Volatility of Valuations of Financial Assets, Twenty Seven Million Unemployed or Underemployed, Stagnating Real Wages, Stagnating Real Disposable Income per Capita, Financial Repression, United States International Trade, World Cyclical Slow Growth and Global Recession Risk
Carlos M. Pelaez
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015
I Twenty Seven Million Unemployed or Underemployed
IA1 Summary of the Employment Situation
IA2 Number of People in Job Stress
IA3 Long-term and Cyclical Comparison of Employment
IA4 Job Creation
IB Stagnating Real Wages
II Stagnating Real Disposable Income and Consumption Expenditures
IB1 Stagnating Real Disposable Income and Consumption Expenditures
IB2 Financial Repression
IIA United States International Trade
III World Financial Turbulence
IIIA Financial Risks
IIIE Appendix Euro Zone Survival Risk
IIIF Appendix on Sovereign Bond Valuation
IV Global Inflation
V World Economic Slowdown
VA United States
VB Japan
VC China
VD Euro Area
VE Germany
VF France
VG Italy
VH United Kingdom
VI Valuation of Risk Financial Assets
VII Economic Indicators
VIII Interest Rates
IX Conclusion
References
Appendixes
Appendix I The Great Inflation
IIIB Appendix on Safe Haven Currencies
IIIC Appendix on Fiscal Compact
IIID Appendix on European Central Bank Large Scale Lender of Last Resort
IIIG Appendix on Deficit Financing of Growth and the Debt Crisis
IIIGA Monetary Policy with Deficit Financing of Economic Growth
IIIGB Adjustment during the Debt Crisis of the 1980s
V World Economic Slowdown. Table V-1 is constructed with the database of the IMF (http://www.imf.org/external/ns/cs.aspx?id=28) to show GDP in dollars in 2012 and the growth rate of real GDP of the world and selected regional countries from 2013 to 2016. The data illustrate the concept often repeated of “two-speed recovery” of the world economy from the recession of 2007 to 2009. The IMF has changed its forecast of the world economy to 3.3 percent in 2013 but accelerating to 3.3 percent in 2014, 3.8 percent in 2015 and 4.0 percent in 2016. Slow-speed recovery occurs in the “major advanced economies” of the G7 that account for $34,523 billion of world output of $72,688 billion, or 47.5 percent, but are projected to grow at much lower rates than world output, 1.9 percent on average from 2013 to 2016 in contrast with 3.6 percent for the world as a whole. While the world would grow 15.2 percent in the four years from 2013 to 2016, the G7 as a whole would grow 8.5 percent. The difference in dollars of 2012 is rather high: growing by 15.2 percent would add around $11.0 trillion of output to the world economy, or roughly, two times the output of the economy of Japan of $5,938 billion but growing by 8.0 percent would add $5.8 trillion of output to the world, or about the output of Japan in 2012. The “two speed” concept is in reference to the growth of the 150 countries labeled as emerging and developing economies (EMDE) with joint output in 2012 of $27,512 billion, or 37.8 percent of world output. The EMDEs would grow cumulatively 20.7 percent or at the average yearly rate of 4.8 percent, contributing $5.7 trillion from 2013 to 2016 or the equivalent of somewhat less than the GDP of $8,387 billion of China in 2012. The final four countries in Table V-1 often referred as BRIC (Brazil, Russia, India, China), are large, rapidly growing emerging economies. Their combined output in 2012 adds to $14,511 billion, or 19.9 percent of world output, which is equivalent to 42.0 percent of the combined output of the major advanced economies of the G7.
Table V-1, IMF World Economic Outlook Database Projections of Real GDP Growth
| GDP USD 2012 | Real GDP ∆% | Real GDP ∆% | Real GDP ∆% | Real GDP ∆% | |
| World | 72,688 | 3.3 | 3.3 | 3.8 | 4.0 |
| G7 | 34,523 | 1.5 | 1.7 | 2.3 | 2.3 |
| Canada | 1,709 | 2.0 | 2.3 | 2.4 | 2.4 |
| France | 2,688 | 0.3 | 0.4 | 1.0 | 1.6 |
| DE | 3,428 | 0.5 | 1.4 | 1.5 | 1.8 |
| Italy | 2,014 | -1.9 | -0.2 | 0.9 | 1.3 |
| Japan | 5,938 | 1.5 | 0.9 | 0.8 | 0.8 |
| UK | 2,471 | 1.7 | 3.2 | 2.7 | 2.4 |
| US | 16,163 | 2.2 | 2.2 | 3.1 | 3.0 |
| Euro Area | 12,220 | -0.4 | 0.8 | 1.3 | 1.7 |
| DE | 3,428 | 0.5 | 1.4 | 1.5 | 1.8 |
| France | 2,688 | 0.3 | 0.4 | 1.0 | 1.6 |
| Italy | 2,014 | -1.9 | -0.2 | 0.9 | 1.3 |
| POT | 212 | -1.4 | 1.0 | 1.5 | 1.7 |
| Ireland | 211 | -0.3 | 1.7 | 2.5 | 2.5 |
| Greece | 249 | -3.9 | 0.6 | 2.9 | 3.7 |
| Spain | 1,323 | -1.2 | 1.3 | 1.7 | 1.8 |
| EMDE | 27,512 | 4.7 | 4.4 | 5.0 | 5.2 |
| Brazil | 2,248 | 2.5 | 0.3 | 1.4 | 2.2 |
| Russia | 2,017 | 1.3 | 0.2 | 0.5 | 1.5 |
| India | 1,859 | 5.0 | 5.6 | 6.4 | 6.5 |
| China | 8,387 | 7.7 | 7.4 | 7.1 | 6.8 |
Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries); POT: Portugal
Source: IMF World Economic Outlook databank http://www.imf.org/external/ns/cs.aspx?id=28
Continuing high rates of unemployment in advanced economies constitute another characteristic of the database of the WEO (http://www.imf.org/external/ns/cs.aspx?id=28). Table V-2 is constructed with the WEO database to provide rates of unemployment from 2012 to 2016 for major countries and regions. In fact, unemployment rates for 2013 in Table I-2 are high for all countries: unusually high for countries with high rates most of the time and unusually high for countries with low rates most of the time. The rates of unemployment are particularly high in 2013 for the countries with sovereign debt difficulties in Europe: 16.2 percent for Portugal (POT), 13.0 percent for Ireland, 27.3 percent for Greece, 26.1 percent for Spain and 12.2 percent for Italy, which is lower but still high. The G7 rate of unemployment is 7.1 percent. Unemployment rates are not likely to decrease substantially if slow growth persists in advanced economies.
Table V-2, IMF World Economic Outlook Database Projections of Unemployment Rate as Percent of Labor Force
| % Labor Force 2012 | % Labor Force 2013 | % Labor Force 2014 | % Labor Force 2015 | % Labor Force 2016 | |
| World | NA | NA | NA | NA | NA |
| G7 | 7.4 | 7.1 | 6.5 | 6.3 | 6.1 |
| Canada | 7.3 | 7.1 | 7.0 | 6.9 | 6.8 |
| France | 9.8 | 10.3 | 10.0 | 10.0 | 9.9 |
| DE | 5.5 | 5.3 | 5.3 | 5.3 | 5.3 |
| Italy | 10.7 | 12.2 | 12.6 | 12.0 | 11.3 |
| Japan | 4.3 | 4.0 | 3.7 | 3.8 | 3.8 |
| UK | 8.0 | 7.6 | 6.3 | 5.8 | 5.5 |
| US | 8.1 | 7.4 | 6.3 | 5.9 | 5.8 |
| Euro Area | 11.3 | 11.9 | 11.6 | 11.2 | 10.7 |
| DE | 5.5 | 5.3 | 5.3 | 5.3 | 5.3 |
| France | 9.8 | 10.3 | 10.0 | 10.0 | 9.9 |
| Italy | 10.7 | 12.2 | 12.6 | 12.0 | 11.3 |
| POT | 15.5 | 16.2 | 14.2 | 13.5 | 13.0 |
| Ireland | 14.7 | 13.0 | 11.2 | 10.5 | 10.1 |
| Greece | 24.2 | 27.3 | 25.8 | 23.8 | 20.9 |
| Spain | 24.8 | 26.1 | 24.6 | 23.5 | 22.4 |
| EMDE | NA | NA | NA | NA | NA |
| Brazil | 5.5 | 5.4 | 5.5 | 6.1 | 5.9 |
| Russia | 5.5 | 5.5 | 5.6 | 6.5 | 6.0 |
| India | NA | NA | NA | NA | NA |
| China | 4.1 | 4.1 | 4.1 | 4.1 | 4.1 |
Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries)
Source: IMF World Economic Outlook databank http://www.imf.org/external/ns/cs.aspx?id=28
Table V-3 provides the latest available estimates of GDP for the regions and countries followed in this blog from IQ2012 to IIIQ2014 available now for all countries. There are preliminary estimates for most countries for IVQ2014. Growth is weak throughout most of the world.
- Japan. The GDP of Japan increased 1.1 percent in IQ2012, 4.3 percent at SAAR (seasonally adjusted annual rate) and 3.5 percent relative to a year earlier but part of the jump could be the low level a year earlier because of the Tōhoku or Great East Earthquake and Tsunami of Mar 11, 2011. Japan is experiencing difficulties with the overvalued yen because of worldwide capital flight originating in zero interest rates with risk aversion in an environment of softer growth of world trade. Japan’s GDP fell 0.4 percent in IIQ2012 at the seasonally adjusted annual rate (SAAR) of minus 1.4 percent, which is much lower than 4.3 percent in IQ2012. Growth of 3.5 percent in IIQ2012 in Japan relative to IIQ2011 has effects of the low level of output because of Tōhoku or Great East Earthquake and Tsunami of Mar 11, 2011. Japan’s GDP contracted 0.5 percent in IIIQ2012 at the SAAR of minus 2.2 percent and increased 0.2 percent relative to a year earlier. Japan’s GDP decreased 0.2 percent in IVQ2012 at the SAAR of minus 0.6 percent and changed 0.0 percent relative to a year earlier. Japan grew 1.4 percent in IQ2013 at the SAAR of 5.6 percent and increased 0.5 percent relative to a year earlier. Japan’s GDP increased 0.8 percent in IIQ2013 at the SAAR of 3.3 percent and increased 1.4 percent relative to a year earlier. Japan’s GDP grew 0.4 percent in IIIQ2013 at the SAAR of 1.4 percent and increased 2.2 percent relative to a year earlier. In IVQ2013, Japan’s GDP decreased 0.3 percent at the SAAR of minus 1.2 percent, increasing 2.3 percent relative to a year earlier. Japan’s GDP increased 1.3 percent in IQ2014 at the SAAR of 5.1 percent and increased 2.4 percent relative to a year earlier. In IIQ2014, Japan’s GDP fell 1.6 percent at the SAAR of minus 6.4 percent and fell 0.3 percent relative to a year earlier. Japan’s GDP contracted 0.7 percent in IIIQ2014 at the SAAR of minus 2.6 percent and fell 1.4 percent relative to a year earlier. In IVQ2014, Japan’s GDP grew 0.4 percent, at the SAAR of 1.5 percent, decreasing 0.8 percent relative to a year earlier.
- China. China’s GDP grew 1.4 percent in IQ2012, annualizing to 5.7 percent, and 8.1 percent relative to a year earlier. The GDP of China grew at 2.1 percent in IIQ2012, which annualizes to 8.7 percent and 7.6 percent relative to a year earlier. China grew at 2.0 percent in IIIQ2012, which annualizes at 8.2 percent and 7.4 percent relative to a year earlier. In IVQ2012, China grew at 1.9 percent, which annualizes at 7.8 percent, and 7.9 percent in IVQ2012 relative to IVQ2011. In IQ2013, China grew at 1.7 percent, which annualizes at 7.0 percent and 7.8 percent relative to a year earlier. In IIQ2013, China grew at 1.8 percent, which annualizes at 7.4 percent and 7.5 percent relative to a year earlier. China grew at 2.3 percent in IIIQ2013, which annualizes at 9.5 percent and 7.9 percent relative to a year earlier. China grew at 1.8 percent in IVQ2013, which annualized to 7.4 percent and 7.6 percent relative to a year earlier. China’s GDP grew 1.6 percent in IQ2014, which annualizes to 6.6 percent, and 7.4 percent relative to a year earlier. China’s GDP grew 1.9 percent in IIQ2014, which annualizes at 7.8 percent, and 7.5 percent relative to a year earlier. China’s GDP grew 1.9 percent in IIIQ2014, which is equivalent to 7.8 percent in a year, and 7.3 percent relative to a year earlier. The GDP of China grew 1.5 percent in IVQ2014, which annualizes at 6.1 percent, and 7.3 percent relative to a year earlier. There is decennial change in leadership in China (http://www.xinhuanet.com/english/special/18cpcnc/index.htm). Growth rates of GDP of China in a quarter relative to the same quarter a year earlier have been declining from 2011 to 2014.
- Euro Area. GDP fell 0.1 percent in the euro area in IQ2012 and decreased 0.4 in IQ2012 relative to a year earlier. Euro area GDP contracted 0.3 percent IIQ2012 and fell 0.8 percent relative to a year earlier. In IIIQ2012, euro area GDP fell 0.1 percent and declined 0.8 percent relative to a year earlier. In IVQ2012, euro area GDP fell 0.4 percent relative to the prior quarter and fell 0.9 percent relative to a year earlier. In IQ2013, the GDP of the euro area fell 0.4 percent and decreased 1.2 percent relative to a year earlier. The GDP of the euro area increased 0.3 percent in IIQ2013 and fell 0.6 percent relative to a year earlier. In IIIQ2013, euro area GDP increased 0.2 percent and fell 0.3 percent relative to a year earlier. The GDP of the euro area increased 0.3 percent in IVQ2013 and increased 0.4 percent relative to a year earlier. In IQ2014, the GDP of the euro area increased 0.3 percent and 1.1 percent relative to a year earlier. The GDP of the euro area increased 0.1 percent in IIQ2014 and increased 0.8 percent relative to a year earlier. The euro area’s GDP increased 0.2 percent in IIIQ2014 and increased 0.8 percent relative to a year earlier. The GDP of the euro area increased 0.3 percent in IVQ2014 and increased 0.9 percent relative to a year earlier.
- Germany. The GDP of Germany increased 0.3 percent in IQ2012 and 1.5 percent relative to a year earlier. In IIQ2012, Germany’s GDP increased 0.1 percent and increased 0.3 percent relative to a year earlier but 0.8 percent relative to a year earlier when adjusted for calendar (CA) effects. In IIIQ2012, Germany’s GDP increased 0.1 percent and 0.1 percent relative to a year earlier. Germany’s GDP contracted 0.4 percent in IVQ2012 and decreased 0.3 percent relative to a year earlier. In IQ2013, Germany’s GDP decreased 0.4 percent and fell 1.8 percent relative to a year earlier. In IIQ2013, Germany’s GDP increased 0.8 percent and 0.5 percent relative to a year earlier. The GDP of Germany increased 0.3 percent in IIIQ2013 and 0.8 percent relative to a year earlier. In IVQ2013, Germany’s GDP increased 0.4 percent and 1.0 percent relative to a year earlier. The GDP of Germany increased 0.8 percent in IQ2014 and 2.6 percent relative to a year earlier. In IIQ2014, Germany’s GDP contracted 0.1 percent and increased 1.0 percent relative to a year earlier. The GDP of Germany increased 0.1 percent in IIIQ2014 and increased 1.2 percent relative to a year earlier. Germany’s GDP increased 0.7 percent in IVQ2014 and increased 1.6 percent relative to a year earlier.
- United States. Growth of US GDP in IQ2012 was 0.6 percent, at SAAR of 2.3 percent and higher by 2.6 percent relative to IQ2011. US GDP increased 0.4 percent in IIQ2012, 1.6 percent at SAAR and 2.3 percent relative to a year earlier. In IIIQ2012, US GDP grew 0.6 percent, 2.5 percent at SAAR and 2.7 percent relative to IIIQ2011. In IVQ2012, US GDP grew 0.0 percent, 0.1 percent at SAAR and 1.6 percent relative to IVQ2011. In IQ2013, US GDP grew at 2.7 percent SAAR, 0.7 percent relative to the prior quarter and 1.7 percent relative to the same quarter in 2013. In IIQ2013, US GDP grew at 1.8 percent in SAAR, 0.4 percent relative to the prior quarter and 1.8 percent relative to IIQ2012. US GDP grew at 4.5 percent in SAAR in IIIQ2013, 1.1 percent relative to the prior quarter and 2.3 percent relative to the same quarter a year earlier (http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html). In IVQ2013, US GDP grew 0.9 percent at 3.5 percent SAAR and 3.1 percent relative to a year earlier. In IQ2014, US GDP decreased 0.5 percent, increased 1.9 percent relative to a year earlier and fell 2.1 percent at SAAR. In IIQ2014, US GDP increased 1.1 percent at 4.6 percent SAAR and increased 2.6 percent relative to a year earlier. US GDP increased 1.2 percent in IIIQ2014 at 5.0 percent SAAR and increased 2.7 percent relative to a year earlier. In IVQ2014, US GDP increased 0.5 percent at SAAR of 2.2 percent and increased 2.4 percent relative to a year earlier.
