Dollar Revaluation and Financial Risk, World Inflation Waves, Mediocre Cyclical United States Economic Growth with GDP Two Trillion Dollars below Trend, Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth, United States Housing Collapse, World Cyclical Slow Growth and Global Recession Risk
Carlos M. Pelaez
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015
I Mediocre Cyclical United States Economic Growth with GDP Two Trillion Dollars below Trend
IA Mediocre Cyclical United States Economic Growth
IA1 Contracting Real Private Fixed Investment
IA2 Swelling Undistributed Corporate Profits
IB 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
II Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth
IIA United States Housing Collapse
III World Financial Turbulence
IIIA Financial Risks
IIIE Appendix Euro Zone Survival Risk
IIIF Appendix on Sovereign Bond Valuation
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
IIIGA Monetary Policy with Deficit Financing of Economic Growth
IIIGB Adjustment during the Debt Crisis of the 1980s
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.
An influential view on the policy required to steer the economy away from the liquidity trap is provided by Paul Krugman (1998). Suppose the central bank faces an increase in inflation. An important ingredient of the control of inflation is the central bank communicating to the public that it will maintain a sustained effort by all available policy measures and required doses until inflation is subdued and price stability is attained. If the public believes that the central bank will control inflation only until it declines to a more benign level but not sufficiently low level, current expectations will develop that inflation will be higher once the central bank abandons harsh measures. During deflation and recession the central bank has to convince the public that it will maintain zero interest rates and other required measures until the rate of inflation returns convincingly to a level consistent with expansion of the economy and stable prices. Krugman (1998, 161) summarizes the argument as:
“The ineffectuality of monetary policy in a liquidity trap is really the result of a looking-glass version of the standard credibility problem: monetary policy does not work because the public expects that whatever the central bank may do now, given the chance, it will revert to type and stabilize prices near their current level. If the central bank can credibly promise to be irresponsible—that is, convince the market that it will in fact allow prices to rise sufficiently—it can bootstrap the economy out of the trap”
This view is consistent with results of research by Christina Romer that “the rapid rates of growth of real output in the mid- and late 1930s were largely due to conventional aggregate demand stimulus, primarily in the form of monetary expansion. My calculations suggest that in the absence of these stimuli the economy would have remained depressed far longer and far more deeply than it actually did” (Romer 1992, 757-8, cited in Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 210-2). The average growth rate of the money supply in 1933-1937 was 10 percent per year and increased in the early 1940s. Romer calculates that GDP would have been much lower without this monetary expansion. The growth of “the money supply was primarily due to a gold inflow, which was in turn due to the devaluation in 1933 and to capital flight from Europe because of political instability after 1934” (Romer 1992, 759). Gold inflow coincided with the decline in real interest rates in 1933 that remained negative through the latter part of the 1930s, suggesting that they could have caused increases in spending that was sensitive to declines in interest rates. Bernanke finds dollar devaluation against gold to have been important in preventing further deflation in the 1930s (Bernanke 2002):
“There have been times when exchange rate policy has been an effective weapon against deflation. A striking example from US history is Franklin Roosevelt’s 40 percent devaluation of the dollar against gold in 1933-34, enforced by a program of gold purchases and domestic money creation. The devaluation and the rapid increase in money supply it permitted ended the US deflation remarkably quickly. Indeed, consumer price inflation in the United States, year on year, went from -10.3 percent in 1932 to -5.1 percent in 1933 to 3.4 percent in 1934. The economy grew strongly, and by the way, 1934 was one of the best years of the century for the stock market”
Fed policy is seeking what Irving Fisher proposed “that great depressions are curable and preventable through reflation and stabilization” (Fisher 1933, 350).
The President of the Federal Reserve Bank of Chicago argues that (Charles Evans 2010):
“I believe the US economy is best described as being in a bona fide liquidity trap. Highly plausible projections are 1 percent for core Personal Consumption Expenditures (PCE) inflation at the end of 2012 and 8 percent for the unemployment rate. For me, the Fed’s dual mandate misses are too large to shrug off, and there is currently no policy conflict between improving employment and inflation outcomes”
There are two types of monetary policies that could be used in this situation. First, the Fed could announce a price-level target to be attained within a reasonable time frame (Evans 2010):
“For example, if the slope of the price path is 2 percent and inflation has been underunning the path for some time, monetary policy would strive to catch up to the path. Inflation would be higher than 2 percent for a time until the path was reattained”
Optimum monetary policy with interest rates near zero could consist of “bringing the price level back up to a level even higher than would have prevailed had the disturbance never occurred” (Gauti Eggertsson and Michael Woodford 2003, 207). Bernanke (2003JPY) explains as follows:
“Failure by the central bank to meet its target in a given period leads to expectations of (and public demands for) increased effort in subsequent periods—greater quantities of assets purchased on the open market for example. So even if the central bank is reluctant to provide a time frame for meetings its objective, the structure of the price-level objective provides a means for the bank to commit to increasing its anti-deflationary efforts when its earlier efforts prove unsuccessful. As Eggertsson and Woodford show, the expectations that an increasing price level gap will give rise to intensified effort by the central bank should lead the public to believe that ultimately inflation will replace deflation, a belief that supports the central bank’s own objectives by lowering the current real rate of interest”
Second, the Fed could use its balance sheet to increase purchases of long-term securities together with credible commitment to maintain the policy until the dual mandates of maximum employment and price stability are attained. Policy continues with reinvestment of principal in securities.
In the restatement of the liquidity trap and large-scale policies of monetary/fiscal stimulus, Krugman (1998, 162) finds:
“In the traditional open economy IS-LM model developed by Robert Mundell [1963] and Marcus Fleming [1962], and also in large-scale econometric models, monetary expansion unambiguously leads to currency depreciation. But there are two offsetting effects on the current account balance. On one side, the currency depreciation tends to increase net exports; on the other side, the expansion of the domestic economy tends to increase imports. For what it is worth, policy experiments on such models seem to suggest that these effects very nearly cancel each other out.
Krugman (1998) uses a different dynamic model with expectations that leads to similar conclusions.
The central bank could also be pursuing competitive devaluation of the national currency in the belief that it could increase inflation to a higher level and promote domestic growth and employment at the expense of growth and unemployment in the rest of the world. An essay by Chairman Bernanke in 1999 on Japanese monetary policy received attention in the press, stating that (Bernanke 2000, 165):
“Roosevelt’s specific policy actions were, I think, less important than his willingness to be aggressive and experiment—in short, to do whatever it took to get the country moving again. Many of his policies did not work as intended, but in the end FDR deserves great credit for having the courage to abandon failed paradigms and to do what needed to be done”
Quantitative easing has never been proposed by Chairman Bernanke or other economists as certain science without adverse effects. What has not been mentioned in the press is another suggestion to the Bank of Japan (BOJ) by Chairman Bernanke in the same essay that is very relevant to current events and the contentious issue of ongoing devaluation wars (Bernanke 2000, 161):
“Because the BOJ has a legal mandate to pursue price stability, it certainly could make a good argument that, with interest rates at zero, depreciation of the yen is the best available tool for achieving its mandated objective. The economic validity of the beggar-thy-neighbor thesis is doubtful, as depreciation creates trade—by raising home country income—as well as diverting it. Perhaps not all those who cite the beggar-thy-neighbor thesis are aware that it had its origins in the Great Depression, when it was used as an argument against the very devaluations that ultimately proved crucial to world economic recovery. A yen trading at 100 to the dollar is in no one’s interest”
Chairman Bernanke is referring to the argument by Joan Robinson based on the experience of the Great Depression that: “in times of general unemployment a game of beggar-my-neighbour is played between the nations, each one endeavouring to throw a larger share of the burden upon the others” (Robinson 1947, 156). Devaluation is one of the tools used in these policies (Robinson 1947, 157). Banking crises dominated the experience of the United States, but countries that recovered were those devaluing early such that competitive devaluations rescued many countries from a recession as strong as that in the US (see references to Ehsan Choudhri, Levis Kochin and Barry Eichengreen in Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 205-9; for the case of Brazil that devalued early in the Great Depression recovering with an increasing trade balance see Pelaez, 1968, 1968b, 1972; Brazil devalued and abandoned the gold standard during crises in the historical period as shown by Pelaez 1976, Pelaez and Suzigan 1981). Beggar-my-neighbor policies did work for individual countries but the criticism of Joan Robinson was that it was not optimal for the world as a whole.
Chairman Bernanke (2013Mar 25) reinterprets devaluation and recovery from the Great Depression:
“The uncoordinated abandonment of the gold standard in the early 1930s gave rise to the idea of "beggar-thy-neighbor" policies. According to this analysis, as put forth by important contemporary economists like Joan Robinson, exchange rate depreciations helped the economy whose currency had weakened by making the country more competitive internationally. Indeed, the decline in the value of the pound after 1931 was associated with a relatively early recovery from the Depression by the United Kingdom, in part because of some rebound in exports. However, according to this view, the gains to the depreciating country were equaled or exceeded by the losses to its trading partners, which became less internationally competitive--hence, ‘beggar thy neighbor.’ Economists still agree that Smoot-Hawley and the ensuing tariff wars were highly counterproductive and contributed to the depth and length of the global Depression. However, modern research on the Depression, beginning with the seminal 1985 paper by Barry Eichengreen and Jeffrey Sachs, has changed our view of the effects of the abandonment of the gold standard. Although it is true that leaving the gold standard and the resulting currency depreciation conferred a temporary competitive advantage in some cases, modern research shows that the primary benefit of leaving gold was that it freed countries to use appropriately expansionary monetary policies. By 1935 or 1936, when essentially all major countries had left the gold standard and exchange rates were market-determined, the net trade effects of the changes in currency values were certainly small. Yet the global economy as a whole was much stronger than it had been in 1931. The reason was that, in shedding the strait jacket of the gold standard, each country became free to use monetary policy in a way that was more commensurate with achieving full employment at home.”
Nurkse (1944) raised concern on the contraction of trade by competitive devaluations during the 1930s. Haberler (1937) dwelled on the issue of flexible exchange rates. Bordo and James (2001) provide perceptive exegesis of the views of Haberler (1937) and Nurkse (1944) together with the evolution of thought by Haberler. Policy coordination among sovereigns may be quite difficult in practice even if there were sufficient knowledge and sound forecasts. Friedman (1953) provided strong case in favor of a system of flexible exchange rates.
Eichengreen and Sachs (1985) argue theoretically with measurements using a two-sector model that it is possible for series of devaluations to improve the welfare of all countries. There were adverse effects of depreciation on other countries but depreciation by many countries could be beneficial for all. The important counterfactual is if depreciations by many countries would have promoted faster recovery from the Great Depression. Depreciation in the model of Eichengreen and Sachs (1985) affected domestic and foreign economies through real wages, profitability, international competitiveness and world interest rates. Depreciation causes increase in the money supply that lowers world interest rates, promoting growth of world output. Lower world interest rates could compensate contraction of output from the shift of demand away from home goods originating in neighbor’s exchange depreciation. Eichengreen and Sachs (1985, 946) conclude:
“This much, however, is clear. We do not present a blanket endorsement of the competitive devaluations of the 1930s. Though it is indisputable that currency depreciation conferred macroeconomic benefits on the initiating country, because of accompanying policies the depreciations of the 1930s had beggar-thy-neighbor effects. Though it is likely that currency depreciation (had it been even more widely adopted) would have worked to the benefit of the world as a whole, the sporadic and uncoordinated approach taken to exchange-rate policy in the 1930s tended, other things being equal, to reduce the magnitude of the benefits.”
There could major difference in the current world economy. The initiating impulse for depreciation originates in zero interest rates on the fed funds rate. The dollar is the world’s reserve currency. Risk aversion intermittently channels capital flight to the safe haven of the dollar and US Treasury securities. In the absence of risk aversion, zero interest rates induce carry trades of short positions in dollars and US debt (borrowing) together with long leveraged exposures in risk financial assets such as stocks, emerging stocks, commodities and high-yield bonds. Without risk aversion, the dollar depreciates against every currency in the world. The dollar depreciated against the euro by 39.3 percent from USD 1.1423/EUR con Jun 26, 2003 to USD 1.5914/EUR on Jun 14, 2008 during unconventional monetary policy before the global recession (Table VI-1). Unconventional monetary policy causes devaluation of the dollar relative to other currencies, which can increases net exports of the US that increase aggregate economic activity (Yellen 2011AS). The country issuing the world’s reserve currency appropriates the advantage from initiating devaluation that in policy intends to generate net exports that increase domestic output.
