Sunday, January 1, 2017

Rules versus Discretionary Authorities in Monetary Policy, Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth, United States Housing, World Cyclical Slow Growth and Global Recession Risk: Part II

 

Rules versus Discretionary Authorities in Monetary Policy, Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth, United States Housing, World Cyclical Slow Growth and Global Recession Risk

Carlos M. Pelaez

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

I Rules versus Discretionary Authorities in Monetary Policy

IA Monetary Policy Rules

IA1 Origins of Rules versus Discretion

IA2 Monetary Policy Rules

IA3 The Taylor Rule

IB Unconventional Monetary Policy

IC Counterfactual of Policies Causing the Financial Crisis and Global Recession

ID Appendix on the Monetary History of Brazil

II Destruction of Household Nonfinancial Wealth with Stagnating Total Real Wealth

IIA United States Housing Collapse

IIA1 Sales of New Houses

IIA2 United States House Prices

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

I Rules versus Discretionary Authorities in Monetary Policy. The objective of this section is to place the alternatives of monetary policy into two perspectives of (1) emphasis on long-term policy in contrast with (2) current emphasis on short-term impulses of unusual magnitude to respond immediately to deviations perceived with significant lags and measurement errors because of the deficiencies of the state of the art.

Unconventional monetary policy since 2003 has consisted of near zero nominal interest rates, negative real rates of interest, massive purchases of securities for the balance sheet of the Fed and intervention in allocation of credit. Professor John B. Taylor (2016Dec 7, 2016Dec20), in Testimony to the Subcommittee on Monetary Policy and Trade Committee on Financial Services, on Dec 7, 2016, analyzes the adverse effects of unconventional monetary policy:

“My research and that of others over the years shows that these policies were not effective, and may have been counterproductive. Economic growth was consistently below the Fed’s forecasts with the policies, and was much weaker than in earlier U.S. recoveries from deep recessions. Job growth has been insufficient to raise the percentage of the population that is working above pre-recession levels. There is a growing consensus that the extra low interest rates and unconventional monetary policy have reached diminishing or negative returns. Many have argued that these policies widen the income distribution, adversely affect savers, and increase the volatility of the dollar exchange rate. Experienced market participants have expressed concerns about bubbles, imbalances, and distortions caused by the policies. The unconventional policies have also raised public policy concerns about the Fed being transformed into a multipurpose institution, intervening in particular sectors and allocating credit, areas where Congress may have a role, but not a limited-purpose independent agency of government.”

Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. 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 IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_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 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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 IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 23.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.0 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 percent below trend under this alternative calculation.

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

IA Monetary Policy Rules considers the use of long-term rules or guidance for optimization of policy over many periods in contrast with discretion or immediate optimization of every perceived deviation from desired paths by policymakers. Section IB Unconventional Monetary Policy raises the issue if excessive short-term impulses cause instability instead of the prime objective of monetary policy of fostering stability. There are adverse effects on resource allocation that prevent an efficient dynamic path of the economy at the time when real disposable income per capita, or what is left per person after taxes and inflation, has stagnated. IC Counterfactual of Policies Causing the Financial Crisis and Global Recession identifies the critical issue of research that economic conditions would have been less unfavorable under alternative monetary policy rules. ID Appendix on the Monetary History of Brazil provides some evidence on the history of Brazil that is of relevance to monetary policy rules. The data in the appendix were part of a research project on the monetary history of Brazil using the NBER framework of Friedman and Schwartz (1963, 1970) and Cagan (1965) as well as the institutional framework of Rondo E. Cameron (1967, 1972) who inspired the research (Pelaez 1974, 1975, 1976a,b, 1977, 1979, Pelaez and Suzigan 1978, 1981). The data were also used to test the correct specification of money and income following Sims (1972; see also Williams et al. 1976) as well as another test of orthogonality of money demand and supply using covariance analysis. Sims (1972, 541) finds that: “If and only if causality runs one way from current and past values of some list of exogenous variables to a given endogenous variable, then in a regression of the endogenous variable on past, current, and future values of the exogenous variables, the future values of the exogenous variables should have zero coefficients.” The objective of research was to verify the quantity theory of money of Friedman and Schwartz (1963, 1970) for the economy of Brazil from 1862 to 1976. The Granger-Sims test postulates that causality runs from money into nominal income if and only if in a regression of nominal income on past, current and future values of money, future coefficients are zero. The results show that the Sims F coefficients are zero for the regressions of nominal income on money and 38 for the coefficients of money on income (Pelaez and Suzigan 1978, Pelaez 1979, 106). There are also covariance tests verifying orthogonality of money demand and money supply and orthogonality of base money and the money multiplier. The quantity theory of money explains money, income and prices in the historical period of Brazil 1862 to 1976. There are two important conclusions.

  • Models of Keynesian multipliers in historical Brazil are inconsistent with these findings
  • Pelaez (1979, 121) concludes following Friedman and Schwartz (1963, 1970) that for the case of Brazil “in historical perspective, it appears that a system of rules, instead of authorities, would have best promoted the interests of the Nation.”

Hill (2007, 763) develops “a simple parametric recursion for VAR coefficients that, for trivariate processes with one scalar auxiliary variable, always allows for sequential linear parametric conditions for non-causality up to horizon h ≥ 1. An empirical analysis of the money-income relationship reveals significant evidence in favor of linear causation of money to income, either directly when we control for cointegration, or indirectly after a delay of 1-3 months in models of first differences.” Shikida, Araujo Jr. e Figueiredo (2014) apply Hill (2007) to the historical experience of Brazil. They conclude that base money influences nominal output but not real output. Their results are consistent with the unpleasant monetarist arithmetic of Sargent and Wallace (1981) that uses base money instead of the stock of money. Because of restrictions on banking and finance (Summerhill 2015, Pelaez 1975), base money should have been more important in influencing nominal income in historical Brazil. It is valid to conclude that monetary policy rules instead of discretionary authorities would have best promoted national interests in historical Brazil.

IA Monetary Policy Rules. The discussion of monetary policy rules is divided into three subsections: IA1 Origins of Rules versus Discretion, IA2 Monetary Policy Rules and IA3 The Taylor Rule.

IA1 Origins of Rules versus Discretion. A classic proposal for rules instead of “authorities” in monetary policy is by Simons (1936, 29-30):

“A democratic, free-enterprise system implies, and requires for its effective functioning and survival, a stable framework of definite rules, laid down in legislation and subject to change only gradually and with careful regard for the vested interests of participants in the economic game. It is peculiarly essential economically that there should be a minimum of uncertainty for enterprisers and investors as to monetary conditions in the future and, politically, that the plausible expedient of setting up "authorities" instead of rules, with respect to matters of such fundamental importance, be avoided, or accepted only as a very temporary arrangement. The most important objective of a sound liberal policy, apart from the establishment of highly competitive conditions in industry and the narrow limitation of political control over relative prices, should be that of securing a monetary system governed by definite rule. The responsibility for carrying out the monetary rules should be lodged in a federal authority, endowed with large administrative powers but closely controlled in their exercise by a sharply defined policy. The powers of the monetary authority should have to do primarily or exclusively with fiscal arrangements with the issue and retirement of paper money (open-market operations in government securities) and perhaps with the relation between government revenues and expenditures; in other words, the monetary rules should be implemented entirely by, and in turn should largely determine, fiscal policy. A monetary rule of maintaining the constancy of some price-index, preferably an index of prices of competitively produced commodities, appears to afford the only promising escape from present monetary chaos and uncertainties.”

The working system of Simons (1936, 1948) consisted of 100 percent reserve requirements on banks (see Allen 1993, Fisher 1936, Graham 1936) to avoid fluctuations from bank runs and fixed quantity of money. Friedman (1967) finds that the monetary reforms proposed by Simons (1948) were not required and moving against desired directions by restricting financial intermediation that would raise costs of capital, inhibiting capital formation. The value in the proposals of Simons (1948) as viewed by Friedman (1967) would be in providing more flexibility for banks that were restricted in the 1960s by regulation inherited from the Great Depression. There are contemporary proposals that resemble the 100 percent reserve requirement in what is known as “narrow banking” (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 71-72).

Historical evolution of monetary policy rules is analyzed by Asso, Khan and Leeson (2007, 2010). In the United States, Irving Fisher and Milton Friedman proposed price and monetary rules (see Bordo and Rockoff 2011). An important departure for considering rules is the existence of three lags of macroeconomic policy (Friedman 1953):

  • The lag between the need for action and the recognition of the need
  • The lag between recognition of the need and taking action
  • The lag between taking action and effects on prices and income

There is not “one hundred percent” confidence in controlling inflation because of the lags in effects of monetary policy impulses and the equally important lags in realization of the need for action and taking of action in addition to the inability to forecast any economic variable. Romer and Romer (2004) find that a one-percentage point tightening of monetary policy is associated with a 4.3 percent decline in industrial production. There is no change in inflation in the first 22 months after monetary policy tightening when it begins to decline steadily, with decrease by 6 percent after 48 months (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 102). Even if there were one hundred percent confidence in reducing inflation by monetary policy, it could take a prolonged period with adverse effects on economic activity. Certainty does not occur in economic policy, in which there are costs that cannot be anticipated.

Friedman (1968) restated his proposal for fixed setting of monetary policy in terms of a fixed rate of increase of the money stock:

“My own prescription is still that the monetary authority go all the way in avoiding such swings by adopting publicly the policy of achieving a steady rate of growth in a specified monetary total. The precise rate of growth, like the precise monetary total, is less important than the adoption of some stated and known rate. I myself have argued for a rate that would on the average achieve rough stability in the level of prices of final products, which I have estimated would call for something like a 3 to 5 per cent per year rate of growth in currency plus all commercial bank deposits or a slightly lower rate of growth in currency plus demand deposits only. But it would be better to have a fixed rate that would on the average produce moderate inflation or moderate deflation, provided it was steady, than to suffer the wide and erratic perturbations we have experienced. Short of the adoption of such a publicly stated policy of a steady rate of monetary growth, it would constitute a major improvement if the monetary authority followed the self-denying ordinance of avoiding wide swings. It is a matter of record that periods of relative stability in the rate of monetary growth have also been periods of relative stability in economic activity, both in the United States and other countries. Periods of wide swings in the rate of monetary growth have also been periods of wide swings in economic activity.”

This was Friedman’s presidential address to the American Economic Association at the onset of wide swings in monetary policy, output and employment in the period of United States economic history known as “the Great Inflation.”

There are three “tactics” in monetary policy identified by Friedman (1982, 100-1):

  • Interest rates as both targets and instruments
  • Monetary targets as instruments and interest rates as targets
  • Base money as instrument and monetary aggregates as targets

The specific policy proposal of Friedman (1982, 101) consists of five ingredients:

“A monetarist policy has five points: first, the target should be growth in some monetary aggregate just which monetary aggregate is a separate question; second, monetary authorities should adopt long-run targets for monetary growth that are consistent with no inflation; third, present rates of growth of monetary aggregates should be modified to achieve the long-run target in a gradual, systematic, and preannounced fashion; fourth, monetary authorities should avoid fine-tuning; fifth, monetary authorities should avoid trying to manipulate either interest rates or exchange rates. Internationally, those countries that have broadly followed the five-point monetarist policy have succeeded in controlling inflation and have done so while achieving relatively satisfactory economic growth.”

The fourth of eight specific proposals for Fed monetary policy at the time states (Friedman, 1982, 117): “set a target path for several years ahead for a single aggregate—for example, M2 or the base. It is less important which aggregate is chosen than that a single aggregate be designated as the target.”

IA2 Monetary Policy Rules. The essence of rules versus discretion monetary policy is posed by McCallum (1999) as follows:

  1. Rules attempt to optimize by using a policy-contingent rule or formula that is implemented every period and is designed such as to apply indefinitely
  2. Discretion consist of re-optimization in every period in accordance with existing conditions assessed by monetary authorities

A rules-based policy optimizes over a long-term period while discretionary policy optimizes individually in every period in accordance with short-term information. A practical definition of monetary policy rule by McCallum (1999) is systematic policy that does not use expectations to cause temporary gains in output.

McCallum (1999) finds that Barro and Gordon (1983b) opened the inclusion of activist policy within a monetary policy rule. The rule provides a reaction function of the central bank that depends on available information. Barro and Gordon (1983b, 606) differentiate as follows: “the presence or absence of precomitment is the most important distinction between rules and discretion.” The reaction function of the central bank h(It-1) depends on information available in the past period, It-1, that would be more restrictive under rules-based policy than under discretion, would include many more variables in the argument. As McCallum (1999) argues, a rule could specify the conditions for activism instead of fixed settings for policy. For example, constant rate of growth of the money stock would be a fixed setting that the central bank would implement indefinitely. A policy in which the interest rate depends on inflation and the divergence of actual and potential output would be activist but under certain guidance.

Current distinction between rules and discretion is provided by Taylor (2012JMCB, 2). There are characteristics of both rules and discretion.

  1. Characteristics of rules

· More predictable and systematic decisions by the central bank on policy instruments

· Dynamic analysis of effects of current decisions on future outcomes

· Decisions based or guided by formulas and equations

· Use of stable relation of policy instruments to outcomes such as inflation and growth

  1. Characteristics of discretion

· Decisions on monetary policy instruments are “less predictable,” focusing on short-term events such as “fine tuning” or policy actions in response to ad hoc movements in economic and financial variables

· Little or no interest by policymakers in agreeing on alternative strategies for fixing magnitudes of instruments of policy

· Evolution of policy instruments over time cannot be captured by equations

IA3 The Taylor Rule. The definition of policy rule by Taylor (1993, 199) is: “Technically, a policy rule is a contingency plan that lasts forever unless there is an explicit cancellation clause.” “Forever” means here “a reasonably long period of time” (Taylor 1993, 1999). The departing theory of Taylor (1993, 1999) is the quantity theory of money equation:

MV = PY (1)

Where M is the money stock, V is income velocity of money, P the price level and Y real output. Velocity depends on the interest rate r and real output or income Y with functional form V(r, Y). The substitution of V(r, Y) in equation (1) yields a relation between the interest rate, r, the price level, P, and real output Y. Taylor (1999) assumes a linear relation of interest rates and the logarithms of the price level P and real output Y. Assuming no lags, deviation of output from stochastic trend and the inflation rate as the first difference of the logarithms of the price level, Taylor (1999) obtains:

r = π + gy + h(π – π*) + rr = (rf – hπ*) + (1 + h)π + gy (2)

Where r is the short-term interest rate, π the inflation rate or percent change in the price level P, y the percentage difference of real output Y from trend and g, h, π* and rf are constants. The settings of monetary policy are the response coefficients g of the deviation of actual output from potential output and (1+h), the response to inflation.

Taylor (1993, 202) provides the simple formula, known as the Taylor Rule, as follows:

r = p + .5y + .5(p-2) + 2 (3)

Where r is the federal funds rate or policy rate of the Fed, p is the rate of inflation in the past four quarters and y is the percentage deviation of actual output from potential output. The fed funds rate increases if the gap between actual and potential output y increases to prevent lower growth below potential and resulting unemployment, and increases if inflation p increases above maximum tolerable inflation of 2 percent. Thus, the policy is guidance on fixing the fed funds rate in response to growth and inflation. Taylor (1993, 202) states: “if both the inflation rate and real GDP are on target, then the federal funds rate would equal 4 percent, or 2 percent in real terms.” If real economic growth is around trend, y is zero, and inflation is around the maximum desired of 2 percent, (p-2) is zero, such that the fed funds rate r is equal to 4 or 2 percent by deducting inflation of 2 percent.

IB Unconventional Monetary Policy. Taylor (1998LB, 1999, 2012JMCB) argues that economic performance has been enhanced during periods of long-term focus of monetary policy in the form of guidance under monetary policy rules. Periods of discretion such as the Great Inflation from the second half of the 1960s to the beginning of the 1980s and the past decade since 2003 have been characterized by instability and mediocre economic performance.

