Accelerating Inflation Throughout the World, Rules, Discretionary Authorities and Slow Productivity Growth, United States Housing, United States Current Account Deficit of Balance of Payments at 3.6 Percent of GDP in IVQ2021 and 3.8 Percent of GDP in IIIQ2021, World Inflation Waves, Stagflation Risk, Worldwide Fiscal, Monetary and External Imbalances, World Cyclical Slow Growth, and Government Intervention in Globalization
Carlos M. Pelaez
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022.
I Rules, Discretionary Authorities and Slow Productivity Growth
IIA United States Housing Collapse
IIA1 Sales of New Houses
IIA2 United States House Prices
II United States Current Account of Balance of Payments
I World Inflation Waves
IA Appendix: Transmission of Unconventional Monetary Policy
IB1 Theory
IB2 Policy
IB3 Evidence
IB4 Unwinding Strategy
IC United States Inflation
IC Long-term US Inflation
ID Current US Inflation
IE Theory and Reality of Economic History, Cyclical Slow Growth Not Secular Stagnation and Monetary Policy Based on Fear of Deflation
III World Financial Turbulence
IV Global Inflation
V World Economic Slowdown
VA United States
VB Japan
VC China
VD Euro Area
VE Germany
VF France
VG Italy
VH United Kingdom
VI Valuation of Risk Financial Assets
VII Economic Indicators
VIII Interest Rates
IX Conclusion
References
Appendixes
Appendix I The Great Inflation
IIIB Appendix on Safe Haven Currencies
IIIC Appendix on Fiscal Compact
IIID Appendix on European Central Bank Large Scale Lender of Last Resort
IIIG Appendix on Deficit Financing of Growth and the Debt Crisis
Preamble. United States total public debt outstanding is $30.3 trillion and debt held by the public $23.8 trillion (https://fiscaldata.treasury.gov/datasets/debt-to-the-penny/debt-to-the-penny). The Net International Investment Position of the United States, or foreign debt, is $16.1 trillion (https://www.bea.gov/sites/default/files/2021-12/intinv321.pdf https://cmpassocregulationblog.blogspot.com/2022/01/increase-in-dec-2021-of-nonfarm-payroll.html). The United States current account deficit is 3.6 percent of GDP in IVQ2021 (Section II https://www.bea.gov/sites/default/files/2022-03/trans421.pdf). The Treasury deficit of the United States reached $2.8 trillion in fiscal year 2021 (https://fiscal.treasury.gov/reports-statements/mts/). Total assets of Federal Reserve Banks reached $9.0 trillion on Mar 23, 2022 and securities held outright reached $8.5 trillion (https://www.federalreserve.gov/releases/h41/current/h41.htm#h41tab1). US GDP nominal NSA reached $24.0 trillion in IVQ2021 (https://apps.bea.gov/iTable/index_nipa.cfm). Total Treasury interest-bearing, marketable debt held by private investors increased from $3635 billion in 2007 to $16,439 billion in Sep 2021 (Fiscal Year 2021) or increase by 352.2 percent (https://fiscal.treasury.gov/reports-statements/treasury-bulletin/).
Chart CPI-H provides 12 months percentage changes of the US Consumer Price Index from 1982 to 2022. The increase of 7.9 percent of the US CPI in the 12 months ending in Feb 2022 is the highest since 8.4 percent in Jan 1982 in the beginning adjustment from the Great Inflation.
Chart CPI-H, US, Consumer Price Index, 12-Month Percentage Change, NSA, 1982-2022
Source: US Bureau of Labor Statistics https://www.bls.gov/cpi/data.htm
Chart VII-4 of the Energy Information Administration provides the price of the Natural Gas Futures Contract increasing from $2.581 on Jan 4, 2021 to $5.187 per million Btu on Mar 22, 2022 or 101.0 percent.
Chart VII-4, US, Natural Gas Futures Contract 1
Source: US Energy Information Administration
https://www.eia.gov/dnav/ng/hist/rngc1d.htm
Chart VII-5 of the US Energy Administration provides US field production of oil decreasing from a peak of 12,966 thousand barrels per day in Nov 2019 to the final point of 11.567 thousand barrels per day in Dec 2021.
Chart VII-5, US, US, Field Production of Crude Oil, Thousand Barrels Per Day
Source: US Energy Information Administration
https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=MCRFPUS2&f=M
Chart VI-6 of the US Energy Information Administration provides imports of crude oil. Imports increased from 245,369 thousand barrels per day in Jan 2021 to 265,228 thousand in Dec 2021.
Chart VII-6, US, US, Imports of Crude Oil and Petroleum Products, Thousand Barrels
Source: US Energy Information Administration
https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=MTTIMUS1&f=M
Chart VI-7 of the EIA provides US Petroleum Consumption, Production, Imports, Exports and Net Imports 1950-2020. There was sharp increase in production in the final segment that reached consumption in 2020.
Chart VI-7, US Petroleum Consumption, Production, Imports, Exports and Net Imports 1950-2020, Million Barrels Per Day
https://www.eia.gov/energyexplained/oil-and-petroleum-products/imports-and-exports.php
I Rules, Discretionary Authorities and Slow Productivity Growth. The Bureau of Labor Statistics (BLS) of the Department of Labor provides the quarterly report on productivity and costs. The operational definition of productivity used by the BLS is (https://www.bls.gov/news.release/pdf/prod2.pdf 1): “Labor productivity, or output per hour, is calculated by dividing an index of real output by an index of hours worked by all persons, including employees, proprietors, and unpaid family workers.” The BLS has revised the estimates for productivity and unit costs. Table II-1 provides estimates for IVQ2021 and revision of the estimates for IIIQ2021 and IIQ2021 together with data for nonfarm business sector productivity and unit labor costs in seasonally adjusted annual equivalent (SAAE) rate and the percentage change from the same quarter a year earlier. Reflecting increase in output at 9.1 percent and increase at 2.4 percent in hours worked, nonfarm business sector labor productivity decreased at the SAAE rate of 6.6 percent in IVQ2021, as shown in column 2 “IVQ2021 SAEE.” The change of labor productivity from IVQ2020 to IVQ2021 was 1.9 percent, reflecting increase in output of 7.0 percent and increase of hours worked of 4.9 percent, as shown in column 3 “IVQ2021 YOY.” Hours worked increased from 5.1 percent in IIQ2021 to 6.2 percent in IIIQ2021 and increased at 2.4 percent in IVQ2021 while output growth decreased from 8.5 percent in IIQ2021 at SAAE to 2.0 percent in IIIQ2021, increasing at 9.1 percent in IVQ2021. The BLS defines unit labor costs as (https://www.bls.gov/news.release/pdf/prod2.pdf 1): “BLS calculates unit labor costs as the ratio of hourly compensation to labor productivity. Increases in hourly compensation tend to increase unit labor costs and increases in productivity, output per hour, tend to reduce them.” Unit labor costs increased at the SAAE rate of 0.9 percent in IVQ2021 and increased 3.5 percent in IVQ2021 relative to IVQ2020. Hourly compensation increased at the SAAE rate of 7.5 percent in IVQ2021, which deflating by the estimated inflation increase SAAE rate in IVQ2021 results in decrease of real hourly compensation at 0.3 percent. Real hourly compensation decreased 1.1 percent in IVQ2021 relative to IVQ2020.
Table II-1, US, Nonfarm Business Sector Productivity and Costs %
| IVQ2021 SAAE | IVQ2021 YOY | IIIQ2021 SAAE | IIIQ2021 YOY | IIQ2021 SAAE | IIQ2021 YOY | |
| Productivity | 6.6 | 1.9 | -3.9 | -0.4 | 3.2 | 2.1 |
| Output | 9.1 | 7.0 | 2.0 | 6.2 | 8.5 | 15.9 |
| Hours | 2.4 | 4.9 | 6.2 | 6.7 | 5.1 | 13.5 |
| Hourly | 7.5 | 5.5 | 6.2 | 6.2 | 9.2 | 3.4 |
| Real Hourly Comp. | -0.3 | -1.1 | -0.4 | 0.9 | 0.9 | -1.4 |
| Unit Labor Costs | 0.9 | 3.5 | 10.6 | 6.7 | 5.8 | 1.2 |
| Unit Nonlabor Payments | 18.6 | 10.0 | -0.4 | 1.2 | 6.6 | 8.4 |
| Implicit Price Deflator | 7.8 | 6.1 | 6.0 | 4.4 | 6.1 | 4.1 |
Notes: SAAE: seasonally adjusted annual equivalent; Comp.: compensation; YoY: Quarter on Same Quarter Year Earlier
The analysis by Kydland (https://www.nobelprize.org/prizes/economic-sciences/2004/kydland/biographical/) and Prescott (https://www.nobelprize.org/prizes/economic-sciences/2004/prescott/biographical/) (1977, 447-80, equation 5) uses the “expectation augmented” Phillips curve with the natural rate of unemployment of Friedman (1968) and Phelps (1968), which in the notation of Barro and Gordon (1983, 592, equation 1) is:
Ut = Unt – α(πt – πe) α > 0 (1)
Where Ut is the rate of unemployment at current time t, Unt is the natural rate of unemployment, πt is the current rate of inflation and πe is the expected rate of inflation by economic agents based on current information. Equation (1) expresses unemployment net of the natural rate of unemployment as a decreasing function of the gap between actual and expected rates of inflation. The system is completed by a social objective function, W, depending on inflation, π, and unemployment, U:
W = W(πt, Ut) (2)
The policymaker maximizes the preferences of the public, (2), subject to the constraint of the tradeoff of inflation and unemployment, (1). The total differential of W set equal to zero provides an indifference map in the Cartesian plane with ordered pairs (πt, Ut - Un) such that the consistent equilibrium is found at the tangency of an indifference curve and the Phillips curve in (1). The indifference curves are concave to the origin. The consistent policy is not optimal. Policymakers without discretionary powers following a rule of price stability would attain equilibrium with unemployment not higher than with the consistent policy. The optimal outcome is obtained by the rule of price stability, or zero inflation, and no more unemployment than under the consistent policy with nonzero inflation and the same unemployment. Taylor (1998LB) attributes the sustained boom of the US economy after the stagflation of the 1970s to following a monetary policy rule instead of discretion (see Taylor 1993, 1999). Professor John B. Taylor (2014Jul15, 2014Jun26) building on advanced research (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB, 2015, 2012 Oct 25; 2013Oct28, 2014 Jan01, 2014Jan3, 2014Jun26, 2014Jul15, 2015, 2016Dec7, 2016Dec20 http://www.johnbtaylor.com/) finds that a monetary policy rule would function best in promoting an environment of low inflation and strong economic growth with stability of financial markets. There is strong case for using rules instead of discretionary authorities in monetary policy (http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). It is not uncommon for effects of regulation differing from those intended by policy. Professors Edward C. Prescott and Lee E. Ohanian (2014Feb), writing on “US productivity growth has taken a dive,” on Feb 3, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303942404579362462611843696?KEYWORDS=Prescott), argue that impressive productivity growth over the long-term constructed US prosperity and wellbeing. Prescott and Ohanian (2014Feb) measure US productivity growth at 2.5 percent per year since 1948. Average US productivity growth has been only 1.1 percent since 2011. Prescott and Ohanian (2014Feb) argue that living standards in the US increased at 28 percent in a decade but with current slow growth of productivity will only increase 12 percent by 2024. There may be collateral effects on productivity growth from policy design similar to those in Kydland and Prescott (1977). Professor Edward P. Lazear (2017Feb27), writing in the Wall Street Journal, on Feb 27, 2017 (https://www.wsj.com/articles/how-trump-can-hit-3-growthmaybe-1488239746), finds that productivity growth was 7 percent between 2009 and 2016 at annual equivalent 1 percent. Lazear measures productivity growth at 2.3 percent per year from 2001 to 2008. Herkenhoff, Ohanian and Prescott (2017) and Ohanian and Prescott (2017Dec) analyze how restriction of land use by states in the United States have been depressing economic activity. Professor Edmund S. Phelps (https://www.nobelprize.org/prizes/economic-sciences/2006/phelps/auto-biography/) argues that there is failed analysis that fiscal stimulus in the form of higher government expenditures and tax reductions caused the recovery of the economy to normal levels by 2017 (Phelps, Edmund S. 2018. The fantasy of fiscal stimulus. The Wall Street Journal Oct 29, 2018 https://www.wsj.com/articles/the-fantasy-of-fiscal-stimulus-1540852299?mod=searchresults&page=1&pos=2). The evidence analyzed by Phelps leads to the conclusion that countries with disorderly government finance grew less rapidly than those with sounder fiscal performance. Phelps concludes convincingly that “there is a strong relationship between the speed of recovery and a proxy of its dynamism—the long-term growth rate of total factor productivity from 1990 to 2007. Some countries have preexisting social institutions and cultural capital that enables them to bounce back from an economic downturn. Much credit of the U.S.’s relatively speedy recovery is owed to this country’s endemic culture of innovation and enterprise.” Professor Edward P. Lazear, writing on “Mind the productivity gap to reduce inequality,” published in the Wall Street Journal on May 6, 2019 (https://www.wsj.com/articles/mind-the-productivity-gap-to-reduce-inequality-11557181134?mod=searchresults&page=1&pos=1), analyzes the causes of the growing differential of wages between the income of the 90th percentile and the 50th percentile in terms of technological change. The improvement of the lower half of wage earners would consist of increasing their skills. Professors John F. Cogan and John B. Taylor, writing in the Wall Street Journal on Oct 6, 2020, measure productivity growth increasing from 0.8 percent per year in 2013-2016 to 1.5 percent per year in 2016-2019 because of deregulation and market-oriented policies. The Bureau of Labor Statistics important report on productivity and costs released on Mar 3, 2022 (https://www.bls.gov/lpc/) supports the argument of decline of productivity growth in the US analyzed by Prescott and Ohanian (2014Feb), Lazear (2017Feb27), Phelps (2018) and Cogan and Taylor (2020Oct6). Table II-2 provides the annual percentage changes of productivity, real hourly compensation and unit labor costs for the entire economic cycle from 2007 to 2021. The estimates incorporate the yearly revision of the US national accounts (https://www.bea.gov/information-updates-national-income-and-product-accounts) and the comprehensive revisions since 1929 (https://apps.bea.gov/national/pdf/2018-ComprehensiveUpdate-Results.pdf). The data confirm the argument of Prescott and Ohanian (2014Feb), Lazear (2017Feb27) and Cogan and Taylor (2020Oct6): productivity increased cumulatively 8.9 percent from 2011 to 2019 at the average annual rate of 1.0 percent. Confirming measurement by Cogan and Taylor (2020Oct6), productivity increased at average 0.7 percent from 2013 to 2016 and at 1.6 percent from 2017 to 2019, using revised data. Average productivity growth for the entire economic cycle from 2007 to 2021 is only 1.5 percent. The argument by Prescott and Ohanian (2014Feb) is proper in choosing the tail of the business cycle because the increase in productivity in 2009 of 3.6 percent and 3.4 percent in 2010 consisted of reducing labor hours. Productivity increased 2.4 percent in 2020 with decrease of output at 4.4 percent and decrease of hours worked at 6.6 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Productivity increased 1.9 percent in 2021 with increase of output of 7.4 percent and hours worked of 5.4 percent.
Table II-2, US, Revised Nonfarm Business Sector Productivity and Costs Annual Average, ∆% Annual Average
| 2017 ∆% | 2018 ∆% | 2019 ∆% | 2020 ∆% | 2021 ∆% | ||
| Productivity | 1.2 | 1.5 | 2.1 | 2.4 | 1.9 | |
| Real Hourly Compensation | 1.4 | 0.9 | 2.0 | 5.7 | 0.7 | |
| Unit Labor Costs | 2.3 | 1.9 | 1.8 | 4.5 | 3.5 | |
| 2016 ∆% | 2015 ∆% | 2014 ∆% | 2013 ∆% | 2012 ∆% | 2011 ∆% | |
| Productivity | 0.4 | 1.2 | 0.7 | 0.5 | 1.0 | 0.0 |
| Real Hourly Compensation | -0.2 | 3.0 | 1.1 | -0.2 | 0.5 | -0.9 |
| Unit Labor Costs | 0.7 | 1.9 | 2.1 | 0.7 | 1.6 | 2.2 |
| 2010 ∆% | 2009 ∆% | 2008 ∆% | 2007∆% | |
| Productivity | 3.4 | 3.6 | 1.3 | 1.6 |
| Real Hourly Compensation | 0.2 | 1.3 | -0.9 | 1.5 |
| Unit Labor Costs | -1.5 | -2.5 | 1.6 | 2.7 |
Source: US Bureau of Labor Statistics
Table II-3, US, Nonfarm Business Output per Productivity jumped in the recovery after the recession from Mar IQ2001 to Nov IVQ2001 (https://www.nber.org/cycles.html) Table II-3 provides quarter on quarter and annual percentage changes in nonfarm business output per hour, or productivity, from 1999 to 2021. The annual average jumped from 2.6 percent in 2001 to 4.3 percent in 2002. Nonfarm business productivity increased at the SAAE rate of 8.6 percent in the first quarter after the recession in IQ2002. Productivity increases decline later in the expansion period. Productivity increases were mediocre during the recession from Dec IVQ2007 to Jun IIIQ2009 (https://www.nber.org/cycles.html) and increased during the first phase of expansion from IIQ2009 to IQ2010, trended lower and collapsed in 2011 and 2012 with sporadic jumps and declines. Productivity increased at 2.8 percent in IVQ2013 and contracted at 4.0 percent in IQ2014. Productivity increased at 3.8 percent in IIQ2014 and at 3.5 percent in IIIQ2014. Productivity contracted at 2.2 percent in IVQ2014 and increased at 3.4 percent in IQ2015. Productivity grew at 1.1 percent in IIQ2015 and changed at minus 0.1 percent in IIIQ2015. Productivity contracted at 1.6 percent in IVQ2015 and increased at 1.3 percent in IQ2016. Productivity decreased at 0.3 percent in IIQ2016 and expanded at 1.6 percent in IIIQ2016. Productivity grew at 2.3 percent in IVQ2016 and increased at 0.3 percent in IQ2017. Productivity decreased at 0.5 percent in IIQ2017 and increased at 3.3 percent in IIIQ2017. Productivity increased at 1.8 percent in IVQ2017 and increased at 1.3 percent in IQ2018. Productivity increased at 1.4 percent in IIQ2018 and increased at 1.3 percent in IIIQ2018. Productivity increased at 0.1 percent in IVQ2018. Productivity increased at 3.5 percent in IQ2019. Productivity increased at 3.7 percent in IIQ2019 and increased at 1.2 percent in IIIQ2019. Productivity increased at 1.3 percent in IVQ2019. Productivity decreased at 2.5 percent in IQ2020, increasing at 10.2 percent in IIQ2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Productivity increased at 6.1 percent in IIIQ2020. Productivity decreased at 2.8 percent in IVQ2020. Productivity increased at 2.4 percent in 2020 with decrease of output at 4.4 percent and decrease of hours worked at 6.6 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Productivity increased at 2.1 percent in IQ2021 and increased at 3.2 percent in IIQ2021. Productivity decreased at 3.9 percent in IIIQ2021. Productivity increased at 6.6 percent in IVQ2021.