- United Kingdom. In IQ2012, UK GDP increased 0.1 percent, increasing 1.0 percent relative to a year earlier. UK GDP fell 0.2 percent in IIQ2012 and increased 0.6 percent relative to a year earlier. UK GDP increased 0.8 percent in IIIQ2012 and increased 0.7 percent relative to a year earlier. UK GDP fell 0.3 percent in IVQ2012 relative to IIIQ2012 and increased 0.4 percent relative to a year earlier. UK GDP increased 0.6 percent in IQ2013 and 0.9 percent relative to a year earlier. UK GDP increased 0.6 percent in IIQ2013 and 1.7 percent relative to a year earlier. In IIIQ2013, UK GDP increased 0.7 percent and 1.6 percent relative to a year earlier. UK GDP increased 0.4 percent in IVQ2013 and 2.4 percent relative to a year earlier. In IQ2014, UK GDP increased 0.9 percent and 2.7 percent relative to a year earlier. UK GDP increased 0.8 percent in IIQ2014 and 2.9 percent relative to a year earlier. In IIIQ2014, UK GDP increased 0.6 percent and increased 2.8 percent relative to a year earlier. UK GDP increased 0.6 percent in IVQ2014 and increased 3.0 percent relative to a year earlier.
- Italy. Italy has experienced decline of GDP in nine consecutive quarters from IIIQ2011 to IIIQ2013 and in IIQ2014 and IIIQ2014. Italy’s GDP fell 0.9 percent in IQ2012 and declined 2.3 percent relative to IQ2011. Italy’s GDP fell 0.6 percent in IIQ2012 and declined 3.1 percent relative to a year earlier. In IIIQ2012, Italy’s GDP fell 0.6 percent and declined 3.1 percent relative to a year earlier. The GDP of Italy contracted 0.6 percent in IVQ2012 and fell 2.7 percent relative to a year earlier. In IQ2013, Italy’s GDP contracted 0.8 percent and fell 2.6 percent relative to a year earlier. Italy’s GDP fell 0.1 percent in IIQ2013 and 2.0 percent relative to a year earlier. The GDP of Italy increased 0.1 percent in IIIQ2013 and declined 1.4 percent relative to a year earlier. Italy’s GDP changed 0.0 percent in IVQ2013 and decreased 0.8 percent relative to a year earlier. In IQ2014, Italy’s GDP decreased 0.1 percent and fell 0.1 percent relative to a year earlier. The GDP of Italy fell 0.2 percent in IIQ2014 and declined 0.3 percent relative to a year earlier. In IIIQ2014, Italy’s GDP contracted 0.1 percent and fell 0.5 percent relative to a year earlier. The GDP of Italy changed 0.0 percent in IVQ20214 and declined 0.5 percent relative to a year earlier
- France. France’s GDP increased 0.2 percent in IQ2012 and increased 0.6 percent relative to a year earlier. France’s GDP decreased 0.3 percent in IIQ2012 and increased 0.4 percent relative to a year earlier. In IIIQ2012, France’s GDP increased 0.2 percent and increased 0.4 percent relative to a year earlier. France’s GDP fell 0.2 percent in IVQ2012 and changed 0.0 percent relative to a year earlier. In IQ2013, France’s GDP decreased 0.1 percent and declined 0.3 percent relative to a year earlier. The GDP of France increased 0.7 percent in IIQ2013 and 0.7 percent relative to a year earlier. France’s GDP decreased 0.1 percent in IIIQ2013 and increased 0.3 percent relative to a year earlier. The GDP of France increased 0.3 percent in IVQ2013 and 0.8 percent relative to a year earlier. In IQ2014, France’s GDP decreased 0.1 percent and increased 0.8 percent relative to a year earlier. In IIQ2014, France’s GDP contracted 0.1 percent and changed 0.0 percent relative to a year earlier. France’s GDP increased 0.3 percent in IIIQ2014 and increased 0.4 percent relative to a year earlier. The GDP of France increased 0.1 percent in IVQ2014 and increased 0.2 percent relative to a year earlier
Table V-3, Percentage Changes of GDP Quarter on Prior Quarter and on Same Quarter Year Earlier, ∆%
| IQ2012/IVQ2011 | IQ2012/IQ2011 | |
| United States | QOQ: 0.6 SAAR: 2.3 | 2.6 |
| Japan | QOQ: 1.1 SAAR: 4.3 | 3.5 |
| China | 1.4 | 8.1 |
| Euro Area | -0.1 | -0.4 |
| Germany | 0.3 | 1.5 |
| France | 0.2 | 0.6 |
| Italy | -0.9 | -2.3 |
| United Kingdom | 0.1 | 1.0 |
| IIQ2012/IQ2012 | IIQ2012/IIQ2011 | |
| United States | QOQ: 0.4 SAAR: 1.6 | 2.3 |
| Japan | QOQ: -0.4 | 3.5 |
| China | 2.1 | 7.6 |
| Euro Area | -0.3 | -0.8 |
| Germany | 0.1 | 0.3 0.8 CA |
| France | -0.3 | 0.4 |
| Italy | -0.6 | -3.1 |
| United Kingdom | -0.2 | 0.6 |
| IIIQ2012/ IIQ2012 | IIIQ2012/ IIIQ2011 | |
| United States | QOQ: 0.6 | 2.7 |
| Japan | QOQ: –0.5 | 0.2 |
| China | 2.0 | 7.4 |
| Euro Area | -0.1 | -0.8 |
| Germany | 0.1 | 0.1 |
| France | 0.2 | 0.4 |
| Italy | -0.6 | -3.1 |
| United Kingdom | 0.8 | 0.7 |
| IVQ2012/IIIQ2012 | IVQ2012/IVQ2011 | |
| United States | QOQ: 0.0 | 1.6 |
| Japan | QOQ: -0.2 SAAR: -0.6 | 0.0 |
| China | 1.9 | 7.9 |
| Euro Area | -0.4 | -0.9 |
| Germany | -0.4 | -0.3 |
| France | -0.2 | 0.0 |
| Italy | -0.6 | -2.7 |
| United Kingdom | -0.3 | 0.4 |
| IQ2013/IVQ2012 | IQ2013/IQ2012 | |
| United States | QOQ: 0.7 | 1.7 |
| Japan | QOQ: 1.4 SAAR: 5.6 | 0.5 |
| China | 1.7 | 7.8 |
| Euro Area | -0.4 | -1.2 |
| Germany | -0.4 | -1.8 |
| France | -0.1 | -0.3 |
| Italy | -0.8 | -2.6 |
| UK | 0.6 | 0.9 |
| IIQ2013/IQ2013 | IIQ2013/IIQ2012 | |
| United States | QOQ: 0.4 SAAR: 1.8 | 1.8 |
| Japan | QOQ: 0.8 SAAR: 3.3 | 1.4 |
| China | 1.8 | 7.5 |
| Euro Area | 0.3 | -0.6 |
| Germany | 0.8 | 0.5 |
| France | 0.7 | 0.7 |
| Italy | -0.1 | -2.0 |
| UK | 0.6 | 1.7 |
| IIIQ2013/IIQ2013 | III/Q2013/ IIIQ2012 | |
| USA | QOQ: 1.1 | 2.3 |
| Japan | QOQ: 0.4 SAAR: 1.4 | 2.2 |
| China | 2.3 | 7.9 |
| Euro Area | 0.2 | -0.3 |
| Germany | 0.3 | 0.8 |
| France | -0.1 | 0.3 |
| Italy | 0.1 | -1.4 |
| UK | 0.7 | 1.6 |
| IVQ2013/IIIQ2013 | IVQ2013/IVQ2012 | |
| USA | QOQ: 0.9 SAAR: 3.5 | 3.1 |
| Japan | QOQ: -0.3 SAAR: -1.2 | 2.3 |
| China | 1.8 | 7.6 |
| Euro Area | 0.3 | 0.4 |
| Germany | 0.4 | 1.0 |
| France | 0.3 | 0.8 |
| Italy | 0.0 | -0.8 |
| UK | 0.4 | 2.4 |
| IQ2014/IVQ2013 | IQ2014/IQ2013 | |
| USA | QOQ -0.5 SAAR -2.1 | 1.9 |
| Japan | QOQ: 1.3 SAAR: 5.1 | 2.4 |
| China | 1.6 | 7.4 |
| Euro Area | 0.3 | 1.1 |
| Germany | 0.8 | 2.6 |
| France | -0.1 | 0.8 |
| Italy | -0.1 | -0.1 |
| UK | 0.9 | 2.7 |
| IIQ2014/IQ2014 | IIQ2014/IIQ2013 | |
| USA | QOQ 1.1 SAAR 4.6 | 2.6 |
| Japan | QOQ: -1.6 SAAR: -6.4 | -0.3 |
| China | 1.9 | 7.5 |
| Euro Area | 0.1 | 0.8 |
| Germany | -0.1 | 1.0 |
| France | -0.1 | 0.0 |
| Italy | -0.2 | -0.3 |
| UK | 0.8 | 2.9 |
| IIIQ2014/IIQ2014 | IIIQ2014/IIIQ2013 | |
| USA | QOQ: 1.2 SAAR: 5.0 | 2.7 |
| Japan | QOQ: -0.7 SAAR: -2.6 | -1.4 |
| China | 1.9 | 7.3 |
| Euro Area | 0.2 | 0.8 |
| Germany | 0.1 | 1.2 |
| France | 0.3 | 0.4 |
| Italy | -0.1 | -0.5 |
| UK | 0.6 | 2.8 |
| IVQ2014/IIIQ2014 | IVQ2014/IVQ2013 | |
| USA | QOQ: 0.5 SAAR: 2.2 | 2.4 |
| Japan | QOQ: 0.4 SAAR: 1.5 | -0.8 |
| China | 1.5 | 7.3 |
| Euro Area | 0.3 | 0.9 |
| Germany | 0.7 | 1.6 |
| France | 0.1 | 0.2 |
| Italy | 0.0 | -0.5 |
| UK | 0.6 | 3.0 |
QOQ: Quarter relative to prior quarter; SAAR: seasonally adjusted annual rate
Source: Country Statistical Agencies http://www.census.gov/aboutus/stat_int.html
Table V-4 provides two types of data: growth of exports and imports in the latest available months and in the past 12 months; and contributions of net trade (exports less imports) to growth of real GDP.
- Japan. Japan provides the most worrisome data (http://cmpassocregulationblog.blogspot.com/2015/03/impatience-with-monetary-policy-of.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/world-financial-turbulence-squeeze-of.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/02/financial-and-international.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/patience-on-interest-rate-increases.html and earlier (http://cmpassocregulationblog.blogspot.com/2014/11/squeeze-of-economic-activity-by-carry.html and earlier http://cmpassocregulationblog.blogspot.com/2014/09/world-inflation-waves-squeeze-of.html and earlier http://cmpassocregulationblog.blogspot.com/2014/08/monetary-policy-world-inflation-waves.html and earlier http://cmpassocregulationblog.blogspot.com/2014/07/world-inflation-waves-united-states.html and earlier (http://cmpassocregulationblog.blogspot.com/2014/06/valuation-risks-world-inflation-waves.html and earlier http://cmpassocregulationblog.blogspot.com/2014/05/united-states-commercial-banks-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2014/05/financial-volatility-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2014/03/interest-rate-risks-world-inflation.html and earlier http://cmpassocregulationblog.blogspot.com/2014/03/financial-risks-slow-cyclical-united.html and earlier http://cmpassocregulationblog.blogspot.com/2014/02/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2013/12/tapering-quantitative-easing-mediocre.html and earlier http://cmpassocregulationblog.blogspot.com/2013/11/risks-of-zero-interest-rates-world.html http://cmpassocregulationblog.blogspot.com/2013/11/global-financial-risk-world-inflation.html http://cmpassocregulationblog.blogspot.com/2013/09/duration-dumping-and-peaking-valuations_8763.html http://cmpass ocregulationblog.blogspot.com/2013/08/interest-rate-risks-duration-dumping.html and earlier http://cmpassocregulationblog.blogspot.com/2013/07/duration-dumping-steepening-yield-curve.html and earlier http://cmpassocregulationblog.blogspot.com/2013/06/paring-quantitative-easing-policy-and_4699.html and earlier at http://cmpassocregulationblog.blogspot.com/2013/05/united-states-commercial-banks-assets.html and earlier http://cmpassocregulationblog.blogspot.com/2013/04/world-inflation-waves-squeeze-of.html and earlier http://cmpassocregulationblog.blogspot.com/2013/03/united-states-commercial-banks-assets.html and earlier at http://cmpassocregulationblog.blogspot.com/2013/02/world-inflation-waves-united-states.html and earlier at http://cmpassocregulationblog.blogspot.com/2013/02/thirty-one-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2012/12/mediocre-and-decelerating-united-states_24.html and earlier http://cmpassocregulationblog.blogspot.com/2012/11/contraction-of-united-states-real_25.html and for GDP http://cmpassocregulationblog.blogspot.com/2013/09/recovery-without-hiring-ten-million.html and earlier http://cmpassocregulationblog.blogspot.com/2013/08/duration-dumping-and-peaking-valuations.html and earlier http://cmpassocreulationblog.blogspot.com/2013/02/recovery-without-hiring-united-states.html). In Feb 2015, Japan’s exports increased 2.4 percent in 12 months while imports decreased 3.6 percent. The second part of Table V-4 shows that net trade deducted 1.4 percentage points from Japan’s growth of GDP in IIQ2012, deducted 1.9 percentage points from GDP growth in IIIQ2012 and deducted 0.4 percentage points from GDP growth in IVQ2012. Net trade added 0.3 percentage points to GDP growth in IQ2012, 1.6 percentage points in IQ2013 and 0.2 percentage points in IIQ2013. In IIIQ2013, net trade deducted 1.5 percentage points from GDP growth in Japan. Net trade ducted 2.1 percentage points from GDP growth in Japan in IVQ2013. Net trade deducted 1.2 percentage point from GDP growth of Japan in IQ2014. Net trade added 4.2 percentage points to GDP growth in IIQ2014. Net trade added 0.2 percentage points to GDP growth in IIIQ2014 and added 0.9 percentage points in IVQ2014.
- China. In Feb 2015, China exports increased 48.3 percent relative to a year earlier and imports decreased 20.5 percent.
- Germany. Germany’s exports decreased 2.1 percent in the month of Jan 2015 and decreased 0.6 percent in the 12 months ending in Jan 2015. Germany’s imports decreased 0.8 percent in the month of Jan 2015 and decreased 0.3 percent in the 12 months ending in Jan. Net trade contributed 0.8 percentage points to growth of GDP in IQ2012, contributed 0.4 percentage points in IIQ2012, contributed 0.3 percentage points in IIIQ2012, deducted 0.5 percentage points in IVQ2012, deducted 0.3 percentage points in IQ2013 and added 0.1 percentage points in IIQ2013. Net traded deducted 0.5 percentage points from Germany’s GDP growth in IIIQ2013 and added 0.5 percentage points to GDP growth in IVQ2013. Net trade deducted 0.1 percentage points from GDP growth in IQ2014. Net trade added 0.0 percentage points to GDP growth in IIQ2014 and added 0.4 percentage points in IIIQ2014. Net trade added 0.2 percentage points to GDP growth in IVQ2014.
- United Kingdom. Net trade contributed 0.7 percentage points in IIQ2013. In IIIQ2013, net trade deducted 1.7 percentage points from UK growth. Net trade contributed 0.1 percentage points to UK value added in IVQ2013. Net trade contributed 0.1 percentage points to UK value added in IQ2014 and 0.2 percentage points in IIQ2014. Net trade deducted 0.5 percentage points to GDP growth in IIIQ2014 and added 0.8 percentage points in IVQ2014.
- France. France’s exports decreased 2.5 percent in Jan 2015 while imports decreased 1.3 percent. France’s exports increased 1.3 percent in the 12 months ending in Jan 2015 and imports decreased 3.1 percent relative to a year earlier. Net traded added 0.1 percentage points to France’s GDP in IIIQ2012 and 0.1 percentage points in IVQ2012. Net trade deducted 0.1 percentage points from France’s GDP growth in IQ2013 and added 0.3 percentage points in IIQ2013, deducting 1.7 percentage points in IIIQ2013. Net trade added 0.1 percentage points to France’s GDP in IVQ2013 and deducted 0.1 percentage points in IQ2014. Net trade deducted 0.2 percentage points from France’s GDP growth in IIQ2014 and deducted 0.2 percentage points in IIIQ2014. Net trade added 0.2 percentage points to France’s GDP growth in IVQ2014.
- United States. US exports decreased 1.6 percent in Feb 2015 and goods exports decreased 4.6 percent in Jan-Feb 2015 relative to a year earlier. Imports decreased 4.4 percent in Feb 2015 and goods imports decreased 3.5 percent in Jan-Feb 2015 relative to a year earlier. Net trade deducted 0.04 percentage points from GDP growth in IIQ2012 and added 0.39 percentage points in IIIQ2012 and 0.79 percentage points in IVQ2012. Net trade deducted 0.08 percentage points from US GDP growth in IQ2013 and deducted 0.54 percentage points in IIQ2013. Net traded added 0.59 percentage points to US GDP growth in IIIQ2013. Net trade added 1.08 percentage points to US GDP growth in IVQ2013. Net trade deducted 1.66 percentage points from US GDP growth in IQ2014 and deducted 0.34 percentage points in IIQ2014. Net trade added 0.78 percentage points to IIIQ2014. Net trade deducted 1.15 percentage points from GDP growth in IVQ2014. Industrial production increased 0.2 percent in Jan 2015 and decreased 0.3 percent in Dec 2014 after increasing 1.1 percent in Nov 2014. Industrial production increased 0.1 percent in Feb 2015 and decreased 0.3 percent in Jan 2015 after decreasing 0.2 percent in Dec 2014, with all data seasonally adjusted. The Federal Reserve completed its annual revision of industrial production and capacity utilization on Mar 28, 2014 (http://www.federalreserve.gov/releases/g17/revisions/Current/DefaultRev.htm). The report of the Board of Governors of the Federal Reserve System states (http://www.federalreserve.gov/releases/g17/Current/default.htm):
“Industrial production increased 0.1 percent in February after decreasing 0.3 percent in January. In February, manufacturing output moved down 0.2 percent, its third consecutive monthly decline. The rates of change for the total index in January and for manufacturing in both December and January are lower than previously reported. The index for mining fell 2.5 percent in February; drops in the indexes for coal mining and for oil and gas well drilling and servicing primarily accounted for the decrease. The output of utilities jumped 7.3 percent, as especially cold temperatures drove up demand for heating. At 105.8 percent of its 2007 average, total industrial production in February was 3.5 percent above its level of a year earlier. Capacity utilization for the industrial sector decreased to 78.9 percent in February, a rate that is 1.2 percentage points below its long-run (1972–2014) average.“ In the six months ending in Feb 2015, United States national industrial production accumulated increase of 1.4 percent at the annual equivalent rate of 2.8 percent, which is lower than growth of 3.5 percent in the 12 months ending in Feb 2015. Excluding growth of 1.2 percent in Nov 2014, growth in the remaining five months from Sep 2014 to Feb 2015 accumulated to 0.2 percent or 0.5 percent annual equivalent. Industrial production declined in three of the past six months. Industrial production contracted at annual equivalent 1.6 percent in the most recent quarter from Dec 2014 to Feb 2015 and expanded at 7.4 percent in the prior quarter Sep to Nov 2014. Business equipment accumulated growth of 0.9 percent in the six months from Sep 2014 to Feb 2015 at the annual equivalent rate of 1.8 percent, which is lower than growth of 4.0 percent in the 12 months ending in Feb 2015. The Fed analyzes capacity utilization of total industry in its report (http://www.federalreserve.gov/releases/g17/Current/default.htm): “Capacity utilization for the industrial sector decreased to 78.9 percent in February, a rate that is 1.2 percentage points below its long-run (1972–2014) average.” United States industry apparently decelerated to a lower growth rate followed by possible acceleration and oscillating growth in past months.