The objective of Levin and Taylor (2009) is to reveal the primary cause of the persistence of inflation drift during the Great Inflation by focusing on the path of inflationary expectations to model monetary policy from 1965 to 1980. They derive three stylized facts by use of measurements of inflation expectations. (1) Inflation began in the mid 1960 while it had been contained since the late 1950s at around 1 percent but inflation expectations accelerate beginning in 1965. (2) Long-term inflation expectations stabilized at a high level in the first half of the 1970s but catapulted toward the end of the decade. (3) Long-run inflation expectations only began to decline at the end of 1980. The central bank reaction function analyzed by Taylor (1993, 202, equation (1)) based on prior research is:

r = p +0.5y + 0.5(p-2) + 2 (3)

In equation (3), the federal funds rate, r, is expressed in terms of the rate of inflation over the previous four quarters, the percentage output gap, y, defined as 100(Y-Y*)/Y*, where Y is actual GDP, Y* is trend real GDP (2.2 percent from IQ1984 to IIIQ1992), and the deviation of inflation from the target of 2 percent, 0.5(p-2). The Taylor policy rule in equation (3) triggers an increase in the fed funds rate when inflation exceeds 2 percent or when GDP exceeds trend GDP. If GDP is equal to target, y = 0, and inflation is also equal to target, p = 2, then the real rate of interest as measured by prior inflation, r-p, equal 2 percent. The fed funds rate calculated with this simple policy rule fits remarkably well the actual fed funds rate in 1987-1992 (Taylor 1993, 204, Figure 1).

The simple rule is restated by Levin and Taylor (2010, 16, equation (1)) to account for discrete shifts in the intercept:

rt = ­r’ + γπ(πt – π*) + γy(yt – y*t) (5)

Equation (5) expresses the short-term real interest rate, rt, in terms of an effect, γπ(πt – π*), of the difference between actual inflation, πt, and the central’s bank objective for inflation, π*, and an effect, γy(yt – y*t), of the deviation of actual output, yt, from trend or steady-state output, y*t, and r’ stands for the steady-state value of the real rate of interest.

Equation (5) is shown by Levy and Taylor (2009) to provide a good fit of experience during the Great Inflation by allowing for shifts in the central bank’s inflation objective π*. Monetary policy during the Great Inflation can be interpreted by three stop-start events occurring in 1968-70, 1974-76 and 1979-80. Levy and Taylor (2009) conclude that in all three “stop and go” episodes monetary policy “fell behind the curve,” permitting rising inflation before belated tightening and abandoning tightening because of the contraction before inflation was reduced to the level before the event. Lags in effect of monetary policy have been amply discussed in the literature and may have proved important in falling behind the curve (see Culbertson 1960, Friedman 1961, Culbertson 1961, Batini and Nelson 2002 and Romer and Romer 2004).

Detailed discussion of the analysis of the Great Inflation is provided in past comments of this blog (http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html http://cmpassocregulationblog.blogspot.com/2011/04/new-economics-of-rose-garden-turned.html http://cmpassocregulationblog.blogspot.com/2011/03/is-there-second-act-of-us-great.html). Unconventional monetary policy played a major role in the financial crisis and global recession as discussed in the balance of this section.

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

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

The answer to these arguments 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.”

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

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

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

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

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

W = Y/r (1)

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

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

Fri May 1

Mon 4

Tue 5

Wed 6

Thu 7

Fri 8

DJIA

18024.06

-0.3%

1.0%

18070.40

0.3%

0.3%

17928.20

-0.5%

-0.8%

17841.98

-1.0%

-0.5%

17924.06

-0.6%

0.5%

18191.11

0.9%

1.5%

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

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

Another hurdle of exit from zero interest rates is “competitive easing” that Professor Raghuram Rajan, governor of the Reserve Bank of India, characterizes as disguised “competitive devaluation” (http://www.centralbanking.com/central-banking-journal/interview/2358995/raghuram-rajan-on-the-dangers-of-asset-prices-policy-spillovers-and-finance-in-india). The fed has been considering increasing interest rates. The European Central Bank (ECB) announced, on Mar 5, 2015, the beginning on Mar 9, 2015 of its quantitative easing program denominated as Public Sector Purchase Program (PSPP), consisting of “combined monthly purchases of EUR 60 bn [billion] in public and private sector securities” (http://www.ecb.europa.eu/mopo/liq/html/pspp.en.html). Expectation of increasing interest rates in the US together with euro rates close to zero or negative cause revaluation of the dollar (or devaluation of the euro and of most currencies worldwide). US corporations suffer currency translation losses of their foreign transactions and investments (http://www.fasb.org/jsp/FASB/Pronouncement_C/SummaryPage&cid=900000010318) while the US becomes less competitive in world trade (Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), Government Intervention in Globalization (2008c)). The DJIA fell 1.5 percent on Mar 6, 2015 and the dollar revalued 2.2 percent from Mar 5 to Mar 6, 2015. The euro has devalued 51.2 percent relative to the dollar from the high on Jul 15, 2008 to Dec 30, 2016.

Fri 27 Feb

Mon 3/2

Tue 3/3

Wed 3/4

Thu 3/5

Fri 3/6

USD/ EUR

1.1197

1.6%

0.0%

1.1185

0.1%

0.1%

1.1176

0.2%

0.1%

1.1081

1.0%

0.9%

1.1030

1.5%

0.5%

1.0843

3.2%

1.7%

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

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

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

Fri Mar 6

Mon 9

Tue 10

Wed 11

Thu 12

Fri 13

USD/ EUR

1.0843

3.2%

1.7%

1.0853

-0.1%

-0.1%

1.0700

1.3%

1.4%

1.0548

2.7%

1.4%

1.0637

1.9%

-0.8%

1.0497

3.2%

1.3%

Fri Mar 13

Mon 16

Tue 17

Wed 18

Thu 19

Fri 20

USD/ EUR

1.0497

3.2%

1.3%

1.0570

-0.7%

-0.7%

1.0598

-1.0%

-0.3%

1.0864

-3.5%

-2.5%

1.0661

-1.6%

1.9%

1.0821

-3.1%

-1.5%

Fri Apr 24

Mon 27

Tue 28

Wed 29

Thu 30

May Fri 1

USD/ EUR

1.0874

-0.6%

-0.4%

1.0891

-0.2%

-0.2%

1.0983

-1.0%

-0.8%

1.1130

-2.4%

-1.3%

1.1223

-3.2%

-0.8%

1.1199

-3.0%

0.2%

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

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

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

Fri May 15

Mon 18

Tue 19

Wed 20

Thu 21

Fri 22

USD/ EUR

1.1449

-2.2%

-0.3%

1.1317

1.2%

1.2%

1.1150

2.6%

1.5%

1.1096

3.1%

0.5%

1.1113

2.9%

-0.2%

1.1015

3.8%

0.9%

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

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

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

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

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

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

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

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

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

Some equity markets fell on Fri Sep 18, 2015:

Fri Sep 11

Mon 14

Tue 15

Wed 16

Thu 17

Fri 18

DJIA

16433.09

2.1%

0.6%

16370.96

-0.4%

-0.4%

16599.85

1.0%

1.4%

16739.95

1.9%

0.8%

16674.74

1.5%

-0.4%

16384.58

-0.3%

-1.7%

Nikkei 225

18264.22

2.7%

-0.2%

17965.70

-1.6%

-1.6%

18026.48

-1.3%

0.3%

18171.60

-0.5%

0.8%

18432.27

0.9%

1.4%

18070.21

-1.1%

-2.0%

DAX

10123.56

0.9%

-0.9%

10131.74

0.1%

0.1%

10188.13

0.6%

0.6%

10227.21

1.0%

0.4%

10229.58

1.0%

0.0%

9916.16

-2.0%

-3.1%

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

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

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

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

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

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

Fri Oct 16

Mon 19

Tue 20

Wed 21

Thu 22

Fri 23

USD/ EUR

1.1350

0.1%

0.3%

1.1327

0.2%

0.2%

1.1348

0.0%

-0.2%

1.1340

0.1%

0.1%

1.1110

2.1%

2.0%

1.1018

2.9%

0.8%

DJIA

17215.97

0.8%

0.4%

17230.54

0.1%

0.1%

17217.11

0.0%

-0.1%

17168.61

-0.3%

-0.3%

17489.16

1.6%

1.9%

17646.70

2.5%

0.9%

Dow Global

2421.58

0.3%

0.6%

2414.33

-0.3%

-0.3%

2411.03

-0.4%

-0.1%

2411.27

-0.4%

0.0%

2434.79

0.5%

1.0%

2458.13

1.5%

1.0%

DJ Asia Pacific

1402.31

1.1%

0.3%

1398.80

-0.3%

-0.3%

1395.06

-0.5%

-0.3%

1402.68

0.0%

0.5%

1396.03

-0.4%

-0.5%

1415.50

0.9%

1.4%

Nikkei 225

18291.80

-0.8%

1.1%

18131.23

-0.9%

-0.9%

18207.15

-0.5%

0.4%

18554.28

1.4%

1.9%

18435.87

0.8%

-0.6%

18825.30

2.9%

2.1%

Shanghai

3391.35

6.5%

1.6%

3386.70

-0.1%

-0.1%

3425.33

1.0%

1.1%

3320.68

-2.1%

-3.1%

3368.74

-0.7%

1.4%

3412.43

0.6%

1.3%

DAX

10104.43

0.1%

0.4%

10164.31

0.6%

0.6%

10147.68

0.4%

-0.2%

10238.10

1.3%

0.9%

10491.97

3.8%

2.5%

10794.54

6.8%

2.9%

Ben Leubsdorf, writing on “Fed’s Yellen: December is “Live Possibility” for First Rate Increase,” on Nov 4, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/feds-yellen-december-is-live-possibility-for-first-rate-increase-1446654282) quotes Chair Yellen that a rate increase in “December would be a live possibility.” The remark of Chair Yellen was during a hearing on supervision and regulation before the Committee on Financial Services, US House of Representatives (http://www.federalreserve.gov/newsevents/testimony/yellen20151104a.htm) and a day before the release of the employment situation report for Oct 2015 (Section I). The dollar revalued 2.4 percent during the week. The euro has devalued 51.2 percent relative to the dollar from the high on Jul 15, 2008 to Dec 30, 2016.

Fri Oct 30

Mon 2

Tue 3

Wed 4

Thu 5

Fri 6

USD/ EUR

1.1007

0.1%

-0.3%

1.1016

-0.1%

-0.1%

1.0965

0.4%

0.5%

1.0867

1.3%

0.9%

1.0884

1.1%

-0.2%

1.0742

2.4%

1.3%

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

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

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

Fri Nov 13

Mon 16

Tue 17

Wed 18

Thu 19

Fri 20

USD/ EUR

1.0774

-0.3%

0.4%

1.0686

0.8%

0.8%

1.0644

1.2%

0.4%

1.0660

1.1%

-0.2%

1.0735

0.4%

-0.7%

1.0647

1.2%

0.8%

DJIA

17245.24

-3.7%

-1.2%

17483.01

1.4%

1.4%

17489.50

1.4%

0.0%

17737.16

2.9%

1.4%

17732.75

2.8%

0.0%

17823.81

3.4%

0.5%

DAX

10708.40

-2.5%

-0.7%

10713.23

0.0%

0.0%

10971.04

2.5%

2.4%

10959.95

2.3%

-0.1%

11085.44

3.5%

1.1%

11119.83

3.8%

0.3%

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

Fri Nov 27

Mon 30

Tue 1

Wed 2

Thu 3

Fri 4

USD/ EUR

1.0594

0.5%

0.2%

1.0565

0.3%

0.3%

1.0634

-0.4%

-0.7%

1.0616

-0.2%

0.2%

1.0941

-3.3%

-3.1%

1.0885

-2.7%

0.5%

DJIA

17798.49

-0.1%

-0.1%

17719.92

-0.4%

-0.4%

17888.35

0.5%

1.0%

17729.68

-0.4%

-0.9%

17477.67

-1.8%

-1.4%

17847.63

0.3%

2.1%

DAX

11293.76

1.6%

-0.2%

11382.23

0.8%

0.8%

11261.24

-0.3%

-1.1%

11190.02

-0.9%

-0.6%

10789.24

-4.5%

-3.6%

10752.10

-4.8%

-0.3%

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

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

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

Fri Dec 11

Mon 14

Tue 15

Wed 16

Thu 17

Fri 18

USD/ EUR

1.0991

-1.0%

-0.4%

1.0993

0.0%

0.0%

1.0932

0.5%

0.6%

1.0913

0.7%

0.2%

1.0827

1.5%

0.8%

1.0868

1.1%

-0.4%

DJIA

17265.21

-3.3%

-1.8%

17368.50

0.6%

0.6%

17524.91

1.5%

0.9%

17749.09

2.8%

1.3%

17495.84

1.3%

-1.4%

17128.55

-0.8%

-2.1%

DAX

10340.06

-3.8%

-2.4%

10139.34

-1.9%

-1.9%

10450.38

-1.1%

3.1%

10469.26

1.2%

0.2%

10738.12

3.8%

2.6%

10608.19

2.6%

-1.2%

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

Fri 22

Mon 25

Tue 26

Wed 27

Thu 28

Fri 29

JPY/ USD

118.77

-1.5%

-0.9%

118.30

0.4%

0.4%

118.42

0.3%

-0.1%

118.68

0.1%

-0.2%

118.82

0.0%

-0.1%

121.13

-2.0%

-1.9%

DJIA

16093.51

0.7%

1.3%

15885.22

-1.3%

-1.3%

16167.23

0.5%

1.8%

15944.46

-0.9%

-1.4%

16069.64

-0.1%

0.8%

16466.30

2.3%

2.5%

Nikkei

16958.53

-1.1%

5.9%

17110.91

0.9%

0.9%

16708.90

-1.5%

-2.3%

17163.92

1.2%

2.7%

17041.45

0.5%

-0.7%

17518.30

3.3%

2.8%

Shanghai

2916.56

0.5%

1.3

2938.51

0.8%

0.8%

2749.79

-5.7%

-6.4%

2735.56

-6.2%

-0.5%

2655.66

-8.9%

-2.9%

2737.60

-6.1%

3.1%

DAX

9764.88

2.3%

2.0%

9736.15

-0.3%

-0.3%

9822.75

0.6%

0.9%

9880.82

1.2%

0.6%

9639.59

-1.3%

-2.4%

9798.11

0.3%

1.6%

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

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

Sovereign Yields 2/12/16

Japan

Germany

USA

2 Year

-0.168

-0.498

0.694

10 Year

0.076

0.262

1.744

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

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

Fri 4

Mon 7

Tue 8

Wed 9

Thu10

Fri 11

USD/ EUR

1.1006

-0.7%

-0.4%

1.1012

-0.1%

-0.1%

1.1013

-0.1%

0.0%

1.0999

0.1%

0.1%

1.1182

-1.6%

-1.7%

1.1151

-1.3%

0.3%

DJIA

17006.77

2.2%

0.4%

17073.95

0.4%

0.4%

16964.10

-0.3%

-0.6%

17000.36

0.0%

0.2%

16995.13

-0.1%

0.0%

17213.31

1.2%

1.3%

DAX

9824.17

3.3%

0.7%

9778.93

-0.5%

0.5%

9692.82

-1.3%

-0.9%

9723.09

-1.0%

0.3%

9498.15

-3.3%

-2.3%

9831.13

0.1%

3.5%

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

clip_image001

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

Source: Board of Governors of the Federal Reserve System

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

clip_image002

Chart VIII-1A, Fed Funds Rate and Yields of Ten-year Treasury Constant Maturity, Jan 2, 2001 to Dec 29, 2016

Source: Board of Governors of the Federal Reserve System

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

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

 

Fed Funds Overnight Rate

10-Year Treasury Constant Maturity

Seasoned Baa Corporate Bond

1/2/2001

6.67

4.92

7.91

10/1/2002

1.85

3.72

7.46

7/3/2003

0.96

3.67

6.39

6/22/2004

1.00

4.72

6.77

6/28/2006

5.06

5.25

6.94

9/17/2008

2.80

3.41

7.25

10/26/2008

0.09

2.16

8.00

10/31/2008

0.22

4.01

9.54

4/6/2009

0.14

2.95

8.63

4/5/2010

0.20

4.01

6.44

2/4/2011

0.17

3.68

6.25

7/25/2012

0.15

1.43

4.73

5/1/13

0.14

1.66

4.48

9/5/13

0.089

2.98

5.53

11/21/2013

0.09

2.79

5.44

11/26/13

0.09

2.74

5.34 (11/26/13)

