Mediocre Cyclical United States Economic Growth with GDP Three Trillion Dollars Below Trend in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide Followed by the Probable Global Recession in the Lockdown of Economic Activity in the COVID-19 Event, Contraction of US GDP at SAAR of 5.0 Percent, Cyclically Stagnating Real Private Fixed Investment, Contraction of Corporate Profits in the Lockdown of Economic Activity of the COVID-19 Event, United States Terms of International Trade, World Inflation Waves, United States Housing, United States House Prices, Probable Global Recession, World Cyclical Slow Growth, and Government Intervention in Globalization: Part III

Mediocre Cyclical United States Economic Growth with GDP Three Trillion Dollars Below Trend in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide Followed by the Probable Global Recession in the Lockdown of Economic Activity in the COVID-19 Event, Contraction of US GDP at SAAR of 5.0 Percent, Cyclically Stagnating Real Private Fixed Investment, Contraction of Corporate Profits in the Lockdown of Economic Activity of the COVID-19 Event, United States Terms of International Trade, World Inflation Waves, United States Housing, United States House Prices, Probable Global Recession, World Cyclical Slow Growth, and Government Intervention in Globalization

Carlos M. Pelaez

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020.

I Mediocre Cyclical United States Economic Growth with GDP Three Trillion Dollars Below Trend in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide Followed by the Probable Global Recession in the Lockdown of Economic Activity in the COVID-19 Event

IA Mediocre Cyclical United States Economic Growth

IA1 Stagnating Real Private Fixed Investment

IA2 Swelling Undistributed Corporate Profits

IID United States Terms of International Trade

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

IIA United States Housing Collapse

IIA1 Sales of New Houses

IIA2 United States House Prices

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

Foreword A.

Table V-3, Percentage Changes of GDP Quarter on Prior Quarter and on Same Quarter Year Earlier, ∆%

	IQ2020/IVQ2019	IQ2020/IQ2019
USA	QOQ: -1.3 SAAR: -5.0	0.2
Japan	QOQ: -0.9 SAAR: -3.4	-2.0
China	-9.8 (-71.0)	-6.8
Germany	-2.2	-1.9 CA -2.3
UK	-2.0	-1.6

QOQ: Quarter relative to prior quarter; SAAR: seasonally adjusted annual rate

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

Foreword B. There is typically significant difference between initial claims for unemployment insurance adjusted and not adjusted for seasonality provided in Table VII-2. Seasonally adjusted claims decreased 323,000 from 2,446,000 on May 16, 2020 to 2,123,000 on May 23, 2020 in the COVID-19 event. Claims not adjusted for seasonality decreased 266,682 from 2,181,640 on May 16, 2020 to 1,914,958 on May 23, 2020.

Table VII-2, US, Initial Claims for Unemployment Insurance

	SA	NSA	4-week MA SA
May 23, 2020	2,123,000	1,914,958	2,608,000
May 16, 2020	2,446,000	2,181,640	3,044,000
Change	-323,000	-266,682	-436,000
May 09, 2020	2,687,000	2,356,626	3,543,000
Prior Year	218,000	199,194	218,250

Note: SA: seasonally adjusted; NSA: not seasonally adjusted; MA: moving average

Source: https://www.dol.gov/ui/data.pdf

Table VII-2A provides the SA and NSA number of uninsured that decreased 3,742,432 NSA from 22,794,138 on May 9, 2020 to 19,051,706 on May 16, 2020.

Table VII-2A, US, Insured Unemployment

	SA	NSA	4-week MA SA
May 16, 2020	21,052,000	19,051,706	22,722,250
May 09, 2020	24,912,000	22,794,138	21,962,000
Change	-3,860,000	-3,742,432	+760,250
May 02, 2020	22,548,000	20,879,704	19,688,750
Prior Year	1,675,000	1,509,265	1,680,000

Note: SA: seasonally adjusted; NSA: not seasonally adjusted; MA: moving average

Source: https://www.dol.gov/ui/data.pdf

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 (https://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
2018	2.1
2019	2.2

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

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
2018	2.3
2019	2.2

Source: US Bureau of Labor Statistics

https://www.bls.gov/ppi/

The producer price index of the US from 1947 to 2020 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, 1947-2020

Source: US Bureau of Labor Statistics

https://www.bls.gov/ppi/

Chart I-7 provides 12-month percentage changes of the producer price index from 1948 to 2020. 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 2020.

Chart I-7, US, Producer Price Index, Finished Goods, 12-Month Percentage Change, NSA, 1948-2020

Source: US Bureau of Labor Statistics

https://www.bls.gov/ppi/

Annual percentage changes of the producer price index from 1948 to 2019 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 and increased 3.1 percent in 2018. Producer prices increased 0.8 percent in 2019. 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 ∆% 1948-2019

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
2018	3.1
2019	0.8

Source: US Bureau of Labor Statistics

https://www.bls.gov/ppi/

Chart I-12 provides the consumer price index NSA from 1913 to 2020. 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, 1913-2020

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

Chart I-13 provides 12-month percentage changes of the consumer price index from 1914 to 2020. 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 1914-2020

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

Table I-2 provides annual percentage changes of United States consumer price inflation from 1914 to 2019. 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. Consumer prices increased 2.4 percent in 2018, increasing at 1.8 percent in 2019. 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 ∆% 1914-2019

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
2018	2.4
2019	1.8

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

Y = ∑_is_iy_i (1)

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

∆Y = ∑_i∆s_iy*_i + ∑_i∆y_is*_i (2)

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

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

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

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

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

Source: US Bureau of Labor Statistics

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

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

Source: Bureau of Labor Statistics

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

The magnitude of the stress in US labor markets is magnified by the increase in the civilian noninstitutional population of the United States from 231.958 million in Jul 2007 to 259.896 million in Apr 2020 or by 27.938 million (https://www.bls.gov/data/). The number with full-time jobs in Apr 2020 is 113.656 million, in the lockdown of economic activity in the COVID-19 event, which is lower by 9.563 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 138.005 million full-time jobs with population of 259.986 million in Apr 2020 (0.531 x 259.896) or 24.349 million fewer full-time jobs relative to actual 113.656 million. There appear to be around 20 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 augmented by the lockdown in economic activity in the COVID-19 event.

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

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

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

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

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

Sources: US Bureau of Labor Statistics

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

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

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

Sources: US Bureau of Labor Statistics

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

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

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

Sources: US Bureau of Labor Statistics

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

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

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

Sources: US Bureau of Labor Statistics

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

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

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

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

Source: Bureau of Labor Statistics

https://www.bls.gov/

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

	ICP	FTE	EMP	CLF	CLFP	EPOP	UNE
2006	228.8	119.7	144.4	151.4	66.2	63.1	7.0
2009	235.8	112.6	139.9	154.1	65.4	59.3	14.3
2012	243.3	114.8	142.5	155.0	63.7	58.6	12.5
2013	245.7	116.3	143.9	155.4	63.2	58.6	11.5
2014	247.9	118.7	146.3	155.9	62.9	59.0	9.6
2015	250.8	121.5	148.8	157.1	62.7	59.3	8.3
2016	253.5	123.8	151.4	159.2	62.8	59.7	7.8
2017	255.1	126.0	153.3	160.3	62.9	60.1	7.0
2018	257.8	128.6	155.8	162.1	62.9	60.4	6.3
2019	259.2	130.6	157.5	163.5	63.1	60.8	6.0
12/07	233.2	121.0	146.3	153.7	65.9	62.8	7.4
9/09	236.3	112.0	139.1	153.6	65.0	58.9	14.5
4/20	259.9	113.7	133.3	155.8	60.0	51.3	22.5

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

Source: Bureau of Labor Statistics

https://www.bls.gov/

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

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

	ICP	EMP	CLF	CLFP	EPOP	UNE	UNER
2006	36.9	20.0	22.4	60.6	54.2	2.4	10.5
2009	37.6	17.6	21.4	56.9	46.9	3.8	17.6
2012	38.8	17.8	21.3	54.9	46.0	3.5	16.2
2013	38.8	18.1	21.4	55.0	46.5	3.3	15.5
2014	38.7	18.4	21.3	55.0	47.6	2.9	13.4
2015	38.6	18.8	21.2	55.0	48.6	2.5	11.6
2016	38.4	19.0	21.2	55.2	49.4	2.2	10.4
2017	38.2	19.2	21.2	55.5	50.3	2.0	9.2
2018	38.0	19.2	21.0	55.2	50.5	1.8	8.6
2019	37.7	19.3	21.1	55.9	51.2	1.8	8.4
12/07	37.5	19.4	21.7	57.8	51.6	2.3	10.7
9/09	37.6	17.0	20.7	55.2	45.1	3.8	18.2
4/20	37.5	13.1	17.9	47.8	35.0	4.8	26.9

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

Source: Bureau of Labor Statistics

https://www.bls.gov/

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.

II I United States Housing Collapse. Data and other information continue to provide depressed conditions in the US housing market in a longer perspective, with recent improvement at the margin. Table IIB-1 shows sales of new houses in the US at seasonally adjusted annual equivalent rate (SAAR). The US Census Bureau revised all seasonally-adjusted new house sales from 2014 to 2019 with the report for Apr 2019 on May 23, 2019 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). The US Census Bureau revised all seasonally adjusted new house sales from 2015 to 2020 with the report of Apr 2020 on May 26, 2020 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). House sales fell in 49 of 112 months from Jan 2011 to Apr 2020 with monthly declines of 5 in 2011, 4 in 2012, 5 in 2013, 6 in 2014, 3 in 2015, 7 in 2016, 5 in 2017, 7 in 2018, 5 in 2019 and 2 in 2020. In Jan-Apr 2012, house sales increased at the annual equivalent rate of 11.8 percent and at 22.3 percent in May-Sep 2012. There was significant strength in Sep-Dec 2011 with annual equivalent rate of 48.4 percent. Sales of new houses fell at 7.0 percent in Oct 2012 with increase of 9.5 percent in Nov 2012. Sales of new houses rebounded 11.8 percent in Jan 2013 with annual equivalent rate of 55.7 percent from Oct 2012 to Jan 2013 because of the increase at 11.8 percent in Jan 2013. New house sales decreased at annual equivalent 3.0 percent in Feb-Mar 2013. New house sales weakened, decreasing at 3.1 percent in annual equivalent from Apr to Dec 2013 with significant volatility illustrated by decline of 20.2 percent in Jul 2013 and increase of 10.2 percent in Oct 2013. New house sales fell 2.9 percent in Dec 2013. New house sales increased 2.3 percent in Jan 2014 and fell 5.2 percent in Feb 2014, decreasing 3.6 percent in Mar 2014. New house sales decreased 0.5 percent in Apr 2014 and increased 11.9 percent in May 2014. New house sales fell 7.3 percent in Jun 2014 and decreased 3.8 percent in Jul 2014. New house sales increased at 8.0 percent in Jan-Aug 2014. New house sales jumped 13.4 percent in Aug 2014 and increased 3.1 percent in Sep 2014. New House sales increased 1.3 percent in Oct 2014 and fell 7.1 percent in Nov 2014. House sales fell at the annual equivalent rate of 11.4 percent in Sep-Nov 2014. New house sales increased 12.4 percent in Dec 2014 and increased 3.6 percent in Jan 2015. Sales of new houses increased 4.9 percent in Feb