The Swiss franc rate relative to the euro (CHF/EUR) appreciated 18.7 percent on Jan 15, 2015. The Swiss franc rate relative to the dollar (CHF/USD) appreciated 17.7 percent. Central banks are taking measures in anticipation of the quantitative easing by the European Central Bank. On Jan 22, 2015, the European Central Bank (ECB) decided to implement an “expanded asset purchase program” with combined asset purchases of €60 billion per month “until at least Sep 2016 (http://www.ecb.europa.eu/press/pr/date/2015/html/pr150122_1.en.html). The DAX index of German equities increased 1.3 percent on Jan 22, 2015 and 2.1 percent on Jan 23, 2015. The euro depreciated from EUR 1.1611/USD (EUR 0.8613/USD) on Wed Jan 21, 2015, to EUR 1.1206/USD (EUR 0.8924/USD) on Fri Jan 23, 2015, or 3.6 percent. Yellen (2011AS, 6) admits that Fed monetary policy results in dollar devaluation with the objective of increasing net exports, which was the policy that Joan Robinson (1947) labeled as “beggar-my-neighbor” remedies for unemployment. Risk aversion erodes devaluation of the dollar.
Pelaez and Pelaez (Regulation of Banks and Finance (2009b), 208-209) summarize the experience of Brazil as follows:
“During 1927–9, Brazil accumulated ￡30 million of foreign exchange of which ￡20 million were deposited at its stabilization fund (Pelaez 1968, 43–4). After the decline in coffee prices and the first impact of the Great Depression in Brazil a hot money movement wiped out foreign exchange reserves. In addition, capital inflows stopped entirely. The deterioration of the terms of trade further complicated matters, as the value of exports in foreign currency declined abruptly. Because of this exchange crisis, the service of the foreign debt of Brazil became impossible. In August 1931, the federal government was forced to cancel the payment of principal on certain foreign loans. The balance of trade in 1931 was expected to yield ￡20 million whereas the service of the foreign debt alone amounted to ￡22.6 million. Part of the solution given to these problems was typical of the 1930s. In September 1931, the government of Brazil required that all foreign transactions were to be conducted through the Bank of Brazil. This monopoly of foreign exchange was exercised by the Bank of Brazil for the following three years. Export permits were granted only after the exchange derived from sales abroad was officially sold to the Bank, which in turn allocated it in accordance with the needs of the economy. An active black market in foreign exchange developed. Brazil was in the first group of countries that abandoned early the gold standard, in 1931, and suffered comparatively less from the Great Depression. The Brazilian federal government, advised by the BOE, increased taxes and reduced expenditures in 1931 to compensate a decline in custom receipts (Pelaez 1968, 40). Expenditures caused by a revolution in 1932 in the state of Sao Paulo and a drought in the northeast explain the deficit. During 1932–6, the federal government engaged in strong efforts to stabilize the budget. Apart from the deliberate efforts to balance the budget during the 1930s, the recovery in economic activity itself may have induced a large part of the reduction of the deficit (Ibid, 41). Brazil’s experience is similar to that of the United States in that fiscal policy did not promote recovery from the Great Depression.”
Is depreciation of the dollar the best available tool currently for achieving the dual mandate of higher inflation and lower unemployment? Bernanke (2002) finds dollar devaluation against gold to have been important in preventing further deflation in the 1930s (http://www.federalreserve.gov/boarddocs/speeches/2002/20021121/default.htm):
“Although a policy of intervening to affect the exchange value of the dollar is nowhere on the horizon today, it's worth noting that there have been times when exchange rate policy has been an effective weapon against deflation. A striking example from U.S. history is Franklin Roosevelt's 40 percent devaluation of the dollar against gold in 1933-34, enforced by a program of gold purchases and domestic money creation. The devaluation and the rapid increase in money supply it permitted ended the U.S. deflation remarkably quickly. Indeed, consumer price inflation in the United States, year on year, went from -10.3 percent in 1932 to -5.1 percent in 1933 to 3.4 percent in 1934.17 The economy grew strongly, and by the way, 1934 was one of the best years of the century for the stock market. If nothing else, the episode illustrates that monetary actions can have powerful effects on the economy, even when the nominal interest rate is at or near zero, as was the case at the time of Roosevelt's devaluation.”
Should the US devalue following Roosevelt? Alternatively, has monetary policy intended devaluation? Fed policy is seeking, deliberately or as a side effect, what Irving Fisher proposed “that great depressions are curable and preventable through reflation and stabilization” (Fisher, 1933, 350). The Fed has created not only high volatility of assets but also what many countries are regarding as a competitive devaluation similar to those criticized by Nurkse (1944). Yellen (2011AS, 6) admits that Fed monetary policy results in dollar devaluation with the objective of increasing net exports, which was the policy that Joan Robinson (1947) labeled as “beggar-my-neighbor” remedies for unemployment.
Unconventional monetary policy of zero interest rates and large-scale purchases of long-term securities for the balance sheet of the central bank is proposed to prevent deflation. The data of CPI inflation of all goods and CPI inflation excluding food and energy for the past six decades does not show even one negative change, as shown in Table CPIEX.
Table CPIEX, Annual Percentage Changes of the CPI All Items Excluding Food and Energy
| Year | Annual ∆% |
| 1958 | 2.4 |
| 1959 | 2.0 |
| 1960 | 1.3 |
| 1961 | 1.3 |
| 1962 | 1.3 |
| 1963 | 1.3 |
| 1964 | 1.6 |
| 1965 | 1.2 |
| 1966 | 2.4 |
| 1967 | 3.6 |
| 1968 | 4.6 |
| 1969 | 5.8 |
| 1970 | 6.3 |
| 1971 | 4.7 |
| 1972 | 3.0 |
| 1973 | 3.6 |
| 1974 | 8.3 |
| 1975 | 9.1 |
| 1976 | 6.5 |
| 1977 | 6.3 |
| 1978 | 7.4 |
| 1979 | 9.8 |
| 1980 | 12.4 |
| 1981 | 10.4 |
| 1982 | 7.4 |
| 1983 | 4.0 |
| 1984 | 5.0 |
| 1985 | 4.3 |
| 1986 | 4.0 |
| 1987 | 4.1 |
| 1988 | 4.4 |
| 1989 | 4.5 |
| 1990 | 5.0 |
| 1991 | 4.9 |
| 1992 | 3.7 |
| 1993 | 3.3 |
| 1994 | 2.8 |
| 1995 | 3.0 |
| 1996 | 2.7 |
| 1997 | 2.4 |
| 1998 | 2.3 |
| 1999 | 2.1 |
| 2000 | 2.4 |
| 2001 | 2.6 |
| 2002 | 2.4 |
| 2003 | 1.4 |
| 2004 | 1.8 |
| 2005 | 2.2 |
| 2006 | 2.5 |
| 2007 | 2.3 |
| 2008 | 2.3 |
| 2009 | 1.7 |
| 2010 | 1.0 |
| 2011 | 1.7 |
| 2012 | 2.1 |
| 2013 | 1.8 |
| 2014 | 1.7 |
Source: Bureau of Labor Statistics
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 |
Source: Bureau of Labor Statistics
The producer price index of the US from 1947 to 2015 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, 1947-2015
Source: US Bureau of Labor Statistics
Chart I-7 provides 12-month percentage changes of the producer price index from 1948 to 2015. The distinguishing event in Chart I-7 is the Great Inflation of the 1970s. The shape of the two-hump Bactrian camel of the 1970s resembles the double hump from 2007 to 2015.
Chart I-7, US, Producer Price Index, Finished Goods, 12-Month Percentage Change, NSA, 1948-2015
Source: US Bureau of Labor Statistics
Annual percentage changes of the producer price index from 1948 to 2014 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 decline 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). There is no evidence in this history of 65 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 ∆% 1948-2014
| 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 | 6.0 |
| 2012 | 1.9 |
| 2013 | 1.2 |
| 2014 | 1.9 |
Source: US Bureau of Labor Statistics
Chart I-12 provides the consumer price index NSA from 1916 to 2015. 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, 1916-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/cpi/data.htm
Chart I-13 provides 12-month percentage changes of the consumer price index from 1916 to 2015. 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 1916-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/cpi/data.htm
Table I-2 provides annual percentage changes of United States consumer price inflation from 1914 to 2014. 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. Fear of deflation on the basis of 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 ∆% 1914-2014
| 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 |
Source: US Bureau of Labor Statistics http://www.bls.gov/cpi/data.htm
Friedman (1969) finds that the optimal rule for the quantity of money is deflation at a rate that results in a zero nominal interest rate (see Ireland 2003 and Cole and Kocherlakota 1998). Atkeson and Kehoe (2004) argue that central bankers are not inclined to implement policies that could result in deflation because of the interpretation of the Great Depression as closely related to deflation. They use panel data on inflation and growth of real output for 17 countries over more than 100 years. The time-series data for each individual country are broken into five-year events with deflation measured as average negative inflation and depression as average negative growth rate of real output. Atkeson and Kehoe (2004) find that the Great Depression from 1929 to 1934 is the only case of association between deflation and depression without any evidence whatsoever of such relation in any other period. Their conclusion is (Atkeson and Kehoe 2004, 99): “Our finding thus suggests that policymakers’ fear of anticipated policy-induced deflation that would result from following, say, the Friedman rule is greatly overblown.” Their conclusion on the experience of Japan is (Atkeson and Kehoe 2004, 99):
“Since 1960, Japan’s average growth rates have basically fallen monotonically, and since 1970, its average inflation rates have too. Attributing this 40-year slowdown to monetary forces is a stretch. More reasonable, we think, is that much of the slowdown is the natural pattern for a country that was far behind the world leaders and had begun to catch up.”
In the sample of Atkeson and Kehoe (2004), there are only eight five-year periods besides the Great Depression with both inflation and depression. Deflation and depression is shown in 65 cases with 21 of depression without deflation. There is no depression in 65 of 73 five-year periods and there is no deflation in 29 episodes of depression. There is a remarkable result of no depression in 90 percent of deflation episodes. Excluding the Great Depression, there is virtually no relation of deflation and depression. Atkeson and Kehoe (2004, 102) find that the average growth rate of Japan of 1.41 percent in the 1990s is “dismal” when compared with 3.20 percent in the United States but is not “dismal” when compared with 1.61 percent for Italy and 1.84 percent for France, which are also catch-up countries in modern economic growth (see Atkeson and Kehoe 1998). The conclusion of Atkeson and Kehoe (2004), without use of controls, is that there is no association of deflation and depression in their dataset.
Benhabib and Spiegel (2009) use a dataset similar to that of Atkeson and Kehoe (2004) but allowing for nonlinearity and inflation volatility. They conclude that in cases of low and negative inflation an increase of average inflation of 1 percent is associated with an increase of 0.31 percent of average annual growth. The analysis of Benhabib and Spiegel (2009) leads to the significantly different conclusion that inflation and economic performance are strongly associated for low and negative inflation. There is no claim of causality by Atkeson and Kehoe (2004) and Benhabib and Spiegel (2009).
Delfim Netto (1959) partly reprinted in Pelaez (1973) conducted two classical nonparametric tests (Mann 1945, Wallis and Moore 1941; see Kendall and Stuart 1968) with coffee-price data in the period of free markets from 1857 to 1906 with the following conclusions (Pelaez, 1976a, 280):
“First, the null hypothesis of no trend was accepted with high confidence; secondly, the null hypothesis of no oscillation was rejected also with high confidence. Consequently, in the nineteenth century international prices of coffee fluctuated but without long-run trend. This statistical fact refutes the extreme argument of structural weakness of the coffee trade.”
In his classic work on the theory of international trade, Jacob Viner (1937, 563) analyzed the “index of total gains from trade,” or “amount of gain per unit of trade,” denoted as T:
T= (∆Pe/∆Pi)∆Q
Where ∆Pe 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.”