12/5/13

0.09

2.88

5.47

12/11/13

0.09

2.89

5.42

12/18/13

0.09

2.94

5.36

12/26/13

0.08

3.00

5.37

1/1/2014

0.08

3.00

5.34

1/8/2014

0.07

2.97

5.28

1/15/2014

0.07

2.86

5.18

1/22/2014

0.07

2.79

5.11

1/30/2014

0.07

2.72

5.08

2/6/2014

0.07

2.73

5.13

2/13/2014

0.06

2.73

5.12

2/20/14

0.07

2.76

5.15

2/27/14

0.07

2.65

5.01

3/6/14

0.08

2.74

5.11

3/13/14

0.08

2.66

5.05

3/20/14

0.08

2.79

5.13

3/27/14

0.08

2.69

4.95

4/3/14

0.08

2.80

5.04

4/10/14

0.08

2.65

4.89

4/17/14

0.09

2.73

4.89

4/24/14

0.10

2.70

4.84

5/1/14

0.09

2.63

4.77

5/8/14

0.08

2.61

4.79

5/15/14

0.09

2.50

4.72

5/22/14

0.09

2.56

4.81

5/29/14

0.09

2.45

4.69

6/05/14

0.09

2.59

4.83

6/12/14

0.09

2.58

4.79

6/19/14

0.10

2.64

4.83

6/26/14

0.10

2.53

4.71

7/2/14

0.10

2.64

4.84

7/10/14

0.09

2.55

4.75

7/17/14

0.09

2.47

4.69

7/24/14

0.09

2.52

4.72

7/31/14

0.08

2.58

4.75

8/7/14

0.09

2.43

4.71

8/14/14

0.09

2.40

4.69

8/21/14

0.09

2.41

4.69

8/28/14

0.09

2.34

4.57

9/04/14

0.09

2.45

4.70

9/11/14

0.09

2.54

4.79

9/18/14

0.09

2.63

4.91

9/25/14

0.09

2.52

4.79

10/02/14

0.09

2.44

4.76

10/09/14

0.08

2.34

4.68

10/16/14

0.09

2.17

4.64

10/23/14

0.09

2.29

4.71

11/13/14

0.09

2.35

4.82

11/20/14

0.10

2.34

4.86

11/26/14

0.10

2.24

4.73

12/04/14

0.12

2.25

4.78

12/11/14

0.12

2.19

4.72

12/18/14

0.13

2.22

4.78

12/23/14

0.13

2.26

4.79

12/30/14

0.06

2.20

4.69

1/8/15

0.12

2.03

4.57

1/15/15

0.12

1.77

4.42

1/22/15

0.12

1.90

4.49

1/29/15

0.11

1.77

4.35

2/05/15

0.12

1.83

4.43

2/12/15

0.12

1.99

4.53

2/19/15

0.12

2.11

4.64

2/26/15

0.11

2.03

4.47

3/5/215

0.11

2.11

4.58

3/12/15

0.11

2.10

4.56

3/19/15

0.12

1.98

4.48

3/26/15

0.11

2.01

4.56

4/03/15

0.12

1.92

4.47

4/9/15

0.12

1.97

4.50

4/16/15

0.13

1.90

4.45

4/23/15

0.13

1.96

4.50

5/1/15

0.08

2.05

4.65

5/7/15

0.13

2.18

4.82

5/14/15

0.13

2.23

4.97

5/21/15

0.12

2.19

4.94

5/28/15

0.12

2.13

4.88

6/04/15

0.13

2.31

5.03

6/11/15

0.13

2.39

5.10

6/18/15

0.14

2.35

5.17

6/25/15

0.13

2.40

5.20

7/1/15

0.13

2.43

5.26

7/9/15

0.13

2.32

5.20

7/16/15

0.14

2.36

5.24

7/23/15

0.13

2.28

5.13

7/30/15

0.14

2.28

5.16

8/06/15

0.14

2.23

5.15

8/20/15

0.15

2.09

5.13

8/27/15

0.14

2.18

5.33

9/03/15

0.14

2.18

5.35

9/10/15

0.14

2.23

5.35

9/17/15

0.14

2.21

5.39

9/25/15

0.14

2.13

5.29

10/01/15

0.13

2.05

5.36

10/08/15

0.13

2.12

5.40

10/15/15

0.13

2.04

5.33

10/22/15

0.12

2.04

5.30

10/29/15

0.12

2.19

5.40

11/05/15

0.12

2.26

5.44

11/12/15

0.12

2.32

5.51

11/19/15

0.12

2.24

5.44

11/25/15

0.12

2.23

5.44

12/03/15

0.13

2.33

5.51

12/10/15

0.14

2.24

5.43

12/17/15

0.37

2.24

5.45

12/23/15

0.36

2.27

5.53

12/30/15

0.35

2.31

5.54

1/07/2016

0.36

2.16

5.44

01/14/16

0.36

2.10

5.46

01/20/16

0.37

2.01

5.41

01/29/16

0.38

2.00

5.48

02/04/16

0.38

1.87

5.40

02/11/16

0.38

1.63

5.26

02/18/16

0.38

1.75

5.37

02/25/16

0.37

1.71

5.27

03/03/16

0.37

1.83

5.30

03/10/16

0.36

1.93

5.23

03/17/16

0.37

1.91

5.11

03/24/16

0.37

1.91

4.97

03/31/16

0.25

1.78

4.90

04/07/16

0.37

1.70

4.76

04/14/16

0.37

1.80

4.79

04/21/16

0.37

1.88

4.79

04/28/16

0.37

1.84

4.73

05/05/16

0.37

1.76

4.62

05/12/16

0.37

1.75

4.66

05/19/16

0.37

1.85

4.70

05/26/16

0.37

1.83

4.69

06/02/16

0.37

1.81

4.64

06/09/16

0.37

1.68

4.53

06/16/16

0.38

1.57

4.47

06/23/16

0.39

1.74

4.60

06/30/16

0.36

1.49

4.41

07/07/16

0.40

1.40

4.19

07/14/16

0.40

1.53

4.23

07/21/16

0.40

1.57

4.25

07/28/16

0.40

1.52

4.20

08/04/16

0.40

1.51

4.27

08/11/16

0.40

1.57

4.27

08/18/16

0.40

1.53

4.23

08/25/16

0.40

1.58

4.21

09/01/16

0.40

1.57

4.19

09/08/16

0.40

1.61

4.28

09/15/16

0.40

1.71

4.43

09/22/16

0.40

1.63

4.32

09/29/16

0.40

1.56

4.23

10/06/16

0.40

1.75

4.36

10/13/16

0.40

1.75

NA*

10/20/16

0.41

1.76

NA*

10/27/16

0.41

1.85

NA*

11/03/16

0.41

1.82

NA*

11/09/16

0.41

2.07

NA*

11/17/16

0.41

2.29

NA*

11/23/16

0.40

2.36

NA*

12/01/16

0.40

2.45

NA*

12/08/16

0.41

2.40

NA*

12/15/16

0.66

2.60

NA*

12/22/16

0.66

2.55

NA*

12/29/16

0.66

2.49

NA*

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

Source: Board of Governors of the Federal Reserve System

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

Chart VIII-2 of the Board of Governors of the Federal Reserve System provides the rate of US dollars (USD) per euro (EUR), USD/EUR. The rate appreciated from USD 1.0955/EUR on Dec 23, 2015 to USD 1.0449/EUR on Dec 30, 2016 or 4.6 percent. The euro has devalued 51.2 percent relative to the dollar from the high on Jul 15, 2008 to Dec 30, 2016. US corporations with foreign transactions and net worth experience losses in their balance sheets in converting revenues from depreciated currencies to the dollar. Corporate profits with IVA and CCA fell at $127.9 billion in IVQ2015 with decrease of domestic industries at $149.8 billion, mostly because of decrease of nonfinancial business at $131.7 billion, and increase of profits from operations in the rest of the world at $22.0 billion. Receipts from the rest of the world fell at $19.9 billion. Corporate profits with IVA and CCA increased at $66.0 billion in IQ2016 with increase of domestic industries at $92.9 billion. Profits from operations from the rest of the world fell at $26.9 billion and payments to the rest of the world increased at $35.6 billion. Corporate profits with IVA and CCA decreased at $12.5 billion in IIQ2016. Profits from domestic industries fell at $50.5 billion and profits from nonfinancial business fell at $56.1 billion. Profits from the rest of the world increased at $38.0 billion. Corporate profits with IVA and CCA increased at $117.8 billion in IIIQ2016. Profits from domestic industries increased at $116.5 billion and profits from nonfinancial business increased at $66.4 billion. Profits from the rest of the world increased at $1.3 billion. Total corporate profits with IVA and CCA were $2138.8 billion in IIIQ2016 of which $1729.9 billion from domestic industries, or 80.9 percent of the total, and $408.9 billion, or 19.1 percent, from the rest of the world. Nonfinancial corporate profits of $1236.9 billion account for 57.8 percent of the total. Uncertainty originating in fiscal, regulatory and monetary policy causes wide swings in expectations and decisions by the private sector with adverse effects on investment, real economic activity and employment. There is increase in corporate profits from devaluing the dollar with unconventional monetary policy of zero interest rates and decrease of corporate profits in revaluing the dollar with attempts at “normalization” or increases in interest rates. Conflicts arise while other central banks differ in their adjustment process. The current account deficit seasonally adjusted increases from 2.3 percent of GDP in IVQ2014 to 2.7 percent in IQ2015. The current account deficit increases to 2.7 percent of GDP in IQ2015 and decreases to 2.5 percent of GDP in IIQ2015. The deficit increases to 2.9 percent of GDP in IIIQ2015, easing to 2.8 percent of GDP in IVQ2015. The net international investment position decreases from minus $7.0 trillion in IVQ2014 to minus $6.8 trillion in IQ2015, decreasing at minus $6.7 trillion in IIQ2015. The net international investment position increases to minus $7.6 trillion in IQ2016 and increases to minus $8.0 trillion in IIQ2016. The BEA explains as follows (http://www.bea.gov/newsreleases/international/intinv/2016/pdf/intinv216.pdf):

The U.S. net international investment position at the end of the second quarter of 2016 was -$8,042.8 billion (preliminary), according to statistics released today by the Bureau of Economic Analysis (BEA). The net investment position at the end of the first quarter was -$7,582.0 billion (revised). The net investment position decreased $460.8 billion or 6.1 percent in the second quarter, compared with a decrease of 4.1 percent in the first quarter, and an average quarterly decrease of 6.1 percent from the first quarter of 2011 through the fourth quarter of 2015. The $460.8 billion decrease in the net position reflected a $479.9 billion decrease in the net position excluding financial derivatives that was partly offset by a $19.1 billion increase in the net position in financial derivatives.”

The BEA explains further (http://www.bea.gov/newsreleases/international/intinv/2016/pdf/intinv216.pdf): “U.S. assets increased $404.1 billion to $24,465.9 billion at the end of the second quarter, reflecting increases in both financial derivatives and assets excluding financial derivatives. Financial derivatives with a positive fair value increased $241.4 billion to $3,223.7 billion, mostly in single-currency interest rate contracts. Assets excluding financial derivatives increased $162.7 billion to $21,242.1 billion, reflecting increases in other investment, portfolio investment, and reserve assets that were partly offset by a decrease in direct investment. Increases resulting from financial transactions were partly offset by depreciation of major foreign currencies against the U.S. dollar that lowered the value of U.S. assets in dollar terms.”

clip_image003

Chart VIII-2, Exchange Rate of US Dollars (USD) per Euro (EUR), Dec 30, 2015 to Dec 30, 2016

Source: Board of Governors of the Federal Reserve System

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

Chart VIII-3 of the Board of Governors of the Federal Reserve System provides the yield of the 10-year Treasury constant maturity note from 1.60 percent on Sep 29 2016 to 2.49 percent on Dec 29, 2016. There is turbulence in financial markets originating in a combination of intentions of normalizing or increasing US policy fed funds rate, quantitative easing in Europe and Japan and increasing perception of financial/economic risks.

clip_image004

Chart VIII-3, Yield of Ten-year Constant Maturity Treasury, Sep 29, 2016 to Dec 29, 2016

Source: Board of Governors of the Federal Reserve System

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

ID. Appendix on the Monetary History of Brazil. According to an influential school of thought, the interrelation of growth and inflation in Latin America is complex, preventing analysis of whether inflation promotes or restricts economic growth (Seers 1962, 191). In this view, there are multiple structural factors of inflation. Successful economic policy requires a development program that ameliorates structural weaknesses. Policy measures in developed countries are not transferable to developing economies.

In extensive research and analysis, Kahil (1973) finds no evidence of the role of structural factors in Brazilian inflation from 1947 to 1963. In fact, Kahil (1973, 329) concludes:

“The immediate causes of the persistent and often violent rise in prices, with which Brazil was plagued from the last month of 1948 to the early months of 1964, are pretty obvious: large and generally growing public deficits, together with too rapid an expansion of bank credit in the first years and, later, exaggerated and more and more frequent increases in the legal minimum wages.”

Kahil (1973, 334) analyzes the impact of inflation on the economy and society of Brazil:

“The real incomes of the various social classes alternately suffered increasingly frequent and sharp fluctuations: no sooner had a group succeeded in its struggle to restore its real income to some previous peak than it witnessed its erosion with accelerated speed; and it soon became apparent to all that the success of any important group in raising its real income, through government actions or by other means, was achieved only by reducing theirs. Social harmony, the general climate of euphoria, and also enthusiasm for government policies, which had tended to prevail until the last months of 1958, gave way in the following years of galloping inflation to intense political and social conflict and to profound disillusionment with public policies. By 1963 when inflation reached its runaway stage, the economy had ceased to grow, industry and transport were convulsed by innumerable strikes, and peasants were invading land in the countryside; and the situation further worsened in the first months of 1964.”

Professor Nathiel H. Leff (1975) at Columbia University identified another important contribution of Kahil (1975, Chapter IV “The supply of capital,” 127-185) of key current relevance to current proposals to promote economic growth and employment by raising inflation targets:

“Contrary to the assertions of some earlier writers on this topic, Kahil concludes that inflation did not lead to accelerated capital formation in Brazil.”

In econometric analysis of Brazil’s inflation from 1947 to 1980, Barbosa (1987) concludes:

“The most important result, based on the empirical evidence presented here, is that in the long run inflation is a monetary phenomenon. It follows that the most challenging task for Brazilian society in the near future is to shape a monetary-fiscal constitution that precludes financing much of the budget deficits through the inflation tax.”

Experience with continuing fiscal deficits and money creation tend to show accelerating inflation. Table III-10 provides average yearly rates of growth of two definitions of the money stock, M1, and M2 that adds also interest-paying deposits. The data were part of a research project on the monetary history of Brazil using the NBER framework of Friedman and Schwartz (1963, 1970) and Cagan (1965) as well as the institutional framework of Rondo E. Cameron (1967, 1972) who inspired the research (Pelaez 1974, 1975, 1976a,b, 1977, 1979, Pelaez and Suzigan 1978, 1981). The data were also used to test the correct specification of money and income following Sims (1972; see also Williams et al. 1976) as well as another test of orthogonality of money demand and supply using covariance analysis. Sims (1972, 541) finds that: “If and only if causality runs one way from current and past values of some list of exogenous variables to a given endogenous variable, then in a regression of the endogenous variable on past, current, and future values of the exogenous variables, the future values of the exogenous variables should have zero coefficients.” The objective of research was to verify the quantity theory of money of Friedman and Schwartz (1963, 1970) for the economy of Brazil from 1862 to 1976. The Granger-Sims test postulates that causality runs from money into nominal income if and only if in a regression of nominal income on past, current and future values of money, future coefficients are zero. The results show that the Sims F coefficients are zero for the regressions of nominal income on money and 38 for the coefficients of money on income (Pelaez and Suzigan 1978, Pelaez 1979, 106). There also covariance tests verifying orthogonality of money demand and money supply and orthogonality of base money and the money multiplier. The quantity theory of money explains money, income and prices in the historical period of Brazil 1862 to 1976. There are two important conclusions.

  • Models of Keynesian multipliers in historical Brazil are inconsistent with these findings
  • Pelaez (1979, 121) concludes following Friedman that for the case of Brazil “in historical perspective, it appears that a system of rules, instead of authorities, would have best promoted the interests of the Nation.”

