Valuation of Risk Financial Assets, United States Commercial Banks Assets and Liabilities, United States Housing, Collapse of United States Dynamism of Income Growth and Employment Creation, World Cyclical Slow Growth and Global Recession Risk
Carlos M. Pelaez
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015
I United States Commercial Banks Assets and Liabilities
IA Transmission of Monetary Policy
IB Functions of Banking
IC United States Commercial Banks Assets and Liabilities
ID Theory and Reality of Economic History, Cyclical Slow growth not Secular Stagnation and Monetary Policy Based on Fear of Deflation
II United States Housing Collapse
II IB Collapse of United States Dynamism of Income Growth and Employment Creation
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
IIB1 United States Housing Collapse. Data and other information continue to provide depressed conditions in the US housing market in a longer perspective, with recent improvement at the margin. Table IIB-1 shows sales of new houses in the US at seasonally adjusted annual equivalent rate (SAAR). House sales fell in 21 of 54 months from Jan 2011 to Jun 2015 with monthly declines of 5 in 2011, 4 in 2012, 4 in 2013, 5 in 2014 and 3 in 2015. In Jan-Apr 2012, house sales increased at the annual equivalent rate of 11.8 percent and at 22.3 percent in May-Sep 2012. There was significant strength in Sep-Dec 2011 with annual equivalent rate of 48.4 percent. Sales of new houses fell 7.0 percent in Oct 2012 with increase of 9.5 percent in Nov 2012. Sales of new houses rebounded 10.8 percent in Jan 2013 with annual equivalent rate of 51.5 percent from Oct 2012 to Jan 2013 because of the increase of 10.8 percent in Jan 2013. New house sales increased at annual equivalent 9.9 percent in Feb-Mar 2013. New house sales weakened, decreasing at 2.3 percent in annual equivalent from Apr to Dec 2013 with significant volatility illustrated by decline of 18.8 percent in Jul 2013 and increase of 11.3 percent in Oct 2013. New house sales fell 1.1 percent in Dec 2013. New house sales increased 1.1 percent in Jan 2014 and fell 6.5 percent in Feb 2014 and 1.7 percent in Mar 2014. New house sales changed 0.0 percent in Apr 2014 and increased 11.5 percent in May 2014. New house sales fell 10.7 percent in Jun 2014 and decreased 1.2 percent in Jul 2014. New house sales jumped 12.7 percent in Aug 2014 and increased 1.1 percent in Sep 2014. New House sales increased 2.8 percent in Oct 2014 and fell 4.9 percent in Nov 2014. House sales fell at the annual equivalent rate of 4.6 percent in Sep-Nov 2014. New house sales increased 10.2 percent in Dec 2014 and increased 5.3 percent in Jan 2015. Sales of new houses increased 4.6 percent in Feb 2015 and fell 11.0 percent in Mar 2015. House sales increased 7.8 percent in Apr 2015. The annual equivalent rate in Dec 2014-Apr 2015 was 44.1 percent. New house sales decreased 1.1 percent in May 2015 and fell 6.8 percent in Jun 2015. New house sales fell at the annual equivalent rate of 38.7 percent May-Jun 2015. There are with wide monthly oscillations. Robbie Whelan and Conor Dougherty, writing on “Builders fuel home sale rise,” on Feb 26, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324338604578327982067761860.html), analyze how builders have provided financial assistance to home buyers, including those short of cash and with weaker credit background, explaining the rise in new home sales and the highest gap between prices of new and existing houses. The 30-year conventional mortgage rate increased from 3.40 on Apr 25, 2013 to 4.58 percent on Aug 22, 2013 (http://www.federalreserve.gov/releases/h15/data.htm), which could also be a factor in recent weakness with improvement after the rate fell to 4.26 in Nov 2013. The conventional mortgage rate rose to 4.48 percent on Dec 26, 2013 and fell to 4.32 percent on Jan 30, 2014. The conventional mortgage rate increased to 4.37 percent on Feb 26, 2014 and 4.40 percent on Mar 27, 2014. The conventional mortgage rate fell to 4.14 percent on Apr 22, 2014, stabilizing at 4.14 on Jun 26, 2014. The conventional mortgage rate stood at 4.04 percent on Jul 23, 2015. The conventional mortgage rate measured in a survey by Freddie Mac (http://www.freddiemac.com/pmms/release.html) is the “contract interest rate on commitments for fixed-rate first mortgages” (http://www.federalreserve.gov/releases/h15/data.htm).
Table IIB-1, US, Sales of New Houses at Seasonally-Adjusted (SA) Annual Equivalent Rate, Thousands and %
| SA Annual Rate | ∆% | |
| Jun 2015 | 482 | -6.8 |
| May | 517 | -1.1 |
| AE ∆% May | -38.7 | |
| Apr | 523 | 7.8 |
| Mar | 485 | -11.0 |
| Feb | 545 | 4.6 |
| Jan | 521 | 5.3 |
| Dec 2014 | 495 | 10.2 |
| AE ∆% Dec-Apr | 44.1 | |
| Nov | 449 | -4.9 |
| Oct | 472 | 2.8 |
| Sep | 459 | 1.1 |
| AE ∆% Sep-Nov | -4.6 | |
| Aug | 454 | 12.7 |
| Jul | 403 | -1.2 |
| Jun | 408 | -10.7 |
| May | 457 | 11.5 |
| Apr | 410 | 0.0 |
| Mar | 410 | -1.7 |
| Feb | 417 | -6.5 |
| Jan | 446 | 1.1 |
| AE ∆% Jan-Aug | 4.6 | |
| Dec 2013 | 441 | -1.1 |
| Nov | 446 | 0.5 |
| Oct | 444 | 11.3 |
| Sep | 399 | 5.0 |
| Aug | 380 | 1.1 |
| Jul | 376 | -18.8 |
| Jun | 463 | 7.7 |
| May | 430 | -4.7 |
| Apr | 451 | 0.4 |
| AE ∆% Apr-Dec | -2.3 | |
| Mar | 449 | 2.3 |
| Feb | 439 | -0.7 |
| AE ∆% Feb-Mar | 9.9 | |
| Jan | 442 | 10.8 |
| Dec 2012 | 399 | 1.8 |
| Nov | 392 | 9.5 |
| Oct | 358 | -7.0 |
| AE ∆% Oct-Jan | 51.5 | |
| Sep | 385 | 2.7 |
| Aug | 375 | 1.6 |
| Jul | 369 | 2.5 |
| Jun | 360 | -2.7 |
| May | 370 | 4.5 |
| AE ∆% May-Sep | 22.3 | |
| Apr | 354 | 0.0 |
| Mar | 354 | -3.3 |
| Feb | 366 | 9.3 |
| Jan | 335 | -1.8 |
| AE ∆% Jan-Apr | 11.8 | |
| Dec 2011 | 341 | 4.0 |
| Nov | 328 | 3.8 |
| Oct | 316 | 3.9 |
| Sep | 304 | 1.7 |
| AE ∆% Sep-Dec | 48.4 | |
| Aug | 299 | 1.0 |
| Jul | 296 | -1.7 |
| Jun | 301 | -1.3 |
| May | 305 | -1.6 |
| AE ∆% May-Aug | -10.3 | |
| Apr | 310 | 3.3 |
| Mar | 300 | 11.1 |
| Feb | 270 | -12.1 |
| Jan | 307 | -5.8 |
| AE ∆% Jan-Apr | -14.2 | |
| Dec 2010 | 326 | 13.6 |
AE: Annual Equivalent
Source: US Census Bureau
http://www.census.gov/construction/nrs/
There is additional information of the report of new house sales in Table IIB-2. The stock of unsold houses fell from rates of 6 to 7 percent of sales in 2011 to 4 to 5 percent in 2013 and 5.4 percent in Jun 2015. Robbie Whelan and Conor Dougherty, writing on “Builders fuel home sale rise,” on Feb 26, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324338604578327982067761860.html), find that inventories of houses have declined as investors acquire distressed houses of higher quality. Median and average house prices oscillate. In Jun 2015, median prices of new houses sold not seasonally adjusted (NSA) increased 0.5 percent after decreasing 3.9 percent in May 2015. Average prices decreased 2.1 percent in Jun 2015 and decreased 0.2 percent in May 2015. Between Dec 2010 and Jun 2015, median prices increased 16.8 percent, partly concentrated in increases of 14.5 percent in Oct 2014, 4.0 percent in Aug 2014, 4.0 percent in May 2014 and 5.2 percent in Mar 2014. Average prices increased 12.7 percent between Dec 2010 and Jun 2015, with increase of 20.3 percent in Oct 2014. Between Dec 2010 and Dec 2012, median prices increased 7.1 percent and average prices increased 2.6 percent. Price increases concentrated in 2012 with increase of median prices of 18.2 percent from Dec 2011 to Dec 2012 and of average prices of 13.8 percent. Median prices increased 16.9 percent from Dec 2012 to Dec 2014, with increase of 14.5 percent in Oct 2014, while average prices increased 24.8 percent, with increase of 20.3 percent in Oct 2014. Robbie Whelan, writing on “New homes hit record as builders cap supply,” on May 24, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323475304578500973445311276.html?mod=WSJ_economy_LeftTopHighlights), finds that homebuilders are continuing to restrict the number of new homes for sale. Restriction of available new homes for sale increases prices paid by buyers.
Table IIB-2, US, New House Stocks and Median and Average New Homes Sales Price
| Unsold* | Median | Month | Average New House Sales Price USD | Month | |
| Jun 2015 | 5.4 | 281,800 | 0.5 | 328,700 | -2.1 |
| May | 4.8 | 280,500 | -3.9 | 335,900 | -0.2 |
| Apr | 4.7 | 292,000 | -0.5 | 336,500 | -4.6 |
| Mar | 5.1 | 293,400 | -0.2 | 352,700 | -0.9 |
| Feb | 4.5 | 293,900 | 0.7 | 355,900 | 0.0 |
| Jan | 4.8 | 292,000 | -3.3 | 356,000 | -4.7 |
| Dec 2014 | 5.1 | 302,000 | -0.2 | 373,500 | 4.1 |
| Nov | 5.6 | 302,700 | 1.1 | 358,800 | -6.6 |
| Oct | 5.3 | 299,400 | 14.5 | 384,000 | 20.3 |
| Sep | 5.5 | 261,500 | -10.4 | 319,100 | -10.4 |
| Aug | 5.4 | 291,700 | 4.0 | 356,200 | 3.2 |
| Jul | 6.1 | 280,400 | -2.3 | 345,200 | 2.1 |
| Jun | 5.8 | 287,000 | 0.5 | 338,100 | 4.5 |
| May | 5.1 | 285,600 | 4.0 | 323,500 | -0.5 |
| Apr | 5.6 | 274,500 | -2.8 | 325,100 | -1.9 |
| Mar | 5.6 | 282,300 | 5.2 | 331,500 | 1.7 |
| Feb | 5.4 | 268,400 | -0.5 | 325,900 | -3.4 |
| Jan | 5.1 | 269,800 | -2.1 | 337,300 | 5.0 |
| Dec 2013 | 5.1 | 275,500 | -0.6 | 321,200 | -4.3 |
| Nov | 5.0 | 277,100 | 4.8 | 335,600 | 0.0 |
| Oct | 4.9 | 264,300 | -2.0 | 335,700 | 4.4 |
| Sep | 5.5 | 269,800 | 5.7 | 321,400 | 3.4 |
| Aug | 5.5 | 255,300 | -2.6 | 310,800 | -5.8 |
| Jul | 5.4 | 262,200 | 0.9 | 329,900 | 7.8 |
| Jun | 4.1 | 259,800 | -1.5 | 306,100 | -2.5 |
| May | 4.5 | 263,700 | -5.6 | 314,000 | -6.8 |
| Apr | 4.3 | 279,300 | 8.5 | 337,000 | 12.3 |
| Mar | 4.1 | 257,500 | -2.9 | 300,200 | -3.9 |
| Feb | 4.2 | 265,100 | 5.4 | 312,500 | 1.8 |
| Jan | 4.0 | 251,500 | -2.6 | 306,900 | 2.6 |
| Dec 2012 | 4.5 | 258,300 | 5.4 | 299,200 | 2.9 |
| Nov | 4.6 | 245,000 | -0.9 | 290,700 | 1.9 |
| Oct | 4.9 | 247,200 | -2.9 | 285,400 | -4.1 |
| Sep | 4.5 | 254,600 | 0.6 | 297,700 | -2.6 |
| Aug | 4.6 | 253,200 | 6.7 | 305,500 | 8.2 |
| Jul | 4.6 | 237,400 | 2.1 | 282,300 | 3.9 |
| Jun | 4.8 | 232,600 | -2.8 | 271,800 | -3.2 |
| May | 4.7 | 239,200 | 1.2 | 280,900 | -2.4 |
| Apr | 4.9 | 236,400 | -1.4 | 287,900 | 1.5 |
| Mar | 4.9 | 239,800 | 0.0 | 283,600 | 3.5 |
| Feb | 4.8 | 239,900 | 8.2 | 274,000 | 3.1 |
| Jan | 5.3 | 221,700 | 1.4 | 265,700 | 1.1 |
| Dec 2011 | 5.3 | 218,600 | 2.0 | 262,900 | 5.2 |
| Nov | 5.7 | 214,300 | -4.7 | 250,000 | -3.2 |
| Oct | 6.0 | 224,800 | 3.6 | 258,300 | 1.1 |
| Sep | 6.3 | 217,000 | -1.2 | 255,400 | -1.5 |
| Aug | 6.5 | 219,600 | -4.5 | 259,300 | -4.1 |
| Jul | 6.7 | 229,900 | -4.3 | 270,300 | -1.0 |
| Jun | 6.6 | 240,200 | 8.2 | 273,100 | 4.0 |
| May | 6.6 | 222,000 | -1.2 | 262,700 | -2.3 |
| Apr | 6.7 | 224,700 | 1.9 | 268,900 | 3.1 |
| Mar | 7.2 | 220,500 | 0.2 | 260,800 | -0.8 |
| Feb | 8.1 | 220,100 | -8.3 | 262,800 | -4.7 |
| Jan | 7.3 | 240,100 | -0.5 | 275,700 | -5.5 |
| Dec 2010 | 7.0 | 241,200 | 9.8 | 291,700 | 3.5 |
*Percent of new houses for sale relative to houses sold
Source: US Census Bureau
http://www.census.gov/construction/nrs/
The depressed level of residential construction and new house sales in the US is evident in Table IIB-3 providing new house sales not seasonally adjusted in Jan-Jun of various years. Sales of new houses are higher in Jan-Jun 2015 relative to Jan-Jun 2014 with increase of 20.3 percent. Sales of new houses in Jan-Jun 2015 are substantially lower than in any year between 1964 and 2014 with the exception of the years from 2009 to 2014. There are only five increases of 16.2 percent relative to Jan-Jun 2013, 43.7 percent relative to Jan-Jun 2012, 73.9 percent relative to Jan-Jun 2011, 50.0 percent relative to Jan-Jun 2010 and 46.0 percent relative to Jan-Jun 2009. Sales of new houses in Jan-Jun 2015 are lower by 3.9 percent relative to Jan-Jun 2008, 39.2 percent relative to 2007, 53.3 percent relative to 2006 and 59.8 percent relative to 2005. The housing boom peaked in 2005 and 2006 when increases in fed funds rates to 5.25 percent in Jun 2006 from 1.0 percent in Jun 2004 affected subprime mortgages that were programmed for refinancing in two or three years on the expectation that price increases forever would raise home equity. Higher home equity would permit refinancing under feasible mortgages incorporating full payment of principal and interest (Gorton 2009EFM; see other references in http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html). Sales of new houses in Jan-Jun 2015 relative to the same period in 2004 fell 57.4 percent and 50.8 percent relative to the same period in 2003. Similar percentage declines are also observed for 2015 relative to years from 2000 to 2004. Sales of new houses in Jan-Jun 2015 fell 19.5 per cent relative to the same period in 1995. The population of the US was 179.3 million in 1960 and 281.4 million in 2000 (Hobbs and Stoops 2002, 16). Detailed historical census reports are available from the US Census Bureau at (http://www.census.gov/population/www/censusdata/hiscendata.html). The US population reached 308.7 million in 2010 (http://2010.census.gov/2010census/data/). The US population increased by 129.4 million from 1960 to 2010 or 72.2 percent. The final row of Table IIB-3 reveals catastrophic data: sales of new houses in Jan-Jun 2015 of 273 thousand units are lower by 6.5 percent relative to 292 thousand units of houses sold in Jan-Jun 1964, the second year when data become available. The civilian noninstitutional population increased from 122.416 million in 1963 to 247.947 million in 2014, or 102.5 percent (http://www.bls.gov/data/). The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (http://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.”