Table II-3, US, Nonfarm Business Output per Hour, Percent Change from Prior Quarter at Annual Rate, 1999-2021
| Year | Qtr1 | Qtr2 | Qtr3 | Qtr4 | Annual |
| 1999 | 5.7 | 1.4 | 3.9 | 6.0 | 3.9 |
| 2000 | -1.5 | 7.9 | -0.1 | 4.1 | 3.0 |
| 2001 | -2.1 | 7.1 | 2.4 | 5.1 | 2.6 |
| 2002 | 8.6 | 0.8 | 2.9 | -0.4 | 4.3 |
| 2003 | 3.9 | 5.4 | 9.0 | 3.7 | 3.7 |
| 2004 | -0.9 | 4.2 | 1.6 | 2.0 | 3.0 |
| 2005 | 4.7 | -0.7 | 2.5 | 0.4 | 2.2 |
| 2006 | 3.0 | -0.7 | -1.2 | 3.3 | 1.0 |
| 2007 | 1.0 | 1.4 | 3.8 | 3.3 | 1.6 |
| 2008 | -2.6 | 4.5 | 0.9 | -2.3 | 1.3 |
| 2009 | 3.8 | 8.7 | 5.4 | 6.3 | 3.6 |
| 2010 | 2.2 | 0.6 | 2.2 | 1.7 | 3.4 |
| 2011 | -2.7 | 0.5 | -1.3 | 2.8 | 0.0 |
| 2012 | 1.8 | 1.7 | -0.4 | -1.3 | 1.0 |
| 2013 | 2.2 | -1.3 | 1.9 | 2.8 | 0.5 |
| 2014 | -4.0 | 3.8 | 3.5 | -2.2 | 0.6 |
| 2015 | 3.4 | 1.1 | -0.1 | -1.6 | 1.2 |
| 2016 | 1.3 | -0.3 | 1.6 | 2.3 | 0.3 |
| 2017 | 0.3 | -0.5 | 3.3 | 1.8 | 1.1 |
| 2018 | 1.3 | 1.4 | 1.3 | 0.1 | 1.5 |
| 2019 | 3.5 | 3.7 | 1.2 | 1.3 | 2.1 |
| 2020 | -2.5 | 10.2 | 6.1 | -2.8 | 2.4 |
| 2021 | 2.1 | 3.2 | -3.9 | 6.6 | 1.9 |
Source: US Bureau of Labor Statistics
Chart II-1 of the Bureau of Labor Statistics (BLS) provides SAAE rates of nonfarm business productivity from 1999 to 2021. There is a clear pattern in both episodes of economic cycles in 2001 and 2007 of rapid expansion of productivity in the transition from contraction to expansion followed by more subdued productivity expansion. Part of the explanation is the reduction in labor utilization resulting from adjustment of business to the sudden shock of collapse of revenue. Productivity rose briefly in the expansion after 2009 but then collapsed and moved to negative change with some positive changes recently at lower rates. Contractions in the cycle from 2007 to 2019 have been more frequent and sharper. Productivity increased in 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021).
Chart II-1, US, Nonfarm Business Output per Hour, Percent Change from Prior Quarter at Annual Rate, 1999-2021
Source: US Bureau of Labor Statistics: http://www.bls.gov/lpc/
Percentage changes from prior quarter at SAAE rates and annual average percentage changes of nonfarm business unit labor costs are provided in Table II-4. Unit labor costs fell during the contractions with continuing negative percentage changes in the early phases of the recovery. Weak labor markets partly explain the decline in unit labor costs. As the economy moves toward full employment, labor markets tighten with increase in unit labor costs. The expansion beginning in IIIQ2009 has been characterized by high unemployment and underemployment. Table II-4 shows continuing subdued increases in unit labor costs in 2011 but with increase at 8.0 percent in IQ2012 followed by increase at 0.3 percent in IIQ2012, increase at 0.8 percent in IIIQ2012 and increase at 12.5 percent in IVQ2012. Unit labor costs decreased at 7.7 percent in IQ2013 and increased at 4.3 percent in IIQ2013. Unit labor costs decreased at 2.9 percent in IIIQ2013 and changed at 0.0 percent in IVQ2013. Unit labor costs increased at 12.5 percent in IQ2014 and decreased at minus 5.4 percent in IIQ2014. Unit labor costs decreased at 0.9 percent in IIIQ2014 and increased at 6.7 percent in IVQ2014. Unit labor costs increased at 1.8 percent in IQ2015 and increased at 2.2 percent in IIQ2015. Unit labor costs increased at 1.4 percent in IIIQ2015 and increased at 1.5 percent in IVQ2015. Unit labor costs decreased at 1.0 percent in IQ2016 and increased at 1.4 percent in IIQ2016. Unit labor costs increased at 0.1 percent in IIIQ2016 and increased at 2.0 percent in IVQ2016. Unit labor costs increased at 3.7 percent in IQ2017 and increased at 2.5 percent in IIQ2017. United labor costs increased at 1.7 percent in IIIQ2017 and increased at 4.0 percent in IVQ2017. Unit labor costs increased at 1.2 percent in IQ2018 and decreased at 0.4 percent in IIQ2018. Unit labor costs increased at 3.8 percent in IIIQ2018 and increased at 1.7 percent in IVQ2018. Unit labor costs increased at 5.7 percent in IQ2019 and decreased at 2.8 percent in IIQ2019. Unit labor costs decreased at 0.9 percent in IIIQ2019 and increased at 3.8 percent in IVQ2019. Unit labor costs increased at 11.4 percent in IQ2020 and increased at 10.3 percent in IIQ2020. Unit labor costs decreased at 10.3 percent in IIIQ2020 and increased at 13.7 percent in IVQ2020. Unit labor costs decreased at 2.7 percent in IQ2021 and increased at 5.8 percent in IIQ2021. Unit labor costs increased at 10.6 percent in IIIQ2021. Unit labor costs increased at 0.9 percent in IVQ2021.
Table II-4, US, Nonfarm Business Unit Labor Costs, Percent Change from Prior Quarter at Annual Rate 1999-2021
| Year | Qtr1 | Qtr2 | Qtr3 | Qtr4 | Annual |
| 1999 | 1.5 | 0.3 | -0.3 | 2.2 | 0.7 |
| 2000 | 16.7 | -6.4 | 8.1 | -1.8 | 3.9 |
| 2001 | 11.7 | -5.1 | -1.8 | -1.3 | 1.7 |
| 2002 | -6.2 | 2.7 | -0.9 | 1.4 | -1.9 |
| 2003 | -1.4 | 1.6 | -2.7 | 1.9 | 0.1 |
| 2004 | 0.2 | 3.6 | 5.6 | -0.3 | 1.5 |
| 2005 | -1.8 | 3.2 | 2.6 | 2.1 | 1.4 |
| 2006 | 5.0 | 1.0 | 1.9 | 3.8 | 2.8 |
| 2007 | 9.0 | -1.6 | -2.2 | 1.4 | 2.7 |
| 2008 | 6.7 | -3.7 | 2.9 | 6.5 | 1.6 |
| 2009 | -13.3 | 1.5 | -3.1 | -3.4 | -2.5 |
| 2010 | -4.6 | 3.4 | -0.5 | 0.5 | -1.5 |
| 2011 | 10.6 | -3.3 | 4.3 | -7.8 | 2.2 |
| 2012 | 8.0 | 0.3 | 0.8 | 12.5 | 1.6 |
| 2013 | -7.7 | 4.3 | -2.9 | 0.0 | 0.7 |
| 2014 | 12.5 | -5.4 | -0.9 | 6.7 | 2.1 |
| 2015 | 1.8 | 2.2 | 1.4 | 1.5 | 1.9 |
| 2016 | -1.0 | 1.4 | 0.1 | 2.0 | 0.7 |
| 2017 | 3.7 | 2.5 | 1.7 | 4.0 | 2.3 |
| 2018 | 1.2 | -0.4 | 3.8 | 1.7 | 1.9 |
| 2019 | 5.7 | -2.8 | -0.9 | 3.8 | 1.8 |
| 2020 | 11.4 | 10.3 | -10.3 | 13.7 | 4.5 |
| 2021 | -2.7 | 5.8 | 10.6 | 0.9 | 3.5 |
Source: US Bureau of Labor Statistics
Chart II-2 provides change of unit labor costs at SAAE from 1999 to 2021. There are multiple oscillations recently with negative changes alternating with positive changes. There is sharp contraction in 2020 followed by rebound in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021).
Chart II-2, US, Nonfarm Business Unit Labor Costs, Percent Change from Prior Quarter at Annual Rate 1999-2021
Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/
Table II-5 provides percentage change from prior quarter at annual rates for nonfarm business real hourly worker compensation. The expansion after the contraction of 2001 was followed by strong recovery of real hourly compensation. Real hourly compensation increased at the rate of 3.2 percent in IQ2011 but fell at annual rates of 7.1 percent in IIQ2011 and 6.9 percent in IVQ2011. Real hourly compensation increased at 7.4 percent in IQ2012, increasing at 1.1 percent in IIQ2012, declining at 1.5 percent in IIIQ2012 and increasing at 8.1 percent in IVQ2012. Real hourly compensation fell at 0.9 percent in 2011 and increased at 0.5 percent in 2012. Real hourly compensation fell at 7.3 percent in IQ2013 and increased at 3.4 percent in IIQ2013, falling at 3.2 percent in IIIQ2013. Real hourly compensation increased at 1.2 percent in IVQ2013 and at 5.3 percent in IQ2014. Real hourly compensation decreased at 3.9 percent in IIQ2014. Real hourly compensation increased at 1.5 percent in IIIQ2014. The annual rate of increase of real hourly compensation for 2013 is minus 0.2 percent. Real hourly compensation increased at 5.3 percent in IVQ2014. The annual rate of increase of real hourly compensation in 2014 is 1.1 percent. Real hourly compensation increased at 8.0 percent in IQ2015 and increased at 0.5 percent in IIQ2015. Real hourly compensation decreased at 0.3 percent in IIIQ2015 and decreased at 0.2 percent in IVQ2015. Real hourly compensation increased at 2.9 percent in 2015. Real hourly compensation increased at 0.5 percent in IQ2016 and decreased at 2.1 percent in IIQ2016. Real hourly compensation changed at 0.0 percent in IIIQ2016 and increased at 1.7 percent in IVQ2016. Real hourly compensation decreased 0.2 percent in 2016. Real hourly compensation increased at 1.2 percent in IQ2017 and increased at 1.3 percent in IIQ2017. Real hourly compensation increased at 3.1 percent in IIIQ2017. Real hourly compensation increased at 2.7 percent in IVQ2017. Real hourly compensation increased 1.3 percent in 2017. Real hourly compensation decreased at 0.6 percent in IQ2018 and decreased at 1.5 percent in IIQ2018. Real hourly compensation increased at 3.5 percent in IIIQ2018 and increased at 0.3 percent in IVQ2018. Real hourly compensation increased 1.0 percent in 2018. Real hourly compensation increased at 8.5 percent in IQ2019 and decreased at 2.6 percent in IIQ2019. Real hourly compensation decreased at 1.0 percent in IIIQ2019, increasing at 2.5 percent in IVQ2019. Real hourly compensation increased 2.0 percent in 2019. Real hourly compensation increased at 7.5 percent in IQ2020 and increased at 25.4 percent in IIQ2020. Real hourly compensation decreased at 9.1 percent in IIIQ2020 and increased at 7.9 percent in IVQ2020. Real hourly compensation increased 5.7 percent in 2020 relative to a year earlier. Real hourly compensation decreased at 4.4 percent in IQ2021, increasing at 0.9 percent in IIQ2021. Real hourly compensation decreased at 0.4 percent in IIIQ2021, decreasing at 0.3 percent in IVQ2021. Real hourly compensation increased 0.7 percent in 2021.
Table II-5, Nonfarm Business Real Hourly Compensation, Percent Change from Prior Quarter at Annual Rate, 1999-2021
| Year | Qtr1 | Qtr2 | Qtr3 | Qtr4 | Annual |
| 1999 | 5.8 | -1.4 | 0.5 | 5.0 | 2.5 |
| 2000 | 10.4 | -2.1 | 4.1 | -0.5 | 3.5 |
| 2001 | 5.3 | -1.3 | -0.5 | 4.1 | 1.5 |
| 2002 | 0.5 | 0.3 | -0.1 | -1.3 | 0.7 |
| 2003 | -1.6 | 7.8 | 3.0 | 4.0 | 1.4 |
| 2004 | -4.0 | 4.7 | 4.7 | -2.6 | 1.8 |
| 2005 | 0.8 | -0.4 | -1.0 | -1.3 | 0.3 |
| 2006 | 6.0 | -3.3 | -3.0 | 9.0 | 0.6 |
| 2007 | 6.0 | -4.7 | -1.0 | -0.2 | 1.5 |
| 2008 | -0.6 | -4.4 | -2.3 | 14.2 | -0.9 |
| 2009 | -7.5 | 8.1 | -1.2 | -0.4 | 1.3 |
| 2010 | -3.1 | 4.2 | 0.5 | -1.0 | 0.2 |
| 2011 | 3.2 | -7.1 | 0.3 | -6.9 | -0.9 |
| 2012 | 7.4 | 1.1 | -1.5 | 8.1 | 0.5 |
| 2013 | -7.3 | 3.4 | -3.2 | 1.2 | -0.2 |
| 2014 | 5.3 | -3.9 | 1.5 | 5.3 | 1.1 |
| 2015 | 8.0 | 0.5 | -0.3 | -0.2 | 2.9 |
| 2016 | 0.5 | -2.1 | 0.0 | 1.7 | -0.2 |
| 2017 | 1.2 | 1.3 | 3.1 | 2.7 | 1.3 |
| 2018 | -0.6 | -1.5 | 3.5 | 0.3 | 1.0 |
| 2019 | 8.5 | -2.6 | -1.0 | 2.5 | 2.0 |
| 2020 | 7.5 | 25.4 | -9.1 | 7.9 | 5.7 |
| 2021 | -4.4 | 0.9 | -0.4 | -0.3 | 0.7 |
Source: US Bureau of Labor Statistics
Chart II-3 provides percentage changes of real hourly compensation in a month relative to the prior month. There are wide swings in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021).
Chart II-3, US, Nonfarm Business Real Hourly Compensation, Percent Change from Prior Quarter at Annual Rate 1999-2021
Source: US Bureau of Labor Statistics https://www.bls.gov/lpc/
Chart II-4 provides percentage change of nonfarm business output per hour in a quarter relative to the same quarter a year earlier. As in most series of real output, productivity increased sharply in 2010 but the momentum was lost after 2011 as with the rest of the real economy. There are more negative yearly changes during the current cycle than in cycle after 1999. There is sharp increase in 2020-2021 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021) followed by recovery.
Chart II-4, US, Nonfarm Business Output per Hour, Percent Change from Same Quarter a Year Earlier 1999-2021
Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/
Chart II-5 provides percentage changes of nonfarm business unit labor costs relative to the same quarter a year earlier. Softening of labor markets caused relatively high yearly percentage changes in the recession of 2001 repeated in the recession in 2009. Recovery was strong in 2010 but then weakened. There is increase with oscillations in 2020-2021 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021).
Chart II-5, US, Nonfarm Business Unit Labor Costs, Percent Change from Same Quarter a Year Earlier 1999-2021
Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/
Chart II-6 provides percentage changes of real hourly compensation in a month relative to the same month a year earlier. There is significant volatility in part because of recurring world inflation waves originating in carry trades from artificially low interest rates. There is increase with fluctuation in 2020-2021 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021) and the recent increase in inflation.
Chart II-6, US, Nonfarm Business Real Hourly Compensation, Percent Change from Same Quarter a Year Earlier 1999-2021
2012=100
Source: US Bureau of Labor Statistics:
In the analysis of Hansen (1939, 3) of secular stagnation, economic progress consists of growth of real income per person driven by growth of productivity. The “constituent elements” of economic progress are “(a) inventions, (b) the discovery and development of new territory and new resources, and (c) the growth of population” (Hansen 1939, 3). Secular stagnation originates in decline of population growth and discouragement of inventions. According to Hansen (1939, 2), US population grew by 16 million in the 1920s but grew by one half or about 8 million in the 1930s with forecasts at the time of Hansen’s writing in 1938 of growth of around 5.3 million in the 1940s. Hansen (1939, 2) characterized demography in the US as “a drastic decline in the rate of population growth.” Hansen’s plea was to adapt economic policy to stagnation of population in ensuring full employment. In the analysis of Hansen (1939, 8), population caused half of the growth of US GDP per year. Growth of output per person in the US and Europe was caused by “changes in techniques and to the exploitation of new natural resources.” In this analysis, population caused 60 percent of the growth of capital formation in the US. Declining population growth would reduce growth of capital formation. Residential construction provided an important share of growth of capital formation. Hansen (1939, 12) argues that market power of imperfect competition discourages innovation with prolonged use of obsolete capital equipment. Trade unions would oppose labor-savings innovations. The combination of stagnating and aging population with reduced innovation caused secular stagnation. Hansen (1939, 12) concludes that there is role for public investments to compensate for lack of dynamism of private investment but with tough tax/debt issues.
The current application of Hansen’s (1938, 1939, 1941) proposition argues that secular stagnation occurs because full employment equilibrium can be attained only with negative real interest rates between minus 2 and minus 3 percent. Professor Lawrence H. Summers (2013Nov8) finds that “a set of older ideas that went under the phrase secular stagnation are not profoundly important in understanding Japan’s experience in the 1990s and may not be without relevance to America’s experience today” (emphasis added). Summers (2013Nov8) argues there could be an explanation in “that the short-term real interest rate that was consistent with full employment had fallen to -2% or -3% sometime in the middle of the last decade. Then, even with artificial stimulus to demand coming from all this financial imprudence, you wouldn’t see any excess demand. And even with a relative resumption of normal credit conditions, you’d have a lot of difficulty getting back to full employment.” The US economy could be in a situation where negative real rates of interest with fed funds rates close to zero as determined by the Federal Open Market Committee (FOMC) do not move the economy to full employment or full utilization of productive resources. Summers (2013Oct8) finds need of new thinking on “how we manage an economy in which the zero nominal interest rates is a chronic and systemic inhibitor of economy activity holding our economies back to their potential.”
Former US Treasury Secretary Robert Rubin (2014Jan8) finds three major risks in prolonged unconventional monetary policy of zero interest rates and quantitative easing: (1) incentive of delaying action by political leaders; (2) “financial moral hazard” in inducing excessive exposures pursuing higher yields of risker credit classes; and (3) major risks in exiting unconventional policy. Rubin (2014Jan8) proposes reduction of deficits by structural reforms that could promote recovery by improving confidence of business attained with sound fiscal discipline.