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 | |
| 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 2013. 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 | 2013 | |
| GDP | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 |
| PCE | 74.0 | 71.9 | 69.2 | 67.1 | 68.5 |
| GDI | 16.4 | 10.9 | 14.2 | 19.3 | 15.9 |
| NRI | 11.1 | 7.3 | 8.3 | 12.8 | 12.2 |
| RSI | 3.9 | 3.4 | 3.5 | 6.0 | 3.1 |
| Net Trade | 0.4 | 0.9 | 1.4 | -5.5 | -3.0 |
| GOVT | 9.2 | 16.3 | 15.2 | 19.1 | 18.6 |
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.
The current application of Hansen’s (1938, 1939, 1941) proposition argues that secular stagnation occurs because full employment equilibrium can be attained only with negative real interest rates between minus 2 and minus 3 percent. Professor Lawrence H. Summers (2013Nov8) finds that “a set of older ideas that went under the phrase secular stagnation are not profoundly important in understanding Japan’s experience in the 1990s and may not be without relevance to America’s experience today” (emphasis added). Summers (2013Nov8) argues there could be an explanation in “that the short-term real interest rate that was consistent with full employment had fallen to -2% or -3% sometime in the middle of the last decade. Then, even with artificial stimulus to demand coming from all this financial imprudence, you wouldn’t see any excess demand. And even with a relative resumption of normal credit conditions, you’d have a lot of difficulty getting back to full employment.” The US economy could be in a situation where negative real rates of interest with fed funds rates close to zero as determined by the Federal Open Market Committee (FOMC) do not move the economy to full employment or full utilization of productive resources. Summers (2013Oct8) finds need of new thinking on “how we manage an economy in which the zero nominal interest rates is a chronic and systemic inhibitor of economy activity holding our economies back to their potential.”
Former US Treasury Secretary Robert Rubin (2014Jan8) finds three major risks in prolonged unconventional monetary policy of zero interest rates and quantitative easing: (1) incentive of delaying action by political leaders; (2) “financial moral hazard” in inducing excessive exposures pursuing higher yields of risker credit classes; and (3) major risks in exiting unconventional policy. Rubin (2014Jan8) proposes reduction of deficits by structural reforms that could promote recovery by improving confidence of business attained with sound fiscal discipline.
Professor John B. Taylor (2014Jan01, 2014Jan3) provides clear thought on the lack of relevance of Hansen’s contention of secular stagnation to current economic conditions. The application of secular stagnation argues that the economy of the US has attained full-employment equilibrium since around 2000 only with negative real rates of interest of minus 2 to minus 3 percent. At low levels of inflation, the so-called full-employment equilibrium of negative interest rates of minus 2 to minus 3 percent cannot be attained and the economy stagnates. Taylor (2014Jan01) analyzes multiple contradictions with current reality in this application of the theory of secular stagnation:
- Secular stagnation would predict idle capacity, in particular in residential investment when fed fund rates were fixed at 1 percent from Jun 2003 to Jun 2004. Taylor (2014Jan01) finds unemployment at 4.4 percent with house prices jumping 7 percent from 2002 to 2003 and 14 percent from 2004 to 2005 before dropping from 2006 to 2007. GDP prices doubled from 1.7 percent to 3.4 percent when interest rates were low from 2003 to 2005.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the application of secular stagnation based on low interest rates because of savings glut and lack of investment opportunities. Taylor (2009) shows that there was no savings glut. The savings rate of the US in the past decade is significantly lower than in the 1980s.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the low ratio of investment to GDP currently and reduced investment and hiring by US business firms.
- Taylor (2014Jan01, 2014Jan3) argues that the financial crisis and global recession were caused by weak implementation of existing regulation and departure from rules-based policies.
- Taylor (2014Jan01, 2014Jan3) argues that the recovery from the global recession was constrained by a change in the regime of regulation and fiscal/monetary policies.
In revealing research, Edward P. Lazear and James R. Spletzer (2012JHJul22) use the wealth of data in the valuable database and resources of the Bureau of Labor Statistics (http://www.bls.gov/data/) in providing clear thought on the nature of the current labor market of the United States. The critical issue of analysis and policy currently is whether unemployment is structural or cyclical. Structural unemployment could occur because of (1) industrial and demographic shifts and (2) mismatches of skills and job vacancies in industries and locations. Consider the aggregate unemployment rate, Y, expressed in terms of share si of a demographic group in an industry i and unemployment rate yi of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):
Y = ∑isiyi (1)
This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):
∆Y = ∑i∆siy*i + ∑i∆yis*i (2)
The first term in (2) captures changes in the demographic and industrial composition of the economy ∆si 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 ∆yi, 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 and 62.5 percent in Feb 2015. 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 (http://cmpassocregulationblog.blogspot.com/2015/02/g20-monetary-policy-recovery-without.html). “Secular stagnation” would be a process over many years and not from one year to another. This is merely another case of theory without reality with dubious policy proposals.
Table I-4b, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1979-2015
| Year | Jan | Feb | Aug | Sep | Oct | Nov | Dec | Annual |
| 1979 | 62.9 | 63.0 | 64.5 | 63.8 | 64.0 | 63.8 | 63.8 | 63.7 |
| 1980 | 63.3 | 63.2 | 64.5 | 63.6 | 63.9 | 63.7 | 63.4 | 63.8 |
| 1981 | 63.2 | 63.2 | 64.6 | 63.5 | 64.0 | 63.8 | 63.4 | 63.9 |
| 1982 | 63.0 | 63.2 | 64.9 | 64.0 | 64.1 | 64.1 | 63.8 | 64.0 |
| 1983 | 63.3 | 63.2 | 65.1 | 64.3 | 64.1 | 64.1 | 63.8 | 64.0 |
| 1984 | 63.3 | 63.4 | 65.2 | 64.4 | 64.6 | 64.4 | 64.3 | 64.4 |
| 1985 | 64.0 | 64.0 | 65.4 | 64.9 | 65.1 | 64.9 | 64.6 | 64.8 |
| 1986 | 64.2 | 64.4 | 66.1 | 65.3 | 65.5 | 65.4 | 65.0 | 65.3 |
| 1987 | 64.7 | 64.8 | 66.5 | 65.5 | 65.9 | 65.7 | 65.5 | 65.6 |
| 1988 | 65.1 | 65.2 | 66.8 | 65.9 | 66.1 | 66.2 | 65.9 | 65.9 |
| 1989 | 65.8 | 65.6 | 67.2 | 66.3 | 66.6 | 66.7 | 66.3 | 66.5 |
| 1990 | 66.0 | 66.0 | 67.1 | 66.4 | 66.5 | 66.3 | 66.1 | 66.5 |
| 1991 | 65.5 | 65.7 | 66.6 | 66.1 | 66.1 | 66.0 | 65.8 | 66.2 |
| 1992 | 65.7 | 65.8 | 67.2 | 66.3 | 66.2 | 66.2 | 66.1 | 66.4 |
| 1993 | 65.6 | 65.8 | 67.0 | 66.1 | 66.4 | 66.3 | 66.2 | 66.3 |
| 1994 | 66.0 | 66.2 | 67.2 | 66.5 | 66.8 | 66.7 | 66.5 | 66.6 |
| 1995 | 66.1 | 66.2 | 67.1 | 66.5 | 66.7 | 66.5 | 66.2 | 66.6 |
| 1996 | 65.8 | 66.1 | 67.2 | 66.8 | 67.1 | 67.0 | 66.7 | 66.8 |
| 1997 | 66.4 | 66.5 | 67.6 | 67.0 | 67.1 | 67.1 | 67.0 | 67.1 |
| 1998 | 66.6 | 66.7 | 67.3 | 67.0 | 67.1 | 67.1 | 67.0 | 67.1 |
| 1999 | 66.7 | 66.8 | 67.3 | 66.8 | 67.0 | 67.0 | 67.0 | 67.1 |
| 2000 | 66.8 | 67.0 | 67.2 | 66.7 | 66.9 | 66.9 | 67.0 | 67.1 |
| 2001 | 66.8 | 66.8 | 66.8 | 66.6 | 66.7 | 66.6 | 66.6 | 66.8 |
| 2002 | 66.2 | 66.6 | 66.8 | 66.6 | 66.6 | 66.3 | 66.2 | 66.6 |
| 2003 | 66.1 | 66.2 | 66.3 | 65.9 | 66.1 | 66.1 | 65.8 | 66.2 |
| 2004 | 65.7 | 65.7 | 66.2 | 65.7 | 66.0 | 66.1 | 65.8 | 66.0 |
| 2005 | 65.4 | 65.6 | 66.5 | 66.1 | 66.2 | 66.1 | 65.9 | 66.0 |
| 2006 | 65.5 | 65.7 | 66.5 | 66.1 | 66.4 | 66.4 | 66.3 | 66.2 |
| 2007 | 65.9 | 65.8 | 66.1 | 66.0 | 66.0 | 66.1 | 65.9 | 66.0 |
| 2008 | 65.7 | 65.5 | 66.4 | 65.9 | 66.1 | 65.8 | 65.7 | 66.0 |
| 2009 | 65.4 | 65.5 | 65.6 | 65.0 | 64.9 | 64.9 | 64.4 | 65.4 |
| 2010 | 64.6 | 64.6 | 65.0 | 64.6 | 64.4 | 64.4 | 64.1 | 64.7 |
| 2011 | 63.9 | 63.9 | 64.3 | 64.2 | 64.1 | 63.9 | 63.8 | 64.1 |
| 2012 | 63.4 | 63.6 | 63.7 | 63.6 | 63.8 | 63.5 | 63.4 | 63.7 |
| 2013 | 63.3 | 63.2 | 63.4 | 63.2 | 62.9 | 62.9 | 62.6 | 63.2 |
| 2014 | 62.5 | 62.7 | 63.0 | 62.8 | 63.0 | 62.8 | 62.5 | 62.9 |
| 2015 | 62.5 | 62.5 |
Source: US Bureau of Labor Statistics
Chart I-12b, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1979-2015
Source: Bureau of Labor Statistics
Broader perspective is provided by Chart I-12c of the US Bureau of Labor Statistics. The United States civilian noninstitutional population has increased along a consistent trend since 1948 that continued through earlier recessions and the global recession from IVQ2007 to IIQ2009 and the cyclical expansion after IIIQ2009.
Chart I-12c, US, Civilian Noninstitutional Population, Thousands, NSA, 1948-2015
Sources: US Bureau of Labor Statistics
The labor force of the United States in Chart I-12d has increased along a trend similar to that of the civilian noninstitutional population in Chart I-12c. There is an evident stagnation of the civilian labor force in the final segment of Chart I-12d during the current economic cycle. This stagnation is explained by cyclical factors similar to those analyzed by Lazear and Spletzer (2012JHJul22) that motivated an increasing population to drop out of the labor force instead of structural factors. Large segments of the potential labor force are not observed, constituting unobserved unemployment and of more permanent nature because those afflicted have been seriously discouraged from working by the lack of opportunities.
Chart I-12d, US, Labor Force, Thousands, NSA, 1948-2015
Sources: US Bureau of Labor Statistics
The rate of labor force participation of the US is in Chart I-12E from 1948 to 2015. There is sudden decline during the global recession after 2007 without recovery explained by cyclic factors (Lazear and Spletzer 2012JHJul22) as many potential workers stopped their job searches disillusioned that there could be an opportunity for them in sharply contracted labor markets.
Chart I-12E, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1948-2015
Sources: US Bureau of Labor Statistics
Table EMP provides the comparison between the labor market in the current whole cycle from 2007 to 2014 and the whole cycle from 1979 to 1988. In the entire cycle from 2007 to 2014, the number employed increased 0.258 million, full-time employed fell 2.373 million, part-time for economic reasons increased 2.812 million and population increased 16.080 million. The number employed increased 0.2 percent, full-time employed fell 2.0 percent, part-time for economic reasons increased 63.9 percent and population increased 6.9 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 1988, the number employed increased 16.144 million, full-time employed increased 12.560 million, part-time for economic reasons 1.629 million and population 19.750 million. In the entire cycle from 1979 to 1988, the number employed increased 16.3 percent, full-time employed 15.2 percent, part-time for economic reasons 45.5 percent and population 12.0 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 |
| ∆2007-2014 | 0.258 | -2,373 | 2.812 | 16.080 |
| ∆% 2007-2013 | 0.2 | -2.0 | 63.9 | 6.9 |
| 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 |
| ∆1979-1988 | 16.144 | 12.560 | 1.629 | 19.750 |
| ∆% 1979-88 | 16.3 | 15.2 | 45.5 | 12.0 |
Source: Bureau of Labor Statistics
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). Youth workers would obtain employment at a premium in an economy with declining population. In fact, 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. This is merely another case of theory without reality with dubious policy proposals. Inferior performance of the US economy and labor markets is the critical current issue of analysis and policy design.
