1941	17.7	1991	-0.1	2011	1.6
1942	18.9	1992	3.5	2012	2.2
1943	17.0	1993	2.8	2013	1.8
1944	8.0	1994	4.0	2014	2.5
1945	-1.0	1995	2.7	2015	2.9
1946	-11.6	1996	3.8	2016	1.6
1947	-1.1	1997	4.4	2017	2.2

Source: US Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm

Characteristics of the four cyclical contractions are in Table I-9B with the first column showing the number of quarters of contraction; the second column the cumulative percentage contraction; and the final column the average quarterly rate of contraction. There were two contractions from IQ1980 to IIIQ1980 and from IIIQ1981 to IVQ1982 separated by three quarters of expansion. The drop of output combining the declines in these two contractions is 4.8 percent, which is almost equal to the decline of 4.0 percent in the contraction from IVQ2007 to IIQ2009. In contrast, during the Great Depression in the four years of 1930 to 1933, 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 http://bea.gov/iTable/index_nipa.cfm). The comparison of the global recession after 2007 with the Great Depression is entirely misleading.

Table I-9B, US, Number of Quarters, GDP Cumulative Percentage Contraction and Average Percentage Annual Equivalent Rate in Cyclical Contractions   

	Number of Quarters	Cumulative Percentage Contraction	Average Percentage Rate
IIQ1953 to IIQ1954	3	-2.4	-0.8
IIIQ1957 to IIQ1958	3	-3.0	-1.0
IVQ1973 to IQ1975	5	-3.1	-0.6
IQ1980 to IIIQ1980	2	-2.2	-1.1
IIIQ1981 to IVQ1982	4	-2.6	-0.65
IVQ2007 to IIQ2009	6	-4.0	-0.7

Sources: Source: Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm

Table I-9C shows the mediocre average annual equivalent growth rate of 2.3 percent of the US economy in the thirty-six quarters of the current cyclical expansion from IIIQ2009 to IIQ2018. In sharp contrast, the average growth rate of GDP was:

• 5.7 percent in the first thirteen quarters of expansion from IQ1983 to IQ1986
• 5.3 percent in the first fifteen quarters of expansion from IQ1983 to IIIQ1986
• 5.1 percent in the first sixteen quarters of expansion from IQ1983 to IVQ1986
• 5.0 percent in the first seventeen quarters of expansion from IQ1983 to IQ1987
• 5.0 percent in the first eighteen quarters of expansion from IQ1983 to IIQ1987
• 4.9 percent in the first nineteen quarters of expansion from IQ1983 to IIIQ1987
• 5.0 percent in the first twenty quarters of expansion from IQ1983 to IVQ1987
• 4.9 percent in the first twenty-first quarters of expansion from IQ1983 to IQ1988
• 4.9 percent in the first twenty-two quarters of expansion from IQ1983 to IIQ1988
• 4.8 percent in the first twenty-three quarters of expansion from IQ1983 to IIIQ1988
• 4.8 percent in the first twenty-four quarters of expansion from IQ1983 to IVQ1988
• 4.8 percent in the first twenty-five quarters of expansion from IQ1983 to IQ1989
• 4.7 percent in the first twenty-six quarters of expansion from IQ1983 to IIQ1989
• 4.6 percent in the first twenty-seven quarters of expansion from IQ1983 to IIIQ1989
• 4.5 percent in the first twenty-eight quarters of expansion from IQ1983 to IVQ1989
• 4.5 percent in the first twenty-nine quarters of expansion from IQ1983 to IQ1990
• 4.4 percent in the first thirty quarters of expansion from IQ1983 to IIQ1990
• 4.3 percent in the first thirty-one quarters of expansion from IQ1983 to IIIQ1990
• 4.0 percent in the first thirty-two quarters of expansion from IQ1983 to IVQ1990
• 3.8 percent in the first thirty-three quarters of expansion from IQ1983 to IQ1991
• 3.8 percent in the first thirty-four quarters of expansion from IQ1983 to IIQ1991
• 3.8 percent in the first thirty-five quarters of expansion from IQ1983 to IIIQ1991
• 3.7 percent in the thirty-six quarters of expansion from IQ1983 to IVQ1991