2015 and fell 11.1 percent in Mar 2015. House sales increased 4.6 percent in Apr 2015. The annual equivalent rate in Dec 2014-Apr 2015 was 35.7 percent. New house sales changed 0.0 percent in May 2015 and fell 4.4 percent in Jun 2015, increasing 5.4 percent in Jul 2015. New house sales fell at annual equivalent 3.1 percent in May-Jul 2015. New house sales increased 2.4 percent in Aug 2015 and fell 12.0 percent in Sep 2015. New house sales decreased at annual equivalent 46.5 percent in Aug-Sep 2015. New house sales increased 5.7 percent in Oct 2015 and increased 4.6 percent in Nov 2015, increasing 8.3 percent in Dec 2015. New house sales increased at the annual equivalent rate of 105.6 percent in Oct-Dec 2015. New house sales decreased 6.8 percent in Jan 2016 at the annual equivalent rate of minus 57.0 percent. New house sales increased 1.2 percent in Feb 2016 and increased 2.1 percent in Mar 2016. New house sales jumped at 8.6 percent in Apr 2016. New house sales increased at the annual equivalent rate of 58.5 percent in Feb-Apr 2016. New house sales decreased 1.9 percent in May 2016 and decreased 0.4 percent in Jun 2016. New house sales jumped 14.5 percent in Jul 2016. New house sales increased at the annual equivalent rate of 56.7 percent in May-Jul 2016. New house sales fell 8.6 percent in Aug 2016 and decreased 2.9 percent in Sep 2016, increasing 1.8 percent in Oct 2016. New house sales fell at the annual equivalent rate of minus 33.4 percent in Aug-Oct 2016. New house sales decreased at 1.0 percent in Nov 2016 and fell at 1.8 percent in Dec 2016. New house sales fell at 15.6 percent annual equivalent in Nov-Dec 2016. New house sales increased at 4.3 percent in Jan 2017 and increased at 2.1 percent in Feb 2017. New house sales increased at 45.8 percent in Jan-Feb 2017. New house sales increased at 5.7 percent in Mar 2017 and fell at 6.7 percent in Apr 2017. New house sales decreased at annual equivalent 8.0 percent in Mar-Apr 2017. New house sales increased at 4.1 percent in May 2017 and increased at 1.1 percent in Jun 2017. New house sales increased at annual equivalent 35.9 percent in May-Jun 2017. New house sales decreased at 8.9 percent in Jul 2017 and decreased at 0.9 percent in Aug 2017, increasing at 13.9 percent in Sep 2017. New house sales increased at annual equivalent 11.8 percent in Jul-Sep 2017. New house sales decreased at 1.7 percent in Oct 2017. New house sales increased at 13.6 percent in Nov 2017. New house sales increased at annual equivalent 93.9 percent in Oct-Nov 2017. New house sales decreased at 7.7 percent in Dec 2017 and decreased at 5.3 percent in Jan 2018. New house sales decreased at annual equivalent 55.4 percent in Dec 2017-Jan 2018. New house sales increased at 2.4 percent in Feb 2018, increasing at 3.9 percent in Mar 2018. New house sales increased at 45.0 percent in Feb-Mar 2018. New house sales decreased at 3.8 percent in Apr 2018 and increased at 3.1 percent in May 2018. New House sales decreased at annual equivalent 4.8 percent in Apr-May 2018. New house sales decreased at 6.7 percent in Jun 2018 and increased at 0.7 percent in Jul 2018. New House sales decreased at annual equivalent 31.2 percent in Jun-Jul 2018. New house sales decreased at 3.1 percent in Aug 2018 and decreased at 0.3 percent in Sep 2018. New house sales decreased at annual equivalent 18.7 percent in Aug-Sep 2018. New house sales fell at 7.4 percent in Oct 2018 and increased at 11.2 percent in Nov 2018. New house sales increased at annual equivalent 19.2 percent in Oct-Nov 2018. New house sales decreased at 8.1 percent in Dec 2018 and increased at 12.9 percent in Jan 2019. New House sales increased at annual equivalent 24.8 percent in Dec 2018-Jan 2019. New house sales increased at 4.4 percent in Feb 2019 and increased at 5.3 percent in Mar 2019. New house sales increased at annual equivalent 76.5 percent in Feb-Mar 2019. New house sales fell at 5.1 percent in Apr 2019 and fell at 9.6 percent in May 2019. New house sales decreased at annual equivalent 60.1 percent in Apr-May 2019. New house sales increased at 21.0 percent in Jun 2019 and decreased at 9.0 percent in Jul 2019. New house sales increased at annual equivalent 78.2 percent in Jun-Jul 2019. New house sales increased at 6.8 percent in Aug 2019 and increased at 2.8 percent in Sep 2019. New house sales decreased at 2.8 percent in Oct 2019. New house sales increased at annual equivalent 29.7 percent in Aug-Oct 2019. New house sales decreased at 1.4 percent in Nov 2019. New house sales decreased at annual equivalent 15.6 percent in Nov 2019. New house sales increased at 5.0 percent in Dec 2019. New house sales increased at 5.9 percent in Jan 2020. New house sales increased at annual equivalent 89.0 percent in Dec 2019-Jan 2020. New house sales decreased at 7.4 percent in Feb 2020. New house sales decreased at 13.7 percent in Mar 2020 in the COVID-19 event. New house sales decreased at annual equivalent 74.0 percent in Feb-Mar 2020. New house sales increased at 0.6 percent in Apr 2020. New house sales increased at annual equivalent 7.4 percent in Apr 2020. There are wide monthly oscillations. Robbie Whelan and Conor Dougherty, writing on “Builders fuel home sale rise,” on Feb 26, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324338604578327982067761860.html), analyze how builders have provided financial assistance to home buyers, including those short of cash and with weaker credit background, explaining the rise in new home sales and the highest gap between prices of new and existing houses. The 30-year conventional mortgage rate increased from 3.40 on Apr 25, 2013 to 4.58 percent on Aug 22, 2013 (http://www.federalreserve.gov/releases/h15/data.htm), which could also be a factor in recent weakness with improvement after the rate fell to 4.26 in Nov 2013. The conventional mortgage rate rose to 4.48 percent on Dec 26, 2013 and fell to 4.32 percent on Jan 30, 2014. The conventional mortgage rate increased to 4.37 percent on Feb 26, 2014 and 4.40 percent on Mar 27, 2014. The conventional mortgage rate fell to 4.14 percent on Apr 22, 2014, stabilizing at 4.14 on Jun 26, 2014. The conventional mortgage rate stood at 3.93 percent on Aug 20, 2015 and at 3.91 percent on Sep 17, 2015. The conventional mortgage rate was at 3.79 percent on Oct 22, 2015. The conventional mortgage rate was 3.97 percent on Nov 20, 2015. The conventional mortgage rate was 3.97 percent on Dec 18, 2015, and 3.92 percent on Jan 14, 2016. The conventional mortgage rate was 3.65 percent on Feb 19, 2016. The commercial mortgage rate was 3.73 percent on Mar 17, 2016 and 3.59 percent on Apr 21, 2016. The conventional mortgage rate was 3.58 on May 19, 2016. The conventional mortgage rate was 3.54 percent on Jun 19, 2016 and 3.45 percent on Jul 21, 2016. The conventional mortgage rate was 3.43 percent on Aug 18, 2016 and 3.48 percent on Sep 22, 2016. The conventional mortgage rate was 3.94 on Nov 17, 2016 and 4.30 percent on Dec 22. The conventional mortgage rate was 4.19 percent on Jan 26, 2017 and 4.15 percent on Feb 17, 2017. The conventional mortgage rate was 4.1 percent on Mar 16, 2017. The conventional mortgage rate was 3.97 percent on Apr 20, 2017. The conventional mortgage rate was 4.05 percent on May 18, 2017. The conventional mortgage rate was 3.90 percent on Jun 22, 2017. The conventional mortgage rate was 3.96 percent on Jul 20, 2017. The conventional mortgage rate was 3.90 percent on Aug 18, 2017. The conventional mortgage rate was 3.83 percent on Sep 21, 2017. The conventional mortgage rate was 3.88 percent on Oct 20, 2017. The conventional mortgage rate was 3.92 percent on Nov 22, 2017 and 3.94 on Dec 21, 2017. The conventional mortgage rate was 4.04 percent on Jan 18, 2018. The conventional mortgage rate was 4.40 percent on Feb 22, 2018. The conventional rate was 4.43 percent on Mar 1, 2018. The conventional mortgage rate was 4.45 percent on Mar 22, 2018. The conventional mortgage rate was 4.47 on Apr 19, 2018. The conventional mortgage rate was 4.87 percent in May 31, 2018. The conventional mortgage rate was 4.57 percent on Jun 21, 2018. The conventional mortgage rate was 4.52 percent on Jul 19, 2018. The conventional mortgage rate was 4.53 percent on Aug 16, 2018. The conventional mortgage rate was 4.65 percent on Sep 20, 2018. The conventional mortgage rate was 4.85 percent on Oct 18, 2018. The conventional mortgage rate was 4.81 percent on Nov 21, 2018. The conventional mortgage rate was 4.35 percent in Feb 2019. The conventional mortgage rate was 4.41 percent on Mar 7, 2019. The conventional mortgage rate was 4.06 percent on Mar 28, 2019. The conventional mortgage rate was 4.12 percent on Apr 5, 2019. The conventional mortgage rate was at 4.06 percent on May 23, 2019. The conventional mortgage rate was 3.84 percent on Jun 20, 2019. The conventional mortgage rate was at 3.81 percent on Jul 18, 2019. The conventional mortgage rate was 3.60 on Aug 22, 2019. The conventional mortgage rate was 3.66 percent on Nov 21, 2019. The conventional mortgage rate was 3.74 percent on

Dec 26, 2019. The conventional mortgage rate was 3.60 on Jan 24, 2020. The conventional mortgage rate was 3.49 percent on Feb 21, 2020. The conventional mortgage rate was 3.65 percent on Mar 19, 2020. The conventional mortgage rate was 3.31 percent on Apr 16, 2020. The conventional mortgage rate was 3.24 percent on May 20, 2020. The conventional mortgage rate measured in a survey by Freddie Mac (http://www.freddiemac.com/pmms/ http://www.freddiemac.com/pmms/abtpmms.htm) is the “interest rate a lender would charge to lend mortgage money to a qualified borrower.