Table IIB-3, US, Sales of New Houses Not Seasonally Adjusted, Thousands and %
| Not Seasonally Adjusted Thousands | |
| Jan-Jun 2015 | 273 |
| Jan-Jun 2014 | 227 |
| ∆% Jan-Jun 2015/Jan-Jun 2014 | 20.3 |
| Jan-Jun 2013 | 235 |
| ∆% Jan-Jun 2015/Jan-Jun 2013 | 16.2 |
| Jan-Jun 2012 | 190 |
| ∆% Jan-Jun 2015/Jan-Jun 2012 | 43.7 |
| Jan-Jun 2011 | 157 |
| ∆% Jan-Jun 2015/Jan-Jun 2011 | 73.9 |
| Jan-Jun 2010 | 182 |
| ∆% Jan-Jun 2015/ | 50.0 |
| Jan-Jun 2009 | 187 |
| ∆% Jan-Jun 2015/ | 46.0 |
| Jan-Jun 2008 | 284 |
| ∆% Jan-Jun 2015/ | -3.9 |
| Jan-Jun 2007 | 449 |
| ∆% Jan-Jun 2015/ | -39.2 |
| Jan-Jun 2006 | 585 |
| ∆% Jan-Jun 2015/Jan-Jun 2006 | -53.3 |
| Jan-Jun 2005 | 679 |
| ∆% Jan-Jun 2015/Jan-Jun 2005 | -59.8 |
| Jan-Jun 2004 | 643 |
| ∆% Jan-Jun 2015/Jan-Jun 2004 | -57.5 |
| Jan-Jun 2003 | 555 |
| ∆% Jan-Jun 2015/ | -50.8 |
| Jan-Jun 2002 | 498 |
| ∆% Jan-Jun 2015/ | -45.2 |
| Jan-Jun 2001 | 494 |
| ∆% Jan-Jun 2015/ | -44.7 |
| Jan-Jun 2000 | 459 |
| ∆% Jan-Jun 2015/ | -40.5 |
| Jan-Jun 1995 | 339 |
| ∆% Jan-Jun 2015/ | -19.5 |
| Jan-Jun 1964 | 292 |
| ∆% Jan-Jun 2015/ | -6.5 |
*Computed using unrounded data
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Table IIB-4 provides the entire available annual series of new house sales from 1963 to 2014. The revised level of 306 thousand new houses sold in 2011 is the lowest since 560 thousand in 1963 in the 48 years of available data while the level of 368 thousand in 2012 is only higher than 323 thousand in 2010. The level of sales of new houses of 437 thousand in 2014 is the lowest from 1963 to 2009 with exception of 412 thousand in 1982 and 436 thousand in 1981. The population of the US increased 129.4 million from 179.3 million in 1960 to 308.7 million in 2010, or 72.2 percent. The civilian noninstitutional population of the US increased from 122.416 million in 1963 to 247.947 million in 2014 or 102.5 percent (http://www.bls.gov/data/). The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (http://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.”
The civilian noninstitutional population is the universe of the labor force. In fact, there is no year from 1963 to 2013 in Table IIA-4 with sales of new houses below 400 thousand with the exception of the immediately preceding years of 2009, 2010, 2011 and 2012.
Table IIB-4, US, New Houses Sold, NSA Thousands
| Period | Sold During Period |
| 1963 | 560 |
| 1964 | 565 |
| 1965 | 575 |
| 1966 | 461 |
| 1967 | 487 |
| 1968 | 490 |
| 1969 | 448 |
| 1970 | 485 |
| 1971 | 656 |
| 1972 | 718 |
| 1973 | 634 |
| 1974 | 519 |
| 1975 | 549 |
| 1976 | 646 |
| 1977 | 819 |
| 1978 | 817 |
| 1979 | 709 |
| 1980 | 545 |
| 1981 | 436 |
| 1982 | 412 |
| 1983 | 623 |
| 1984 | 639 |
| 1985 | 688 |
| 1986 | 750 |
| 1987 | 671 |
| 1988 | 676 |
| 1989 | 650 |
| 1990 | 534 |
| 1991 | 509 |
| 1992 | 610 |
| 1993 | 666 |
| 1994 | 670 |
| 1995 | 667 |
| 1996 | 757 |
| 1997 | 804 |
| 1998 | 886 |
| 1999 | 880 |
| 2000 | 877 |
| 2001 | 908 |
| 2002 | 973 |
| 2003 | 1,086 |
| 2004 | 1,203 |
| 2005 | 1,283 |
| 2006 | 1,051 |
| 2007 | 776 |
| 2008 | 485 |
| 2009 | 375 |
| 2010 | 323 |
| 2011 | 306 |
| 2012 | 368 |
| 2013 | 429 |
| 2014 | 437 |
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Chart IIB-1 of the US Bureau of the Census shows the sharp decline of sales of new houses in the US. Sales rose temporarily until about mid 2010 but then declined to a lower plateau followed by increase and stability.
Chart IIB-1, US, New One-Family Houses Sold in the US, SAAR (Seasonally Adjusted Annual Rate)
Source: US Census Bureau
http://www.census.gov/briefrm/esbr/www/esbr051.html
Percentage changes and average rates of growth of new house sales for selected periods are shown in Table IIB-5. The percentage change of new house sales from 1963 to 2014 is minus 22.0 percent. Between 1991 and 2001, sales of new houses rose 78.4 percent at the average yearly rate of 6.0 percent. Between 1995 and 2005 sales of new houses increased 92.4 percent at the yearly rate of 6.8 percent. There are similar rates in all years from 2000 to 2005. The boom in housing construction and sales began in the 1980s and 1990s. The collapse of real estate culminated several decades of housing subsidies and policies to lower mortgage rates and borrowing terms (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009b), 42-8). Sales of new houses sold in 2014 fell 34.5 percent relative to the same period in 1995 and 65.9 percent relative to 2005.
Table IIB-5, US, Percentage Change and Average Yearly Rate of Growth of Sales of New One-Family Houses
| ∆% | Average Yearly % Rate | |
| 1963-2014 | -22.0 | NA |
| 1991-2001 | 78.4 | 6.0 |
| 1995-2005 | 92.4 | 6.8 |
| 2000-2005 | 46.3 | 7.9 |
| 1995-2014 | -34.5 | NA |
| 2000-2014 | -50.2 | NA |
| 2005-2014 | -65.9 | NA |
NA: Not Applicable
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Chart IIB-2 of the US Bureau of the Census provides the entire monthly sample of new houses sold in the US between Jan 1963 and Jun 2015 without seasonal adjustment. The series is almost stationary until the 1990s. There is sharp upward trend from the early 1990s to 2005-2006 after which new single-family houses sold collapse to levels below those in the beginning of the series.
Chart IIB-2, US, New Single-family Houses Sold, NSA, 1963-2015
Source: US Census Bureau
http://www.census.gov/construction/nrs/
The available historical annual data of median and average prices of new houses sold in the US between 1963 and 2014 is provided in Table IIB-6. On a yearly basis, median and average prices reached a peak in 2007 and then fell substantially. There is recovery in 2012-2014.
Table IIB-6, US, Median and Average Prices of New Houses Sold, Annual Data
| Period | Median | Average |
| 1963 | $18,000 | $19,300 |
| 1964 | $18,900 | $20,500 |
| 1965 | $20,000 | $21,500 |
| 1966 | $21,400 | $23,300 |
| 1967 | $22,700 | $24,600 |
| 1968 | $24,700 | $26,600 |
| 1969 | $25,600 | $27,900 |
| 1970 | $23,400 | $26,600 |
| 1971 | $25,200 | $28,300 |
| 1972 | $27,600 | $30,500 |
| 1973 | $32,500 | $35,500 |
| 1974 | $35,900 | $38,900 |
| 1975 | $39,300 | $42,600 |
| 1976 | $44,200 | $48,000 |
| 1977 | $48,800 | $54,200 |
| 1978 | $55,700 | $62,500 |
| 1979 | $62,900 | $71,800 |
| 1980 | $64,600 | $76,400 |
| 1981 | $68,900 | $83,000 |
| 1982 | $69,300 | $83,900 |
| 1983 | $75,300 | $89,800 |
| 1984 | $79,900 | $97,600 |
| 1985 | $84,300 | $100,800 |
| 1986 | $92,000 | $111,900 |
| 1987 | $104,500 | $127,200 |
| 1988 | $112,500 | $138,300 |
| 1989 | $120,000 | $148,800 |
| 1990 | $122,900 | $149,800 |
| 1991 | $120,000 | $147,200 |
| 1992 | $121,500 | $144,100 |
| 1993 | $126,500 | $147,700 |
| 1994 | $130,000 | $154,500 |
| 1995 | $133,900 | $158,700 |
| 1996 | $140,000 | $166,400 |
| 1997 | $146,000 | $176,200 |
| 1998 | $152,500 | $181,900 |
| 1999 | $161,000 | $195,600 |
| 2000 | $169,000 | $207,000 |
| 2001 | $175,200 | $213,200 |
| 2002 | $187,600 | $228,700 |
| 2003 | $195,000 | $246,300 |
| 2004 | $221,000 | $274,500 |
| 2005 | $240,900 | $297,000 |
| 2006 | $246,500 | $305,900 |
| 2007 | $247,900 | $313,600 |
| 2008 | $232,100 | $292,600 |
| 2009 | $216,700 | $270,900 |
| 2010 | $221,800 | $272,900 |
| 2011 | $227,200 | $267,900 |
| 2012 | $245,200 | $292,200 |
| 2013 | $268,900 | $324,500 |
| 2014 | $282,800 | $345,800 |
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Percentage changes of median and average prices of new houses sold in selected years are shown in Table IIB-7. Prices rose sharply between 2000 and 2005. In fact, prices in 2014 are higher than in 2000. Between 2006 and 2014, median prices of new houses sold increased 14.7 percent and average prices increased 13.0 percent. Between 2013 and 2014, median prices increased 5.2 percent and average prices increased 6.6 percent.
Table IIB-7, US, Percentage Change of New Houses Median and Average Prices, NSA, ∆%
| Median New | Average New Home Sales Prices ∆% | |
| ∆% 2000 to 2003 | 15.4 | 19.0 |
| ∆% 2000 to 2005 | 42.5 | 43.5 |
| ∆% 2000 to 2014 | 67.7 | 66.8 |
| ∆% 2005 to 2014 | 17.4 | 16.4 |
| ∆% 2000 to 2006 | 45.9 | 47.8 |
| ∆% 2006 to 2014 | 14.7 | 13.0 |
| ∆% 2009 to 2014 | 30.5 | 27.6 |
| ∆% 2010 to 2014 | 27.5 | 26.7 |
| ∆% 2011 to 2014 | 24.5 | 29.1 |
| ∆% 2012 to 2014 | 15.3 | 18.3 |
| ∆% 2013 to 2014 | 5.2 | 6.6 |
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Chart IIB-3 of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 1963 to Jun 2015. There is long-term sharp upward trend with few declines until the current collapse. Median prices increased sharply during the Great Inflation of the 1960s and 1970s and paused during the savings and loans crisis of the late 1980s and the recession of 1991. Housing subsidies throughout the 1990s caused sharp upward trend of median new house prices that accelerated after the fed funds rate of 1 percent from 2003 to 2004. There was sharp reduction of prices after 2006 with recovery recently toward earlier prices.
Chart IIB-3, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2015
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Chart IIB-4 of the US Census Bureau provides average prices of new houses sold from the mid-1970s to Jun 2015. There is similar behavior as with median prices of new houses sold in Chart IIB-3. The only stress occurred in price pauses during the savings and loans crisis of the late 1980s and the collapse after 2006 with recent recovery.
Chart IIB-4, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1975-2015
Source: US Census Bureau
http://www.census.gov/construction/nrs/
Chart IIB-5 of the Board of Governors of the Federal Reserve System provides the rate for the 30-year conventional mortgage, the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1954 to 2015. All rates decline throughout the period from the Great Inflation of the 1970s through the following Great Moderation and until currently. In Apr 1971, the fed funds rate was 4.15 percent and the conventional mortgage rate 7.31 percent. In November 2012, the fed funds rate was 0.16 percent, the yield of the 30-year Treasury 2.80 percent and the conventional mortgage rate 3.35. The final segment shows an increase in the yield of the 30-year Treasury to 3.61 percent in July 2013 with the fed funds rate at 0.09 percent and the conventional mortgage at 4.37 percent. The final data point shows marginal increase of the conventional mortgage rate to 3.98 percent in Jun 2015 with the yield of the 30-year Treasury bond at 3.11 percent and overnight rate on fed funds at 0.13 percent. The recent increase in interest rates if sustained could affect the US real estate market. Shayndi Raice and Nick Timiraos, writing on “Banks cut as mortgage boom ends,” on Jan 9, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303754404579310940019239208), analyze the drop in mortgage applications to a 13-year low, as measured by the Mortgage Bankers Association. Nick Timiraos, writing on “Demand for home loans plunges,” on Apr 24, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304788404579522051733228402?mg=reno64-wsj), analyzes data in Inside Mortgage Finance that mortgage lending of $235 billion in IQ2014 is 58 percent lower than a year earlier and 23 percent below IVQ2013. Mortgage lending collapsed to the lowest level in 14 years. In testimony before the Committee on the Budget of the US Senate on May 8, 2004, Chair Yellen provides analysis of the current economic situation and outlook (http://www.federalreserve.gov/newsevents/testimony/yellen20140507a.htm): “One cautionary note, though, is that readings on housing activity--a sector that has been recovering since 2011--have remained disappointing so far this year and will bear watching.”
Chart IIB-5, US, Thirty-year Conventional Mortgage, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1954-2015
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/H15/default.htm
Table IIB-8 provides the monthly data in Chart IIB-5 from Dec 2012 to May 2015. While the fed funds rate fell from 0.16 percent in Dec 2012 to 0.07 percent in Jan 2014, the yield of the constant maturity 30-year Treasury bond rose from 2.88 percent in Dec 2012 to 3.77 percent in Jan 2014 and the conventional mortgage rate increased from 3.35 percent in Dec 2012 to 4.43 percent in Jan 2014. In Jun 2015, the fed funds rate stabilized at 0.13 percent with increase to 3.11 percent of the 30-year yield and increase at 3.98 percent of the conventional mortgage rate.
Table IIB-8, US, Fed Funds Rate, Thirty Year Treasury Bond and Conventional Mortgage Rate, Monthly, Percent per Year, Dec 2012 to Jun 2015
| Fed Funds Rate | Yield of Thirty Year Constant Maturity Bond | Conventional Mortgage Rate | |
| 2012-12 | 0.16 | 2.88 | 3.35 |
| 2013-01 | 0.14 | 3.08 | 3.41 |
| 2013-02 | 0.15 | 3.17 | 3.53 |
| 2013-03 | 0.14 | 3.16 | 3.57 |
| 2013-04 | 0.15 | 2.93 | 3.45 |
| 2013-05 | 0.11 | 3.11 | 3.54 |
| 2013-06 | 0.09 | 3.4 | 4.07 |
| 2013-07 | 0.09 | 3.61 | 4.37 |
| 2013-08 | 0.08 | 3.76 | 4.46 |
| 2013-09 | 0.08 | 3.79 | 4.49 |
| 2013-10 | 0.09 | 3.68 | 4.19 |
| 2013-11 | 0.08 | 3.8 | 4.26 |
| 2013-12 | 0.09 | 3.89 | 4.46 |
| 2014-01 | 0.07 | 3.77 | 4.43 |
| 2014-02 | 0.07 | 3.66 | 4.3 |
| 2014-03 | 0.08 | 3.62 | 4.34 |
| 2014-04 | 0.09 | 3.52 | 4.34 |
| 2014-05 | 0.09 | 3.39 | 4.19 |
| 2014-06 | 0.1 | 3.42 | 4.16 |
| 2014-07 | 0.09 | 3.33 | 4.13 |
| 2014-08 | 0.09 | 3.2 | 4.12 |
| 2014-09 | 0.09 | 3.26 | 4.16 |
| 2014-10 | 0.09 | 3.04 | 4.04 |
| 2014-11 | 0.09 | 3.04 | 4 |
| 2014-12 | 0.12 | 2.83 | 3.86 |
| 2015-01 | 0.11 | 2.46 | 3.71 |
| 2015-02 | 0.11 | 2.57 | 3.71 |
| 2015-03 | 0.11 | 2.63 | 3.77 |
| 2015-04 | 0.12 | 2.59 | 3.67 |
| 2015-05 | 0.12 | 2.96 | 3.84 |
| 2015-06 | 0.13 | 3.11 | 3.98 |
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/H15/default.htm
IIB2 United States House Prices. The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). Table IIA2-1 provides the FHFA HPI for purchases only, which shows behavior similar to that of the Case-Shiller index but with lower magnitudes. House prices catapulted from 2000 to 2003, 2005 and 2006. From IVQ2000 to IVQ2006, the index for the US as a whole rose 55.0 percent, with 62.1 percent for New England, 72.0 percent for Middle Atlantic, 71.2 percent for South Atlantic but only by 33.1 percent for East South Central. Prices fell relative to 2014 for the US and all regions from 2006 with exception of increase of 2.6 percent for East South Central. Prices for the US increased 4.9 percent in IVQ2014 relative to IVQ2013 and 12.9 percent from IVQ2012 to IVQ2014. From IVQ2000 to IVQ2014, prices rose for the US and the four regions in Table IIA2-1.
Table IIA2-1, US, FHFA House Price Index Purchases Only NSA ∆%
| United States | New England | Middle Atlantic | South Atlantic | East South Central | |
| IVQ2000 | 24.0 | 40.6 | 35.8 | 25.9 | 11.0 |
| IVQ2000 | 50.5 | 65.0 | 67.6 | 62.9 | 25.4 |
| IVQ2000 to | 55.0 | 62.1 | 72.0 | 71.2 | 33.1 |
| IVQ2005 to | -1.5 | -8.7 | -2.3 | -7.4 | 8.9 |
| IVQ2006 | -4.4 | -7.1 | -4.8 | -11.9 | 2.6 |
| IVQ2007 to | -1.9 | -5.1 | -5.0 | -8.6 | 0.7 |
| IVQ2011 to | 18.9 | 7.3 | 6.9 | 19.9 | 11.8 |
| IVQ2012 to | 12.9 | 6.8 | 5.7 | 13.8 | 8.6 |
| IVQ2013 to IVQ2014 | 4.9 | 2.5 | 2.2 | 5.1 | 4.2 |
| IVQ2000 to | 48.3 144.27 | 50.6 138.40 | 63.7 127.30 | 50.9 140.28 | 36.6 146.07 |
Source: Federal Housing Finance Agency
http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx
Data of the FHFA HPI for the remaining US regions are in Table IIA2-2. Behavior is not very different from that in Table IIA2-1 with the exception of East North Central. House prices in the Pacific region doubled between 2000 and 2006. Although prices of houses declined sharply from 2005 and 2006 to 2014 with exception of West South Central and West North Central, there was still appreciation relative to 2000.