In revealing research, Edward P. Lazear and James R. Spletzer (2012JHJul22) use the wealth of data in the valuable database and resources of the Bureau of Labor Statistics (http://www.bls.gov/data/) in providing clear thought on the nature of the current labor market of the United States. The critical issue of analysis and policy currently is whether unemployment is structural or cyclical. Structural unemployment could occur because of (1) industrial and demographic shifts and (2) mismatches of skills and job vacancies in industries and locations. Consider the aggregate unemployment rate, Y, expressed in terms of share si of a demographic group in an industry i and unemployment rate yi of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):
Y = ∑isiyi (1)
This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):
∆Y = ∑i∆siy*i + ∑i∆yis*i (2)
The first term in (2) captures changes in the demographic and industrial composition of the economy ∆si 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 ∆yi, 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.”
The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. There are accentuated cyclic factors for both the entire population and the young population of ages 16 to 24 years. Table Summary Total provides the total noninstitutional population (ICP) of the US, full-time employment level (FTE), employment level (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP), employment/population ratio (EPOP) and unemployment level (UNE). Secular stagnation would spread over long periods instead of immediately. All indicators of the labor market weakened sharply during the contraction and did not recover. Population continued to grow but all other variables collapsed and did not recover. The theory of secular stagnation departs from an aggregate production function in which output grows with the use of labor, capital and technology (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), 11-16). Hansen (1938, 1939) finds secular stagnation in lower growth of an aging population. In the current US economy, Table Summary shows that population is dynamic while the labor market is fractured. There is key explanation in the behavior of the civilian labor force participation rate (CLFP) and the employment population ratio (EPOP) that collapsed during the global recession with inadequate recovery. Abandoning job searches are difficult to capture in labor statistics but likely explain the decline in the participation of the population in the labor force. Allowing for abandoning job searches, the total number of people unemployed or underemployed is 28.2 million or 16.5 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html).
Table Summary Total, US, Total Noninstitutional Civilian Population, Full-time Employment, Employment, Civilian Labor Force, Civilian Labor Force Participation Rate, Employment Population Ratio, Unemployment, NSA, Millions and Percent
| ICP | FTE | EMP | CLF | CLFP | EPOP | UNE | |
| 2006 | 228.8 | 119.7 | 144.4 | 151.4 | 66.2 | 63.1 | 7.0 |
| 2009 | 235.8 | 112.6 | 139.9 | 154.1 | 65.4 | 59.3 | 14.3 |
| 2012 | 243.3 | 114.8 | 142.5 | 155.0 | 63.7 | 58.6 | 12.5 |
| 2013 | 245.7 | 116.3 | 143.9 | 155.4 | 63.2 | 58.6 | 11.5 |
| 2014 | 247.9 | 118.7 | 146.3 | 155.9 | 62.9 | 59.0 | 9.6 |
| 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 |
| 2/15 | 249.9 | 119.3 | 147.1 | 156.2 | 62.5 | 58.9 | 9.1 |
ICP: Total Noninstitutional Civilian Population; FT: 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
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 |
| 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 |
| 2/15 | 38.7 | 18.2 | 20.7 | 53.6 | 47.1 | 2.5 | 12.2 |
ICP: Youth Noninstitutional Civilian Population; EMP: Youth Employment Level; CLF: Youth Civilian Labor Force; CLFP: Youth Civilian Labor Force Participation Rate; EPOP: Youth Employment Population Ratio; UNE: Unemployment; UNER: Youth Unemployment Rate
Source: Bureau of Labor Statistics
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.”
IIA Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth. The valuable report on Financial Accounts of the United States formerly Flow of Funds Accounts of the United States provided by the Board of Governors of the Federal Reserve System (http://www.federalreserve.gov/releases/z1/Current/ http://www.federalreserve.gov/apps/fof/) is rich in important information and analysis. Table IIA-1, updated in this blog for every new quarterly release, shows the balance sheet of US households combined with nonprofit organizations in 2007, 2011, 2013 and IVQ2014. The data show the strong shock to US wealth during the contraction. Assets fell from $81.1 trillion in 2007 to $77.2 trillion in 2011 even after nine consecutive quarters of growth beginning in IIIQ2009 (Section I and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html), for decline of $3.9 trillion or 4.8 percent. Assets stood at $92.6 trillion in 2013 for gain of $11.5 trillion relative to $81.1 trillion in 2007 or increase by 14.2 percent. Assets increased to $97.1 trillion in IVQ2014 by $15.9 trillion relative to 2007 or 19.6 percent. Liabilities declined from $14.4 trillion in 2007 to $13.6 trillion in 2011 or by $821.3 billion equivalent to decline by 5.7 percent. Liabilities declined $604.5 billion or 4.2 percent from 2007 to 2013 and increased 1.6 percent from 2011 to 2013. Liabilities fell from $14.4 trillion in 2007 to $14.2 trillion in IVQ2014, by $241.4 billion or decline of 1.7 percent. Net worth shrank from $66.7 trillion in 2007 to $63.7 trillion in 2011, that is, $3.1 trillion equivalent to decline of 4.6 percent. Net worth increased from $66,749.8 billion in 2007 to $82,912.1 billion in IVQ2014 by $16,162.4 billion or 24.2 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 234.812 in Dec 2014 (http://www.bls.gov/cpi/data.htm) or 11.8 percent. Net worth adjusted by CPI inflation increased 11.1 percent from 2007 to IVQ2014. Nonfinancial assets increased $898.5 billion from $28,176.0 billion in 2007 to $29,074.5 billion in IVQ2014 or 0.7 percent. There was increase from 2007 to IVQ2013 of $172.1 trillion in real estate assets or by 5.2 percent. Real estate assets adjusted for CPI inflation fell 9.9 percent between 2007 and IVQ2014. The National Association of Realtors estimated that the gains in net worth in homes by Americans were about $4 trillion between 2000 and 2005 (quoted in Pelaez and Pelaez, The Global Recession Risk (2007), 224-5).
Table IIA-1, US, Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars Outstanding End of Period, NSA
| 2007 | 2011 | 2013 | IVQ2014 | |
| Assets | 81,145.7 | 77,241.5 | 92,635.6 | 97,066.7 |
| Nonfinancial | 28,176.0 | 23,375.1 | 27,688.5 | 29,074.5 |
| Real Estate | 23,366.5 | 18,249.5 | 22,315.2 | 23,538.6 |
| Durable Goods | 4,476.0 | 4,723.3 | 4,942.2 | 5,086.6 |
| Financial | 52,969.8 | 53,866.3 | 64,947.2 | 67,992.2 |
| Deposits | 7,560.4 | 8,747.6 | 9,655.3 | 10,231.4 |
| Credit Market | 3,997.8 | 4,390.0 | 3,874.8 | 3,355.8 |
| Mutual Fund Shares | 4,590.3 | 4,658.5 | 7,142.1 | 7,804.2 |
| Equities Corporate | 9,912/5 | 8,455.3 | 12,406.8 | 13,365.2 |
| Equity Noncorporate | 8,935.2 | 7,363.1 | 9,001.1 | 9,337.7 |
| Pension | 15,267.2 | 17,444.7 | 19,893.7 | 20,814.3 |
| Liabilities | 14,395.9 | 13,573.8 | 13,791.4 | 14,154.5 |
| Home Mortgages | 10,613.3 | 9,698.3 | 9,405.8 | 9,379.8 |
| Consumer Credit | 2,615.7 | 2,755.9 | 3,097.9 | 3,316.3 |
| Net Worth | 66,749.8 | 63,667.7 | 78,844.2 | 82,912.2 |
Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
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 II-2 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 95.4 percent in the 10-city composite of the Case-Shiller home price index, 81.0 percent in the 20-city composite and 65.6 percent in the US national home price index between Dec 2000 and Dec 2005. Prices rose around 100 percent from Dec 2000 to Dec 2006, increasing 95.8 percent for the 10-city composite, 84.2 percent for the 20-city composite and 68.4 percent in the US national index. House prices rose 37.6 percent between Dec 2003 and Dec 2005 for the 10-city composite, 34.2 percent for the 20-city composite and 29.0 percent for the US national propelled by low fed funds rates of 1.0 percent between Jun 2003 and Jun 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 decline of yields of mortgage-backed securities with intended decrease in mortgage rates. Similarly, between Dec 2003 and Dec 2006, the 10-city index gained 37.9 percent, the 20-city index increased 35.1 percent and the US national 31.2 percent. House prices have fallen from Dec 2006 to Dec 2014 by 15.5 percent for the 10-city composite, 14.9 percent for the 20-city composite and 9.0 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 Dec 2014, house prices increased 4.3 percent in the 10-city composite and increased 4.5 percent in the 20-city composite and 4.6 percent in the US national. Table II-2 also shows that house prices increased 65.4 percent between Dec 2000 and Dec 2014 for the 10-city composite and increased 55.1 percent for the 20-city composite and 53.3 percent 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 10-city composite fell 17.0 percent from the peak in Jun 2006 to Dec 2014 and the 20-city composite fell 16.2 percent from the peak in Jul 2006 to Dec 2014. The US national fell 9.6 percent from the peaks of the 10-city and 20-city composites to Dec 2014. 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 annual growth rate between Dec 1987 and Dec 2014 for the 10-city composite was 3.7 percent and 3.4 percent for the US national. Data for the 20-city composite are available only beginning in Jan 2000. House prices accelerated in the 1990s with the average rate of the 10-city composite of 5.0 percent between Dec 1992 and Dec 2000 while the average rate for the period Dec 1987 to Dec 2000 was 3.8 percent. The average rate for the US national was 3.4 percent from Dec 1987 and Dec 2014 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 of the 10-city composite between Dec 2000 and Dec 2014 was 3.7 percent while the rate of the 20-city composite was 3.0 percent and 3.1 percent for the US national.