Manufacturing fell 21.9 from the peak in Jun 2007 to the trough in Apr 2009 and increased by 25.0 percent from the trough in Apr 2009 to Dec 2014. Manufacturing grew 24.3 percent from the trough in Apr 2009 to Feb 2015. Manufacturing output in Feb 2015 is 2.9 percent below the peak in Jun 2007. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IVQ2014 would have accumulated to 23.0 percent. GDP in IVQ2014 would be $18,438.0 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,143.3 billion than actual $16,294.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 26.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.1 percent of the effective labor force (Section I and earlier http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html). US GDP in IVQ2014 is 11.6 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,294.7 billion in IVQ2014 or 8.7 percent at the average annual equivalent rate of 1.2 percent. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth at average 3.3 percent per year from Feb 1919 to Feb 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 125.2379 in Feb 2015. The actual index NSA in Feb 2015 is 100.0312, which is 20.1 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014, raising the index at trend to 117.6250 in Feb 2015. The output of manufacturing at 100.0312 in Feb 2015 is 15.0 percent below trend under this alternative calculation.
Table V-4, Growth of Trade and Contributions of Net Trade to GDP Growth, ∆% and % Points
| Exports | Exports 12 M ∆% | Imports | Imports 12 M ∆% | |
| USA | -1.6 Jan | -4.6 Jan-Feb | -4.4 Jan | -3.5 Jan-Feb |
| Japan | Feb 2015 2.4 Jan 17.0 Dec 12.9 Nov 4.9 Oct 9.6 Sep 6.9 Aug -1.3 Jul 3.9 Jun -2.0 May 2014 -2.7 Apr 2014 5.1 Mar 2014 1.8 Feb 2014 9.5 Jan 2014 9.5 Dec 2013 15.3 Nov 2013 18.4 Oct 2013 18.6 Sep 2013 11.5 Aug 2013 14.7 Jul 2013 12.2 Jun 2013 7.4 May 2013 10.1 Apr 2013 3.8 Mar 2013 1.1 Feb 2013 -2.9 Jan 2013 6.4 Dec -5.8 Nov -4.1 Oct -6.5 Sep -10.3 Aug -5.8 Jul -8.1 | Feb 2015 -3.6 Jan -9.0 Dec 1.9 Nov -1.7 Oct 2.7 Sep 6.2 Aug -1.5 Jul 2.3 Jun 8.4 May 2014 -3.6 Apr 2013 3.4 Mar 2014 18.1 Feb 2014 9.0 Jan 2014 25.0 Dec 2013 24.7 Nov 2013 21.1 Oct 2013 26.1 Sep 2013 16.5 Aug 2013 16.0 Jul 2013 19.6 Jun 2013 11.8 May 2013 10.0 Apr 2013 9.4 Mar 2013 5.5 Feb 2013 7.3 Jan 2013 7.3 Dec 1.9 Nov 0.8 Oct -1.6 Sep 4.1 Aug -5.4 Jul 2.1 | ||
| China | 2015 48.3 Feb -3.3 Jan 2014 9.7 Dec 4.7 Nov 11.6 Oct 15.3 Sep 9.4 Aug 14.5 Jul 7.2 Jun 7.0 May 0.9 Apr -6.6 Mar -18.1 Feb 10.6 Jan 2013 4.3 Dec 12.7 Nov 5.6 Oct -0.3 Sep 7.2 Aug 5.1 Jul -3.1 Jun 1.0 May 14.7 Apr 10.0 Mar 21.8 Feb 25.0 Jan | 2015 -20.5 Feb -19.9 Jan 2014 -2.4 Dec -6.7 Nov 4.6 Oct 7.0 Sep -2.4 Aug -1.6 Jul 5.5 Jun -1.6 May -0.8 Apr -11.3 Mar 10.1 Feb 10.0 Jan 2013 8.3 Dec 5.3 Nov 7.6 Oct 7.4 Sep 7.0 Aug 10.9 Jul -0.7 Jun -0.3 May 16.8 Apr 14.1 Mar -15.2 Feb 28.8 Jan | ||
| Euro Area | -0.4 12 M-Jan | 2.2 Jan-Dec | -5.6 12-M Jan | 0.1 Jan-Dec |
| Germany | -2.1 Jan CSA | -0.6 Jan | -0.8 Jan CSA | -0.3 Jan |
| France Jan | -2.5 | 1.3 | -1.3 | -3.1 |
| Italy Jan | -2.5 | -4.2 | 1.0 | -4.2 |
| UK | -2.3 Jan | -5.6 Nov 14-Jan 15 /Nov 13-Jan 14 | -5.6 Jan | -2.9 Nov 14-Jan 15 /Nov 13-Jan 14 |
| Net Trade % Points GDP Growth | % Points | |||
| USA | IVQ2014 -1.15 IIIQ2014 0.78 IIQ2014 -0.34 IQ2014 -1.66 IVQ2013 1.08 IIIQ2013 0.59 IIQ2013 -0.54 IQ2013 -0.08 IVQ2012 +0.79 IIIQ2012 0.39 IIQ2012 -0.04 IQ2012 -0.11 | |||
| Japan | 0.3 IQ2012 -1.4 IIQ2012 -1.9 IIIQ2012 -0.4 IVQ2012 1.6 IQ2013 0.2 IIQ2013 -1.5 IIIQ2013 -2.1 IVQ2013 -1.2 IQ2014 4.2 IIQ2014 0.2 IIIQ2014 0.9 IVQ2014 | |||
| Germany | IQ2012 0.8 IIQ2012 0.4 IIIQ2012 0.3 IVQ2012 -0.5 IQ2013 -0.3 IIQ2013 0.1 IIIQ2013 -0.5 IVQ2013 0.5 IQ2014 -0.1 IIQ2014 0.0 IIIQ2014 0.4 IVQ2014 0.2 | |||
| France | 0.1 IIIQ2012 0.1 IVQ2012 -0.1 IQ2013 0.3 IIQ2013 -1.7 IIIQ2013 0.1 IVQ2013 -0.1 IQ2014 -0.2 IIQ2014 -0.2 IIIQ2014 0.2 IVQ2014 | |||
| UK | 0.7 IIQ2013 -1.7 IIIQ2013 0.1 IVQ2013 0.1 IQ2014 0.2 IIQ2214 -0.5 IIIQ2014 0.8 IVQ2014 |
Sources: Country Statistical Agencies http://www.census.gov/foreign-trade/
The geographical breakdown of exports and imports of Japan with selected regions and countries is in Table VB-5 for Feb 2015. The share of Asia in Japan’s trade is close to one-half for 51.6 percent of exports and 50.6 percent of imports. Within Asia, exports to China are 15.0 percent of total exports and imports from China 26.0 percent of total imports. While exports to China decreased 17.3 percent in the 12 months ending in Feb 2015, imports from China increased 39.4 percent. The largest export market for Japan in Feb 2015 is the US with share of 20.3 percent of total exports, which is close to that of China, and share of imports from the US of 9.2 percent in total imports. Japan’s exports to the US increased 14.3 percent in the 12 months ending in Feb 2015 and imports from the US increased 0.5 percent. Western Europe has share of 10.7 percent in Japan’s exports and of 10.7 percent in imports. Rates of growth of exports of Japan in Feb 2015 are 14.3 percent for exports to the US, 14.4 percent for exports to Brazil and minus 4.4 percent for exports to Germany. Comparisons relative to 2011 may have some bias because of the effects of the Tōhoku or Great East Earthquake and Tsunami of Mar 11, 2011. Deceleration of growth in China and the US and threat of recession in Europe can reduce world trade and economic activity. Growth rates of imports in the 12 months ending in Feb 2015 are mixed. Imports from Asia decreased 3.0 percent in the 12 months ending in Feb 2015 while imports from China increased 16.8 percent. Data are in millions of yen, which may have effects of recent depreciation of the yen relative to the United States dollar (USD).
Table VB-5, Japan, Value and 12-Month Percentage Changes of Exports and Imports by Regions and Countries, ∆% and Millions of Yen
| Feb 2015 | Exports | 12 months ∆% | Imports Millions Yen | 12 months ∆% |
| Total | 5,941,062 | 2.4 | 6,365,660 | -3.6 |
| Asia | 3,068,435 % Total 51.6 | -1.1 | 3,218,465 % Total 50.6 | 16.8 |
| China | 888,893 % Total 15.0 | -17.3 | 1,657,797 % Total 26.0 | 39.4 |
| USA | 1,215,673 % Total 20.5 | 14.3 | 583,982 % Total 9.2 | 0.5 |
| Canada | 81,977 | 19.1 | 78,456 | -8.8 |
| Brazil | 44,487 | 14.4 | 85,814 | -10.4 |
| Mexico | 96,650 | 16.4 | 43,839 | 21.5 |
| Western Europe | 634,818 % Total 10.7 | 1.7 | 679,425 % Total 10.7 | -3.9 |
| Germany | 158,600 | -4.4 | 194,294 | -9.4 |
| France | 54,121 | 6.4 | 86,072 | 7.3 |
| UK | 86,176 | -1.9 | 51,802 | -1.7 |
| Middle East | 256,291 | 11.0 | 763,137 | -42.6 |
| Australia | 129,414 | 2.5 | 364,356 | -2.7 |
Source: Japan, Ministry of Finance http://www.customs.go.jp/toukei/info/index_e.htm
World trade projections of the IMF are in Table V-6. There is increasing growth of the volume of world trade of goods and services from 3.0 percent in 2013 to 5.0 percent in 2015 and 5.6 percent on average from 2016 to 2019. World trade would be slower for advanced economies while emerging and developing economies (EMDE) experience faster growth. World economic slowdown would be more challenging with lower growth of world trade.
Table V-6, IMF, Projections of World Trade, USD Billions, USD/Barrel and Annual ∆%
| 2013 | 2014 | 2015 | Average ∆% 2016-2019 | |
| World Trade Volume (Goods and Services) | 3.0 | 3.8 | 5.0 | 5.6 |
| Exports Goods & Services | 3.2 | 3.7 | 5.0 | 5.5 |
| Imports Goods & Services | 2.8 | 3.9 | 5.0 | 5.6 |
| World Trade Value of Exports Goods & Services USD Billion | 23,114 | 23,928 | 24,948 | Average ∆% 2006-2015 20,259 |
| Value of Exports of Goods USD Billion | 18,671 | 19,299 | 20,107 | Average ∆% 2006-2015 16,312 |
| Average Oil Price USD/Barrel | 104.07 | 102.76 | 99.36 | Average ∆% 2006-2015 88.85 |
| Average Annual ∆% Export Unit Value of Manufactures | -1.1 | -0.2 | -0.5 | Average ∆% 2006-2015 -0.6 |
| Exports of Goods & Services | 2013 | 2014 | 2015 | Average ∆% 2016-2019 |
| Euro Area | 1.8 | 3.5 | 4.3 | 4.7 |
| EMDE | 4.4 | 3.9 | 5.8 | 6.1 |
| G7 | 1.8 | 2.9 | 4.2 | 4.9 |
| Imports Goods & Services | ||||
| Euro Area | 0.5 | 3.4 | 3.9 | 4.7 |
| EMDE | 5.3 | 4.4 | 6.1 | 6.3 |
| G7 | 1.2 | 3.6 | 4.1 | 4.9 |
| Terms of Trade of Goods & Services | ||||
| Euro Area | 0.8 | -0.4 | -0.3 | -0.1 |
| EMDE | -0.2 | -0.02 | -0.6 | -0.4 |
| G7 | 0.8 | 0.7 | -0.2 | 0.0 |
| Terms of Trade of Goods | ||||
| Euro Area | 1.2 | 0.03 | -0.02 | -0.2 |
| EMDE | -0.2 | 0.2 | -0.4 | -0.3 |
| G7 | 0.9 | 0.3 | -0.1 | -0.1 |
Notes: Commodity Price Index includes Fuel and Non-fuel Prices; Commodity Industrial Inputs Price includes agricultural raw materials and metal prices; Oil price is average of WTI, Brent and Dubai
Source: International Monetary Fund World Economic Outlook databank
http://www.imf.org/external/ns/cs.aspx?id=28
The JP Morgan Global All-Industry Output Index of the JP Morgan Manufacturing and Services PMI™, produced by JP Morgan and Markit in association with ISM and IFPSM, with high association with world GDP, increased to 53.9 in Feb from 53.0 in Jan, indicating expansion at slightly higher rate (http://www.markiteconomics.com/Survey/PressRelease.mvc/febc72758d7e4f4da8961c17c008d1f9). This index has remained above the contraction territory of 50.0 during 67 consecutive months. The employment index did not change from 51.7 in Jan to 51.7 in Feb with input prices rising at faster rate, new orders increasing at faster rate and output increasing at faster rate (http://www.markiteconomics.com/Survey/PressRelease.mvc/febc72758d7e4f4da8961c17c008d1f9). David Hensley, Director of Global Economic Coordination at JP Morgan, finds moderate acceleration of world economic growth in IQ2015 (http://www.markiteconomics.com/Survey/PressRelease.mvc/febc72758d7e4f4da8961c17c008d1f9). The JP Morgan Global Manufacturing PMI™, produced by JP Morgan and Markit in association with ISM and IFPSM, decreased to 51.8 in Mar from 51.9 in Feb (http://www.markiteconomics.com/Survey/PressRelease.mvc/667a34f2b7664124b9bacd22a85f00d9). New export orders expanded for the twentieth consecutive month. David Hensley, Director of Global Economics Coordination at JP Morgan Chase, finds continuing growth in global manufacturing with output increasing at rates around those in past months (http://www.markiteconomics.com/Survey/PressRelease.mvc/667a34f2b7664124b9bacd22a85f00d9). The HSBC Brazil Composite Output Index, compiled by Markit, increased from 49.2 in Jan to 51.3 in Feb, indicating moderate contraction in activity of Brazil’s private sector (http://www.markiteconomics.com/Survey/PressRelease.mvc/e3331afa76fc4a1bb68dc6331473ddb0). The HSBC Brazil Services Business Activity index, compiled by Markit, increased from 48.4 in Jan to 52.3 in Feb, indicating stronger services activity (http://www.markiteconomics.com/Survey/PressRelease.mvc/e3331afa76fc4a1bb68dc6331473ddb0). Pollyana De Lima, Economist at Markit, finds stronger services activity with uncertainties (http://www.markiteconomics.com/Survey/PressRelease.mvc/e3331afa76fc4a1bb68dc6331473ddb0). The HSBC Brazil Purchasing Managers’ IndexTM (PMI™) decreased from 49.6 in Feb to 46.2 in Mar, indicating deterioration in manufacturing (http://www.markiteconomics.com/Survey/PressRelease.mvc/a7a09c9443da4a7b91e8017781be1fd3). Pollyanna De Lima, Economist at Markit, finds the fastest contraction of manufacturing output in three and a half years (http://www.markiteconomics.com/Survey/PressRelease.mvc/a7a09c9443da4a7b91e8017781be1fd3).