Table IIB-1, US, Sales of New Houses at Seasonally Adjusted (SA) Annual Equivalent Rate, Thousands and % 

	SA Annual Rate Thousands	∆%
Apr	623	0.6
AE ∆% Apr		7.4
Mar	619	-13.7
Feb	717	-7.4
AE ∆% Mar-Feb		-74.0
Jan	774	5.9
Dec 2019	731	5.0
AE ∆% Dec-Jan		89.0
Nov	696	-1.4
AE ∆% Nov		-15.6
Oct	706	-2.8
Sep	726	2.8
Aug	706	6.8
AE ∆% Aug-Oct		29.7
Jul	661	-9.0
Jun	726	21.0
AE ∆% Jun-Jul		78.2
May	600	-9.6
Apr	664	-5.1
AE ∆% Apr-May		-60.1
Mar	700	5.3
Feb	665	4.4
AE ∆% Feb-Mar		76.5
Jan	637	12.9
Dec 2018	564	-8.1
AE ∆% Dec-Jan		24.8
Nov	614	11.2
Oct	552	-7.4
AE ∆% Oct-Nov		19.2
Sep	596	-0.3
Aug	598	-3.1
AE ∆% Aug-Sep		-18.7
Jul	617	0.7
Jun	613	-6.7
AE ∆% Jun-Jul		-31.2
May	657	3.1
Apr	637	-3.8
AE ∆% Apr-May		-4.8
Mar	662	3.9
Feb	637	2.4
AE ∆% Feb-Mar		45.0
Jan	622	-5.3
Dec 2017	657	-7.7
AE ∆% Dec-Jan		-55.4
Nov	712	13.6
Oct	627	-1.7
AE ∆% Oct-Nov		93.9
Sep	638	13.9
Aug	560	-0.9
Jul	565	-8.9
AE ∆% Jul-Sep		11.8
Jun	620	1.1
May	613	4.1
AE ∆% May -Jun		35.9
Apr	589	-6.7
Mar	631	5.7
AE ∆% Mar-Apr		-8.0
Feb	597	2.1
Jan	585	4.3
AE ∆% Jan-Feb		45.8
Dec 2016	561	-1.8
Nov	571	-1.0
AE ∆% Nov-Dec		-15.6
Oct	577	1.8
Sep	567	-2.9
Aug	584	-8.6
AE ∆% Aug-Oct		-33.4
Jul	639	14.5
Jun	558	-0.4
May	560	-1.9
AE ∆% May-Jul		56.7
Apr	571	8.6
Mar	526	2.1
Feb	515	1.2
AE ∆% Feb-Apr		58.5
Jan	509	-6.8
AE ∆% Jan		-57.0
Dec 2015	546	8.3
Nov	504	4.6
Oct	482	5.7
AE ∆% Oct-Dec		105.6
Sep	456	-12.0
Aug	518	2.4
AE ∆% Aug-Sep		-46.5
Jul	506	5.4
Jun	480	-4.4
May	502	0.0
AE ∆% May-Jul		-3.1
Apr	502	4.6
Mar	480	-11.1
Feb	540	4.9
Jan	515	3.6
Dec 2014	497	12.4
AE ∆% Dec-Apr		35.7
Nov	442	-7.1
Oct	476	1.3
Sep	470	3.1
AE ∆% Sep-Nov		-11.4
Aug	456	13.4
Jul	402	-3.8
Jun	418	-7.3
May	451	11.9
Apr	403	-0.5
Mar	405	-3.6
Feb	420	-5.2
Jan	443	2.3
AE ∆% Jan-Aug		8.0
Dec 2013	433	-2.9
Nov	446	0.5
Oct	444	10.2
Sep	403	5.8
Aug	381	1.6
Jul	375	-20.2
Jun	470	9.8
May	428	-2.9
Apr	441	-0.7
AE ∆% Apr-Dec		-3.1
Mar	444	-0.7
Feb	447	0.2
AE ∆% Feb-Mar		-3.0
Jan	446	11.8
Dec 2012	399	1.8
Nov	392	9.5
Oct	358	-7.0
AE ∆% Oct-Jan		55.7
Sep	385	2.7
Aug	375	1.6
Jul	369	2.5
Jun	360	-2.7
May	370	4.5
AE ∆% May-Sep		22.3
Apr	354	0.0
Mar	354	-3.3
Feb	366	9.3
Jan	335	-1.8
AE ∆% Jan-Apr		11.8
Dec 2011	341	4.0
Nov	328	3.8
Oct	316	3.9
Sep	304	1.7
AE ∆% Sep-Dec		48.4
Aug	299	1.0
Jul	296	-1.7
Jun	301	-1.3
May	305	-1.6
AE ∆% May-Aug		-10.3
Apr	310	3.3
Mar	300	11.1
Feb	270	-12.1
Jan	307	-5.8
AE ∆% Jan-Apr		-14.2
Dec 2010	326	13.6

AE: Annual Equivalent

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

There is additional information of the report of new house sales in Table IIB-2. The stock of unsold houses fell from rates of 6 to 8 percent of sales in 2011 to 4 to 5 percent in 2013 and 6.3 percent in Apr 2020. Robbie Whelan and Conor Dougherty, writing on “Builders fuel home sale rise,” on Feb 26, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324338604578327982067761860.html), find that inventories of houses have declined as investors acquire distressed houses of higher quality. Median and average house prices oscillate. In Apr 2020, median prices of new houses sold not seasonally adjusted (NSA) decreased 5.2 percent after decreasing 1.4 percent in

Apr 2020. Average prices decreased 3.4 percent in Apr 2020 and decreased 1.8 percent in Mar 2020. Between Dec 2010 and Apr 2020, median prices increased 28.5 percent, with increases of 6.0 percent in Feb 2016, 4.9 percent in Nov 2015, 2.2 percent in Sep 2015, 13.6 percent in Oct 2014, 4.0 percent in Aug 2014, 4.0 percent in May 2014 and 5.2 percent in Mar 2014. Average prices increased 25.0 percent between Dec 2010 and Apr 2020, with increases of 5.1 percent in Mar 2016, 4.0 percent in Sep 2015, 4.4 percent in Jul 2015 and 18.3 percent in Oct 2014. Between Dec 2010 and Dec 2012, median prices increased 7.1 percent and average prices increased 2.6 percent. Price increases concentrated in 2012 with increase of median prices of 18.2 percent from Dec 2011 to Dec 2012 and of average prices of 13.8 percent. Median prices increased 16.7 percent from Dec 2012 to Dec 2014, with increase of 13.6 percent in Oct 2014, while average prices increased 24.7 percent, with increase of 18.3 percent in Oct 2014. Median prices decreased 1.5 percent from Dec 2014 to Dec 2015 while average prices fell 5.5 percent. Median prices increased 10.1 percent from Dec 2015 to Dec 2016 while average prices increased 8.5 percent. Median prices increased 5.0 percent from Dec 2016 to Dec 2017 while average prices increased 5.3 percent. Median prices decreased 4.0 percent from Dec 2017 to Dec 2018 while average prices decreased 5.2 percent. Median prices decreased 0.1 percent from Dec 2018 to Dec 2019 while average prices decreased 1.1 percent. Median prices decreased 8.6 percent from Apr 2019 to Apr 2020 while average prices decreased 5.4 percent. Robbie Whelan, writing on “New homes hit record as builders cap supply,” on May 24, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323475304578500973445311276.html?mod=WSJ_economy_LeftTopHighlights), finds that homebuilders are continuing to restrict the number of new homes for sale. Restriction of available new homes for sale increases prices paid by buyers.

Table IIB-2, US, New House Stocks and Median and Average New Homes Sales Price

	Unsold* Stocks in Equiv. Months of Sales SA %	Median New House Sales Price USD NSA	Month ∆%	Average New House Sales Price USD NSA	Month ∆%
Apr 2020	6.3	309,900	-5.2	364,500	-3.4
Mar	6.4	326,900	-1.4	377,400	-1.8
Feb	5.5	331,400	0.8	384,300	0.1
Jan	5.0	328,900	-0.2	384,000	1.7
Dec 2019	5.3	329,500	0.5	377,700	-1.7
Nov	5.6	328,000	1.7	384,400	1.1
Oct	5.5	322,400	2.1	380,300	2.2
Sep	5.3	315,700	-3.5	372,100	-5.2
Aug	5.5	327,000	6.1	392,700	5.1
Jul	6.0	308,300	-1.1	373,500	3.2
Jun	5.5	311,800	-0.3	361,900	-4.5
May	6.7	312,700	-7.8	379,100	-1.6
Apr	6.1	339,000	9.1	385,400	3.4
Mar	5.8	310,600	-3.2	372,700	-2.8
Feb	6.1	320,800	5.0	383,600	6.2
Jan	6.5	305,400	-7.4	361,100	-5.4
Dec 2018	7.4	329,700	6.9	381,800	4.0
Nov	6.5	308,500	-6.0	367,100	-7.0
Oct	7.2	328,300	0.0	394,900	2.2
Sep	6.5	328,300	2.1	386,400	1.4
Aug	6.4	321,400	-1.9	380,900	-2.9
Jul	6.1	327,500	5.5	392,300	6.0
Jun	6.1	310,500	-2.0	370,100	-0.7
May	5.5	316,700	0.7	372,600	-3.2
Apr	5.7	314,400	-6.3	385,100	4.3
Mar	5.4	335,400	2.5	369,200	-1.2
Feb	5.6	327,200	-0.7	373,600	-1.1
Jan	5.6	329,600	-4.0	377,800	-6.2
Dec 2017	5.4	343,300	0.0	402,900	3.7
Nov	4.9	343,400	7.5	388,500	-1.4
Oct	5.5	319,500	-3.6	394,000	3.9
Sep	5.3	331,500	5.5	379,300	2.7
Aug	6.0	314,200	-2.7	369,200	-0.9
Jul	5.9	322,900	2.4	372,400	0.5
Jun	5.3	315,200	-2.6	370,600	-2.1
May	5.3	323,600	4.0	378,400	3.4
Apr	5.5	311,100	-3.3	365,800	-4.8
Mar	5.0	321,700	8.0	384,400	3.8
Feb	5.2	298,000	-5.5	370,500	3.6
Jan	5.3	315,200	-3.6	357,700	-6.5
Dec 2016	5.4	327,000	3.8	382,500	5.3
Nov	5.2	315,000	4.0	363,400	3.2
Oct	5.1	302,800	-3.8	352,200	-3.8
Sep	5.2	314,800	5.3	366,100	3.1
Aug	4.9	298,900	0.5	355,100	0.6
Jul	4.5	297,400	-4.4	353,000	-1.3
Jun	5.2	311,200	5.4	357,800	2.3
May	5.2	295,200	-7.3	349,700	-5.3
Apr	5.1	318,300	5.0	369,300	2.9
Mar	5.5	303,200	-0.9	359,000	5.1
Feb	5.6	305,800	6.0	341,700	-5.4
Jan	5.6	288,400	-2.9	361,200	2.5
Dec 2015	5.1	297,100	-5.0	352,500	-5.5
Nov	5.5	312,600	4.9	373,200	1.2
Oct	5.6	298,000	-0.5	368,900	3.3
Sep	5.9	299,500	2.2	357,200	4.0
Aug	5.0	293,000	0.2	343,300	0.6
Jul	5.2	292,300	2.5	341,200	4.4
Jun	5.4	285,100	-0.8	326,900	-2.8
May	5.0	287,500	-2.4	336,200	-1.2
Apr	4.9	294,500	2.8	340,400	-2.5
Mar	5.1	286,600	0.0	349,300	0.9
Feb	4.5	286,600	-1.8	346,300	-0.6
Jan	4.8	292,000	-3.2	348,300	-6.7
Dec 2014	5.1	301,500	1.1	373,200	7.0
Nov	5.7	298,300	0.4	348,900	-7.6
Oct	5.3	297,000	13.6	377,500	18.3
Sep	5.4	261,500	-10.4	319,100	-10.4
Aug	5.4	291,700	4.0	356,200	3.2
Jul	6.2	280,400	-2.3	345,200	2.1
Jun	5.7	287,000	0.5	338,100	4.5
May	5.2	285,600	4.0	323,500	-0.5
Apr	5.7	274,500	-2.8	325,100	-1.9
Mar	5.6	282,300	5.2	331,500	1.7
Feb	5.3	268,400	-0.5	325,900	-3.4
Jan	5.1	269,800	-2.1	337,300	5.0
Dec 2013	5.2	275,500	-0.6	321,200	-4.3
Nov	5.0	277,100	4.8	335,600	0.0
Oct	4.9	264,300	-2.0	335,700	4.4
Sep	5.4	269,800	5.7	321,400	3.4
Aug	5.5	255,300	-2.6	310,800	-5.8
Jul	5.5	262,200	0.9	329,900	7.8
Jun	4.1	259,800	-1.5	306,100	-2.5
May	4.6	263,700	-5.6	314,000	-6.8
Apr	4.4	279,300	8.5	337,000	12.3
Mar	4.2	257,500	-2.9	300,200	-3.9
Feb	4.1	265,100	5.4	312,500	1.8
Jan	4.0	251,500	-2.6	306,900	2.6
Dec 2012	4.5	258,300	5.4	299,200	2.9
Nov	4.6	245,000	-0.9	290,700	1.9
Oct	4.9	247,200	-2.9	285,400	-4.1
Sep	4.5	254,600	0.6	297,700	-2.6
Aug	4.6	253,200	6.7	305,500	8.2
Jul	4.6	237,400	2.1	282,300	3.9
Jun	4.8	232,600	-2.8	271,800	-3.2
May	4.7	239,200	1.2	280,900	-2.4
Apr	4.9	236,400	-1.4	287,900	1.5
Mar	4.9	239,800	0.0	283,600	3.5
Feb	4.8	239,900	8.2	274,000	3.1
Jan	5.3	221,700	1.4	265,700	1.1
Dec 2011	5.3	218,600	2.0	262,900	5.2
Nov	5.7	214,300	-4.7	250,000	-3.2
Oct	6.0	224,800	3.6	258,300	1.1
Sep	6.3	217,000	-1.2	255,400	-1.5
Aug	6.5	219,600	-4.5	259,300	-4.1
Jul	6.7	229,900	-4.3	270,300	-1.0
Jun	6.6	240,200	8.2	273,100	4.0
May	6.6	222,000	-1.2	262,700	-2.3
Apr	6.7	224,700	1.9	268,900	3.1
Mar	7.2	220,500	0.2	260,800	-0.8
Feb	8.1	220,100	-8.3	262,800	-4.7
Jan	7.3	240,100	-0.5	275,700	-5.5
Dec 2010	7.0	241,200	9.8	291,700	3.5