Table I-5, US, Percentage Changes of Standard & Poor’s Case-Shiller Home Price Indices, Not Seasonally Adjusted, ∆%
| 10-City Composite | 20-City Composite | US National | |
| ∆% Dec 2000 to Dec 2003 | 42.0 | 34.9 | 28.3 |
| ∆% Dec 2000 to Dec 2005 | 95.4 | 81.0 | 65.6 |
| ∆% Dec 2003 to Dec 2005 | 37.6 | 34.2 | 29.0 |
| ∆% Dec 2000 to Dec 2006 | 95.8 | 82.2 | 68.4 |
| ∆% Dec 2003 to Dec 2006 | 37.9 | 35.1 | 31.2 |
| ∆% Dec 2005 to Dec 2014 | -15.4 | -14.3 | -7.4 |
| ∆% Dec 2006 to Dec 2014 | -15.5 | -14.9 | -9.0 |
| ∆% Dec 2009 to Dec 2014 | 19.2 | 18.3 | 12.9 |
| ∆% Dec 2010 to Dec 2014 | 20.4 | 21.5 | 18.6 |
| ∆% Dec 2011 to Dec 2014 | 25.6 | 26.7 | 23.4 |
| ∆% Dec 2012 to Dec 2014 | 18.4 | 18.4 | 15.9 |
| ∆% Dec 2013 to Dec 2014 | 4.3 | 4.5 | 4.6 |
| ∆% Dec 2000 to Dec 2014 | 65.4 | 55.1 | 53.3 |
| ∆% Peak Jun 2006 Dec 2014 | -17.0 | -9.6 | |
| ∆% Peak Jul 2006 Dec 2014 | -16.2 | -9.6 | |
| Average ∆% Dec 1987-Dec 2014 | 3.7 | NA | 3.4 |
| Average ∆% Dec 1987-Dec 2000 | 3.8 | NA | 3.6 |
| Average ∆% Dec 1992-Dec 2000 | 5.0 | NA | 4.5 |
| Average ∆% Dec 2000-Dec 2014 | 3.7 | 3.2 | 3.1 |
Source: http://us.spindices.com/index-family/real-estate/sp-case-shiller
Price increases measured by the Case-Shiller house price indices show “slight uptick” throughout the US with increases in nine cities (https://www.spice-indices.com/idpfiles/spice-assets/resources/public/documents/144743_cshomeprice-release-0224.pdf?force_download=true). Monthly house prices increased sharply from Feb 2013 to Jan 2014 for both the 10- and 20-city composites, as shown in Table II-3. In Jan 2013, the seasonally adjusted 10-city composite increased 0.8 percent and the 20-city increased 0.9 percent while the 10-city not seasonally adjusted changed 0.0 percent and the 20-city changed 0.0 percent. House prices increased at high monthly percentage rates from Feb to Nov 2013. With the exception of Feb through Apr 2012, house prices seasonally adjusted declined in every month for both the 10-city and 20-city Case-Shiller composites from Dec 2010 to Jan 2012, as shown in Table I-6. The most important seasonal factor in house prices is school changes for wealthier homeowners with more expensive houses. Without seasonal adjustment, house prices fell from Dec 2010 throughout Mar 2011 and then increased in every month from Apr to Aug 2011 but fell in every month from Sep 2011 to Feb 2012. The not seasonally adjusted index registers decline in Mar 2012 of 0.1 percent for the 10-city composite and is flat for the 20-city composite. Not seasonally adjusted house prices increased 1.4 percent in Apr 2012 and at high monthly percentage rates until Sep 2012. House prices not seasonally adjusted stalled from Oct 2012 to Jan 2013 and surged from Feb to Sep 2013, decelerating in Oct 2013-Feb 2014. House prices grew at fast rates in Mar 2014. The 10-city NSA index increased 0.1 percent in Dec 2014 and the 20-city decreased 0.1 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 II-3, US, Monthly Percentage Change of S&P Case-Shiller Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%
| 10-City Composite SA | 10-City Composite NSA | 20-City Composite SA | 20-City Composite NSA | |
| Dec 2014 | 0.8 | 0.1 | 0.9 | 0.1 |
| Nov | 0.8 | -0.3 | 0.8 | -0.2 |
| Oct | 0.7 | -0.2 | 0.7 | -0.1 |
| Sep | 0.2 | -0.1 | 0.3 | -0.1 |
| Aug | -0.1 | 0.1 | -0.1 | 0.2 |
| Jul | -0.4 | 0.6 | -0.4 | 0.6 |
| Jun | -0.1 | 1.0 | -0.2 | 1.0 |
| May | -0.3 | 1.1 | -0.3 | 1.1 |
| Apr | 0.0 | 1.1 | 0.0 | 1.2 |
| Mar | 1.1 | 0.8 | 1.3 | 0.9 |
| Feb | 1.0 | 0.0 | 0.7 | 0.0 |
| Jan | 0.7 | -0.1 | 0.7 | -0.1 |
| Dec 2013 | 0.7 | -0.1 | 0.7 | -0.1 |
| Nov | 0.9 | 0.0 | 0.9 | -0.1 |
| Oct | 1.0 | 0.2 | 1.1 | 0.2 |
| Sep | 1.0 | 0.7 | 1.1 | 0.7 |
| Aug | 1.0 | 1.3 | 1.0 | 1.3 |
| Jul | 0.8 | 1.9 | 0.8 | 1.8 |
| Jun | 1.0 | 2.2 | 0.9 | 2.2 |
| May | 1.1 | 2.5 | 1.1 | 2.5 |
| Apr | 1.6 | 2.6 | 1.5 | 2.6 |
| Mar | 1.6 | 1.3 | 1.7 | 1.3 |
| Feb | 1.3 | 0.3 | 1.0 | 0.2 |
| Jan | 0.8 | 0.0 | 0.9 | 0.0 |
| Dec 2012 | 1.0 | 0.2 | 1.0 | 0.2 |
| Nov | 0.7 | -0.3 | 0.8 | -0.2 |
| Oct | 0.7 | -0.2 | 0.7 | -0.1 |
| Sep | 0.5 | 0.3 | 0.6 | 0.3 |
| Aug | 0.5 | 0.8 | 0.6 | 0.9 |
| Jul | 0.4 | 1.5 | 0.5 | 1.6 |
| Jun | 0.9 | 2.1 | 1.0 | 2.3 |
| May | 0.8 | 2.2 | 1.0 | 2.4 |
| Apr | 0.4 | 1.4 | 0.4 | 1.4 |
| Mar | 0.3 | -0.1 | 0.5 | 0.0 |
| Feb | 0.0 | -0.9 | 0.0 | -0.8 |
| Jan | -0.3 | -1.1 | -0.1 | -1.0 |
| Dec 2011 | -0.5 | -1.2 | -0.4 | -1.1 |
| Nov | -0.5 | -1.4 | -0.5 | -1.3 |
| Oct | -0.5 | -1.3 | -0.5 | -1.4 |
| Sep | -0.4 | -0.6 | -0.4 | -0.7 |
| Aug | -0.3 | 0.1 | -0.3 | 0.1 |
| Jul | -0.2 | 0.9 | -0.1 | 1.0 |
| Jun | -0.1 | 1.0 | -0.1 | 1.2 |
| May | -0.3 | 1.0 | -0.3 | 1.0 |
| Apr | -0.2 | 0.6 | -0.3 | 0.6 |
| Mar | -0.5 | -1.0 | -0.5 | -1.0 |
| Feb | -0.4 | -1.3 | -0.4 | -1.2 |
| Jan | -0.3 | -1.1 | -0.3 | -1.1 |
| Dec 2010 | -0.2 | -0.9 | -0.2 | -1.0 |
Source: http://us.spindices.com/index-family/real-estate/sp-case-shiller
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 $10.4 trillion or 12.7 percent from 2007 to 2008 and $8.8 trillion or 10.9 percent to 2009. Net worth fell $10.2 trillion from 2007 to 2008 or 15.3 percent and $8.5 trillion to 2009 or 12.7 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 | 81,145.7 | 70,788.9 | -10,356.8 | 72,314.1 | -8,831.6 |
| Non | 28,176.0 | 24,791.0 | -3,385.0 | 23,699.6 | -4,476.4 |
| RE | 23,366.5 | 19,856.8 | -3,509.7 | 18,743.2 | -4,623.3 |
| FIN | 52,969.8 | 45,997.8 | -6,972.0 | 48,614.4 | -4,355.4 |
| LIAB | 14,395.9 | 14,279.9 | -116.0 | 14,063.4 | -332.5 |
| NW | 66,749.8 | 56,508.9 | -10,240.9 | 58,250.7 | -8,499.1 |
A: Assets; Non FIN: Nonfinancial Assets; RE: Real Estate; FIN: Financial Assets; LIAB: Liabilities; NW: Net Worth
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
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 68.5 percent of GDP in IVQ2014 (Section I and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IVQ2014, real estate increased in value by $898.5 billion and financial assets increased $15,022.4 billion for net gain of real estate and financial assets of $15,920.9 billion, explaining most of the increase in net worth of $16,162.4 billion obtained by adding the decrease in liabilities of $241.4 billion to the increase of assets of $15,921.0 billion. Net worth increased from $66,749.8 billion in 2007 to $82,912.2 billion in IVQ2014 by $16,162.4 billion or 24.2 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 234.812 in Dec 2014 (http://www.bls.gov/cpi/data.htm) or 11.8 percent. Net worth adjusted by CPI inflation increased 11.1 percent from 2007 to IVQ2014. Real estate assets adjusted for CPI inflation fell 9.9 percent from 2007 to IVQ2014. 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.” US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 22 quarters from IIIQ2009 to IVQ2014. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IVQ2014 (http://www.bea.gov/newsreleases/national/gdp/2015/pdf/gdp4q14_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (Section I and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/financial-and-international.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 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 and at 7.8 percent from IQ1983 to IVQ1983 (Section I and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/02/financial-and-international.html). 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 IVQ2014 would have accumulated to 23.0 percent. GDP in IVQ2014 would be $18,438.0 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,143.3 billion than actual $16,294.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 27.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.5 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html and earlier http://cmpassocregulationblog.blogspot.com/2015/01/fluctuating-valuations-of-risk.html). US GDP in IVQ2014 is 11.6 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,294.7 billion in IVQ2014 or 8.7 percent at the average annual equivalent rate of 1.2 percent. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. 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 at average 3.3 percent per year from Feb 1919 to Feb 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 125.2379 in Feb 2015. The actual index NSA in Feb 2015 is 100.0312, which is 20.1 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014, raising the index at trend to 117.6250 in Feb 2015. The output of manufacturing at 100.0312 in Feb 2015 is 15.0 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 2011, 2013 and IVQ2014
| Value 2007 | Change to 2011 | Change to 2013 | Change to IVQ2014 | |
| Assets | 81,145.7 | -3,904.2 | 11,489.9 | 15,921.0 |
| Nonfinancial | 28,176.0 | -4,800.9 | -487.5 | 898.5 |
| Real Estate | 23,366.5 | -5,117.0 | -1,051.3 | 172.1 |
| Financial | 52,969.8 | 896.5 | 11,977.4 | 15,022.4 |
| Liabilities | 14,395.9 | -822.1 | -604.5 | -241.4 |
| Net Worth | 66,749.8 | -3,082.1 | 12,094.4 | 16,162.4 |
Net Worth = Assets – Liabilities
Source: Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System.
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
The comparison of net worth of households and nonprofit organizations in the entire economic cycle from IQ1980 (and from IVQ1979) to IIQ1988 and from IVQ2007 to IVQ2014 is provided in Table IIA-5. The data reveal the following facts for the cycles in the 1980s:
- IVQ1979 to IIQ1988. Net worth increased 108.9 percent from IVQ1979 to IIQ1988, the all items CPI index increased 53.8 percent from 76.7 in Dec 1979 to 118.0 in Jun 1988 and real net worth increased 35.8 percent.
- IQ1980 to IVQ1985. Net worth increased 65.4 percent, the all items CPI index increased 36.5 percent from 80.1 in Mar 1980 to 109.3 in Dec 1985 and real net worth increased 21.2 percent.
- IVQ1979 to IVQ1985. Net worth increased 68.8 percent, the all items CPI index increased 42.5 percent from 76.7 in Dec 1979 to 109.3 in Dec 1985 and real net worth increased 18.5 percent.
- IQ1980 to IIQ1988. Net worth increased 104.6 percent, the all items CPI index increased 47.3 percent from 80.1 in Mar 1980 to 118.0 in Jun 1988 and real net worth increased 38.9 percent.
There is disastrous performance in the current economic cycle:
- IVQ2007 to IVQ2014. Net worth increased 24.2 percent, the all items CPI increased 11.8 percent from 210.036 in Dec 2007 to 234.812 in Dec 2014 and real or inflation adjusted net worth increased 11.1 percent. Real estate assets adjusted for inflation fell 9.9 percent.
The explanation is partly in the sharp decline of wealth of households and nonprofit organizations and partly in the mediocre growth rates of the cyclical expansion beginning in IIIQ2009. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 22 quarters from IIIQ2009 to IVQ2014. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IVQ2014 (http://www.bea.gov/newsreleases/national/gdp/2015/pdf/gdp4q14_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (Section I and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/financial-and-international.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 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 and at 7.8 percent from IQ1983 to IVQ1983 (Section I and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/02/financial-and-international.html). 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 IVQ2014 would have accumulated to 23.0 percent. GDP in IVQ2014 would be $18,438.0 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,143.3 billion than actual $16,294.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 27.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.5 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html and earlier http://cmpassocregulationblog.blogspot.com/2015/01/fluctuating-valuations-of-risk.html). US GDP in IVQ2014 is 11.6 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,294.7 billion in IVQ2014 or 8.7 percent at the average annual equivalent rate of 1.2 percent. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. 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 at average 3.3 percent per year from Feb 1919 to Feb 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 125.2379 in Feb 2015. The actual index NSA in Feb 2015 is 100.0312, which is 20.1 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014, raising the index at trend to 117.6250 in Feb 2015. The output of manufacturing at 100.0312 in Feb 2015 is 15.0 percent below trend under this alternative calculation.