VA United States. The Markit Flash US Manufacturing Purchasing Managers’ Index™ (PMI™) seasonally adjusted increased to 55.3 in Mar from 55.1 in Feb (http://www.markiteconomics.com/Survey/PressRelease.mvc/2861da71797f4fa9bc41b8cbee173955). New export orders declined. Chris Williamson, Chief Economist at Markit, finds that manufacturing expanding with challenges to competitiveness from the strong dollar (http://www.markiteconomics.com/Survey/PressRelease.mvc/2861da71797f4fa9bc41b8cbee173955). The Markit Flash US Services PMI™ Business Activity Index increased from 57.1 in Feb to 58. in Mar (http://www.markiteconomics.com/Survey/PressRelease.mvc/89c23ff78b0f4f29a791fd417b52764d). The Markit Flash US Composite PMI™ Output Index increased from 57.2 in Feb to 58.5 in Mar. Chris Williamson, Chief Economist at Markit, finds that the surveys are consistent with slowing GDP growth that may accelerate in the second quarter (http://www.markiteconomics.com/Survey/PressRelease.mvc/89c23ff78b0f4f29a791fd417b52764d). The Markit US Composite PMI™ Output Index of Manufacturing and Services increased to 57.2 in Feb from 54.4 in Jan (http://www.markiteconomics.com/Survey/PressRelease.mvc/de457cc1d40b4f0182e3e9adca09f723). The Markit US Services PMI™ Business Activity Index increased from 54.2 in Jan to 57.1 in Feb (http://www.markiteconomics.com/Survey/PressRelease.mvc/de457cc1d40b4f0182e3e9adca09f723). Chris Williamson, Chief Economist at Markit, finds the indexes consistent with US growth at around 2.2 percent annual in IQ2015 (http://www.markiteconomics.com/Survey/PressRelease.mvc/de457cc1d40b4f0182e3e9adca09f723). The Markit US Manufacturing Purchasing Managers’ Index™ (PMI™) increased to 55.7 in Mar from 55.1 in Feb, which indicates expansion at faster rate (http://www.markiteconomics.com/Survey/PressRelease.mvc/ddc73e386ca84f55aef2be31c5af4241). New foreign orders stagnated. Tim Moore, Senior Economist at Markit, finds that the index suggests restrain of foreign orders because of dollar appreciation (http://www.markiteconomics.com/Survey/PressRelease.mvc/ddc73e386ca84f55aef2be31c5af4241). The purchasing managers’ index (PMI) of the Institute for Supply Management (ISM) Report on Business® decreased 1.4 percentage points from 52.9 in Feb to 51.5 in Mar, which indicates growth at slower rate (http://www.ism.ws/ISMReport/MfgROB.cfm?navItemNumber=29253). The index of new orders decreased 0.7 percentage points from 52.5 in Feb to 51.8 in Mar. The index of new export orders decreased 1.0 percentage points from 48.5 in Feb to 47.5 in Mar, contracting at faster rate. The Non-Manufacturing ISM Report on Business® PMI increased 0.2 percentage points from 56.7 in Jan to 56.9 in Feb, indicating growth of business activity/production during 67 consecutive months, while the index of new orders decreased 2.8 percentage points from 59.5 in Jan to 56.7 in Feb (http://www.ism.ws/ISMReport/NonMfgROB.cfm?navItemNumber=29043). Table USA provides the country economic indicators for the US.
Table USA, US Economic Indicators
| Consumer Price Index | Feb 12 months NSA ∆%: 0.0; ex food and energy ∆%: 1.7 Feb month SA ∆%: 0.2; ex food and energy ∆%: 0.2 |
| Producer Price Index | Finished Goods Feb 12-month NSA ∆%: -3.4; ex food and energy ∆% 1.5 Final Demand Feb 12-month NSA ∆%: -0.6; ex food and energy ∆% 1.0 |
| PCE Inflation | Feb 12-month NSA ∆%: headline 0.3; ex food and energy ∆% 1.4 |
| Employment Situation | Household Survey: Mar Unemployment Rate SA 5.5% |
| Nonfarm Hiring | Nonfarm Hiring fell from 63.3 million in 2006 to 54.2 million in 2013 or by 9.1 million and to 58.7 million in 2014 or by 4.6 million |
| GDP Growth | BEA Revised National Income Accounts IIQ2012/IIQ2011 2.3 IIIQ2012/IIIQ2011 2.7 IVQ2012/IVQ2011 1.6 IQ2013/IQ2012 1.7 IIQ2013/IIQ2012 1.8 IIIQ2013/IIIQ2012 2.3 IVQ2013/IVQ2012 3.1 IQ2014/IQ2013 1.9 IIQ2014/IIQ2013 2.6 IIIQ2014/IIIQ2013 2.7 IVQ2014/IVQ2013 2.4 IQ2012 SAAR 2.3 IIQ2012 SAAR 1.6 IIIQ2012 SAAR 2.5 IVQ2012 SAAR 0.1 IQ2013 SAAR 2.7 IIQ2013 SAAR 1.8 IIIQ2013 SAAR 4.5 IVQ2013 SAAR 3.5 IQ2014 SAAR -2.1 IIQ2014 SAAR 4.6 IIIQ2014 SAAR 5.0 IVQ2014 SAAR 2.2 |
| Real Private Fixed Investment | SAAR IVQ2014 4.5 ∆% IVQ2007 to IVQ2014: 3.3% Blog 3/29/15 |
| Corporate Profits | IVQ2014 SAAR: Corporate Profits -1.4; Undistributed Profits -6.6 Blog 3/29/15 |
| Personal Income and Consumption | Feb month ∆% SA Real Disposable Personal Income (RDPI) SA ∆% 0.2 |
| Quarterly Services Report | IVQ14/IVQ13 NSA ∆%: Financial & Insurance 5.2 |
| Employment Cost Index | Compensation Private IVQ2014 SA ∆%: 0.6 |
| Industrial Production | Feb month SA ∆%: 0.1 Manufacturing Feb SA -0.2 ∆% Feb 12 months SA ∆% 3.3, NSA 5.5 |
| Productivity and Costs | Nonfarm Business Productivity IVQ2014∆% SAAE -2.2; IVQ2014/IVQ2013 ∆% -0.1; Unit Labor Costs SAAE IVQ2014 ∆% 4.1; IVQ2014/IVQ2013 ∆%: 2.6 Blog 3/8/15 |
| New York Fed Manufacturing Index | General Business Conditions From Feb 7.78 to Mar 6.90 |
| Philadelphia Fed Business Outlook Index | General Index from Feb 5.2 to Mar 5.0 |
| Manufacturing Shipments and Orders | New Orders SA Feb ∆% 0.2 Ex Transport 0.8 Jan-Feb NSA New Orders ∆% minus 5.4 Ex transport minus 5.9 |
| Durable Goods | Feb New Orders SA ∆%: minus 1.4; ex transport ∆%: minus 0.4 |
| Sales of New Motor Vehicles | Mar 2015 3,954,544; Mar 2014 3,743,742. Mar 15 SAAR 17.15 million, Feb 15 SAAR 16.23 million, Mar 2014 SAAR 16.49 million Blog 4/5/15 |
| Sales of Merchant Wholesalers | Jan 2015/Jan 2014 NSA ∆%: Total -3.8; Durable Goods: 2.9; Nondurable |
| Sales and Inventories of Manufacturers, Retailers and Merchant Wholesalers | Jan 15 12-M NSA ∆%: Sales Total Business -2.2; Manufacturers -4.1 |
| Sales for Retail and Food Services | Jan-Feb 2015/Jan-Feb 2014 ∆%: Retail and Food Services 2.3; Retail ∆% 1.4 |
| Value of Construction Put in Place | Feb SAAR month SA ∆%: minus 0.1 Feb 12-month NSA:3.1 |
| Case-Shiller Home Prices | Jan 2015/ Jan 2014 ∆% NSA: 10 Cities 4.4; 20 Cities: 4.6; National: 4.5 |
| FHFA House Price Index Purchases Only | Jan SA ∆% 0.3; |
| New House Sales | Feb 2015 month SAAR ∆%: minus 19.1 |
| Housing Starts and Permits | Feb Starts month SA ∆% -17.0; Permits ∆%: 3.0 |
| Trade Balance | Balance Feb SA -$35,444 million versus Jan -$42,676 million |
| Export and Import Prices | Feb 12-month NSA ∆%: Imports -9.4; Exports -5.9 |
| Consumer Credit | Jan ∆% annual rate: Total 4.2; Revolving -1.6; Nonrevolving 6.3 |
| Net Foreign Purchases of Long-term Treasury Securities | Jan Net Foreign Purchases of Long-term US Securities: minus $39.3 billion |
| Treasury Budget | Fiscal Year 2015/2014 ∆% Feb: Receipts 7.1; Outlays 6.0; Individual Income Taxes 8.1 Deficit Fiscal Year 2012 $1,087 billion Deficit Fiscal Year 2013 $680 billion Deficit Fiscal Year 2014 $483 billion Blog 3/15/2015 |
| CBO Budget and Economic Outlook | 2012 Deficit $1087 B 6.8% GDP Debt $11,281 B 70.4% GDP 2013 Deficit $680 B, 4.1% GDP Debt $11,983 B 72.3% GDP 2014 Deficit $483 B 2.8% GDP Debt $12,779 B 74.1% GDP 2025 Deficit $1,088B, 4.0% GDP Debt $21,605B 78.7% GDP 2039: Long-term Debt/GDP 106% Blog 8/26/12 11/18/12 2/10/13 9/22/13 2/16/14 8/24/14 9/14/14 3/1/15 |
| Commercial Banks Assets and Liabilities | Dec 2014 SAAR ∆%: Securities 24.2 Loans 7.1 Cash Assets -52.4 Deposits 7.6 Blog 1/25/15 |
| Flow of Funds Net Worth of Families and Nonprofits | IVQ2014 ∆ since 2007 Assets +$15,921.0 BN Nonfinancial $898.5 BN Real estate $172.1 BN Financial +15,022.4 BN Net Worth +$16,162.4 BN Blog 3/29/15 |
| Current Account Balance of Payments | IVQ2014 -111,222 MM % GDP 2.6 Blog 3/22/15 |
| Collapse of United States Dynamism of Income Growth and Employment Creation | Blog 1/25/15 |
Links to blog comments in Table USA:
3/29/15 http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html
3/22/15 http://cmpassocregulationblog.blogspot.com/2015/03/impatience-with-monetary-policy-of.html
3/15/15 http://cmpassocregulationblog.blogspot.com/2015/03/global-exchange-rate-struggle-recovery.html
3/8/15 http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html
3/1/15 http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html
2/1/15 http://cmpassocregulationblog.blogspot.com/2015/02/financial-and-international.html
1/25/15 http://cmpassocregulationblog.blogspot.com/2015/01/competitive-currency-conflicts-world.html
9/14/14 http://cmpassocregulationblog.blogspot.com/2014/09/geopolitics-monetary-policy-and.html
8/24/14 http://cmpassocregulationblog.blogspot.com/2014/08/monetary-policy-world-inflation-waves.html
2/16/14 http://cmpassocregulationblog.blogspot.com/2014/02/theory-and-reality-of-cyclical-slow.html
9/22/13 http://cmpassocregulationblog.blogspot.com/2013/09/duration-dumping-and-peaking-valuations.html
2/10/13 http://cmpassocregulationblog.blogspot.com/2013/02/united-states-unsustainable-fiscal.html
Motor vehicle sales and production in the US have been in long-term structural change. Table VA-1 provides the data on new motor vehicle sales and domestic car production in the US from 1990 to 2010. New motor vehicle sales grew from 14,137 thousand in 1990 to the peak of 17,806 thousand in 2000 or 29.5 percent. In that same period, domestic car production fell from 6,231 thousand in 1990 to 5,542 thousand in 2000 or -11.1 percent. New motor vehicle sales fell from 17,445 thousand in 2005 to 11,772 in 2010 or 32.5 percent while domestic car production fell from 4,321 thousand in 2005 to 2,840 thousand in 2010 or 34.3 percent. In Mar 2015, light vehicle sales accumulated to 3,954,544, which is higher by 5.6 percent relative to 3,743,742 a year earlier (http://motorintelligence.com/m_frameset.html). The seasonally adjusted annual rate of light vehicle sales in the US reached 17.15 million in Mar 2015, higher than 16.23 million in Feb 2015 and higher than 16.49 million in Mar 2014 (http://motorintelligence.com/m_frameset.html).
Table VA-1, US, New Motor Vehicle Sales and Car Production, Thousand Units
| New Motor Vehicle Sales | New Car Sales and Leases | New Truck Sales and Leases | Domestic Car Production | |
| 1990 | 14,137 | 9,300 | 4,837 | 6,231 |
| 1991 | 12,725 | 8,589 | 4,136 | 5,454 |
| 1992 | 13,093 | 8,215 | 4,878 | 5,979 |
| 1993 | 14,172 | 8,518 | 5,654 | 5,979 |
| 1994 | 15,397 | 8,990 | 6,407 | 6,614 |
| 1995 | 15,106 | 8,536 | 6,470 | 6,340 |
| 1996 | 15,449 | 8,527 | 6,922 | 6,081 |
| 1997 | 15,490 | 8,273 | 7,218 | 5,934 |
| 1998 | 15,958 | 8,142 | 7,816 | 5,554 |
| 1999 | 17,401 | 8,697 | 8,704 | 5,638 |
| 2000 | 17,806 | 8,852 | 8,954 | 5,542 |
| 2001 | 17,468 | 8,422 | 9,046 | 4,878 |
| 2002 | 17,144 | 8,109 | 9,036 | 5,019 |
| 2003 | 16,968 | 7,611 | 9,357 | 4,510 |
| 2004 | 17,298 | 7,545 | 9,753 | 4,230 |
| 2005 | 17,445 | 7,720 | 9,725 | 4,321 |
| 2006 | 17,049 | 7,821 | 9,228 | 4,367 |
| 2007 | 16,460 | 7,618 | 8,683 | 3,924 |
| 2008 | 13,494 | 6,814 | 6.680 | 3,777 |
| 2009 | 10,601 | 5,456 | 5,154 | 2,247 |
| 2010 | 11,772 | 5,729 | 6,044 | 2,840 |
Source: US Census Bureau
http://www.census.gov/compendia/statab/cats/wholesale_retail_trade/motor_vehicle_sales.html
Chart VA-1 of the Board of Governors of the Federal Reserve provides output of motor vehicles and parts in the United States from 1972 to 2015. Output virtually stagnated since the late 1990s.
Chart VA-1, US, Motor Vehicles and Parts Output, 1972-2015
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/g17/Current/default.htm
Manufacturers’ shipments increased 0.7 percent in Feb 2015 and decreased 2.3 percent in Jan 2015 after decreasing 0.9 percent in Dec 2014. New orders increased 0.2 percent in Feb 2015, after decreasing 0.7 percent in Jan 2015 and decreasing 3.5 percent in Dec 2014, as shown in Table VA-2. These data are very volatile. Volatility is illustrated by increase of 2642.2 percent of new orders of nondefense aircraft in Sep 2012 following decline by 97.2 percent in Aug 2012. New orders excluding transportation equipment increased 0.8 percent in Feb 2015 after decreasing 2.3 percent in Jan 2015 and decreasing 2.3 percent in Dec 2014. Capital goods new orders, indicating investment, decreased 1.2 percent in Feb 2015 after increasing 7.1 percent in Jan 2015 and decreasing 10.3 percent in Dec 2014. New orders of nondefense capital goods decreased 2.3 percent in Feb 2015 after increasing 8.5 percent in Jan 2015 and decreasing 10.1 percent in Dec 2014. Excluding more volatile aircraft, capital goods orders decreased 1.1 percent in Feb 2015 after decreasing 0.3 percent in Jan 2015 and decreasing 0.5 percent in Dec 2014.
Table VA-2, US, Value of Manufacturers’ Shipments and New Orders, SA, Month ∆%
| Feb 2015 | Jan 2015 | Dec 2014 ∆% | |
| Total | |||
| S | 0.7 | -2.3 | -0.9 |
| NO | 0.2 | -0.7 | -3.5 |
| Excluding | |||
| S | 0.9 | -2.5 | -1.8 |
| NO | 0.8 | -2.3 | -2.3 |
| Excluding | |||
| S | 0.8 | -2.0 | -1.1 |
| NO | 0.4 | -0.6 | -3.3 |
| Durable Goods | |||
| S | -0.2 | -1.4 | 1.5 |
| NO | -1.4 | 1.9 | -3.7 |
| Machinery | |||
| S | 0.6 | -2.6 | -0.9 |
| NO | -1.6 | -0.2 | -3.3 |
| Computers & Electronic Products | |||
| S | 0.6 | -1.2 | -0.2 |
| NO | -0.7 | 0.8 | -1.5 |
| Computers | |||
| S | 0.6 | -1.8 | -2.4 |
| NO | 0.0 | 23.9 | -31.1 |
| Transport | |||
| S | -0.2 | -1.1 | 4.0 |
| NO | -3.3 | 8.9 | -10.0 |
| Automobiles | |||
| S | 0.0 | -10.0 | 3.5 |
| Motor Vehicles | |||
| S | -0.7 | -0.5 | 2.9 |
| NO | -1.2 | 0.4 | 1.5 |
| Nondefense | |||
| S | -1.2 | 10.4 | 3.4 |
| NO | -8.8 | 122.3 | -58.1 |
| Capital Goods | |||
| S | 0.1 | -0.9 | 1.2 |
| NO | -1.2 | 7.1 | -10.3 |
| Nondefense Capital Goods | |||
| S | 0.1 | 0.8 | 0.8 |
| NO | -2.3 | 8.5 | -10.1 |
| Capital Goods ex Aircraft | |||
| S | 0.3 | -0.6 | 0.4 |
| NO | -1.1 | -0.3 | -0.5 |
| Nondurable Goods | |||
| S | 1.8 | -3.2 | -3.3 |
| NO | 1.8 | -3.2 | -3.3 |
Note: Mfg: manufacturing; S: shipments; NO: new orders; Transport: transportation
Source: US Census Bureau
http://www.census.gov/manufacturing/m3/
Chart VA-2 of the US Census Bureau provides new orders of manufacturers from Mar 2014 to Feb 2015. There is significant volatility that prevents discerning clear trends. New orders fell in five of the past six months.
Chart VA-2, US, Manufacturers’ New Orders 2014-2015 Seasonally Adjusted, Month ∆%
Source: US Census Bureau
http://www.census.gov/briefrm/esbr/www/esbr022.html
Chart VA-3 of the US Census Bureau provides total value of manufacturers’ new orders, seasonally adjusted, from 1992 to 2015. Seasonal adjustment reduces sharp oscillations. The series dropped nearly vertically during the global recession but rose along a path even steeper than in the high-growth period before the recession. The final segment suggests deceleration but similar segments occurred in earlier periods followed with continuing growth and stability currently.