*Percent of new houses for sale relative to houses sold

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

The depressed level of residential construction and new house sales in the US is evident in Table IIB-3 providing new house sales not seasonally adjusted in Jan-Apr of various years. New house sales increased 1.7 percent from Jan-Apr 2019 to Jan-Apr 2020. New house sales increased 5.7 percent from Jan-Apr 2018 to Jan-Apr 2020. New house sales increased 13.6 percent from Jan-Apr 2017 to Jan-Apr 2020. Sales of new houses are higher in Jan-Apr 2020 relative to Jan-Apr 2016 with increase of 28.0 percent. Sales of new houses are higher in Jan-Apr 2020 relative to Jan-Apr 2015 with increase of 36.0 percent. Sales of new houses in Jan-Apr 2020 are substantially lower than in many years between 1996 and 2020 except for the years from 2008 to 2019. There are several other increases of 65.8 percent relative to 2014, 59.2 percent relative to Jan-Apr 2013, 100.0 percent relative to Jan-Apr 2012, 139.6 percent relative to Jan-Apr 2011, 89.1 percent relative to Jan-Apr 2010, and 108.6 percent relative to Jan-Apr 2009. New house sales in Jan-Apr 2020 are 27.4 percent higher than in Jan-Apr 2008. Sales of new houses in Jan-Apr 2020 are lower by 18.5 percent relative to Jan-Apr 2007, 37.1 percent relative to 2006, 45.5 percent relative to 2005 and 42.8 percent relative to 2004. The housing boom peaked in 2005 and 2006 when increases in fed funds rates to 5.25 percent in Jun 2006 from 1.0 percent in Jun 2004 affected subprime mortgages that were programmed for refinancing in two or three years on the expectation that price increases forever would raise home equity. Higher home equity would permit refinancing under feasible mortgages incorporating full payment of principal and interest (Gorton 2009EFM; see other references in http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html). Sales of new houses in Jan-Mar 2020 relative to the same period in 2003 fell 30.3 percent and 25.8 percent relative to the same period in 2002. Similar percentage declines are also for 2020 relative to years from 2000 to 2004. Sales of new houses in Jan-Apr 2020 decreased 19.9 per cent relative to the same period in 1998. The population of the US was 179.3 million in 1960 and 281.4 million in 2000 (Hobbs and Stoops 2002, 16). Detailed historical census reports are available from the US Census Bureau at (http://www.census.gov/population/www/censusdata/hiscendata.html). The estimate of the US population is 418.8 million in 2015. The US population increased by 133.6 percent from 1960 to 2015. The final row of Table IIB-3 reveals catastrophic data: sales of new houses in Jan-Mar 2020 of 242 thousand units are lower by 16.6 percent relative to 290 thousand units of houses sold in Jan-Apr 1977, which is fifteenth year when data become available in 1963. The civilian noninstitutional population increased from 122.416 million in 1963 to 259.175 million in 2019, or 111.7 percent (https://www.bls.gov/data/). The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (http://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.” Note: there are two equal total new houses sold in 2015 of 84 (39 in Jan and 45 in Feb) and 84 in 2016 (39 in Jan and 45 in Feb). There are two other equal total new houses sold of 68 in 2013 (32 in Jan and 36 in Feb) and 68 in 2014 (33 in Jan and 35 in 2014).

Table IIB-3, US, Sales of New Houses Not Seasonally Adjusted, Thousands and %

Jan-Apr 2020	242
Jan-Apr 2019	238
∆% Jan-Apr 2020/Jan-Apr 2019	1.7
Jan-Apr 2018	229
∆% Jan-Apr 2020/Jan-Apr 2018	5.7
Jan-Apr 2017	213
Jan-Apr 2020/Jan-Apr 2017	13.6
Jan-Apr 2016	189
∆% Jan-Apr 2020/Jan-Apr 2016	28.0
Jan-Apr 2015	178
∆% Jan-Apr 2020/Jan-Apr 2015	36.0
Jan-Apr 2014	146
∆% Jan-Apr 2020/Jan-Apr 2014	65.8
Jan-Apr 2013	152
∆% Jan-Apr 2020/Jan-Apr 2013	59.2
Jan-Apr 2012	121
∆% Jan-Apr 2020/Jan-Apr 2012	100.0
Jan-Apr 2011	101
∆% Jan-Apr 2020/  Jan-Apr 2011	139.6
Jan-Apr 2010	128
∆% Jan-Apr 2020/  Jan-Apr 2010	89.1
Jan-Apr 2009	116
∆% Jan-Apr 2020/  Jan-Apr 2009	108.6
Jan-Apr 2008	190
∆% Jan-Apr 2020/ Jan-Apr 2008	27.4
Jan-Apr 2007	297
∆% Jan-Apr 2020/Jan-Apr 2007	-18.5
Jan-Apr 2006	385
∆% Jan-Apr 2020/Jan-Apr 2006	-37.1
Jan-Apr 2005	444
∆% Jan-Apr 2020/Jan-Apr 2005	-45.5
Jan-Apr 2004	423
∆% Jan-Apr 2020/ Jan-Apr 2004	-42.8
Jan-Apr 2003	347
∆% Jan-Apr 2020/ Jan-Apr 2003	-30.3
Jan-Apr 2002	326
∆% Jan-Apr 2020/ Jan-Apr 2002	-25.8
Jan-Apr 2001	335
∆% Jan-Apr 2020/Jan-Apr 2001	-27.8
Jan-Apr 2000	311
∆% Jan-Apr 2020/Jan-Apr 2000	-22.2
Jan-Apr 1998	302
∆% Jan-Apr 2020/Jan-Apr 1998	-19.9
Jan-Apr 1977	290
∆% Jan-Apr 2020/Jan-Apr 1977	-16.6

*Computed using unrounded data

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

The revised level of 306 thousand new houses sold in 2011 is the lowest since 560 thousand in 1963 in the 53 years of available data while the level of 368 thousand in 2012 is only higher than 323 thousand in 2010. The level of sales of new houses of 437 thousand in 2014 is the lowest from 1963 to 2009 with exception of 412 thousand in 1982 and 436 thousand in 1981. The population of the US increased 129.4 million from 179.3 million in 1960 to 308.7 million in 2010, or 72.2 percent. The estimate of the US population is 418.8 million in 2015. The US population increased 133.6 percent from 1960 to 2015. The civilian noninstitutional population increased from 122.416 million in 1963 to 259.175 million in 2019, or 111.7 percent (https://www.bls.gov/data/). The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (https://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.”

Table IIB-4, US, New Houses Sold, NSA Thousands

Period	Sold During Period
1963	560
1964	565
1965	575
1966	461
1967	487
1968	490
1969	448
1970	485
1971	656
1972	718
1973	634
1974	519
1975	549
1976	646
1977	819
1978	817
1979	709
1980	545
1981	436
1982	412
1983	623
1984	639
1985	688
1986	750
1987	671
1988	676
1989	650
1990	534
1991	509
1992	610
1993	666
1994	670
1995	667
1996	757
1997	804
1998	886
1999	880
2000	877
2001	908
2002	973
2003	1,086
2004	1,203
2005	1,283
2006	1,051
2007	776
2008	485
2009	375
2010	323
2011	306
2012	368
2013	429
2014	437
2015	501
2016	561
2017	613
2018	617
2019	683

Source: US Census Bureau https://www.census.gov/construction/nrs/index.html

Chart IIB-1 of the US Bureau of the Census shows the sharp decline of sales of new houses in the US. Sales rose temporarily until about mid 2010 but then declined to a lower plateau followed by increase, stability and new oscillating increase. There is decrease in the final segment followed by marginal increase. There is renewed decline and stabilization.

Chart IIB-1, US, New One-Family Houses Sold in the US, SAAR (Seasonally Adjusted Annual Rate) 

Source: US Census Bureau

https://www.census.gov/construction/nrs/img/c25_curr.gif

Between 1991 and 2001, sales of new houses rose 78.4 percent at the average yearly rate of 6.0 percent, as shown in Table IB-5. Between 1995 and 2005 sales of new houses increased 92.4 percent at the yearly rate of 6.8 percent. There are similar rates in all years from 2000 to 2005. The boom in housing construction and sales began in the 1980s and 1990s. The collapse of real estate culminated several decades of housing subsidies and policies to lower mortgage rates and borrowing terms (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009b), 42-8). Sales of new houses sold in 2019 fell 9.8 percent relative to the same period in 1996 and fell 46.8 percent relative to 2005.

Table IIB-5, US, Percentage Change and Average Yearly Rate of Growth of Sales of New One-Family Houses

	∆%	Average Yearly % Rate
1963-2019	22.0	0.4
1991-2001	78.4	6.0
1995-2005	92.4	6.8
2000-2005	46.3	7.9
1996-2019	-9.8	NA
2000-2019	-22.1	NA
2005-2019	-46.8	NA

NA: Not Applicable

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

Chart IIB-2 of the US Bureau of the Census provides the entire monthly sample of new houses sold in the US between Jan 1963 and Apr 2020 without seasonal adjustment. The series is almost stationary until the 1990s. There is sharp upward trend from the early 1990s to 2005-2006 after which new single-family houses sold collapse to levels below those in the beginning of the series.

Chart IIB-2, US, New Single-family Houses Sold, NSA, 1963-2020

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

The available historical annual data of median and average prices of new houses sold in the US between 1963 and 2019 is in Table IIB-6. On a yearly basis, median and average prices reached a peak in 2007 and then fell substantially. There is recovery in 2012-2018 followed by decline in 2019.