Professor John B. Taylor (2014Jan01, 2014Jan3) provides clear thought on the lack of relevance of Hansen’s contention of secular stagnation to current economic conditions. The application of secular stagnation argues that the economy of the US has attained full-employment equilibrium since around 2000 only with negative real rates of interest of minus 2 to minus 3 percent. At low levels of inflation, the so-called full-employment equilibrium of negative interest rates of minus 2 to minus 3 percent cannot be attained and the economy stagnates. Taylor (2014Jan01) analyzes multiple contradictions with current reality in this application of the theory of secular stagnation:
- Secular stagnation would predict idle capacity, in particular in residential investment when fed fund rates were fixed at 1 percent from Jun 2003 to Jun 2004. Taylor (2014Jan01) finds unemployment at 4.4 percent with house prices jumping 7 percent from 2002 to 2003 and 14 percent from 2004 to 2005 before dropping from 2006 to 2007. GDP prices doubled from 1.7 percent to 3.4 percent when interest rates were low from 2003 to 2005.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the application of secular stagnation based on low interest rates because of savings glut and lack of investment opportunities. Taylor (2009) shows that there was no savings glut. The savings rate of the US in the past decade is significantly lower than in the 1980s.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the low ratio of investment to GDP currently and reduced investment and hiring by US business firms.
- Taylor (2014Jan01, 2014Jan3) argues that the financial crisis and global recession were caused by weak implementation of existing regulation and departure from rules-based policies.
Taylor (2014Jan01, 2014Jan3) argues that the recovery from the global recession was constrained by a change in the regime of regulation and fiscal/monetary policies.
The analysis by Kydland (https://www.nobelprize.org/prizes/economic-sciences/2004/kydland/biographical/) and Prescott (https://www.nobelprize.org/prizes/economic-sciences/2004/prescott/biographical/) (1977, 447-80, equation 5) uses the “expectation augmented” Phillips curve with the natural rate of unemployment of Friedman (1968) and Phelps (1968), which in the notation of Barro and Gordon (1983, 592, equation 1) is:
Ut = Unt – α(πt – πe) α > 0 (1)
Where Ut is the rate of unemployment at current time t, Unt is the natural rate of unemployment, πt is the current rate of inflation and πe is the expected rate of inflation by economic agents based on current information. Equation (1) expresses unemployment net of the natural rate of unemployment as a decreasing function of the gap between actual and expected rates of inflation. The system is completed by a social objective function, W, depending on inflation, π, and unemployment, U:
W = W(πt, Ut) (2)
The policymaker maximizes the preferences of the public, (2), subject to the constraint of the tradeoff of inflation and unemployment, (1). The total differential of W set equal to zero provides an indifference map in the Cartesian plane with ordered pairs (πt, Ut - Un) such that the consistent equilibrium is found at the tangency of an indifference curve and the Phillips curve in (1). The indifference curves are concave to the origin. The consistent policy is not optimal. Policymakers without discretionary powers following a rule of price stability would attain equilibrium with unemployment not higher than with the consistent policy. The optimal outcome is obtained by the rule of price stability, or zero inflation, and no more unemployment than under the consistent policy with nonzero inflation and the same unemployment. Taylor (1998LB) attributes the sustained boom of the US economy after the stagflation of the 1970s to following a monetary policy rule instead of discretion (see Taylor 1993, 1999). Professor John B. Taylor (2014Jul15, 2014Jun26) building on advanced research (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB, 2015, 2012 Oct 25; 2013Oct28, 2014 Jan01, 2014Jan3, 2014Jun26, 2014Jul15, 2015, 2016Dec7, 2016Dec20 http://www.johnbtaylor.com/) finds that a monetary policy rule would function best in promoting an environment of low inflation and strong economic growth with stability of financial markets. There is strong case for using rules instead of discretionary authorities in monetary policy (http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). It is not uncommon for effects of regulation differing from those intended by policy. Professors Edward C. Prescott and Lee E. Ohanian (2014Feb), writing on “US productivity growth has taken a dive,” on Feb 3, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303942404579362462611843696?KEYWORDS=Prescott), argue that impressive productivity growth over the long-term constructed US prosperity and wellbeing. Prescott and Ohanian (2014Feb) measure US productivity growth at 2.5 percent per year since 1948. Average US productivity growth has been only 1.1 percent since 2011. Prescott and Ohanian (2014Feb) argue that living standards in the US increased at 28 percent in a decade but with current slow growth of productivity will only increase 12 percent by 2024. There may be collateral effects on productivity growth from policy design similar to those in Kydland and Prescott (1977). Professor Edward P. Lazear (2017Feb27), writing in the Wall Street Journal, on Feb 27, 2017 (https://www.wsj.com/articles/how-trump-can-hit-3-growthmaybe-1488239746), finds that productivity growth was 7 percent between 2009 and 2016 at annual equivalent 1 percent. Lazear measures productivity growth at 2.3 percent per year from 2001 to 2008. Herkenhoff, Ohanian and Prescott (2017) and Ohanian and Prescott (2017Dec) analyze how restriction of land use by states in the United States have been depressing economic activity. Professor Edmund S. Phelps (https://www.nobelprize.org/prizes/economic-sciences/2006/phelps/auto-biography/) argues that there is failed analysis that fiscal stimulus in the form of higher government expenditures and tax reductions caused the recovery of the economy to normal levels by 2017 (Phelps, Edmund S. 2018. The fantasy of fiscal stimulus. The Wall Street Journal Oct 29, 2018 https://www.wsj.com/articles/the-fantasy-of-fiscal-stimulus-1540852299?mod=searchresults&page=1&pos=2). The evidence analyzed by Phelps leads to the conclusion that countries with disorderly government finance grew less rapidly than those with sounder fiscal performance. Phelps concludes convincingly that “there is a strong relationship between the speed of recovery and a proxy of its dynamism—the long-term growth rate of total factor productivity from 1990 to 2007. Some countries have preexisting social institutions and cultural capital that enables them to bounce back from an economic downturn. Much credit of the U.S.’s relatively speedy recovery is owed to this country’s endemic culture of innovation and enterprise.” Professor Edward P. Lazear, writing on “Mind the productivity gap to reduce inequality,” published in the Wall Street Journal on May 6, 2019 (https://www.wsj.com/articles/mind-the-productivity-gap-to-reduce-inequality-11557181134?mod=searchresults&page=1&pos=1), analyzes the causes of the growing differential of wages between the income of the 90th percentile and the 50th percentile in terms of technological change. The improvement of the lower half of wage earners would consist of increasing their skills. Professors John F. Cogan and John B. Taylor, writing in the Wall Street Journal on Oct 6, 2020, measure productivity growth increasing from 0.8 percent per year in 2013-2016 to 1.5 percent per year in 2016-2019 because of deregulation and market-oriented policies. The Bureau of Labor Statistics important report on productivity and costs released on Jun 3, 2021 (https://www.bls.gov/lpc/) supports the argument of decline of productivity growth in the US analyzed by Prescott and Ohanian (2014Feb), Lazear (2017Feb27), Phelps (2018) and Cogan and Taylor (2020Oct6). Table II-2 provides the annual percentage changes of productivity, real hourly compensation and unit labor costs for the entire economic cycle from 2007 to 2021. The estimates incorporate the yearly revision of the US national accounts (https://www.bea.gov/information-updates-national-income-and-product-accounts) and the comprehensive revisions since 1929 (https://apps.bea.gov/national/pdf/2018-ComprehensiveUpdate-Results.pdf). The data confirm the argument of Prescott and Ohanian (2014Feb), Lazear (2017Feb27) and Cogan and Taylor (2020Oct6): productivity increased cumulatively 8.9 percent from 2011 to 2019 at the average annual rate of 1.0 percent. Confirming measurement by Cogan and Taylor (2020Oct6), productivity increased at average 0.7 percent from 2013 to 2016 and at 1.6 percent from 2017 to 2019, using revised data. Average productivity growth for the entire economic cycle from 2007 to 2021 is only 1.5 percent. The argument by Prescott and Ohanian (2014Feb) is proper in choosing the tail of the business cycle because the increase in productivity in 2009 of 3.6 percent and 3.4 percent in 2010 consisted of reducing labor hours. Productivity increased 2.4 percent in 2020 with decrease of output at 4.4 percent and decrease of hours worked at 6.6 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Productivity increased 1.9 percent in 2021 with increase of output of 7.4 percent and hours worked of 5.4 percent.
In revealing research, Edward P. Lazear and James R. Spletzer (2012JHJul22) use the wealth of data in the valuable database and resources of the Bureau of Labor Statistics (http://www.bls.gov/data/) in providing clear thought on the nature of the current labor market of the United States. The critical issue of analysis and policy currently is whether unemployment is structural or cyclical. Structural unemployment could occur because of (1) industrial and demographic shifts and (2) mismatches of skills and job vacancies in industries and locations. Consider the aggregate unemployment rate, Y, expressed in terms of share si of a demographic group in an industry i and unemployment rate yi of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):
Y = ∑isiyi (1)
This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):
∆Y = ∑i∆siy*i + ∑i∆yis*i (2)
The first term in (2) captures changes in the demographic and industrial composition of the economy ∆si multiplied by the average rate of unemployment y*i , or structural factors. The second term in (2) captures changes in the unemployment rate specific to a group, or ∆yi, multiplied by the average share of the group s*i, or cyclical factors. There are also mismatches in skills and locations relative to available job vacancies. A simple observation by Lazear and Spletzer (2012JHJul22) casts intuitive doubt on structural factors: the rate of unemployment jumped from 4.4 percent in the spring of 2007 to 10 percent in October 2009. By nature, structural factors should be permanent or occur over relative long periods. The revealing result of the exhaustive research of Lazear and Spletzer (2012JHJul22) is:
“The analysis in this paper and in others that we review do not provide any compelling evidence that there have been changes in the structure of the labor market that are capable of explaining the pattern of persistently high unemployment rates. The evidence points to primarily cyclic factors.”
The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. The theory of secular stagnation departs from an aggregate production function in which output grows with the use of labor, capital and technology (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), 11-6). Simon Kuznets (1971) analyzes modern economic growth in his Lecture in Memory of Alfred Nobel:
“The major breakthroughs in the advance of human knowledge, those that constituted dominant sources of sustained growth over long periods and spread to a substantial part of the world, may be termed epochal innovations. And the changing course of economic history can perhaps be subdivided into economic epochs, each identified by the epochal innovation with the distinctive characteristics of growth that it generated. Without considering the feasibility of identifying and dating such economic epochs, we may proceed on the working assumption that modern economic growth represents such a distinct epoch - growth dating back to the late eighteenth century and limited (except in significant partial effects) to economically developed countries. These countries, so classified because they have managed to take adequate advantage of the potential of modern technology, include most of Europe, the overseas offshoots of Western Europe, and Japan—barely one quarter of world population.”
Chart II-7 provides nonfarm-business labor productivity, measured by output per hour, from 1947 to 2021. The rate of productivity increase continued in the early part of the 2000s but then softened and fell during the global recession. The interruption of productivity increases occurred exclusively in the current business cycle. Lazear and Spletzer (2012JHJul22) find “primarily cyclic” factors in explaining the frustration of currently depressed labor markets in the United States. Stagnation of productivity is another cyclic event and not secular trend. The theory and application of secular stagnation to current US economic conditions is void of reality.
Chart II-7, US, Nonfarm Business Labor Productivity, Output per Hour, 1947-2021, Index 2012=100
Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/
Table II-6 expands Table II-2 providing more complete measurements of the Productivity and Cost research of the Bureau of Labor Statistics. The proper emphasis of Prescott and Ohanian (2014Feb) is on the low productivity increases from 2011 to 2019. Labor productivity increased 3.4 percent in 2010 and 3.6 percent in 2009. There is much stronger yet not sustained performance in 2010 with productivity growing 3.4 percent because of growth of output of 3.3 percent with decline of hours worked of 0.1 percent. Productivity growth of 3.6 percent in 2009 consists of decline of output by 3.9 percent while hours worked collapsed 7.1 percent, which is not a desirable route to progress. The expansion phase of the economic cycle concentrated in one year, 2010, with underperformance in the remainder of the expansion from 2011 to 2019 of productivity growth at average 1.0 percent per year. Productivity increased 2.4 percent in 2020 with decrease of output at 4.4 percent and decrease of hours worked at 6.6 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Productivity increased 1.9 percent in 2021 with increase of output of 7.4 percent and of hours worked of 5.4 percent.
Table II-6, US, Productivity and Costs, Annual Percentage Changes 2007-2021
| 2017 | 2018 | 2019 | 2020 | 2021 | |
| Productivity | 1.1 | 1.5 | 2.1 | 2.4 | 1.9 |
| Output | 2.7 | 3.5 | 2.7 | -4.4 | 7.4 |
| Hours Worked | 1.6 | 2.0 | 0.7 | -6.6 | 5.4 |
| Employment | 1.6 | 1.9 | 1.2 | -6.5 | 4.1 |
| Average Weekly Hours Worked | 0.0 | 0.1 | -0.5 | -0.1 | 1.2 |
| Unit Labor Costs | 2.3 | 1.9 | 1.8 | 4.5 | 3.5 |
| Hourly Compensation | 3.5 | 3.4 | 3.9 | 7.0 | 5.4 |
| Consumer Price Inflation | 2.1 | 2.4 | 1.8 | 1.2 | 4.7 |
| Real Hourly Compensation | 1.3 | 1.0 | 2.0 | 5.7 | 0.7 |
| Non-labor Payments | 3.6 | 6.1 | 4.0 | -9.1 | 12.6 |
| Output per Job | 1.1 | 1.6 | 1.5 | 2.2 | 3.1 |
| 2016 | 2015 | 2014 | 2013 | 2012 | |
| Productivity | 0.3 | 1.2 | 0.6 | 0.5 | 1.0 |
| Output | 1.8 | 3.4 | 3.0 | 2.2 | 3.3 |
| Hours Worked | 1.5 | 2.2 | 2.3 | 1.7 | 2.3 |
| Employment | 1.9 | 2.3 | 2.1 | 1.9 | 2.0 |
| Average Weekly Hours Worked | -0.4 | -0.1 | 0.2 | -0.1 | 0.3 |
| Unit Labor Costs | 0.7 | 1.9 | 2.1 | 0.7 | 1.6 |
| Hourly Compensation | 1.1 | 3.1 | 2.8 | 1.2 | 2.7 |
| Consumer Price Inflation | 1.3 | 0.1 | 1.6 | 1.5 | 2.1 |
| Real Hourly Compensation | -0.2 | 2.9 | 1.1 | -0.2 | 0.5 |
| Non-labor Payments | 3.0 | 2.7 | 4.5 | 4.6 | 5.4 |
| Output per Job | 0.0 | 1.1 | 0.9 | 0.4 | 1.3 |
| 2011 | 2010 | 2009 | 2008 | 2007 | |
| Productivity | 0.0 | 3.4 | 3.6 | 1.3 | 1.6 |
| Output | 2.0 | 3.3 | -3.9 | -0.9 | 2.3 |
| Hours Worked | 2.0 | -0.1 | -7.1 | -2.1 | 0.7 |
| Employment | 1.6 | -1.2 | -5.7 | -1.4 | 0.9 |
| Average Weekly Hours Worked | 0.4 | 1.1 | -1.6 | -0.7 | -0.2 |
| Unit Labor Costs | 2.2 | -1.5 | -2.5 | 1.6 | 2.7 |
| Hourly Compensation | 2.2 | 1.9 | 0.9 | 2.9 | 4.3 |
| Consumer Price Inflation | 3.2 | 1.6 | -0.4 | 3.8 | 2.8 |
| Real Hourly Compensation | -0.9 | 0.2 | 1.3 | -0.9 | 1.5 |
| Non-labor Payments | 3.7 | 8.0 | 0.5 | 0.5 | 3.4 |
| Output per Job | 0.5 | 4.6 | 1.9 | 0.6 | 1.4 |
Source: US Bureau of Labor Statistics https://www.bls.gov/lpc/
Productivity growth can bring about prosperity while productivity regression can jeopardize progress. Cobet and Wilson (2002) provide estimates of output per hour and unit labor costs in national currency and US dollars for the US, Japan and Germany from 1950 to 2000 (see Pelaez and Pelaez, The Global Recession Risk (2007), 137-44). The average yearly rate of productivity change from 1950 to 2000 was 2.9 percent in the US, 6.3 percent for Japan and 4.7 percent for Germany while unit labor costs in USD increased at 2.6 percent in the US, 4.7 percent in Japan and 4.3 percent in Germany. From 1995 to 2000, output per hour increased at the average yearly rate of 4.6 percent in the US, 3.9 percent in Japan and 2.6 percent in Germany while unit labor costs in USD fell at minus 0.7 percent in the US, 4.3 percent in Japan and 7.5 percent in Germany. There was increase in productivity growth in Japan and France within the G7 in the second half of the 1990s but significantly lower than the acceleration of 1.3 percentage points per year in the US. Table II-7 provides average growth rates of indicators in the research of productivity and growth of the US Bureau of Labor Statistics. There is dramatic decline of productivity growth from 2.1 percent per year on average from 1947 to 2019 to 1.4 percent per year on average in the whole cycle from 2007 to 2019. Productivity increased at the average rate of 2.3 percent from 1947 to 2007. Productivity increased 2.4 percent from 2019 to 2020 with decrease of output at 4.4 percent and decrease of hours worked at 6.6 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Productivity increased 1.9 percent from 2020 to 2021 with growth of output of 7.4 percent and of hours worked of 5.4 percent. There is profound drop in the average rate of output growth from 3.4 percent on average from 1947 to 2019 to 1.9 percent from 2007 to 2019. Output grew at 3.7 percent per year on average from 1947 to 2007. Output contracted at 4.4 percent from 2019 to 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). 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 2.2 percent on average in the cyclical expansion in the 50 quarters from IIIQ2009 to IVQ2021 and in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). 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, 201 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) (https://apps.bea.gov/iTable/index_nipa.cfm) and the second estimate of GDP for IVQ2021 (https://www.bea.gov/sites/default/files/2022-02/gdp4q21_2nd.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 2.9 percent obtained by dividing GDP of $15,605.6 billion in IIQ2010 by GDP of $15,161.8 billion in IIQ2009 {[($15,605.6/$15,161.8) -1]100 = 2.9%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2022/02/us-gdp-growing-at-saar-of-70-percent-in.html and earlier https://cmpassocregulationblog.blogspot.com/2022/01/fomc-states-with-inflation-well-above-2.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IQ1988, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989, 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ1992, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993, 3.7 percent from IQ1983 to IVQ1993, 3.7 percent from IQ1983 to IQ1994, 3.7 percent from IQ1983 to IIQ1994, 3.7 percent from IQ1983 to IIIQ1994, 3.7 percent from IQ1983 to IVQ1994, 3.6 percent from IQ1983 to IQ1995, 3.6 percent from IQ1983 to IIQ1995 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2022/02/us-gdp-growing-at-saar-of-70-percent-in.html and earlier https://cmpassocregulationblog.blogspot.com/2022/01/fomc-states-with-inflation-well-above-2.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.4 percent from the pre-recession peak of $9404.5 billion of chained 2012 dollars in IIIQ1990 to the trough of $9275.3 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IVQ2021 and in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021) would have accumulated to 51.3 percent. GDP in IVQ2021 would be $23,849.2 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $4038.6 billion than actual $19,810.6 billion. There are more than four trillion dollars of GDP less than at trend, explaining the 23.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 13.3 percent of the effective labor force with the largest part originating in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2022/03/increase-in-feb-2022-of-nonfarm-payroll.html and earlier https://cmpassocregulationblog.blogspot.com/2022/02/increase-in-jan-2022-of-nonfarm-payroll.html). Unemployment is decreasing while employment is increasing in initial adjustment of the lockdown of economic activity in the global recession resulting from the COVID-19 event (https://www.bls.gov/covid19/effects-of-covid-19-pandemic-and-response-on-the-employment-situation-news-release.htm). US GDP in IVQ2021 is 16.9 percent lower than at trend. US GDP grew from $15,767.1 billion in IVQ2007 in constant dollars to $19,810.6 billion in IVQ2021 or 25.6 percent at the average annual equivalent rate of 1.6 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 3.0 percent per year from Feb 1919 to Feb 2022. Growth at 3.0 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 162.3669 in Feb 2022. The actual index NSA in Feb 2022 is 101.4579 which is 37.5 percent below trend. The underperformance of manufacturing in Mar-Nov 2020 originates partly in the earlier global recession augmented by the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 169.1965 in Feb 2022. The actual index NSA in Feb 2022 is 101.4579, which is 40.0 percent below trend. Manufacturing output grew at average 1.8 percent between Dec 1986 and Feb 2022. Using trend growth of 1.8 percent per year, the index would increase to 137.5298 in Feb 2022. The output of manufacturing at 101.4579 in Feb 2022 is 26.2 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 108.5167 in Jul 2007 to the low of 84.7321 in May 2009 or 21.9 percent. The NAICS manufacturing index increased from 84.7321 in Apr 2009 to 102.5311 in Feb 2022 or 21.0 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 104.6868 in Dec 2007 to 170.4304 in Feb 2022. The NAICS index at 102.5311 in Feb 2021 is 39.8 percent below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 104.6868 in Dec 2007 to 132.9246 in Feb 2022. The NAICS index at 102.5311 in Feb 2022 is 22.9 percent below trend under this alternative calculation.