The line “average first four quarters in four expansions” provides the average growth rate of 7.7 percent with 7.8 percent from IIIQ1954 to IIQ1955, 9.2 percent from IIIQ1958 to IIQ1959, 6.1 percent from IIIQ1975 to IIQ1976 and 7.9 percent from IQ1983 to IVQ1983. The United States missed this opportunity of high growth in the initial phase of recovery.  BEA data show the US economy in standstill relative to historical experience with annual growth of 2.6 percent in 2010 decelerating to 1.6 percent annual growth in 2011, 2.2 percent in 2012, 1.8 percent in 2013, 2.5 percent in 2014, 2.9 percent in 2015, 1.6 percent in 2016 and 2.2 percent in 2017 (http://www.bea.gov/iTable/index_nipa.cfm).  The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.1 percent from IQ1983 to IVQ1986, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IQ1988, 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 and at 7.9 percent from IQ1983 to IVQ1983. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://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 (http://www.bea.gov/iTable/index_nipa.cfm). GDP grew 2.8 percent in the first four quarters of the expansion from IIIQ2009 to IIQ2010. GDP growth in the twenty-six quarters from 2012 to 2018 accumulated to 15.7 percent. This growth is equivalent to 2.3 percent per year, obtained by dividing GDP in IIQ2018 of $18,514.6 billion by GDP in IVQ2011 of $16,004.1 billion and compounding by 4/26: {[($18,511.6/$16,004.1)^4/26 -1]100 = 2.3 percent}.

Table I-9C, US, Number of Quarters, Cumulative Growth and Average Annual Equivalent Growth Rate in Cyclical Expansions

	Number of Quarters	Cumulative Growth ∆%	Average Annual Equivalent Growth Rate
IIIQ 1954 to IQ1957	11	12.8	4.5
First Four Quarters IIIQ1954 to IIQ1955	4	7.8
IIQ1958 to IIQ1959	5	10.0	7.9
First Four Quarters IIIQ1958 to IIQ1959	4	9.2
IIQ1975 to IVQ1976	8	8.3	4.1
First Four Quarters IIIQ1975 to IIQ1976	4	6.1
IQ1983-IQ1986 IQ1983-IIIQ1986 IQ1983-IVQ1986 IQ1983-IQ1987 IQ1983-IIQ1987 IQ1983 to IIIQ1987 IQ1983 to IVQ1987 IQ1983 to IQ1988 IQ1983 to IIQ1988 IQ1983 to IIIQ1988 IQ1983 to IVQ1988 IQ1983 to IQ1989 IQ1983 to IIQ1989 IQ1983 to IIIQ1989 IQ1983 to IVQ1989 IQ1983 to IQ1990 IQ1983 to IIQ1990 IQ1983 to IIIQ1990 IQ1983 to IVQ1990 IQ1983 to IQ1991 IQ1983 to IIQ1991 IQ1983 to IIIQ1991 IQ1983 to IVQ1991	13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36	19.8 21.5 22.1 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	5.7 5.3 5.1 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
First Four Quarters IQ1983 to IVQ1983	4	7.9
Average First Four Quarters in Four Expansions*		7.7
IIIQ2009 to IIQ2018	36	22.3	2.3
First Four Quarters IIIQ2009 to IIQ2010		2.8

*First Four Quarters: 7.8% IIIQ1954-IIQ1955; 9.2% IIIQ1958-IIQ1959; 6.1% IIIQ1975-IQ1976; 7.8% IQ1983-IVQ1983