Table IIA2-2, US, FHFA House Price Index Purchases Only NSA ∆%
| West South Central | West North Central | East North Central | Mountain | Pacific | |
| IVQ2000 | 11.1 | 18.3 | 14.7 | 18.9 | 44.6 |
| IVQ2000 | 23.9 | 31.0 | 23.8 | 58.0 | 107.7 |
| IVQ2000 to IVQ2006 | 31.6 | 33.7 | 23.7 | 68.6 | 108.7 |
| IVQ2005 to | 26.6 | 4.7 | -5.4 | -2.6 | -14.7 |
| IVQ2006 | 19.1 | 2.6 | -5.4 | -8.7 | -15.1 |
| IVQ2007 to | 15.2 | 3.2 | -2.1 | -5.6 | -6.0 |
| IVQ2011 to | 18.1 | 13.5 | 14.2 | 32.9 | 37.6 |
| IVQ2012 to | 12.1 | 8.9 | 11.1 | 17.9 | 24.4 |
| IVQ2013 to IVQ2014 | 5.9 | 4.0 | 4.6 | 5.5 | 7.3 |
| IVQ2000 to IVQ2014 | 56.8 145.53 | 37.1 158.59 | 17.1 155.13 | 53.9 172.46 | 77.1 132.21 |
Source: Federal Housing Finance Agency
http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx
Monthly and 12-month percentage changes of the FHFA House Price Index are in Table IIA2-3. Percentage monthly increases of the FHFA index were positive from Apr to Jul 2011 with exception of declines in May and Aug 2011 while 12 months percentage changes improved steadily from around minus 6 percent in Mar to May 2011 to minus 4.4 percent in Jun 2011. The FHFA house price index fell 0.6 percent in Oct 2011 and fell 3.0 percent in the 12 months ending in Oct 2011. There was significant recovery in Nov 2012 with increase in the house price index of 0.4 percent and reduction of the 12-month rate of decline to 2.3 percent. The house price index rose 0.4 percent in Dec 2011 and the 12-month percentage change improved to minus 1.2 percent. There was further improvement with revised change of minus 0.1 percent in Jan 2012 and decline of the 12-month percentage change to minus 1.0 percent. The index improved to positive change of 0.2 percent in Feb 2012 and increase of 0.2 percent in the 12 months ending in Feb 2012. There was strong improvement in Mar 2012 with gain in prices of 0.9 percent and 2.3 percent in 12 months. The house price index of FHFA increased 0.6 percent in Apr 2012 and 2.7 percent in 12 months and improvement continued with increase of 0.6 percent in May 2012 and 3.6 percent in the 12 months ending in May 2012. Improvement consolidated with increase of 0.4 percent in Jun 2012 and 3.6 percent in 12 months. In Jul 2012, the house price index increased 0.1 percent and 3.5 percent in 12 months. Strong increase of 0.5 percent in Aug 2012 pulled the 12-month change to 4.3 percent. There was another increase of 0.7 percent in Oct and 5.3 percent in 12 months followed by increase of 0.5 percent in Nov 2012 and 5.4 percent in 12 months. The FHFA house price index increased 0.8 percent in Jan 2013 and 6.3 percent in 12 months. Improvement continued with increase of 0.5 percent in Apr 2013 and 7.3 percent in 12 months. In May 2013, the house price indexed increased 0.7 percent and 7.4 percent in 12 months. The FHFA house price index increased 0.7 percent in Jun 2013 and 7.7 percent in 12 months. In Jul 2013, the FHFA house price index increased 0.7 percent and 8.3 percent in 12 months. Improvement continued with increase of 0.4 percent in Aug 2013 and 8.2 percent in 12 months. In Sep 2013, the house price index increased 0.5 percent and 8.2 percent in 12 months. The house price index increased 0.5 percent in Oct 2013 and 7.9 percent in 12 months. In Nov 2013, the house price index changed 0.0 percent and increased 7.3 percent in 12 months. The house price index rose 0.7 percent in Dec 2013 and 7.6 percent in 12 months. Improvement continued with increase of 0.6 percent in Jan 2014 and 7.4 percent in 12 months. In Feb 2014, the house price index increased 0.4 percent and 7.0 percent in 12 months. The house price index increased 0.4 percent in Mar 2014 and 6.4 percent in 12 months. In Apr 2014, the house price index increased 0.2 percent and increased 6.1 percent in 12 months. The house price index increased 0.2 percent in May 2014 and 5.6 percent in 12 months. In Jun 2014, the house price index increased 0.4 percent and 5.3 percent in 12 months. The house price index increased 0.3 percent in Jul 2014 and 4.9 percent in 12 months. In Sep 2014, the house price index increased 0.2 percent and increased 4.6 percent in 12 months. The house price index increased 0.5 percent in Oct 2014 and 4.6 percent in 12 months. In Nov 2014, the house price index increased 0.7 percent and 5.4 percent in 12 months. The house price index increased 0.8 percent in Dec 2014 and increased 5.5 percent in 12 months. In Feb 2015, the house price index increased 0.7 percent and increased 5.5 percent in 12 months. The house price index increased 0.3 percent in Mar 2015 and 5.4 percent in 12 months. In Apr 2015, the house price index increased 0.4 percent and 5.5 percent in 12 months. The house price index increased 0.4 percent in May 2015 and 5.7 percent in 12 months.
Table IIA2-3, US, FHFA House Price Index Purchases Only SA. Month and NSA 12-Month ∆%
| Month ∆% SA | 12 Month ∆% NSA | |
| May 2015 | 0.4 | 5.7 |
| Apr | 0.4 | 5.5 |
| Mar | 0.3 | 5.4 |
| Feb | 0.7 | 5.5 |
| Jan | 0.3 | 5.1 |
| Dec 2014 | 0.8 | 5.5 |
| Nov | 0.7 | 5.4 |
| Oct | 0.5 | 4.6 |
| Sep | 0.2 | 4.6 |
| Aug | 0.5 | 5.0 |
| Jul | 0.3 | 4.9 |
| Jun | 0.4 | 5.3 |
| May | 0.2 | 5.6 |
| Apr | 0.2 | 6.1 |
| Mar | 0.4 | 6.4 |
| Feb | 0.4 | 7.0 |
| Jan | 0.6 | 7.4 |
| Dec 2013 | 0.7 | 7.6 |
| Nov | 0.0 | 7.3 |
| Oct | 0.5 | 7.9 |
| Sep | 0.5 | 8.2 |
| Aug | 0.4 | 8.2 |
| Jul | 0.7 | 8.3 |
| Jun | 0.7 | 7.7 |
| May | 0.7 | 7.4 |
| Apr | 0.5 | 7.3 |
| Mar | 1.1 | 7.4 |
| Feb | 0.8 | 7.0 |
| Jan | 0.8 | 6.3 |
| Dec 2012 | 0.5 | 5.5 |
| Nov | 0.5 | 5.4 |
| Oct | 0.7 | 5.3 |
| Sep | 0.5 | 4.1 |
| Aug | 0.5 | 4.3 |
| Jul | 0.1 | 3.5 |
| Jun | 0.4 | 3.6 |
| May | 0.6 | 3.6 |
| Apr | 0.6 | 2.7 |
| Mar | 0.9 | 2.3 |
| Feb | 0.2 | 0.2 |
| Jan | -0.1 | -1.0 |
| Dec 2011 | 0.4 | -1.2 |
| Nov | 0.4 | -2.3 |
| Oct | -0.6 | -3.0 |
| Sep | 0.6 | -2.4 |
| Aug | -0.3 | -3.8 |
| Jul | 0.3 | -3.4 |
| Jun | 0.4 | -4.4 |
| May | -0.2 | -5.9 |
| Apr | 0.2 | -5.8 |
| Mar | -1.0 | -5.9 |
| Feb | -1.1 | -5.0 |
| Jan | -0.4 | -4.5 |
| Dec 2010 | -3.9 | |
| Dec 2009 | -2.0 | |
| Dec 2008 | -10.2 | |
| Dec 2007 | -3.2 | |
| Dec 2006 | 2.5 | |
| Dec 2005 | 9.8 | |
| Dec 2004 | 10.2 | |
| Dec 2003 | 8.0 | |
| Dec 2002 | 7.8 | |
| Dec 2001 | 6.7 | |
| Dec 2000 | 7.2 | |
| Dec 1999 | 6.1 | |
| Dec 1998 | 5.9 | |
| Dec 1997 | 3.4 | |
| Dec 1996 | 2.8 | |
| Dec 1995 | 2.9 | |
| Dec 1994 | 2.6 | |
| Dec 1993 | 3.1 | |
| Dec 1992 | 2.4 |
Source: Federal Housing Finance Agency
The bottom part of Table IIA2-3 provides 12-month percentage changes of the FHFA house price index since 1992 when data become available for 1991. Table IIA2-4 provides percentage changes and average rates of percent change per year for various periods. Between 1992 and 2014, the FHFA house price index increased 108.1 percent at the yearly average rate of 3.4 percent. In the period 1992-2000, the FHFA house price index increased 39.4 percent at the average yearly rate of 4.2 percent. The average yearly rate of price increase accelerated to 7.5 percent in the period 2000-2003, 8.5 percent in 2000-2005 and 7.5 percent in 2000-2006. At the margin, the average rate jumped to 10.0 percent in 2003-2005 and 7.5 percent in 2003-2006. House prices measured by the FHFA house price index declined 3.1 percent between 2006 and 2014 and 0.7 percent between 2005 and 2014.
Table IIA2-4, US, FHFA House Price Index, Percentage Change and Average Rate of Percentage Change per Year, Selected Dates 1992-2013
| Dec | ∆% | Average ∆% per Year |
| 1992-2014 | 108.1 | 3.4 |
| 1992-2000 | 39.4 | 4.2 |
| 2000-2003 | 24.2 | 7.5 |
| 2000-2005 | 50.4 | 8.5 |
| 2003-2005 | 21.1 | 10.0 |
| 2005-2014 | -0.7 | NA |
| 2000-2006 | 54.1 | 7.5 |
| 2003-2006 | 24.1 | 7.5 |
| 2006-2014 | -3.1 | NA |
Source: Federal Housing Finance Agency
The valuable report on Financial Accounts of the United States formerly Flow of Funds Accounts of the United States provided by the Board of Governors of the Federal Reserve System (http://www.federalreserve.gov/releases/z1/Current/ http://www.federalreserve.gov/apps/fof/) is rich in important information and analysis. Table IIA-1, updated in this blog for every new quarterly release, shows the balance sheet of US households combined with nonprofit organizations in 2007, 2011, 2014 and IQ2015. The data show the strong shock to US wealth during the contraction. Assets fell from $81.1 trillion in 2007 to $77.4 trillion in 2011 even after nine consecutive quarters of growth beginning in IIIQ2009 (http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.html), for decline of $3.7 trillion or 4.5 percent. Assets stood at $97.5 trillion in 2014 for gain of $16.4 trillion relative to $81.1 trillion in 2007 or increase by 20.2 percent. Assets increased to $99.1 trillion in IQ2015 by $18.0 trillion relative to 2007 or 22.1 percent. Liabilities declined from $14.4 trillion in 2007 to $13.6 trillion in 2011 or by $824.4 billion equivalent to decline by 5.7 percent. Liabilities declined $226.4 billion or 1.6 percent from 2007 to 2014. Liabilities fell from $14.4 trillion in 2007 to $14.1 trillion in IQ2015, by $243.7 billion or decline of 1.7 percent. Net worth shrank from $66.7 trillion in 2007 to $63.9 trillion in 2011, that is, $2.8 trillion equivalent to decline of 4.3 percent. Net worth increased from $66,721.8 billion in 2007 to $84,924.6 billion in IQ2015 by $18,202.8 billion or 27.3 percent. The US consumer price index for all items increased from 210.036 in Dec 2007 to 236.119 in Mar 2015 (http://www.bls.gov/cpi/data.htm) or 12.4 percent. Net worth adjusted by CPI inflation increased 13.2 percent from 2007 to IQ2015. Nonfinancial assets increased $898.5 billion from $28,149.7 billion in 2007 to $29,693.7 billion in IQ2015 or 5.5 percent. There was increase from 2007 to IQ2015 of $778.5 billion in real estate assets or by 3.3 percent. Real estate assets adjusted for CPI inflation fell 8.1 percent between 2007 and IQ2015. The National Association of Realtors estimated that the gains in net worth in homes by Americans were about $4 trillion between 2000 and 2005 (quoted in Pelaez and Pelaez, The Global Recession Risk (2007), 224-5).
Table IIA-1, US, Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars Outstanding End of Period, NSA
| 2007 | 2011 | 2014 | IQ2015 | |
| Assets | 81,117.1 | 77,449.5 | 97,464.9 | 99,076.3 |
| Nonfinancial | 28,149.7 | 23,378.2 | 29,150.2 | 29,693.7 |
| Real Estate | 23,340.2 | 18,252.6 | 23,615.4 | 24,118.7 |
| Durable Goods | 4,476.0 | 4,723.3 | 5,085.5 | 5,121.1 |
| Financial | 52,967.4 | 54,071.3 | 68,314.7 | 69,382.6 |
| Deposits | 7,560.4 | 8,716.1 | 10,144.1 | 10,287.2 |
| Credit Market | 3,997.0 | 4,395.5 | 3,314.5 | 3,271.4 |
| Mutual Fund Shares | 4,591.5 | 4,622.5 | 7,695.3 | 7,918.8 |
| Equities Corporate | 9,912.5 | 8,498.4 | 13,360.7 | 13,640.8 |
| Equity Noncorporate | 8,933.1 | 7,587.0 | 9,924.7 | 10,156.2 |
| Pension | 15,267.2 | 17,447.7 | 20,783.7 | 20,991.9 |
| Liabilities | 14,395.3 | 13,570.9 | 14,168.9 | 14,151.6 |
| Home Mortgages | 10,613.3 | 9,695.9 | 9,403.1 | 9,370.5 |
| Consumer Credit | 2,615.1 | 2,755.4 | 3,317.2 | 3,321.6 |
| Net Worth | 66,721.8 | 63,878.6 | 83,296.0 | 84,924.6 |
Net Worth = Assets – Liabilities
Source: Board of Governors of the Federal Reserve System. 2015. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2015. Washington, DC, Federal Reserve System, Jun 11. http://www.federalreserve.gov/releases/z1/.
The explanation of the sharp contraction of household wealth can probably be found in the origins of the financial crisis and global recession. Let V(T) represent the value of the firm’s equity at time T and B stand for the promised debt of the firm to bondholders and assume that corporate management, elected by equity owners, is acting on the interests of equity owners. Robert C. Merton (1974, 453) states:
“On the maturity date T, the firm must either pay the promised payment of B to the debtholders or else the current equity will be valueless. Clearly, if at time T, V(T) > B, the firm should pay the bondholders because the value of equity will be V(T) – B > 0 whereas if they do not, the value of equity would be zero. If V(T) ≤ B, then the firm will not make the payment and default the firm to the bondholders because otherwise the equity holders would have to pay in additional money and the (formal) value of equity prior to such payments would be (V(T)- B) < 0.”
Pelaez and Pelaez (The Global Recession Risk (2007), 208-9) apply this analysis to the US housing market in 2005-2006 concluding:
“The house market [in 2006] is probably operating with low historical levels of individual equity. There is an application of structural models [Duffie and Singleton 2003] to the individual decisions on whether or not to continue paying a mortgage. The costs of sale would include realtor and legal fees. There could be a point where the expected net sale value of the real estate may be just lower than the value of the mortgage. At that point, there would be an incentive to default. The default vulnerability of securitization is unknown.”
There are multiple important determinants of the interest rate: “aggregate wealth, the distribution of wealth among investors, expected rate of return on physical investment, taxes, government policy and inflation” (Ingersoll 1987, 405). Aggregate wealth is a major driver of interest rates (Ibid, 406). Unconventional monetary policy, with zero fed funds rates and flattening of long-term yields by quantitative easing, causes uncontrollable effects on risk taking that can have profound undesirable effects on financial stability. Excessively aggressive and exotic monetary policy is the main culprit and not the inadequacy of financial management and risk controls.
The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Ibid). According to a subsequent restatement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption decisions is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:
W = Y/r (1)
Equation (1) shows that as r goes to zero, r →0, W grows without bound, W→∞.
Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment but the exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at close to zero interest rates, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV).
The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper to purchase default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever increasing house prices. The suspension of auctions of 30-year Treasuries was designed to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity because of the penalty in the form of low interest rates and unsound credit decisions because the put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4).