Table IIA-5, Net Worth of Households and Nonprofit Organizations in Billions of Dollars, IVQ1979 to IVQ1985 and IVQ2007 to IVQ2012
| Period IQ1980 to IIQ1988 | |
| Net Worth of Households and Nonprofit Organizations USD Millions | |
| IVQ1979 IQ1980 | 9,047.8 9,238.6 |
| IVQ1985 IIIQ1986 IVQ1986 IQ1987 IIQ1987 IIIQ1987 IVQ1987 IQ1988 II1988 | 15,277.2 16,290.7 16,840.3 17,494.6 17,784.0 18,195.2 18,021.9 18,459.2 18,900.2 |
| ∆ USD Billions IVQ1985 IVQ1979 to IIQ1988 IQ1980-IVQ1985 IQ1980-IIIQ1986 IQ1980-IVQ1986 IQ1980-IQ1987 IQ1980-IIQ1987 IQ1980-IIIQ1987 IQ1980-IVQ1987 IQ1980-IQ1988 IQ1980-IIQ1988 | +6,229.4 ∆%68.8 R∆%18.5 +9852.4 ∆%108.9 R∆%35.8 +6,038.6 ∆%65.4 R∆%21.2 +7,052.1 ∆%76.3 R∆%28.2 +7,601.7 ∆%82.3 R∆%32.1 +8,256.0 ∆%89.4 R∆%35.3 +8,545.4 ∆%92.5 R∆%35.9 +8,956.6 ∆%96.9 R∆%37.2 +8783.3 ∆%95.1 R∆%35.4 +9226.6 ∆%100.2 R∆%37.6 +9661.6 ∆%104.6 R∆38.9 |
| Period IVQ2007 to IVQ2014 | |
| Net Worth of Households and Nonprofit Organizations USD Millions | |
| IVQ2007 | 66,749.8 |
| IVQ2014 | 82,912.2 |
| ∆ USD Billions | +16,162.4 ∆%24.2 R∆%11.1 |
Net Worth = Assets – Liabilities. R∆% real percentage change or adjusted for CPI percentage change.
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Chart IIA-1 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ2007 to IIIQ2014. There is remarkable stop and go behavior in this series with two sharp declines and two standstills in the 22 quarters of expansion of the economy beginning in IIIQ2009. The increase in net worth of households and nonprofit organizations is the result of increases in valuations of risk financial assets and compressed liabilities resulting from zero interest rates. Wealth of households and nonprofits organization increased 11.1 percent from IVQ2007 to IVQ2014 when adjusting for consumer price inflation.
Chart IIA-1, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ2007 to IVQ2014
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Chart IIA-2 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ1979 to IIQ1988. There are changes in the rates of growth of wealth suggested by the changing slopes but there is smooth upward trend. There was significant financial turmoil during the 1980s. Benston and Kaufman (1997, 139) find that there was failure of 1150 US commercial and savings banks between 1983 and 1990, or about 8 percent of the industry in 1980, which is nearly twice more than between the establishment of the Federal Deposit Insurance Corporation in 1934 through 1983. More than 900 savings and loans associations, representing 25 percent of the industry, were closed, merged or placed in conservatorships (see Pelaez and Pelaez, Regulation of Banks and Finance (2008b), 74-7). The Financial Institutions Reform, Recovery and Enforcement Act of 1989 (FIRREA) created the Resolution Trust Corporation (RTC) and the Savings Association Insurance Fund (SAIF) that received $150 billion of taxpayer funds to resolve insolvent savings and loans. The GDP of the US in 1989 was $5657.7 billion (http://www.bea.gov/iTable/index_nipa.cfm), such that the partial cost to taxpayers of that bailout was around 2.65 percent of GDP in a year. US GDP in 2014 is estimated at $17,418.9 billion, such that the bailout would be equivalent to cost to taxpayers of about $461.6 billion in current GDP terms. A major difference with the Troubled Asset Relief Program (TARP) for private-sector banks is that most of the costs were recovered with interest gains whereas in the case of savings and loans there was no recovery. Money center banks were under extraordinary pressure from the default of sovereign debt by various emerging nations that represented a large share of their net worth (see Pelaez 1986). Net worth of households and nonprofit organizations increased 108.9 percent from IVQ1979 to IIQ1988 and 35.8 percent when adjusting for consumer price inflation. Net worth of households and nonprofit organizations increased 104.6 percent from IQ1980 to IIQ1988 and 38.9 percent when adjusting for consumer price inflation.
Chart IIA-2, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1979 to IIQ1988
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Chart IIA-3 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ1945 at $767.3 billion to IVQ2014 at $82,912.2 billion or increase of 10,705.7 percent. The consumer price index not seasonally adjusted was 18.2 in Dec 1945 jumping to 234.812 in Dec 2014 or increase of 1,190.2 percent. There was a gigantic increase of US net worth of households and nonprofit organizations over 69 years with inflation-adjusted increase from $42.159 in dollars of 1945 to $353.100 in IVQ2014 or 737.5 percent. In a simple formula: {[($82,912.2/$767.3)/(234.812/18.2)-1]100 = 737.5%}. Wealth of households and nonprofit organizations increased from $767.3 billion at year-end 1945 to $82,912.2 billion at the end of IVQ2014 or 10,705.7 percent. The consumer price index increased from 18.2 in Dec 1945 to 234.812 in Dec 2014 or 1190.2 percent. Net wealth of households and nonprofit organizations in dollars of 1945 increased from $42.159 in 1945 to $353.100 in IVQ2014 or 737.5 percent at the average yearly rate of 3.1 percent. US real GDP grew at the average rate of 2.9 percent from 1945 to 2014 (http://www.bea.gov/iTable/index_nipa.cfm). The combination of collapse of values of real estate and financial assets during the global recession of IVQ2007 to IIQ2009 caused sharp contraction of net worth of US households and nonprofit organizations. Recovery has been in stop-and-go fashion during the worst cyclical expansion in the 68 years when US GDP grew at 2.3 percent on average in the twenty-two quarters between IIIQ2009 and IVQ2014 (Section I and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html). US GDP was $228.2 billion in 1945 and net worth of households and nonprofit organizations $767.3 billion for ratio of wealth to GDP of 3.36. The ratio of net worth of households and nonprofits of $66,749.8 billion in 2007 to GDP of $14,477.6 billion was 4.61. The ratio of net worth of households and nonprofits of $82,912.2 billion in 2014 to GDP of 17,418.9 billion was 4.75. The final data point in Chart IIA-3 is net worth of household and nonprofit institutions at $82,912.2 billion in IVQ2014 for increase of 10,705.7 percent relative to $767.3 billion in IVQ1945. CPI adjusted net worth of household and nonprofit institutions increased from $42.159 in IVQ1945 to $353.100 in IVQ2014 or 737.5 percent at the annual equivalent rate of 3.1 percent.
Chart IIA-3, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1945 to IVQ2014
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Table IIA-6 provides percentage changes of nonfinancial domestic sector debt. Households increased debt by 10.3 percent in 2006 but reduced debt from 2009 to 2011. Households have increased debt moderately since 2012. Financial repression by zero fed funds rates or negative interest rates intends to increase debt and reduce savings. Business had not been as exuberant in acquiring debt and has been increasing debt benefitting from historically low costs while increasing cash holdings to around $2 trillion by swelling undistributed profits because of the uncertainty of capital budgeting. The key to growth and hiring consists in creating the incentives for business to invest and hire. States and local government were forced into increasing debt by the decline in revenues but began to contract in IQ2011, decreasing again from IQ2011 to IVQ2011, increasing at 2.1 percent in IIQ2012 and decreasing at 0.2 percent in IIIQ2012 and 2.6 percent in IVQ2012. State and local government increased debt at 1.9 percent in IQ2013 and decreased at 0.2 percent in IIQ2013. State and local government decreased debt at 3.7 percent in IIIQ2013 and at 3.3 percent in IVQ2013. State and local government reduced debt at 1.3 percent in IQ2014 and increased at 1.2 percent in IIQ2014. State and local government reduced debt at 2.8 percent in IIIQ2014 and increased at 1.1 percent in IVQ2014. Opposite behavior is found for the federal government that has been rapidly accumulating debt but without success in the self-assigned goal of promoting economic growth. Financial repression constitutes seigniorage of government debt (http://cmpassocregulationblog.blogspot.com/2011/05/global-inflation-seigniorage-financial.html http://cmpassocregulationblog.blogspot.com/2011/05/global-inflation-seigniorage-monetary.html).
Table IIA-6, US, Percentage Change of Nonfinancial Domestic Sector Debt
| Total | Households | Business | State & | Federal | |
| IVQ2014 | 4.7 | 2.7 | 7.2 | 1.1 | 5.4 |
| IIIQ2014 | 4.4 | 2.8 | 5.0 | -2.8 | 7.2 |
| IIQ2014 | 3.4 | 3.5 | 5.0 | 1.2 | 2.5 |
| IQ2014 | 4.3 | 2.3 | 6.1 | -1.3 | 6.0 |
| IVQ2013 | 4.4 | 0.6 | 4.4 | -3.3 | 10.5 |
| IIIQ2013 | 3.5 | 3.1 | 7.0 | -3.7 | 2.6 |
| IIQ2013 | 3.0 | 1.6 | 4.9 | -0.2 | 3.5 |
| IQ2013 | 4.1 | 0.7 | 3.5 | 1.9 | 9.1 |
| IVQ2012 | 5.2 | 1.9 | 6.9 | -2.6 | 9.3 |
| 2014 | 4.3 | 2.9 | 5.9 | -0.5 | 5.4 |
| 2013 | 3.8 | 1.5 | 5.1 | -1.3 | 6.5 |
| 2012 | 5.0 | 1.5 | 4.8 | -0.2 | 10.9 |
| 2011 | 3.6 | -0.2 | 3.0 | -1.7 | 11.4 |
| 2010 | 4.1 | -1.1 | -0.9 | 2.3 | 20.2 |
| 2009 | 3.3 | 0.0 | -4.3 | 4.0 | 22.7 |
| 2008 | 6.2 | 1.1 | 5.8 | 0.6 | 24.2 |
| 2007 | 8.2 | 7.1 | 12.4 | 5.5 | 4.9 |
| 2006 | 8.4 | 10.3 | 9.8 | 3.9 | 3.9 |
| 2005 | 9.0 | 11.2 | 8.1 | 5.8 | 7.0 |
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Table IIA-7 provides wealth of US households and nonprofit organizations since 2005 in billions of current dollars at the end of period, NSA. Wealth fell from $66,750 billion in 2007 to $58,251 billion in 2009 or 12.7 percent and to $63,668 billion in 2011 or 4.6 percent. Wealth increased 24.2 percent from 2007 to IVQ2014, increasing 11.1 percent after adjustment for inflation, primarily because of bloating financial assets while nonfinancial assets declined in real terms.
Table IIA-7, US, Net Worth of Households and Nonprofit Organizations, Billions of Dollars, Amounts Outstanding at End of Period, NSA
| Quarter | Net Worth |
| IVQ2014 | 82,912 |
| IIIQ2014 | 81,395 |
| IIQ2014 | 81,409 |
| IQ2014 | 80,015 |
| IVQ2013 | 78,844 |
| IIIQ2013 | 76,215 |
| IIQ2013 | 73,850 |
| IQ2013 | 72,272 |
| IVQ2012 | 69,509 |
| IIIQ2012 | 68,245 |
| IIQ2012 | 66,046 |
| IQ2012 | 65,842 |
| 2014 | 82,912 |
| 2013 | 78,844 |
| 2012 | 69,509 |
| 2011 | 63,668 |
| 2010 | 62,448 |
| 2009 | 58,251 |
| 2008 | 56,509 |
| 2007 | 66,750 |
| 2006 | 66,334 |
| 2005 | 61,839 |
| 2004 | 55,950 |
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
IIA United States Housing Collapse. The objective of this section is to provide the latest data and analysis of US housing. Subsection IIB1 United New House Sales analyzes the collapse of US new house sales. Subsection IIB2 United States House Prices considers the latest available data on house prices. Subsection IIB3 Factors of US Housing Collapse provides the analysis of the causes of the housing crisis of the US.