Source: Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm

Table EMP provides the comparison between the labor market in the current whole cycle from 2007 to 2017 and the whole cycle from 1979 to 1989. In the entire cycle from 2007 to 2017, the number employed increased 7.290 million, full-time employed increased 4.876 million, part-time for economic reasons increased 0.849 million and population increased 23.212 million. The number employed increased 5.0 percent, full-time employed increased 4.0 percent, part-time for economic reasons increased 19.3 percent and population increased 10.0 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
∆2007-2017	7.290	4.876	0.849	23.212
∆% 2007-2016	5.0	4.0	19.3	10.0
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

http://www.bls.gov/

The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. There are accentuated cyclic factors for both the entire population and the young population of ages 16 to 24 years. Table Summary Total provides the total noninstitutional population (ICP) of the US, full-time employment level (FTE), employment level (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP), employment/population ratio (EPOP) and unemployment level (UNE). Secular stagnation would spread over long periods instead of immediately. All indicators of the labor market weakened sharply during the contraction and did not recover. Population continued to grow but all other variables collapsed and did not recover. 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), 11-16). Hansen (1938, 1939) finds secular stagnation in lower growth of an aging population. In the current US economy, Table Summary shows that population is dynamic while the labor market is fractured. There is key explanation in the behavior of the civilian labor force participation rate (CLFP) and the employment population ratio (EPOP) that collapsed during the global recession with inadequate recovery. Abandoning job searches are difficult to capture in labor statistics but likely explain the decline in the participation of the population in the labor force. Allowing for abandoning job searches, the total number of people unemployed or underemployed is 20.7 million or 12.1 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html).

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
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	58.9	14.5
9/18	258.3	129.5	156.2	162.0	62.7	60.5	5.8

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

http://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
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
8/18	38.0	19.4	21.1	55.5	51.0	1.7	8.2

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

http://www.bls.gov/

The United States is experiencing high youth unemployment as in European economies. Table I-10 provides the employment level for ages 16 to 24 years of age estimated by the Bureau of Labor Statistics. On an annual basis, youth employment fell from 20.041 million in 2006 to 17.362 million in 2011 or 2.679 million fewer youth jobs and to 17.834 million in 2012 or 2.207 million fewer jobs. Youth employment fell from 20.041 million in 2006 to 18.057 million in 2013 or 1.984 million fewer jobs. Youth employment fell from 20.041 million in 2006 to 18.442 million in 2014 or 1.599 million. Youth employment fell from 20.041 million in 2006 to 18.756 Youth employment fell from 20.041 million in 2006 to 18.756 million in 2015 or 1.285 million. Youth employment fell from 20.041 million in 2006 to 18.992 million in 2016 or 1.049 million. Youth employment fell from 20.041 million in 2006 to 19.206 million in 2017 or 0.835 million. The level of youth jobs fell from 20.129 million in Dec 2006 to 18.347 million in Dec 2014 for 1.782 million fewer youth jobs. The level of youth jobs fell from 20.129 million in Dec 2006 to 18.720 million in Dec 2015 or 1.409 million fewer jobs. Youth jobs fell from 20.129 million in Dec 2006 to 18.830 million in Dec 2016 or 1.299 million. Youth jobs fell from 21.167 million in Aug 2006 to 20.038 million in Aug 2017 or 1.129 million. During the seasonal peak months of youth employment in the summer from Jun to Aug, youth employment has fallen by more than two million jobs relative to 21.167 million in Aug 2006 to 18.972 million in Aug 2014 for 2.195 million fewer jobs. Youth employment fell from 21.914 million in Jul 2006 to 20.085 million in Jul 2014 for 1.829 million fewer youth jobs. The number of youth jobs fell from 21.268 million in Jun 2006 million to 19.421 million in Jun 2014 or 1.847 million fewer youth jobs. The number of jobs ages 16 to 24 years fell from 21.167 million in Aug 2006 to 18.636 million in Aug 2013 or by 2.531 million. The number of youth jobs fell from 19.604 million in Sep 2006 to 18.043 million in Sep 2013 or 1.561 million fewer youth jobs. The number of youth jobs fell from 20.129 million in Dec 2006 to 18.106 million in Dec 2013 or 2.023 million fewer jobs. The civilian noninstitutional population ages 16 to 24 years increased from 37.443 million in Jul 2007 to 38.861 million in Jul 2013 or by 1.418 million while the number of jobs for ages 16 to 24 years fell by 2.230 million from 21.914 million in Jul 2006 to 19.684 million in Jul 2013. The civilian noninstitutional population for ages 16 to 24 years increased from 37.455 million in Aug 2007 to 38.841 million in Aug 2013 or by 1.386 million while the number of youth jobs fell by 1.777 million. The civilian noninstitutional population increased from 37.467 million in Sep 2007 to 38.822 million in Sep 2013 or by 1.355 million while the number of youth jobs fell by 1.455 million. The civilian noninstitutional population increased from 37.480 million in Oct 2007 to 38.804 million in Oct 2013 or by 1.324 million while the number of youth jobs decreased 1.877 million from Oct 2006 to Oct 2013. The civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.798 million in Nov 2013 or by 1.722 million while the number of youth jobs fell 1.799 million. The civilian noninstitutional population increased from 37.518 million in Dec 2007 to 38.790 million in Dec 2013 or by 1.272 million while the number of youth jobs fell 2.023 million from Dec 2006 to Dec 2013. The youth civilian noninstitutional population increased 1.488 million from 37.282 million in in Jan 2007 to 38.770 million in Jan 2014 while the number of youth jobs fell 2.035 million. The youth civilian noninstitutional population increased 1.464 million from 37.302 in Feb 2007 to 38.766 million in Feb 2014 while the number of youth jobs decreased 2.058 million. The civilian noninstitutional population increased 1.437 million from 37.324 million in Mar 2007 to 38.761 million in Mar 2014 while jobs for ages 16 to 24 years decreased 1.599 million from 19.538 million in Mar 2007 to 17.939 million in Mar 2014. The civilian noninstitutional population ages 16 to 24 years increased 1.410 million from 37.349 million in Apr 2007 to 38.759 million in Apr 2014 while the number of youth jobs fell 1.347 million. The civilian noninstitutional population increased 1.370 million from 37.379 million in May 2007 to 38.749 million in May 2014 while the number of youth jobs decreased 1.128 million. The civilian noninstitutional population increased 1.330 million from 37.410 million in Jun 2007 to 38.740 million in Jun 2014 while the number of youth jobs fell 1.847 million from 21.268 million in Jun 2006 to 19.421 million in Jun 2014. 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 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.