Hill (2007, 763) develops “a simple parametric recursion for VAR coefficients that, for trivariate processes with one scalar auxiliary variable, always allows for sequential linear parametric conditions for non-causality up to horizon h ≥ 1. An empirical analysis of the money-income relationship reveals significant evidence in favor of linear causation of money to income, either directly when we control for cointegration, or indirectly after a delay of 1-3 months in models of first differences.” Shikida, Araujo Jr. e Figueiredo (2014) apply Hill (2007) to the historical experience of Brazil. They conclude that base money influences nominal output but not real output. Their results are consistent with the unpleasant monetarist arithmetic of Sargent and Wallace (1981) that uses base money instead of the stock of money. Because of restrictions on banking and finance (Summerhill 2015, Pelaez 1975), base money should have been more important in influencing nominal income in historical Brazil. It is still valid to conclude that monetary policy rules instead of discretionary authorities would have best promoted national interests in historical Brazil.

The average yearly rates of inflation are high for almost any period in 1861-1970, even when prices were declining at 1 percent in 19th century England, and accelerated to 27.1 percent in 1945-1970. There may be concern in an uncontrolled deficit monetized by sharp increases in base money. The Fed may have desired to control inflation at 2 percent after lowering the fed funds rate to 1 percent in 2003 but inflation rose to 4.1 percent in 2007. There is not “one hundred percent” confidence in controlling inflation because of the lags in effects of monetary policy impulses and the equally important lags in realization of the need for action and taking of action and also the inability to forecast any economic variable. Romer and Romer (2004) find that a one percentage point tightening of monetary policy is associated with a 4.3 percent decline in industrial production. There is no change in inflation in the first 22 months after monetary policy tightening when it begins to decline steadily, with decrease by 6 percent after 48 months (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 102). Even if there were one hundred percent confidence in reducing inflation by monetary policy, it could take a prolonged period with adverse effects on economic activity. Certainty does not occur in economic policy, which is characterized by costs that cannot be anticipated.

Table III-10, Brazil, Yearly Growth Rates of M1, M2, Nominal Income (Y), Real Income (y), Real Income per Capita (y/n) and Prices (P)

 

M1

M2

Y

y

y/N

P

1861-1970

9.3

6.2

10.2

4.6

2.4

5.8

1861-1900

5.4

5.9

5.9

4.4

2.6

1.6

1861-1913

4.7

4.7

5.3

4.4

2.4

0.1

1861-1929

5.5

5.6

6.4

4.3

2.3

2.1

1900-1970

13.9

13.9

15.2

4.9

2.6

10.3

1900-1929

8.9

8.9

10.8

4.2

2.1

6.6

1900-1945

8.6

9.1

9.2

4.3

2.2

4.9

1920-1970

17.8

17.3

19.4

5.3

2.8

14.1

1920-1945

8.3

8.7

7.5

4.3

2.2

3.2

1920-1929

5.4

6.9

11.1

5.3

3.3

5.8

1929-1939

8.9

8.1

11.7

6.3

4.1

5.4

1945-1970

30.3

29.2

33.2

6.1

3.1

27.1

Note: growth rates are obtained by regressions of the natural logarithms on time. M1 and M2 definitions of the money stock; Y nominal GDP; y real GDP; y/N real GDP per capita; P prices.

Source: See Pelaez and Suzigan (1978), 143; M1 and M2 from Pelaez and Suzigan (1981); money income and real income from Contador and Haddad (1975) and Haddad (1974); prices by the exchange rate adjusted by British wholesale prices until 1906 and then from Villela and Suzigan (1973); national accounts after 1947 from Fundação Getúlio Vargas.

Chart III-1 shows in semi-logarithmic scale from 1861 to 1970 in descending order two definitions of income velocity, money income, M1, M2, an indicator of prices and real income.

clip_image005

Chart III-1, Brazil, Money, Income and Prices 1861-1970.

Source: © Carlos Manuel Pelaez and Wilson Suzigan. 1981. História Monetária do Brasil Segunda Edição. Coleção Temas Brasileiros. Brasília: Universidade de Brasília, 21.

Table III-11 provides yearly percentage changes of GDP, GDP per capita, base money, prices and the current account in millions of dollars during the acceleration of inflation after 1947. There was an explosion of base money or the issue of money and three waves of inflation identified by Kahil (1973). Inflation accelerated together with issue of money and political instability from 1960 to 1964. There must be a role for expectations in inflation but there is not much sound knowledge and measurement as Rajan (2012May8) argues. There have been inflation waves documented in periodic comments in this blog (see Section I and earlier at http://cmpassocregulationblog.blogspot.com/2012/04/fractured-labor-market-with-hiring.html). The risk is ignition of adverse expectations at the crest of one of worldwide inflation waves. Lack of credibility of the commitment by the FOMC to contain inflation could ignite such perverse expectations. Deficit financing of economic growth can lead to inflation and financial instability.

Table III-11, Brazil, GDP, GDP per Capita, Base Money, Prices and Current Account of the Balance of Payments, ∆% and USD Millions, 1947-1971

 

GDP

∆%

GDP per Capita

∆%

Base Money

∆%

Prices

∆%

Current
Account BOP

USD Millions

1947

2.4

0.1

-1.4

14.0

162

1948

7.4

4.9

4.6

7.6

-24

1949

6.6

4.2

14.5

4.0

-74

1950

6.5

4.0

23.0

10.0

52

1951

5.9

2.9

15.3

21.9

-291

1952

8.7

5.6

17.7

10.2

-615

1953

2.5

-0.5

15.5

12.1

16

1954

10.1

6.9

23.4

31.0

-203

1955

6.9

3.8

18.0

14.0

17

1956

3.2

0.2

16.9

21.6

194

1957

8.1

4.9

30.5

13.9

-180

1958

7.7

4.6

26.1

10.4

-253

1959

5.6

2.5

32.3

37.7

-154

1960

9.7

6.5

42.4

27.6

-410

1961

10.3

7.1

54.4

36.1

115

1962

5.3

2.2

66.4

54.1

-346

1963

1.6

-1.4

78.4

75.2

-244

1964

2.9

-0.1

82.5

89.7

40

1965

2.7

-0.6

67.6

62.0

331

1966

4.4

1.5

25.8

37.9

153

1967

4.9

2.0

33.9

28.7

-245

1968

11.2

8.1

31.4

25.2

32

1969

9.9

6.9

22.4

18.2

549

1970

8.9

5.8

20.2

20.7

545

1971

13.3

10.2

29.8

22.0

530

Sources: Fundação Getúlio Vargas, Banco Central do Brasil and Pelaez and Suzigan (1981). Carlos Manuel Pelaez, História Econômica do Brasil: Um Elo entre a Teoria e a Realidade Econômica. São Paulo: Editora Atlas, 1979, 94.

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, 2014, 2015 and IIIQ2016. The contraction caused a strong shock to US wealth. Assets fell from $80.9 trillion in 2007 to $76.9 trillion in 2011 even after nine consecutive quarters of growth beginning in IIIQ2009 (http://cmpassocregulationblog.blogspot.com/2016/08/and-as-ever-economic-outlook-is.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/business-fixed-investment-has-been-soft.html), for decline of $4.0 trillion or 4.9 percent. Assets stood at $101.7 trillion in 2015 for gain of $20.8 trillion relative to $80.9 trillion in 2007 or increase by 25.7 percent. Assets increased to $105.1 trillion in IIIQ2016 by $24.2 trillion relative to 2007 or 30.0 percent. Liabilities declined from $14.4 trillion in 2007 to $13.6 trillion in 2011 or by $752.8 billion equivalent to decline by 5.2 percent. Liabilities increased $182.7 billion or 1.3 percent from 2007 to 2015. Liabilities increased from $14.4 trillion in 2007 to $14.9 trillion in IIIQ2016, by $513.8 billion or increase of 3.6 percent. Net worth shrank from $66.5 trillion in 2007 to $63.3 trillion in 2011, that is, $3.2 trillion equivalent to decline of 4.8 percent. Net worth increased from $66,464.0 billion in 2007 to $90,196.1 billion in IIIQ2016 by $23,732.1 billion or 35.7 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 241.428 in Sep 2016 (http://www.bls.gov/cpi/data.htm) or 14.9 percent. Net worth adjusted by CPI inflation increased 18.1 percent from 2007 to IIIQ2016. Nonfinancial assets increased $3890.3 billion from $28,074.5 billion in 2007 to $31,964.8 billion in IIIQ2016 or 13.9 percent. There was increase from 2007 to IIIQ2016 of $2844.8 billion in real estate assets or by 12.2 percent. Real estate assets adjusted for CPI inflation fell 2.4 percent between 2007 and IIIQ2016. 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

2014

2015

IIIQ2016

Assets

80,860.2

97,976.5

101,696.8

105,106.1

Nonfinancial

28,074.5

28,706.7

30,473.6

31,964.8

  Real Estate

23,265.0

23,200.5

24,766.9

26,109.8

  Durable Goods

  4,476.0

5,052.9

  5,236.8

5,373.2

Financial

52,785.7

69,269.8

71,223.2

73,141.2

  Deposits

  7,562.3

10,145.9

  10,737.7

11,126.0

  Debt Secs.

  4,080.6

3,993.1

  4,440.0

3,733.7

  Mutual Fund Shares

   4,314.9

6,726.3

   6,504.4

6,875.2

  Equities Corporate

   10,046.8

14,356.7

   14,159.8

14,748.4

  Equity Noncorporate

   8,815.5

10,097.5

   10,829.4

11,174.0

  Pension

15,073.4

20,658.6

21,247.6

22,087.5

Liabilities

14,396.1

14,232.5

14,578.8

14,909.9

  Home Mortgages

10,613.1

9,461.1

  9,547.2

9,707.5

  Consumer Credit

   2,609.9

3,318.0

   3,535.7

3,696.0

Net Worth

66,464.0

83,744.0

87,118.0

90,196.1

Net Worth = Assets – Liabilities

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 IIA-1 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 96.0 percent in the 10-city composite of the Case-Shiller home price index, 81.2 percent in the 20-city composite and 65.9 percent in the US national home price index between Oct 2000 and Oct 2005. Prices rose around 100 percent from Oct 2000 to Oct 2006, increasing 101.1 percent for the 10-city composite, 86.7 percent for the 20-city composite and 70.9 percent in the US national index. House prices rose 38.9 percent between Oct 2003 and Oct 2005 for the 10-city composite, 34.9 percent for the 20-city composite and 29.6 percent for the US national propelled by low fed funds rates of 1.0 percent between Oct 2003 and Oct 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Oct 2004 until Oct 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 Oct 2003 and Oct 2006, the 10-city index gained 42.5 percent; the 20-city index increased 39.0 percent; and the US national 33.4 percent. House prices have fallen from Oct 2006 to Oct 2016 by 8.5 percent for the 10-city composite and 6.6 percent for the 20-city composite, increasing 0.5 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 Oct 2016, house prices increased 4.3 percent in the 10-city composite, increasing 5.1 percent in the 20-city composite and 5.6 percent in the US national. Table IIA-1 also shows that house prices increased 83.9 percent between Oct 2000 and Oct 2016 for the 10-city composite, increasing 74.3 percent for the 20-city composite and 71.8 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 9.2 percent from the peak in Jun 2006 to Oct 2016 and the 20-city composite fell 7.1 percent from the peak in Jul 2006 to Oct 2016. The US national increased 0.3 percent from the peak of the 10-city composite to Jun 2016 and 0.2 percent from the peak of the 20-city composite to Jul 2016. 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 2015 for the 10-city composite was 3.8 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 to Dec 2015 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 2015 was 3.7 percent while the rate of the 20-city composite was 3.3 percent and 3.2 percent for the US national.

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

 

10-City Composite

20-City Composite

US National

∆% Oct 2000 to Oct 2003

41.1

34.3

28.1

∆% Oct 2000 to Oct 2005

96.0

81.2

65.9

∆% Oct 2003 to Oct 2005

38.9

34.9

29.6

∆% Oct 2000 to Oct 2006

101.1

86.7

70.9

∆% Oct 2003 to Oct 2006

42.5

39.0

33.4

∆% Oct 2005 to Oct 2016

-6.2

-3.8

3.5

∆% Oct 2006 to Oct 2016

-8.5

-6.6

0.5

∆% Oct 2009 to Oct 2016

29.5

30.9

24.5

∆% Oct 2010 to Oct 2016

29.4

32.1

29.3

∆% Oct 2011 to Oct 2016

33.9

36.9

33.7

∆% Oct 2012 to Oct 2016

29.6

31.3

28.5

∆% Oct 2013 to Oct 2016

14.0

15.6

15.9

∆% Oct 2014 to Oct 2016

9.3

10.7

10.8

∆% Oct 2015 to Oct 2016

4.3

5.1

5.6

∆% Oct 2000 to Oct 2016

83.9

74.3

71.8

∆% Peak Jun 2006 Oct 2016

-9.2

  

0.3

∆% Peak Jul 2006 Oct 2016

  

-7.1

0.2

Average ∆% Dec 1987-Dec 2015

3.8

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 2015

3.7

3.3

3.2

Source: http://us.spindices.com/index-family/real-estate/sp-corelogic-case-shiller

Price increases measured by the Case-Shiller house price indices show in data for Oct 2016 that “home prices continued their rise across the country over the last 12 months” (https://www.spice-indices.com/idpfiles/spice-assets/resources/public/documents/461749_cshomeprice-release-1227.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 IIA-2. 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 Mar through Apr 2012, house prices seasonally adjusted declined in most months for both the 10-city and 20-city Case-Shiller composites from Dec 2010 to Jan 2012, as shown in Table IIA-2. 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 changed 0.0 percent in Oct 2016 and the 20-city increased 0.1 percent. The 10-city SA increased 0.6 percent in Oct 2016 and the 20-city composite SA increased 0.6 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses.

Table IIA-2, US, Monthly Percentage Change of S&P 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

Oct 2016

0.6

0.0

0.6

0.1

Sep

0.4

0.1

0.5

0.1

Aug

0.3

0.3

0.3

0.4

Jul

0.0

0.5

0.1

0.5

Jun

-0.1

0.7

0.0

0.8

May

-0.2

0.8

-0.1

0.9

Apr

-0.3

1.0

-0.3

1.1

Mar

1.1

0.9

1.1

1.0

Feb

0.5

0.2

0.6

0.2

Jan

0.6

-0.1

0.7

0.0

Dec 2015

0.5

-0.1

0.6

0.0

Nov

0.8

0.0

0.8

0.0

Oct

0.5

-0.1

0.6

0.0

Sep

0.4

0.1

0.5

0.1

Aug

0.2

0.2

0.2

0.3

Jul

0.1

0.6

0.1

0.7

Jun

0.1

0.9

0.1

1.0

May

0.1

1.0

0.1

1.1

Apr

-0.3

1.1

-0.3

1.1

Mar

1.0

0.8

1.1

0.9

Feb

0.9

0.5

0.9

0.5

Jan

0.6

-0.1

0.6

-0.1

Dec 2014

0.7

0.0

0.7

0.0

Nov

0.5

-0.3

0.6

-0.2

Oct

0.5

-0.1

0.5

-0.1

Sep

0.2

-0.1

0.3

-0.1

Aug

0.1

0.2

0.1

0.2

Jul

0.0

0.6

0.0

0.6

Jun

0.1

1.0

0.1

1.0

May

0.0

1.1

0.1

1.1

Apr

-0.2

1.1

-0.2

1.2

Mar

1.0

0.8

1.0

0.9

Feb

0.5

0.0

0.5

0.0

Jan

0.7

-0.1

0.6

-0.1

Dec 2013

0.6

-0.1

0.6

-0.1

Nov

0.8

0.0

0.8

-0.1

Oct

0.9

0.2

0.9

0.2

Sep

1.0

0.7

1.1

0.7

Aug

1.2

1.3

1.2

1.3

Jul

1.1

1.9

1.1

1.8

Jun

1.2

2.2

1.1

2.2

May

1.3

2.5

1.3

2.5

Apr

1.4

2.6

1.4

2.6

Mar

1.5

1.3

1.5

1.3

Feb

1.0

0.3

0.9

0.2

Jan

0.8

0.0

0.9

0.0

Dec 2012

0.9

0.2

0.9

0.2

Nov

0.6

-0.3

0.7

-0.2

Oct

0.6

-0.2

0.7

-0.1

Sep

0.6

0.3

0.6

0.3

Aug

0.6

0.8

0.7

0.9

Jul

0.6

1.5

0.7

1.6

Jun

1.0

2.1

1.1

2.3

May

1.0

2.2

1.1

2.4

Apr

0.3

1.4

0.4

1.4

Mar

0.2

-0.1

0.2

0.0

Feb

-0.1

-0.9

0.0

-0.8

Jan

-0.3

-1.1

-0.2

-1.0

Dec 2011

-0.5

-1.2

-0.4

-1.1

Nov

-0.6

-1.4

-0.5

-1.3

Oct

-0.6

-1.3

-0.6

-1.4

Sep

-0.3

-0.6

-0.4

-0.7

Aug

-0.2

0.1

-0.2

0.1

Jul

-0.1

0.9

0.0

1.0

Jun

-0.1

1.0

-0.1

1.2

May

-0.2

1.0

-0.2

1.0

Apr

-0.3

0.6

-0.2

0.6

Mar

-0.6

-1.0

-0.7

-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-corelogic-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 $10.5 trillion or 13.0 percent from 2007 to 2008 and $8.9 trillion or 11.1 percent to 2009. Net worth fell $10.4 trillion from 2007 to 2008 or 15.7 percent and $8.7 trillion to 2009 or 13.0 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