There are significant elements of the theory of bank financial fragility of Diamond and Dybvig (1983) and Diamond and Rajan (2000, 2001a, 2001b) that help to explain the financial fragility of banks during the credit/dollar crisis (see also Diamond 2007). The theory of Diamond and Dybvig (1983) as exposed by Diamond (2007) is that banks funding with demand deposits have a mismatch of liquidity (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 58-66). A run occurs when too many depositors attempt to withdraw cash at the same time. All that is needed is an expectation of failure of the bank. Three important functions of banks are providing evaluation, monitoring and liquidity transformation. Banks invest in human capital to evaluate projects of borrowers in deciding if they merit credit. The evaluation function reduces adverse selection or financing projects with low present value. Banks also provide important monitoring services of following the implementation of projects, avoiding moral hazard that funds be used for, say, real estate speculation instead of the original project of factory construction. The transformation function of banks involves both assets and liabilities of bank balance sheets. Banks convert an illiquid asset or loan for a project with cash flows in the distant future into a liquid liability in the form of demand deposits that can be withdrawn immediately.
In the theory of banking of Diamond and Rajan (2000, 2001a, 2001b), the bank creates liquidity by tying human assets to capital. The collection skills of the relationship banker convert an illiquid project of an entrepreneur into liquid demand deposits that are immediately available for withdrawal. The deposit/capital structure is fragile because of the threat of bank runs. In these days of online banking, the run on Washington Mutual was through withdrawals online. A bank run can be triggered by the decline of the value of bank assets below the value of demand deposits.
Pelaez and Pelaez (Regulation of Banks and Finance 2009b, 60, 64-5) find immediate application of the theories of banking of Diamond, Dybvig and Rajan to the credit/dollar crisis after 2007. It is a credit crisis because the main issue was the deterioration of the credit portfolios of securitized banks as a result of default of subprime mortgages. It is a dollar crisis because of the weakening dollar resulting from relatively low interest rate policies of the US. It caused systemic effects that converted into a global recession not only because of the huge weight of the US economy in the world economy but also because the credit crisis transferred to the UK and Europe. Management skills or human capital of banks are illustrated by the financial engineering of complex products. The increasing importance of human relative to inanimate capital (Rajan and Zingales 2000) is revolutionizing the theory of the firm (Zingales 2000) and corporate governance (Rajan and Zingales 2001). Finance is one of the most important examples of this transformation. Profits were derived from the charter in the original banking institution. Pricing and structuring financial instruments was revolutionized with option pricing formulas developed by Black and Scholes (1973) and Merton (1973, 1974, 1998) that permitted the development of complex products with fair pricing. The successful financial company must attract and retain finance professionals who have invested in human capital, which is a sunk cost to them and not of the institution where they work.
The complex financial products created for securitized banking with high investments in human capital are based on houses, which are as illiquid as the projects of entrepreneurs in the theory of banking. The liquidity fragility of the securitized bank is equivalent to that of the commercial bank in the theory of banking (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65). Banks created off-balance sheet structured investment vehicles (SIV) that issued commercial paper receiving AAA rating because of letters of liquidity guarantee by the banks. The commercial paper was converted into liquidity by its use as collateral in SRPs at the lowest rates and minimal haircuts because of the AAA rating of the guarantor bank. In the theory of banking, default can be triggered when the value of assets is perceived as lower than the value of the deposits. Commercial paper issued by SIVs, securitized mortgages and derivatives all obtained SRP liquidity on the basis of illiquid home mortgage loans at the bottom of the pyramid. The run on the securitized bank had a clear origin (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65):
“The increasing default of mortgages resulted in an increase in counterparty risk. Banks were hit by the liquidity demands of their counterparties. The liquidity shock extended to many segments of the financial markets—interbank loans, asset-backed commercial paper (ABCP), high-yield bonds and many others—when counterparties preferred lower returns of highly liquid safe havens, such as Treasury securities, than the risk of having to sell the collateral in SRPs at deep discounts or holding an illiquid asset. The price of an illiquid asset is near zero.”
Gorton and Metrick (2010H, 507) provide a revealing quote to the work in 1908 of Edwin R. A. Seligman, professor of political economy at Columbia University, founding member of the American Economic Association and one of its presidents and successful advocate of progressive income taxation. The intention of the quote is to bring forth the important argument that financial crises are explained in terms of “confidence” but as Professor Seligman states in reference to historical banking crises in the US the important task is to explain what caused the lack of confidence. It is instructive to repeat the more extended quote of Seligman (1908, xi) on the explanations of banking crises:
“The current explanations may be divided into two categories. Of these the first includes what might be termed the superficial theories. Thus it is commonly stated that the outbreak of a crisis is due to lack of confidence,--as if the lack of confidence was not in itself the very thing which needs to be explained. Of still slighter value is the attempt to associate a crisis with some particular governmental policy, or with some action of a country’s executive. Such puerile interpretations have commonly been confined to countries like the United States, where the political passions of democracy have had the fullest way. Thus the crisis of 1893 was ascribed by the Republicans to the impending Democratic tariff of 1894; and the crisis of 1907 has by some been termed the ‘[Theodore] Roosevelt panic,” utterly oblivious of the fact that from the time of President Jackson, who was held responsible for the troubles of 1837, every successive crisis had had its presidential scapegoat, and has been followed by a political revulsion. Opposed to these popular, but wholly unfounded interpretations, is the second class of explanations, which seek to burrow beneath the surface and to discover the more occult and fundamental causes of the periodicity of crises.”
Scholars ignore superficial explanations in the effort to seek good and truth. The problem of economic analysis of the credit/dollar crisis is the lack of a structural model with which to attempt empirical determination of causes (Gorton and Metrick 2010SB). There would still be doubts even with a well-specified structural model because samples of economic events do not typically permit separating causes and effects. There is also confusion is separating the why of the crisis and how it started and propagated, all of which are extremely important.
In true heritage of the principles of Seligman (1908), Gorton (2009EFM) discovers a prime causal driver of the credit/dollar crisis. The objective of subprime and Alt-A mortgages was to facilitate loans to populations with modest means so that they could acquire a home. These borrowers would not receive credit because of (1) lack of funds for down payments; (2) low credit rating and information; (3) lack of information on income; and (4) errors or lack of other information. Subprime mortgage “engineering” was based on the belief that both lender and borrower could benefit from increases in house prices over the short run. The initial mortgage would be refinanced in two or three years depending on the increase of the price of the house. According to Gorton (2009EFM, 13, 16):
“The outstanding amounts of Subprime and Alt-A [mortgages] combined amounted to about one quarter of the $6 trillion mortgage market in 2004-2007Q1. Over the period 2000-2007, the outstanding amount of agency mortgages doubled, but subprime grew 800%! Issuance in 2005 and 2006 of Subprime and Alt-A mortgages was almost 30% of the mortgage market. Since 2000 the Subprime and Alt-A segments of the market grew at the expense of the Agency (i.e., the government sponsored entities of Fannie Mae and Freddie Mac) share, which fell from almost 80% (by outstanding or issuance) to about half by issuance and 67% by outstanding amount. The lender’s option to rollover the mortgage after an initial period is implicit in the subprime mortgage. The key design features of a subprime mortgage are: (1) it is short term, making refinancing important; (2) there is a step-up mortgage rate that applies at the end of the first period, creating a strong incentive to refinance; and (3) there is a prepayment penalty, creating an incentive not to refinance early.”
The prime objective of successive administrations in the US during the past 20 years and actually since the times of Roosevelt in the 1930s has been to provide “affordable” financing for the “American dream” of home ownership. The US housing finance system is mixed with public, public/private and purely private entities. The Federal Home Loan Bank (FHLB) system was established by Congress in 1932 that also created the Federal Housing Administration in 1934 with the objective of insuring homes against default. In 1938, the government created the Federal National Mortgage Association, or Fannie Mae, to foster a market for FHA-insured mortgages. Government-insured mortgages were transferred from Fannie Mae to the Government National Mortgage Association, or Ginnie Mae, to permit Fannie Mae to become a publicly-owned company. Securitization of mortgages began in 1970 with the government charter to the Federal Home Loan Mortgage Corporation, or Freddie Mac, with the objective of bundling mortgages created by thrift institutions that would be marketed as bonds with guarantees by Freddie Mac (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 42-8). In the third quarter of 2008, total mortgages in the US were $12,057 billion of which 43.5 percent, or $5423 billion, were retained or guaranteed by Fannie Mae and Freddie Mac (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 45). In 1990, Fannie Mae and Freddie Mac had a share of only 25.4 percent of total mortgages in the US. Mortgages in the US increased from $6922 billion in 2002 to $12,088 billion in 2007, or by 74.6 percent, while the retained or guaranteed portfolio of Fannie and Freddie rose from $3180 billion in 2002 to $4934 billion in 2007, or by 55.2 percent.
According to Pinto (2008) in testimony to Congress:
“There are approximately 25 million subprime and Alt-A loans outstanding, with an unpaid principal amount of over $4.5 trillion, about half of them held or guaranteed by Fannie and Freddie. Their high risk activities were allowed to operate at 75:1 leverage ratio. While they may deny it, there can be no doubt that Fannie and Freddie now own or guarantee $1.6 trillion in subprime, Alt-A and other default prone loans and securities. This comprises over 1/3 of their risk portfolios and amounts to 34% of all the subprime loans and 60% of all Alt-A loans outstanding. These 10.5 million unsustainable, nonprime loans are experiencing a default rate 8 times the level of the GSEs’ 20 million traditional quality loans. The GSEs will be responsible for a large percentage of an estimated 8.8 million foreclosures expected over the next 4 years, accounting for the failure of about 1 in 6 home mortgages. Fannie and Freddie have subprimed America.”
In perceptive analysis of growth and macroeconomics in the past six decades, Rajan (2012FA) argues that “the West can’t borrow and spend its way to recovery.” The Keynesian paradigm is not applicable in current conditions. Advanced economies in the West could be divided into those that reformed regulatory structures to encourage productivity and others that retained older structures. In the period from 1950 to 2000, Cobet and Wilson (2002) find that US productivity, measured as output/hour, grew at the average yearly rate of 2.9 percent while Japan grew at 6.3 percent and Germany at 4.7 percent (see Pelaez and Pelaez, The Global Recession Risk (2007), 135-44). In the period from 1995 to 2000, output/hour grew at the average yearly rate of 4.6 percent in the US but at lower rates of 3.9 percent in Japan and 2.6 percent in the US. Rajan (2012FA) argues that the differential in productivity growth was accomplished by deregulation in the US at the end of the 1970s and during the 1980s. In contrast, Europe did not engage in reform with the exception of Germany in the early 2000s that empowered the German economy with significant productivity advantage. At the same time, technology and globalization increased relative remunerations in highly-skilled, educated workers relative to those without skills for the new economy. It was then politically appealing to improve the fortunes of those left behind by the technological revolution by means of increasing cheap credit. As Rajan (2012FA) argues:
“In 1992, Congress passed the Federal Housing Enterprises Financial Safety and Soundness Act, partly to gain more control over Fannie Mae and Freddie Mac, the giant private mortgage agencies, and partly to promote affordable homeownership for low-income groups. Such policies helped money flow to lower-middle-class households and raised their spending—so much so that consumption inequality rose much less than income inequality in the years before the crisis. These policies were also politically popular. Unlike when it came to an expansion in government welfare transfers, few groups opposed expanding credit to the lower-middle class—not the politicians who wanted more growth and happy constituents, not the bankers and brokers who profited from the mortgage fees, not the borrowers who could now buy their dream houses with virtually no money down, and not the laissez-faire bank regulators who thought they could pick up the pieces if the housing market collapsed. The Federal Reserve abetted these shortsighted policies. In 2001, in response to the dot-com bust, the Fed cut short-term interest rates to the bone. Even though the overstretched corporations that were meant to be stimulated were not interested in investing, artificially low interest rates acted as a tremendous subsidy to the parts of the economy that relied on debt, such as housing and finance. This led to an expansion in housing construction (and related services, such as real estate brokerage and mortgage lending), which created jobs, especially for the unskilled. Progressive economists applauded this process, arguing that the housing boom would lift the economy out of the doldrums. But the Fed-supported bubble proved unsustainable. Many construction workers have lost their jobs and are now in deeper trouble than before, having also borrowed to buy unaffordable houses. Bankers obviously deserve a large share of the blame for the crisis. Some of the financial sector’s activities were clearly predatory, if not outright criminal. But the role that the politically induced expansion of credit played cannot be ignored; it is the main reason the usual checks and balances on financial risk taking broke down.”
In fact, Raghuram G. Rajan (2005) anticipated low liquidity in financial markets resulting from low interest rates before the financial crisis that caused distortions of risk/return decisions provoking the credit/dollar crisis and global recession from IVQ2007 to IIQ2009. Near zero interest rates of unconventional monetary policy induced excessive risks and low liquidity in financial decisions that were critical as a cause of the credit/dollar crisis after 2007. Rajan (2012FA) argues that it is not feasible to return to the employment and income levels before the credit/dollar crisis because of the bloated construction sector, financial system and government budgets.
Table IIA-1 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 94.7 percent in the 10-city composite of the Case-Shiller home price index, 78.0 percent in the 20-city composite and 62.9 percent in the US national home price index between Apr 2000 and Apr 2005. Prices rose around 100 percent from Apr 2000 to Apr 2006, increasing 116.3 percent for the 10-city composite, 98.0 percent for the 20-city composite and 79.1 percent in the US national index. House prices rose 38.3 percent between Apr 2003 and Apr 2005 for the 10-city composite, 33.0 percent for the 20-city composite and 28.0 percent for the US national propelled by low fed funds rates of 1.0 percent between Jun 2003 and Jun 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Aprket 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 Apr 2003 and Apr 2006, the 10-city index gained 53.6 percent, the 20-city index increased 47.8 percent and the US national 40.7 percent. House prices have fallen from Apr 2006 to Apr 2015 by 14.8 percent for the 10-city composite, 13.6 percent for the 20-city composite and 7.4 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 Apr 2015, house prices increased 4.6 percent in the 10-city composite, increased 4.9 percent in the 20-city composite and 4.2 percent in the US national. Table IIA-6 also shows that house prices increased 84.4 percent between Apr 2000 and Apr 2015 for the 10-city composite, increased 71.0 percent for the 20-city composite and 65.8 percent for the US national. House prices are close to the lowest level since peaks during the boom before the financial crisis and global recession. The 10-city composite fell 15.2 percent from the peak in Jun 2006 to Apr 2015 and the 20-city composite fell 14.3 percent from the peak in Jul 2006 to Apr 2015. The US national fell 7.9 percent from the peaks of the 10-city composite to Apr 2015 and 7.9 percent from the peak of the 20-city composite to Apr 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 to 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-1, US, Percentage Changes of Standard & Poor’s Case-Shiller Home Price Indices, Not Seasonally Adjusted, ∆%
| 10-City Composite | 20-City Composite | US National | |
| ∆% Apr 2000 to Apr 2003 | 40.8 | 33.9 | 27.3 |
| ∆% Apr 2000 to Apr 2005 | 94.7 | 78.0 | 62.9 |
| ∆% Apr 2003 to Apr 2005 | 38.3 | 33.0 | 28.0 |
| ∆% Apr 2000 to Apr 2006 | 116.3 | 98.0 | 79.1 |
| ∆% Apr 2003 to Apr 2006 | 53.6 | 47.8 | 40.7 |
| ∆% Apr 2005 to Apr 2015 | -5.3 | -3.9 | 1.8 |
| ∆% Apr 2006 to Apr 2015 | -14.8 | -13.6 | -7.4 |
| ∆% Apr 2009 to Apr 2015 | 27.5 | 27.1 | 15.7 |
| ∆% Apr 2010 to Apr 2015 | 21.9 | 22.4 | 16.9 |
| ∆% Apr 2011 to Apr 2015 | 26.4 | 27.9 | 22.2 |
| ∆% Apr 2012 to Apr 2015 | 29.2 | 30.2 | 22.7 |
| ∆% Apr 2013 to Apr 2015 | 16.0 | 16.3 | 12.6 |
| ∆% Apr 2014 to Apr 2015 | 4.6 | 4.9 | 4.2 |
| ∆% Apr 2000 to Apr 2015 | 84.4 | 71.0 | 65.8 |
| ∆% Peak Jun 2006 Apr 2015 | -15.2 | -7.9 | |
| ∆% Peak Jul 2006 Apr 2015 | -14.3 | -7.9 | |
| 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 that “home prices continued their rise across the country over the last 12 months” (https://www.spice-indices.com/idpfiles/spice-assets/resources/public/documents/200749_cshomeprice-release-0630.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 1.0 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 increased 1.0 percent in Apr 2015 and the 20-city increased 1.1 percent. The 10-city SA increased 0.4 percent in Apr 2015 and the 20-city composite SA increased 0.3 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses.