Table II-7, US, Productivity and Costs, Average Annual Percentage Changes 2007-2019, 1947-2007, 1947-2019 and 2019-2021
| Average Annual Percentage Rate 2007-2019 | Average Annual Percentage Rate 1947-2007 | Average Annual Percentage Rate 1947-2019 | Percentage Change 2019-2021 | |
| Productivity | 1.4 | 2.3 | 2.1 | 4.3 |
| Output | 1.9 | 3.7 | 3.4 | 3.5 |
| Hours | 0.5 | 1.4 | 1.2 | -1.6 |
| Employment | 0.6 | 1.6 | 1.5 | -2.7 |
| Average Weekly Hours | -1.3* | -14.4* | -15.6* | 1.1 |
| Hourly Compensation | 2.5 | 5.4 | 4.9 | 12.7 |
| Consumer Price Inflation | 1.8 | 3.8 | 3.4 | 6.0 |
| Real Hourly Compensation | 0.7 | 1.7 | 1.5 | 6.4 |
| Unit Labor Costs | 1.1 | 3.0 | 2.7 | 8.1 |
| Unit Non-Labor Payments | 1.9 | 3.5 | 3.2 | -0.3 |
| Output per Job | 1.3 | 2.0 | 1.9 | 5.4 |
* Percentage Change
Source: US Bureau of Labor Statistics https://www.bls.gov/lpc/
Unit labor costs increased sharply during the Great Inflation from the late 1960s to 1981 as shown by sharper slope in Chart II-8. Unit labor costs continued to increase but at a lower rate because of cyclic factors and not because of imaginary secular stagnation.
Chart II-8, US, Nonfarm Business, Unit Labor Costs, 1947-2021, Index 2012=100
Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/
Real hourly compensation increased at relatively high rates after 1947 to the early 1970s but reached a plateau that lasted until the early 1990s, as shown in Chart II-9. There were rapid increases until the global recession. Cyclic factors and not alleged secular stagnation explain the interruption of increases in real hourly compensation.
Chart II-9, US, Nonfarm Business, Real Hourly Compensation, 1947-2021, Index 2012=100
Source: US Bureau of Labor Statistics: https://www.bls.gov/lpc/
II I United States Housing Collapse. Data and other information continue to provide depressed conditions in the US housing market in a longer perspective, with recent improvement at the margin. Table IIB-1 shows sales of new houses in the US at seasonally adjusted annual equivalent rate (SAAR). The US Census Bureau revised all seasonally adjusted new house sales from 2014 to 2019 with the report for Apr 2019 on May 23, 2019 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). The US Census Bureau revised all seasonally adjusted new house sales from 2015 to 2020 with the report of Apr 2020 on May 26, 2020 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). The US Census Bureau revised all seasonally adjusted new house sales from 2016 to 2021 with the report for Apr 2021 on May 25, 2021 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). There is significant oscillation of monthly house sales. Recovery from the global recession after 2007 was inadequate. New house sales dropped 14.7 percent in Mar 2020 and decreased 6.6 percent in Apr 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Recovery was quite sharp with SAARs jumping to 993 thousand in Jan 2021. Sales eased to 772 thousand in Feb 2022.
Table IIB-1, US, Sales of New Houses Seasonally Adjusted Annual Rate (SAAR), Thousands and %
| SAAR Thousands | ∆% | ||
| Dec-2010 | 326 | 13.6 | |
| Jan-2011 | 307 | -5.8 | |
| Feb-2011 | 270 | -12.1 | |
| Mar-2011 | 300 | 11.1 | |
| Apr-2011 | 310 | 3.3 | |
| May-2011 | 305 | -1.6 | |
| Jun-2011 | 301 | -1.3 | |
| Jul-2011 | 296 | -1.7 | |
| Aug-2011 | 299 | 1.0 | |
| Sep-2011 | 304 | 1.7 | |
| Oct-2011 | 316 | 3.9 | |
| Nov-2011 | 328 | 3.8 | |
| Dec-2011 | 341 | 4.0 | |
| Jan-2012 | 335 | -1.8 | |
| Feb-2012 | 366 | 9.3 | |
| Mar-2012 | 354 | -3.3 | |
| Apr-2012 | 354 | 0.0 | |
| May-2012 | 370 | 4.5 | |
| Jun-2012 | 360 | -2.7 | |
| Jul-2012 | 369 | 2.5 | |
| Aug-2012 | 375 | 1.6 | |
| Sep-2012 | 385 | 2.7 | |
| Oct-2012 | 358 | -7.0 | |
| Nov-2012 | 392 | 9.5 | |
| Dec-2012 | 399 | 1.8 | |
| Jan-2013 | 446 | 11.8 | |
| Feb-2013 | 447 | 0.2 | |
| Mar-2013 | 444 | -0.7 | |
| Apr-2013 | 441 | -0.7 | |
| May-2013 | 428 | -2.9 | |
| Jun-2013 | 470 | 9.8 | |
| Jul-2013 | 375 | -20.2 | |
| Aug-2013 | 381 | 1.6 | |
| Sep-2013 | 403 | 5.8 | |
| Oct-2013 | 444 | 10.2 | |
| Nov-2013 | 446 | 0.5 | |
| Dec-2013 | 433 | -2.9 | |
| Jan-2014 | 443 | 2.3 | |
| Feb-2014 | 420 | -5.2 | |
| Mar-2014 | 405 | -3.6 | |
| Apr-2014 | 403 | -0.5 | |
| May-2014 | 451 | 11.9 | |
| Jun-2014 | 418 | -7.3 | |
| Jul-2014 | 402 | -3.8 | |
| Aug-2014 | 456 | 13.4 | |
| Sep-2014 | 470 | 3.1 | |
| Oct-2014 | 476 | 1.3 | |
| Nov-2014 | 442 | -7.1 | |
| Dec-2014 | 497 | 12.4 | |
| Jan-2015 | 515 | 3.6 | |
| Feb-2015 | 540 | 4.9 | |
| Mar-2015 | 480 | -11.1 | |
| Apr-2015 | 502 | 4.6 | |
| May-2015 | 502 | 0.0 | |
| Jun-2015 | 480 | -4.4 | |
| Jul-2015 | 506 | 5.4 | |
| Aug-2015 | 518 | 2.4 | |
| Sep-2015 | 456 | -12.0 | |
| Oct-2015 | 482 | 5.7 | |
| Nov-2015 | 504 | 4.6 | |
| Dec-2015 | 546 | 8.3 | |
| Jan-2016 | 505 | -7.5 | |
| Feb-2016 | 517 | 2.4 | |
| Mar-2016 | 532 | 2.9 | |
| Apr-2016 | 576 | 8.3 | |
| May-2016 | 571 | -0.9 | |
| Jun-2016 | 557 | -2.5 | |
| Jul-2016 | 628 | 12.7 | |
| Aug-2016 | 575 | -8.4 | |
| Sep-2016 | 558 | -3.0 | |
| Oct-2016 | 575 | 3.0 | |
| Nov-2016 | 571 | -0.7 | |
| Dec-2016 | 561 | -1.8 | |
| Jan-2017 | 578 | 3.0 | |
| Feb-2017 | 601 | 4.0 | |
| Mar-2017 | 643 | 7.0 | |
| Apr-2017 | 604 | -6.1 | |
| May-2017 | 627 | 3.8 | |
| Jun-2017 | 612 | -2.4 | |
| Jul-2017 | 553 | -9.6 | |
| Aug-2017 | 550 | -0.5 | |
| Sep-2017 | 622 | 13.1 | |
| Oct-2017 | 625 | 0.5 | |
| Nov-2017 | 718 | 14.9 | |
| Dec-2017 | 658 | -8.4 | |
| Jan-2018 | 610 | -7.3 | |
| Feb-2018 | 644 | 5.6 | |
| Mar-2018 | 680 | 5.6 | |
| Apr-2018 | 658 | -3.2 | |
| May-2018 | 680 | 3.3 | |
| Jun-2018 | 598 | -12.1 | |
| Jul-2018 | 600 | 0.3 | |
| Aug-2018 | 582 | -3.0 | |
| Sep-2018 | 584 | 0.3 | |
| Oct-2018 | 546 | -6.5 | |
| Nov-2018 | 618 | 13.2 | |
| Dec-2018 | 566 | -8.4 | |
| Jan-2019 | 628 | 11.0 | |
| Feb-2019 | 675 | 7.5 | |
| Mar-2019 | 721 | 6.8 | |
| Apr-2019 | 689 | -4.4 | |
| May-2019 | 619 | -10.2 | |
| Jun-2019 | 711 | 14.9 | |
| Jul-2019 | 636 | -10.5 | |
| Aug-2019 | 677 | 6.4 | |
| Sep-2019 | 706 | 4.3 | |
| Oct-2019 | 703 | -0.4 | |
| Nov-2019 | 700 | -0.4 | |
| Dec-2019 | 733 | 4.7 | |
| Jan-2020 | 756 | 3.1 | |
| Feb-2020 | 730 | -3.4 | |
| Mar-2020 | 623 | -14.7 | |
| Apr-2020 | 582 | -6.6 | |
| May-2020 | 704 | 21.0 | |
| Jun-2020 | 839 | 19.2 | |
| Jul-2020 | 972 | 15.9 | |
| Aug-2020 | 977 | 0.5 | |
| Sep-2020 | 971 | -0.6 | |
| Oct-2020 | 969 | -0.2 | |
| Nov-2020 | 865 | -10.7 | |
| Dec-2020 | 943 | 9.0 | |
| Jan-2021 | 993 | 5.3 | |
| Feb-2021 | 823 | -17.1 | |
| Mar-2021 | 873 | 6.1 | |
| Apr-2021 | 796 | -8.8 | |
| May-2021 | 733 | -7.9 | |
| Jun-2021 | 683 | -6.8 | |
| Jul-2021 | 704 | 3.1 | |
| Aug-2021 | 668 | -5.1 | |
| Sep-2021 | 725 | 8.5 | |
| Oct-2021 | 667 | -8.0 | |
| Nov-2021 | 753 | 12.9 | |
| Dec-2021 | 860 | 14.2 | |
| Jan-2022 | 788 | -8.4 | |
| Feb-2022 | 772 | -2.0 |
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
The depressed level of residential construction and new house sales in the US is evident in Table IIB-3 providing new house sales not seasonally adjusted in Jan-Feb-Feb of various years. New house sales decreased 12.2 percent from Jan-Feb 2021 to Jan-Feb 2022 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). There is an ongoing boom in real estate acquisitions. Comparisons are strong relative to current house sales in contrast with weakness in earlier periods after the global recession from IVQ2007 to IIQ2009. New house sales increased 5.7 percent from Jan-Feb 2020 to Jan-Feb 2022. New house sales increased 21.7 percent from Jan-Feb 2019 to Jan-Feb 2022. New house sales increased 26.5 percent from Jan-Feb 2018 to Jan-Feb 2022. New house sales increased 34.4 percent from Jan-Feb 2017 to Jan-Feb 2022. Sales of new houses are higher in Jan-Feb 2022 relative to Jan-Feb 2016 with increase of 53.6 percent. Sales of new houses are higher in Jan-Feb 2022 relative to Jan-Feb 2015 with increase of 53.6 percent. Sales of new houses in Jan-Feb 2022 were substantially lower than in many years between 1996 and 2019 except for the years from 2008 to 2019. There are several other increases of 89.7 percent relative to 2014, 89.7 percent relative to Jan-Feb 2013, 143.4 percent relative to Jan-Feb 2012, 200.0 percent relative to Jan-Feb 2011, 152.9 percent relative to Jan-Feb 2010, and 143.4 percent relative to Jan-Feb 2009. New house sales in Jan-Feb 2022 are 40.2 percent higher than in Jan-Feb 2008. Sales of new houses in Jan-Feb 2022 are lower by 3.7 percent relative to Jan-Feb 2007. Sales of new houses are lower by 27.1 percent relative to Jan-Feb 2006, 35.8 percent relative to 2005 and 32.5 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-Feb 2022 relative to the same period in 2003 fell 15.8 percent and decreased 3.0 percent relative to the same period in 2002. Similar percentage declines are also for 2022 relative to years from 2000 to 2004. Sales of new houses in Jan-Feb 2022 changed 0.0 per cent relative to the same period in 1998. The population of the US was 179.3 million in 1960 and 281.4 million in 2000 (Hobbs and Stoops 2002, 16). Detailed historical census reports are available from the US Census Bureau at (http://www.census.gov/population/www/censusdata/hiscendata.html). The estimate of the US population is 418.8 million in 2015. The US population increased by 133.6 percent from 1960 to 2015. The final row of Table IIB-3 reveals catastrophic data: sales of new houses in Jan-Feb 2022 of 129 thousand units are only 3.2 percent higher relative to 125 thousand units of houses sold in Jan 1977, which is fifteenth year when data become available in 1963. The civilian noninstitutional population increased from 122.416 million in 1963 to 259.175 million in 2019, or 111.7 percent (https://www.bls.gov/data/), to 260.329 million in 2020 or 112.7 percent and to 261.445 million in 2021 or 113.6 percent. The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (http://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.” Note: there are two equal total new houses sold in 2015 of 84 (39 in Jan and 45 in Feb) and 84 in 2016 (39 in Jan and 45 in Feb). There are two other equal total new houses sold of 68 in 2013 (32 in Jan and 36 in Feb) and 68 in 2014 (33 in Jan and 35 in 2014).
Table IIB-3, US, Sales of New Houses Not Seasonally Adjusted, Thousands and %
| Jan-Feb 2022 | 129 |
| Jan-Feb 2021 | 147 |
| ∆% Jan-Feb 22/Jan-Feb 2021 | -12.2 |
| Jan-Feb 2020 | 122 |
| ∆% Jan-Feb 22/Jan-Feb 2020 | 5.7 |
| Jan-Feb 2019 | 106 |
| ∆% Jan-Feb 2022/Jan-Feb 2019 | 21.7 |
| Jan-Feb 2018 | 102 |
| ∆% Jan-Feb 2022/Jan-Feb 2018 | 26.5 |
| Jan-Feb 2017 | 96 |
| ∆% Jan-Feb 2022/Jan-Feb 2017 | 34.4 |
| Jan-Feb 2016 | 84 |
| ∆% Jan-Feb 2022/Jan-Feb 2016 | 53.6 |
| Jan-Feb 2015 | 84 |
| ∆% Jan-Feb 2022/Jan-Feb 2015 | 53.6 |
| Jan-Feb 2014 | 68 |
| ∆% Jan-Feb 2022/Jan-Feb 2014 | 89.7 |
| Jan-Feb 2013 | 68 |
| ∆% Jan-Feb 2022/Jan-Feb 2013 | 89.7 |
| Jan-Feb 2012 | 53 |
| ∆% Jan-Feb 2022/ | 143.4 |
| Jan-Feb 2011 | 43 |
| ∆% Jan-Feb 2022/ | 200.0 |
| Jan-Feb 2010 | 51 |
| ∆% Jan-Feb 2022/ | 152.9 |
| Jan-Feb 2009 | 53 |
| ∆% Jan-Feb 2022/ | 143.4 |
| Jan-Feb 2008 | 92 |
| ∆% Jan-Feb 2022/Jan-Feb 2008 | 40.2 |
| Jan-Feb 2007 | 134 |
| ∆% Jan-Feb 2022/Jan-Feb 2007 | -3.7 |
| Jan-Feb 2006 | 177 |
| ∆% Jan-Feb 2022/Jan-Feb 2006 | -27.1 |
| Jan-Feb 2005 | 201 |
| ∆% Jan-Feb 2022/ | -35.8 |
| Jan-Feb 2004 | 191 |
| ∆% Jan-Feb 2022/ | -32.5 |
| Jan-Feb 2003 | 158 |
| ∆% Jan-Feb 2022/ | -18.4 |
| Jan-Feb 2002 | 150 |
| ∆% Jan-Feb 2022/Jan-Feb 2002 | -14.0 |
| Jan-Feb 2001 | 157 |
| ∆% Jan-Feb 2022/Jan-Feb 2001 | -17.8 |
| Jan-Feb 2000 | 145 |
| ∆% Jan-Feb 2022/Jan-Feb 2000 | -11.0 |
| Jan-Feb 1998 | 139 |
| ∆% Jan-Feb 2020/Jan-Feb 1998 | -7.2 |
| Jan-Feb 1977 | 125 |
| ∆% Jan-Feb 2022/Jan-Feb 1977 | 3.2 |
*Computed using unrounded data
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Table IIB-4 provides annual sales of new houses. The revised level of 306 thousand new houses sold in 2011 is the lowest since 560 thousand in 1963 in the 59 years of available data while the level of 368 thousand in 2012 is only higher than 323 thousand in 2010. The level of sales of new houses of 437 thousand in 2014 is the lowest from 1963 to 2009 with exception of 412 thousand in 1982 and 436 thousand in 1981. The population of the US increased 129.4 million from 179.3 million in 1960 to 308.7 million in 2010, or 72.2 percent. The estimate of the US population is 418.8 million in 2015. The US population increased 133.6 percent from 1960 to 2015. The civilian noninstitutional population increased from 122.416 million in 1963 to 259.175 million in 2019, or 111.7 percent (https://www.bls.gov/data/), to 260.329 million in 2020 or 112.7 percent and 261.445 million in 2021 or 113.6 percent. The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (https://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.”