Chart VA-3, US, Value of Total Manufacturers’ New Orders, Seasonally Adjusted, 1992-2015
Source: US Census Bureau
http://www.census.gov/manufacturing/m3/
Additional perspective on manufacturers’ shipments and new orders is provided by Table VA-3. Values are cumulative millions of dollars in Jan-Feb 2015 not seasonally adjusted (NSA). Shipments of all manufacturing industries in Jan-Feb 2015 total $881.8 billion and new orders total $866.7 billion, growing respectively by minus 3.7 percent and minus 5.4 percent relative to the same period in 2014. Excluding transportation equipment, shipments fell 5.3 percent and new orders decreased 6.1 percent. Excluding defense, shipments fell 4.1 percent and new orders fell 5.9 percent. Durable goods shipments reached $442.4 billion in Jan-Feb 2015, or 50.2 percent of the total, growing by 3.0 percent, and new orders $427.3 billion, or 49.3 percent of the total, decreasing by 0.7 percent. Important information in Table VA-3 is the large share of nondurable goods with shipments of $439.4 billion or 49.8 percent of the total, decreasing by 9.7 percent. Capital goods have relatively high value of $155.9 billion for shipments, growing 4.0 percent, and new orders $152.3 billion, decreasing 2.2 percent, which could be an indicator of future investment. Excluding aircraft, capital goods shipments reached $124.8 billion, growing 3.4 percent, and new orders $129.7 billion, decreasing 0.1 percent. There is no suggestion in these data that the US economy is close to recession but manufacturing accounts for 11.3 percent of US national income in IVQ2014. These data are not adjusted for inflation.
Table VA-3, US, Value of Manufacturers’ Shipments and New Orders, NSA, Millions of Dollars
| Feb 2015 | Shipments | ∆% Jan-Feb 2015/Jan-Feb | New Orders | ∆% Jan-Feb 2015/Jan-Feb |
| Total | 881,805 | -3.7 | 866,725 | -5.4 |
| Excluding Transport | 748,637 | -5.3 | 742,368 | -5.9 |
| Excluding Defense | 862,242 | -3.7 | 850,775 | -5.4 |
| Durable Goods | 442,425 | 3.0 | 427,345 | -0.7 |
| Machinery | 63,148 | -0.5 | 64,607 | -7.7 |
| Computers & Electronic Products | 50,286 | -2.2 | 39,090 | 3.4 |
| Computers | 510 | -27.1 | 594 | 16.7 |
| Transport Equipment | 133,168 | 6.3 | 124,357 | -2.9 |
| Automobiles | 17,134 | -6.8 | ||
| Motor Vehicles | 42,364 | 8.0 | 42,239 | 8.8 |
| Nondefense Aircraft | 22,448 | 18.8 | 15,779 | -32.3 |
| Capital Goods | 155,858 | 4.0 | 152,316 | -2.2 |
| Nondefense Capital Goods | 139,885 | 5.3 | 138,130 | -2.5 |
| Capital Goods ex Aircraft | 124,837 | 3.4 | 129,746 | -0.1 |
| Nondurable Goods | 439,380 | -9.7 | 439,380 | -9.7 |
| Food Products | 127,968 | 2.3 | ||
| Petroleum Refineries | 82,966 | -35.4 | ||
| Chemical Products | 114,722 | -4.1 |
Note: Transport: transportation Source: US Census Bureau
Source: US Census Bureau
http://www.census.gov/manufacturing/m3/
Chart VA-4 of the US Census Bureau provides value of manufacturer’s new orders not seasonally adjusted from Jan 1992 to Feb 2015. Fluctuations are evident, which are smoothed by seasonal adjustment in the above Chart VA-3. The series drops nearly vertically during the global contraction and then resumes growth in a steep upward trend, flattening recently.
Chart VA-4, US, Value of Total Manufacturers’ New Orders, Not Seasonally Adjusted, 1992-2015
Source: US Census Bureau
http://www.census.gov/manufacturing/m3/
Construction spending at seasonally adjusted annualized rate (SAAR) reached $967.1 billion in Feb 2015, which was lower by 0.1 percent than in the prior month of Jan 2015, as shown in Table VA-4. Residential construction, with $355.6 billion accounting for 36.8 percent of total value of construction, decreased 0.1 percent in Feb and nonresidential construction, with $611.5 billion accounting for 63.2 percent of the total, decreased 20.1 percent. Public construction decreased 0.8 percent while private construction increased 0.5 percent. Data in Table VA-4 show that nonresidential construction at $611.5 billion is much higher in value than residential construction at $355.6 billion while total private construction at $698.2 billion is much higher than public construction at $268.9 billion, all in SAAR. Residential and nonresidential construction contributed positively to growth of GDP in the US in all quarters in 2012. Nonresidential investment added 0.20 percentage points to GDP growth in IQ2013 while residential construction added 0.22 percentage points. Nonresidential construction added 0.21 percentage points to GDP growth in IIQ2013 with residential construction adding 0.53 percentage points. Nonresidential construction added 0.67 percentage points to GDP growth in IIIQ2013 while residential construction added 0.34 percentage points. Nonresidential construction added 1.23 percentage points to GDP growth in IVQ2013 while residential construction deducted 0.28 percentage points. In 2012, residential construction added 0.33 percentage points to GDP growth and added 0.01 percentage points in 2011. Residential construction added 0.33 percentage points to GDP growth in 2013. Nonresidential construction added 0.84 percentage points to GDP growth in 2012 and 0.85 percentage points in 2011. Nonresidential construction added 0.37 percentage points to GDP growth in 2013. Residential construction added 0.05 percentage points to GDP growth in 2014 and nonresidential contruction added 0.78 percentage points. In IQ2014, residential construction deducted 0.17 percentage points from GDP growth and nonresidential construction added 0.20 percentage points. Nonresidential construction added 1.18 percentage points to GDP growth in IIQ2014 and residential construction added 0.27 percentage points. Nonresidential construction added 1.10 percentage points to GDP growth in IIIQ2014 while residential construction added 0.10 percentage points. In IVQ2014, nonresidential construction added 0.60 percentage points to GDP growth and residential construction added 0.12 percentage points (http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html).
Table VA-4, Construction Put in Place in the United States Seasonally Adjusted Annual Rate Million Dollars and Month and 12-Month ∆%
| Feb 2015 | Jan 2014 SAAR $ Millions | Month ∆% | 12-Month ∆% |
| Total | 967,170 | -0.1 | 2.1 |
| Residential | 355,625 | -0.1 | -1.9 |
| Nonresidential | 611,545 | -0.1 | 4.6 |
| Total Private | 698,237 | 0.2 | 1.8 |
| Private Residential | 349,852 | -0.2 | -2.1 |
| New Single Family | 203,867 | -1.4 | 9.7 |
| New Multi-Family | 50,869 | 4.1 | 31.5 |
| Private Nonresidential | 344,385 | 0.5 | 5.9 |
| Total Public | 268,933 | -0.8 | 3.1 |
| Public Residential | 5,773 | 3.4 | 13.9 |
| Public Nonresidential | 263,160 | -0.8 | 2.9 |
SAAR: seasonally adjusted annual rate; B: billions
Source: US Census Bureau http://www.census.gov/construction/c30/c30index.html
Further information on construction spending is provided in Table VA-5. The original monthly estimates not-seasonally adjusted (NSA) and their 12-month rates of change are provided in the first two columns while the SAARs and their monthly changes are provided in the final two columns. There has been improvement in construction in the US. There are only ten declines in the monthly rate from Dec 2011 to Feb 2015, with four in 2014. Growth in 12 months NSA fell from 8.2 percent in Dec 2012 to 3.1 percent in Feb 2015.
Table VA-5, US, Value and Percentage Change in Value of Construction Put in Place, Dollars Millions and ∆%
| Value NSA | 12-Month ∆% NSA | Value | Month ∆% SA* | |
| Feb 2015 | 65,826 | 3.1 | 967,170 | -0.1 |
| Jan | 67,298 | 0.8 | 967,930 | -1.7 |
| Dec 2014 | 76,374 | 3.4 | 984,502 | 1.0 |
| Nov | 82,054 | 2.1 | 974,301 | -0.6 |
| Oct | 90,522 | 3.9 | 980,422 | 1.4 |
| Sep | 89,530 | 4.0 | 966,432 | 0.6 |
| Aug | 89,992 | 2.4 | 961,066 | 0.1 |
| Jul | 86,888 | 4.3 | 960,043 | 0.3 |
| Jun | 86,141 | 5.1 | 957,120 | -1.6 |
| May | 82,778 | 7.6 | 972,844 | 1.3 |
| Apr | 76,940 | 8.5 | 960,349 | 1.4 |
| Mar | 70,064 | 8.8 | 947,303 | 0.0 |
| Feb | 63,817 | 7.8 | 947,088 | -0.8 |
| Jan | 66,486 | 11.5 | 954,642 | -0.7 |
| Dec 2013 | 73,893 | 9.6 | 961,158 | 0.9 |
| Nov | 80,373 | 7.5 | 952,531 | 1.3 |
| Oct | 87,163 | 5.8 | 939,933 | 1.7 |
| Sep | 86,101 | 6.4 | 924,153 | 1.0 |
| Aug | 86,909 | 5.8 | 915,286 | 1.0 |
| Jul | 83,299 | 6.2 | 906,644 | 0.7 |
| Jun | 81,961 | 3.4 | 900,334 | 0.4 |
| May | 76,938 | 4.6 | 896,603 | 1.3 |
| Apr | 70,900 | 5.5 | 884,966 | 1.5 |
| Mar | 64,404 | 4.3 | 871,888 | -0.9 |
| Feb | 59,188 | 4.3 | 879,609 | 1.2 |
| Jan | 59,636 | 5.4 | 869,232 | -1.5 |
| Dec 2012 | 67,431 | 8.2 | 882,637 | 0.4 |
| Nov | 74,754 | 8.6 | 879,539 | -0.5 |
| Oct | 82,353 | 10.9 | 883,853 | 0.9 |
| Sep | 80,890 | 7.3 | 876,091 | 0.9 |
| Aug | 82,183 | 6.6 | 868,246 | 0.4 |
| Jul | 78,457 | 8.8 | 864,687 | -1.0 |
| Jun | 79,262 | 8.8 | 873,069 | 1.3 |
| May | 73,571 | 11.8 | 861,913 | 2.3 |
| Apr | 67,234 | 9.4 | 842,898 | 1.0 |
| Mar | 61,772 | 9.2 | 834,810 | 1.1 |
| Feb | 56,761 | 12.2 | 825,774 | 0.3 |
| Jan | 56,578 | 10.9 | 823,583 | 0.8 |
| Dec 2011 | 62,319 | 3.4 | 817,198 | 0.9 |
SAAR: Seasonally Adjusted Annual Rate
Source: US Census Bureau http://www.census.gov/construction/c30/c30index.html
The sharp contraction of the value of construction in the US is revealed by Table VA-6. Construction spending in Jan-Feb 2015, not seasonally adjusted, reached $132.9 billion, which is higher by 2.0 percent than $130.3 billion in the same period in 2014. The depth of the contraction is shown by the decline of construction spending from $162.5 billion in Jan-Feb 2006 to $132.9 billion in the same period in 2015, or decline by minus 18.3 percent. The decline in inflation-adjusted terms is much higher. The all-items not seasonally adjusted CPI (consumer price index) increased from 198.7 in Feb 2006 to 234.7 in Feb 2015 (http://www.bls.gov/cpi/data.htm) or by 18.1 percent. The comparable decline from Jan-Feb 2005 to Jan-Feb 2015 is minus 7.4 percent. Construction spending in Jan-Feb 2015 increased by 12.2 percent relative to the same period in 2003. Construction spending is lower by 0.6 percent in Jan-Feb 2015 relative to the same period in 2009. Construction has been weaker than the economy as a whole.
Table VA-6, US, Value of Construction Put in Place in the United States, Not Seasonally Adjusted, $ Millions and ∆%
| Jan-Feb 2015 $ MM | 132,850 |
| Jan-Feb 2014 | 130,303 |
| ∆% to 2015 | 2.0 |
| Jan-Feb 2013 | 118,824 |
| ∆% to 2015 | 11.8 |
| Jan-Feb 2012 $ MM | 113,339 |
| ∆% to 2015 | 17.2 |
| Jan-Feb 2011 $ MM | 101,602 |
| ∆% to 2015 | 30.8 |
| Jan-Feb 2010 $MM | 109,786 |
| ∆% to 2015 | 21.0 |
| Jan-Feb 2009 | 133,617 |
| ∆% to 2015 | -0.6 |
| Jan-Feb 2006 $ MM | 162,536 |
| ∆% to 2015 | -18.3 |
| Jan-Feb 2005 $ MM | 143,522 |
| ∆% to 2015 | -7.4 |
| Jan-Feb 2003 $ MM | 118,403 |
| ∆% to 2015 | 12.2 |
Source: US Census Bureau http://www.census.gov/construction/c30/c30index.html
Chart VA-5 of the US Census Bureau provides value of construction spending in the US not seasonally adjusted from 2002 to 2015. There are wide oscillations requiring seasonal adjustment to compare adjacent data. There was sharp decline during the global recession followed in recent periods by a stationary series that may be moving upward again with vacillation in the final segment.
Chart VA-5, Value of Construction Spending not Seasonally Adjusted, Millions of Dollars, 2002-2015
Source: US Census Bureau http://www.census.gov/construction/c30/c30index.html
Table VA-7 provides the value of construction in the US not seasonally adjusted in selected months from 2002 to 2015. Construction in Feb 2015 of $65.8 billion increased 3.1 percent relative to $63.8 billion in Feb 2014 and increased 30.1 percent relative to $50.6 billion in Feb 2010. Construction in Feb 2015 of $65.8 billion is lower by 19.2 percent relative to $81.5 billion in Feb 2005.
Table VA-7, US, Value of Construction Spending Not Seasonally Adjusted, Millions of Dollars
| Year | Jan | Feb | Sep | Oct | Nov | Dec |
| 2002 | 59,516 | 58,588 | 76,542 | 75,710 | 71,362 | 63,984 |
| 2003 | 59,877 | 58,526 | 83,841 | 83,133 | 77,915 | 71,050 |
| 2004 | 64,934 | 64,138 | 92,538 | 90,582 | 86,394 | 77,733 |
| 2005 | 71,474 | 72,048 | 103,269 | 102,339 | 97,549 | 88,172 |
| 2006 | 81,058 | 81,478 | 104,191 | 101,582 | 95,339 | 86,436 |
| 2007 | 79,406 | 79,177 | 105,150 | 103,847 | 94,822 | 84,218 |
| 2008 | 77,365 | 77,253 | 96,858 | 95,722 | 86,176 | 76,763 |
| 2009 | 67,127 | 66,490 | 81,344 | 80,071 | 72,013 | 64,178 |
| 2010 | 55,674 | 54,112 | 74,892 | 73,607 | 68,100 | 60,246 |
| 2011 | 51,012 | 50,590 | 75,388 | 74,246 | 68,845 | 62,319 |
| 2012 | 56,578 | 56,761 | 80,890 | 82,353 | 74,754 | 67,431 |
| 2013 | 59,636 | 59,188 | 86,101 | 87,163 | 80,373 | 73,893 |
| 2014 | 66,486 | 63,817 | 89,530 | 90,522 | 82,054 | 76,374 |
| 2015 | 67,024 | 65,826 | NA | NA | NA | NA |
Source: US Census Bureau http://www.census.gov/construction/c30/c30index.html
Chart VA-6 of the US Census Bureau shows SAARs of construction spending for the US since 1993. Construction spending surged in nearly vertical slope after the stimulus of 2003 combining near zero interest rates together with other housing subsidies and subsequent slow adjustment in 17 doses of increases by 25 basis points between Jun 2004 and Jun 2006. Construction spending collapsed after subprime mortgages defaulted with the fed funds rate increasing from 1.00 percent in Jun 2004 to 5.25 percent in Jun 2006. Subprime mortgages were programmed for refinancing in two years after increases in homeowner equity in the assumption that fed funds rates would remain low forever or increase in small increments (Gorton 2009EFM see http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html). Price declines of houses or even uncertainty prevented refinancing of subprime mortgages that defaulted, causing the financial crisis that eventually triggered the global recession. Chart VA-6 shows a trend of increase in the final segment but it is difficult to assess if it is sustainable.
Chart VA-6, US, Construction Expenditures SAAR 1993-2015
Source: US Census Bureau
http://www.census.gov/briefrm/esbr/www/esbr050.html
Construction spending at SAARs in the five months Jan-Feb and Oct-Dec is shown in Table VA-8 for the years between 2002 and 2015. There is a peak in 2005 to 2007 with subsequent collapse of SAARs and rebound in 2012-2015.
Table VA-8, US, Value of Construction Spending SAAR Millions of Dollars
| Year | Jan | Feb | Oct | Nov | Dec |
| 2002 | 858,654 | 862,338 | 839,690 | 844,697 | 855,921 |
| 2003 | 863,855 | 859,225 | 925,732 | 925,985 | 948,491 |
| 2004 | 938,826 | 938,656 | 1,015,562 | 1,023,210 | 1,037,684 |
| 2005 | 1,036,187 | 1,056,492 | 1,145,663 | 1,156,977 | 1,178,305 |
| 2006 | 1,183,861 | 1,199,767 | 1,139,292 | 1,137,488 | 1,153,491 |
| 2007 | 1,149,899 | 1,156,008 | 1,152,511 | 1,127,558 | 1,108,958 |
| 2008 | 1,106,498 | 1,092,911 | 1,051,176 | 1,031,004 | 994,216 |
| 2009 | 965,270 | 962,482 | 870,697 | 852,896 | 832,484 |
| 2010 | 818,468 | 798,063 | 802,013 | 799,636 | 779,919 |
| 2011 | 757,900 | 754,970 | 806,849 | 809,833 | 817,198 |
| 2012 | 823,583 | 825,774 | 883,853 | 879,539 | 882,637 |
| 2013 | 869,232 | 879,609 | 939,933 | 952,531 | 961,158 |
| 2014 | 954,642 | 947,088 | 980,422 | 974,301 | 984,502 |
| 2015 | 967,930 | 967,170 | NA | NA | NA |
Source: US Census Bureau http://www.census.gov/construction/c30/c30index.html
Chart VA-7 of the US Census Bureau provides SAARs of value of construction from 2002 to 2015. There is clear acceleration after 2003 when fed funds rates were fixed at 1.0 percent from Jun 2003 until Jun 2004. Construction peaked in 2005-2006, stabilizing in 2007 at a lower level and then collapsed in a nearly vertical drop until 2011 with increases into 2012 and marginal drop in Jan 2013 followed by increase in Feb 2013 and decline in Mar 2013 followed by continuing increase in Apr-May 2013 and Aug-Nov 2013. The rate of growth slowed in 2014-2015.