80,860.2

70,342.9

-10,517.3

71,919.9

-8,940.3

Non
FIN

28,074.5

24,388.3

-3,686.2

23,399.0

-4,675.5

RE

23,265.0

19,454.1

-3,810.9

18,442.6

-4,822.4

FIN

52,785.7

45,954.5

-6,831.2

48,520.9

-4,264.8

LIAB

14,396.1

14,296.1

-100.0

14,108.6

-287.5

NW

66,464.0

56,046.8

-10,417.2

57,811.3

-8,652.7

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. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 68.7 percent of GDP in IIIQ2016 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IIIQ2016, real estate increased in value by $2844.8 billion and financial assets increased $20,355.5 billion for net gain of real estate and financial assets of $23,200.3 billion, explaining most of the increase in net worth of $23,732.1 billion obtained by deducting the increase in liabilities of $513.8 billion from the increase of assets of $22,245.9 billion. Net worth increased from $66,464.0 billion in 2007 to $90,196.1 billion in IIIQ2016 by $23,732.1 billion or 35.7 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 241.428 in Sep 2016 (http://www.bls.gov/cpi/data.htm) or 14.9 percent. Net worth adjusted by CPI inflation increased 18.1 percent from 2007 to IIQ2016. Real estate assets adjusted for CPI inflation fell 2.3 percent from 2007 to IIIQ2016. There are multiple complaints that unconventional monetary policy concentrates income on wealthier individuals because of their holdings of financial assets while the middle class has gained less because of fewer holdings of financial assets and higher share of real estate in family wealth. There is nothing new in these arguments. Interest rate ceilings on deposits and loans have been commonly used. The Banking Act of 1933 imposed prohibition of payment of interest on demand deposits and ceilings on interest rates on time deposits. These measures were justified by arguments that the banking panic of the 1930s was caused by competitive rates on bank deposits that led banks to engage in high-risk loans (Friedman, 1970, 18; see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 74-5). The objective of policy was to prevent unsound loans in banks. Savings and loan institutions complained of unfair competition from commercial banks that led to continuing controls with the objective of directing savings toward residential construction. Friedman (1970, 15) argues that controls were passive during periods when rates implied on demand deposit were zero or lower and when Regulation Q ceilings on time deposits were above market rates on time deposits. The Great Inflation or stagflation of the 1960s and 1970s changed the relevance of Regulation Q. Friedman (1970, 26-7) predicted the future:

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

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

“These are the people who have the fewest alternative ways to invest their limited assets and are least sophisticated about the alternatives.” Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. 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 IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_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 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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 IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 23.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.0 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 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, 2015 and IQ2016

 

Value 2007

Change to 2014

Change to 2015

Change to IIIQ2016

Assets

80,860.2

17,116.3

20,836.6

22,245.9

Nonfinancial

28,074.5

632.2

2,399.1

3,890.3

Real Estate

23,265.0

-64.5

1,501.9

2,844.8

Financial

52,785.7

16,484.1

18,437.5

20,355.5

Liabilities

14,396.1

-163.6

182.7

513.8

Net Worth

66,464.0

17,280.0

20,654.0

23,732.1

Net Worth = Assets – Liabilities

Source: Net Worth = Assets – Liabilities

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 IQ1990 and from IVQ2007 to IIIQ2016 is in Table IIA-5. The data reveal the following facts for the cycles in the 1980s:

  • IVQ1979 to IQ1990. Net worth increased 138.1 percent from IVQ1979 to IQ1990, the all items CPI index increased 67.8 percent from 76.7 in Dec 1979 to 128.7 in Mar 1990 and real net worth increased 41.9 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 68.9 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 IQ1989. Net worth increased 118.0 percent, the all items CPI index increased 52.7 percent from 80.1 in Mar 1980 to 122.3 in Mar 1989 and real net worth increased 42.8 percent.
  • IQ1980 to IIQ1989. Net worth increased 122.2 percent, the all items CPI index increased 54.9 percent from 80.1 in Mar 1980 to 124.1 in Jun 1989 and real net worth increased 43.4 percent.
  • IQ1980 to IIIQ1989. Net worth increased 128.1 percent, the all items CPI index increased 56.1 percent from 80.1 in Mar 1980 to 125.0 in Sep 1989 and real net worth increased 46.2 percent.
  • IQ1980 to IVQ1989. Net worth increased 132.1 percent, the all items CPI index increased 57.4 from 80.1 in Mar 1980 to 126.1 in Dec 1989 and real net worth increased 47.4 percent.
  • IQ1980 to IQ1990. Net worth increased 133.2 percent, the all items CPI indexed increased 60.7 percent from 80.1 in Mar 1980 to 128.7 in Mar 1990 and real net worth increased 45.1 percent.

There is disastrous performance in the current economic cycle:

  • IVQ2007 to IIIQ2016. Net worth increased 35.7 percent, the all items CPI increased 14.9 percent from 210.036 in Dec 2007 to 241.428 in Sep 2016 and real or inflation adjusted net worth increased 18.1 percent. Real estate assets adjusted for inflation fell 2.4 percent. Growth of real net worth at the long-term average of 3.1 percent per year from IVQ1945 to IIIQ2016 would have accumulated to 30.6 percent in the entire cycle from IVQ2007 to IIIQ2016, much higher than actual 18.1 percent.

The explanation is partly in the sharp decline of wealth of households and nonprofit organizations and partly in the mediocre growth rates of the cyclical expansion beginning in IIIQ2009. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. 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 IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_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 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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 IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 23.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.0 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 percent below trend under this alternative calculation.

Table IIA-5, Net Worth of Households and Nonprofit Organizations in Billions of Dollars, IVQ1979 to IIIQ1989 and IVQ2007 to IQ2016

Period IQ1980 to IVQ1989

  

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

IIQ1988

IIIQ1988

IVQ1988

IQ1989

IIQ1989

IIIQ1989

IVQ1989

IQ1990

15,278.3

16,292.1

16,841.0

17,494.9

17,782.0

18,192.3

18,019.7

18,492.5

18,902.6

19,209.5

19,690.0

20,136.4

20,529.2

21,077.3

21,443.5

21,542.1

∆ USD Billions IVQ1985

IVQ1979 to IQ1990

IQ1980-IVQ1985

IQ1980-IIIQ1986

IQ1980-IVQ1986

IQ1980-IQ1987

IQ1980-IIQ1987

IQ1980-IIIQ1987

IQ1980-IVQ1987

IQ1980-IQ1988

IQ1980-IIQ1988

IQ1980-IIIQ1988

IQ1980-IVQ1988

IQ1980-IQ1989

IQ1980-IIQ1989

IQ1980-IIIQ1989

IQ1980-IVQ1989

IQ1980-IQ1990

+6,230.5  ∆%68.9 R∆%18.5

+12,494.3  ∆%138.1 R∆%41.9

+6,039.7 ∆%65.4 R∆%21.2

+7,053.5 ∆%76.3 R∆%28.2

+7,602.4 ∆%82.3 R∆%32.1

+8,256.3 ∆%89.4 R∆%35.3

+8,543.4 ∆%92.5 R∆%35.8

+8,953.7 ∆%96.9 R∆%37.2

+8781.1 ∆%95.0 R∆%35.4

+9253.9 ∆%100.2 R∆%37.6

+9664.0 ∆%104.6 R∆%38.9

+9970.9 ∆%107.9 R∆%39.0

+10451.4 ∆%113.1 R∆%41.7

+10897.8 ∆%118.0 R∆%42.8

+11,290.6 ∆%122.2 R∆% 43.4

+11,838.7 ∆%128.1 R∆% 46.2

+12,204.9 ∆%132.1 R∆%47.4

+12,303.5 ∆%133.2 R∆%45.1

Period IVQ2007 to IIIQ2016

  

Net Worth of Households and Nonprofit Organizations USD Millions

  

IVQ2007

66,464.0

IIIQ2016

90,196.1

∆ USD Billions

+23,732.1 ∆%35.7 R∆%18.1

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

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 IIIQ2016. There is remarkable stop and go behavior in this series with two sharp declines and two standstills in the 28 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 18.1 percent from IVQ2007 to IIIQ2016 when adjusting for consumer price inflation.

clip_image006

Chart IIA-1, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ2007 to IIIQ2016

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 IQ1990. 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. The Bureau of Economic Analysis estimates US GDP in 2015 at $18,036.6 billion, such that the bailout would be equivalent to cost to taxpayers of about $477.97 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 138.1 percent from IVQ1979 to IQ1990 and 41.9 percent when adjusting for consumer price inflation. Net worth of households and nonprofit organizations increased 133.2 percent from IQ1980 to IQ1990 and 45.1 percent when adjusting for consumer price inflation.

clip_image007

Chart IIA-2, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1979 to IQ1990

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 $802.4 billion to IVQ2015 at $87,118.0 billion or increase of 10,757.2 percent. The consumer price index not seasonally adjusted was 18.2 in Dec 1945 jumping to 236.525 in Dec 2015 or increase of 1,199.6 percent. There was a gigantic increase of US net worth of households and nonprofit organizations over 70 years with inflation-adjusted increase from $44.088 in dollars of 1945 to $373.594 in IIIQ2016 or 747.4 percent. In a simple formula: {[($90196.1.7/$802.4)/(241.428/18.2)-1]100 = 747.4%}. Wealth of households and nonprofit organizations increased from $802.4 billion at year-end 1945 to $90,196.1 billion at the end of IIIQ2016 or 11,140.8 percent. The consumer price index increased from 18.2 in Dec 1945 to 241.428 in Jun 2016 or 1,226.6 percent. Net wealth of households and nonprofit organizations in dollars of 1945 increased from $44.088 in 1945 to $373.594 in IIIQ2016 or 747.4 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 2015 (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 70 years when US GDP grew at 2.1 percent on average in the twenty-nine quarters between IIIQ2009 and IIIQ2016 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html). US GDP was $228.2 billion in 1945 and net worth of households and nonprofit organizations $802.4 billion for ratio of wealth to GDP of 3.52. The ratio of net worth of households and nonprofits of $66,464.0 billion in 2007 to GDP of $14,477.6 billion was 4.59. The ratio of net worth of households and nonprofits of $87,118.0 billion in 2015 to GDP of 18,036.6 billion was 4.83. The final data point in Chart IIA-3 is net worth of household and nonprofit institutions at $90,196.1 billion in IIIQ2016 for increase of 11,140.8 percent relative to $802.4 billion in IVQ1945. CPI adjusted net worth of household and nonprofit institutions increased from $44.088 in IVQ1945 to $373.594 in IIIQ2016 or 747.4 percent at the annual equivalent rate of 3.1 percent.

clip_image008

Chart IIA-3, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1945 to IIIQ2016

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. http://www.federalreserve.gov/releases/z1/.

Table IIA-6 provides percentage changes of nonfinancial domestic sector debt. Households increased debt by 10.5 percent in 2006 but reduced debt from 2010 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 1.1 percent in IIQ2013. State and local government decreased debt at 3.0 percent in IIIQ2013 and at 2.8 percent in IVQ2013. State and local government reduced debt at 1.7 percent in IQ2014 and decreased at 0.4 percent in IIQ2014. State and local government reduced debt at 2.7 percent in IIIQ2014 and increased at 0.7 percent in IVQ2014. State and local government increased debt at 1.8 percent in IQ2015 and increased at 0.2 percent in IIIQ2015. State and local government decreased debt at 1.2 percent in IVQ2015. State and local government increased debt at 0.7 percent in IQ2016 and increased at 2.2 percent in IIQ2016. State and local government increased debt at 0.8 percent in IIIQ2016. Opposite behavior is 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 &
Local Govern-ment

Federal

IIIQ2016

5.8

4.0

6.0

0.8

8.2

IIQ2016

4.3

4.3

4.1

2.2

5.0

IQ2016

5.4

2.7

9.4

0.7

5.6

IVQ2015

7.7

3.7

5.4

-1.2

15.4

IIIQ2015

2.7

1.4

5.4

0.2

2.1

IIQ2015

4.4

4.0

7.9

0.5

2.7

IQ2015

2.7

2.1

7.4

1.8

-0.3

IVQ2014

3.5

2.2

6.3

0.7

3.1

IIIQ2014

5.1

2.8

6.5

-2.7

7.9

2015

4.4

2.8

6.7

0.3

5.0

2014

4.3

3.1

6.1

-1.2

5.4

2013

3.8

1.8

4.7

-1.8

6.7

2012

5.0

1.9

4.7

-0.2

10.1

2011

3.5

-0.5

2.8

-1.5

10.8

2010

4.4

-0.4

-0.7

2.4

18.5

2009

3.6

0.5

-4.2

4.4

20.4

2008

5.8

0.0

6.0

1.2

21.4

2007

8.1

7.2

12.4

6.0

4.7

2006

8.4

10.5

9.8

4.4

3.9

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 $66,464 billion in 2007 to $57,811 billion in 2009 or 13.0 percent and to $63,258 billion in 2011 or 4.8 percent. Wealth increased 35.7 percent from 2007 to IIIQ2016, increasing 18.1 percent after adjustment for inflation, primarily because of bloating financial assets while nonfinancial assets declined/stagnated 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

IIIQ2016

90,196

IIQ2016

88,603

IQ2016

87,765

IVQ2015

87,118

IIIQ2015

85,018

IIQ2015

86,298

IQ2015

85,678

IVQ2014

83,744

IIIQ2014

81,916

IIQ2014

81,605

IQ2014

79,995

IVQ2013

78,773

IIIQ2013

76,049

IIQ2013

73,590

IQ2013

72,088

IVQ2012

69,113

2015

87,118

2014

83,744

2013

78,773

2012

69,113

2011

63,258

2010

61,946

2009

57,811

2008

56,047

2007

66,464

2006

66,184

2005

61,867

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. http://www.federalreserve.gov/releases/z1/.