Table IIB-4, US, New Houses Sold, NSA Thousands
| Period | Sold During Period |
| 1963 | 560 |
| 1964 | 565 |
| 1965 | 575 |
| 1966 | 461 |
| 1967 | 487 |
| 1968 | 490 |
| 1969 | 448 |
| 1970 | 485 |
| 1971 | 656 |
| 1972 | 718 |
| 1973 | 634 |
| 1974 | 519 |
| 1975 | 549 |
| 1976 | 646 |
| 1977 | 819 |
| 1978 | 817 |
| 1979 | 709 |
| 1980 | 545 |
| 1981 | 436 |
| 1982 | 412 |
| 1983 | 623 |
| 1984 | 639 |
| 1985 | 688 |
| 1986 | 750 |
| 1987 | 671 |
| 1988 | 676 |
| 1989 | 650 |
| 1990 | 534 |
| 1991 | 509 |
| 1992 | 610 |
| 1993 | 666 |
| 1994 | 670 |
| 1995 | 667 |
| 1996 | 757 |
| 1997 | 804 |
| 1998 | 886 |
| 1999 | 880 |
| 2000 | 877 |
| 2001 | 908 |
| 2002 | 973 |
| 2003 | 1,086 |
| 2004 | 1,203 |
| 2005 | 1,283 |
| 2006 | 1,051 |
| 2007 | 776 |
| 2008 | 485 |
| 2009 | 375 |
| 2010 | 323 |
| 2011 | 306 |
| 2012 | 368 |
| 2013 | 429 |
| 2014 | 437 |
| 2015 | 501 |
| 2016 | 561 |
| 2017 | 613 |
| 2018 | 617 |
| 2019 | 683 |
| 2020 | 822 |
| 2021 | 770 |
Source: US Census Bureau https://www.census.gov/construction/nrs/index.html
Chart IIB-1 of the US Bureau of the Census shows the sharp decline of sales of new houses in the US. Sales rose temporarily until about mid 2010 but then declined to a lower plateau followed by increase, stability and new oscillating increase. There is decrease in the final segment followed by marginal increase. There is renewed decline and stabilization with recovery in oscillations in May-2020-Feb 2022.
Chart IIB-1, US, New One-Family Houses Sold in the US, SAAR (Seasonally Adjusted Annual Rate)
Source: US Census Bureau
https://www.census.gov/construction/nrs/img/c25_curr.gif
Between 1991 and 2001, sales of new houses rose 78.4 percent at the average yearly rate of 6.0 percent, as shown in Table IB-5. Between 1995 and 2005 sales of new houses increased 92.4 percent at the yearly rate of 6.8 percent. There are similar rates in all years from 2000 to 2005. The boom in housing construction and sales began in the 1980s and 1990s. The collapse of real estate culminated several decades of housing subsidies and policies to lower mortgage rates and borrowing terms (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009b), 42-8). Sales of new houses sold in 2019 fell 9.8 percent relative to the same period in 1996 and fell 46.8 percent relative to 2005. Sales of new houses increased 20.4 percent from 2019 to 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Sales of new houses increased 12.7 percent from 2019 to 2021 and fell 6.3 percent in 2021 relative to 2020.
Table IIB-5, US, Percentage Change and Average Yearly Rate of Growth of Sales of New One-Family Houses
| ∆% | Average Yearly % Rate | |
| 1963-2019 | 22.0 | 0.4 |
| 1991-2001 | 78.4 | 6.0 |
| 1995-2005 | 92.4 | 6.8 |
| 2000-2005 | 46.3 | 7.9 |
| 1996-2019 | -9.8 | NA |
| 2000-2019 | -22.1 | NA |
| 2005-2019 | -46.8 | NA |
| 2019-2020 | 20.4 | NA |
| 2019-2021 | 12.7 | NA |
| 2020-2021 | -6.3 | NA |
NA: Not Applicable
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
IMPORTANT NOTE: Charts IIB-2 through IIB-2A cannot be updated because of the discontinuance of support of the Adobe Flash Player (https://www.adobe.com/products/flashplayer/end-of-life.html). There are updates with Fusion charts.
Chart IIB-2, US, New Single-family Houses Sold, NSA, 1963-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-2F, US, New Single-family Houses Sold, NSA, 1963-2022
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-2A, US, New Single-family Houses Sold, NSA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-2AF, US, New Single-family Houses Sold, NSA, 2019-2022
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-2A1, US, New Single-family Houses Sold, SA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-2A1F, US, New Single-family Houses Sold, SAAR, 2019-2022
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
The available historical annual data of median and average prices of new houses sold in the US between 1963 and 2021 is in Table IIB-6. On a yearly basis, median and average prices reached a peak in 2007 and then fell substantially. There is recovery in 2012-2018 followed by decline in 2019. Prices recovered in 2020 and 2021 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021).
Table IIB-6, US, Median and Average Prices of New Houses Sold, Annual Data
| Period | Median | Average |
| 1963 | $18,000 | $19,300 |
| 1964 | $18,900 | $20,500 |
| 1965 | $20,000 | $21,500 |
| 1966 | $21,400 | $23,300 |
| 1967 | $22,700 | $24,600 |
| 1968 | $24,700 | $26,600 |
| 1969 | $25,600 | $27,900 |
| 1970 | $23,400 | $26,600 |
| 1971 | $25,200 | $28,300 |
| 1972 | $27,600 | $30,500 |
| 1973 | $32,500 | $35,500 |
| 1974 | $35,900 | $38,900 |
| 1975 | $39,300 | $42,600 |
| 1976 | $44,200 | $48,000 |
| 1977 | $48,800 | $54,200 |
| 1978 | $55,700 | $62,500 |
| 1979 | $62,900 | $71,800 |
| 1980 | $64,600 | $76,400 |
| 1981 | $68,900 | $83,000 |
| 1982 | $69,300 | $83,900 |
| 1983 | $75,300 | $89,800 |
| 1984 | $79,900 | $97,600 |
| 1985 | $84,300 | $100,800 |
| 1986 | $92,000 | $111,900 |
| 1987 | $104,500 | $127,200 |
| 1988 | $112,500 | $138,300 |
| 1989 | $120,000 | $148,800 |
| 1990 | $122,900 | $149,800 |
| 1991 | $120,000 | $147,200 |
| 1992 | $121,500 | $144,100 |
| 1993 | $126,500 | $147,700 |
| 1994 | $130,000 | $154,500 |
| 1995 | $133,900 | $158,700 |
| 1996 | $140,000 | $166,400 |
| 1997 | $146,000 | $176,200 |
| 1998 | $152,500 | $181,900 |
| 1999 | $161,000 | $195,600 |
| 2000 | $169,000 | $207,000 |
| 2001 | $175,200 | $213,200 |
| 2002 | $187,600 | $228,700 |
| 2003 | $195,000 | $246,300 |
| 2004 | $221,000 | $274,500 |
| 2005 | $240,900 | $297,000 |
| 2006 | $246,500 | $305,900 |
| 2007 | $247,900 | $313,600 |
| 2008 | $232,100 | $292,600 |
| 2009 | $216,700 | $270,900 |
| 2010 | $221,800 | $272,900 |
| 2011 | $227,200 | $267,900 |
| 2012 | $245,200 | $292,200 |
| 2013 | $268,900 | $324,500 |
| 2014 | $288,500 | $347,700 |
| 2015 | $294,200 | $352,700 |
| 2016 | $307,800 | $360,900 |
| 2017 | $323,100 | $384,900 |
| 2018 | $326,400 | $385,000 |
| 2019 | $321,500 | $383,900 |
| 2020 | $336,900 | $391,900 |
| 2021 | $398,800 | $462,000 |
Source: US Census Bureau https://www.census.gov/construction/nrs/index.html
Prices rose sharply between 2000 and 2005 as shown in Table IIB-7. In fact, prices in 2019 are higher than in 2000. Between 2006 and 2019, median prices of new houses sold increased 30.4 percent and average prices increased 25.5 percent. Between 2018 and 2019, median prices decreased 1.5 percent and average prices decreased 0.3 percent. Median prices increased 36.7 percent from 2006 to 2020 while average prices increased 28.1 percent. Median prices increased 4.8 percent from 2019 to 2020 while average prices increased 2.1 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event. Median prices increased 61.8 percent from 2006 to 2021 while average prices increased 51.0 percent. Median prices increased 18.4 percent from 2020 to 2021 while average prices increased 17.9 percent.
Table IIB-7, US, Percentage Change of New Houses Median and Average Prices, NSA, ∆%
| Median New | Average New Home Sales Prices ∆% | |
| ∆% 2000 to 2003 | 15.4 | 19.0 |
| ∆% 2000 to 2005 | 42.5 | 43.5 |
| ∆% 2000 to 2019 | 90.2 | 85.5 |
| ∆% 2005 to 2019 | 33.5 | 29.3 |
| ∆% 2000 to 2006 | 45.9 | 47.8 |
| ∆% 2006 to 2019 | 30.4 | 25.5 |
| ∆% 2009 to 2019 | 48.4 | 41.7 |
| ∆% 2010 to 2019 | 45.0 | 40.7 |
| ∆% 2011 to 2019 | 41.5 | 43.3 |
| ∆% 2012 to 2019 | 31.1 | 31.4 |
| ∆% 2013 to 2019 | 19.6 | 18.3 |
| ∆% 2014 to 2019 | 11.4 | 10.4 |
| ∆% 2015 to 2019 | 9.3 | 8.8 |
| ∆% 2016 to 2019 | 4.5 | 6.4 |
| ∆% 2017 to 2019 | -0.5 | -0.3 |
| ∆% 2018 to 2019 | -1.5 | -0.3 |
| ∆% 2006 to 2020 | 36.7 | 28.1 |
| ∆% 2019 to 2020 | 4.8 | 2.1 |
| ∆% 2006 to 2021 | 61.8 | 51.0 |
| ∆% 2019 to 2021 | 24.0 | 20.3 |
| ∆% 2020 to 2021 | 18.4 | 17.9 |
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
IMPORTANT NOTE: Charts IIB-3 through IIB-4A cannot be updated because of the discontinuance of support of the Adobe Flash Player (https://www.adobe.com/products/flashplayer/end-of-life.html). There are updates with Fusion charts.
Chart IIB-3 of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 1963 to Nov 2020. There is long-term sharp upward trend with few declines until the current collapse. Median prices increased sharply during the Great Inflation of the 1960s and 1970s and paused during the savings and loans crisis of the late 1980s and the recession of 1991. Housing subsidies throughout the 1990s caused sharp upward trend of median new house prices that accelerated after the fed funds rate of 1 percent from 2003 to 2004. There was sharp reduction of prices after 2006 with recovery recently above earlier prices.
Chart IIB-3, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-3F, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2022
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-3A of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 2019 to Nov 2020. There is sharp decline of prices in Mar-Apr 2020 in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event followed by vigorous recovery in May-Jun 2020 with decline in Jul-Aug 2020, recovery in Sep 2020 and decline in Oct-Nov 2020.
Chart IIB-3A, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-3AF, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2022
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4F of the US Census Bureau provides average prices of new houses sold from the mid-1970s to Nov 2020. There is similar behavior as with median prices of new houses sold in Chart IIB-3. The only stress occurred in price pauses during the savings and loans crisis of the late 1980s and the collapse after 2006 with recent recovery, interrupted in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021).
Chart IIB-4F, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1975-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4F, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1975-2022
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4A of the US Census Bureau provides average prices of new houses sold from Jan 2019 to Nov 2020. Prices declined in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/cycles.html), in the lockdown of economic activity in the COVID-19 event with vigorous recovery in May-Jun 2020 followed by decline in Jul 2020. There is sharp recovery in Sep-Aug 2020 followed by decline in Oct 2020 and mild increase in Nov 2020.
Chart IIB-4A, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4AF, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2022
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4A, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2020
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-4AF, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 2019-2021
Source: US Census Bureau
https://www.census.gov/construction/nrs/index.html
Chart IIB-5 of the Board of Governors of the Federal Reserve System provides the rate for the 30-year conventional mortgage, the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1954 to 2016. All rates decline throughout the period from the Great Inflation of the 1970s through the following Great Moderation and until currently. In Apr 1971, the fed funds rate was 4.15 percent and the conventional mortgage rate 7.31 percent. In November 2012, the fed funds rate was 0.16 percent, the yield of the 30-year Treasury 2.80 percent and the conventional mortgage rate 3.35. The final segment shows an increase in the yield of the 30-year Treasury to 3.61 percent in July 2013 with the fed funds rate at 0.09 percent and the conventional mortgage at 4.37 percent. The final data point shows marginal decrease of the conventional mortgage rate to 3.60 percent in May 2016 with the yield of the 30-year Treasury bond at 2.63 percent and overnight rate on fed funds at 0.37 percent. The recent increase in interest rates if sustained could affect the US real estate market. Shayndi Raice and Nick Timiraos, writing on “Banks cut as mortgage boom ends,” on Jan 9, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303754404579310940019239208), analyze the drop in mortgage applications to a 13-year low, as measured by the Mortgage Bankers Association. Nick Timiraos, writing on “Demand for home loans plunges,” on Apr 24, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304788404579522051733228402?mg=reno64-wsj), analyzes data in Inside Mortgage Finance that mortgage lending of $235 billion in IQ2014 is 58 percent lower than a year earlier and 23 percent below IVQ2013. Mortgage lending collapsed to the lowest level in 14 years. In testimony before the Committee on the Budget of the US Senate on May 8, 2004, Chair Yellen provides analysis of the current economic situation and outlook (http://www.federalreserve.gov/newsevents/testimony/yellen20140507a.htm): “One cautionary note, though, is that readings on housing activity--a sector that has been recovering since 2011--have remained disappointing so far this year and will bear watching.”
Chart IIB-5, US, Thirty-year Conventional Mortgage, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1954-2016
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/H15/default.htm
Chart IIB-5A of the Board of Governors of the Federal Reserve System provides the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1977 to 2022. The Board of Governors of the Federal Reserve System discontinued the conventional mortgage rate in its data bank. The final data point is 0.08 percent for the fed funds rate in Feb 2022 and 2.25 percent for the thirty-year Treasury bond resulting from the massive unconventional monetary policy in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). The conventional mortgage rate stood at 3.76 percent in Feb 2022.
Chart IIB-5A, US, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1977-2022
Source: Board of Governors of the Federal Reserve System
https://www.federalreserve.gov/releases/H15/default.htm
Table IIB-8, US, Fed Funds Rate, Thirty Year Treasury Bond and Conventional Mortgage Rate, Monthly, Percent per Year, Dec 2012 to Feb 2022
| Fed Funds Rate | Yield of Thirty-Year Constant Maturity | Conventional Mortgage Rate | |
| 2012-12 | 0.16 | 2.88 | 3.35 |
| 2013-01 | 0.14 | 3.08 | 3.41 |
| 2013-02 | 0.15 | 3.17 | 3.53 |
| 2013-03 | 0.14 | 3.16 | 3.57 |
| 2013-04 | 0.15 | 2.93 | 3.45 |
| 2013-05 | 0.11 | 3.11 | 3.54 |
| 2013-06 | 0.09 | 3.4 | 4.07 |
| 2013-07 | 0.09 | 3.61 | 4.37 |
| 2013-08 | 0.08 | 3.76 | 4.46 |
| 2013-09 | 0.08 | 3.79 | 4.49 |
| 2013-10 | 0.09 | 3.68 | 4.19 |
| 2013-11 | 0.08 | 3.8 | 4.26 |
| 2013-12 | 0.09 | 3.89 | 4.46 |
| 2014-01 | 0.07 | 3.77 | 4.43 |
| 2014-02 | 0.07 | 3.66 | 4.3 |
| 2014-03 | 0.08 | 3.62 | 4.34 |
| 2014-04 | 0.09 | 3.52 | 4.34 |
| 2014-05 | 0.09 | 3.39 | 4.19 |
| 2014-06 | 0.1 | 3.42 | 4.16 |
| 2014-07 | 0.09 | 3.33 | 4.13 |
| 2014-08 | 0.09 | 3.2 | 4.12 |
| 2014-09 | 0.09 | 3.26 | 4.16 |
| 2014-10 | 0.09 | 3.04 | 4.04 |
| 2014-11 | 0.09 | 3.04 | 4 |
| 2014-12 | 0.12 | 2.83 | 3.86 |
| 2015-01 | 0.11 | 2.46 | 3.67 |
| 2015-02 | 0.11 | 2.57 | 3.71 |
| 2015-03 | 0.11 | 2.63 | 3.77 |
| 2015-04 | 0.12 | 2.59 | 3.67 |
| 2015-05 | 0.12 | 2.96 | 3.84 |
| 2015-06 | 0.13 | 3.11 | 3.98 |
| 2015-07 | 0.13 | 3.07 | 4.05 |
| 2015-08 | 0.14 | 2.86 | 3.91 |
| 2015-09 | 0.14 | 2.95 | 3.89 |
| 2015-10 | 0.12 | 2.89 | 3.8 |
| 2015-11 | 0.12 | 3.03 | 3.94 |
| 2015-12 | 0.24 | 2.97 | 3.96 |
| 2016-01 | 0.34 | 2.86 | 3.87 |
| 2016-02 | 0.38 | 2.62 | 3.66 |
| 2016-03 | 0.36 | 2.68 | 3.69 |
| 2016-04 | 0.37 | 2.62 | 3.61 |
| 2016-05 | 0.37 | 2.63 | 3.6 |
| 2016-06 | 0.38 | 2.45 | 3.57 |
| 2016-07 | 0.39 | 2.23 | 3.44 |
| 2016-08 | 0.4 | 2.26 | 3.44 |
| 2016-09 | 0.4 | 2.35 | 3.46 |
| 2016-10 | 0.4 | 2.5 | 3.47 |
| 2016-11 | 0.41 | 2.86 | 3.77 |
| 2016-12 | 0.54 | 3.11 | 4.2 |
| 2017-01 | 0.65 | 3.02 | 4.15 |
| 2017-02 | 0.66 | 3.03 | 4.17 |
| 2017-03 | 0.79 | 3.08 | 4.2 |
| 2017-04 | 0.9 | 2.94 | 4.05 |
| 2017-05 | 0.91 | 2.96 | 4.01 |
| 2017-06 | 1.04 | 2.8 | 3.9 |
| 2017-07 | 1.15 | 2.88 | 3.97 |
| 2017-08 | 1.16 | 2.8 | 3.88 |
| 2017-09 | 1.15 | 2.78 | 3.81 |
| 2017-10 | 1.15 | 2.88 | 3.9 |
| 2017-11 | 1.16 | 2.8 | 3.92 |
| 2017-12 | 1.3 | 2.77 | 3.95 |
| 2018-01 | 1.41 | 2.88 | 4.03 |
| 2018-02 | 1.42 | 3.13 | 4.33 |
| 2018-03 | 1.51 | 3.09 | 4.44 |
| 2018-04 | 1.69 | 3.07 | 4.47 |
| 2018-05 | 1.7 | 3.13 | 4.59 |
| 2018-06 | 1.82 | 3.05 | 4.57 |
| 2018-07 | 1.91 | 3.01 | 4.53 |
| 2018-08 | 1.91 | 3.04 | 4.55 |
| 2018-09 | 1.95 | 3.15 | 4.63 |
| 2018-10 | 2.19 | 3.34 | 4.83 |
| 2018-11 | 2.2 | 3.36 | 4.87 |
| 2018-12 | 2.27 | 3.10 | 4.64 |
| 2019-01 | 2.40 | 3.04 | 4.46 |
| 2019-02 | 2.40 | 3.02 | 4.37 |
| 2019-03 | 2.41 | 2.98 | 4.27 |
| 2019-04 | 2.42 | 2.94 | 4.14 |
| 2019-05 | 2.39 | 2.82 | 4.07 |
| 2019-06 | 2.38 | 2.57 | 3.80 |
| 2019-07 | 2.40 | 2.57 | 3.77 |
| 2019-08 | 2.13 | 2.12 | 3.62 |
| 2019-09 | 2.04 | 2.16 | 3.61 |
| 2019-10 | 1.83 | 2.19 | 3.69 |
| 2019-11 | 1.55 | 2.28 | 3.70 |
| 2019-12 | 1.55 | 2.30 | 3.72 |
| 2020-01 | 1.55 | 2.22 | 3.62 |
| 2020-02 | 1.58 | 1.97 | 3.47 |
| 2020-03 | 0.65 | 1.46 | 3.45 |
| 2020-04 | 0.05 | 1.27 | 3.31 |
| 2020-05 | 0.05 | 1.38 | 3.23 |
| 2020-06 | 0.08 | 1.49 | 3.16 |
| 2020-07 | 0.09 | 1.31 | 3.02 |
| 2020-08 | 0.10 | 1.36 | 2.94 |
| 2020-09 | 0.09 | 1.42 | 2.89 |
| 2020-10 | 0.09 | 1.57 | 2.83 |
| 2020-11 | 0.09 | 1.62 | 2.77 |
| 2020-12 | 0.09 | 1.67 | 2.68 |
| 2021-01 | 0.09 | 1.82 | 2.74 |
| 2021-02 | 0.08 | 2.04 | 2.81 |
| 2021-03 | 0.07 | 2.34 | 3.08 |
| 2021-04 | 0.07 | 2.30 | 3.06 |
| 2021-05 | 0.06 | 2.32 | 2.96 |
| 2021-06 | 0.08 | 2.16 | 2.98 |
| 2021-7 | 0.10 | 1.94 | 2.87 |
| 2021-8 | 0.09 | 1.92 | 2.84 |
| 2021-9 | 0.08 | 1.94 | 2.90 |
| 2021-10 | 0.08 | 2.06 | 3.07 |
| 2021-11 | 0.08 | 1.94 | 3.07 |
| 2021-12 | 0.08 | 1.85 | 3.10 |
| 2022-01 | 0.08 | 2.10 | 3.45 |
| 2022-02 | 0.08 | 2.25 | 3.76 |
Source: Board of Governors of the Federal Reserve System
https://www.federalreserve.gov/releases/H15/default.htm
http://www.freddiemac.com/pmms/pmms30.html
IIB2 United States House Prices. The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). Table IIA2-1 provides the FHFA HPI for purchases only, which shows behavior similar to that of the Case-Shiller index but with lower magnitudes. House prices catapulted from 2000 to 2003, 2005 and 2006. From IVQ2000 to IVQ2006, the index for the US as a whole rose 55.0 percent, with 62.1 percent for New England, 72.0 percent for Middle Atlantic, 71.2 percent for South Atlantic but only by 33.1 percent for East South Central. Prices fell relative to 2014 for the US and all regions from 2006 with exception of increase of 2.6 percent for East South Central. Prices for the US increased 4.9 percent in IVQ2014 relative to IVQ2013 and 12.9 percent from IVQ2012 to IVQ2014. From IVQ2000 to IVQ2014, prices rose for the US and the four regions in Table IIA2-1.