Chart VA-7, US, Construction Expenditures SAAR 2002-2015
Source: US Census Bureau
http://www.census.gov/construction/c30/c30index.html
Chart VA-8 of the US Census Bureau provides monthly residential construction in the US not seasonally adjusted from 2002 to 2015. There was steep increase until 2006 followed by sharp contraction. The series stabilized at the bottom and increased in the final segment with subsequent stability.
Chart VA-8, US, Residential Construction, Not Seasonally Adjusted, Millions of Dollars, 2002-2015
Source: US Census Bureau
http://www.census.gov/construction/c30/c30index.html
Chart VA-9 of the US Census Bureau provides monthly nonresidential construction in the US not seasonally adjusted. There is similar acceleration until 2006 followed by milder contraction than for residential construction. The final segment appears stationary.
Chart VA-9, US, Nonresidential Construction, Not Seasonally Adjusted, Millions of Dollars, 2002-2015
http://www.census.gov/construction/c30/c30index.html
Annual available data for the value of construction put in place in the US between 1993 and 2014 are provided in Table VA-9. Data from 1993 to 2001 are available for public and private construction with breakdown in residential and nonresidential only for private construction. Data beginning in 2002 provide aggregate residential and nonresidential values. Total construction value put in place in the US increased 97.8 percent between 1993 and 2014 but most of the growth, 65.3 percent, was concentrated in 1993 to 2000 with increase of 19.7 percent between 2000 and 2014. Total value of construction increased 13.3 percent between 2002 and 2014 with value of nonresidential construction increasing 36.0 percent while value of residential construction fell 11.9 percent. Value of total construction fell 13.0 percent between 2005 and 2014, with value of residential construction declining 42.6 percent while value of nonresidential construction rose 24.6 percent. Value of total construction fell 17.7 percent between 2006 and 2014, with value of nonresidential construction increasing 10.8 percent while value of residential construction fell 42.9 percent. In 2002, nonresidential construction had share of 52.6 percent in total construction while the share of residential construction was 47.4 percent. In 2014, the share of nonresidential construction in total value rose to 63.1 percent while that of residential construction fell to 36.9 percent.
Table VA-9, Annual Value of Construction Put in Place 1993-2014, Millions of Dollars and ∆%
| Total | Private Nonresidential | Private Residential | |
| 1993 | 485,548 | 150,006 | 208,180 |
| 1994 | 531,892 | 160,438 | 241,033 |
| 1995 | 548,666 | 180,534 | 228,121 |
| 1996 | 599,693 | 195,523 | 257,495 |
| 1997 | 631,853 | 213,720 | 264,696 |
| 1998 | 688,515 | 237,394 | 296,343 |
| 1999 | 744,551 | 249,167 | 326,302 |
| 2000 | 802,756 | 275,293 | 346,138 |
| 2001 | 840,249 | 273,922 | 364,414 |
| Total | Total Nonresidential | Total Residential | |
| 2002 | 847,874 | 445,914 | 401,960 |
| 2003 | 891,497 | 440,246 | 451,251 |
| 2004 | 991,356 | 452,948 | 538,408 |
| 2005 | 1,104,136 | 486,629 | 617,507 |
| 2006 | 1,167,222 | 547,408 | 619,814 |
| 2007 | 1,152,351 | 651,883 | 500,468 |
| 2008 | 1,068,436 | 710,690 | 357,746 |
| 2009 | 904,929 | 651,001 | 253,928 |
| 2010 | 806,040 | 556,928 | 249,112 |
| 2011 | 788,343 | 535,686 | 252,657 |
| 2012 | 861,245 | 574,399 | 286,847 |
| 2013 | 910,764 | 568,561 | 342,203 |
| 2014 | 960,586 | 606,363 | 354,222 |
| ∆% 1993-2014 | 97.8 | ||
| ∆% 1993-2000 | 65.3 | ||
| ∆% 2000-2014 | 19.7 | ||
| ∆% 2002-2014 | 13.3 | 36.0 | -11.9 |
| ∆% 2005-2014 | -13.0 | 24.6 | -42.6 |
| ∆% 2006-2014 | -17.7 | 10.8 | -42.9 |
Source: US Census Bureau http://www.census.gov/construction/c30/c30index.html
Table IIA-4 summarizes the brutal drops in assets and net worth of US households and nonprofit organizations from 2007 to 2008 and 2009. Total assets fell $10.4 trillion or 12.7 percent from 2007 to 2008 and $8.8 trillion or 10.9 percent to 2009. Net worth fell $10.2 trillion from 2007 to 2008 or 15.3 percent and $8.5 trillion to 2009 or 12.7 percent. Subsidies to housing prolonged over decades together with interest rates at 1.0 percent from Jun 2003 to Jun 2004 inflated valuations of real estate and risk financial assets such as equities. The increase of fed funds rates by 25 basis points until 5.25 percent in Jun 2006 reversed carry trades through exotic vehicles such as subprime adjustable rate mortgages (ARM) and world financial markets. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9).
Table IIA-4, Difference of Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars from 2007 to 2008 and 2009
| 2007 | 2008 | Change to 2008 | 2009 | Change to 2009 | |
| A | 81,145.7 | 70,788.9 | -10,356.8 | 72,314.1 | -8,831.6 |
| Non | 28,176.0 | 24,791.0 | -3,385.0 | 23,699.6 | -4,476.4 |
| RE | 23,366.5 | 19,856.8 | -3,509.7 | 18,743.2 | -4,623.3 |
| FIN | 52,969.8 | 45,997.8 | -6,972.0 | 48,614.4 | -4,355.4 |
| LIAB | 14,395.9 | 14,279.9 | -116.0 | 14,063.4 | -332.5 |
| NW | 66,749.8 | 56,508.9 | -10,240.9 | 58,250.7 | -8,499.1 |
A: Assets; Non FIN: Nonfinancial Assets; RE: Real Estate; FIN: Financial Assets; LIAB: Liabilities; NW: Net Worth
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
The 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.
There are two approaches in theory considered by Bordo (2012Nov20) and Bordo and Lane (2013). The first approach is in the classical works of Milton Friedman and Anna Jacobson Schwartz (1963a, 1987) and Karl Brunner and Allan H. Meltzer (1973). There is a similar approach in Tobin (1969). Friedman and Schwartz (1963a, 66) trace the effects of expansionary monetary policy into increasing initially financial asset prices: “It seems plausible that both nonbank and bank holders of redundant balances will turn first to securities comparable to those they have sold, say, fixed-interest coupon, low-risk obligations. But as they seek to purchase these they will tend to bid up the prices of those issues. Hence they, and also other holders not involved in the initial central bank open-market transactions, will look farther afield: the banks, to their loans; the nonbank holders, to other categories of securities-higher risk fixed-coupon obligations, equities, real property, and so forth.”
The second approach is by the Austrian School arguing that increases in asset prices can become bubbles if monetary policy allows their financing with bank credit. Professor Michael D. Bordo provides clear thought and empirical evidence on the role of “expansionary monetary policy” in inflating asset prices (Bordo2012Nov20, Bordo and Lane 2013). Bordo and Lane (2013) provide revealing narrative of historical episodes of expansionary monetary policy. Bordo and Lane (2013) conclude that policies of depressing interest rates below the target rate or growth of money above the target influences higher asset prices, using a panel of 18 OECD countries from 1920 to 2011. Bordo (2012Nov20) concludes: “that expansionary money is a significant trigger” and “central banks should follow stable monetary policies…based on well understood and credible monetary rules.” Taylor (2007, 2009) explains the housing boom and financial crisis in terms of expansionary monetary policy.
Another hurdle of exit from zero interest rates is “competitive easing” that Professor Raghuram Rajan, governor of the Reserve Bank of India, characterizes as disguised “competitive devaluation” (http://www.centralbanking.com/central-banking-journal/interview/2358995/raghuram-rajan-on-the-dangers-of-asset-prices-policy-spillovers-and-finance-in-india). The fed has been considering increasing interest rates. The European Central Bank (ECB) announced, on Mar 5, 2015, the beginning on Mar 9, 2015 of its quantitative easing program denominated as Public Sector Purchase Program (PSPP), consisting of “combined monthly purchases of EUR 60 bn [billion] in public and private sector securities” (http://www.ecb.europa.eu/mopo/liq/html/pspp.en.html). Expectation of increasing interest rates in the US together with euro rates close to zero or negative cause revaluation of the dollar (or devaluation of the euro and of most currencies worldwide). US corporations suffer currency translation losses of their foreign transactions and investments (http://www.fasb.org/jsp/FASB/Pronouncement_C/SummaryPage&cid=900000010318) while the US becomes less competitive in world trade (Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), Government Intervention in Globalization (2008c)). The DJIA fell 1.5 percent on Mar 6, 2015 and the dollar revalued 2.2 percent from Mar 5 to Mar 6, 2015. The euro has devalued 44.9 percent relative to the dollar from the high on Jul 15, 2008 to Apr 3, 2015.
| Fri 27 Feb | Mon 3/2 | Tue 3/3 | Wed 3/4 | Thu 3/5 | Fri 3/6 |
| USD/ EUR 1.1197 1.6% 0.0% | 1.1185 0.1% 0.1% | 1.1176 0.2% 0.1% | 1.1081 1.0% 0.9% | 1.1030 1.5% 0.5% | 1.0843 3.2% 1.7% |
Chair Yellen explained the removal of the word “patience” from the advanced guidance at the press conference following the FOMC meeting on Mar 18, 2015(http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20150318.pdf):
“In other words, just because we removed the word “patient” from the statement doesn’t mean we are going to be impatient. Moreover, even after the initial increase in the target funds rate, our policy is likely to remain highly accommodative to support continued progress toward our objectives of maximum employment and 2 percent inflation.”
Exchange rate volatility is increasing in response of “impatience” in financial markets with monetary policy guidance and measures:
| Fri Mar 6 | Mon 9 | Tue 10 | Wed 11 | Thu 12 | Fri 13 |
| USD/ EUR 1.0843 3.2% 1.7% | 1.0853 -0.1% -0.1% | 1.0700 1.3% 1.4% | 1.0548 2.7% 1.4% | 1.0637 1.9% -0.8% | 1.0497 3.2% 1.3% |
| Fri Mar 13 | Mon 16 | Tue 17 | Wed 18 | Thu 19 | Fri 20 |
| USD/ EUR 1.0497 3.2% 1.3% | 1.0570 -0.7% -0.7% | 1.0598 -1.0% -0.3% | 1.0864 -3.5% -2.5% | 1.0661 -1.6% 1.9% | 1.0821 -3.1% -1.5% |
The apparent improvement in Table IIA-4A is mostly because of increases in valuations of risk financial assets by the carry trade from zero interest rates to leveraged exposures in risk financial assets such as stocks, high-yield bonds, emerging markets, commodities and so on. Zero interest rates also act to increase net worth by reducing debt or liabilities. The net worth of households has become an instrument of unconventional monetary policy by zero interest rates in the theory that increases in net worth increase consumption that accounts for 68.5 percent of GDP in IVQ2014 (Section I and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html), generating demand to increase aggregate economic activity and employment. There are neglected and counterproductive risks in unconventional monetary policy. Between 2007 and IVQ2014, real estate increased in value by $898.5 billion and financial assets increased $15,022.4 billion for net gain of real estate and financial assets of $15,920.9 billion, explaining most of the increase in net worth of $16,162.4 billion obtained by adding the decrease in liabilities of $241.4 billion to the increase of assets of $15,921.0 billion. Net worth increased from $66,749.8 billion in 2007 to $82,912.2 billion in IVQ2014 by $16,162.4 billion or 24.2 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 234.812 in Dec 2014 (http://www.bls.gov/cpi/data.htm) or 11.8 percent. Net worth adjusted by CPI inflation increased 11.1 percent from 2007 to IVQ2014. Real estate assets adjusted for CPI inflation fell 9.9 percent from 2007 to IVQ2014. There are multiple complaints that unconventional monetary policy concentrates income on wealthier individuals because of their holdings of financial assets while the middle class has gained less because of fewer holdings of financial assets and higher share of real estate in family wealth. There is nothing new in these arguments. Interest rate ceilings on deposits and loans have been commonly used. The Banking Act of 1933 imposed prohibition of payment of interest on demand deposits and ceilings on interest rates on time deposits. These measures were justified by arguments that the banking panic of the 1930s was caused by competitive rates on bank deposits that led banks to engage in high-risk loans (Friedman, 1970, 18; see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 74-5). The objective of policy was to prevent unsound loans in banks. Savings and loan institutions complained of unfair competition from commercial banks that led to continuing controls with the objective of directing savings toward residential construction. Friedman (1970, 15) argues that controls were passive during periods when rates implied on demand deposit were zero or lower and when Regulation Q ceilings on time deposits were above market rates on time deposits. The Great Inflation or stagflation of the 1960s and 1970s changed the relevance of Regulation Q. Friedman (1970, 26-7) predicted the future:
“The banks have been forced into costly structural readjustments, the European banking system has been given an unnecessary competitive advantage, and London has been artificially strengthened as a financial center at the expense of New York.”
In short, Depression regulation exported the US financial system to London and offshore centers. What is vividly relevant currently from this experience is the argument by Friedman (1970, 27) that the controls affected the most people with lower incomes and wealth who were forced into accepting controlled-rates on their savings that were lower than those that would be obtained under freer markets. As Friedman (1970, 27) argues:
“These are the people who have the fewest alternative ways to invest their limited assets and are least sophisticated about the alternatives.” US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 22 quarters from IIIQ2009 to IVQ2014. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IVQ2014 (http://www.bea.gov/newsreleases/national/gdp/2015/pdf/gdp4q14_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IVQ2014 would have accumulated to 23.0 percent. GDP in IVQ2014 would be $18,438.0 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,143.3 billion than actual $16,294.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 26.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.1 percent of the effective labor force (Section I and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html). US GDP in IVQ2014 is 11.6 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,294.7 billion in IVQ2014 or 8.7 percent at the average annual equivalent rate of 1.2 percent. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth at average 3.3 percent per year from Feb 1919 to Feb 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 125.2379 in Feb 2015. The actual index NSA in Feb 2015 is 100.0312, which is 20.1 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014, raising the index at trend to 117.6250 in Feb 2015. The output of manufacturing at 100.0312 in Feb 2015 is 15.0 percent below trend under this alternative calculation.
Table IIA-4A, US, Difference of Balance Sheet of Households and Nonprofit Organizations Billions of Dollars from 2007 to 2011, 2013 and IVQ2014
| Value 2007 | Change to 2011 | Change to 2013 | Change to IVQ2014 | |
| Assets | 81,145.7 | -3,904.2 | 11,489.9 | 15,921.0 |
| Nonfinancial | 28,176.0 | -4,800.9 | -487.5 | 898.5 |
| Real Estate | 23,366.5 | -5,117.0 | -1,051.3 | 172.1 |
| Financial | 52,969.8 | 896.5 | 11,977.4 | 15,022.4 |
| Liabilities | 14,395.9 | -822.1 | -604.5 | -241.4 |
| Net Worth | 66,749.8 | -3,082.1 | 12,094.4 | 16,162.4 |
Net Worth = Assets – Liabilities
Source: Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System.
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
The comparison of net worth of households and nonprofit organizations in the entire economic cycle from IQ1980 (and from IVQ1979) to IIQ1988 and from IVQ2007 to IVQ2014 is provided in Table IIA-5. The data reveal the following facts for the cycles in the 1980s:
- IVQ1979 to IIQ1988. Net worth increased 108.9 percent from IVQ1979 to IIQ1988, the all items CPI index increased 53.8 percent from 76.7 in Dec 1979 to 118.0 in Jun 1988 and real net worth increased 35.8 percent.
- IQ1980 to IVQ1985. Net worth increased 65.4 percent, the all items CPI index increased 36.5 percent from 80.1 in Mar 1980 to 109.3 in Dec 1985 and real net worth increased 21.2 percent.
- IVQ1979 to IVQ1985. Net worth increased 68.8 percent, the all items CPI index increased 42.5 percent from 76.7 in Dec 1979 to 109.3 in Dec 1985 and real net worth increased 18.5 percent.
- IQ1980 to IIQ1988. Net worth increased 104.6 percent, the all items CPI index increased 47.3 percent from 80.1 in Mar 1980 to 118.0 in Jun 1988 and real net worth increased 38.9 percent.
There is disastrous performance in the current economic cycle:
- IVQ2007 to IVQ2014. Net worth increased 24.2 percent, the all items CPI increased 11.8 percent from 210.036 in Dec 2007 to 234.812 in Dec 2014 and real or inflation adjusted net worth increased 11.1 percent. Real estate assets adjusted for inflation fell 9.9 percent.