Table IIB-6, US, Median and Average Prices of New Houses Sold, Annual Data

Period	Median	Average
1963	$18,000	$19,300
1964	$18,900	$20,500
1965	$20,000	$21,500
1966	$21,400	$23,300
1967	$22,700	$24,600
1968	$24,700	$26,600
1969	$25,600	$27,900
1970	$23,400	$26,600
1971	$25,200	$28,300
1972	$27,600	$30,500
1973	$32,500	$35,500
1974	$35,900	$38,900
1975	$39,300	$42,600
1976	$44,200	$48,000
1977	$48,800	$54,200
1978	$55,700	$62,500
1979	$62,900	$71,800
1980	$64,600	$76,400
1981	$68,900	$83,000
1982	$69,300	$83,900
1983	$75,300	$89,800
1984	$79,900	$97,600
1985	$84,300	$100,800
1986	$92,000	$111,900
1987	$104,500	$127,200
1988	$112,500	$138,300
1989	$120,000	$148,800
1990	$122,900	$149,800
1991	$120,000	$147,200
1992	$121,500	$144,100
1993	$126,500	$147,700
1994	$130,000	$154,500
1995	$133,900	$158,700
1996	$140,000	$166,400
1997	$146,000	$176,200
1998	$152,500	$181,900
1999	$161,000	$195,600
2000	$169,000	$207,000
2001	$175,200	$213,200
2002	$187,600	$228,700
2003	$195,000	$246,300
2004	$221,000	$274,500
2005	$240,900	$297,000
2006	$246,500	$305,900
2007	$247,900	$313,600
2008	$232,100	$292,600
2009	$216,700	$270,900
2010	$221,800	$272,900
2011	$227,200	$267,900
2012	$245,200	$292,200
2013	$268,900	$324,500
2014	$288,500	$347,700
2015	$294,200	$352,700
2016	$307,800	$360,900
2017	$323,100	$384,900
2018	$326,400	$385,000
2019	$321,500	$383,900

Source: US Census Bureau https://www.census.gov/construction/nrs/index.html

Prices rose sharply between 2000 and 2005 as shown in Table IIB-7. In fact, prices in 2019 are higher than in 2000. Between 2006 and 2019, median prices of new houses sold increased 30.2 percent and average prices increased 25.3 percent. Between 2018 and 2019, median prices decreased 1.5 percent and average prices decreased 0.3 percent.

Table IIB-7, US, Percentage Change of New Houses Median and Average Prices, NSA, ∆%

	Median New  Home Sales Prices ∆%	Average New Home Sales Prices ∆%
∆% 2000 to 2003	15.4	19.0
∆% 2000 to 2005	42.5	43.5
∆% 2000 to 2019	90.2	85.5
∆% 2005 to 2019	33.5	29.3
∆% 2000 to 2006	45.9	47.8
∆% 2006 to 2019	30.4	25.5
∆% 2009 to 2019	48.4	41.7
∆% 2010 to 2019	45.0	40.7
∆% 2011 to 2019	41.5	43.3
∆% 2012 to 2019	31.1	31.4
∆% 2013 to 2019	19.6	18.3
∆% 2014 to 2019	11.4	10.4
∆% 2015 to 2019	9.3	8.8
∆% 2016 to 2019	4.5	6.4
∆% 2017 to 2019	-0.5	-0.3
∆% 2018 to 2019	-1.5	-0.3

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

Chart IIB-3 of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 1963 to Apr 2020. There is long-term sharp upward trend with few declines until the current collapse. Median prices increased sharply during the Great Inflation of the 1960s and 1970s and paused during the savings and loans crisis of the late 1980s and the recession of 1991. Housing subsidies throughout the 1990s caused sharp upward trend of median new house prices that accelerated after the fed funds rate of 1 percent from 2003 to 2004. There was sharp reduction of prices after 2006 with recovery recently above earlier prices.

Chart IIB-3, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2020

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

Chart IIB-4 of the US Census Bureau provides average prices of new houses sold from the mid-1970s to Apr 2020. There is similar behavior as with median prices of new houses sold in Chart IIB-3. The only stress occurred in price pauses during the savings and loans crisis of the late 1980s and the collapse after 2006 with recent recovery.

Chart IIB-4, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1975-2020

Source: US Census Bureau

https://www.census.gov/construction/nrs/index.html

Chart IIB-5 of the Board of Governors of the Federal Reserve System provides the rate for the 30-year conventional mortgage, the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1954 to 2016. All rates decline throughout the period from the Great Inflation of the 1970s through the following Great Moderation and until currently. In Apr 1971, the fed funds rate was 4.15 percent and the conventional mortgage rate 7.31 percent. In November 2012, the fed funds rate was 0.16 percent, the yield of the 30-year Treasury 2.80 percent and the conventional mortgage rate 3.35. The final segment shows an increase in the yield of the 30-year Treasury to 3.61 percent in July 2013 with the fed funds rate at 0.09 percent and the conventional mortgage at 4.37 percent. The final data point shows marginal decrease of the conventional mortgage rate to 3.60 percent in May 2016 with the yield of the 30-year Treasury bond at 2.63 percent and overnight rate on fed funds at 0.37 percent. The recent increase in interest rates if sustained could affect the US real estate market. Shayndi Raice and Nick Timiraos, writing on “Banks cut as mortgage boom ends,” on Jan 9, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303754404579310940019239208), analyze the drop in mortgage applications to a 13-year low, as measured by the Mortgage Bankers Association. Nick Timiraos, writing on “Demand for home loans plunges,” on Apr 24, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304788404579522051733228402?mg=reno64-wsj), analyzes data in Inside Mortgage Finance that mortgage lending of $235 billion in IQ2014 is 58 percent lower than a year earlier and 23 percent below IVQ2013. Mortgage lending collapsed to the lowest level in 14 years. In testimony before the Committee on the Budget of the US Senate on May 8, 2004, Chair Yellen provides analysis of the current economic situation and outlook (http://www.federalreserve.gov/newsevents/testimony/yellen20140507a.htm): “One cautionary note, though, is that readings on housing activity--a sector that has been recovering since 2011--have remained disappointing so far this year and will bear watching.”

Chart IIB-5, US, Thirty-year Conventional Mortgage, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1954-2016

Source: Board of Governors of the Federal Reserve System

http://www.federalreserve.gov/releases/H15/default.htm

Chart IIB-5A of the Board of Governors of the Federal Reserve System provides the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1977 to 2020. The Board of Governors of the Federal Reserve System discontinued the conventional mortgage rate in its data bank. The final data point is 0.05 percent for the fed funds rate in Apr 2020 and 1.27 percent for the thirty-year Treasury bond resulting from the massive unconventional monetary policy in the COVID-19 event. The conventional mortgage rate stood at 3.31 percent in Apr 2020.

Chart IIB-5A, US, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1977-2020

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H15/default.htm

Table IIB-8, US, Fed Funds Rate, Thirty Year Treasury Bond and Conventional Mortgage Rate, Monthly, Percent per Year, Dec 2012 to Jan 2020

	Fed Funds Rate	Yield of Thirty Year Constant Maturity	Conventional Mortgage Rate
2012-12	0.16	2.88	3.35
2013-01	0.14	3.08	3.41
2013-02	0.15	3.17	3.53
2013-03	0.14	3.16	3.57
2013-04	0.15	2.93	3.45
2013-05	0.11	3.11	3.54
2013-06	0.09	3.4	4.07
2013-07	0.09	3.61	4.37
2013-08	0.08	3.76	4.46
2013-09	0.08	3.79	4.49
2013-10	0.09	3.68	4.19
2013-11	0.08	3.8	4.26
2013-12	0.09	3.89	4.46
2014-01	0.07	3.77	4.43
2014-02	0.07	3.66	4.3
2014-03	0.08	3.62	4.34
2014-04	0.09	3.52	4.34
2014-05	0.09	3.39	4.19
2014-06	0.1	3.42	4.16
2014-07	0.09	3.33	4.13
2014-08	0.09	3.2	4.12
2014-09	0.09	3.26	4.16
2014-10	0.09	3.04	4.04
2014-11	0.09	3.04	4
2014-12	0.12	2.83	3.86
2015-01	0.11	2.46	3.67
2015-02	0.11	2.57	3.71
2015-03	0.11	2.63	3.77
2015-04	0.12	2.59	3.67
2015-05	0.12	2.96	3.84
2015-06	0.13	3.11	3.98
2015-07	0.13	3.07	4.05
2015-08	0.14	2.86	3.91
2015-09	0.14	2.95	3.89
2015-10	0.12	2.89	3.8
2015-11	0.12	3.03	3.94
2015-12	0.24	2.97	3.96
2016-01	0.34	2.86	3.87
2016-02	0.38	2.62	3.66
2016-03	0.36	2.68	3.69
2016-04	0.37	2.62	3.61
2016-05	0.37	2.63	3.6
2016-06	0.38	2.45	3.57
2016-07	0.39	2.23	3.44
2016-08	0.4	2.26	3.44
2016-09	0.4	2.35	3.46
2016-10	0.4	2.5	3.47
2016-11	0.41	2.86	3.77
2016-12	0.54	3.11	4.2
2017-01	0.65	3.02	4.15
2017-02	0.66	3.03	4.17
2017-03	0.79	3.08	4.2
2017-04	0.9	2.94	4.05
2017-05	0.91	2.96	4.01
2017-06	1.04	2.8	3.9
2017-07	1.15	2.88	3.97
2017-08	1.16	2.8	3.88
2017-09	1.15	2.78	3.81
2017-10	1.15	2.88	3.9
2017-11	1.16	2.8	3.92
2017-12	1.3	2.77	3.95
2018-01	1.41	2.88	4.03
2018-02	1.42	3.13	4.33
2018-03	1.51	3.09	4.44
2018-04	1.69	3.07	4.47
2018-05	1.7	3.13	4.59
2018-06	1.82	3.05	4.57
2018-07	1.91	3.01	4.53
2018-08	1.91	3.04	4.55
2018-09	1.95	3.15	4.63
2018-10	2.19	3.34	4.83
2018-11	2.2	3.36	4.87
2018-12	2.27	3.10	4.64
2019-01	2.40	3.04	4.46
2019-02	2.40	3.02	4.37
2019-03	2.41	2.98	4.27
2019-04	2.42	2.94	4.14
2019-05	2.39	2.82	4.07
2019-06	2.38	2.57	3.80
2019-07	2.40	2.57	3.77
2019-08	2.13	2.12	3.62
2019-09	2.04	2.16	3.61
2019-10	1.83	2.19	3.69
2019-11	1.55	2.28	3.70
2019-12	1.55	2.30	3.72
2020-01	1.55	2.22	3.62
2020-02	1.58	1.97	3.47
2020-03	0.65	1.46	3.45
2020-04	0.05	1.27	3.31

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H15/default.htm

http://www.freddiemac.com/pmms/pmms30.html

IIB2 United States House Prices. The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). Table IIA2-1 provides the FHFA HPI for purchases only, which shows behavior similar to that of the Case-Shiller index but with lower magnitudes. House prices catapulted from 2000 to 2003, 2005 and 2006. From IVQ2000 to IVQ2006, the index for the US as a whole rose 55.0 percent, with 62.1 percent for New England, 72.0 percent for Middle Atlantic, 71.2 percent for South Atlantic but only by 33.1 percent for East South Central. Prices fell relative to 2014 for the US and all regions from 2006 with exception of increase of 2.6 percent for East South Central. Prices for the US increased 4.9 percent in IVQ2014 relative to IVQ2013 and 12.9 percent from IVQ2012 to IVQ2014. From IVQ2000 to IVQ2014, prices rose for the US and the four regions in Table IIA2-1.