I United States Housing Collapse. Data and other information continue to provide depressed conditions in the US housing market in a longer perspective, with recent improvement at the margin. Table IIB-1 shows sales of new houses in the US at seasonally adjusted annual equivalent rate (SAAR). House sales fell in 28 of 71 months from Jan 2011 to Nov 2016 with monthly declines of 5 in 2011, 4 in 2012, 4 in 2013, 6 in 2014, 3 in 2015 and 6 in 2016. 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 10.8 percent in Jan 2013 with annual equivalent rate of 51.5 percent from Oct 2012 to Jan 2013 because of the increase of 10.8 percent in Jan 2013. New house sales increased at annual equivalent 9.9 percent in Feb-Mar 2013. New house sales weakened, decreasing at 2.3 percent in annual equivalent from Apr to Dec 2013 with significant volatility illustrated by decline of 18.8 percent in Jul 2013 and increase of 11.3 percent in Oct 2013. New house sales fell 1.1 percent in Dec 2013. New house sales increased 1.4 percent in Jan 2014 and fell 5.4 percent in Feb 2014, decreasing 3.1 percent in Mar 2014. New house sales decreased 2.2 percent in Apr 2014 and increased 12.7 percent in May 2014. New house sales fell 8.0 percent in Jun 2014 and decreased 3.4 percent in Jul 2014. New house sales jumped 11.7 percent in Aug 2014 and increased 3.8 percent in Sep 2014. New House sales increased 1.7 percent in Oct 2014 and fell 5.9 percent in Nov 2014. House sales fell at the annual equivalent rate of 2.6 percent in Sep-Nov 2014. New house sales increased 10.3 percent in Dec 2014 and increased 6.5 percent in Jan 2015. Sales of new houses increased 4.8 percent in Feb 2015 and fell 10.7 percent in Mar 2015. House sales increased 2.0 percent in Apr 2015. The annual equivalent rate in Dec 2014-Apr 2015 was 31.6 percent. New house sales increased 1.4 percent in May 2015 and fell 6.9 percent in Jun 2015, increasing 5.5 percent in Jul 2015. New house sales fell at annual equivalent 1.6 percent in May-Jul 2015. New house sales increased 1.4 percent in Aug 2015 and fell 9.5 percent in Sep 2015. New house sales decreased at annual equivalent 40.3 percent in Aug-Sep 2015. New house sales increased 4.6 percent in Oct 2015 and increased 6.3 percent in Nov 2015, increasing 5.9 percent in Dec 2015. New house sales increased at the annual equivalent rate of 92.2 percent in Oct-Dec 2015. New house sales decreased 2.2 percent in Jan 2016 at the annual equivalent rate of minus 23.4 percent. New house sales decreased 0.2 percent in Feb 2016 and increased 2.3 percent in Mar 2016. New house sales jumped at 6.1 percent in Apr 2016. New house sales increased at the annual equivalent rate of 37.7 percent in Feb-Apr 2016. New house sales decreased 0.7 percent in May 2016 and decreased 1.4 percent in Jun 2016. New house sales jumped 11.5 percent in Aug 2016. New house sales increased at the annual equivalent rate of 42.0 percent in May-Jul 2016. New house sales fell 10.1 percent in Aug 2016 and increased 2.1 percent in Sep 2016, decreasing 1.4 percent in Oct 2016. New house sales fell at the annual equivalent rate of minus 32.9 percent in Aug-Oct 2016. New house sales increased at 5.2 percent in Nov 2016, which is equivalent to 83.7 percent in a year. There are 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 3.93 percent on Aug 20, 2015 and at 3.91 percent on Sep 17, 2015. The conventional mortgage rate was at 3.79 percent on Oct 22, 2015. The conventional mortgage rate was 3.97 percent on Nov 20, 2015. The conventional mortgage rate was 3.97 percent on Dec 18, 2015, and 3.92 percent on Jan 14, 2016. The conventional mortgage rate was 3.65 percent on Feb 19, 2016. The commercial mortgage rate was 3.73 percent on Mar 17, 2016 and 3.59 percent on Apr 21, 2016. The conventional mortgage rate was 3.58 on May 19, 2016. The conventional mortgage rate was 3.54 percent on Jun 19, 2016 and 3.45 percent on Jul 21, 2016. The conventional mortgage rate was 3.43 percent on Aug 18, 2016 and 3.48 percent on Sep 22, 2016. The conventional mortgage rate was 3.94 on Nov 17, 2016 and 4.30 percent on Dec 22. The conventional mortgage rate measured in a survey by Freddie Mac (http://www.freddiemac.com/pmms/ http://www.freddiemac.com/pmms/abtpmms.htm) is the “interest rate a lender would charge to lend mortgage money to a qualified borrower.”

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

 

SA Annual Rate
Thousands

∆%

Nov 2016

592

5.2

AE ∆% Nov

  

83.7

Oct

563

-1.4

Sep

571

2.1

Aug

559

-10.1

AE ∆% Aug-Oct

  

-32.9

Jul

622

11.5

Jun

558

-1.4

May

566

-0.7

AE ∆% May-Jul

  

42.0

Apr

570

6.1

Mar

537

2.3

Feb

525

-0.2

AE ∆% Feb-Apr

  

37.7

Jan

526

-2.2

AE ∆% Jan

  

-23.4

Dec 2015

538

5.9

Nov

508

6.3

Oct

478

4.6

AE ∆% Oct-Dec

  

92.2

Sep

457

-9.5

Aug

505

1.4

AE ∆% Aug-Sep

  

-40.3

Jul

498

5.5

Jun

472

-6.9

May

507

1.4

AE ∆% May-Jul

  

-1.6

Apr

500

2.0

Mar

490

-10.7

Feb

549

4.8

Jan

524

6.5

Dec 2014

492

10.3

AE ∆% Dec-Apr

  

31.6

Nov

446

-5.9

Oct

474

1.7

Sep

466

3.8

AE ∆% Sep-Nov

  

-2.6

Aug

449

11.7

Jul

402

-3.4

Jun

416

-8.0

May

452

12.7

Apr

401

-2.2

Mar

410

-3.1

Feb

423

-5.4

Jan

447

1.4

AE ∆% Jan-Aug

  

2.6

Dec 2013

441

-1.1

Nov

446

0.5

Oct

444

11.3

Sep

399

5.0

Aug

380

1.1

Jul

376

-18.8

Jun

463

7.7

May

430

-4.7

Apr

451

0.4

AE ∆% Apr-Dec

  

-2.3

Mar

449

2.3

Feb

439

-0.7

AE ∆% Feb-Mar

  

9.9

Jan

442

10.8

Dec 2012

399

1.8

Nov

392

9.5

Oct

358

-7.0

AE ∆% Oct-Jan

  

51.5

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 8 percent of sales in 2011 to 4 to 5 percent in 2013 and 5.1 percent in Nov 2016. 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 Nov 2016, median prices of new houses sold not seasonally adjusted (NSA) increased 0.9 percent after decreasing 5.3 percent in

Oct 2016. Average prices increased 1.5 percent in Nov 2016 and decreased 4.3 percent in Oct 2016. Between Dec 2010 and Nov 2016, median prices increased 26.6 percent, partly concentrated in increases of 6.9 percent in Feb 2016, 6.1 percent in Nov 2015, 2.5 percent in Sep 2015, 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 23.4 percent between Dec 2010 and Nov 2016, with increases of 5.2 percent in Mar 2016, 5.4 percent in Sep 2015, 3.8 percent in Jul 2015 and 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 16.9 percent from Dec 2012 to Dec 2014, with increase of 14.5 percent in Oct 2014, while average prices increased 24.8 percent, with increase of 20.3 percent in Oct 2014. Median prices decreased 1.0 percent from Dec 2014 to Dec 2015 while average prices fell 4.1 percent. Median prices decreased 3.7 percent from Nov 2015 to Nov 2016 while average prices decreased 4.5 percent. Robbie Whelan, writing on “New homes hit record as builders cap supply,” on May 24, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323475304578500973445311276.html?mod=WSJ_economy_LeftTopHighlights), finds that homebuilders are continuing to restrict the number of new homes for sale. Restriction of available new homes for sale increases prices paid by buyers.

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

 

Unsold*
Stocks in Equiv.
Months
of Sales
SA %

Median
New House Sales Price USD
NSA

Month
∆%

Average New House Sales Price USD
NSA

Month
∆%

Nov 2016

5.1

305,400

0.9

359,900

1.5

Oct

5.2

302,700

-5.3

354,700

-4.3

Sep

5.0

319,800

5.8

370,600

1.6

Aug

5.2

302,400

2.5

364,700

2.7

Jul

4.6

295,000

-8.3

355,000

-2.6

Jun

5.2

321,600

8.6

364,300

4.1

May

5.1

296,000

-7.9

350,000

-7.9

Apr

5.1

321,300

3.2

380,000

3.3

Mar

5.5

311,400

0.0

367,700

5.2

Feb

5.5

311,300

6.9

349,400

-4.4

Jan

5.5

291,100

-2.6

365,600

2.1

Dec 2015

5.2

299,000

-5.7

358,100

-5.0

Nov

5.4

317,000

6.1

376,800

2.7

Oct

5.6

298,700

-2.9

366,900

-0.2

Sep

5.8

307,600

2.5

367,800

5.4

Aug

5.2

300,200

1.4

348,800

2.0

Jul

5.2

296,000

2.4

341,900

3.8

Jun

5.5

289,200

0.6

329,300

-3.4

May

5.0

287,400

-1.8

340,800

1.8

Apr

5.0

292,700

-0.2

334,700

-5.1

Mar

5.0

293,400

-0.2

352,700

-0.9

Feb

4.5

293,900

0.7

355,900

0.0

Jan

4.8

292,000

-3.3

356,000

-4.7

Dec 2014

5.1

302,000

-0.2

373,500

4.1

Nov

5.7

302,700

1.1

358,800

-6.6

Oct

5.3

299,400

14.5

384,000

20.3

Sep

5.3

261,500

-10.4

319,100

-10.4

Aug

5.5

291,700

4.0

356,200

3.2

Jul

6.1

280,400

-2.3

345,200

2.1

Jun

5.7

287,000

0.5

338,100

4.5

May

5.1

285,600

4.0

323,500

-0.5

Apr

5.7

274,500

-2.8

325,100

-1.9

Mar

5.6

282,300

5.2

331,500

1.7

Feb

5.3

268,400

-0.5

325,900

-3.4

Jan

5.1

269,800

-2.1

337,300

5.0

Dec 2013

5.1

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

5.4

262,200

0.9

329,900

7.8

Jun

4.1

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

4.1

257,500

-2.9

300,200

-3.9

Feb

4.2

265,100

5.4

312,500

1.8

Jan

4.0

251,500

-2.6

306,900

2.6

Dec 2012

4.5

258,300

5.4

299,200

2.9

Nov

4.6

245,000

-0.9

290,700

1.9

Oct

4.9

247,200

-2.9

285,400

-4.1

Sep

4.5

254,600

0.6

297,700

-2.6

Aug

4.6

253,200

6.7

305,500

8.2

Jul

4.6

237,400

2.1

282,300

3.9

Jun

4.8

232,600

-2.8

271,800

-3.2

May

4.7

239,200

1.2

280,900

-2.4

Apr

4.9

236,400

-1.4

287,900

1.5

Mar

4.9

239,800

0.0

283,600

3.5

Feb

4.8

239,900

8.2

274,000

3.1

Jan

5.3

221,700

1.4

265,700

1.1

Dec 2011

5.3

218,600

2.0

262,900

5.2

Nov

5.7

214,300

-4.7

250,000

-3.2

Oct

6.0

224,800

3.6

258,300

1.1

Sep

6.3

217,000

-1.2

255,400

-1.5

Aug

6.5

219,600

-4.5

259,300

-4.1

Jul

6.7

229,900

-4.3

270,300

-1.0

Jun

6.6

240,200

8.2

273,100

4.0

May

6.6

222,000

-1.2

262,700

-2.3

Apr

6.7

224,700

1.9

268,900

3.1

Mar

7.2

220,500

0.2

260,800

-0.8

Feb

8.1

220,100

-8.3

262,800

-4.7

Jan

7.3

240,100

-0.5

275,700

-5.5

Dec 2010

7.0

241,200

9.8

291,700

3.5

*Percent of new houses for sale relative to houses sold

Source: US Census Bureau

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-Nov of various years. Sales of new houses are higher in Jan-Nov 2016 relative to Jan-Nov 2015 with increase of 12.7 percent. Sales of new houses are higher in Jan-Nov 2016 relative to Jan-Nov 2014 with increase of 29.2 percent. Sales of new houses in Jan-Nov 2016 are substantially lower than in many years between 1971 and 2016 with the exception of the years from 2008 to 2015. There are only six other increases of 31.2 percent relative to Jan-Nov 2013, 53.1 percent relative to Jan-Nov 2012, 85.8 percent relative to Jan-Nov 2011, 74.6 percent relative to Jan-Nov 2010, 49.1 percent relative to Jan-Nov 2009 and 13.7 percent relative to Jan-Nov 2008. Sales of new houses in Jan-Nov 2016 are lower by 28.7 percent relative to Jan-Nov 2007, 46.8 percent relative to 2006, 56.4 percent relative to 2005 and 53.4 percent relative to 2004. The housing boom peaked in 2005 and 2006 when increases in fed funds rates to 5.25 percent in Jun 2006 from 1.0 percent in Jun 2004 affected subprime mortgages that were programmed for refinancing in two or three years on the expectation that price increases forever would raise home equity. Higher home equity would permit refinancing under feasible mortgages incorporating full payment of principal and interest (Gorton 2009EFM; see other references in http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html). Sales of new houses in Jan-Jul 2016 relative to the same period in 2003 fell 48.5 percent and 42.1 percent relative to the same period in 2002. Similar percentage declines are also for 2016 relative to years from 2000 to 2004. Sales of new houses in Jan-Nov 2016 fell 15.8 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 estimate of the US population is 418.8 million in 2015. The US population increased by 133.6 percent from 1960 to 2015. The final row of Table IIB-3 reveals catastrophic data: sales of new houses in Jan-Nov 2016 of 522 thousand units are lower by 3.0 percent relative to 538 thousand units of houses sold in Jan-Nov 1965, which is two years after data become available. The civilian noninstitutional population increased from 122.416 million in 1963 to 250.801 million in 2015, or 104.9 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-Nov 2016

522

Jan-Nov 2015

463

∆% Jan-Nov 2016/Jan-Nov 2015

12.7

Jan-Nov 2014

404

∆% Jan-Nov 2016/Jan-Nov 2014

29.2

Jan-Nov 2013

398

∆% Jan-Nov 2016/Jan-Nov 2013

31.2

Jan-Nov 2012

341

∆% Jan-Nov 2016/Jan-Nov 2012

53.1

Jan-Nov 2011

281

∆% Jan-Nov 2016/Jan-Nov 2011

85.8

Jan-Nov 2010

299

∆% Jan-Nov 2016/ 
Jan-Nov 2010

74.6

Jan-Nov 2009

350

∆% Jan-Nov 2016/ 
Jan-Nov 2009

49.1

Jan-Nov 2008

459

∆% Jan-Nov 2016/ 
Jan-Nov 2008

13.7

Jan-Nov 2007

732

∆% Jan-Nov 2016/
Jan-Nov 2007

-28.7

Jan-Nov 2006

981

∆% Jan-Nov 2016/Jan-Nov 2006

-46.8

Jan-Nov 2005

1196

∆% Jan-Nov 2016/Jan-Nov 2005

-56.4

Jan-Nov 2004

1120

∆% Jan-Nov 2016/Jan-Nov 2004

-53.4

Jan-Nov 2003

1013

∆% Jan-Nov 2016/
Jan-Nov  2003

-48.5

Jan-Nov 2002

902

∆% Jan-Nov 2016/
Jan-Nov 2002

-42.1

Jan-Nov 2001

843

∆% Jan-Nov 2016/
Jan-Nov 2001

-38.1

Jan-Nov 2000

812

∆% Jan-Nov 2016/
Jan-Nov 2000

-35.7

Jan-Nov 1995

620

∆% Jan-Nov 2016/
Jan-Nov 1995

-15.8

Jan-Nov 1965

538

∆% Jan-Nov 2016/
Jan-Nov 1965

-3.0

*Computed using unrounded data

Source: US Census Bureau

http://www.census.gov/construction/nrs/

The revised level of 306 thousand new houses sold in 2011 is the lowest since 560 thousand in 1963 in the 53 years of available data while the level of 368 thousand in 2012 is only higher than 323 thousand in 2010. The level of sales of new houses of 437 thousand in 2014 is the lowest from 1963 to 2009 with exception of 412 thousand in 1982 and 436 thousand in 1981. The population of the US increased 129.4 million from 179.3 million in 1960 to 308.7 million in 2010, or 72.2 percent. The estimate of the US population is 418.8 million in 2015. The US population increased 133.6 percent from 1960 to 2015. The civilian noninstitutional population increased from 122.416 million in 1963 to 250.801 million in 2015, or 104.9 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

Period

Sold During Period

1963

560

1964

565

1965

575

1966

461

1967

487

1968

490

1969

448

1970

485

1971

656

1972

718

1973

634

1974

519

1975

549

1976

646

1977

819

1978

817

1979

709

1980

545

1981

436

1982

412

1983

623

1984

639

1985

688

1986

750

1987

671

1988

676

1989

650

1990

534

1991

509

1992

610

1993

666

1994

670

1995

667

1996

757

1997

804

1998

886

1999

880

2000

877

2001

908

2002

973

2003

1,086

2004

1,203

2005

1,283

2006

1,051

2007

776

2008

485

2009

375

2010

323

2011

306

2012

368

2013

429

2014

437

2015

501

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, stability and new oscillating increase.

clip_image010

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 in Table IIB-5. The percentage change of new house sales from 1963 to 2015 is minus 10.5 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 2015 fell 24.9 percent relative to the same period in 1995 and 61.0 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

1963-2015

-10.5

NA

1991-2001

78.4

6.0

1995-2005

92.4

6.8

2000-2005

46.3

7.9

1995-2015

-24.9

NA

2000-2015

-42.9

NA

2005-2015

-61.0

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 Nov 2016 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.

clip_image011

Chart IIB-2, US, New Single-family Houses Sold, NSA, 1963-2016

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 2015 is in Table IIB-6. On a yearly basis, median and average prices reached a peak in 2007 and then fell substantially. There is recovery in 2012-2015.