Table IIA2-1, US, FHFA House Price Index Purchases Only NSA ∆%
| United States | New England | Middle Atlantic | South Atlantic | East South Central | |
| IVQ2000 | 24.0 | 40.6 | 35.8 | 25.9 | 11.0 |
| IVQ2000 | 50.5 | 65.0 | 67.6 | 62.9 | 25.4 |
| IVQ2000 to | 55.0 | 62.1 | 72.0 | 71.2 | 33.1 |
| IVQ2005 to | -1.5 | -8.7 | -2.3 | -7.4 | 8.9 |
| IVQ2006 | -4.4 | -7.1 | -4.8 | -11.9 | 2.6 |
| IVQ2007 to | -1.9 | -5.1 | -5.0 | -8.6 | 0.7 |
| IVQ2011 to | 18.9 | 7.3 | 6.9 | 19.9 | 11.8 |
| IVQ2012 to | 12.9 | 6.8 | 5.7 | 13.8 | 8.6 |
| IVQ2013 to IVQ2014 | 4.9 | 2.5 | 2.2 | 5.1 | 4.2 |
| IVQ2000 to | 48.3 144.27 | 50.6 138.40 | 63.7 127.30 | 50.9 140.28 | 36.6 146.07 |
Source: Federal Housing Finance Agency
http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx
Data of the FHFA HPI for the remaining US regions are in Table IIA2-2. Behavior is not very different from that in Table IIA2-1 with the exception of East North Central. House prices in the Pacific region doubled between 2000 and 2006. Although prices of houses declined sharply from 2005 and 2006 to 2014 with exception of West South Central and West North Central, there was still appreciation relative to 2000.
Table IIA2-2, US, FHFA House Price Index Purchases Only NSA ∆%
| West South Central | West North Central | East North Central | Mountain | Pacific | |
| IVQ2000 | 11.1 | 18.3 | 14.7 | 18.9 | 44.6 |
| IVQ2000 | 23.9 | 31.0 | 23.8 | 58.0 | 107.7 |
| IVQ2000 to IVQ2006 | 31.6 | 33.7 | 23.7 | 68.6 | 108.7 |
| IVQ2005 to | 26.6 | 4.7 | -5.4 | -2.6 | -14.7 |
| IVQ2006 | 19.1 | 2.6 | -5.4 | -8.7 | -15.1 |
| IVQ2007 to | 15.2 | 3.2 | -2.1 | -5.6 | -6.0 |
| IVQ2011 to | 18.1 | 13.5 | 14.2 | 32.9 | 37.6 |
| IVQ2012 to | 12.1 | 8.9 | 11.1 | 17.9 | 24.4 |
| IVQ2013 to IVQ2014 | 5.9 | 4.0 | 4.6 | 5.5 | 7.3 |
| IVQ2000 to IVQ2014 | 56.8 145.53 | 37.1 158.59 | 17.1 155.13 | 53.9 172.46 | 77.1 132.21 |
Source: Federal Housing Finance Agency
http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx
Monthly and 12-month percentage changes of the FHFA House Price Index are in Table IIA2-3. Percentage monthly increases of the FHFA index were positive from Apr to Jul 2011 with exception of declines in May and Aug 2011 while 12-month percentage changes improved steadily from around minus 6.0 percent in Mar to May 2011 to minus 4.6 percent in Jun 2011. The house price index increased 0.6 percent in Jan 2019 and increased 5.4 percent in 12 months. House prices increased 0.4 percent in Feb 2019 and increased 5.0 percent in 12 months. The house price index increased 0.3 percent in Mar 2019 and increased 4.9 percent in 12 months. House prices increased 0.6 percent in Apr 2019 and increased 5.2 percent in 12 months. The house price index increased 0.5 percent in May 2019 and increased 5.1 percent in 12 months. House prices increased 0.3 percent in Jun 2019 and increased 4.9 percent in 12 months. The house price index increased 0.4 percent in Jul 2019 and increased 4.9 percent in 12 months. House prices increased 0.2 percent in Aug 2019 and increased 4.8 percent in 12 months. The house price index increased 0.6 percent in Sep 2019 and increased 5.3 percent in 12 months. House prices increased 0.4 percent in Oct 2019 and increased 5.4 percent in 12 months. The house price index increased 0.5 percent in Nov 2019 and increased 5.3 percent in 12 months. House prices increased 0.8 percent in Dec 2019 and increased 5.9 percent in 12 months. The house price index increased 0.8 percent in Jan 2020 and increased 6.0 percent in 12 months. House prices increased 0.8 percent in Feb 2020 and increased 6.5 percent in 12 months. The house price index increased 0.2 percent in Mar 2020 and increased 6.3 percent in 12 months. House prices increased 0.3 percent in Apr 2020 and increased 6.0 percent in 12 months. The house price index decreased 0.2 percent in May 2020 and increased 5.2 percent in 12 months. House prices increased 1.1 percent in Jun 2020 and increased 6.0 percent in 12 months. The house price index increased 1.2 percent in Jul 2020 and increased 6.9 percent in 12 months. House prices increased 1.6 percent in Aug 2020 and increased 8.4 percent in 12 months. The house price index increased 1.5 percent in Sep 2020 and increased 9.5 percent in 12 months. House prices increased 1.5 percent in Oct 2020 and increased 10.6 percent in 12 months. The house price index increased 1.1 percent in Nov 2020 and increased 11.3 percent in 12 months. House prices increased 1.2 percent in Dec 2020 and increased 11.8 percent in 12 months. The house price index increased 1.3 percent in Jan 2021 and increased 12.4 percent in 12 months. House prices increased 1.1 percent in Feb 2021 and increased 12.7 percent in 12 months. The house price index increased 1.6 percent in Mar 2021 and increased 14.3 percent in 12 months. House prices increased 1.8 percent in Apr 2021 and increased 16.0 percent in 12 months. The house price index increased 1.8 percent in May 2021 and increased 18.3 percent in 12 months. House prices increased 1.7 percent in Jun 2021 and increased 19.1 percent in 12 months. The house price index increased 1.4 percent in Jul 2021 and increased 19.3 percent in 12 months. House prices increased 1.0 percent in Aug 2021 and increased 18.6 percent in 12 months. The house price index increased 0.9 percent in Sep 2021 and increased 17.9 percent in 12 months. House prices increased 1.1 percent in Oct 2021 and increased 17.5 percent in 12 months. The house price index increased 1.2 percent in Nov 2021 and increased 17.7 percent in 12 months. House prices increased 1.2 percent in Dec 2021 and increased 17.7 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 | ||||
| 12/1/2021 | 1.2 | 17.7 | |||
| 11/1/2021 | 1.2 | 17.7 | |||
| 10/1/2021 | 1.1 | 17.5 | |||
| 9/1/2021 | 0.9 | 17.9 | |||
| 8/1/2021 | 1.0 | 18.6 | |||
| 7/1/2021 | 1.4 | 19.3 | |||
| 6/1/2021 | 1.7 | 19.1 | |||
| 5/1/2021 | 1.8 | 18.3 | |||
| 4/1/2021 | 1.8 | 16.0 | |||
| 3/1/2021 | 1.6 | 14.3 | |||
| 2/1/2021 | 1.1 | 12.7 | |||
| 1/1/2021 | 1.3 | 12.4 | |||
| 12/1/2020 | 1.2 | 11.8 | |||
| 11/1/2020 | 1.1 | 11.3 | |||
| 10/1/2020 | 1.5 | 10.6 | |||
| 9/1/2020 | 1.5 | 9.5 | |||
| 8/1/2020 | 1.6 | 8.4 | |||
| 7/1/2020 | 1.2 | 6.9 | |||
| 6/1/2020 | 1.1 | 6.0 | |||
| 5/1/2020 | -0.2 | 5.2 | |||
| 4/1/2020 | 0.3 | 5.9 | |||
| 3/1/2020 | 0.2 | 6.3 | |||
| 2/1/2020 | 0.8 | 6.5 | |||
| 1/1/2020 | 0.8 | 6.0 | |||
| 12/1/2019 | 0.8 | 5.9 | |||
| 11/1/2019 | 0.5 | 5.3 | |||
| 10/1/2019 | 0.4 | 5.4 | |||
| 9/1/2019 | 0.6 | 5.3 | |||
| 8/1/2019 | 0.2 | 4.8 | |||
| 7/1/2019 | 0.4 | 4.9 | |||
| 6/1/2019 | 0.3 | 4.9 | |||
| 5/1/2019 | 0.5 | 5.1 | |||
| 4/1/2019 | 0.6 | 5.2 | |||
| 3/1/2019 | 0.3 | 4.9 | |||
| 2/1/2019 | 0.4 | 5.0 | |||
| 1/1/2019 | 0.6 | 5.4 | |||
| 12/1/2018 | 0.2 | 5.5 | |||
| 11/1/2018 | 0.6 | 5.7 | |||
| 10/1/2018 | 0.4 | 5.8 | |||
| 9/1/2018 | 0.1 | 5.9 | |||
| 8/1/2018 | 0.5 | 6.0 | |||
| 7/1/2018 | 0.4 | 6.2 | |||
| 6/1/2018 | 0.4 | 6.3 | |||
| 5/1/2018 | 0.6 | 6.4 | |||
| 4/1/2018 | 0.3 | 6.4 | |||
| 3/1/2018 | 0.3 | 6.9 | |||
| 2/1/2018 | 0.8 | 7.3 | |||
| 1/1/2018 | 0.8 | 7.1 | |||
| 12/1/2017 | 0.4 | 6.2 | |||
| 11/1/2017 | 0.7 | 6.4 | |||
| 10/1/2017 | 0.4 | 6.2 | |||
| 9/1/2017 | 0.4 | 6.3 | |||
| 8/1/2017 | 0.7 | 6.4 | |||
| 7/1/2017 | 0.6 | 6.1 | |||
| 6/1/2017 | 0.4 | 6.1 | |||
| 5/1/2017 | 0.5 | 6.4 | |||
| 4/1/2017 | 0.8 | 6.4 | |||
| 3/1/2017 | 0.7 | 6.2 | |||
| 2/1/2017 | 0.6 | 6.3 | |||
| 1/1/2017 | 0.0 | 5.8 | |||
| 12/1/2016 | 0.6 | 6.2 | |||
| 11/1/2016 | 0.5 | 5.9 | |||
| 10/1/2016 | 0.5 | 5.9 | |||
| 9/1/2016 | 0.6 | 5.9 | |||
| 8/1/2016 | 0.4 | 5.9 | |||
| 7/1/2016 | 0.5 | 5.6 | |||
| 6/1/2016 | 0.6 | 5.5 | |||
| 5/1/2016 | 0.4 | 5.5 | |||
| 4/1/2016 | 0.5 | 5.7 | |||
| 3/1/2016 | 0.8 | 5.6 | |||
| 2/1/2016 | 0.1 | 5.2 | |||
| 1/1/2016 | 0.4 | 5.8 | |||
| 12/1/2015 | 0.4 | 5.4 | |||
| 11/1/2015 | 0.5 | 5.6 | |||
| 10/1/2015 | 0.5 | 5.5 | |||
| 9/1/2015 | 0.5 | 5.5 | |||
| 8/1/2015 | 0.2 | 5.1 | |||
| 7/1/2015 | 0.5 | 5.2 | |||
| 6/1/2015 | 0.4 | 5.2 | |||
| 5/1/2015 | 0.6 | 5.4 | |||
| 4/1/2015 | 0.3 | 5.0 | |||
| 3/1/2015 | 0.4 | 5.1 | |||
| 2/1/2015 | 0.8 | 5.0 | |||
| 1/1/2015 | 0.1 | 4.6 | |||
| 12/1/2014 | 0.6 | 4.9 | |||
| 11/1/2014 | 0.5 | 4.8 | |||
| 10/1/2014 | 0.6 | 4.3 | |||
| 9/1/2014 | 0.1 | 4.0 | |||
| 8/1/2014 | 0.4 | 4.4 | |||
| 7/1/2014 | 0.4 | 4.4 | |||
| 6/1/2014 | 0.5 | 4.6 | |||
| 5/1/2014 | 0.2 | 4.7 | |||
| 4/1/2014 | 0.3 | 5.4 | |||
| 3/1/2014 | 0.4 | 5.7 | |||
| 2/1/2014 | 0.4 | 6.3 | |||
| 1/1/2014 | 0.4 | 6.5 | |||
| 12/1/2013 | 0.6 | 6.7 | |||
| 11/1/2013 | 0.0 | 6.6 | |||
| 10/1/2013 | 0.2 | 7.0 | |||
| 9/1/2013 | 0.5 | 7.3 | |||
| 8/1/2013 | 0.3 | 7.2 | |||
| 7/1/2013 | 0.6 | 7.7 | |||
| 6/1/2013 | 0.6 | 7.4 | |||
| 5/1/2013 | 0.8 | 7.1 | |||
| 4/1/2013 | 0.5 | 6.9 | |||
| 3/1/2013 | 1.0 | 7.0 | |||
| 2/1/2013 | 0.6 | 6.6 | |||
| 1/1/2013 | 0.7 | 6.2 | |||
| 12/1/2012 | 0.5 | 5.0 | |||
| 11/1/2012 | 0.5 | 4.8 | |||
| 10/1/2012 | 0.5 | 4.8 | |||
| 9/1/2012 | 0.4 | 3.8 | |||
| 8/1/2012 | 0.7 | 4.0 | |||
| 7/1/2012 | 0.2 | 3.1 | |||
| 6/1/2012 | 0.4 | 3.1 | |||
| 5/1/2012 | 0.6 | 2.9 | |||
| 4/1/2012 | 0.5 | 2.1 | |||
| 3/1/2012 | 0.9 | 1.8 | |||
| 2/1/2012 | 0.2 | -0.4 | |||
| 1/1/2012 | -0.4 | -1.4 | |||
| 12/1/2011 | 0.3 | -1.5 | |||
| 11/1/2011 | 0.4 | -2.6 | |||
| 10/1/2011 | -0.5 | -3.3 | |||
| 9/1/2011 | 0.6 | -2.6 | |||
| 8/1/2011 | -0.3 | -4.0 | |||
| 7/1/2011 | 0.2 | -3.8 | |||
| 6/1/2011 | 0.3 | -4.6 | |||
| 5/1/2011 | -0.2 | -5.9 | |||
| 4/1/2011 | 0.2 | -5.8 | |||
| 3/1/2011 | -1.1 | -5.9 | |||
| 2/1/2011 | -0.9 | -5.1 | |||
| 1/1/2011 | -0.6 | -4.5 | |||
| 12/1/2010 | -0.7 | -3.9 | |||
| 12/1/2009 | -1.0 | -2.0 | |||
| 12/1/2008 | -0.3 | -10.4 | |||
| 12/1/2007 | -0.5 | -3.4 | |||
| 12/1/2006 | 0.0 | 2.2 | |||
| 12/1/2005 | 0.6 | 9.8 | |||
| 12/1/2004 | 0.9 | 10.2 | |||
| 12/1/2003 | 0.8 | 7.9 | |||
| 12/1/2002 | 0.7 | 7.8 | |||
| 12/1/2001 | 0.7 | 6.7 | |||
| 12/1/2000 | 0.6 | 7.1 | |||
| 12/1/1999 | 0.5 | 6.1 | |||
| 12/1/1998 | 0.5 | 5.9 | |||
| 12/1/1997 | 0.3 | 3.3 | |||
| 12/1/1996 | 0.3 | 2.7 | |||
| 12/1/1995 | 0.4 | 3.0 | |||
| 12/1/1994 | 0.0 | 2.5 | |||
| 12/1/1993 | 0.5 | 3.1 | |||
| 12/1/1992 | -0.1 | 2.3 |
Source: Federal Housing Finance Agency
https://www.fhfa.gov/DataTools
The bottom part of Table IIA2-3 provides 12-month percentage changes of the FHFA house price index since 1992 when data become available for 1991. Table IIA2-4 provides percentage changes and average rates of percent change per year for various periods. Between 1992 and 2019, the FHFA house price index increased 167.1 percent at the yearly average rate of 3.7 percent. In the period 1992-2000, the FHFA house price index increased 39.0 percent at the average yearly rate of 4.2 percent. The average yearly rate of price increase accelerated to 7.5 percent in the period 2000-2003, 8.5 percent in 2000-2005 and 7.4 percent in 2000-2006. At the margin, the average rate jumped to 10.0 percent in 2003-2005 and 7.4 percent in 2003-2006. House prices measured by the FHFA house price index increased 25.2 percent at the average yearly rate of 1.7 percent between 2006 and 2019 and 28.0 percent between 2005 and 2019 at the average yearly rate of 1.8 percent. The FHFA house price index increased 31.6 percent from 2019 to 2021 at the average yearly rate of 9.6 percent in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021).