IIB1 United States New House Sales. 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). House sales fell in 20 of fifty months from Jan 2011 to Feb 2015 with monthly declines of five in 2011, four in 2012, six in 2013 and five in 2014. 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 7.0 percent in Oct 2012 with increase of 9.5 percent in Nov 2012. Sales of new houses rebounded 13.5 percent in Jan 2013 with annual equivalent rate of 62.9 percent from Oct 2012 to Jan 2013 because of the increase of 13.5 percent in Jan 2013. New house sales fell at annual equivalent 16.1 percent in Feb-Mar 2013. New house sales weakened, increasing at 0.8 percent in annual equivalent from Apr to Dec 2013 with significant volatility illustrated by decline of 20.0 percent in Jul 2013 and increase of 12.8 percent in Oct 2013. New house sales fell 0.7 percent in Dec 2013. New house sales increased 3.4 percent in Jan 2014 and fell 5.5 percent in Feb 2014 and 6.7 percent in Mar 2014. New house sales increased 2.5 percent in Apr 2014 and 10.9 percent in May 2014. New house sales fell 10.7 percent in Jun 2014 and decreased 2.4 percent in Jul 2014. New house sales jumped 12.3 percent in Aug 2014 and increased 1.8 percent in Sep 2014. New House sales increased 2.9 percent in Oct 2014 and fell 4.5 percent in Nov 2014. New house sales increased 6.9 percent in Dec 2014 and increased 4.4 percent in Jan 2015. Sales of new houses increased 7.8 percent in Feb 2015. The annual equivalent rate in Jan-Aug 2014 was 2.1 percent and 44.9 percent in Sep 2014-Feb 2015 with 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 3.69 percent on Mar 26, 2015. The conventional mortgage rate measured in a survey by Freddie Mac (http://www.freddiemac.com/pmms/release.html) is the “contract interest rate on commitments for fixed-rate first mortgages” (http://www.federalreserve.gov/releases/h15/data.htm).
Table IIB-1, US, Sales of New Houses at Seasonally-Adjusted (SA) Annual Equivalent Rate, Thousands and %
| SA Annual Rate | ∆% | |
| Feb 2015 | 539 | 7.8 |
| Jan | 500 | 4.4 |
| Dec 2014 | 479 | 6.9 |
| Nov | 448 | -4.5 |
| Oct | 469 | 2.9 |
| Sep | 456 | 1.8 |
| AE ∆% Sep-Feb | 44.9 | |
| Aug | 448 | 12.3 |
| Jul | 399 | -2.4 |
| Jun | 409 | -10.7 |
| May | 458 | 10.9 |
| Apr | 413 | 2.5 |
| Mar | 403 | -6.7 |
| Feb | 432 | -5.5 |
| Jan | 457 | 3.4 |
| AE ∆% Jan-Aug | 2.1 | |
| Dec 2013 | 442 | -0.7 |
| Nov | 445 | -1.1 |
| Oct | 450 | 12.8 |
| Sep | 399 | 5.3 |
| Aug | 379 | 3.3 |
| Jul | 367 | -20.0 |
| Jun | 459 | 6.5 |
| May | 431 | -4.6 |
| Apr | 452 | 2.7 |
| AE ∆% Apr-Dec | 0.8 | |
| Mar | 440 | -1.8 |
| Feb | 448 | -1.1 |
| AE ∆% Feb-Mar | -16.1 | |
| Jan | 453 | 13.5 |
| Dec 2012 | 399 | 1.8 |
| Nov | 392 | 9.5 |
| Oct | 358 | -7.0 |
| AE ∆% Oct-Jan | 62.9 | |
| 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
http://www.census.gov/construction/nrs/
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 7 percent of sales in 2011 to 4 to 5 percent in 2013 and 4.7 percent in Feb 2015. 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 Feb 2015, median prices of new houses sold not seasonally adjusted (NSA) decreased 4.8 percent after decreasing 2.1 percent in Jan 2015. Average prices decreased 0.9 percent in Feb 2014 and decreased 6.3 percent in Jan 2015. Between Dec 2010 and Feb 2015, median prices increased 14.2 percent, partly concentrated in increases of 14.5 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 16.9 percent between Dec 2010 and Feb 2015, with increase of 20.3 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 14.4 percent from Dec 2012 to Dec 2014, with increase of 14.5 percent in Oct 2014, while average prices increased 22.7 percent, with increase of 20.3 percent in Oct 2014. 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* | Median | Month | Average New House Sales Price USD | Month | |
| Feb 2015 | 4.7 | 275,500 | -4.8 | 341,000 | -0.9 |
| Jan | 5.1 | 289,400 | -2.1 | 344,100 | -6.3 |
| Dec 2014 | 5.3 | 295,500 | -2.4 | 367,200 | 2.3 |
| Nov | 5.7 | 302,700 | 1.1 | 358,800 | -6.6 |
| Oct | 5.4 | 299,400 | 14.5 | 384,000 | 20.3 |
| Sep | 5.5 | 261,500 | -10.4 | 319,100 | -10.4 |
| Aug | 5.5 | 291,700 | 4.0 | 356,200 | 3.2 |
| Jul | 6.2 | 280,400 | -2.3 | 345,200 | 2.1 |
| Jun | 5.8 | 287,000 | 0.5 | 338,100 | 4.5 |
| May | 5.0 | 285,600 | 4.0 | 323,500 | -0.5 |
| Apr | 5.5 | 274,500 | -2.8 | 325,100 | -1.9 |
| Mar | 5.7 | 282,300 | 5.2 | 331,500 | 1.7 |
| Feb | 5.2 | 268,400 | -0.5 | 325,900 | -3.4 |
| Jan | 5.0 | 269,800 | -2.1 | 337,300 | 5.0 |
| Dec 2013 | 5.1 | 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 | 5.5 | 269,800 | 5.7 | 321,400 | 3.4 |
| Aug | 5.5 | 255,300 | -2.6 | 310,800 | -5.8 |
| Jul | 5.6 | 262,200 | 0.9 | 329,900 | 7.8 |
| Jun | 4.2 | 259,800 | -1.5 | 306,100 | -2.5 |
| May | 4.5 | 263,700 | -5.6 | 314,000 | -6.8 |
| Apr | 4.3 | 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 | 3.9 | 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
http://www.census.gov/construction/nrs/
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-Feb of various years. Sales of new houses are higher in Jan-Feb 2015 relative to Jan-Feb 2014 with increase of 19.1 percent. Sales of new houses in Jan-Feb 2015 are substantially lower than in any year between 1963 and 2014 with the exception of the years from 2009 to 2014. There are only five increases of 19.1 percent relative to Jan-Feb 2013, 52.8 percent relative to Jan-Feb 2012, 88.4 percent relative to Jan-Feb 2011, 58.8 percent relative to Jan 2010 and 52.8 percent relative to Jan-Feb 2009. Sales of new houses in Jan-Feb 2015 are lower by 12.0 percent relative to Jan 2008, 40.0 percent relative to 2007, 54.2 percent relative to 2006 and 59.7 percent relative to 2005. 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 2014 relative to the same period in 2004 fell 57.6 percent and 48.7 percent relative to the same period in 2003. Similar percentage declines are also observed for 2014 relative to years from 2000 to 2004. Sales of new houses in Jan-Feb 2014 fell 13.8 per cent relative to the same period in 1995. 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 US population reached 308.7 million in 2010 (http://2010.census.gov/2010census/data/). The US population increased by 129.4 million from 1960 to 2010 or 72.2 percent. The final row of Table IIB-3 reveals catastrophic data: sales of new houses in Jan-Feb 2015 of 81 thousand units are higher by 5.2 percent relative to 77 thousand units of houses sold in Jan-Feb 1963, the first year when data become available. The civilian noninstitutional population increased from 122.416 million in 1963 to 247.947 million in 2014, or 102.5 percent (http://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.”
Table IIB-3, US, Sales of New Houses Not Seasonally Adjusted, Thousands and %
| Not Seasonally Adjusted Thousands | |
| Jan-Feb 2015 | 81 |
| Jan-Feb 2014 | 68 |
| ∆% Jan-Feb 2015/Jan-Feb 2014 | 19.1 |
| Jan-Feb 2013 | 68 |
| ∆% Jan-Feb 2015/Jan-Feb 2013 | 19.1 |
| Jan-Feb 2012 | 53 |
| ∆% Jan-Feb 2015/Jan-Feb 2012 | 52.8 |
| Jan-Feb 2011 | 43 |
| ∆% Jan-Feb 2015/Jan-Feb 2011 | 88.4 |
| Jan-Feb 2010 | 51 |
| ∆% Jan-Feb 2015/ | 58.8 |
| Jan-Feb 2009 | 53 |
| ∆% Jan-Feb 2015/ | 52.8 |
| Jan-Feb 2008 | 92 |
| ∆% Jan-Feb 2015/ | -12.0 |
| Jan-Feb 2007 | 134 |
| ∆% Jan-Feb 2015/ | -40.0 |
| Jan-Feb 2006 | 177 |
| ∆% Jan-Feb 2015/Jan-Feb 2006 | -54.2 |
| Jan-Feb 2005 | 201 |
| ∆% Jan-Feb 2015/Jan-Feb 2005 | -59.7 |
| Jan-Feb 2004 | 191 |
| ∆% Jan-Feb 2015/Jan-Feb 2004 | -57.6 |
| Jan-Feb 2003 | 158 |
| ∆% Jan-Feb 2015/ | -48.7 |
| Jan-Feb 2002 | 150 |
| ∆% Jan-Feb 2015/ | -46.0 |
| Jan-Feb 2001 | 157 |
| ∆% Jan-Feb 2015/ | -48.4 |
| Jan-Feb 2000 | 145 |
| ∆% Jan-Feb 2015/ | -44.1 |
| Jan-Feb 1995 | 94 |
| ∆% Jan-Feb 2015/ | -13.8 |
| Jan-Feb 1963 | 77 |
| ∆% Jan-Feb 2014/ | 5.2 |
*Computed using unrounded data
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Table IIB-4 provides the entire available annual series of new house sales from 1963 to 2014. The revised level of 306 thousand new houses sold in 2011 is the lowest since 560 thousand in 1963 in the 48 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 436 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 civilian noninstitutional population of the US increased from 122.416 million in 1963 to 247.947 million in 2014 or 102.5 percent (http://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.”
The civilian noninstitutional population is the universe of the labor force. In fact, there is no year from 1963 to 2013 in Table IIA-4 with sales of new houses below 400 thousand with the exception of the immediately preceding years of 2009, 2010, 2011 and 2012.
Table IIB-4, US, New Houses Sold, NSA Thousands
| Year | New Houses Sold |
| 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 | 436 |
Source: US Census Bureau
http://www.census.gov/construction/nrs
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 and stability.
Chart IIB-1, US, New One-Family Houses Sold in the US, SAAR (Seasonally Adjusted Annual Rate)
Source: US Census Bureau
http://www.census.gov/briefrm/esbr/www/esbr051.html
Percentage changes and average rates of growth of new house sales for selected periods are shown in Table IIB-5. The percentage change of new house sales from 1963 to 2014 is minus 22.1 percent. Between 1991 and 2001, sales of new houses rose 78.4 percent at the average yearly rate of 6.0 percent. 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 2014 fell 34.5 percent relative to the same period in 1995 and 66.0 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 | |
| 1963-2014 | -22.1 | NA |
| 1991-2001 | 78.4 | 6.0 |
| 1995-2005 | 92.4 | 6.8 |
| 2000-2005 | 46.3 | 7.9 |
| 1995-2014 | -34.5 | NA |
| 2000-2014 | -50.3 | NA |
| 2005-2014 | -66.0 | NA |
NA: Not Applicable
Source: US Census Bureau
http://www.census.gov/construction/nrs/
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 Feb 2015 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, 1963-2015
Source: US Census Bureau
http://www.census.gov/construction/nrs/
The available historical annual data of median and average prices of new houses sold in the US between 1963 and 2014 is provided 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 2012-2014.
Table IIB-6, US, Median and Average Prices of New Houses Sold, Annual Data
| Year | Median Price | Average Price |
| 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 | $283,400 | $345,300 |
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Percentage changes of median and average prices of new houses sold in selected years are shown in Table IIB-7. Prices rose sharply between 2000 and 2005. In fact, prices in 2014 are higher than in 2000. Between 2006 and 2014, median prices of new houses sold increased 15.0 percent and average prices increased 12.9 percent. Between 2013 and 2014, median prices increased 5.4 percent and average prices increased 6.4 percent.