Table IIA-2, US, Monthly Percentage Change of S&P Case-Shiller Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%
| 10-City Composite SA | 10-City Composite NSA | 20-City Composite SA | 20-City Composite NSA | |
| Apr 2015 | 0.4 | 1.0 | 0.3 | 1.1 |
| Mar | 0.8 | 0.8 | 1.0 | 0.9 |
| Feb | 1.1 | 0.5 | 1.1 | 0.4 |
| Jan | 0.8 | -0.1 | 0.8 | -0.1 |
| Dec 2014 | 0.8 | 0.1 | 0.9 | 0.0 |
| Nov | 0.7 | -0.3 | 0.7 | -0.2 |
| Oct | 0.6 | -0.1 | 0.7 | -0.1 |
| Sep | 0.2 | -0.1 | 0.3 | -0.1 |
| Aug | -0.1 | 0.2 | 0.0 | 0.2 |
| Jul | -0.3 | 0.6 | -0.3 | 0.6 |
| Jun | -0.1 | 1.0 | -0.2 | 1.0 |
| May | -0.5 | 1.1 | -0.5 | 1.1 |
| Apr | 0.4 | 1.1 | 0.3 | 1.2 |
| Mar | 0.9 | 0.8 | 1.0 | 0.9 |
| Feb | 0.7 | 0.0 | 0.7 | 0.0 |
| Jan | 0.9 | -0.1 | 0.8 | -0.1 |
| Dec 2013 | 0.7 | -0.1 | 0.7 | -0.1 |
| Nov | 0.9 | 0.0 | 0.9 | -0.1 |
| Oct | 1.0 | 0.2 | 1.0 | 0.2 |
| Sep | 1.0 | 0.7 | 1.1 | 0.7 |
| Aug | 1.1 | 1.3 | 1.1 | 1.3 |
| Jul | 0.9 | 1.9 | 0.9 | 1.8 |
| Jun | 1.0 | 2.2 | 0.9 | 2.2 |
| May | 0.9 | 2.5 | 0.9 | 2.5 |
| Apr | 1.9 | 2.6 | 1.7 | 2.6 |
| Mar | 1.4 | 1.3 | 1.5 | 1.3 |
| Feb | 1.1 | 0.3 | 1.0 | 0.2 |
| Jan | 0.9 | 0.0 | 1.0 | 0.0 |
| Dec 2012 | 1.0 | 0.2 | 1.0 | 0.2 |
| Nov | 0.6 | -0.3 | 0.7 | -0.2 |
| Oct | 0.6 | -0.2 | 0.7 | -0.1 |
| Sep | 0.5 | 0.3 | 0.6 | 0.3 |
| Aug | 0.5 | 0.8 | 0.6 | 0.9 |
| Jul | 0.4 | 1.5 | 0.5 | 1.6 |
| Jun | 0.9 | 2.1 | 1.0 | 2.3 |
| May | 0.7 | 2.2 | 0.8 | 2.4 |
| Apr | 0.6 | 1.4 | 0.6 | 1.4 |
| Mar | 0.2 | -0.1 | 0.3 | 0.0 |
| Feb | -0.1 | -0.9 | 0.0 | -0.8 |
| Jan | -0.2 | -1.1 | -0.1 | -1.0 |
| Dec 2011 | -0.5 | -1.2 | -0.4 | -1.1 |
| Nov | -0.6 | -1.4 | -0.5 | -1.3 |
| Oct | -0.6 | -1.3 | -0.6 | -1.4 |
| Sep | -0.4 | -0.6 | -0.5 | -0.7 |
| Aug | -0.2 | 0.1 | -0.2 | 0.1 |
| Jul | -0.1 | 0.9 | -0.1 | 1.0 |
| Jun | -0.1 | 1.0 | -0.1 | 1.2 |
| May | -0.3 | 1.0 | -0.4 | 1.0 |
| Apr | -0.1 | 0.6 | -0.2 | 0.6 |
| Mar | -0.5 | -1.0 | -0.6 | -1.0 |
| Feb | -0.4 | -1.3 | -0.3 | -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
IB Collapse of United States Dynamism of Income Growth and Employment Creation. There are four major approaches to the analysis of the depth of the financial crisis and global recession from IVQ2007 (Dec) to IIQ2009 (Jun) and the subpar recovery from IIIQ2009 (Jul) to the present:
(1) Deeper contraction and slower recovery in recessions with financial crises
(2) Counterfactual of avoiding deeper contraction by fiscal and monetary policies
(3) Theory and Reality of Secular Stagnation
(4) Counterfactual that the financial crises and global recession would have been avoided had economic policies been different
(5) Evidence that growth rates are higher after deeper recessions with financial crises.
A counterfactual consists of theory and measurements of what would have occurred otherwise if economic policies or institutional arrangements had been different. This task is quite difficult because economic data are observed with all effects as they actually occurred while the counterfactual attempts to evaluate how data would differ had policies and institutional arrangements been different (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008b), 125, 136; Pelaez 1979, 26-8). Counterfactual data are unobserved and must be calculated using theory and measurement methods. The measurement of costs and benefits of projects or applied welfare economics (Harberger 1971, 1997) specifies and attempts to measure projects such as what would be economic welfare with or without a bridge or whether markets would be more or less competitive in the absence of antitrust and regulation laws (Winston 2006). The “new economic history” of the United States used counterfactuals to measure the economy with or without railroads (Fishlow 1965, Fogel 1964) and in analyzing slavery (Fogel and Engerman 1974). A critical counterfactual in economic history is how Britain surged ahead of France (North and Weingast 1989). These four approaches are discussed below in turn followed with comparison of the two recessions of the 1980s from IQ1980 (Jan) to IIIQ1980 (Jul) and from IIIQ1981 (Jul) to IVQ1982 (Nov) with the recession from IVQ2007 (Dec) to IIQ2009 (Jun) as dated by the National Bureau of Economic Research (NBER http://www.nber.org/cycles.html). These comparisons are not idle exercises, defining the interpretation of history and even possibly critical policies and institutional arrangements. There is active debate on these issues (Bordo 2012Oct 21 http://www.bloomberg.com/news/2012-10-21/why-this-u-s-recovery-is-weaker.html Reinhart and Rogoff, 2012Oct14 http://www.economics.harvard.edu/faculty/rogoff/files/Is_US_Different_RR_3.pdf Taylor 2012Oct 25 http://www.johnbtaylorsblog.blogspot.co.uk/2012/10/an-unusually-weak-recovery-as-usually.html, Wolf 2012Oct23 http://www.ft.com/intl/cms/s/0/791fc13a-1c57-11e2-a63b-00144feabdc0.html#axzz2AotsUk1q).
(1) Lower Growth Rates in Recoveries from Recessions with Financial Crises. A monumental effort of data gathering, calculation and analysis by Professors Carmen M. Reinhart and Kenneth Rogoff at Harvard University is highly relevant to banking crises, financial crash, debt crises and economic growth (Reinhart 2010CB; Reinhart and Rogoff 2011AF, 2011Jul14, 2011EJ, 2011CEPR, 2010FCDC, 2010GTD, 2009TD, 2009AFC, 2008TDPV; see also Reinhart and Reinhart 2011Feb, 2010AF and Reinhart and Sbrancia 2011). See http://cmpassocregulationblog.blogspot.com/2011/07/debt-and-financial-risk-aversion-and.html. The dataset of Reinhart and Rogoff (2010GTD, 1) is quite unique in breadth of countries and over time periods:
“Our results incorporate data on 44 countries spanning about 200 years. Taken together, the data incorporate over 3,700 annual observations covering a wide range of political systems, institutions, exchange rate and monetary arrangements and historic circumstances. We also employ more recent data on external debt, including debt owed by government and by private entities.”
Reinhart and Rogoff (2010GTD, 2011CEPR) classify the dataset of 2317 observations into 20 advanced economies and 24 emerging market economies. In each of the advanced and emerging categories, the data for countries is divided into buckets according to the ratio of gross central government debt to GDP: below 30, 30 to 60, 60 to 90 and higher than 90 (Reinhart and Rogoff 2010GTD, Table 1, 4). Median and average yearly percentage growth rates of GDP are calculated for each of the buckets for advanced economies. There does not appear to be any relation for debt/GDP ratios below 90. The highest growth rates are for debt/GDP ratios below 30: 3.7 percent for the average and 3.9 percent for the median. Growth is significantly lower for debt/GDP ratios above 90: 1.7 percent for the average and 1.9 percent for the median. GDP growth rates for the intermediate buckets are in a range around 3 percent: the highest 3.4 percent average is for the bucket 60 to 90 and 3.1 percent median for 30 to 60. There is even sharper contrast for the United States: 4.0 percent growth for debt/GDP ratio below 30; 3.4 percent growth for debt/GDP ratio of 30 to 60; 3.3 percent growth for debt/GDP ratio of 60 to 90; and minus 1.8 percent, contraction, of GDP for debt/GDP ratio above 90.
For the five countries with systemic financial crises—Iceland, Ireland, UK, Spain and the US—real average debt levels have increased by 75 percent between 2007 and 2009 (Reinhart and Rogoff 2010GTD, Figure 1). The cumulative increase in public debt in the three years after systemic banking crisis in a group of episodes after World War II is 86 percent (Reinhart and Rogoff 2011CEPR, Figure 2, 10).
An important concept is “this time is different syndrome,” which “is rooted in the firmly-held belief that financial crises are something that happens to other people in other countries at other times; crises do not happen here and now to us” (Reinhart and Rogoff 2010FCDC, 9). There is both an arrogance and ignorance in “this time is different” syndrome, as explained by Reinhart and Rogoff (2010FCDC, 34):
“The ignorance, of course, stems from the belief that financial crises happen to other people at other time in other places. Outside a small number of experts, few people fully appreciate the universality of financial crises. The arrogance is of those who believe they have figured out how to do things better and smarter so that the boom can long continue without a crisis.”
There is sober warning by Reinhart and Rogoff (2011CEPR, 42) based on the momentous effort of their scholarly data gathering, calculation and analysis:
“Despite considerable deleveraging by the private financial sector, total debt remains near its historic high in 2008. Total public sector debt during the first quarter of 2010 is 117 percent of GDP. It has only been higher during a one-year stint at 119 percent in 1945. Perhaps soaring US debt levels will not prove to be a drag on growth in the decades to come. However, if history is any guide, that is a risky proposition and over-reliance on US exceptionalism may only be one more example of the ‘This Time is Different’ syndrome.”
As both sides of the Atlantic economy maneuver around defaults, the experience on debt and growth deserves significant emphasis in research and policy. The world economy is slowing with high levels of unemployment in advanced economies. Countries do not grow themselves out of unsustainable debts but rather through de facto defaults by means of financial repression and in some cases through inflation. The conclusion is that this time is not different.
Professor Alan M. Taylor (2012) at the University of Virginia analyzes own and collaborative research on 140 years of history with data from 14 advanced economies in the effort to elucidate experience preceding, during and after financial crises. The conclusion is (Allan M. Taylor 2012, 8):
“Recessions might be painful, but they tend to be even more painful when combined with financial crises or (worse) global crises, and we already know that post-2008 experience will not overturn this conclusion. The impact on credit is also very strong: financial crises lead to strong setbacks in the rate of growth of loans as compared to what happens in normal recessions, and this effect is strong for global crises. Finally, inflation generally falls in recessions, but the downdraft is stronger in financial crisis times.”
Alan M. Taylor (2012) also finds that advanced economies entered the global recession with the largest financial sector in history. There was doubling after 1980 of the ratio of loans to GDP and tripling of the size of bank balance sheets. In contrast, in the period from 1950 to 1970 there was high investment, savings and growth in advanced economies with firm regulation of finance and controls of foreign capital flows.
(2) Counterfactual of the Global Recession. There is a difficult decision on when to withdraw the fiscal stimulus that could have adverse consequences on current growth and employment analyzed by Krugman (2011Jun18). CBO (2011JunLTBO, Chapter 2) considers the timing of withdrawal as well as the equally tough problems that result from not taking prompt action to prevent a possible debt crisis in the future. Krugman (2011Jun18) refers to Eggertsson and Krugman (2010) on the possible contractive effects of debt. The world does not become poorer as a result of debt because an individual’s asset is another’s liability. Past levels of credit may become unacceptable by credit tightening, such as during a financial crisis. Debtors are forced into deleveraging, which results in expenditure reduction, but there may not be compensatory effects by creditors who may not be in need of increasing expenditures. The economy could be pushed toward the lower bound of zero interest rates, or liquidity trap, remaining in that threshold of deflation and high unemployment.
Analysis of debt can lead to the solution of the timing of when to cease stimulus by fiscal spending (Krugman 2011Jun18). Excessive debt caused the financial crisis and global recession and it is difficult to understand how more debt can recover the economy. Krugman (2011Jun18) argues that the level of debt is not important because one individual’s asset is another individual’s liability. The distribution of debt is important when economic agents with high debt levels are encountering different constraints than economic agents with low debt levels. The opportunity for recovery may exist in borrowing by some agents that can adjust the adverse effects of past excessive borrowing by other agents. As Krugman (2011Jun18, 20) states:
“Suppose, in particular, that the government can borrow for a while, using the borrowed money to buy useful things like infrastructure. The true social cost of these things will be very low, because the spending will be putting resources that would otherwise be unemployed to work. And government spending will also make it easier for highly indebted players to pay down their debt; if the spending is sufficiently sustained, it can bring the debtors to the point where they’re no longer so severely balance-sheet constrained, and further deficit spending is no longer required to achieve full employment. Yes, private debt will in part have been replaced by public debt – but the point is that debt will have been shifted away from severely balance-sheet-constrained players, so that the economy’s problems will have been reduced even if the overall level of debt hasn’t fallen. The bottom line, then, is that the plausible-sounding argument that debt can’t cure debt is just wrong. On the contrary, it can – and the alternative is a prolonged period of economic weakness that actually makes the debt problem harder to resolve.”
Besides operational issues, the consideration of this argument would require specifying and measuring two types of gains and losses from this policy: (1) the benefits in terms of growth and employment currently; and (2) the costs of postponing the adjustment such as in the exercise by CBO (2011JunLTO, 28-31) in Table 11. It may be easier to analyze the costs and benefits than actual measurement.
An analytical and empirical approach is followed by Blinder and Zandi (2010), using the Moody’s Analytics model of the US economy with four different scenarios: (1) baseline with all policies used; (2) counterfactual including all fiscal stimulus policies but excluding financial stimulus policies; (3) counterfactual including all financial stimulus policies but excluding fiscal stimulus; and (4) a scenario excluding all policies. The scenario excluding all policies is an important reference or the counterfactual of what would have happened if the government had been entirely inactive. A salient feature of the work by Blinder and Zandi (2010) is the consideration of both fiscal and financial policies. There was probably more activity with financial policies than with fiscal policies. Financial policies included the Fed balance sheet, 11 facilities of direct credit to illiquid segments of financial markets, interest rate policy, the Financial Stability Plan including stress tests of banks, the Troubled Asset Relief Program (TARP) and others (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009b), 157-67; Regulation of Banks and Finance (2009a), 224-7).
Blinder and Zandi (2010, 4) find that:
“In the scenario that excludes all the extraordinary policies, the downturn continues into 2011. Real GDP falls a stunning 7.4% in 2009 and another 3.7% in 2010 (see Table 3). The peak-to-trough decline in GDP is therefore close to 12%, compared to an actual decline of about 4%. By the time employment hits bottom, some 16.6 million jobs are lost in this scenario—about twice as many as actually were lost. The unemployment rate peaks at 16.5%, and although not determined in this analysis, it would not be surprising if the underemployment rate approached one-fourth of the labor force. The federal budget deficit surges to over $2 trillion in fiscal year 2010, $2.6 trillion in fiscal year 2011, and $2.25 trillion in FY 2012. Remember, this is with no policy response. With outright deflation in prices and wages in 2009-2011, this dark scenario constitutes a 1930s-like depression.”
The conclusion by Blinder and Zandi (2010) is that if the US had not taken massive fiscal and financial measures the economy could have suffered far more during a prolonged period. There are still a multitude of questions that cloud understanding of the impact of the recession and what would have happened without massive policy impulses. Some effects are quite difficult to measure. An important argument by Blinder and Zandi (2010) is that this evaluation of counterfactuals is relevant to the need of stimulus if economic conditions worsened again.
(3) Theory and Reality of Cyclical Stagnation Not Secular Stagnation. There is current interest in past theories of “secular stagnation.” Alvin H. Hansen (1939, 4, 7; see Hansen 1938, 1941; for an early critique see Simons 1942) argues:
“Not until the problem of full employment of our productive resources from the long-run, secular standpoint was upon us, were we compelled to give serious consideration to those factors and forces in our economy which tend to make business recoveries weak and anaemic (sic) and which tend to prolong and deepen the course of depressions. This is the essence of secular stagnation-sick recoveries which die in their infancy and depressions which feed on themselves and leave a hard and seemingly immovable core of unemployment. Now the rate of population growth must necessarily play an important role in determining the character of the output; in other words, the composition of the flow of final goods. Thus a rapidly growing population will demand a much larger per capita volume of new residential building construction than will a stationary population. A stationary population with its larger proportion of old people may perhaps demand more personal services; and the composition of consumer demand will have an important influence on the quantity of capital required. The demand for housing calls for large capital outlays, while the demand for personal services can be met without making large investment expenditures. It is therefore not unlikely that a shift from a rapidly growing population to a stationary or declining one may so alter the composition of the final flow of consumption goods that the ratio of capital to output as a whole will tend to decline.”
In the analysis of Hansen (1939, 3) of secular stagnation, economic progress consists of growth of real income per person driven by growth of productivity. The “constituent elements” of economic progress are “(a) inventions, (b) the discovery and development of new territory and new resources, and (c) the growth of population” (Hansen 1939, 3). Secular stagnation originates in decline of population growth and discouragement of inventions. According to Hansen (1939, 2), US population grew by 16 million in the 1920s but grew by one half or about 8 million in the 1930s with forecasts at the time of Hansen’s writing in 1938 of growth of around 5.3 million in the 1940s. Hansen (1939, 2) characterized demography in the US as “a drastic decline in the rate of population growth. Hansen’s plea was to adapt economic policy to stagnation of population in ensuring full employment. In the analysis of Hansen (1939, 8), population caused half of the growth of US GDP per year. Growth of output per person in the US and Europe was caused by “changes in techniques and to the exploitation of new natural resources.” In this analysis, population caused 60 percent of the growth of capital formation in the US. Declining population growth would reduce growth of capital formation. Residential construction provided an important share of growth of capital formation. Hansen (1939, 12) argues that market power of imperfect competition discourages innovation with prolonged use of obsolete capital equipment. Trade unions would oppose labor-savings innovations. The combination of stagnating and aging population with reduced innovation caused secular stagnation. Hansen (1939, 12) concludes that there is role for public investments to compensate for lack of dynamism of private investment but with tough tax/debt issues.