The explanation is partly in the sharp decline of wealth of households and nonprofit organizations and partly in the mediocre growth rates of the cyclical expansion beginning in IIIQ2009. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.3 percent on average in the cyclical expansion in the 22 quarters from IIIQ2009 to IVQ2014. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IVQ2014 (http://www.bea.gov/newsreleases/national/gdp/2015/pdf/gdp4q14_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IVQ2014 would have accumulated to 23.0 percent. GDP in IVQ2014 would be $18,438.0 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,143.3 billion than actual $16,294.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 26.7 million unemployed or underemployed equivalent to actual unemployment/underemployment of 16.1 percent of the effective labor force (Section I and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/global-competitive-devaluation-rules.html and earlier http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html). US GDP in IVQ2014 is 11.6 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,294.7 billion in IVQ2014 or 8.7 percent at the average annual equivalent rate of 1.2 percent. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth at average 3.3 percent per year from Feb 1919 to Feb 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 125.2379 in Feb 2015. The actual index NSA in Feb 2015 is 100.0312, which is 20.1 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014, raising the index at trend to 117.6250 in Feb 2015. The output of manufacturing at 100.0312 in Feb 2015 is 15.0 percent below trend under this alternative calculation.
Table IIA-5, Net Worth of Households and Nonprofit Organizations in Billions of Dollars, IVQ1979 to IVQ1985 and IVQ2007 to IVQ2012
| Period IQ1980 to IIQ1988 | |
| Net Worth of Households and Nonprofit Organizations USD Millions | |
| IVQ1979 IQ1980 | 9,047.8 9,238.6 |
| IVQ1985 IIIQ1986 IVQ1986 IQ1987 IIQ1987 IIIQ1987 IVQ1987 IQ1988 II1988 | 15,277.2 16,290.7 16,840.3 17,494.6 17,784.0 18,195.2 18,021.9 18,459.2 18,900.2 |
| ∆ USD Billions IVQ1985 IVQ1979 to IIQ1988 IQ1980-IVQ1985 IQ1980-IIIQ1986 IQ1980-IVQ1986 IQ1980-IQ1987 IQ1980-IIQ1987 IQ1980-IIIQ1987 IQ1980-IVQ1987 IQ1980-IQ1988 IQ1980-IIQ1988 | +6,229.4 ∆%68.8 R∆%18.5 +9852.4 ∆%108.9 R∆%35.8 +6,038.6 ∆%65.4 R∆%21.2 +7,052.1 ∆%76.3 R∆%28.2 +7,601.7 ∆%82.3 R∆%32.1 +8,256.0 ∆%89.4 R∆%35.3 +8,545.4 ∆%92.5 R∆%35.9 +8,956.6 ∆%96.9 R∆%37.2 +8783.3 ∆%95.1 R∆%35.4 +9226.6 ∆%100.2 R∆%37.6 +9661.6 ∆%104.6 R∆38.9 |
| Period IVQ2007 to IVQ2014 | |
| Net Worth of Households and Nonprofit Organizations USD Millions | |
| IVQ2007 | 66,749.8 |
| IVQ2014 | 82,912.2 |
| ∆ USD Billions | +16,162.4 ∆%24.2 R∆%11.1 |
Net Worth = Assets – Liabilities. R∆% real percentage change or adjusted for CPI percentage change.
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Chart IIA-1 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ2007 to IIIQ2014. There is remarkable stop and go behavior in this series with two sharp declines and two standstills in the 22 quarters of expansion of the economy beginning in IIIQ2009. The increase in net worth of households and nonprofit organizations is the result of increases in valuations of risk financial assets and compressed liabilities resulting from zero interest rates. Wealth of households and nonprofits organization increased 11.1 percent from IVQ2007 to IVQ2014 when adjusting for consumer price inflation.
Chart IIA-1, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ2007 to IVQ2014
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Chart IIA-2 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ1979 to IIQ1988. There are changes in the rates of growth of wealth suggested by the changing slopes but there is smooth upward trend. There was significant financial turmoil during the 1980s. Benston and Kaufman (1997, 139) find that there was failure of 1150 US commercial and savings banks between 1983 and 1990, or about 8 percent of the industry in 1980, which is nearly twice more than between the establishment of the Federal Deposit Insurance Corporation in 1934 through 1983. More than 900 savings and loans associations, representing 25 percent of the industry, were closed, merged or placed in conservatorships (see Pelaez and Pelaez, Regulation of Banks and Finance (2008b), 74-7). The Financial Institutions Reform, Recovery and Enforcement Act of 1989 (FIRREA) created the Resolution Trust Corporation (RTC) and the Savings Association Insurance Fund (SAIF) that received $150 billion of taxpayer funds to resolve insolvent savings and loans. The GDP of the US in 1989 was $5657.7 billion (http://www.bea.gov/iTable/index_nipa.cfm), such that the partial cost to taxpayers of that bailout was around 2.65 percent of GDP in a year. US GDP in 2014 is estimated at $17,418.9 billion, such that the bailout would be equivalent to cost to taxpayers of about $461.6 billion in current GDP terms. A major difference with the Troubled Asset Relief Program (TARP) for private-sector banks is that most of the costs were recovered with interest gains whereas in the case of savings and loans there was no recovery. Money center banks were under extraordinary pressure from the default of sovereign debt by various emerging nations that represented a large share of their net worth (see Pelaez 1986). Net worth of households and nonprofit organizations increased 108.9 percent from IVQ1979 to IIQ1988 and 35.8 percent when adjusting for consumer price inflation. Net worth of households and nonprofit organizations increased 104.6 percent from IQ1980 to IIQ1988 and 38.9 percent when adjusting for consumer price inflation.
Chart IIA-2, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1979 to IIQ1988
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/.
Chart IIA-3 of the Board of Governors of the Federal Reserve System provides US wealth of households and nonprofit organizations from IVQ1945 at $767.3 billion to IVQ2014 at $82,912.2 billion or increase of 10,705.7 percent. The consumer price index not seasonally adjusted was 18.2 in Dec 1945 jumping to 234.812 in Dec 2014 or increase of 1,190.2 percent. There was a gigantic increase of US net worth of households and nonprofit organizations over 69 years with inflation-adjusted increase from $42.159 in dollars of 1945 to $353.100 in IVQ2014 or 737.5 percent. In a simple formula: {[($82,912.2/$767.3)/(234.812/18.2)-1]100 = 737.5%}. Wealth of households and nonprofit organizations increased from $767.3 billion at year-end 1945 to $82,912.2 billion at the end of IVQ2014 or 10,705.7 percent. The consumer price index increased from 18.2 in Dec 1945 to 234.812 in Dec 2014 or 1190.2 percent. Net wealth of households and nonprofit organizations in dollars of 1945 increased from $42.159 in 1945 to $353.100 in IVQ2014 or 737.5 percent at the average yearly rate of 3.1 percent. US real GDP grew at the average rate of 2.9 percent from 1945 to 2014 (http://www.bea.gov/iTable/index_nipa.cfm). The combination of collapse of values of real estate and financial assets during the global recession of IVQ2007 to IIQ2009 caused sharp contraction of net worth of US households and nonprofit organizations. Recovery has been in stop-and-go fashion during the worst cyclical expansion in the 68 years when US GDP grew at 2.3 percent on average in the twenty-two quarters between IIIQ2009 and IVQ2014 (http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html). US GDP was $228.2 billion in 1945 and net worth of households and nonprofit organizations $767.3 billion for ratio of wealth to GDP of 3.36. The ratio of net worth of households and nonprofits of $66,749.8 billion in 2007 to GDP of $14,477.6 billion was 4.61. The ratio of net worth of households and nonprofits of $82,912.2 billion in 2014 to GDP of 17,418.9 billion was 4.75. The final data point in Chart IIA-3 is net worth of household and nonprofit institutions at $82,912.2 billion in IVQ2014 for increase of 10,705.7 percent relative to $767.3 billion in IVQ1945. CPI adjusted net worth of household and nonprofit institutions increased from $42.159 in IVQ1945 to $353.100 in IVQ2014 or 737.5 percent at the annual equivalent rate of 3.1 percent.
Chart IIA-3, Net Worth of Households and Nonprofit Organizations in Millions of Dollars, IVQ1945 to IVQ2014
Source: Board of Governors of the Federal Reserve System. 2014. Flow of funds, balance sheets and integrated macroeconomic accounts: fourth quarter 2014. Washington, DC, Federal Reserve System, Mar 12. http://www.federalreserve.gov/releases/z1/
Table IIA-6 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 93.4 percent in the 10-city composite of the Case-Shiller home price index, 76.4 percent in the 20-city composite and 60.1 percent in the US national home price index between Jan 2000 and Jan 2005. Prices rose around 100 percent from Jan 2000 to Jan 2006, increasing 122.5 percent for the 10-city composite, 102.4 percent for the 20-city composite and 80.8 percent in the US national index. House prices rose 35.3 percent between Jan 2003 and Jan 2005 for the 10-city composite, 30.1 percent for the 20-city composite and 25.5 percent for the US national propelled by low fed funds rates of 1.0 percent between Jun 2003 and Jun 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Jun 2004 until Jun 2006, reaching 5.25 percent. Simultaneously, the suspension of auctions of the 30-year Treasury bond caused decline of yields of mortgage-backed securities with intended decrease in mortgage rates. Similarly, between Jan 2003 and Jan 2006, the 10-city index gained 55.7 percent, the 20-city index increased 49.2 percent and the US national 41.7 percent. House prices have fallen from Jan 2006 to Jan 2015 by 15.6 percent for the 10-city composite, 14.6 percent for the 20-city composite and 7.8 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 Jan 2015, house prices increased 4.4 percent in the 10-city composite, increased 4.6 percent in the 20-city composite and 4.5 percent in the US national. Table IIA-6 also shows that house prices increased 87.8 percent between Jan 2000 and Jan 2015 for the 10-city composite and increased 72.9 percent for the 20-city composite and 66.7 percent for the US national. House prices are close to the lowest level since peaks during the boom before the financial crisis and global recession. The 10-city composite fell 17.0 percent from the peak in Jun 2006 to Jan 2015 and the 20-city composite fell 16.3 percent from the peak in Jul 2006 to Jan 2015. The US national fell 9.7 percent from the peaks of the 10-city and 20-city composites to Jan 2015. The final part of Table II-2 provides average annual percentage rates of growth of the house price indexes of Standard & Poor’s Case-Shiller. The average annual growth rate between Dec 1987 and Dec 2014 for the 10-city composite was 3.7 percent and 3.4 percent for the US national. Data for the 20-city composite are available only beginning in Jan 2000. House prices accelerated in the 1990s with the average rate of the 10-city composite of 5.0 percent between Dec 1992 and Dec 2000 while the average rate for the period Dec 1987 to Dec 2000 was 3.8 percent. The average rate for the US national was 3.4 percent from Dec 1987 and Dec 2014 and 3.6 percent from Dec 1987 to Dec 2000. Although the global recession affecting the US between IVQ2007 (Dec) and IIQ2009 (Jun) caused decline of house prices of slightly above 30 percent, the average annual growth rate of the 10-city composite between Dec 2000 and Dec 2014 was 3.7 percent while the rate of the 20-city composite was 3.2 percent and 3.1 percent for the US national.
Table IIA-6, US, Percentage Changes of Standard & Poor’s Case-Shiller Home Price Indices, Not Seasonally Adjusted, ∆%
| 10-City Composite | 20-City Composite | US National | |
| ∆% Jan 2000 to Jan 2003 | 42.9 | 35.6 | 27.7 |
| ∆% Jan 2000 to Jan 2005 | 93.4 | 76.4 | 60.1 |
| ∆% Jan 2003 to Jan 2005 | 35.3 | 30.1 | 25.5 |
| ∆% Jan 2000 to Jan 2006 | 122.5 | 102.4 | 80.8 |
| ∆% Jan 2003 to Jan 2006 | 55.7 | 49.2 | 41.7 |
| ∆% Jan 2005 to Jan 2015 | -2.9 | -2.0 | 4.1 |
| ∆% Jan 2006 to Jan 2015 | -15.6 | -14.6 | -7.8 |
| ∆% Jan 2009 to Jan 2015 | 18.9 | 18.2 | 11.6 |
| ∆% Jan 2010 to Jan 2015 | 19.0 | 19.0 | 14.9 |
| ∆% Jan 2011 to Jan 2015 | 21.7 | 22.8 | 19.9 |
| ∆% Jan 2012 to Jan 2015 | 26.9 | 27.9 | 24.2 |
| ∆% Jan 2013 to Jan 2015 | 18.4 | 18.3 | 15.4 |
| ∆% Jan 2014 to Jan 2015 | 4.4 | 4.6 | 4.5 |
| ∆% Jan 2000 to Jan 2015 | 87.8 | 72.9 | 66.7 |
| ∆% Peak Jun 2006 Jan 2015 | -17.0 | -9.7 | |
| ∆% Peak Jul 2006 Jan 2015 | -16.3 | -9.7 | |
| Average ∆% Dec 1987-Dec 2014 | 3.7 | NA | 3.4 |
| Average ∆% Dec 1987-Dec 2000 | 3.8 | NA | 3.6 |
| Average ∆% Dec 1992-Dec 2000 | 5.0 | NA | 4.5 |
| Average ∆% Dec 2000-Dec 2014 | 3.7 | 3.2 | 3.1 |
Source: http://us.spindices.com/index-family/real-estate/sp-case-shiller
Price increases measured by the Case-Shiller house price indices show “higher price increases” throughout the US with increases in prices in fourteen cities in the 12 months ending in Jan 2015 (https://www.spice-indices.com/idpfiles/spice-assets/resources/public/documents/161742_cshomeprice-release-0331.pdf?force_download=true). Monthly house prices increased sharply from Feb 2013 to Jan 2014 for both the 10- and 20-city composites, as shown in Table IIA-7. In Jan 2013, the seasonally adjusted 10-city composite increased 0.9 percent and the 20-city increased 0.9 percent while the 10-city not seasonally adjusted changed 0.0 percent and the 20-city changed 0.0 percent. House prices increased at high monthly percentage rates from Feb to Nov 2013. With the exception of Mar through Apr 2012, house prices seasonally adjusted declined in every month for both the 10-city and 20-city Case-Shiller composites from Dec 2010 to Jan 2012, as shown in Table I-6. The most important seasonal factor in house prices is school changes for wealthier homeowners with more expensive houses. Without seasonal adjustment, house prices fell from Dec 2010 throughout Mar 2011 and then increased in every month from Apr to Aug 2011 but fell in every month from Sep 2011 to Feb 2012. The not seasonally adjusted index registers decline in Mar 2012 of 0.1 percent for the 10-city composite and is flat for the 20-city composite. Not seasonally adjusted house prices increased 1.4 percent in Apr 2012 and at high monthly percentage rates until Sep 2012. House prices not seasonally adjusted stalled from Oct 2012 to Jan 2013 and surged from Feb to Sep 2013, decelerating in Oct 2013-Feb 2014. House prices grew at fast rates in Mar 2014. The 10-city NSA index changed 0.0 percent in Jan 2015 and the 20-city changed 0.0 percent. The 10-city SA increased 0.9 percent in Jan 2015 and the 20-city composite SA 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-7, US, Monthly Percentage Change of S&P Case-Shiller Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%
| 10-City Composite SA | 10-City Composite NSA | 20-City Composite SA | 20-City Composite NSA | |
| Jan 2015 | 0.9 | 0.0 | 0.9 | 0.0 |
| Dec 2014 | 0.9 | 0.1 | 0.9 | 0.1 |
| Nov | 0.8 | -0.3 | 0.8 | -0.2 |
| Oct | 0.7 | -0.2 | 0.7 | -0.1 |
| Sep | 0.2 | -0.1 | 0.3 | -0.1 |
| Aug | -0.1 | 0.1 | -0.1 | 0.2 |
| Jul | -0.4 | 0.6 | -0.4 | 0.6 |
| Jun | -0.1 | 1.0 | -0.2 | 1.0 |
| May | -0.3 | 1.1 | -0.3 | 1.1 |
| Apr | 0.0 | 1.0 | 0.1 | 1.2 |
| Mar | 1.3 | 0.8 | 1.3 | 0.9 |
| Feb | 0.6 | 0.0 | 0.6 | 0.0 |
| Jan | 0.8 | -0.1 | 0.8 | -0.1 |
| Dec 2013 | 0.8 | -0.1 | 0.7 | -0.1 |
| Nov | 1.0 | 0.0 | 0.9 | -0.1 |
| Oct | 1.0 | 0.2 | 1.0 | 0.2 |
| Sep | 1.0 | 0.7 | 1.0 | 0.7 |
| Aug | 1.0 | 1.3 | 1.0 | 1.3 |
| Jul | 0.8 | 1.9 | 0.8 | 1.8 |
| Jun | 1.0 | 2.2 | 0.9 | 2.2 |
| May | 1.1 | 2.5 | 1.1 | 2.5 |
| Apr | 1.6 | 2.6 | 1.5 | 2.6 |
| Mar | 1.8 | 1.3 | 1.7 | 1.3 |
| Feb | 0.9 | 0.3 | 0.9 | 0.2 |
| Jan | 0.9 | 0.0 | 0.9 | 0.0 |
| Dec 2012 | 1.0 | 0.2 | 1.0 | 0.2 |
| Nov | 0.7 | -0.3 | 0.8 | -0.2 |
| Oct | 0.7 | -0.2 | 0.7 | -0.1 |
| Sep | 0.5 | 0.3 | 0.6 | 0.3 |
| Aug | 0.5 | 0.8 | 0.5 | 0.9 |
| Jul | 0.4 | 1.5 | 0.5 | 1.6 |
| Jun | 0.9 | 2.1 | 1.0 | 2.3 |
| May | 0.8 | 2.2 | 1.0 | 2.4 |
| Apr | 0.5 | 1.4 | 0.4 | 1.4 |
| Mar | 0.4 | -0.1 | 0.5 | 0.0 |
| Feb | -0.2 | -0.9 | -0.1 | -0.8 |
| Jan | -0.2 | -1.1 | -0.1 | -1.0 |
| Dec 2011 | -0.4 | -1.2 | -0.3 | -1.1 |
| Nov | -0.5 | -1.4 | -0.5 | -1.3 |
| Oct | -0.5 | -1.3 | -0.5 | -1.4 |
| Sep | -0.4 | -0.6 | -0.5 | -0.7 |
| Aug | -0.3 | 0.1 | -0.3 | 0.1 |
| Jul | -0.2 | 0.9 | -0.1 | 1.0 |
| Jun | -0.1 | 1.0 | 0.0 | 1.2 |
| May | -0.3 | 1.0 | -0.3 | 1.0 |
| Apr | -0.2 | 0.6 | -0.3 | 0.6 |
| Mar | -0.4 | -1.0 | -0.5 | -1.0 |
| Feb | -0.5 | -1.3 | -0.4 | -1.2 |
| Jan | -0.2 | -1.1 | -0.2 | -1.1 |
| Dec 2010 | -0.2 | -0.9 | -0.2 | -1.0 |
Source: http://us.spindices.com/index-family/real-estate/sp-case-shiller
VB Japan. The GDP of Japan grew at 1.0 percent per year on average from 1991 to 2002, with the GDP implicit deflator falling at 0.8 percent per year on average. The average growth rate of Japan’s GDP was 4 percent per year on average from the middle of the 1970s to 1992 (Ito 2004). Low growth in Japan in the 1990s is commonly labeled as “the lost decade” (see Pelaez and Pelaez, The Global Recession Risk (2007), 81-115). Table VB-GDP provides yearly growth rates of Japan’s GDP from 1995 to 2014. Growth weakened from 1.9 per cent in 1995 and 2.6 percent in 1996 to contractions of 2.0 percent in 1998 and 0.2 percent in 1999. Growth rates were below 2 percent with exception of 2.3 percent in 2000, 2.4 percent in 2004 and 2.2 percent in 2007. Japan’s GDP contracted sharply by 1.0 percent in 2008 and 5.5 percent in 2009. As in most advanced economies, growth was robust at 4.7 percent in 2010 but mediocre at minus 0.5 percent in 2011 because of the tsunami and 1.8 percent in 2012. Japan’s GDP grew 1.6 percent in 2013 and stagnated in 2014. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). Japan’s real GDP in calendar year 2014 is 0.7 percent higher than in calendar year 2007 (http://www.esri.cao.go.jp/index-e.html).