Table IIA2-4, US, FHFA House Price Index, Percentage Change and Average Rate of Percentage Change per Year, Selected Dates 1992-2020
| Dec | ∆% | Average ∆% per Year |
| 1992-2019 | 167.1 | 3.7 |
| 1992-2020 | 198.6 | 4.0 |
| 1992-2021 | 251.3 | 4.4 |
| 1992-2000 | 39.0 | 4.2 |
| 2000-2003 | 24.1 | 7.5 |
| 2000-2005 | 50.2 | 8.5 |
| 2003-2005 | 21.0 | 10.0 |
| 2005-2019 | 28.0 | 1.8 |
| 2005-2020 | 43.1 | 2.4 |
| 2005-2021 | 68.3 | 3.5 |
| 2000-2006 | 53.5 | 7.4 |
| 2003-2006 | 23.7 | 7.4 |
| 2006-2019 | 25.2 | 1.7 |
| 2006-2020 | 39.9 | 2.4 |
| 2006-2021 | 64.6 | 3.4 |
| 2019-2021 | 31.6 | 9.6 |
Source: Federal Housing Finance Agency
https://www.fhfa.gov/DataTools
The explanation of the sharp contraction of household wealth can probably be found in the origins of the financial crisis and global recession. Let V(T) represent the value of the firm’s equity at time T and B stand for the promised debt of the firm to bondholders and assume that corporate management, elected by equity owners, is acting on the interests of equity owners. Robert C. Merton (1974, 453) states:
“On the maturity date T, the firm must either pay the promised payment of B to the debtholders or else the current equity will be valueless. Clearly, if at time T, V(T) > B, the firm should pay the bondholders because the value of equity will be V(T) – B > 0 whereas if they do not, the value of equity would be zero. If V(T) ≤ B, then the firm will not make the payment and default the firm to the bondholders because otherwise the equity holders would have to pay in additional money and the (formal) value of equity prior to such payments would be (V(T)- B) < 0.”
Pelaez and Pelaez (The Global Recession Risk (2007), 208-9) apply this analysis to the US housing market in 2005-2006 concluding:
“The house market [in 2006] is probably operating with low historical levels of individual equity. There is an application of structural models [Duffie and Singleton 2003] to the individual decisions on whether or not to continue paying a mortgage. The costs of sale would include realtor and legal fees. There could be a point where the expected net sale value of the real estate may be just lower than the value of the mortgage. At that point, there would be an incentive to default. The default vulnerability of securitization is unknown.”
There are multiple important determinants of the interest rate: “aggregate wealth, the distribution of wealth among investors, expected rate of return on physical investment, taxes, government policy and inflation” (Ingersoll 1987, 405). Aggregate wealth is a major driver of interest rates (Ibid, 406). Unconventional monetary policy, with zero fed funds rates and flattening of long-term yields by quantitative easing, causes uncontrollable effects on risk taking that can have profound undesirable effects on financial stability. Excessively aggressive and exotic monetary policy is the main culprit and not the inadequacy of financial management and risk controls.
The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Ibid). According to a subsequent restatement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption decisions is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:
W = Y/r (1)
Equation (1) shows that as r goes to zero, r →0, W grows without bound, W→∞.
Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment but the exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at close to zero interest rates, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV).
The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever increasing house prices. The suspension of auctions of 30-year Treasuries was designed to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity because of the penalty in the form of low interest rates and unsound credit decisions because the put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4).
There are significant elements of the theory of bank financial fragility of Diamond and Dybvig (1983) and Diamond and Rajan (2000, 2001a, 2001b) that help to explain the financial fragility of banks during the credit/dollar crisis (see also Diamond 2007). The theory of Diamond and Dybvig (1983) as exposed by Diamond (2007) is that banks funding with demand deposits have a mismatch of liquidity (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 58-66). A run occurs when too many depositors attempt to withdraw cash at the same time. All that is needed is an expectation of failure of the bank. Three important functions of banks are providing evaluation, monitoring and liquidity transformation. Banks invest in human capital to evaluate projects of borrowers in deciding if they merit credit. The evaluation function reduces adverse selection or financing projects with low present value. Banks also provide important monitoring services of following the implementation of projects, avoiding moral hazard that funds be used for, say, real estate speculation instead of the original project of factory construction. The transformation function of banks involves both assets and liabilities of bank balance sheets. Banks convert an illiquid asset or loan for a project with cash flows in the distant future into a liquid liability in the form of demand deposits that can be withdrawn immediately.
In the theory of banking of Diamond and Rajan (2000, 2001a, 2001b), the bank creates liquidity by tying human assets to capital. The collection skills of the relationship banker convert an illiquid project of an entrepreneur into liquid demand deposits that are immediately available for withdrawal. The deposit/capital structure is fragile because of the threat of bank runs. In these days of online banking, the run on Washington Mutual was through withdrawals online. A bank run can be triggered by the decline of the value of bank assets below the value of demand deposits.
Pelaez and Pelaez (Regulation of Banks and Finance 2009b, 60, 64-5) find immediate application of the theories of banking of Diamond, Dybvig and Rajan to the credit/dollar crisis after 2007. It is a credit crisis because the main issue was the deterioration of the credit portfolios of securitized banks as a result of default of subprime mortgages. It is a dollar crisis because of the weakening dollar resulting from relatively low interest rate policies of the US. It caused systemic effects that converted into a global recession not only because of the huge weight of the US economy in the world economy but also because the credit crisis transferred to the UK and Europe. Management skills or human capital of banks are illustrated by the financial engineering of complex products. The increasing importance of human relative to inanimate capital (Rajan and Zingales 2000) is revolutionizing the theory of the firm (Zingales 2000) and corporate governance (Rajan and Zingales 2001). Finance is one of the most important examples of this transformation. Profits were derived from the charter in the original banking institution. Pricing and structuring financial instruments was revolutionized with option pricing formulas developed by Black and Scholes (1973) and Merton (1973, 1974, 1998) that permitted the development of complex products with fair pricing. The successful financial company must attract and retain finance professionals who have invested in human capital, which is a sunk cost to them and not of the institution where they work.
The complex financial products created for securitized banking with high investments in human capital are based on houses, which are as illiquid as the projects of entrepreneurs in the theory of banking. The liquidity fragility of the securitized bank is equivalent to that of the commercial bank in the theory of banking (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65). Banks created off-balance sheet structured investment vehicles (SIV) that issued commercial paper receiving AAA rating because of letters of liquidity guarantee by the banks. The commercial paper was converted into liquidity by its use as collateral in SRPs at the lowest rates and minimal haircuts because of the AAA rating of the guarantor bank. In the theory of banking, default can be triggered when the value of assets is perceived as lower than the value of the deposits. Commercial paper issued by SIVs, securitized mortgages and derivatives all obtained SRP liquidity on the basis of illiquid home mortgage loans at the bottom of the pyramid. The run on the securitized bank had a clear origin (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65):
“The increasing default of mortgages resulted in an increase in counterparty risk. Banks were hit by the liquidity demands of their counterparties. The liquidity shock extended to many segments of the financial markets—interbank loans, asset-backed commercial paper (ABCP), high-yield bonds and many others—when counterparties preferred lower returns of highly liquid safe havens, such as Treasury securities, than the risk of having to sell the collateral in SRPs at deep discounts or holding an illiquid asset. The price of an illiquid asset is near zero.”
Gorton and Metrick (2010H, 507) provide a revealing quote to the work in 1908 of Edwin R. A. Seligman, professor of political economy at Columbia University, founding member of the American Economic Association and one of its presidents and successful advocate of progressive income taxation. The intention of the quote is to bring forth the important argument that financial crises are explained in terms of “confidence” but as Professor Seligman states in reference to historical banking crises in the US, the important task is to explain what caused the lack of confidence. It is instructive to repeat the more extended quote of Seligman (1908, xi) on the explanations of banking crises:
“The current explanations may be divided into two categories. Of these the first includes what might be termed the superficial theories. Thus it is commonly stated that the outbreak of a crisis is due to lack of confidence,--as if the lack of confidence was not in itself the very thing which needs to be explained. Of still slighter value is the attempt to associate a crisis with some particular governmental policy, or with some action of a country’s executive. Such puerile interpretations have commonly been confined to countries like the United States, where the political passions of democracy have had the fullest way. Thus the crisis of 1893 was ascribed by the Republicans to the impending Democratic tariff of 1894; and the crisis of 1907 has by some been termed the ‘[Theodore] Roosevelt panic,” utterly oblivious of the fact that from the time of President Jackson, who was held responsible for the troubles of 1837, every successive crisis had had its presidential scapegoat, and has been followed by a political revulsion. Opposed to these popular, but wholly unfounded interpretations, is the second class of explanations, which seek to burrow beneath the surface and to discover the more occult and fundamental causes of the periodicity of crises.”
Scholars ignore superficial explanations in the effort to seek good and truth. The problem of economic analysis of the credit/dollar crisis is the lack of a structural model with which to attempt empirical determination of causes (Gorton and Metrick 2010SB). There would still be doubts even with a well-specified structural model because samples of economic events do not typically permit separating causes and effects. There is also confusion is separating the why of the crisis and how it started and propagated, all of which are extremely important.
In true heritage of the principles of Seligman (1908), Gorton (2009EFM) discovers a prime causal driver of the credit/dollar crisis. The objective of subprime and Alt-A mortgages was to facilitate loans to populations with modest means so that they could acquire a home. These borrowers would not receive credit because of (1) lack of funds for down payments; (2) low credit rating and information; (3) lack of information on income; and (4) errors or lack of other information. Subprime mortgage “engineering” was based on the belief that both lender and borrower could benefit from increases in house prices over the short run. The initial mortgage would be refinanced in two or three years depending on the increase of the price of the house. According to Gorton (2009EFM, 13, 16):
“The outstanding amounts of Subprime and Alt-A [mortgages] combined amounted to about one quarter of the $6 trillion mortgage market in 2004-2007Q1. Over the period 2000-2007, the outstanding amount of agency mortgages doubled, but subprime grew 800%! Issuance in 2005 and 2006 of Subprime and Alt-A mortgages was almost 30% of the mortgage market. Since 2000 the Subprime and Alt-A segments of the market grew at the expense of the Agency (i.e., the government sponsored entities of Fannie Mae and Freddie Mac) share, which fell from almost 80% (by outstanding or issuance) to about half by issuance and 67% by outstanding amount. The lender’s option to rollover the mortgage after an initial period is implicit in the subprime mortgage. The key design features of a subprime mortgage are: (1) it is short term, making refinancing important; (2) there is a step-up mortgage rate that applies at the end of the first period, creating a strong incentive to refinance; and (3) there is a prepayment penalty, creating an incentive not to refinance early.”
The prime objective of successive administrations in the US during the past 20 years and actually since the times of Roosevelt in the 1930s has been to provide “affordable” financing for the “American dream” of home ownership. The US housing finance system is mixed with public, public/private and purely private entities. The Federal Home Loan Bank (FHLB) system was established by Congress in 1932 that also created the Federal Housing Administration in 1934 with the objective of insuring homes against default. In 1938, the government created the Federal National Mortgage Association, or Fannie Mae, to foster a market for FHA-insured mortgages. Government-insured mortgages were transferred from Fannie Mae to the Government National Mortgage Association, or Ginnie Mae, to permit Fannie Mae to become a publicly-owned company. Securitization of mortgages began in 1970 with the government charter to the Federal Home Loan Mortgage Corporation, or Freddie Mac, with the objective of bundling mortgages created by thrift institutions that would be marketed as bonds with guarantees by Freddie Mac (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 42-8). In the third quarter of 2008, total mortgages in the US were $12,057 billion of which 43.5 percent, or $5423 billion, were retained or guaranteed by Fannie Mae and Freddie Mac (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 45). In 1990, Fannie Mae and Freddie Mac had a share of only 25.4 percent of total mortgages in the US. Mortgages in the US increased from $6922 billion in 2002 to $12,088 billion in 2007, or by 74.6 percent, while the retained or guaranteed portfolio of Fannie and Freddie rose from $3180 billion in 2002 to $4934 billion in 2007, or by 55.2 percent.
According to Pinto (2008) in testimony to Congress:
“There are approximately 25 million subprime and Alt-A loans outstanding, with an unpaid principal amount of over $4.5 trillion, about half of them held or guaranteed by Fannie and Freddie. Their high risk activities were allowed to operate at 75:1 leverage ratio. While they may deny it, there can be no doubt that Fannie and Freddie now own or guarantee $1.6 trillion in subprime, Alt-A and other default prone loans and securities. This comprises over 1/3 of their risk portfolios and amounts to 34% of all the subprime loans and 60% of all Alt-A loans outstanding. These 10.5 million unsustainable, nonprime loans are experiencing a default rate 8 times the level of the GSEs’ 20 million traditional quality loans. The GSEs will be responsible for a large percentage of an estimated 8.8 million foreclosures expected over the next 4 years, accounting for the failure of about 1 in 6 home mortgages. Fannie and Freddie have subprimed America.”
In perceptive analysis of growth and macroeconomics in the past six decades, Rajan (2012FA) argues that “the West can’t borrow and spend its way to recovery.” The Keynesian paradigm is not applicable in current conditions. Advanced economies in the West could be divided into those that reformed regulatory structures to encourage productivity and others that retained older structures. In the period from 1950 to 2000, Cobet and Wilson (2002) find that US productivity, measured as output/hour, grew at the average yearly rate of 2.9 percent while Japan grew at 6.3 percent and Germany at 4.7 percent (see Pelaez and Pelaez, The Global Recession Risk (2007), 135-44). In the period from 1995 to 2000, output/hour grew at the average yearly rate of 4.6 percent in the US but at lower rates of 3.9 percent in Japan and 2.6 percent in the US. Rajan (2012FA) argues that the differential in productivity growth was accomplished by deregulation in the US at the end of the 1970s and during the 1980s. In contrast, Europe did not engage in reform with the exception of Germany in the early 2000s that empowered the German economy with significant productivity advantage. At the same time, technology and globalization increased relative remunerations in highly-skilled, educated workers relative to those without skills for the new economy. It was then politically appealing to improve the fortunes of those left behind by the technological revolution by means of increasing cheap credit. As Rajan (2012FA) argues:
“In 1992, Congress passed the Federal Housing Enterprises Financial Safety and Soundness Act, partly to gain more control over Fannie Mae and Freddie Mac, the giant private mortgage agencies, and partly to promote affordable homeownership for low-income groups. Such policies helped money flow to lower-middle-class households and raised their spending—so much so that consumption inequality rose much less than income inequality in the years before the crisis. These policies were also politically popular. Unlike when it came to an expansion in government welfare transfers, few groups opposed expanding credit to the lower-middle class—not the politicians who wanted more growth and happy constituents, not the bankers and brokers who profited from the mortgage fees, not the borrowers who could now buy their dream houses with virtually no money down, and not the laissez-faire bank regulators who thought they could pick up the pieces if the housing market collapsed. The Federal Reserve abetted these shortsighted policies. In 2001, in response to the dot-com bust, the Fed cut short-term interest rates to the bone. Even though the overstretched corporations that were meant to be stimulated were not interested in investing, artificially low interest rates acted as a tremendous subsidy to the parts of the economy that relied on debt, such as housing and finance. This led to an expansion in housing construction (and related services, such as real estate brokerage and mortgage lending), which created jobs, especially for the unskilled. Progressive economists applauded this process, arguing that the housing boom would lift the economy out of the doldrums. But the Fed-supported bubble proved unsustainable. Many construction workers have lost their jobs and are now in deeper trouble than before, having also borrowed to buy unaffordable houses. Bankers obviously deserve a large share of the blame for the crisis. Some of the financial sector’s activities were clearly predatory, if not outright criminal. But the role that the politically induced expansion of credit played cannot be ignored; it is the main reason the usual checks and balances on financial risk taking broke down.”
In fact, Raghuram G. Rajan (2005) anticipated low liquidity in financial markets resulting from low interest rates before the financial crisis that caused distortions of risk/return decisions provoking the credit/dollar crisis and global recession from IVQ2007 to IIQ2009. Near zero interest rates of unconventional monetary policy induced excessive risks and low liquidity in financial decisions that were critical as a cause of the credit/dollar crisis after 2007. Rajan (2012FA) argues that it is not feasible to return to the employment and income levels before the credit/dollar crisis because of the bloated construction sector, financial system and government budgets.
Table IIA-1 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 65.6 percent in the US national home price index between Dec 2000 and Dec 2005. Prices rose 68.4 percent in the US national index from Dec 2000 to Dec 2006. House prices rose 29.0 percent between Dec 2003 and Dec 2005 for the US national propelled by low fed funds rates of 1.0 percent between Dec 2003 and Dec 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Jun 2004 until Jun 2006, reaching 5.25 percent. Simultaneously, the suspension of auctions of the 30-year Treasury bond caused decrease of yields of mortgage-backed securities with intended increase in mortgage rates. Similarly, between Dec 2003 and Dec 2006 the US national increased 31.2 percent. House prices have increased from Dec 2006 to Dec 2021 by 52.1 percent for the US national. Measuring house prices is quite difficult because of the lack of homogeneity that is typical of standardized commodities. In the 12 months ending in Dec 2021, house prices increased 18.8 percent in the US national. Table IIA-1 also shows that house prices increased 156.1 percent between Dec 2000 and Dec 2021 for the US national. House prices had been close to the lowest level since peaks during the boom before the financial crisis and global recession. The US national increased 51.0 percent in Dec 2021 from the peak in Jun 2006 and increased 51.0 percent from the peak in Jul 2006. The final part of Table II-2 provides average annual percentage rates of growth of the house price indexes of Standard & Poor’s Case-Shiller. The average rate for the US national was 4.2 percent from Dec 1987 to Dec 2021 and 3.6 percent from Dec 1987 to Dec 2000. Although the global recession affecting the US between IVQ2007 (Dec) and IIQ2009 (Jun) caused decline of house prices of slightly above 30 percent, the average annual growth rate between Dec 2000 and Dec 2021 was 4.6 percent for the US national.