Table SE1 provides contributions to growth of GDP in the 1930s. These data were not available until much more recently. Residential investment (RSI) contributed 1.03 percentage points to growth of GDP of 8.0 percent in 1939, which is a high percentage of the contribution of gross private domestic investment of 2.39 percentage points. Residential investment contributed 0.42 percentage points to GDP growth of 8.8 percent in 1940 with gross private domestic investment contributing 3.99 percentage points.
Table SE1, US, Contributions to Growth of GDP
| GDP ∆% | PCE PP | GDI PP | NRI PP | RSI PP | Net Trade PP | GOVT | |
| 1930 | -8.5 | -3.96 | -5.18 | -1.84 | -1.50 | -0.31 | 0.94 |
| 1931 | -6.4 | -2.37 | -4.28 | -3.32 | -0.40 | -0.22 | 0.48 |
| 1932 | -12.9 | -7.00 | -5.28 | -2.78 | -1.02 | -0.20 | -0.42 |
| 1933 | -1.3 | -1.79 | 1.16 | -0.44 | -0.24 | -0.11 | -0.52 |
| 1934 | 10.8 | 5.71 | 2.83 | 1.31 | 0.38 | 0.33 | 1.91 |
| 1935 | 8.9 | 4.69 | 4.54 | 1.41 | 0.56 | -0.83 | 0.50 |
| 1936 | 12.9 | 7.68 | 2.58 | 2.10 | 0.47 | 0.24 | 2.44 |
| 1937 | 5.1 | 2.72 | 2.57 | 1.42 | 0.17 | 0.45 | -0.64 |
| 1938 | -3.3 | -1.15 | -4.13 | -2.13 | 0.01 | 0.88 | 1.09 |
| 1939 | 8.0 | 4.11 | 2.39 | 0.71 | 1.03 | 0.07 | 1.41 |
| 1940 | 8.8 | 3.72 | 3.99 | 1.60 | 0.42 | 0.52 | 0.57 |
GDP ∆%: Annual Growth of GDP; PCE PP: Percentage Points Contributed by Personal Consumption Expenditures (PCE); GDI PP: Percentage Points Contributed by Gross Private Domestic Investment (GDI); NRI PP: Percentage Points Contributed by Nonresidential Investment (NRI); RSI: Percentage Points Contributed by Residential Investment; Net Trade PP: Percentage Points Contributed by Net Exports less Imports of Goods and Services; GOVT PP: Percentage Points Contributed by Government Consumption and Gross Investment
Source: Bureau of Economic Analysis
http://www.bea.gov/iTable/index_nipa.cfm
Table ES2 provides percentage shares of GDP in 1929, 1939, 1940, 2006 and 2013. The share of residential investment was 3.9 percent in 1929, 3.4 percent in 1939 and 6.0 percent in 2006 at the peak of the real estate boom. The share of residential investment in GDP has not been very high historically.
Table ES2, Percentage Shares in GDP
| 1929 | 1939 | 1940 | 2006 | 2013 | |
| GDP | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 |
| PCE | 74.0 | 71.9 | 69.2 | 67.1 | 68.5 |
| GDI | 16.4 | 10.9 | 14.2 | 19.3 | 15.9 |
| NRI | 11.1 | 7.3 | 8.3 | 12.8 | 12.2 |
| RSI | 3.9 | 3.4 | 3.5 | 6.0 | 3.1 |
| Net Trade | 0.4 | 0.9 | 1.4 | -5.5 | -3.0 |
| GOVT | 9.2 | 16.3 | 15.2 | 19.1 | 18.6 |
PCE: Personal Consumption Expenditures; GDI: Gross Domestic Investment; NRI: Nonresidential Investment; RSI: Residential Investment; Net Trade: Net Exports less Imports of Goods and Services; GOVT: Government Consumption and Gross Investment
Source: Bureau of Economic Analysis
PCE: Personal Consumption Expenditures; GDI: Gross Private Domestic Investment; NRI: Nonresidential Investment; RSI: Residential Investment; Net Trade: Net Exports less Imports of Goods and Services; GOVT: Government Consumption and Gross Investment
Source: Bureau of Economic Analysis
http://www.bea.gov/iTable/index_nipa.cfm
An interpretation of the New Deal is that fiscal stimulus must be massive in recovering growth and employment and that it should not be withdrawn prematurely to avoid a sharp second contraction as it occurred in 1937 (Christina Romer 2009). Proposals for another higher dose of stimulus explain the current weakness by insufficient fiscal expansion and warn that failure to spend more can cause another contraction as in 1937. According to a different interpretation, private hours worked declined by 25 percent by 1939 compared with the level in 1929, suggesting that the economy fell to a lower path of expansion than in 1929 (works by Harold Cole and Lee Ohanian (1999) (cited in Pelaez and Pelaez, Regulation of Banks and Finance, 215-7). Major real variables of output and employment were below trend by 1939: -26.8 percent for GNP, -25.4 percent for consumption, -51 percent for investment and -25.6 percent for hours worked. Surprisingly, total factor productivity increased by 3.1 percent and real wages by 21.8 percent (Cole and Ohanian 1999). The policies of the Roosevelt administration encouraged increasing unionization to maintain high wages with lower hours worked and high prices by lax enforcement of antitrust law to encourage cartels or collusive agreements among producers. The encouragement by the government of labor bargaining by unions and higher prices by collusion depressed output and employment throughout the 1930s until Roosevelt abandoned the policies during World War II after which the economy recovered full employment (Cole and Ohanian 1999). The fortunate ones who worked during the New Deal received higher real wages at the expense of many who never worked again. In a way, the administration behaved like the father of the unionized workers and the uncle of the collusive rich, neglecting the majority in the middle. Inflation-adjusted GDP increased by 10.8 percent in 1934, 8.9 percent in 1935, 12.9 percent in 1936 but only 5.1 percent in 1937, contracting by -3.3 percent in 1938 (US Bureau of Economic Analysis cited in Pelaez and Pelaez, Financial Regulation after the Global Recession, 151, Globalization and the State, Vol. II, 206). The competing explanation is that the economy did not decline from 1937 to 1938 because of lower government spending in 1937 but rather because of the expansion of unions promoted by the New Deal and increases in tax rates (Thomas Cooley and Lee Ohanian 2010). Government spending adjusted for inflation fell only 0.7 percent in 1936 and 1937 and could not explain the decline of GDP by 3.4 percent in 1938. In 1936, the administration imposed a tax on retained profits not distributed to shareholders according to a sliding scale of 7 percent for retaining 1 percent of total net income up to 27 percent for retaining 70 percent of total net income, increasing costs of investment that were mostly financed in that period with retained earnings (Cooley and Ohanian 2010). The tax rate on dividends jumped from 10.1 percent in 1929 to 15.9 percent in 1932 and doubled by 1936. A recent study finds that “tax rates on dividends rose dramatically during the 1930s and imply significant declines in investment and equity values and nontrivial declines in GDP and hours of work” (Ellen McGrattan 2010), explaining a significant part of the decline of 26 percent in business fixed investment in 1937-1938. The National Labor Relations Act of 1935 caused an increase in union membership from 12 percent in 1934 to 25 percent in 1938. The alternative lesson from the 1930s is that capital income taxes and higher unionization caused increases in business costs that perpetuated job losses of the recession with current risks of repeating the 1930s (Cooley and Ohanian 1999).
In the analysis of Hansen (1939, 3) of secular stagnation, economic progress consists of growth of real income per person driven by growth of productivity. The “constituent elements” of economic progress are “(a) inventions, (b) the discovery and development of new territory and new resources, and (c) the growth of population” (Hansen 1939, 3). Secular stagnation originates in decline of population growth and discouragement of inventions. According to Hansen (1939, 2), US population grew by 16 million in the 1920s but grew by one half or about 8 million in the 1930s with forecasts at the time of Hansen’s writing in 1938 of growth of around 5.3 million in the 1940s. Hansen (1939, 2) characterized demography in the US as “a drastic decline in the rate of population growth. Hansen’s plea was to adapt economic policy to stagnation of population in ensuring full employment. In the analysis of Hansen (1939, 8), population caused half of the growth of US GDP per year. Growth of output per person in the US and Europe was caused by “changes in techniques and to the exploitation of new natural resources.” In this analysis, population caused 60 percent of the growth of capital formation in the US. Declining population growth would reduce growth of capital formation. Residential construction provided an important share of growth of capital formation. Hansen (1939, 12) argues that market power of imperfect competition discourages innovation with prolonged use of obsolete capital equipment. Trade unions would oppose labor-savings innovations. The combination of stagnating and aging population with reduced innovation caused secular stagnation. Hansen (1939, 12) concludes that there is role for public investments to compensate for lack of dynamism of private investment but with tough tax/debt issues.
The current application of Hansen’s (1938, 1939, 1941) proposition argues that secular stagnation occurs because full employment equilibrium can be attained only with negative real interest rates between minus 2 and minus 3 percent. Professor Lawrence H. Summers (2013Nov8) finds that “a set of older ideas that went under the phrase secular stagnation are not profoundly important in understanding Japan’s experience in the 1990s and may not be without relevance to America’s experience today” (emphasis added). Summers (2013Nov8) argues there could be an explanation in “that the short-term real interest rate that was consistent with full employment had fallen to -2% or -3% sometime in the middle of the last decade. Then, even with artificial stimulus to demand coming from all this financial imprudence, you wouldn’t see any excess demand. And even with a relative resumption of normal credit conditions, you’d have a lot of difficulty getting back to full employment.” The US economy could be in a situation where negative real rates of interest with fed funds rates close to zero as determined by the Federal Open Market Committee (FOMC) do not move the economy to full employment or full utilization of productive resources. Summers (2013Oct8) finds need of new thinking on “how we manage an economy in which the zero nominal interest rates is a chronic and systemic inhibitor of economy activity holding our economies back to their potential.”
Former US Treasury Secretary Robert Rubin (2014Jan8) finds three major risks in prolonged unconventional monetary policy of zero interest rates and quantitative easing: (1) incentive of delaying action by political leaders; (2) “financial moral hazard” in inducing excessive exposures pursuing higher yields of risker credit classes; and (3) major risks in exiting unconventional policy. Rubin (2014Jan8) proposes reduction of deficits by structural reforms that could promote recovery by improving confidence of business attained with sound fiscal discipline.
Professor John B. Taylor (2014Jan01, 2014Jan3) provides clear thought on the lack of relevance of Hansen’s contention of secular stagnation to current economic conditions. The application of secular stagnation argues that the economy of the US has attained full-employment equilibrium since around 2000 only with negative real rates of interest of minus 2 to minus 3 percent. At low levels of inflation, the so-called full-employment equilibrium of negative interest rates of minus 2 to minus 3 percent cannot be attained and the economy stagnates. Taylor (2014Jan01) analyzes multiple contradictions with current reality in this application of the theory of secular stagnation:
- Secular stagnation would predict idle capacity, in particular in residential investment when fed fund rates were fixed at 1 percent from Jun 2003 to Jun 2004. Taylor (2014Jan01) finds unemployment at 4.4 percent with house prices jumping 7 percent from 2002 to 2003 and 14 percent from 2004 to 2005 before dropping from 2006 to 2007. GDP prices doubled from 1.7 percent to 3.4 percent when interest rates were low from 2003 to 2005.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the application of secular stagnation based on low interest rates because of savings glut and lack of investment opportunities. Taylor (2009) shows that there was no savings glut. The savings rate of the US in the past decade is significantly lower than in the 1980s.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the low ratio of investment to GDP currently and reduced investment and hiring by US business firms.
- Taylor (2014Jan01, 2014Jan3) argues that the financial crisis and global recession were caused by weak implementation of existing regulation and departure from rules-based policies.
- Taylor (2014Jan01, 2014Jan3) argues that the recovery from the global recession was constrained by a change in the regime of regulation and fiscal/monetary policies.
In revealing research, Edward P. Lazear and James R. Spletzer (2012JHJul22) use the wealth of data in the valuable database and resources of the Bureau of Labor Statistics (http://www.bls.gov/data/) in providing clear thought on the nature of the current labor market of the United States. The critical issue of analysis and policy currently is whether unemployment is structural or cyclical. Structural unemployment could occur because of (1) industrial and demographic shifts and (2) mismatches of skills and job vacancies in industries and locations. Consider the aggregate unemployment rate, Y, expressed in terms of share si of a demographic group in an industry i and unemployment rate yi of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):
Y = ∑isiyi (1)
This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):
∆Y = ∑i∆siy*i + ∑i∆yis*i (2)
The first term in (2) captures changes in the demographic and industrial composition of the economy ∆si multiplied by the average rate of unemployment y*i , or structural factors. The second term in (2) captures changes in the unemployment rate specific to a group, or ∆yi, multiplied by the average share of the group s*i, or cyclical factors. There are also mismatches in skills and locations relative to available job vacancies. A simple observation by Lazear and Spletzer (2012JHJul22) casts intuitive doubt on structural factors: the rate of unemployment jumped from 4.4 percent in the spring of 2007 to 10 percent in October 2009. By nature, structural factors should be permanent or occur over relative long periods. The revealing result of the exhaustive research of Lazear and Spletzer (2012JHJul22) is:
“The analysis in this paper and in others that we review do not provide any compelling evidence that there have been changes in the structure of the labor market that are capable of explaining the pattern of persistently high unemployment rates. The evidence points to primarily cyclic factors.”
Table I-4b and Chart I-12-b provide the US labor force participation rate or percentage of the labor force in population. It is not likely that simple demographic trends caused the sharp decline during the global recession and failure to recover earlier levels. The civilian labor force participation rate dropped from the peak of 66.9 percent in Jul 2006 to 62.6 percent in Dec 2013, 62.5 percent in Dec 2014 and 63.1 percent in Jun 2015. The civilian labor force participation rate was 63.7 percent on an annual basis in 1979 and 63.4 percent in Dec 1980 and Dec 1981, reaching even 62.9 percent in both Apr and May 1979. The civilian labor force participation rate jumped with the recovery to 64.8 percent on an annual basis in 1985 and 65.9 percent in Jul 1985. Structural factors cannot explain these sudden changes vividly shown visually in the final segment of Chart I-12b. Seniors would like to delay their retiring especially because of the adversities of financial repression on their savings. Labor force statistics are capturing the disillusion of potential workers with their chances in finding a job in what Lazear and Spletzer (2012JHJul22) characterize as accentuated cyclical factors. The argument that anemic population growth causes “secular stagnation” in the US (Hansen 1938, 1939, 1941) is as misplaced currently as in the late 1930s (for early dissent see Simons 1942). There is currently population growth in the ages of 16 to 24 years but not enough job creation and discouragement of job searches for all ages (http://cmpassocregulationblog.blogspot.com/2015/07/oscillating-valuations-of-risk.html). “Secular stagnation” would be a process over many years and not from one year to another. This is merely another case of theory without reality with dubious policy proposals.