Table IIA2-1, US, FHFA House Price Index Purchases Only NSA ∆%

	United States	New England	Middle Atlantic	South Atlantic	East South Central
IVQ2000 to IVQ2003	24.0	40.6	35.8	25.9	11.0
IVQ2000 to IVQ2005	50.5	65.0	67.6	62.9	25.4
IVQ2000 to IVQ2006	55.0	62.1	72.0	71.2	33.1
IVQ2005 to IVQ2014	-1.5	-8.7	-2.3	-7.4	8.9
IVQ2006 to IVQ2014	-4.4	-7.1	-4.8	-11.9	2.6
IVQ2007 to IVQ2014	-1.9	-5.1	-5.0	-8.6	0.7
IVQ2011 to IVQ2014	18.9	7.3	6.9	19.9	11.8
IVQ2012 to IVQ2014	12.9	6.8	5.7	13.8	8.6
IVQ2013 to IVQ2014	4.9	2.5	2.2	5.1	4.2
IVQ2000 to IVQ2014	48.3 144.27	50.6 138.40	63.7 127.30	50.9 140.28	36.6 146.07

Source: Federal Housing Finance Agency

http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx

Data of the FHFA HPI for the remaining US regions are in Table IIA2-2. Behavior is not very different from that in Table IIA2-1 with the exception of East North Central. House prices in the Pacific region doubled between 2000 and 2006. Although prices of houses declined sharply from 2005 and 2006 to 2014 with exception of West South Central and West North Central, there was still appreciation relative to 2000.

Table IIA2-2, US, FHFA House Price Index Purchases Only NSA ∆%

	West South Central	West North Central	East North Central	Mountain	Pacific
IVQ2000 to IVQ2003	11.1	18.3	14.7	18.9	44.6
IVQ2000 to IVQ2005	23.9	31.0	23.8	58.0	107.7
IVQ2000 to IVQ2006	31.6	33.7	23.7	68.6	108.7
IVQ2005 to IVQ2014	26.6	4.7	-5.4	-2.6	-14.7
IVQ2006 to IVQ2014	19.1	2.6	-5.4	-8.7	-15.1
IVQ2007 to IVQ2014	15.2	3.2	-2.1	-5.6	-6.0
IVQ2011 to IVQ2014	18.1	13.5	14.2	32.9	37.6
IVQ2012 to IVQ2014	12.1	8.9	11.1	17.9	24.4
IVQ2013 to IVQ2014	5.9	4.0	4.6	5.5	7.3
IVQ2000 to IVQ2014	56.8 145.53	37.1 158.59	17.1 155.13	53.9 172.46	77.1 132.21

Source: Federal Housing Finance Agency

http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx

Monthly and 12-month percentage changes of the FHFA House Price Index are in Table IIA2-3. Percentage monthly increases of the FHFA index were positive from Apr to Jul 2011 with exception of declines in May and Aug 2011 while 12-month percentage changes improved steadily from around minus 6.0 percent in Mar to May 2011 to minus 4.5 percent in Jun 2011. The FHFA house price index fell 0.6 percent in Oct 2011 and fell 3.3 percent in the 12 months ending in Oct 2011. There was significant recovery in Nov 2011 with increase in the house price index of 0.5 percent and reduction of the 12-month rate of decline to 2.4 percent. The house price index rose 0.3 percent in Dec 2011 and the 12-month percentage change improved to minus 1.5 percent. There was further improvement with revised change of minus 0.3 percent in Jan 2012 and decline of the 12-month percentage change to minus 1.3 percent. The index improved to positive change of 0.2 percent in Feb 2012 and decrease of 0.2 percent in the 12 months ending in Feb 2012. There was strong improvement in Mar 2012 with gain in prices of 0.8 percent and 1.7 percent in 12 months. The house price index of FHFA increased 0.5 percent in Apr 2012 and 2.1 percent in 12 months and improvement continued with increase of 0.6 percent in May 2012 and 3.1 percent in the 12 months ending in May 2012. Improvement consolidated with increase of 0.4 percent in Jun 2012 and 3.1 percent in 12 months. In Jul 2012, the house price index increased 0.3 percent and 3.2 percent in 12 months. Strong increase of 0.6 percent in Aug 2012 pulled the 12-month change to 4.1 percent. There was another increase of 0.5 percent in Oct 2012 and 4.8 percent in 12 months followed by increase of 0.5 percent in Nov 2012 and 4.8 percent in 12 months. The FHFA house price index increased 0.7 percent in Jan 2013 and 6.2 percent in 12 months. Improvement continued with increase of 0.5 percent in Apr 2013 and 7.0 percent in 12 months. In May 2013, the house price indexed increased 0.8 percent and 7.2 percent in 12 months. The FHFA house price index increased 0.6 percent in Jun 2013 and 7.4 percent in 12 months. In Jul 2013, the FHFA house price index increased 0.6 percent and 7.7 percent in 12 months. Improvement continued with increase of 0.3 percent in Aug 2013 and 7.3 percent in 12 months. In Sep 2013, the house price index increased 0.6 percent and 7.5 percent in 12 months. The house price index increased 0.3 percent in Oct 2013 and 7.2 percent in 12 months. In Nov 2013, the house price index increased 0.1 percent and increased 6.8 percent in 12 months. The house price index rose 0.5 percent in Dec 2013 and 6.8 percent in 12 months. Improvement continued with increase of 0.5 percent in Jan 2014 and 6.6 percent in 12 months. In Feb 2014, the house price index increased 0.5 percent and 6.4 percent in 12 months. The house price index increased 0.3 percent in Mar 2014 and 5.8 percent in 12 months. In Apr 2014, the house price index increased 0.3 percent and increased 5.5 percent in 12 months. The house price index increased 0.2 percent in May 2014 and 4.8 percent in 12 months. In Jun 2014, the house price index increased 0.5 percent and 4.7 percent in 12 months. The house price index increased 0.3 percent in Jul 2014 and 4.4 percent in 12 months. In Sep 2014, the house price index increased 0.1 percent and increased 4.1 percent in 12 months. The house price index increased 0.6 percent in Oct 2014 and 4.5 percent in 12 months. In Nov 2014, the house price index increased 0.4 percent and 4.8 percent in 12 months. The house price index increased 0.6 percent in Dec 2014 and increased 5.0 percent in 12 months. In Mar 2015, the house price index increased 0.3 percent and increased 5.2 percent in 12 months. In Apr 2015, the house price index increased 0.3 percent and 5.1 percent in 12 months. The house price index increased 0.6 percent in May 2015 and 5.5 percent in 12 months. House prices increased 0.4 percent in Jun 2015 and 5.3 percent in 12 months. The house price index increased 0.5 percent in Jul 2015 and increased 5.4 percent in 12 months. House prices increased 0.1 percent in Aug 2015 and increased 5.1 percent in 12 months. In Sep 2015, the house price index increased 0.6 percent and increased 5.6 percent in 12 months. The house price index increased 0.6 percent in Oct 2015 and increased 5.6 percent in 12 months. House prices increased 0.6 percent in Nov 2015 and increased 5.7 percent in 12 months. The house price index increased 0.4 percent in Dec 2015 and increased 5.5 percent in 12 months. House prices increased 0.5 percent in Jan 2016 and increased 6.0 percent in 12 months. The house price index increased 0.2 percent in Feb 2016 and increased 5.5 percent in 12 months. House prices increased 0.7 percent in Mar 2016 and increased 5.8 percent in 12 months. The house price index increased 0.4 percent in Apr 2016 and increased 5.8 percent in 12 months. House prices increased 0.4 percent in May 2016 and increased 5.6 percent in 12 months. The house price index increased 0.6 percent in Jun 2016 and increased 5.6 percent in 12 months. House prices increased 0.5 percent in Jul 2016 and increased 5.6 percent in 12 months. The house price index increased 0.5 percent in Aug 2016 and increased 6.1 percent in 12 months. House prices increased 0.6 percent in Sep 2016 and increased 6.1 percent in 12 months. The house price index increased 0.6 percent in Oct 2016 and increased 6.1 percent in 12 months. House prices increased 0.5 percent in Nov 2016 and increased 6.1 percent in 12 months. The house price index increased 0.5 percent in Dec 2016 and increased 6.3 percent in 12 months. House prices increased 0.0 percent in Jan 2017 and increased 5.7 percent in 12 months. In Feb 2017, the house price index increased 0.8 percent and increased 6.4 percent in 12 months. House prices increased 0.6 percent in Mar 2017 and increased 6.3 percent in 12 months. In Apr 2017, the house price index increased 0.7 percent and increased 6.6 percent in 12 months. House prices increased 0.4 percent in May 2017 and increased 6.5 percent in 12 months. The house price index increased 0.4 percent in Jun 2017 and increased 6.2 percent in 12 months. House prices increased 0.6 percent in Jul 2017 and increased 6.4 percent in 12 months. The house price index increased 0.7 percent in Aug 2017 and increased 6.6 percent in 12 months. House prices increased 0.5 percent in Sep 2017 and increased 6.4 percent in 12 months. The house price index increased 0.6 percent in Oct 2017 and increased 6.5 percent in 12 months. House prices increased 0.6 percent in Nov 2017 and increased 6.6 percent in 12 months. The house price index increased 0.4 percent in Dec 2017 and increased 6.5 percent in 12 months. The house price index increased 0.8 percent in Jan 2018 and increased 7.4 percent in 12 months. House prices increased 0.8 percent in Feb 2018 and increased 7.5 percent in 12 months. The house price index increased 0.3 percent in Mar 2018 and increased 7.1 percent in 12 months. House prices increased 0.3 percent in Apr 2018 and increased 6.6 percent in 12 months. The house price index increased 0.4 percent in May 2018 and increased 6.7 percent in 12 months ending in May 2018. House prices increased 0.5 percent in Jun 2018 and increased 6.7 percent in 12 months. The house price index increased 0.3 percent in July 2018 and increased 6.4 percent in 12 months. House prices increased 0.6 percent in Aug 2018 and increased 6.4 percent in 12 months. The house price index increased 0.2 percent in Sep 2018 and increased 6.2 percent in 12 months. House prices increased 0.5 percent in Oct 2018 and increased 6.1 percent in 12 months. The house price index increased 0.5 percent in Nov 2018 and increased 5.9 percent in 12 months. House prices increased 0.5 percent in Dec 2018 and increased 6.0 percent in 12 months. The house price index increased 0.6 percent in Jan 2019 and increased 5.8 percent in 12 months. House prices increased 0.3 percent in Feb 2019 and increased 5.4 percent in 12 months. The house price index increased 0.3 percent in Mar 2019 and increased 5.3 percent in 12 months. House prices increased 0.5 percent in Apr 2019 and increased 5.5 percent in 12 months. The house price index increased 0.3 percent in May 2019 and increased 5.3 percent in 12 months. House prices increased 0.3 percent in Jun 2019 and increased 5.1 percent in 12 months. The house price index increased 0.5 percent in Jul 2019 and increased 5.3 percent in 12 months. House prices increased 0.2 percent in Aug 2019 and increased 4.9 percent in 12 months. The house price index increased 0.8 percent in Sep 2019 and increased 5.5 percent in 12 months. House prices increased 0.4 percent in Oct 2019 and increased 5.4 percent in 12 months. The house price index increased 0.3 percent in Nov 2019 and increased 5.2 percent in 12 months. House prices increased 0.8 percent in Dec 2019 and increased 5.5 percent in 12 months. The house price index increased 0.5 percent in Jan 2020 and increased 5.5 percent in 12 months. House prices increased 0.8 percent in Feb 2020 and increased 6.1 percent in 12 months. The house price index increased 0.1 percent in Mar 2020 and increased 5.9 percent in 12 months.