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

Year	Jun	Jul	Aug	Sep	Dec	Annual
2001	21212	22042	20529	19706	19547	20088
2002	20828	21501	20653	19466	19394	19683
2003	20432	20950	20181	18909	19136	19351
2004	20587	21447	20660	19158	19619	19630
2005	20949	21749	20814	19503	19733	19770
2006	21268	21914	21167	19604	20129	20041
2007	21098	21717	20413	19498	19361	19875
2008	20466	21021	20096	18818	18378	19202
2009	18726	19304	18270	16972	16615	17601
2010	17920	18564	18061	16874	16727	17077
2011	18180	18632	18067	17238	17234	17362
2012	18907	19461	18171	17687	17604	17834
2013	19125	19684	18636	18043	18106	18057
2014	19421	20085	18972	18104	18347	18442
2015	19789	20333	19577	18355	18720	18756
2016	19967	20456	19876	18693	18830	18992
2017	20330	20890	20038	19119	18610	19206
2018	20332	20897	19372	18786

Sources: US Bureau of Labor Statistics

http://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, 2001-2018

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

Chart I-21A provides the US civilian noninstitutional population ages 16 to 24 years not seasonally adjusted from 2001 to 2018. 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 civilian noninstitutional population ages 16 to 24 years increased from 37.443 million in Jul 2007 to 38.861 million in Jul 2013 or by 1.418 million while the number of jobs for ages 16 to 24 years fell by 2.230 million from 21.914 million in Jul 2006 to 19.684 million in Jul 2013. The civilian noninstitutional population for ages 16 to 24 years increased from 37.455 million in Aug 2007 to 38.841 million in Aug 2013 or by 1.386 million while the number of youth jobs fell by 1.777 million. The civilian noninstitutional population increased from 37.467 million in Sep 2007 to 38.822 million in Sep 2013 or by 1.355 million while the number of youth jobs fell by 1.455 million. The civilian noninstitutional population increased from 37.480 million in Oct 2007 to 38.804 million in Oct 2013 or by 1.324 million while the number of youth jobs decreased 1.877 million from Oct 2006 to Oct 2013. The civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.798 million in Nov 2013 or by 1.722 million while the number of youth jobs fell 1.799 million. The civilian noninstitutional population increased from 37.518 million in Dec 2007 to 38.790 million in Dec 2013 or by 1.272 million while the number of youth jobs fell 2.023 million from Dec 2006 to Dec 2013. The youth civilian noninstitutional population increased 1.488 million from 37.282 million in in Jan 2007 to 38.770 million in Jan 2014 while the number of youth jobs fell 2.035 million. The youth civilian noninstitutional population increased 1.464 million from 37.302 in Feb 2007 to 38.766 million in Feb 2014 while the number of youth jobs decreased 2.058 million. The civilian noninstitutional population increased 1.437 million from 37.324 million in Mar 2007 to 38.761 million in Mar 2014 while jobs for ages 16 to 24 years decreased 1.599 million from 19.538 million in Mar 2007 to 17.939 million in Mar 2014. The civilian noninstitutional population ages 16 to 24 years increased 1.410 million from 37.349 million in Apr 2007 to 38.759 million in Apr 2014 while the number of youth jobs fell 1.347 million. The civilian noninstitutional population increased 1.370 million from 37.379 million in May 2007 to 38.749 million in May 2014 while the number of youth jobs decreased 1.128 million. The civilian noninstitutional population increased 1.330 million from 37.410 million in Jun 2007 to 38.740 million in Jun 2014 while the number of youth jobs fell 1.847 million from 21.268 million in Jun 2006 to 19.421 million in Jun 2014. 