Table I-4b, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1979-2015
| Year | Jan | Feb | Mar | Apr | May | Jun | Sep | Oct | Nov | Dec | Annual |
| 1979 | 62.9 | 63.0 | 63.2 | 62.9 | 62.9 | 64.5 | 63.8 | 64.0 | 63.8 | 63.8 | 63.7 |
| 1980 | 63.3 | 63.2 | 63.2 | 63.2 | 63.5 | 64.6 | 63.6 | 63.9 | 63.7 | 63.4 | 63.8 |
| 1981 | 63.2 | 63.2 | 63.5 | 63.6 | 63.9 | 64.6 | 63.5 | 64.0 | 63.8 | 63.4 | 63.9 |
| 1982 | 63.0 | 63.2 | 63.4 | 63.3 | 63.9 | 64.8 | 64.0 | 64.1 | 64.1 | 63.8 | 64.0 |
| 1983 | 63.3 | 63.2 | 63.3 | 63.2 | 63.4 | 65.1 | 64.3 | 64.1 | 64.1 | 63.8 | 64.0 |
| 1984 | 63.3 | 63.4 | 63.6 | 63.7 | 64.3 | 65.5 | 64.4 | 64.6 | 64.4 | 64.3 | 64.4 |
| 1985 | 64.0 | 64.0 | 64.4 | 64.3 | 64.6 | 65.5 | 64.9 | 65.1 | 64.9 | 64.6 | 64.8 |
| 1986 | 64.2 | 64.4 | 64.6 | 64.6 | 65.0 | 66.3 | 65.3 | 65.5 | 65.4 | 65.0 | 65.3 |
| 1987 | 64.7 | 64.8 | 65.0 | 64.9 | 65.6 | 66.3 | 65.5 | 65.9 | 65.7 | 65.5 | 65.6 |
| 1988 | 65.1 | 65.2 | 65.2 | 65.3 | 65.5 | 66.7 | 65.9 | 66.1 | 66.2 | 65.9 | 65.9 |
| 1989 | 65.8 | 65.6 | 65.7 | 65.9 | 66.2 | 67.4 | 66.3 | 66.6 | 66.7 | 66.3 | 66.5 |
| 1990 | 66.0 | 66.0 | 66.2 | 66.1 | 66.5 | 67.4 | 66.4 | 66.5 | 66.3 | 66.1 | 66.5 |
| 1991 | 65.5 | 65.7 | 65.9 | 66.0 | 66.0 | 67.2 | 66.1 | 66.1 | 66.0 | 65.8 | 66.2 |
| 1992 | 65.7 | 65.8 | 66.0 | 66.0 | 66.4 | 67.6 | 66.3 | 66.2 | 66.2 | 66.1 | 66.4 |
| 1993 | 65.6 | 65.8 | 65.8 | 65.6 | 66.3 | 67.3 | 66.1 | 66.4 | 66.3 | 66.2 | 66.3 |
| 1994 | 66.0 | 66.2 | 66.1 | 66.0 | 66.5 | 67.2 | 66.5 | 66.8 | 66.7 | 66.5 | 66.6 |
| 1995 | 66.1 | 66.2 | 66.4 | 66.4 | 66.4 | 67.2 | 66.5 | 66.7 | 66.5 | 66.2 | 66.6 |
| 1996 | 65.8 | 66.1 | 66.4 | 66.2 | 66.7 | 67.4 | 66.8 | 67.1 | 67.0 | 66.7 | 66.8 |
| 1997 | 66.4 | 66.5 | 66.9 | 66.7 | 67.0 | 67.8 | 67.0 | 67.1 | 67.1 | 67.0 | 67.1 |
| 1998 | 66.6 | 66.7 | 67.0 | 66.6 | 67.0 | 67.7 | 67.0 | 67.1 | 67.1 | 67.0 | 67.1 |
| 1999 | 66.7 | 66.8 | 66.9 | 66.7 | 67.0 | 67.7 | 66.8 | 67.0 | 67.0 | 67.0 | 67.1 |
| 2000 | 66.8 | 67.0 | 67.1 | 67.0 | 67.0 | 67.7 | 66.7 | 66.9 | 66.9 | 67.0 | 67.1 |
| 2001 | 66.8 | 66.8 | 67.0 | 66.7 | 66.6 | 67.2 | 66.6 | 66.7 | 66.6 | 66.6 | 66.8 |
| 2002 | 66.2 | 66.6 | 66.6 | 66.4 | 66.5 | 67.1 | 66.6 | 66.6 | 66.3 | 66.2 | 66.6 |
| 2003 | 66.1 | 66.2 | 66.2 | 66.2 | 66.2 | 67.0 | 65.9 | 66.1 | 66.1 | 65.8 | 66.2 |
| 2004 | 65.7 | 65.7 | 65.8 | 65.7 | 65.8 | 66.5 | 65.7 | 66.0 | 66.1 | 65.8 | 66.0 |
| 2005 | 65.4 | 65.6 | 65.6 | 65.8 | 66.0 | 66.5 | 66.1 | 66.2 | 66.1 | 65.9 | 66.0 |
| 2006 | 65.5 | 65.7 | 65.8 | 65.8 | 66.0 | 66.7 | 66.1 | 66.4 | 66.4 | 66.3 | 66.2 |
| 2007 | 65.9 | 65.8 | 65.9 | 65.7 | 65.8 | 66.6 | 66.0 | 66.0 | 66.1 | 65.9 | 66.0 |
| 2008 | 65.7 | 65.5 | 65.7 | 65.7 | 66.0 | 66.6 | 65.9 | 66.1 | 65.8 | 65.7 | 66.0 |
| 2009 | 65.4 | 65.5 | 65.4 | 65.4 | 65.5 | 66.2 | 65.0 | 64.9 | 64.9 | 64.4 | 65.4 |
| 2010 | 64.6 | 64.6 | 64.8 | 64.9 | 64.8 | 65.1 | 64.6 | 64.4 | 64.4 | 64.1 | 64.7 |
| 2011 | 63.9 | 63.9 | 64.0 | 63.9 | 64.1 | 64.5 | 64.2 | 64.1 | 63.9 | 63.8 | 64.1 |
| 2012 | 63.4 | 63.6 | 63.6 | 63.4 | 63.8 | 64.3 | 63.6 | 63.8 | 63.5 | 63.4 | 63.7 |
| 2013 | 63.3 | 63.2 | 63.1 | 63.1 | 63.5 | 64.0 | 63.2 | 62.9 | 62.9 | 62.6 | 63.2 |
| 2014 | 62.5 | 62.7 | 62.9 | 62.6 | 62.9 | 63.4 | 62.8 | 63.0 | 62.8 | 62.5 | 62.9 |
| 2015 | 62.5 | 62.5 | 62.5 | 62.6 | 63.0 | 63.1 |
Source: US Bureau of Labor Statistics
Chart I-12b, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1979-2015
Source: Bureau of Labor Statistics
Broader perspective is provided by Chart I-12c of the US Bureau of Labor Statistics. The United States civilian noninstitutional population has increased along a consistent trend since 1948 that continued through earlier recessions and the global recession from IVQ2007 to IIQ2009 and the cyclical expansion after IIIQ2009.
Chart I-12c, US, Civilian Noninstitutional Population, Thousands, NSA, 1948-2015
Sources: US Bureau of Labor Statistics
The labor force of the United States in Chart I-12d has increased along a trend similar to that of the civilian noninstitutional population in Chart I-12c. There is an evident stagnation of the civilian labor force in the final segment of Chart I-12d during the current economic cycle. This stagnation is explained by cyclical factors similar to those analyzed by Lazear and Spletzer (2012JHJul22) that motivated an increasing population to drop out of the labor force instead of structural factors. Large segments of the potential labor force are not observed, constituting unobserved unemployment and of more permanent nature because those afflicted have been seriously discouraged from working by the lack of opportunities. The civilian labor force consists of people who are available and willing to work and who have searched for employment recently. The labor force of the US grew 9.4 percent from 142.828 million in Jan 2001 to 156.255 million in Jul 2009. The civilian labor force is 1.3 percent higher at 158.283 million in Jun 2015 than in Jul 2009, all numbers not seasonally adjusted. Chart I-3 shows the flattening of the curve of expansion of the labor force and its decline in 2010 and 2011. The ratio of the labor force of 154.871 million in Jul 2007 to the noninstitutional population of 231.958 million in Jul 2007 was 66.8 percent while the ratio of the labor force of 158.823 million in Jun 2015 to the noninstitutional population of 250.663 million in Jun 2015 was 63.1 percent. The labor force of the US in Jun 2015 corresponding to 66.8 percent of participation in the population would be 167.443 million (0.668 x 250.663). The difference between the measured labor force in Jun 2015 of 158.823 million and the labor force in Jun 2015 with participation rate of 66.8 percent (as in Jul 2007) of 167.443 million is 8.620 million. The level of the labor force in the US has stagnated and is 8.620 million lower than what it would have been had the same participation rate been maintained. Millions of people have abandoned their search for employment because they believe there are no jobs available for them. The key issue is whether the decline in participation of the population in the labor force is the result of people giving up on finding another job.
The number employed in Jun 2015 was 149.645 million (NSA) or 2.330 million more people with jobs relative to the peak of 147.315 million in Jul 2007 while the civilian noninstitutional population of ages 16 years and over increased from 231.958 million in Jul 2007 to 250.663 million in Jun 2015 or by 18.705 million. The number employed increased 1.6 percent from Jul 2007 to Jun 2015 while the noninstitutional civilian population of ages of 16 years and over, or those available for work, increased 8.1 percent. The ratio of employment to population in Jul 2007 was 63.5 percent (147.315 million employment as percent of population of 231.958 million). The same ratio in Jun 2015 would result in 159.171 million jobs (0.635 multiplied by noninstitutional civilian population of 250.663 million). There are effectively 9.526 million fewer jobs in Jun 2015 than in Jul 2007, or 159.171 million minus 149.645 million. There is actually not sufficient job creation in merely absorbing new entrants in the labor force.
Chart I-12d, US, Labor Force, Thousands, NSA, 1948-2015
Sources: US Bureau of Labor Statistics
The rate of labor force participation of the US is in Chart I-12E from 1948 to 2015. There is sudden decline during the global recession after 2007 without recovery explained by cyclic factors (Lazear and Spletzer 2012JHJul22) as many potential workers stopped their job searches disillusioned that there could be an opportunity for them in sharply contracted labor markets.
Chart I-12E, US, Labor Force Participation Rate, Percent of Labor Force in Population, NSA, 1948-2015
Sources: US Bureau of Labor Statistics
Table EMP provides the comparison between the labor market in the current whole cycle from 2007 to 2014 and the whole cycle from 1979 to 1988. In the entire cycle from 2007 to 2014, the number employed increased 0.258 million, full-time employed fell 2.373 million, part-time for economic reasons increased 2.812 million and population increased 16.080 million. The number employed increased 0.2 percent, full-time employed fell 2.0 percent, part-time for economic reasons increased 63.9 percent and population increased 6.9 percent. There is sharp contrast with the contractions of the 1980s and with most economic history of the United States. In the whole cycle from 1979 to 1988, the number employed increased 16.144 million, full-time employed increased 12.560 million, part-time for economic reasons 1.629 million and population 19.750 million. In the entire cycle from 1979 to 1988, the number employed increased 16.3 percent, full-time employed 15.2 percent, part-time for economic reasons 45.5 percent and population 12.0 percent. The difference between the 1980s and the current cycle after 2007 is in the high rate of growth after the contraction that maintained trend growth around 3.0 percent for the entire cycle and per capital growth at 2.0 percent. The evident fact is that current weakness in labor markets originates in cyclical slow growth and not in imaginary secular stagnation.
Table EMP, US, Annual Level of Employed, Full-Time Employed, Employed Part-Time for Economic Reasons and Noninstitutional Civilian Population, Millions
| Employed | Full-Time Employed | Part Time Economic Reasons | Noninstitutional Civilian Population | |
| 2000s | ||||
| 2000 | 136.891 | 113.846 | 3.227 | 212.577 |
| 2001 | 136.933 | 113.573 | 3.715 | 215.092 |
| 2002 | 136.485 | 112.700 | 4.213 | 217.570 |
| 2003 | 137.736 | 113.324 | 4.701 | 221.168 |
| 2004 | 139.252 | 114.518 | 4.567 | 223.357 |
| 2005 | 141.730 | 117.016 | 4.350 | 226.082 |
| 2006 | 144.427 | 119.688 | 4.162 | 228.815 |
| 2007 | 146.047 | 121.091 | 4.401 | 231.867 |
| 2008 | 145.362 | 120.030 | 5.875 | 233.788 |
| 2009 | 139.877 | 112.634 | 8.913 | 235.801 |
| 2010 | 139.064 | 111.714 | 8.874 | 237.830 |
| 2011 | 139.869 | 112.556 | 8.560 | 239.618 |
| 2012 | 142.469 | 114.809 | 8.122 | 243.284 |
| 2013 | 143.929 | 116.314 | 7.935 | 245.679 |
| 2014 | 146.305 | 118.718 | 7.213 | 247.947 |
| ∆2007-2014 | 0.258 | -2,373 | 2.812 | 16.080 |
| ∆% 2007-2013 | 0.2 | -2.0 | 63.9 | 6.9 |
| 1980s | ||||
| 1979 | 98.824 | 82.654 | 3.577 | 164.863 |
| 1980 | 99.303 | 82.562 | 4.321 | 167.745 |
| 1981 | 100.397 | 83.243 | 4.768 | 170.130 |
| 1982 | 99.526 | 81.421 | 6.170 | 172.271 |
| 1983 | 100.834 | 82.322 | 6.266 | 174.215 |
| 1984 | 105.005 | 86.544 | 5.744 | 176.383 |
| 1985 | 107.150 | 88.534 | 5.590 | 178.206 |
| 1986 | 109.597 | 90.529 | 5.588 | 180.587 |
| 1987 | 112.440 | 92.957 | 5.401 | 182.753 |
| 1988 | 114.968 | 95.214 | 5.206 | 184.613 |
| 1989 | 117.342 | 97.369 | 4.894 | 186.393 |
| ∆1979-1988 | 16.144 | 12.560 | 1.629 | 19.750 |
| ∆% 1979-88 | 16.3 | 15.2 | 45.5 | 12.0 |
Source: Bureau of Labor Statistics
The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. There are accentuated cyclic factors for both the entire population and the young population of ages 16 to 24 years. Table Summary Total provides the total noninstitutional population (ICP) of the US, full-time employment level (FTE), employment level (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP), employment/population ratio (EPOP) and unemployment level (UNE). Secular stagnation would spread over long periods instead of immediately. All indicators of the labor market weakened sharply during the contraction and did not recover. Population continued to grow but all other variables collapsed and did not recover. The theory of secular stagnation departs from an aggregate production function in which output grows with the use of labor, capital and technology (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), 11-16). Hansen (1938, 1939) finds secular stagnation in lower growth of an aging population. In the current US economy, Table Summary shows that population is dynamic while the labor market is fractured. There is key explanation in the behavior of the civilian labor force participation rate (CLFP) and the employment population ratio (EPOP) that collapsed during the global recession with inadequate recovery. Abandoning job searches are difficult to capture in labor statistics but likely explain the decline in the participation of the population in the labor force. Allowing for abandoning job searches, the total number of people unemployed or underemployed is 25.0 million or 15.1 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html).
Table Summary Total, US, Total Noninstitutional Civilian Population, Full-time Employment, Employment, Civilian Labor Force, Civilian Labor Force Participation Rate, Employment Population Ratio, Unemployment, NSA, Millions and Percent
| ICP | FTE | EMP | CLF | CLFP | EPOP | UNE | |
| 2006 | 228.8 | 119.7 | 144.4 | 151.4 | 66.2 | 63.1 | 7.0 |
| 2009 | 235.8 | 112.6 | 139.9 | 154.1 | 65.4 | 59.3 | 14.3 |
| 2012 | 243.3 | 114.8 | 142.5 | 155.0 | 63.7 | 58.6 | 12.5 |
| 2013 | 245.7 | 116.3 | 143.9 | 155.4 | 63.2 | 58.6 | 11.5 |
| 2014 | 247.9 | 118.7 | 146.3 | 155.9 | 62.9 | 59.0 | 9.6 |
| 12/07 | 233.2 | 121.0 | 146.3 | 153.7 | 65.9 | 62.8 | 7.4 |
| 9/09 | 236.3 | 112.0 | 139.1 | 153.6 | 65.0 | 58.9 | 14.5 |
| 6/15 | 250.7 | 122.3 | 149.6 | 158.2 | 63.1 | 59.7 | 8.6 |
ICP: Total Noninstitutional Civilian Population; FT: Full-time Employment Level, EMP: Total Employment Level; CLF: Civilian Labor Force; CLFP: Civilian Labor Force Participation Rate; EPOP: Employment Population Ratio; UNE: Unemployment
Source: Bureau of Labor Statistics
The same situation is present in the labor market for young people in ages 16 to 24 years with data in Table Summary Youth. The youth noninstitutional civilian population (ICP) continued to increase during and after the global recession. There is the same disastrous labor market with decline for young people in employment (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP) and employment population ratio (EPOP). There are only increases for unemployment of young people (UNE) and youth unemployment rate (UNER). If aging were a factor of secular stagnation, growth of population of young people would attract a premium in remuneration in labor markets. The sad fact is that young people are also facing tough labor markets. The application of the theory of secular stagnation to the US economy and labor markets is void of reality in the form of key facts, which are best explained by accentuated cyclic factors analyzed by Lazear and Spletzer (2012JHJul22).