Table IIA2-3, US, FHFA House Price Index Purchases Only SA. Month and NSA 12-Month ∆%

		Month ∆% SA			12-Month ∆% NSA
3/1/2020		0.1			5.9
2/1/2020		0.8			6.1
1/1/2020		0.5			5.5
12/1/2019		0.8			5.5
11/1/2019		0.3			5.2
10/1/2019		0.4			5.4
9/1/2019		0.8			5.5
8/1/2019		0.2			4.9
7/1/2019		0.5			5.3
6/1/2019		0.3			5.1
5/1/2019		0.3			5.3
4/1/2019		0.5			5.5
3/1/2019		0.3			5.3
2/1/2019		0.3			5.4
1/1/2019		0.6			5.8
12/1/2018		0.5			6
11/1/2018		0.5			5.9
10/1/2018		0.5			6.1
9/1/2018		0.2			6.2
8/1/2018		0.6			6.4
7/1/2018		0.3			6.4
6/1/2018		0.5			6.7
5/1/2018		0.4			6.7
4/1/2018		0.3			6.6
3/1/2018		0.3			7.1
2/1/2018		0.8			7.5
1/1/2018		0.8			7.4
12/1/2017		0.4			6.5
11/1/2017		0.6			6.6
10/1/2017		0.6			6.5
9/1/2017		0.5			6.4
8/1/2017		0.7			6.6
7/1/2017		0.6			6.4
6/1/2017		0.4			6.2
5/1/2017		0.4			6.5
4/1/2017		0.7			6.6
3/1/2017		0.6			6.3
2/1/2017		0.8			6.4
1/1/2017		0			5.7
12/1/2016		0.5			6.3
11/1/2016		0.5			6.1
10/1/2016		0.6			6.1
9/1/2016		0.6			6.1
8/1/2016		0.5			6.1
7/1/2016		0.5			5.6
6/1/2016		0.6			5.6
5/1/2016		0.4			5.6
4/1/2016		0.4			5.8
3/1/2016		0.7			5.8
2/1/2016		0.2			5.5
1/1/2016		0.5			6
12/1/2015		0.4			5.5
11/1/2015		0.6			5.7
10/1/2015		0.6			5.6
9/1/2015		0.6			5.6
8/1/2015		0.1			5.1
7/1/2015		0.5			5.4
6/1/2015		0.4			5.3
5/1/2015		0.6			5.5
4/1/2015		0.3			5.1
3/1/2015		0.3			5.2
2/1/2015		0.8			5.1
1/1/2015		0.1			4.7
12/1/2014		0.6			5
11/1/2014		0.4			4.8
10/1/2014		0.6			4.5
9/1/2014		0.1			4.1
8/1/2014		0.5			4.6
7/1/2014		0.3			4.4
6/1/2014		0.5			4.7
5/1/2014		0.2			4.8
4/1/2014		0.3			5.5
3/1/2014		0.3			5.8
2/1/2014		0.5			6.4
1/1/2014		0.5			6.6
12/1/2013		0.5			6.8
11/1/2013		0.1			6.8
10/1/2013		0.3			7.2
9/1/2013		0.6			7.5
8/1/2013		0.3			7.3
7/1/2013		0.6			7.7
6/1/2013		0.6			7.4
5/1/2013		0.8			7.2
4/1/2013		0.5			7
3/1/2013		1			7.1
2/1/2013		0.7			6.7
1/1/2013		0.7			6.2
12/1/2012		0.5			5.2
11/1/2012		0.5			4.8
10/1/2012		0.5			4.8
9/1/2012		0.3			3.7
8/1/2012		0.6			4.1
7/1/2012		0.3			3.2
6/1/2012		0.4			3.1
5/1/2012		0.6			3.1
4/1/2012		0.5			2.1
3/1/2012		0.8			1.7
2/1/2012		0.2			-0.2
1/1/2012		-0.3			-1.3
12/1/2011		0.3			-1.5
11/1/2011		0.5			-2.4
10/1/2011		-0.6			-3.3
9/1/2011		0.6			-2.5
8/1/2011		-0.3			-4
7/1/2011		0.2			-3.7
6/1/2011		0.4			-4.5
5/1/2011		-0.3			-5.9
4/1/2011		0.2			-5.7
3/1/2011		-1			-5.9
2/1/2011		-1			-5.2
1/1/2011		-0.5			-4.5
12/1/2010		-0.7			-3.9
12/1/2009		-1			-2
12/1/2008		-0.3			-10.5
12/1/2007		-0.5			-3.4
12/1/2006		0.1			2.3
12/1/2005		0.6			9.8
12/1/2004		0.9			10.2
12/1/2003		0.8			8
12/1/2002		0.7			7.8
12/1/2001		0.6			6.7
12/1/2000		0.6			7.1
12/1/1999		0.5			6.1
12/1/1998		0.4			5.9
12/1/1997		0.3			3.4
12/1/1996		0.3			2.7
12/1/1995		0.4			3
12/1/1994		0			2.5
12/1/1993		0.5			3.1
12/1/1992		-0.1			2.4

Source: Federal Housing Finance Agency

https://www.fhfa.gov/DataTools

The bottom part of Table IIA2-3 provides 12-month percentage changes of the FHFA house price index since 1992 when data become available for 1991. Table IIA2-4 provides percentage changes and average rates of percent change per year for various periods. Between 1992 and 2019, the FHFA house price index increased 170.5 percent at the yearly average rate of 3.8 percent. In the period 1992-2000, the FHFA house price index increased 39.2 percent at the average yearly rate of 4.2 percent. The average yearly rate of price increase accelerated to 7.5 percent in the period 2000-2003, 8.5 percent in 2000-2005 and 7.4 percent in 2000-2006. At the margin, the average rate jumped to 10.0 percent in 2003-2005 and 7.4 percent in 2003-2006. House prices measured by the FHFA house price index increased 26.4 percent at the average yearly rate of 1.8 percent between 2006 and 2019 and 29.4 percent between 2005 and 2019 at the average yearly rate of 1.9 percent.

Table IIA2-4, US, FHFA House Price Index, Percentage Change and Average Rate of Percentage Change per Year, Selected Dates 1992-2019

Dec	∆%	Average ∆% per Year
1992-2019	170.5	3.8
1992-2000	39.2	4.2
2000-2003	24.2	7.5
2000-2005	50.2	8.5
2003-2005	21.0	10.0
2005-2019	29.4	1.9
2000-2006	53.7	7.4
2003-2006	23.8	7.4
2006-2019	26.4	1.8

Source: Federal Housing Finance Agency

https://www.fhfa.gov/DataTools

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

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

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

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

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

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

W = Y/r (1)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

According to Pinto (2008) in testimony to Congress:

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

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

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

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

Table IIA-2 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 62.2 percent in the US national home price index between Mar 2000 and Mar 2005. Prices rose 80.1 percent in the US national index from Mar 2000 to Mar 2006. House prices rose 27.3 percent between Mar 2003 and Mar 2005 for the US national propelled by low fed funds rates of 1.0 percent between Jul 2003 and Jul 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Jun 2004 until Jun 2006, reaching 5.25 percent. Simultaneously, the suspension of auctions of the 30-year Treasury bond caused decrease of yields of mortgage-backed securities with intended increase in mortgage rates. Similarly, between Mar 2003 and Mar 2006 the US national increased 41.3 percent. House prices have increased from Mar 2006 to Mar 2020 by 17.6 percent for the US national. Measuring house prices is quite difficult because of the lack of homogeneity that is typical of standardized commodities. In the 12 months ending in Mar 2019, house prices increased 4.4 percent in the US national. Table IIA-1 also shows that house prices increased 111.8 percent between Mar 2000 and Mar 2020 for the US national. House prices are close to the lowest level since peaks during the boom before the financial crisis and global recession. The US national increased 16.5 percent in Mar 2020 from the peak in Jun 2006 and increased 16.4 percent from the peak in Jul 2006. The final part of Table II-2 provides average annual percentage rates of growth of the house price indexes of Standard & Poor’s Case-Shiller. The average rate for the US national was 3.6 percent from Dec 1987 to Dec 2019 and 3.6 percent from Dec 1987 to Dec 2000. Although the global recession affecting the US between IVQ2007 (Dec) and IIQ2009 (Jun) caused decline of house prices of slightly above 30 percent, the average annual growth rate between Dec 2000 and Dec 2019 was 3.6 percent for the US national.

Table IIA-1, US, Percentage Changes of Standard & Poor’s Case-Shiller National Home Price Indices, Not Seasonally Adjusted, ∆%

	US National
∆% Mar 2000 to Mar 2003	27.4
∆% Mar 2000 to Mar 2005	62.2
∆% Mar 2003 to Mar 2005	27.3
∆% Mar 2000 to Mar 2006	80.1
∆% Mar 2003 to Mar 2006	41.3
∆% Mar 2005 to Mar 2020	30.6
∆% Mar 2006 to Mar 2020	17.6
∆% Mar 2009 to Mar 2020	46.7
∆% Mar 2010 to Mar 2020	49.7
∆% Mar 2011 to Mar 2020	56.0
∆% Mar 2012 to Mar 2020	58.2
∆% Mar 2013 to Mar 2020	45.3
∆% Mar 2014 to Mar 2020	33.3
∆% Mar 2015 to Mar 2020	27.8
∆% Mar 2016 to Mar 2020	21.7
∆% Mar 2017 to Mar 2020	15.2
∆% Mar 2018 to Mar 2020	8.2
∆% Mar 2019 to Mar 2020	4.4
∆% Mar 2000 to Mar 2020	111.8
∆% Peak Jun 2006 to Mar 2020	16.5
∆% Peak Jul 2006 to Mar 2020	16.4
Average ∆% Dec 1987-Dec 2019	3.6
Average ∆% Dec 1987-Dec 2000	3.6
Average ∆% Dec 1992-Dec 2000	4.5
Average ∆% Dec 2000-Dec 2019	3.6

Source: https://my.spindices.com/indices/real-estate/sp-corelogic-case-shiller-us-national-home-price-nsa-index

Monthly house prices increased sharply from Feb 2013 to Jan 2014 for both the SA and NSA national house price, as shown in Table IIA-3. In Jan 2013, the seasonally adjusted national house price index increased 0.9 percent and the NSA increased 0.3. House prices increased at high monthly percentage rates from Feb to Nov 2013. The most important seasonal factor in house prices is school changes for wealthier homeowners with more expensive houses. With seasonal adjustment, house prices fell from Dec 2010 throughout Mar 2011 and then increased in every month from Apr to Jul 2011 but fell in every month from Aug 2011 to Feb 2012. The not seasonally adjusted index registers increase in Mar 2012 of 1.4 percent. Not seasonally adjusted house prices increased 1.9 percent in Apr 2012 and at high monthly percentage rates through Aug 2012. House prices not seasonally adjusted stalled from Oct 2012 to Dec 2012 and surged from Feb to Sep 2013, decelerating in Oct 2013-Jan 2014. House prices grew at fast rates in Mar-Jul 2014. The SA national house price index increased 0.5 percent in Mar 2020 and the NSA index changed 0.8 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses.