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 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, 2001-2018

Source: US Bureau of Labor Statistics http://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. 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, 2001-2018

Source: US Bureau of Labor Statistics http://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. 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, 2001-2018

Source: US Bureau of Labor Statistics http://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. Chart I-21D shows vertical drop during the global recession without recovery.

Chart I-21D, US, Employment Population Ratio Ages 16 to 24 Years, Thousands NSA, 2001-2018

Source: US Bureau of Labor Statistics http://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 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 ages 16 to 24 years decreased from 2.297 million in Sep 2006 to 1.715 million in Sep 2018 or decrease by 0.582 million. This situation may persist for many years.

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

Year	Jul	Aug	Sep	Dec	Annual
2001	2585	2461	2301	2412	2371
2002	3034	2688	2506	2374	2683
2003	3200	2724	2698	2248	2746
2004	3018	2585	2493	2294	2638
2005	2688	2519	2339	2055	2521
2006	2750	2467	2297	2007	2353
2007	2622	2388	2419	2323	2342
2008	3408	2990	2904	2928	2830
2009	4387	4004	3774	3532	3760
2010	4374	3903	3604	3352	3857
2011	4110	3820	3541	3161	3634
2012	4011	3672	3174	3153	3451
2013	3821	3453	3139	2536	3324
2014	3353	2844	2854	2317	2853
2015	2829	2390	2256	2114	2467
2016	2648	2221	2126	1859	2211
2017	2217	1930	1878	1640	1955
2018	2119	1720	1715

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-22 provides the unemployment level for ages 16 to 24 from 2001 to 2018. The level rose sharply from 2007 to 2010 with tepid improvement into 2012 and deterioration into 2013-2014 with recent marginal improvement in 2015-2018 alternating with deterioration.

Chart I-22, US, Unemployment Level 16-24 Years, Thousands SA, 2001-2018

Source: US Bureau of Labor Statistics http://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. 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	Jun	Jul	Aug	Sep	Dec	Annual
2001	11.6	10.5	10.7	10.5	11.0	10.6
2002	13.2	12.4	11.5	11.4	10.9	12.0
2003	14.8	13.3	11.9	12.5	10.5	12.4
2004	13.4	12.3	11.1	11.5	10.5	11.8
2005	12.6	11.0	10.8	10.7	9.4	11.3
2006	11.9	11.2	10.4	10.5	9.1	10.5
2007	12.0	10.8	10.5	11.0	10.7	10.5
2008	14.4	14.0	13.0	13.4	13.7	12.8
2009	19.9	18.5	18.0	18.2	17.5	17.6
2010	20.0	19.1	17.8	17.6	16.7	18.4
2011	18.9	18.1	17.5	17.0	15.5	17.3
2012	18.1	17.1	16.8	15.2	15.2	16.2
2013	18.0	16.3	15.6	14.8	12.3	15.5
2014	15.0	14.3	13.0	13.6	11.2	13.4
2015	13.7	12.2	10.9	10.9	10.1	11.6
2016	12.3	11.5	10.1	10.2	9.0	10.4
2017	10.5	9.6	8.8	8.9	8.1	9.2
2018	10.2	9.2	8.2	8.4