Table IIB-7, US, Percentage Change of New Houses Median and Average Prices, NSA, ∆%
| Median New | Average New Home Sales Prices ∆% | |
| ∆% 2000 to 2003 | 15.4 | 19.0 |
| ∆% 2000 to 2005 | 42.5 | 43.5 |
| ∆% 2000 to 2014 | 67.7 | 66.8 |
| ∆% 2005 to 2014 | 17.6 | 16.3 |
| ∆% 2000 to 2006 | 45.9 | 47.8 |
| ∆% 2006 to 2014 | 15.0 | 12.9 |
| ∆% 2009 to 2014 | 30.8 | 27.5 |
| ∆% 2010 to 2014 | 27.8 | 26.5 |
| ∆% 2011 to 2014 | 24.7 | 28.9 |
| ∆% 2012 to 2014 | 15.6 | 18.2 |
| ∆% 2013 to 2014 | 5.4 | 6.4 |
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Chart IIB-3 of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 1963 to Feb 2015. 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 toward earlier prices.
Chart IIB-3, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2015
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Chart IIB-4 of the US Census Bureau provides average prices of new houses sold from the mid-1970s to Feb 2015. 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, 1975-2015
Source: US Census Bureau
http://www.census.gov/construction/nrs/
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 2015. 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 3.71 percent in Feb 2015 with the yield of the 30-year Treasury bond at 2.57 percent and overnight rate on fed funds at 0.11 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, 1954-2015
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/H15/default.htm
Table IIB-8 provides the monthly data in Chart IIB-5 from Dec 2012 to Feb 2015. While the fed funds rate fell from 0.16 percent in Dec 2012 to 0.07 percent in Jan 2014, the yield of the constant maturity 30-year Treasury bond rose from 2.88 percent in Dec 2012 to 3.77 percent in Jan 2014 and the conventional mortgage rate increased from 3.35 percent in Dec 2012 to 4.43 percent in Jan 2014. In Feb 2015, the fed funds rate stabilized at 0.11 percent with increase to 2.57 percent of the 30-year yield and decline at 3.71 percent of the conventional mortgage rate.
Table IIB-8, US, Fed Funds Rate, Thirty Year Treasury Bond and Conventional Mortgage Rate, Monthly, Percent per Year, Dec 2012 to Feb 2015
| Fed Funds Rate | Yield of Thirty Year Constant Maturity Bond | 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 | 3.80 | 4.26 |
| 2013-12 | 0.09 | 3.89 | 4.46 |
| 2014-01 | 0.07 | 3.77 | 4.43 |
| 2014-02 | 0.07 | 3.66 | 4.30 |
| 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 | 0.10 | 3.42 | 4.16 |
| 2014-07 | 0.09 | 3.33 | 4.13 |
| 2014-08 | 0.09 | 3.20 | 4.12 |
| 2014-09 | 0.09 | 3.26 | 4.16 |
| 2014-10 | 0.09 | 3.04 | 4.04 |
| 2014-11 | 0.09 | 3.04 | 4.00 |
| 2014-12 | 0.12 | 2.83 | 3.86 |
| 2015-01 | 0.11 | 2.46 | 3.71 |
| 2015-02 | 0.11 | 2.57 | 3.71 |
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/H15/default.htm
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). 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 | 24.0 | 40.6 | 35.8 | 25.9 | 11.0 |
| IVQ2000 | 50.5 | 65.0 | 67.6 | 62.9 | 25.4 |
| IVQ2000 to | 55.0 | 62.1 | 72.0 | 71.2 | 33.1 |
| IVQ2005 to | -1.5 | -8.7 | -2.3 | -7.4 | 8.9 |
| IVQ2006 | -4.4 | -7.1 | -4.8 | -11.9 | 2.6 |
| IVQ2007 to | -1.9 | -5.1 | -5.0 | -8.6 | 0.7 |
| IVQ2011 to | 18.9 | 7.3 | 6.9 | 19.9 | 11.8 |
| IVQ2012 to | 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 | 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 | 11.1 | 18.3 | 14.7 | 18.9 | 44.6 |
| IVQ2000 | 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 | 26.6 | 4.7 | -5.4 | -2.6 | -14.7 |
| IVQ2006 | 19.1 | 2.6 | -5.4 | -8.7 | -15.1 |
| IVQ2007 to | 15.2 | 3.2 | -2.1 | -5.6 | -6.0 |
| IVQ2011 to | 18.1 | 13.5 | 14.2 | 32.9 | 37.6 |
| IVQ2012 to | 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 months percentage changes improved steadily from around minus 6 percent in Mar to May 2011 to minus 4.3 percent in Jun 2011. The FHFA house price index fell 0.5 percent in Oct 2011 and fell 3.0 percent in the 12 months ending in Oct 2011. There was significant recovery in Nov 2012 with increase in the house price index of 0.4 percent and reduction of the 12-month rate of decline to 2.2 percent. The house price index rose 0.4 percent in Dec 2011 and the 12-month percentage change improved to minus 1.2 percent. There was further improvement with revised decline of 0.1 percent in Jan 2012 and decline of the 12-month percentage change to minus 1.0 percent. The index improved to positive change of 0.1 percent in Feb 2012 and increase of 0.2 percent in the 12 months ending in Feb 2012. There was strong improvement in Mar 2012 with gain in prices of 1.0 percent and 2.3 percent in 12 months. The house price index of FHFA increased 0.6 percent in Apr 2012 and 2.8 percent in 12 months and improvement continued with increase of 0.6 percent in May 2012 and 3.7 percent in the 12 months ending in May 2012. Improvement consolidated with increase of 0.3 percent in Jun 2012 and 3.6 percent in 12 months. In Jul 2012, the house price index increased 0.1 percent and 3.6 percent in 12 months. Strong increase of 0.5 percent in Aug 2012 pulled the 12-month change to 4.3 percent. There was another increase of 0.6 percent in Oct and 5.3 percent in 12 months followed by increase of 0.6 percent in Nov 2012 and 5.4 percent in 12 months. The FHFA house price index increased 0.8 percent in Jan 2013 and 6.4 percent in 12 months. Improvement continued with increase of 0.4 percent in Apr 2013 and 7.2 percent in 12 months. In May 2013, the house price indexed increased 0.8 percent and 7.5 percent in 12 months. The FHFA house price index increased 0.6 percent in Jun 2013 and 7.8 percent in 12 months. In Jul 2013, the FHFA house price index increased 0.7 percent and 8.4 percent in 12 months. Improvement continued with increase of 0.4 percent in Aug 2013 and 8.3 percent in 12 months. In Sep 2013, the house price index increased 0.5 percent and 8.3 percent in 12 months. The house price index increased 0.5 percent in Oct 2013 and 8.1 percent in 12 months. In Nov 2013, the house price index changed 0.0 percent and increased 7.4 percent in 12 months. The house price index rose 0.7 percent in Dec 2013 and 7.7 percent in 12 months. Improvement continued with increase of 0.6 percent in Jan 2014 and 7.4 percent in 12 months. In Feb 2014, the house price index increased 0.4 percent and 7.1 percent in 12 months. The house price index increased 0.6 percent in Mar 2014 and 6.5 percent in 12 months. In Apr 2014, the house price index increased 0.1 percent and increased 6.1 percent in 12 months. The house price index increased 0.3 percent in May 2014 and 5.6 percent in 12 months. In Jun 2014, the house price index increased 0.4 percent and 5.4 percent in 12 months. The house price index increased 0.2 percent in Jul 2014 and 4.9 percent in 12 months. In Sep 2014, the house price index increased 0.1 percent and increased 4.6 percent in 12 months. The house price index increased 0.5 percent in Oct 2014 and 4.5 percent in 12 months. In Nov 2014, the house price index increased 0.7 percent and 5.3 percent in 12 months. The house price index increased 0.7 percent in Dec 2014 and increased 5.4 percent in 12 months. In Feb 2015, the house price index increased 0.3 percent and increased 5.1 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 | |
| Jan 2015 | 0.3 | 5.1 |
| Dec 2014 | 0.7 | 5.4 |
| Nov | 0.7 | 5.3 |
| Oct | 0.5 | 4.5 |
| Sep | 0.1 | 4.6 |
| Aug | 0.5 | 5.0 |
| Jul | 0.2 | 4.9 |
| Jun | 0.4 | 5.4 |
| May | 0.3 | 5.6 |
| Apr | 0.1 | 6.1 |
| Mar | 0.6 | 6.5 |
| Feb | 0.4 | 7.1 |
| Jan | 0.6 | 7.4 |
| Dec 2013 | 0.7 | 7.7 |
| Nov | 0.0 | 7.4 |
| Oct | 0.5 | 8.1 |
| Sep | 0.5 | 8.3 |
| Aug | 0.4 | 8.3 |
| Jul | 0.7 | 8.4 |
| Jun | 0.6 | 7.8 |
| May | 0.8 | 7.5 |
| Apr | 0.4 | 7.2 |
| Mar | 1.2 | 7.4 |
| Feb | 0.8 | 7.1 |
| Jan | 0.8 | 6.4 |
| Dec 2012 | 0.4 | 5.5 |
| Nov | 0.6 | 5.4 |
| Oct | 0.6 | 5.3 |
| Sep | 0.5 | 4.1 |
| Aug | 0.5 | 4.3 |
| Jul | 0.1 | 3.6 |
| Jun | 0.3 | 3.6 |
| May | 0.6 | 3.7 |
| Apr | 0.6 | 2.8 |
| Mar | 1.0 | 2.3 |
| Feb | 0.1 | 0.2 |
| Jan | -0.1 | -1.0 |
| Dec 2011 | 0.4 | -1.2 |
| Nov | 0.4 | -2.2 |
| Oct | -0.5 | -3.0 |
| Sep | 0.6 | -2.4 |
| Aug | -0.3 | -3.7 |
| Jul | 0.2 | -3.4 |
| Jun | 0.4 | -4.3 |
| May | -0.2 | -5.9 |
| Apr | 0.1 | -5.8 |
| Mar | -1.0 | -6.0 |
| Feb | -1.1 | -5.0 |
| Jan | -0.4 | -4.6 |
| Dec 2010 | -3.8 | |
| Dec 2009 | -2.0 | |
| Dec 2008 | -10.2 | |
| Dec 2007 | -3.2 | |
| Dec 2006 | 2.5 | |
| Dec 2005 | 9.8 | |
| Dec 2004 | 10.2 | |
| Dec 2003 | 8.0 | |
| Dec 2002 | 7.8 | |
| Dec 2001 | 6.7 | |
| Dec 2000 | 7.2 | |
| Dec 1999 | 6.1 | |
| Dec 1998 | 5.9 | |
| Dec 1997 | 3.4 | |
| Dec 1996 | 2.8 | |
| Dec 1995 | 3.0 | |
| Dec 1994 | 2.6 | |
| Dec 1993 | 3.1 | |
| Dec 1992 | 2.4 |
Source: Federal Housing Finance Agency
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 2014, the FHFA house price index increased 108.5 percent at the yearly average rate of 3.4 percent. In the period 1992-2000, the FHFA house price index increased 39.4 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 7.5 percent in 2000-2006. At the margin, the average rate jumped to 10.0 percent in 2003-2005 and 7.5 percent in 2003-2006. House prices measured by the FHFA house price index declined 3.1 percent between 2006 and 2014 and 0.7 percent between 2005 and 2014.
Table IIA2-4, US, FHFA House Price Index, Percentage Change and Average Rate of Percentage Change per Year, Selected Dates 1992-2013
| Dec | ∆% | Average ∆% per Year |
| 1992-2014 | 108.2 | 3.4 |
| 1992-2000 | 39.4 | 4.2 |
| 2000-2003 | 24.2 | 7.5 |
| 2000-2005 | 50.4 | 8.5 |
| 2003-2005 | 21.1 | 10.0 |
| 2005-2014 | -0.7 | NA |
| 2000-2006 | 54.2 | 7.5 |
| 2003-2006 | 24.1 | 7.5 |
| 2006-2014 | -3.1 | NA |
Source: Federal Housing Finance Agency
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015.
No comments:
Post a Comment