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

Period

Median

Average

1963

$18,000

$19,300

1964

$18,900

$20,500

1965

$20,000

$21,500

1966

$21,400

$23,300

1967

$22,700

$24,600

1968

$24,700

$26,600

1969

$25,600

$27,900

1970

$23,400

$26,600

1971

$25,200

$28,300

1972

$27,600

$30,500

1973

$32,500

$35,500

1974

$35,900

$38,900

1975

$39,300

$42,600

1976

$44,200

$48,000

1977

$48,800

$54,200

1978

$55,700

$62,500

1979

$62,900

$71,800

1980

$64,600

$76,400

1981

$68,900

$83,000

1982

$69,300

$83,900

1983

$75,300

$89,800

1984

$79,900

$97,600

1985

$84,300

$100,800

1986

$92,000

$111,900

1987

$104,500

$127,200

1988

$112,500

$138,300

1989

$120,000

$148,800

1990

$122,900

$149,800

1991

$120,000

$147,200

1992

$121,500

$144,100

1993

$126,500

$147,700

1994

$130,000

$154,500

1995

$133,900

$158,700

1996

$140,000

$166,400

1997

$146,000

$176,200

1998

$152,500

$181,900

1999

$161,000

$195,600

2000

$169,000

$207,000

2001

$175,200

$213,200

2002

$187,600

$228,700

2003

$195,000

$246,300

2004

$221,000

$274,500

2005

$240,900

$297,000

2006

$246,500

$305,900

2007

$247,900

$313,600

2008

$232,100

$292,600

2009

$216,700

$270,900

2010

$221,800

$272,900

2011

$227,200

$267,900

2012

$245,200

$292,200

2013

$268,900

$324,500

2014

$282,800

$345,800

2015

$296,400

$360,600

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 in Table IIB-7. Prices rose sharply between 2000 and 2005. In fact, prices in 2015 are higher than in 2000. Between 2006 and 2015, median prices of new houses sold increased 20.2 percent and average prices increased 17.9 percent. Between 2014 and 2015, median prices increased 4.8 percent and average prices increased 4.3 percent.

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

 

Median New 
Home Sales Prices ∆%

Average New Home Sales Prices ∆%

∆% 2000 to 2003

15.4

19.0

∆% 2000 to 2005

42.5

43.5

∆% 2000 to 2015

75.4

74.2

∆% 2005 to 2015

23.0

21.4

∆% 2000 to 2006

45.9

47.8

∆% 2006 to 2015

20.2

17.9

∆% 2009 to 2015

36.8

33.1

∆% 2010 to 2015

33.6

32.1

∆% 2011 to 2015

30.5

34.6

∆% 2012 to 2015

20.9

23.4

∆% 2013 to 2015

10.2

11.1

∆% 2014 to 2015

4.8

4.3

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 Nov 2016. There is long-term sharp upward trend with few declines until the current collapse. Median prices increased sharply during the Great Inflation of the 1960s and 1970s and paused during the savings and loans crisis of the late 1980s and the recession of 1991. Housing subsidies throughout the 1990s caused sharp upward trend of median new house prices that accelerated after the fed funds rate of 1 percent from 2003 to 2004. There was sharp reduction of prices after 2006 with recovery recently above earlier prices.

clip_image012

Chart IIB-3, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2016

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 Nov 2016. 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.

clip_image013

Chart IIB-4, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1975-2016

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

clip_image014

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

Source: Board of Governors of the Federal Reserve System

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

Chart IIB-5A of the Board of Governors of the Federal Reserve System provides the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1954 to 2016. The Board of Governors of the Federal Reserve System discontinued the conventional mortgage rate in its data bank. The final data point is 0.41 percent for the fed funds rate in Nov 2016 and 2.86 percent for the thirty-year Treasury bond. The conventional mortgage rate stood at 3.77 percent in Nov 2016.

clip_image015

Chart IIB-5A, US, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 2001-2016

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 Aug 2016. 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 Nov 2016, the fed funds rate stood at 0.41 percent with increase to 2.86 percent of the 30-year yield and increase at 3.77 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 Aug 2016

 

Fed Funds Rate

Yield of Thirty Year Constant Maturity

Conventional Mortgage Rate

2012-12

0.16

2.88

3.35

2013-01

0.14

3.08

3.41

2013-02

0.15

3.17

3.53

2013-03

0.14

3.16

3.57

2013-04

0.15

2.93

3.45

2013-05

0.11

3.11

3.54

2013-06

0.09

3.40

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

0.1

3.42

4.16

2014-07

0.09

3.33

4.13

2014-08

0.09

3.2

4.12

2014-09

0.09

3.26

4.16

2014-10

0.09

3.04

4.04

2014-11

0.09

3.04

4.00

2014-12

0.12

2.83

3.86

2015-01

0.11

2.46

3.67

2015-02

0.11

2.57

3.71

2015-03

0.11

2.63

3.77

2015-04

0.12

2.59

3.67

2015-05

0.12

2.96

3.84

2015-06

0.13

3.11

3.98

2015-07

0.13

3.07

4.05

2015-08

0.14

2.86

3.91

2015-09

0.14

2.95

3.89

2015-10

0.12

2.89

3.80

2015-11

0.12

3.03

3.94

2015-12

0.24

2.97

3.96

2016-01

0.34

2.86

3.87

2016-02

0.38

2.62

3.66

2016-03

0.36

2.68

3.69

2016-04

0.37

2.62

3.61

2016-05

0.37

2.63

3.60

2016-06

0.38

2.45

3.57

2016-07

0.39

2.23

3.44

2016-08

0.40

2.26

3.44

2016-09

0.40

2.35

3.46

2016-10

0.40

2.50

3.47

2016-11

0.41

2.86

3.77

Source: Board of Governors of the Federal Reserve System

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

http://www.freddiemac.com/pmms/pmms30.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). The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). Table IIA2-1 provides the FHFA HPI for purchases only, which shows behavior similar to that of the Case-Shiller index but with lower magnitudes. House prices catapulted from 2000 to 2003, 2005 and 2006. From IVQ2000 to IVQ2006, the index for the US as a whole rose 55.0 percent, with 62.1 percent for New England, 72.0 percent for Middle Atlantic, 71.2 percent for South Atlantic but only by 33.1 percent for East South Central. Prices fell relative to 2014 for the US and all regions from 2006 with exception of increase of 2.6 percent for East South Central. Prices for the US increased 4.9 percent in IVQ2014 relative to IVQ2013 and 12.9 percent from IVQ2012 to IVQ2014. From IVQ2000 to IVQ2014, prices rose for the US and the four regions in Table IIA2-1.

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

 

United States

New England

Middle Atlantic

South Atlantic

East South Central

IVQ2000
to
IVQ2003

24.0

40.6

35.8

25.9

11.0

IVQ2000
to
IVQ2005

50.5

65.0

67.6

62.9

25.4

IVQ2000 to
IVQ2006

55.0

62.1

72.0

71.2

33.1

IVQ2005 to
IVQ2014

-1.5

-8.7

-2.3

-7.4

8.9

IVQ2006
to
IVQ2014

-4.4

-7.1

-4.8

-11.9

2.6

IVQ2007 to
IVQ2014

-1.9

-5.1

-5.0

-8.6

0.7

IVQ2011 to
IVQ2014

18.9

7.3

6.9

19.9

11.8

IVQ2012 to
IVQ2014

12.9

6.8

5.7

13.8

8.6

IVQ2013 to IVQ2014

4.9

2.5

2.2

5.1

4.2

IVQ2000 to
IVQ2014

48.3

144.27

50.6

138.40

63.7

127.30

50.9

140.28

36.6

146.07

Source: Federal Housing Finance Agency

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

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

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

 

West South Central

West North Central

East North Central

Mountain

Pacific

IVQ2000
to
IVQ2003

11.1

18.3

14.7

18.9

44.6

IVQ2000
to
IVQ2005

23.9

31.0

23.8

58.0

107.7

IVQ2000 to IVQ2006

31.6

33.7

23.7

68.6

108.7

IVQ2005 to
IVQ2014

26.6

4.7

-5.4

-2.6

-14.7

IVQ2006
to
IVQ2014

19.1

2.6

-5.4

-8.7

-15.1

IVQ2007 to
IVQ2014

15.2

3.2

-2.1

-5.6

-6.0

IVQ2011 to
IVQ2014

18.1

13.5

14.2

32.9

37.6

IVQ2012 to
IVQ2014

12.1

8.9

11.1

17.9

24.4

IVQ2013 to IVQ2014

5.9

4.0

4.6

5.5

7.3

IVQ2000 to IVQ2014

56.8

145.53

37.1

158.59

17.1

155.13

53.9

172.46

77.1

132.21

Source: Federal Housing Finance Agency

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

Monthly and 12-month percentage changes of the FHFA House Price Index are in Table IIA2-3. Percentage monthly increases of the FHFA index were positive from Apr to Jul 2011 with exception of declines in May and Aug 2011 while 12 months percentage changes improved steadily from around minus 6.0 percent in Mar to May 2011 to minus 4.4 percent in Jun 2011. The FHFA house price index fell 0.6 percent in Oct 2011 and fell 3.1 percent in the 12 months ending in Oct 2011. There was significant recovery in Nov 2011 with increase in the house price index of 0.5 percent and reduction of the 12-month rate of decline to 2.3 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 change of minus 0.3 percent in Jan 2012 and decline of the 12-month percentage change to minus 1.2 percent. The index improved to positive change of 0.3 percent in Feb 2012 and increase of 0.1 percent in the 12 months ending in Feb 2012. There was strong improvement in Mar 2012 with gain in prices of 0.9 percent and 2.1 percent in 12 months. The house price index of FHFA increased 0.6 percent in Apr 2012 and 2.5 percent in 12 months and improvement continued with increase of 0.6 percent in May 2012 and 3.4 percent in the 12 months ending in May 2012. Improvement consolidated with increase of 0.4 percent in Jun 2012 and 3.4 percent in 12 months. In Jul 2012, the house price index increased 0.2 percent and 3.3 percent in 12 months. Strong increase of 0.6 percent in Aug 2012 pulled the 12-month change to 4.2 percent. There was another increase of 0.6 percent in Oct and 5.2 percent in 12 months followed by increase of 0.5 percent in Nov 2012 and 5.2 percent in 12 months. The FHFA house price index increased 0.7 percent in Jan 2013 and 6.4 percent in 12 months. Improvement continued with increase of 0.5 percent in Apr 2013 and 7.1 percent in 12 months. In May 2013, the house price indexed increased 0.8 percent and 7.3 percent in 12 months. The FHFA house price index increased 0.6 percent in Jun 2013 and 7.6 percent in 12 months. In Jul 2013, the FHFA house price index increased 0.6 percent and 8.1 percent in 12 months. Improvement continued with increase of 0.4 percent in Aug 2013 and 7.9 percent in 12 months. In Sep 2013, the house price index increased 0.4 percent and 7.8 percent in 12 months. The house price index increased 0.5 percent in Oct 2013 and 7.7 percent in 12 months. In Nov 2013, the house price index changed 0.0 percent and increased 7.1 percent in 12 months. The house price index rose 0.6 percent in Dec 2013 and 7.3 percent in 12 months. Improvement continued with increase of 0.5 percent in Jan 2014 and 7.1 percent in 12 months. In Feb 2014, the house price index increased 0.4 percent and 6.8 percent in 12 months. The house price index increased 0.4 percent in Mar 2014 and 6.2 percent in 12 months. In Apr 2014, the house price index increased 0.2 percent and increased 5.8 percent in 12 months. The house price index increased 0.3 percent in May 2014 and 5.2 percent in 12 months. In Jun 2014, the house price index increased 0.4 percent and 5.0 percent in 12 months. The house price index increased 0.4 percent in Jul 2014 and 4.8 percent in 12 months. In Sep 2014, the house price index increased 0.2 percent and increased 4.5 percent in 12 months. The house price index increased 0.6 percent in Oct 2014 and 4.6 percent in 12 months. In Nov 2014, the house price index increased 0.5 percent and 5.1 percent in 12 months. The house price index increased 0.8 percent in Dec 2014 and increased 5.4 percent in 12 months. In Feb 2015, the house price index increased 0.8 percent and increased 5.4 percent in 12 months. The house price index increased 0.3 percent in Mar 2015 and 5.3 percent in 12 months. In Apr 2015, the house price index increased 0.6 percent and 5.6 percent in 12 months. The house price index increased 0.5 percent in May 2015 and 5.8 percent in 12 months. House prices increased 0.4 percent in Jun 2015 and 5.7 percent in 12 months. The house price index increased 0.5 percent in Jul 2015 and increased 5.8 percent in 12 months. House prices increased 0.3 percent in Aug 2015 and increased 5.5 percent in 12 months. In Sep 2015, the house price index increased 0.8 percent and increased 6.1 percent in 12 months. The house price index increased 0.4 percent in Oct 2015 and increased 6.0 percent in 12 months. House prices increased 0.7 percent in Nov 2015 and increased 6.1 percent in 12 months. The house price index increased 0.5 percent in Dec 2015 and increased 5.9 percent in 12 months. House prices increased 0.4 percent in Jan 2016 and increased 6.2 percent in 12 months. The house price index increased 0.5 percent in Feb 2016 and increased 5.9 percent in 12 months. House prices increased 0.9 percent in Mar 2016 and increased 6.5 percent in 12 months. The house price index increased 0.2 percent in Apr 2016 and increased 6.1 percent in 12 months. House prices increased 0.3 percent in May 2016 and increased 5.9 percent in 12 months. The house price index increased 0.4 percent in Jun 2016 and increased 5.9 percent in 12 months. House prices increased 0.5 percent in Jul 2016 and increased 6.0 percent in 12 months. The house price index increased 0.7 percent in Aug 2016 and increased 6.4 percent in 12 months. House prices increased 0.6 percent in Sep 2016 and increased 6.3 percent in 12 months. The house price index increased 0.4 percent in Oct 2016 and increased 6.2 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

Oct 2016

0.4

6.2

Sep

0.6

6.3

Aug

0.7

6.4

Jul

0.5

6.0

Jun

0.4

5.9

May

0.3

5.9

Apr

0.2

6.1

Mar

0.8

6.5

Feb

0.5

5.9

Jan

0.4

6.2

Dec 2015

0.5

5.9

Nov

0.7

6.1

Oct

0.4

6.0

Sep

0.8

6.1

Aug

0.3

5.5

Jul

0.5

5.8

Jun

0.4

5.7

May

0.5

5.8

Apr

0.6

5.6

Mar

0.3

5.3

Feb

0.8

5.4

Jan

0.1

5.0

Dec 2014

0.8

5.4

Nov

0.5

5.1

Oct

0.6

4.6

Sep

0.2

4.5

Aug

0.5

4.8

Jul

0.4

4.8

Jun

0.4

5.0

May

0.3

5.2

Apr

0.2

5.8

Mar

0.4

6.2

Feb

0.4

6.8

Jan

0.5

7.1

Dec 2013

0.6

7.3

Nov

0.0

7.1

Oct

0.5

7.7

Sep

0.4

7.8

Aug

0.4

7.9

Jul

0.6

8.1

Jun

0.6

7.6

May

0.8

7.3

Apr

0.5

7.1

Mar

1.2

7.2

Feb

0.6

6.8

Jan

0.7

6.4

Dec 2012

0.5

5.3

Nov

0.5

5.2

Oct

0.6

5.2

Sep

0.5

4.0

Aug

0.6

4.2

Jul

0.2

3.3

Jun

0.4

3.4

May

0.6

3.4

Apr

0.6

2.5

Mar

0.9

2.1

Feb

0.3

0.1

Jan

-0.3

-1.2

Dec 2011

0.4

-1.2

Nov

0.5

-2.3

Oct

-0.6

-3.1

Sep

0.6

-2.3

Aug

-0.3

-3.8

Jul

0.3

-3.5

Jun

0.4

-4.4

May

-0.3

-5.9

Apr

0.2

-5.7

Mar

-0.9

-5.9

Feb

-1.1

-5.1

Jan

-0.4

-4.5

Dec 2010

  

-3.9

Dec 2009

  

-2.0

Dec 2008

  

-10.3

Dec 2007

  

-3.3

Dec 2006

  

2.4

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

http://www.fhfa.gov/DataTools

The bottom part of Table IIA2-3 provides 12-month percentage changes of the FHFA house price index since 1992 when data become available for 1991. Table IIA2-4 provides percentage changes and average rates of percent change per year for various periods. Between 1992 and 2015, the FHFA house price index increased 118.2 percent at the yearly average rate of 3.5 percent. In the period 1992-2000, the FHFA house price index increased 39.3 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 7.4 percent in 2003-2006. House prices measured by the FHFA house price index increased 1.7 percent at the average yearly rate of 0.2 percent between 2006 and 2015 and 4.2 percent between 2005 and 2015 at the average yearly rate of 0.4 percent.