Table IIA-1, US, Percentage Changes of Standard & Poor’s Case-Shiller National Home Price Indices, Not Seasonally Adjusted, ∆%
| US National | |
| ∆% Dec 2000 to Dec 2003 | 28.3 |
| ∆% Dec 2000 to Dec 2005 | 65.6 |
| ∆% Dec 2003 to Dec 2005 | 29.0 |
| ∆% Dec 2000 to Dec 2006 | 68.4 |
| ∆% Dec 2003 to Dec 2006 | 31.2 |
| ∆% Dec 2005 to Dec 2021 | 54.7 |
| ∆% Dec 2006 to Dec 2021 | 52.1 |
| ∆% Dec 2009 to Dec 2021 | 90.0 |
| ∆% Dec 2010 to Dec 2021 | 98.1 |
| ∆% Dec 2011 to Dec 2021 | 106.1 |
| ∆% Dec 2012 to Dec 2021 | 93.7 |
| ∆% Dec 2013 to Dec 2021 | 74.9 |
| ∆% Dec 2014 to Dec 2021 | 67.4 |
| ∆% Dec 2015 to Dec 2021 | 59.1 |
| ∆% Dec 2016 to Dec 2021 | 51.1 |
| ∆% Dec 2017 to Dec 2021 | 42.2 |
| ∆% Dec 2018 to Dec 2021 | 36.1 |
| ∆% Dec 2019 to Dec 2021 | 31.2 |
| ∆% Dec 2020 to Dec 2021 | 18.8 |
| ∆% Dec 2000 to Dec 2021 | 156.1 |
| ∆% Peak Jun 2006 to Dec 2021 | 51.0 |
| ∆% Peak Jul 2006 to Dec 2021 | 51.0 |
| Average ∆% Dec 1987-Dec 2021 | 4.2 |
| Average ∆% Dec 1987-Dec 2000 | 3.6 |
| Average ∆% Dec 1992-Dec 2000 | 4.5 |
| Average ∆% Dec 2000-Dec 2021 | 4.6 |
Monthly house prices increased sharply from Feb 2013 to Jan 2014 for both the SA and NSA national house price, as shown in Table IIA-3. In Jan 2013, the seasonally adjusted national house price index increased 0.9 percent and the NSA increased 0.3. House prices increased at high monthly percentage rates from Feb to Nov 2013. The most important seasonal factor in house prices is school changes for wealthier homeowners with more expensive houses. With seasonal adjustment, house prices fell from Dec 2010 throughout Mar 2011 and fell in every month from Aug 2011 to Feb 2012. The not seasonally adjusted index registers increase in Mar 2012 of 1.4 percent. Not seasonally adjusted house prices increased 1.9 percent in Apr 2012 and at high monthly percentage rates through Aug 2012. House prices not seasonally adjusted stalled from Oct 2012 to Dec 2012 and surged from Feb to Sep 2013, decelerating in Oct 2013-Jan 2014. House prices grew at fast rates in Mar-Jul 2014. The SA national house price index increased 1.3 percent in Dec 2021 and the NSA index increased 0.9 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses.
Table IIA-2, US, Monthly Percentage Change of S&P Corelogic Case-Shiller National Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%
| ∆% SA | ∆% NSA | |||
| December 2021 | 1.3 | 0.9 | ||
| November 2021 | 1.2 | 0.9 | ||
| October 2021 | 1.0 | 0.8 | ||
| September 2021 | 1.1 | 0.9 | ||
| August 2021 | 1.4 | 1.2 | ||
| July 2021 | 1.6 | 1.7 | ||
| June 2021 | 1.9 | 2.2 | ||
| May 2021 | 1.8 | 2.3 | ||
| April 2021 | 1.7 | 2.3 | ||
| March 2021 | 1.7 | 2.1 | ||
| February 2021 | 1.3 | 1.2 | ||
| January 2021 | 1.4 | 0.9 | ||
| December 2020 | 1.3 | 0.9 | ||
| November 2020 | 1.4 | 1.1 | ||
| October 2020 | 1.6 | 1.3 | ||
| September 2020 | 1.4 | 1.2 | ||
| August 2020 | 1.3 | 1.1 | ||
| July 2020 | 0.7 | 0.8 | ||
| June 2020 | 0.2 | 0.6 | ||
| May 2020 | 0.1 | 0.6 | ||
| April 2020 | 0.4 | 1.0 | ||
| March 2020 | 0.5 | 0.9 | ||
| February 2020 | 0.5 | 0.4 | ||
| January 2020 | 0.5 | 0.1 | ||
| December 2019 | 0.4 | 0.1 | ||
| November 2019 | 0.4 | 0.1 | ||
| October 2019 | 0.3 | 0.0 | ||
| September 2019 | 0.3 | 0.1 | ||
| August 2019 | 0.4 | 0.2 | ||
| July 2019 | 0.3 | 0.4 | ||
| June 2019 | 0.2 | 0.6 | ||
| May 2019 | 0.3 | 0.8 | ||
| April 2019 | 0.3 | 0.9 | ||
| March 2019 | 0.3 | 0.7 | ||
| February 2019 | 0.2 | 0.1 | ||
| January 2019 | 0.2 | -0.2 | ||
| December 2018 | 0.2 | -0.2 | ||
| November 2018 | 0.2 | -0.1 | ||
| October 2018 | 0.3 | 0.0 | ||
| September 2018 | 0.3 | 0.0 | ||
| August 2018 | 0.4 | 0.2 | ||
| July 2018 | 0.3 | 0.4 | ||
| June 2018 | 0.4 | 0.8 | ||
| May 2018 | 0.4 | 0.9 | ||
| April 2018 | 0.4 | 1.0 | ||
| March 2018 | 0.4 | 0.8 | ||
| February 2018 | 0.5 | 0.4 | ||
| January 2018 | 0.6 | 0.1 | ||
| December 2017 | 0.6 | 0.2 | ||
| November 2017 | 0.6 | 0.2 | ||
| October 2017 | 0.5 | 0.1 | ||
| September 2017 | 0.5 | 0.2 | ||
| August 2017 | 0.6 | 0.4 | ||
| July 2017 | 0.5 | 0.7 | ||
| June 2017 | 0.5 | 0.9 | ||
| May 2017 | 0.5 | 1.1 | ||
| April 2017 | 0.4 | 1.1 | ||
| March 2017 | 0.4 | 0.8 | ||
| February 2017 | 0.3 | 0.2 | ||
| January 2017 | 0.6 | 0.1 | ||
| December 2016 | 0.5 | 0.1 | ||
| November 2016 | 0.5 | 0.1 | ||
| October 2016 | 0.5 | 0.0 | ||
| September 2016 | 0.5 | 0.2 | ||
| August 2016 | 0.6 | 0.3 | ||
| July 2016 | 0.4 | 0.6 | ||
| June 2016 | 0.4 | 0.9 | ||
| May 2016 | 0.4 | 1.0 | ||
| April 2016 | 0.3 | 1.1 | ||
| March 2016 | 0.3 | 0.8 | ||
| February 2016 | 0.2 | 0.1 | ||
| January 2016 | 0.4 | 0.0 | ||
| December 2015 | 0.5 | 0.0 | ||
| November 2015 | 0.5 | 0.1 | ||
| October 2015 | 0.6 | 0.0 | ||
| September 2015 | 0.5 | 0.1 | ||
| August 2015 | 0.5 | 0.3 | ||
| July 2015 | 0.4 | 0.6 | ||
| June 2015 | 0.3 | 0.9 | ||
| May 2015 | 0.3 | 1.1 | ||
| April 2015 | 0.3 | 1.1 | ||
| March 2015 | 0.4 | 0.9 | ||
| February 2015 | 0.3 | 0.2 | ||
| January 2015 | 0.4 | -0.1 | ||
| December 2014 | 0.4 | -0.1 | ||
| November 2014 | 0.4 | -0.2 | ||
| October 2014 | 0.4 | -0.2 | ||
| September 2014 | 0.4 | -0.1 | ||
| August 2014 | 0.4 | 0.2 | ||
| July 2014 | 0.3 | 0.6 | ||
| June 2014 | 0.2 | 0.9 | ||
| May 2014 | 0.2 | 1.1 | ||
| April 2014 | 0.2 | 1.1 | ||
| March 2014 | 0.3 | 0.8 | ||
| February 2014 | 0.4 | 0.3 | ||
| January 2014 | 0.6 | 0.1 | ||
| December 2013 | 0.6 | -0.1 | ||
| November 2013 | 0.5 | -0.1 | ||
| October 2013 | 0.6 | -0.1 | ||
| September 2013 | 0.8 | 0.2 | ||
| August 2013 | 0.9 | 0.7 | ||
| July 2013 | 0.9 | 1.2 | ||
| June 2013 | 0.9 | 1.7 | ||
| May 2013 | 0.9 | 1.9 | ||
| April 2013 | 1.0 | 2.0 | ||
| March 2013 | 1.5 | 1.9 | ||
| February 2013 | 0.6 | 0.6 | ||
| January 2013 | 0.9 | 0.3 | ||
| December 2012 | 0.6 | -0.1 | ||
| November 2012 | 0.7 | 0.0 | ||
| October 2012 | 0.5 | -0.3 | ||
| September 2012 | 0.4 | -0.2 | ||
| August 2012 | 0.4 | 0.3 | ||
| July 2012 | 0.5 | 0.8 | ||
| June 2012 | 0.6 | 1.5 | ||
| May 2012 | 0.7 | 1.9 | ||
| April 2012 | 0.9 | 1.9 | ||
| March 2012 | 1.0 | 1.4 | ||
| February 2012 | -0.1 | -0.1 | ||
| January 2012 | 0.0 | -0.7 | ||
| December 2011 | -0.3 | -1.1 | ||
| November 2011 | -0.6 | -1.3 | ||
| October 2011 | -0.5 | -1.3 | ||
| September 2011 | -0.5 | -1.1 | ||
| August 2011 | -0.3 | -0.4 | ||
| July 2011 | -0.1 | 0.3 | ||
| June 2011 | 0.0 | 0.9 | ||
| May 2011 | -0.1 | 1.1 | ||
| April 2011 | 0.0 | 1.0 | ||
| March 2011 | -0.3 | 0.0 | ||
| February 2011 | -0.8 | -0.9 | ||
| January 2011 | -0.4 | -1.1 | ||
| December 2010 | -0.1 | -0.8 |
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 $8.9 trillion or 10.5 percent from 2007 to 2008 and $8.6 trillion or 10.2 percent to 2009. Net worth fell $8.8 trillion from 2007 to 2008 or 12.6 percent and $8.4 trillion to 2009 or 12.0 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 summarizes the brutal drops in assets and net worth of US households and nonprofit organizations from 2007 to 2008 and 2009. Total assets fell $8.9 trillion or 10.5 percent from 2007 to 2008 and $8.6 trillion or 10.2 percent to 2009. Net worth fell $8.8 trillion from 2007 to 2008 or 12.6 percent and $8.4 trillion to 2009 or 11.9 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 | 84,697.8 | 75,772.0 | -8,925.8 | 76,096.3 | -8,601.5 |
| Non | 30,637.4 | 28,164.1 | -2,473.3 | 26,139.6 | -4,497.8 |
| RE | 25,845.9 | 23,249.9 | -2,596.0 | 21,209.1 | -4,636.8 |
| FIN | 54,060.4 | 47,607.8 | -6,452.6 | 49,956.7 | -4,103.7 |
| LIAB | 14,613.2 | 14,508.7 | -104.5 | 14,383.6 | -229.6 |
| NW | 70,084.6 | 61,263.3 | -8,821.3 | 61,712.7 | -8,371.9 |
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2021. Washington, DC, Federal Reserve System, Nov 9. https://www.federalreserve.gov/releases/z1/current/default.htm
The apparent improvement in Table IIA-4A is mostly because of increases in valuations of risk financial assets by the carry trade from zero interest rates to leveraged exposures in risk financial assets such as stocks, high-yield bonds, emerging markets, commodities and so on. Zero interest rates also act to increase net worth by reducing debt or liabilities. The net worth of households has become an instrument of unconventional monetary policy by zero interest rates in the theory that increases in net worth increase consumption that accounts for 68.8 percent of GDP in IIIQ2021 (https://cmpassocregulationblog.blogspot.com/2021/11/us-gdp-growing-at-21-saar-in-iiiq2021.html and earlier https://cmpassocregulationblog.blogspot.com/2021/10/us-gdp-growing-at-20-saar-in-iiiq2021.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IIIQ2021, real estate increased in value by $15,079.0 billion. Financial assets increased $59,996.6 billion, explaining most of the increase in net worth of $74,624.8 billion obtained by deducting the increase in liabilities of $3355.4 billion from the increase of assets of $77,980.2 billion (with minor rounding error). Net worth increased from $70,084.6 billion in IVQ2007 to $144,709.4 billion in IIIQ2021 by $74,624.8 billion or 106.5 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 274.310 in Sep 2021 (https://www.bls.gov/cpi/data.htm) or 30.6 percent. Net worth adjusted by CPI inflation increased 58.1 percent from 2007 to IIIQ2021. Real estate assets adjusted for CPI inflation increased 21.2 percent from 2007 to IIIQ2021. 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 2.2 percent on average in the cyclical expansion in the 50 quarters from IIIQ2009 to IVQ2021 and in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). 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, 201 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) (https://apps.bea.gov/iTable/index_nipa.cfm) and the second estimate of GDP for IVQ2021 (https://www.bea.gov/sites/default/files/2022-02/gdp4q21_2nd.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 2.9 percent obtained by dividing GDP of $15,605.6 billion in IIQ2010 by GDP of $15,161.8 billion in IIQ2009 {[($15,605.6/$15,161.8) -1]100 = 2.9%], or accumulating the quarter on quarter growth rates (https://cmpassocregulationblog.blogspot.com/2022/02/us-gdp-growing-at-saar-of-70-percent-in.html and earlier https://cmpassocregulationblog.blogspot.com/2022/01/fomc-states-with-inflation-well-above-2.html). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.3 percent from IQ1983 to IIIQ1986, 5.1 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IQ1988, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.6 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989, 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990, 4.0 percent from IQ1983 to IVQ1990, 3.8 percent from IQ1983 to IQ1991, 3.8 percent from IQ1983 to IIQ1991, 3.8 percent from IQ1983 to IIIQ1991, 3.7 percent from IQ1983 to IVQ1991, 3.7 percent from IQ1983 to IQ1992, 3.7 percent from IQ1983 to IIQ1992, 3.7 percent from IQ1983 to IIIQ1992, 3.8 percent from IQ1983 to IVQ1992, 3.7 percent from IQ1983 to IQ1993, 3.6 percent from IQ1983 to IIQ1993, 3.6 percent from IQ1983 to IIIQ1993, 3.7 percent from IQ1983 to IVQ1993, 3.7 percent from IQ1983 to IQ1994, 3.7 percent from IQ1983 to IIQ1994, 3.7 percent from IQ1983 to IIIQ1994, 3.7 percent from IQ1983 to IVQ1994, 3.6 percent from IQ1983 to IQ1995, 3.6 percent from IQ1983 to IIQ1995 and at 7.9 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2022/02/us-gdp-growing-at-saar-of-70-percent-in.html and earlier https://cmpassocregulationblog.blogspot.com/2022/01/fomc-states-with-inflation-well-above-2.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (https://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.4 percent from the pre-recession peak of $9404.5 billion of chained 2012 dollars in IIIQ1990 to the trough of $9275.3 billion in IQ1991 (https://apps.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IVQ2021 and in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021) would have accumulated to 51.3 percent. GDP in IVQ2021 would be $23,849.2 billion (in constant dollars of 2012) if the US had grown at trend, which is higher by $4038.6 billion than actual $19,810.6 billion. There are more than four trillion dollars of GDP less than at trend, explaining the 23.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 13.3 percent of the effective labor force with the largest part originating in the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event (https://cmpassocregulationblog.blogspot.com/2022/03/increase-in-feb-2022-of-nonfarm-payroll.html and earlier https://cmpassocregulationblog.blogspot.com/2022/02/increase-in-jan-2022-of-nonfarm-payroll.html). Unemployment is decreasing while employment is increasing in initial adjustment of the lockdown of economic activity in the global recession resulting from the COVID-19 event (https://www.bls.gov/covid19/effects-of-covid-19-pandemic-and-response-on-the-employment-situation-news-release.htm). US GDP in IVQ2021 is 16.9 percent lower than at trend. US GDP grew from $15,767.1 billion in IVQ2007 in constant dollars to $19,810.6 billion in IVQ2021 or 25.6 percent at the average annual equivalent rate of 1.6 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 3.0 percent per year from Feb 1919 to Feb 2022. Growth at 3.0 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 162.3669 in Feb 2022. The actual index NSA in Feb 2022 is 101.4579 which is 37.5 percent below trend. The underperformance of manufacturing in Mar-Nov 2020 originates partly in the earlier global recession augmented by the global recession, with output in the US reaching a high in Feb 2020 (https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions), in the lockdown of economic activity in the COVID-19 event and the through in Apr 2020 (https://www.nber.org/news/business-cycle-dating-committee-announcement-july-19-2021). Manufacturing grew at the average annual rate of 3.3 percent between Dec 1986 and Dec 2006. Growth at 3.3 percent per year would raise the NSA index of manufacturing output (SIC, Standard Industrial Classification) from 106.8161 in Dec 2007 to 169.1965 in Feb 2022. The actual index NSA in Feb 2022 is 101.4579, which is 40.0 percent below trend. Manufacturing output grew at average 1.8 percent between Dec 1986 and Feb 2022. Using trend growth of 1.8 percent per year, the index would increase to 137.5298 in Feb 2022. The output of manufacturing at 101.4579 in Feb 2022 is 26.2 percent below trend under this alternative calculation. Using the NAICS (North American Industry Classification System), manufacturing output fell from the high of 108.5167 in Jul 2007 to the low of 84.7321 in May 2009 or 21.9 percent. The NAICS manufacturing index increased from 84.7321 in Apr 2009 to 102.5311 in Feb 2022 or 21.0 percent. The NAICS manufacturing index increased at the annual equivalent rate of 3.5 percent from Dec 1986 to Dec 2006. Growth at 3.5 percent would increase the NAICS manufacturing output index from 104.6868 in Dec 2007 to 170.4304 in Feb 2022. The NAICS index at 102.5311 in Feb 2021 is 39.8 percent below trend. The NAICS manufacturing output index grew at 1.7 percent annual equivalent from Dec 1999 to Dec 2006. Growth at 1.7 percent would raise the NAICS manufacturing output index from 104.6868 in Dec 2007 to 132.9246 in Feb 2022. The NAICS index at 102.5311 in Feb 2022 is 22.9 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 2019 2020 and IIIQ2021
| Value 2007 | Change to 2019 | Change to 2020 | Change to IIIQ2021 | |
| Assets | 84,697.8 | 48,621.3 | 63,341.6 | 77,980.2 |
| Nonfinancial | 30,637.4 | 9,295.0 | 12,884.9 | 17,983.6 |
| Real Estate | 25,845.9 | 7,702.5 | 10,903.6 | 15,079.0 |
| Financial | 54,060.4 | 39,326.3 | 50,456.7 | 59,996.6 |
| Liabilities | 14,613.2 | 1,891.9 | 2,510.1 | 3,355.4 |
| Net Worth | 70,084.6 | 46,729.3 | 60,831.5 | 74,624.8 |
Notes: Deposits: Total Time and Savings Deposits FL15303005; Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2021. Flow of funds, balance sheets and integrated macroeconomic accounts: third quarter 2021. Washington, DC, Federal Reserve System, Nov 9. https://www.federalreserve.gov/releases/z1/current/default.htm
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022.
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