Sources: US Bureau of Labor Statistics

http://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 2018. 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, 2001-2018

Source: US Bureau of Labor Statistics http://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 2018. 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. 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 3.8 percent from IQ1983 to IIQ1991 compared with 2.2 percent on average during the first 35 quarters of expansion from IIIQ2009 to IQ2018. US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 36 quarters from IIIQ2009 to IIQ2018. 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 IIQ2018 (https://www.bea.gov/system/files/2018-09/gdp2q18_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/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.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 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://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 (http://www.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 IIQ2018 would have accumulated to 36.4 percent. GDP in IIQ2018 would be $21,499.4 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $2987.8 billion than actual $18,511.6 billion. There are about two trillion dollars of GDP less than at trend, explaining the 20.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 12.1 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html). US GDP in IIQ2018 is 13.9 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,511.6 billion in IIQ2018 or 17.4 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 Sep 1919 to Sep 2018. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.3221 in Dec 2007 to 150.3999 in Sep 2018. The actual index NSA in Sep 2018 is 106.1908, which is 29.4 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Sep 2018. Using trend growth of 2.1 percent per year, the index would increase to 135.4390 in Sep 2018. The output of manufacturing at 106.1908 in Sep 2018 is 21.6 percent below trend under this alternative calculation.

Chart I-24, US, Unemployment Rate 16-24 Years, Percent NSA, 1948-2018

Source: US Bureau of Labor Statistics http://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 2.252 million in May 2017 and at 1.174 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 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 36 quarters from IIIQ2009 to IIQ2018. 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 IIQ2018 (https://www.bea.gov/system/files/2018-09/gdp2q18_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/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.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 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2018/09/fomc-increases-policy-interest-rate.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/revision-of-united-states-national.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://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 (http://www.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 IIQ2018 would have accumulated to 36.4 percent. GDP in IIQ2018 would be $21,499.4 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $2987.8 billion than actual $18,511.6 billion. There are about two trillion dollars of GDP less than at trend, explaining the 20.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 12.1 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2018/10/twenty-one-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2018/09/twenty-one-million-unemployed-or.html). US GDP in IIQ2018 is 13.9 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,511.6 billion in IIQ2018 or 17.4 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 Sep 1919 to Sep 2018. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.3221 in Dec 2007 to 150.3999 in Sep 2018. The actual index NSA in Sep 2018 is 106.1908, which is 29.4 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Sep 2018. Using trend growth of 2.1 percent per year, the index would increase to 135.4390 in Sep 2018. The output of manufacturing at 106.1908 in Sep 2018 is 21.6 percent below trend under this alternative calculation.

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

Year	Jul	Aug	Sep	Dec	Annual
2000	1253	1339	1254	1217	1249
2001	1539	1640	1586	1901	1576
2002	2173	2114	1966	2210	2114
2003	2281	2301	2157	2130	2253
2004	2116	2082	1951	2086	2149
2005	2119	1895	1992	1963	2009
2006	1985	1869	1710	1794	1848
2007	2053	1956	1854	2120	1966
2008	2492	2695	2595	3485	2540
2009	4757	4683	4560	4960	4500
2010	4821	5128	4640	4762	4879
2011	4772	4592	4426	4182	4537
2012	4405	4179	3899	3927	4133
2013	3727	3607	3535	3378	3719
2014	3083	3037	2640	2667	3000
2015	2666	2693	2388	2317	2574
2016	2619	2565	2414	2360	2485
2017	2335	2444	2140	2079	2238
2018	2235	2096	1872

Sources: US Bureau of Labor Statistics

http://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, 1976-2018

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

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018.