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

1992-2015

118.2

3.5

1992-2000

39.3

4.2

2000-2003

24.2

7.5

2000-2005

50.3

8.5

2003-2005

21.0

10.0

2005-2015

4.2

0.4

2000-2006

54.0

7.5

2003-2006

23.9

7.4

2006-2015

1.7

0.2

Source: Federal Housing Finance Agency

http://www.fhfa.gov/DataTools

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, 2014, 2015 and IIIQ2016. The contraction caused a strong shock to US wealth. Assets fell from $80.9 trillion in 2007 to $76.9 trillion in 2011 even after nine consecutive quarters of growth beginning in IIIQ2009 (http://cmpassocregulationblog.blogspot.com/2016/08/and-as-ever-economic-outlook-is.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/business-fixed-investment-has-been-soft.html), for decline of $4.0 trillion or 4.9 percent. Assets stood at $101.7 trillion in 2015 for gain of $20.8 trillion relative to $80.9 trillion in 2007 or increase by 25.7 percent. Assets increased to $105.1 trillion in IIIQ2016 by $24.2 trillion relative to 2007 or 30.0 percent. Liabilities declined from $14.4 trillion in 2007 to $13.6 trillion in 2011 or by $752.8 billion equivalent to decline by 5.2 percent. Liabilities increased $182.7 billion or 1.3 percent from 2007 to 2015. Liabilities increased from $14.4 trillion in 2007 to $14.9 trillion in IIIQ2016, by $513.8 billion or increase of 3.6 percent. Net worth shrank from $66.5 trillion in 2007 to $63.3 trillion in 2011, that is, $3.2 trillion equivalent to decline of 4.8 percent. Net worth increased from $66,464.0 billion in 2007 to $90,196.1 billion in IIIQ2016 by $23,732.1 billion or 35.7 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 241.428 in Sep 2016 (http://www.bls.gov/cpi/data.htm) or 14.9 percent. Net worth adjusted by CPI inflation increased 18.1 percent from 2007 to IIIQ2016. Nonfinancial assets increased $3890.3 billion from $28,074.5 billion in 2007 to $31,964.8 billion in IIIQ2016 or 13.9 percent. There was increase from 2007 to IIIQ2016 of $2844.8 billion in real estate assets or by 12.2 percent. Real estate assets adjusted for CPI inflation fell 2.4 percent between 2007 and IIIQ2016. 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

2014

2015

IIIQ2016

Assets

80,860.2

97,976.5

101,696.8

105,106.1

Nonfinancial

28,074.5

28,706.7

30,473.6

31,964.8

  Real Estate

23,265.0

23,200.5

24,766.9

26,109.8

  Durable Goods

  4,476.0

5,052.9

  5,236.8

5,373.2

Financial

52,785.7

69,269.8

71,223.2

73,141.2

  Deposits

  7,562.3

10,145.9

  10,737.7

11,126.0

  Debt Secs.

  4,080.6

3,993.1

  4,440.0

3,733.7

  Mutual Fund Shares

   4,314.9

6,726.3

   6,504.4

6,875.2

  Equities Corporate

   10,046.8

14,356.7

   14,159.8

14,748.4

  Equity Noncorporate

   8,815.5

10,097.5

   10,829.4

11,174.0

  Pension

15,073.4

20,658.6

21,247.6

22,087.5

Liabilities

14,396.1

14,232.5

14,578.8

14,909.9

  Home Mortgages

10,613.1

9,461.1

  9,547.2

9,707.5

  Consumer Credit

   2,609.9

3,318.0

   3,535.7

3,696.0

Net Worth

66,464.0

83,744.0

87,118.0

90,196.1

Net Worth = Assets – Liabilities

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 IIA-1 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 96.0 percent in the 10-city composite of the Case-Shiller home price index, 81.2 percent in the 20-city composite and 65.9 percent in the US national home price index between Oct 2000 and Oct 2005. Prices rose around 100 percent from Oct 2000 to Oct 2006, increasing 101.1 percent for the 10-city composite, 86.7 percent for the 20-city composite and 70.9 percent in the US national index. House prices rose 38.9 percent between Oct 2003 and Oct 2005 for the 10-city composite, 34.9 percent for the 20-city composite and 29.6 percent for the US national propelled by low fed funds rates of 1.0 percent between Oct 2003 and Oct 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Oct 2004 until Oct 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 Oct 2003 and Oct 2006, the 10-city index gained 42.5 percent; the 20-city index increased 39.0 percent; and the US national 33.4 percent. House prices have fallen from Oct 2006 to Oct 2016 by 8.5 percent for the 10-city composite and 6.6 percent for the 20-city composite, increasing 0.5 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 Oct 2016, house prices increased 4.3 percent in the 10-city composite, increasing 5.1 percent in the 20-city composite and 5.6 percent in the US national. Table IIA-1 also shows that house prices increased 83.9 percent between Oct 2000 and Oct 2016 for the 10-city composite, increasing 74.3 percent for the 20-city composite and 71.8 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 9.2 percent from the peak in Jun 2006 to Oct 2016 and the 20-city composite fell 7.1 percent from the peak in Jul 2006 to Oct 2016. The US national increased 0.3 percent from the peak of the 10-city composite to Jun 2016 and 0.2 percent from the peak of the 20-city composite to Jul 2016. 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 2015 for the 10-city composite was 3.8 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 to Dec 2015 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 2015 was 3.7 percent while the rate of the 20-city composite was 3.3 percent and 3.2 percent for the US national.

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

 

10-City Composite

20-City Composite

US National

∆% Oct 2000 to Oct 2003

41.1

34.3

28.1

∆% Oct 2000 to Oct 2005

96.0

81.2

65.9

∆% Oct 2003 to Oct 2005

38.9

34.9

29.6

∆% Oct 2000 to Oct 2006

101.1

86.7

70.9

∆% Oct 2003 to Oct 2006

42.5

39.0

33.4

∆% Oct 2005 to Oct 2016

-6.2

-3.8

3.5

∆% Oct 2006 to Oct 2016

-8.5

-6.6

0.5

∆% Oct 2009 to Oct 2016

29.5

30.9

24.5

∆% Oct 2010 to Oct 2016

29.4

32.1

29.3

∆% Oct 2011 to Oct 2016

33.9

36.9

33.7

∆% Oct 2012 to Oct 2016

29.6

31.3

28.5

∆% Oct 2013 to Oct 2016

14.0

15.6

15.9

∆% Oct 2014 to Oct 2016

9.3

10.7

10.8

∆% Oct 2015 to Oct 2016

4.3

5.1

5.6

∆% Oct 2000 to Oct 2016

83.9

74.3

71.8

∆% Peak Jun 2006 Oct 2016

-9.2

  

0.3

∆% Peak Jul 2006 Oct 2016

  

-7.1

0.2

Average ∆% Dec 1987-Dec 2015

3.8

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 2015

3.7

3.3

3.2

Source: http://us.spindices.com/index-family/real-estate/sp-corelogic-case-shiller

Price increases measured by the Case-Shiller house price indices show in data for Oct 2016 that “home prices continued their rise across the country over the last 12 months” (https://www.spice-indices.com/idpfiles/spice-assets/resources/public/documents/461749_cshomeprice-release-1227.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 IIA-2. 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 Mar through Apr 2012, house prices seasonally adjusted declined in most months for both the 10-city and 20-city Case-Shiller composites from Dec 2010 to Jan 2012, as shown in Table IIA-2. 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 changed 0.0 percent in Oct 2016 and the 20-city increased 0.1 percent. The 10-city SA increased 0.6 percent in Oct 2016 and the 20-city composite SA increased 0.6 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses.

Table IIA-2, US, Monthly Percentage Change of S&P 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

Oct 2016

0.6

0.0

0.6

0.1

Sep

0.4

0.1

0.5

0.1

Aug

0.3

0.3

0.3

0.4

Jul

0.0

0.5

0.1

0.5

Jun

-0.1

0.7

0.0

0.8

May

-0.2

0.8

-0.1

0.9

Apr

-0.3

1.0

-0.3

1.1

Mar

1.1

0.9

1.1

1.0

Feb

0.5

0.2

0.6

0.2

Jan

0.6

-0.1

0.7

0.0

Dec 2015

0.5

-0.1

0.6

0.0

Nov

0.8

0.0

0.8

0.0

Oct

0.5

-0.1

0.6

0.0

Sep

0.4

0.1

0.5

0.1

Aug

0.2

0.2

0.2

0.3

Jul

0.1

0.6

0.1

0.7

Jun

0.1

0.9

0.1

1.0

May

0.1

1.0

0.1

1.1

Apr

-0.3

1.1

-0.3

1.1

Mar

1.0

0.8

1.1

0.9

Feb

0.9

0.5

0.9

0.5

Jan

0.6

-0.1

0.6

-0.1

Dec 2014

0.7

0.0

0.7

0.0

Nov

0.5

-0.3

0.6

-0.2

Oct

0.5

-0.1

0.5

-0.1

Sep

0.2

-0.1

0.3

-0.1

Aug

0.1

0.2

0.1

0.2

Jul

0.0

0.6

0.0

0.6

Jun

0.1

1.0

0.1

1.0

May

0.0

1.1

0.1

1.1

Apr

-0.2

1.1

-0.2

1.2

Mar

1.0

0.8

1.0

0.9

Feb

0.5

0.0

0.5

0.0

Jan

0.7

-0.1

0.6

-0.1

Dec 2013

0.6

-0.1

0.6

-0.1

Nov

0.8

0.0

0.8

-0.1

Oct

0.9

0.2

0.9

0.2

Sep

1.0

0.7

1.1

0.7

Aug

1.2

1.3

1.2

1.3

Jul

1.1

1.9

1.1

1.8

Jun

1.2

2.2

1.1

2.2

May

1.3

2.5

1.3

2.5

Apr

1.4

2.6

1.4

2.6

Mar

1.5

1.3

1.5

1.3

Feb

1.0

0.3

0.9

0.2

Jan

0.8

0.0

0.9

0.0

Dec 2012

0.9

0.2

0.9

0.2

Nov

0.6

-0.3

0.7

-0.2

Oct

0.6

-0.2

0.7

-0.1

Sep

0.6

0.3

0.6

0.3

Aug

0.6

0.8

0.7

0.9

Jul

0.6

1.5

0.7

1.6

Jun

1.0

2.1

1.1

2.3

May

1.0

2.2

1.1

2.4

Apr

0.3

1.4

0.4

1.4

Mar

0.2

-0.1

0.2

0.0

Feb

-0.1

-0.9

0.0

-0.8

Jan

-0.3

-1.1

-0.2

-1.0

Dec 2011

-0.5

-1.2

-0.4

-1.1

Nov

-0.6

-1.4

-0.5

-1.3

Oct

-0.6

-1.3

-0.6

-1.4

Sep

-0.3

-0.6

-0.4

-0.7

Aug

-0.2

0.1

-0.2

0.1

Jul

-0.1

0.9

0.0

1.0

Jun

-0.1

1.0

-0.1

1.2

May

-0.2

1.0

-0.2

1.0

Apr

-0.3

0.6

-0.2

0.6

Mar

-0.6

-1.0

-0.7

-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-corelogic-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 $10.5 trillion or 13.0 percent from 2007 to 2008 and $8.9 trillion or 11.1 percent to 2009. Net worth fell $10.4 trillion from 2007 to 2008 or 15.7 percent and $8.7 trillion to 2009 or 13.0 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

80,860.2

70,342.9

-10,517.3

71,919.9

-8,940.3

Non
FIN

28,074.5

24,388.3

-3,686.2

23,399.0

-4,675.5

RE

23,265.0

19,454.1

-3,810.9

18,442.6

-4,822.4

FIN

52,785.7

45,954.5

-6,831.2

48,520.9

-4,264.8

LIAB

14,396.1

14,296.1

-100.0

14,108.6

-287.5

NW

66,464.0

56,046.8

-10,417.2

57,811.3

-8,652.7

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. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. 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 68.7 percent of GDP in IIIQ2016 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IIIQ2016, real estate increased in value by $2844.8 billion and financial assets increased $20,355.5 billion for net gain of real estate and financial assets of $23,200.3 billion, explaining most of the increase in net worth of $23,732.1 billion obtained by deducting the increase in liabilities of $513.8 billion from the increase of assets of $22,245.9 billion. Net worth increased from $66,464.0 billion in 2007 to $90,196.1 billion in IIIQ2016 by $23,732.1 billion or 35.7 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 241.428 in Sep 2016 (http://www.bls.gov/cpi/data.htm) or 14.9 percent. Net worth adjusted by CPI inflation increased 18.1 percent from 2007 to IIQ2016. Real estate assets adjusted for CPI inflation fell 2.3 percent from 2007 to IIIQ2016. There are multiple complaints that unconventional monetary policy concentrates income on wealthier individuals because of their holdings of financial assets while the middle class has gained less because of fewer holdings of financial assets and higher share of real estate in family wealth. There is nothing new in these arguments. Interest rate ceilings on deposits and loans have been commonly used. The Banking Act of 1933 imposed prohibition of payment of interest on demand deposits and ceilings on interest rates on time deposits. These measures were justified by arguments that the banking panic of the 1930s was caused by competitive rates on bank deposits that led banks to engage in high-risk loans (Friedman, 1970, 18; see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 74-5). The objective of policy was to prevent unsound loans in banks. Savings and loan institutions complained of unfair competition from commercial banks that led to continuing controls with the objective of directing savings toward residential construction. Friedman (1970, 15) argues that controls were passive during periods when rates implied on demand deposit were zero or lower and when Regulation Q ceilings on time deposits were above market rates on time deposits. The Great Inflation or stagflation of the 1960s and 1970s changed the relevance of Regulation Q. Friedman (1970, 26-7) predicted the future:

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

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

“These are the people who have the fewest alternative ways to invest their limited assets and are least sophisticated about the alternatives.” Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. 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 IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_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 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). 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 IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 23.6 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.0 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 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, 2015 and IQ2016

 

Value 2007

Change to 2014

Change to 2015

Change to IIIQ2016

Assets

80,860.2

17,116.3

20,836.6

22,245.9

Nonfinancial

28,074.5

632.2

2,399.1

3,890.3

Real Estate

23,265.0

-64.5

1,501.9

2,844.8

Financial

52,785.7

16,484.1

18,437.5

20,355.5

Liabilities

14,396.1

-163.6

182.7

513.8

Net Worth

66,464.0

17,280.0

20,654.0

23,732.1

Net Worth = Assets – Liabilities

Source: Net Worth = Assets – Liabilities

Source: Board of Governors of the Federal Reserve System. 2016. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2016. Washington, DC, Federal Reserve System, Dec 8. http://www.federalreserve.gov/releases/z1/.

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

Posted by at

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)