Table IIA-2, US, Monthly Percentage Change of S&P Corelogic Case-Shiller National Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%

December 2010		-0.1			-0.8
January 2011		-0.4			-1.1
February 2011		-0.8			-0.9
March 2011		-0.3			0.0
April 2011		0.0			1.0
May 2011		-0.1			1.1
June 2011		0.0			0.9
July 2011		-0.1			0.3
August 2011		-0.3			-0.4
September 2011		-0.5			-1.1
October 2011		-0.5			-1.3
November 2011		-0.6			-1.3
December 2011		-0.3			-1.1
January 2012		0.0			-0.7
February 2012		-0.1			-0.1
March 2012		1.0			1.4
April 2012		0.9			1.9
May 2012		0.7			1.9
June 2012		0.6			1.5
July 2012		0.5			0.8
August 2012		0.4			0.3
September 2012		0.4			-0.2
October 2012		0.5			-0.3
November 2012		0.7			0.0
December 2012		0.6			-0.1
January 2013		0.9			0.3
February 2013		0.6			0.6
March 2013		1.5			1.9
April 2013		1.0			2.0
May 2013		0.9			1.9
June 2013		0.9			1.7
July 2013		0.9			1.2
August 2013		0.9			0.7
September 2013		0.8			0.2
October 2013		0.6			-0.1
November 2013		0.5			-0.1
December 2013		0.6			0.0
January 2014		0.6			0.1
February 2014		0.4			0.3
March 2014		0.3			0.8
April 2014		0.2			1.1
May 2014		0.2			1.1
June 2014		0.2			0.9
July 2014		0.3			0.6
August 2014		0.4			0.2
September 2014		0.4			-0.1
October 2014		0.4			-0.2
November 2014		0.4			-0.1
December 2014		0.4			-0.1
January 2015		0.4			-0.1
February 2015		0.3			0.2
March 2015		0.4			0.9
April 2015		0.3			1.1
May 2015		0.3			1.1
June 2015		0.3			0.9
July 2015		0.4			0.6
August 2015		0.5			0.3
September 2015		0.5			0.1
October 2015		0.6			0.0
November 2015		0.5			0.1
December 2015		0.5			0.0
January 2016		0.4			0.0
February 2016		0.2			0.1
March 2016		0.3			0.8
April 2016		0.3			1.1
May 2016		0.4			1.0
June 2016		0.3			0.9
July 2016		0.4			0.6
August 2016		0.6			0.4
September 2016		0.5			0.2
October 2016		0.5			0.0
November 2016		0.6			0.1
December 2016		0.5			0.1
January 2017		0.6			0.1
February 2017		0.3			0.2
March 2017		0.4			0.8
April 2017		0.4			1.1
May 2017		0.4			1.1
June 2017		0.4			0.9
July 2017		0.5			0.7
August 2017		0.6			0.4
September 2017		0.6			0.2
October 2017		0.5			0.1
November 2017		0.6			0.2
December 2017		0.6			0.2
January 2018		0.6			0.1
February 2018		0.5			0.4
March 2018		0.4			0.8
April 2018		0.4			1.0
May 2018		0.3			0.9
June 2018		0.3			0.8
July 2018		0.3			0.5
August 2018		0.4			0.2
September 2018		0.3			0.0
October 2018		0.4			0.0
November 2018		0.2			-0.1
December 2018		0.2			-0.2
January 2019		0.2			-0.2
February 2019		0.2			0.1
March 2019		0.3			0.7
April 2019		0.3			0.9
May 2019		0.2			0.8
June 2019		0.2			0.6
July 2019		0.2			0.4
August 2019		0.4			0.2
September 2019		0.4			0.1
October 2019		0.4			0.0
November 2019		0.4			0.1
December 2019		0.4			0.0
January 2020		0.4			0.0
February 2020		0.5			0.4
March 2020		0.5			0.8

Source: https://my.spindices.com/indices/real-estate/sp-corelogic-case-shiller-us-national-home-price-nsa-index

Table IIA-4 summarizes the brutal drops in assets and net worth of US households and nonprofit organizations from 2007 to 2008 and 2009. Total assets fell $9.1 trillion or 10.6 percent from 2007 to 2008 and $8.1 trillion or 9.5 percent to 2009. Net worth fell $8.9 trillion from 2007 to 2008 or 12.7 percent and $7.9 trillion to 2009 or 11.1 percent. Subsidies to housing prolonged over decades together with interest rates at 1.0 percent from Jun 2003 to Jun 2004 inflated valuations of real estate and risk financial assets such as equities. The increase of fed funds rates by 25 basis points until 5.25 percent in Jun 2006 reversed carry trades through exotic vehicles such as subprime adjustable rate mortgages (ARM) and world financial markets. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9).

Table IIA-4, Difference of Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars from 2007 to 2008 and 2009

	2007	2008	Change to 2008	2009	Change to 2009
A	85,145.5	76,094.7	-9,050.8	77,054.6	-8,090.9
Non FIN	30,542.1	27,982.7	-2,559.4	26,021.7	-4,520.4
RE	25,745.4	23,063.0	-2,682.4	21,085.2	-4,660.2
FIN	54,603.3	48,112.0	-6,491.3	51,032.9	-3,570.4
LIAB	14,503.2	14,399.4	-103.8	14,277.0	-226.2
NW	70,642.3	61,695.3	-8,9467.0	62,777.5	-7,864.8

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

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

The apparent improvement in Table IIA-4A is mostly because of increases in valuations of risk financial assets by the carry trade from zero interest rates to leveraged exposures in risk financial assets such as stocks, high-yield bonds, emerging markets, commodities and so on. Zero interest rates also act to increase net worth by reducing debt or liabilities. The net worth of households has become an instrument of unconventional monetary policy by zero interest rates in the theory that increases in net worth increase consumption that accounts for 67.6 percent of GDP in IQ2020 (Section I and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IVQ2019, real estate increased in value by $7163.9 billion and financial assets increased $41,006.5 billion for net gain of real estate and financial assets of $48,170.4 billion, explaining most of the increase in net worth of $47,725.9 billion obtained by deducting the increase in liabilities of $2073.0 billion from the increase of assets of $49,798.9 billion (with minor rounding error). Net worth increased from $70,642.3 billion in IVQ2007 to $118,368.2 billion in IVQ2019 by $47,725.9 billion or 67.6 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 256.974 in Dec 2019 (https://www.bls.gov/cpi/data.htm) or 22.3 percent. Net worth adjusted by CPI inflation increased 37.0 percent from 2007 to IVQ2019. Real estate assets adjusted for CPI inflation increased 4.5 percent from 2007 to IVQ2019. There are multiple complaints that unconventional monetary policy concentrates income on wealthier individuals because of their holdings of financial assets while the middle class has gained less because of fewer holdings of financial assets and higher share of real estate in family wealth. There is nothing new in these arguments. Interest rate ceilings on deposits and loans have been commonly used. The Banking Act of 1933 imposed prohibition of payment of interest on demand deposits and ceilings on interest rates on time deposits. These measures were justified by arguments that the banking panic of the 1930s was caused by competitive rates on bank deposits that led banks to engage in high-risk loans (Friedman, 1970, 18; see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 74-5). The objective of policy was to prevent unsound loans in banks. Savings and loan institutions complained of unfair competition from commercial banks that led to continuing controls with the objective of directing savings toward residential construction. Friedman (1970, 15) argues that controls were passive during periods when rates implied on demand deposit were zero or lower and when Regulation Q ceilings on time deposits were above market rates on time deposits. The Great Inflation or stagflation of the 1960s and 1970s changed the relevance of Regulation Q. Friedman (1970, 26-7) predicted the future:

“The banks have been forced into costly structural readjustments, the European banking system has been given an unnecessary competitive advantage, and London has been artificially strengthened as a financial center at the expense of New York.”

In short, Depression regulation exported the US financial system to London and offshore centers. What is vividly relevant currently from this experience is the argument by Friedman (1970, 27) that the controls affected the most people with lower incomes and wealth who were forced into accepting controlled-rates on their savings that were lower than those that would be obtained under freer markets. As Friedman (1970, 27) argues:

“These are the people who have the fewest alternative ways to invest their limited assets and are least sophisticated about the alternatives.” 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 2.1 percent on average in the cyclical expansion in the 43 quarters from IIIQ2009 to IQ2020. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the first estimate of GDP for IQ2020 (https://www.bea.gov/system/files/2020-04/gdp1q20_adv.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.8 percent obtained by dividing GDP of $15,557.3 billion in IIQ2010 by GDP of $15,134.1 billion in IIQ2009 {[($15,557.3/$15,134.1) -1]100 = 2.8%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ2019, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2020/05/mediocre-cyclical-united-states.html and earlier https://cmpassocregulationblog.blogspot.com/2020/03/weekly-rise-of-valuations-of-risk.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2020 and the lockdown of economic activity in COVID-19 would have accumulated to 43.6 percent. GDP in IQ2020 would be $22,634.2 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $3646.3 billion than actual $18,987.9 billion. There are more than three trillion dollars of GDP less than at trend, explaining the 51.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 30.0 percent of the effective labor force with the largest part originating in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2020/05/fifty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2020/04/lockdown-of-economic-activity-in.html). Unemployment is increasing sharply while employment is declining rapidly because of the lockdown of economic activity in the probable global recession resulting from the COVID-19 event (https://www.bls.gov/cps/employment-situation-covid19-faq-april-2020.pdf). US GDP in IQ2020 is 16.1 percent lower than at trend. US GDP grew from $15,762.0 billion in IVQ2007 in constant dollars to $18,987.9 billion in IQ2020 or 20.5 percent at the average annual equivalent rate of 1.5 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 2.9 percent per year from Apr 1919 to Apr 2020. Growth at 2.9 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 154.0798 in Apr 2020. The actual index NSA in Apr 2020 is 84.8550 which is 44.9 percent below trend. The deterioration of manufacturing in Apr 2020 originates in the lockdown of economic activity in the COVID-19 event. Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 108.2987 in Dec 2007 to 161.6318 in Apr 2020. The actual index NSA in Apr 2020 is 84.8550, which is 47.5 percent below trend. Manufacturing output grew at average 1.3 percent between Dec 1986 and Apr 2020. Using trend growth of 1.3 percent per year, the index would increase to 127.0007 in Apr 2020. The output of manufacturing at 84.8550 in Apr 2020 is 33.2 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 110.5147 in Jun 2007 to the low of 86.3800 in Apr 2009 or 21.8 percent. The NAICS manufacturing index decreased from 86.3800 in Apr 2009 to 85.8317 in Apr 2020 or minus 0.6 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 106.6777 in Dec 2007 to 163.0563 in Apr 2020. The NAICS index at 85.8317 in Apr 2020 is 47.4 below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 106.6777 in Dec 2007 to 131.3304 in Apr 2020. The NAICS index at 85.8317 in Apr 2020 is 34.6 percent below trend under this alternative calculation.

Table IIA-4A, US, Difference of Balance Sheet of Households and Nonprofit Organizations Billions of Dollars from 2007 to 2017, 2018 and IVQ2019

	Value 2007	Change to 2017	Change to 2018	Change to 2019
Assets	85,145.5	36,402.0	38,101.9	49,798.9
Nonfinancial	30,542.10	5,418.2	7,325.3	8,792.5
Real Estate	25,745.4	4,310.2	5,966.9	7,163.9
Financial	54,603.3	30,984.0	30,776.7	41,006.5
Liabilities	14,503.2	1,041.5	1,525.3	2,073.0
Net Worth	70,642.3	35,360.5	36,576.7	47,725.9

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

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

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020.