Table VB-GDP, Japan, Yearly Percentage Change of GDP ∆%
| Calendar Year | ∆% |
| 1995 | 1.9 |
| 1996 | 2.6 |
| 1997 | 1.6 |
| 1998 | -2.0 |
| 1999 | -0.2 |
| 2000 | 2.3 |
| 2001 | 0.4 |
| 2002 | 0.3 |
| 2003 | 1.7 |
| 2004 | 2.4 |
| 2005 | 1.3 |
| 2006 | 1.7 |
| 2007 | 2.2 |
| 2008 | -1.0 |
| 2009 | -5.5 |
| 2010 | 4.7 |
| 2011 | -0.5 |
| 2012 | 1.8 |
| 2013 | 1.6 |
| 2014 | 0.0 |
Source: Source: Japan Economic and Social Research Institute, Cabinet Office
http://www.esri.cao.go.jp/index-e.html
http://www.esri.cao.go.jp/en/sna/sokuhou/sokuhou_top.html
Table VB-BOJF provides the forecasts of economic activity and inflation in Japan by the majority of members of the Policy Board of the Bank of Japan, which is part of their Outlook for Economic Activity and Prices (https://www.boj.or.jp/en/announcements/release_2015/k150121a.pdf) with changes on Jul 21, 2015 (https://www.boj.or.jp/en/announcements/release_2015/k150121a.pdf). For fiscal 2014, the forecast is of growth of GDP between minus 0.7 to minus 0.3 percent, with the all items CPI less fresh food 2.9 to 3.3 percent (https://www.boj.or.jp/en/announcements/release_2015/k150121a.pdf). The critical difference is forecast of the CPI excluding fresh food of 0.3 to 1.4 percent in 2015 and 0.9 to 2.3 percent in 2016 (https://www.boj.or.jp/en/announcements/release_2015/k150121a.pdf). Consumer price inflation in Japan excluding fresh food was minus 0.2 percent in Dec 2014 and 2.5 percent in 12 months (http://www.stat.go.jp/english/data/cpi/1581.htm), significantly because of the increase of the tax on value added of consumption in Apr 2014. The new monetary policy of the Bank of Japan aims to increase inflation to 2 percent. These forecasts are biannual in Apr and Oct. The Cabinet Office, Ministry of Finance and Bank of Japan released on Jan 22, 2013, a “Joint Statement of the Government and the Bank of Japan on Overcoming Deflation and Achieving Sustainable Economic Growth” (http://www.boj.or.jp/en/announcements/release_2013/k130122c.pdf) with the important change of increasing the inflation target of monetary policy from 1 percent to 2 percent:
“The Bank of Japan conducts monetary policy based on the principle that the policy shall be aimed at achieving price stability, thereby contributing to the sound development of the national economy, and is responsible for maintaining financial system stability. The Bank aims to achieve price stability on a sustainable basis, given that there are various factors that affect prices in the short run.
The Bank recognizes that the inflation rate consistent with price stability on a sustainable basis will rise as efforts by a wide range of entities toward strengthening competitiveness and growth potential of Japan's economy make progress. Based on this recognition, the Bank sets the price stability target at 2 percent in terms of the year-on-year rate of change in the consumer price index.
Under the price stability target specified above, the Bank will pursue monetary easing and aim to achieve this target at the earliest possible time. Taking into consideration that it will take considerable time before the effects of monetary policy permeate the economy, the Bank will ascertain whether there is any significant risk to the sustainability of economic growth, including from the accumulation of financial imbalances.”
The Bank of Japan also provided explicit analysis of its view on price stability in a “Background note regarding the Bank’s thinking on price stability” (http://www.boj.or.jp/en/announcements/release_2013/data/rel130123a1.pdf http://www.boj.or.jp/en/announcements/release_2013/rel130123a.htm/). The Bank of Japan also amended “Principal terms and conditions for the Asset Purchase Program” (http://www.boj.or.jp/en/announcements/release_2013/rel130122a.pdf): “Asset purchases and loan provision shall be conducted up to the maximum outstanding amounts by the end of 2013. From January 2014, the Bank shall purchase financial assets and provide loans every month, the amount of which shall be determined pursuant to the relevant rules of the Bank.”
Financial markets in Japan and worldwide were shocked by new bold measures of “quantitative and qualitative monetary easing” by the Bank of Japan (http://www.boj.or.jp/en/announcements/release_2013/k130404a.pdf). The objective of policy is to “achieve the price stability target of 2 percent in terms of the year-on-year rate of change in the consumer price index (CPI) at the earliest possible time, with a time horizon of about two years” (http://www.boj.or.jp/en/announcements/release_2013/k130404a.pdf). The main elements of the new policy are as follows:
- Monetary Base Control. Most central banks in the world pursue interest rates instead of monetary aggregates, injecting bank reserves to lower interest rates to desired levels. The Bank of Japan (BOJ) has shifted back to monetary aggregates, conducting money market operations with the objective of increasing base money, or monetary liabilities of the government, at the annual rate of 60 to 70 trillion yen. The BOJ estimates base money outstanding at “138 trillion yen at end-2012) and plans to increase it to “200 trillion yen at end-2012 and 270 trillion yen at end 2014” (http://www.boj.or.jp/en/announcements/release_2013/k130404a.pdf).
- Maturity Extension of Purchases of Japanese Government Bonds. Purchases of bonds will be extended even up to bonds with maturity of 40 years with the guideline of extending the average maturity of BOJ bond purchases from three to seven years. The BOJ estimates the current average maturity of Japanese government bonds (JGB) at around seven years. The BOJ plans to purchase about 7.5 trillion yen per month (http://www.boj.or.jp/en/announcements/release_2013/rel130404d.pdf). Takashi Nakamichi, Tatsuo Ito and Phred Dvorak, wiring on “Bank of Japan mounts bid for revival,” on Apr 4, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323646604578401633067110420.html), find that the limit of maturities of three years on purchases of JGBs was designed to avoid views that the BOJ would finance uncontrolled government deficits.
- Seigniorage. The BOJ is pursuing coordination with the government that will take measures to establish “sustainable fiscal structure with a view to ensuring the credibility of fiscal management” (http://www.boj.or.jp/en/announcements/release_2013/k130404a.pdf).
- Diversification of Asset Purchases. The BOJ will engage in transactions of exchange traded funds (ETF) and real estate investment trusts (REITS) and not solely on purchases of JGBs. Purchases of ETFs will be at an annual rate of increase of one trillion yen and purchases of REITS at 30 billion yen.
- Bank Lending Facility and Growth Supporting Funding Facility. At the meeting on Feb 18, the Bank of Japan doubled the scale of these lending facilities to prevent their expiration in the near future (http://www.boj.or.jp/en/announcements/release_2014/k140218a.pdf).
Table VB-BOJF, Bank of Japan, Forecasts of the Majority of Members of the Policy Board, % Year on Year
| Fiscal Year | Real GDP | CPI All Items Less Fresh Food | Excluding Effects of Consumption Tax Hikes |
| 2013 | |||
| Apr 2014 | +2.2 to +2.3 | +0.8 | |
| Jan 2014 | +2.5 to +2.9 [+2.7] | +0.7 to +0.9 [+0.7] | |
| Oct 2013 | +2.6 to +3.0 [+2.7] | +0.6 to +1.0 [+0.7] | |
| Jul 2013 | +2.5 to +3.0 [+2.8] | +0.5 to +0.8 [+0.6] | |
| 2014 | |||
| Jan 2015 | -0.6 to -0.4 [-0.5] | +2.9 to +3.2 [+2.9] | +0.9 to +1.2 [+0.9] |
| Oct 2014 | +0.2 to +0.7 [+0.5] | +3.1 to +3.4 [+3.2] | +1.1 to +1.4 [+1.2] |
| Jul 2014 | +0.6 to +1.3 [+1.0] | +3.2 to +3.5 [+3.3] | +1.2 to +1.5 [+1.3] |
| Apr 2014 | +0.8 to +1.3 | +3.0 to +3.5 | +1.0 to +1.5 |
| Jan 2014 | +0.9 to 1.5 [+1.4] | +2.9 to +3.6 [+3.3] | +0.9 to +1.6 [+1.3] |
| Oct 2013 | +0.9 to +1.5 [+1.5] | +2.8 to +3.6 [+3.3] | +0.8 to +1.6 [+1.3] |
| Jul 2013 | +0.8 to +1.5 [+1.3] | +2.7 to +3.6 [+3.3] | +0.7 to +1.6 [+1.3] |
| 2015 | |||
| Jan 2015 | +1.8 to +2.3 [+2.1] | +0.4 to +1.3 [+1.0] | +0.4 to +1.3 [+1.0] |
| Oct 2014 | +1.2 to +1.7 [+1.5] | +1.8 to 2.6 [+2.4] | +1.1 to +1.9 [+1.7] |
| Jul 2014 | +1.2 to +1.6 [+1.5] | +1.9 to +2.8 [+2.6] | +1.2 to +2.1 [+1.9] |
| Apr 2014 | +1.2 to +1.5 | +1.9 to +2.8 | +1.2 to +2.1 |
| Jan 2014 | +1.2 to +1.8 [+1.5] | +1.7 to +2.9 [+2.6] | +1.0 to +2.2 [+1.9] |
| Oct 2013 | +1.3 to +1.8 [+1.5] | +1.6 to +2.9 [+2.6] | +0.9 to +2.2 [+1.9] |
| Jul 2013 | +1.3 to +1.9 [+1.5] | +1.6 to +2.9 [+2.6] | +0.9 to +2.2 [+1.9] |
| 2016 | |||
| Jan 2015 | +1.5 to +1.7 [+1.6] | +1.5 to +2.3 [+2.2] | +1.5 to +2.3 [+2.2] |
| Oct 2014 | +1.0 to +1.4 [+1.2] | +1.9 to 3.0 [+2.8] | +1.2 to 2.3 [+2.1] |
| Jul 2014 | +1.0 to +1.5 [+1.3] | +2.0 to +3.0 [+2.8] | +1.3 to +2.3 [+2.1] |
| Apr 2014 | +1.0 to +1.5 | +2.0 to +3.0 | +1.3 to +2.3 |
Figures in brackets are the median of forecasts of Policy Board members
Source: Policy Board, Bank of Japan
https://www.boj.or.jp/en/announcements/release_2015/k150121a.pdf
https://www.boj.or.jp/en/announcements/release_2014/k140715a.pdf
The Markit/JMMA Flash Japan Manufacturing PMI Index™ with the Flash Japan Manufacturing PMI™ decreased from 51.6 in Feb to 50.4 in Mar and the Flash Japan Manufacturing Output Index™ decreased from 53.5 in Feb to 52.0 in Mar (http://www.markiteconomics.com/Survey/PressRelease.mvc/f8c440b1bcca4e76b3f2d6ec4a5d3e37). New export orders increased at slower pace. Amy Brownbill, Economist at Markit, finds weaker improvement in Japan’s manufacturing (http://www.markiteconomics.com/Survey/PressRelease.mvc/f8c440b1bcca4e76b3f2d6ec4a5d3e37). The Markit Composite Output PMI Index decreased from 50.0 in Feb to 48.4 in Mar, indicating mildly deteriorating business activity (http://www.markiteconomics.com/Survey/PressRelease.mvc/74103110a2a2461e922e8e3112487235). The Markit Business Activity Index of Services decreased to 48.4 in Mar from 48.5 in Feb (http://www.markiteconomics.com/Survey/PressRelease.mvc/74103110a2a2461e922e8e3112487235). Amy Brownbill, Ecoomist at Markit and author of the report, finds weak current conditions with positive business expectations (http://www.markiteconomics.com/Survey/PressRelease.mvc/74103110a2a2461e922e8e3112487235). The Markit/JMMA Purchasing Managers’ Index™ (PMI™), seasonally adjusted, decreased from 51.6 in Feb to 50.3 in Mar (http://www.markiteconomics.com/Survey/PressRelease.mvc/b911afd82d224220a79b04adddf770f4). New orders declined while foreign orders increased. Amy Brownbill, Economist at Markit, finds manufacturing improvement with increasing foreign orders influenced by devaluation of the yen (http://www.markiteconomics.com/Survey/PressRelease.mvc/b911afd82d224220a79b04adddf770f4).Table JPY provides the country data table for Japan.
Table JPY, Japan, Economic Indicators
| Historical GDP and CPI | 1981-2010 Real GDP Growth and CPI Inflation 1981-2010 |
| Corporate Goods Prices | Feb ∆% 0.0 |
| Consumer Price Index | Feb NSA ∆% -0.2; Feb 12 months NSA ∆% 2.2 |
| Real GDP Growth | IVQ2014 ∆%: 0.4 on IIIQ2014; IVQ2014 SAAR 1.5; |
| Employment Report | Feb Unemployed 2.26 million Change in unemployed since last year: minus 60 thousand |
| All Industry Indices | Jan month SA ∆% 1.9 Blog 3/22/15 |
| Industrial Production | Feb SA month ∆%: -3.4 |
| Machine Orders | Total Jan ∆% 814.2 Private ∆%: 10.7 Jan ∆% Excluding Volatile Orders minus 1.7 |
| Tertiary Index | Jan month SA ∆% 1.4 |
| Wholesale and Retail Sales | Feb 12 months: |
| Family Income and Expenditure Survey | Feb 12-month ∆% total nominal consumption -0.4, real -2.9 Blog 3/29/15 |
| Trade Balance | Exports Feb 12 months ∆%: 12.4 Imports Feb 12 months ∆% -3.6 Blog 3/22/15 |
Links to blog comments in Table JPY:
3/29/15 http://cmpassocregulationblog.blogspot.com/2015/03/dollar-revaluation-and-financial-risk.html
3/22/15 http://cmpassocregulationblog.blogspot.com/2015/03/impatience-with-monetary-policy-of.html
3/1/15 http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html
2/22/15 http://cmpassocregulationblog.blogspot.com/2015/02/world-financial-turbulence-squeeze-of.html
12/14/14 http://cmpassocregulationblog.blogspot.com/2014/12/global-financial-and-economic-risk.html
11/23/14 http://cmpassocregulationblog.blogspot.com/2014/11/squeeze-of-economic-activity-by-carry.htm
9/14/14 http://cmpassocregulationblog.blogspot.com/2014/09/geopolitics-monetary-policy-and.html
8/17/2014 http://cmpassocregulationblog.blogspot.com/2014/08/weakening-world-economic-growth.html
6/15/2014 http://cmpassocregulationblog.blogspot.com/2014/06/financialgeopolitical-risks-recovery.html
5/18/14 http://cmpassocregulationblog.blogspot.com/2014/05/world-inflation-waves-squeeze-of.html
3/16/2014 http://cmpassocregulationblog.blogspot.com/2014/03/global-financial-risks-recovery-without.html
2/23/14 http://cmpassocregulationblog.blogspot.com/2014/02/squeeze-of-economic-activity-by-carry.html
12/15/13 http://cmpassocregulationblog.blogspot.com/2013/12/theory-and-reality-of-secular.html
11/17/13 http://cmpassocregulationblog.blogspot.com/2013/11/risks-of-unwinding-monetary-policy.html
9/15/13 http://cmpassocregulationblog.blogspot.com/2013/09/recovery-without-hiring-ten-million.html
8/18/13 http://cmpassocregulationblog.blogspot.com/2013/08/duration-dumping-and-peaking-valuations.html
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015.
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