Table Summary Youth, US, Youth, Ages 16 to 24 Years, Noninstitutional Civilian Population, Full-time Employment, Employment, Civilian Labor Force, Civilian Labor Force Participation Rate, Employment Population Ratio, Unemployment, NSA, Millions and Percent
| ICP | EMP | CLF | CLFP | EPOP | UNE | UNER | |
| 2006 | 36.9 | 20.0 | 22.4 | 60.6 | 54.2 | 2.4 | 10.5 |
| 2009 | 37.6 | 17.6 | 21.4 | 56.9 | 46.9 | 3.8 | 17.6 |
| 2012 | 38.8 | 17.8 | 21.3 | 54.9 | 46.0 | 3.5 | 16.2 |
| 2013 | 38.8 | 18.1 | 21.4 | 55.0 | 46.5 | 3.3 | 15.5 |
| 2014 | 38.7 | 18.4 | 21.3 | 55.0 | 47.6 | 2.9 | 13.4 |
| 12/07 | 37.5 | 19.4 | 21.7 | 57.8 | 51.6 | 2.3 | 10.7 |
| 9/09 | 37.6 | 17.0 | 20.7 | 55.2 | 45.1 | 3.8 | 18.2 |
| 6/15 | 38.6 | 19.8 | 22.9 | 59.4 | 51.3 | 3.1 | 13.7 |
ICP: Youth Noninstitutional Civilian Population; EMP: Youth Employment Level; CLF: Youth Civilian Labor Force; CLFP: Youth Civilian Labor Force Participation Rate; EPOP: Youth Employment Population Ratio; UNE: Unemployment; UNER: Youth Unemployment Rate
Source: Bureau of Labor Statistics
The United States is experiencing high youth unemployment as in European economies. Table I-10 provides the employment level for ages 16 to 24 years of age estimated by the Bureau of Labor Statistics. On an annual basis, youth employment fell from 20.041 million in 2006 to 17.362 million in 2011 or 2.679 million fewer youth jobs and to 17.834 million in 2012 or 2.207 million fewer jobs. Youth employment fell from 20.041 million in 2006 to 18.057 million in 2013 or 1.984 million fewer jobs. Youth employment fell from 20.041 million in 2006 to 18.442 million in 2014 or 1.599 million. The level of youth jobs fell from 20.129 million in Dec 2006 to 18.347 million in Dec 2014 for 1.782 million fewer youth jobs. The level of youth jobs fell from 21.268 million in Jun 2006 to 19.789 million in Jun 2015 or 1.479 million fewer jobs. During the seasonal peak months of youth employment in the summer from Jun to Aug, youth employment has fallen by more than two million jobs relative to 21.167 million in Aug 2006 to 18.972 million in Aug 2014 for 2.195 million fewer jobs. Youth employment fell from 21.914 million in Jul 2006 to 20.085 million in Jul 2014 for 1.829 million fewer youth jobs. The number of youth jobs fell from 21.268 million in Jun 2006 million to 19.421 million in Jun 2014 or 1.847 million fewer youth jobs. The number of jobs ages 16 to 24 years fell from 21.167 million in Aug 2006 to 18.636 million in Aug 2013 or by 2.531 million. The number of youth jobs fell from 19.604 million in Sep 2006 to 18.043 million in Sep 2013 or 1.561 million fewer youth jobs. The number of youth jobs fell from 20.129 million in Dec 2006 to 18.106 million in Dec 2013 or 2.023 million fewer jobs. The civilian noninstitutional population ages 16 to 24 years increased from 37.443 million in Jul 2007 to 38.861 million in Jul 2013 or by 1.418 million while the number of jobs for ages 16 to 24 years fell by 2.230 million from 21.914 million in Jul 2006 to 19.684 million in Jul 2013. The civilian noninstitutional population for ages 16 to 24 years increased from 37.455 million in Aug 2007 to 38.841 million in Aug 2013 or by 1.386 million while the number of youth jobs fell by 1.777 million. The civilian noninstitutional population increased from 37.467 million in Sep 2007 to 38.822 million in Sep 2013 or by 1.355 million while the number of youth jobs fell by 1.455 million. The civilian noninstitutional population increased from 37.480 million in Oct 2007 to 38.804 million in Oct 2013 or by 1.324 million while the number of youth jobs decreased 1.877 million from Oct 2006 to Oct 2013. The civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.798 million in Nov 2013 or by 1.722 million while the number of youth jobs fell 1.799 million. The civilian noninstitutional population increased from 37.518 million in Dec 2007 to 38.790 million in Dec 2013 or by 1.272 million while the number of youth jobs fell 2.023 million from Dec 2006 to Dec 2013. The youth civilian noninstitutional population increased 1.488 million from 37.282 million in in Jan 2007 to 38.770 million in Jan 2014 while the number of youth jobs fell 2.035 million. The youth civilian noninstitutional population increased 1.464 million from 37.302 in Feb 2007 to 38.766 million in Feb 2014 while the number of youth jobs decreased 2.058 million. The civilian noninstitutional population increased 1.437 million from 37.324 million in Mar 2007 to 38.761 million in Mar 2014 while jobs for ages 16 to 24 years decreased 1.599 million from 19.538 million in Mar 2007 to 17.939 million in Mar 2014. The civilian noninstitutional population ages 16 to 24 years increased 1.410 million from 37.349 million in Apr 2007 to 38.759 million in Apr 2014 while the number of youth jobs fell 1.347 million. The civilian noninstitutional population increased 1.370 million from 37.379 million in May 2007 to 38.749 million in May 2014 while the number of youth jobs decreased 1.128 million. The civilian noninstitutional population increased 1.330 million from 37.410 million in Jun 2007 to 38.740 million in Jun 2014 while the number of youth jobs fell 1.847 million from 21.268 million in Jun 2006 to 19.421 million in Jun 2014. The youth civilian noninstitutional population increased by 1.292 million from 37.443 million in Jul 2007 to 38.735 million in Jul 2014 while the number of youth jobs fell 1.632 million. The youth civilian noninstitutional population increased from 37.445 million in Aug 2007 to 38.706 million in Aug 2014 or 1.251 million while the number of youth jobs fell 1.441 million. The youth civilian noninstitutional population increased 1.652 million from 37.027 million in Sep 2006 to 38.679 million in Sep 2014 while the number of youth jobs fell 1.500 million. The youth civilian noninstitutional population increased from 37.047 million in Oct 2006 to 38.650 million in Oct 2014 or 1.603 million while the number of youth jobs fell 1.072 million. The youth civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.628 million in Nov 2014 or 1.552 million while the number of youth jobs fell 1.327 million. The civilian noninstitutional population increased from 37.100 million in Dec 2006 to 38.606 million in Dec 2014 or 1.506 million while the number of youth jobs fell 1.782 million. The civilian noninstitutional population increased 1.971 million from 36.761 million in Jan 2006 to 38.732 million in Jan 2015 while the number of youth jobs fell 1.091 million. The civilian noninstitutional population increased 1.914 million from 36.791 million in Feb 2006 to 38.705 million in Feb 2015 while the number of youth jobs fell 0.960 million. The civilian noninstitutional population increased 1.858 million from 36.821 million in Mar 2006 to 38.679 million in Mar 2015 while the number of youth jobs fell 1.215 million. The youth civilian noninstitutional population increased 1.800 million from 36.854 million in Apr 2006 to 38.654 million in Apr 2015 while the number of youth jobs fell 1.165 million. The youth civilian noninstitutional population increased 1,733 million from 36.897 million in May 2006 to 38.630 million in May 2015 while the number of youth jobs fell 1.060 million. The youth civilian noninstitutional population increased 1.666 million from 36.943 million in Jun 2006 to 38.609 million in Jun 2015 while the number of youth jobs fell 1.479 million. The hardship does not originate in low growth of population but in underperformance of the economy in the expansion from the business cycle. There are two hardships behind these data. First, young people cannot find employment after finishing high school and college, reducing prospects for achievement in older age. Second, students with more modest means cannot find employment to keep them in college.
Table I-10, US, Employment Level 16-24 Years, Thousands, NSA
| Year | Jan | Feb | Mar | Apr | May | Jun | Oct | Dec |
| 2001 | 19678 | 19745 | 19800 | 19778 | 19648 | 21212 | 19694 | 19547 |
| 2002 | 18653 | 19074 | 19091 | 19108 | 19484 | 20828 | 19542 | 19394 |
| 2003 | 18811 | 18880 | 18709 | 18873 | 19032 | 20432 | 19139 | 19136 |
| 2004 | 18852 | 18841 | 18752 | 19184 | 19237 | 20587 | 19609 | 19619 |
| 2005 | 18858 | 18670 | 18989 | 19071 | 19356 | 20949 | 19794 | 19733 |
| 2006 | 19003 | 19182 | 19291 | 19406 | 19769 | 21268 | 19853 | 20129 |
| 2007 | 19407 | 19415 | 19538 | 19368 | 19457 | 21098 | 19564 | 19361 |
| 2008 | 18724 | 18546 | 18745 | 19161 | 19254 | 20466 | 18757 | 18378 |
| 2009 | 17467 | 17606 | 17564 | 17739 | 17588 | 18726 | 16671 | 16615 |
| 2010 | 16166 | 16412 | 16587 | 16764 | 17039 | 17920 | 16867 | 16727 |
| 2011 | 16512 | 16638 | 16898 | 16970 | 17045 | 18180 | 17532 | 17234 |
| 2012 | 16944 | 17150 | 17301 | 17387 | 17681 | 18907 | 17842 | 17604 |
| 2013 | 17183 | 17257 | 17271 | 17593 | 17704 | 19125 | 17976 | 18106 |
| 2014 | 17372 | 17357 | 17939 | 18021 | 18329 | 19421 | 18781 | 18347 |
| 2015 | 17912 | 18222 | 18076 | 18241 | 18709 | 19789 |
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-21 provides US employment level ages 16 to 24 years from 2002 to 2015. Employment level is sharply lower in May 2015 relative to the peak in 2007. The following Chart I-21A relates youth employment and youth civilian noninstitutional population.
Chart I-21, US, Employment Level 16-24 Years, Thousands SA, 2001-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-21A provides the US civilian noninstitutional population ages 16 to 24 years not seasonally adjusted from 2001 to 2015. The civilian noninstitutional population ages 16 to 24 years increased from 37.443 million in Jul 2007 to 38.861 million in Jul 2013 or by 1.418 million while the number of jobs for ages 16 to 24 years fell by 2.230 million from 21.914 million in Jul 2006 to 19.684 million in Jul 2013. The civilian noninstitutional population for ages 16 to 24 years increased from 37.455 million in Aug 2007 to 38.841 million in Aug 2013 or by 1.386 million while the number of youth jobs fell by 1.777 million. The civilian noninstitutional population increased from 37.467 million in Sep 2007 to 38.822 million in Sep 2013 or by 1.355 million while the number of youth jobs fell by 1.455 million. The civilian noninstitutional population increased from 37.480 million in Oct 2007 to 38.804 million in Oct 2013 or by 1.324 million while the number of youth jobs decreased 1.877 million from Oct 2006 to Oct 2013. The civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.798 million in Nov 2013 or by 1.722 million while the number of youth jobs fell 1.799 million. The civilian noninstitutional population increased from 37.518 million in Dec 2007 to 38.790 million in Dec 2013 or by 1.272 million while the number of youth jobs fell 2.023 million from Dec 2006 to Dec 2013. The youth civilian noninstitutional population increased 1.488 million from 37.282 million in in Jan 2007 to 38.770 million in Jan 2014 while the number of youth jobs fell 2.035 million. The youth civilian noninstitutional population increased 1.464 million from 37.302 in Feb 2007 to 38.766 million in Feb 2014 while the number of youth jobs decreased 2.058 million. The civilian noninstitutional population increased 1.437 million from 37.324 million in Mar 2007 to 38.761 million in Mar 2014 while jobs for ages 16 to 24 years decreased 1.599 million from 19.538 million in Mar 2007 to 17.939 million in Mar 2014. The civilian noninstitutional population ages 16 to 24 years increased 1.410 million from 37.349 million in Apr 2007 to 38.759 million in Apr 2014 while the number of youth jobs fell 1.347 million. The civilian noninstitutional population increased 1.370 million from 37.379 million in May 2007 to 38.749 million in May 2014 while the number of youth jobs decreased 1.128 million. The civilian noninstitutional population increased 1.330 million from 37.410 million in Jun 2007 to 38.740 million in Jun 2014 while the number of youth jobs fell 1.847 million from 21.268 million in Jun 2006 to 19.421 million in Jun 2014. The youth civilian noninstitutional population increased by 1.292 million from 37.443 million in Jul 2007 to 38.735 million in Jul 2014 while the number of youth jobs fell 1.632 million. The youth civilian noninstitutional population increased from 37.445 million in Aug 2007 to 38.706 million in Aug 2014 or 1.251 million while the number of youth jobs fell 1.441 million. The youth civilian noninstitutional population increased 1.652 million from 37.027 million in Sep 2006 to 38.679 million in Sep 2014 while the number of youth jobs fell 1.500 million. The youth civilian noninstitutional population increased from 37.047 million in Oct 2006 to 38.650 million in Oct 2014 or 1.603 million while the number of youth jobs fell 1.072 million. The youth civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.628 million in Nov 2014 or 1.552 million while the number of youth jobs fell 1.327 million. The civilian noninstitutional population increased from 37.100 million in Dec 2006 to 38.606 million in Dec 2014 or 1.506 million while the number of youth jobs fell 1.782 million. The civilian noninstitutional population increased 1.971 million from 36.761 million in Jan 2006 to 38.732 million in Jan 2015 while the number of youth jobs fell 1.091 million. The civilian noninstitutional population increased 1.914 million from 36.791 million in Feb 2006 to 38.705 million in Feb 2015 while the number of youth jobs fell 0.960 million. The civilian noninstitutional population increased 1.858 million from 36.821 million in Mar 2006 to 38.679 million in Mar 2015 while the number of youth jobs fell 1.215 million. The youth civilian noninstitutional population increased 1.800 million from 36.854 million in Apr 2006 to 38.654 million in Apr 2015 while the number of youth jobs fell 1.165 million. The youth civilian noninstitutional population increased 1,733 million from 36.897 million in May 2006 to 38.630 million in May 2015 while the number of youth jobs fell 1.060 million. The youth civilian noninstitutional population increased 1.666 million from 36.943 million in Jun 2006 to 38.609 million in Jun 2015 while the number of youth jobs fell 1.479 million. The hardship does not originate in low growth of population but in underperformance of the economy in the expansion from the business cycle. There are two hardships behind these data. First, young people cannot find employment after finishing high school and college, reducing prospects for achievement in older age. Second, students with more modest means cannot find employment to keep them in college.
Chart I-21A, US, Civilian Noninstitutional Population Ages 16 to 24 Years, Thousands NSA, 2001-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-21B provides the civilian labor force of the US ages 16 to 24 years NSA from 2001 to 2015. The US civilian labor force ages 16 to 24 years fell from 24.339 million in Jul 2007 to 23.506 million in Jul 2013, by 0.833 million or decline of 3.4 percent, while the civilian noninstitutional population NSA increased from 37.443 million in Jul 2007 to 38.861 million in Jul 2013, by 1.418 million or 3.8 percent. The US civilian labor force ages 16 to 24 fell from 22.801 million in Aug 2007 to 22.089 million in Aug 2013, by 0.712 million or 3.1 percent, while the noninstitutional population for ages 16 to 24 years increased from 37.455 million in Aug 2007 to 38.841 million in Aug 2013, by 1.386 million or 3.7 percent. The US civilian labor force ages 16 to 24 years fell from 21.917 million in Sep 2007 to 21.183 million in Sep 2013, by 0.734 million or 3.3 percent while the civilian noninstitutional youth population increased from 37.467 million in Sep 2007 to 38.822 million in Sep 2013 by 1.355 million or 3.6 percent. The US civilian labor force fell from 21.821 million in Oct 2007 to 21.003 million in Oct 2013, by 0.818 million or 3.7 percent while the noninstitutional youth population increased from 37.480 million in Oct 2007 to 38.804 million in Oct 2013, by 1.324 million or 3.5 percent. The US youth civilian labor force fell from 21.909 million in Nov 2007 to 20.825 million in Nov 2013, by 1.084 million or 4.9 percent while the civilian noninstitutional youth population increased from 37.076 million in Nov 2006 to 38.798 million in Nov 2013 or by 1.722 million. The US youth civilian labor force fell from 21.684 million in Dec 2007 to 20.642 million in Dec 2013, by 1.042 million or 4.8 percent, while the civilian noninstitutional population increased from 37.518 million in Dec 2007 to 38.790 million in Dec 2013, by 1.272 million or 3.4 percent. The youth civilian labor force of the US fell from 21.770 million in Jan 2007 to 20.423 million in Jan 2014, by 1.347 million or 6.2 percent while the youth civilian noninstitutional population increased 37.282 million in Jan 2007 to 38.770 million in Jan 2014, by 1.488 million or 4.0 percent. The youth civilian labor force of the US fell 1.255 million from 21.645 million in Feb 2007 to 20.390 million in Feb 2014 while the youth civilian noninstitutional population increased 1.464 million from 37.302 million in Feb 2007 to 38.766 million in Feb 2014. The youth civilian labor force of the US fell 0.693 million from 21.634 million in Mar 2007 to 20.941 million in Mar 2014 or 3.2 person while the youth noninstitutional civilian population 1.437 million from 37.324 million in Mar 2007 to 38.761 million in Mar 2014 or 3.9 percent. The US youth civilian labor force fell 981 thousand from 21.442 million in Apr 2007 to 20.461 million in Apr 2014 while the youth civilian noninstitutional population increased from 37.349 million in Apr 2007 to 38.759 million in Apr 2014 by 1.410 thousand or 3.8 percent. The youth civilian labor force decreased from 21.659 million in May 2007 to 21.160 million in May 2014 by 499 thousand or 2.3 percent while the youth civilian noninstitutional population increased 1.370 million from 37.739 million in May 2007 to 38.749 million in May 2007 or by 2.7 percent. The youth civilian labor force decreased from 24.128 million in Jun 2006 to 22.851 million in Jun 2014 by 1.277 million or 5.3 percent while the civilian noninstitutional population increased from 36.943 million in Jun 2006 to 38.740 million in Jun 2014 by 1.797 million or 4.9 percent. The youth civilian labor force fell from 24.664 million in Jul 2006 to 23.437 million in Jul 2014 while the civilian noninstitutional population increased from 36.989 million in Jul 2006 to 38.735 million in Jul 2014. The youth civilian labor force fell 1.818 million from 23.634 million in Aug 2006 to 21.816 million in Aug 2014 while the civilian noninstitutional population increased from 37.008 million in Aug 2006 to 38.706 million in Aug 2914 or 1.698 million. The youth civilian labor force fell 0.942 million from 21.901 million in Sep 2006 to 20.959 million in Sep 2014 while the noninstitutional population increased 1.652 million from 37.027 million in Sep 2006 to 38.679 million in Sep 2014. The youth civilian labor force decreased 0.702 million from 22.105 million in Oct 2006 to 21.403 million in Oct 2014 while the youth civilian noninstitutional population increased from 37.047 million in Oct 2006 to 38.650 million in Oct 2014 or 1.603 million. The youth civilian labor force decreased 1.111 million from 22.145 million in Nov 2006 to 21.034 million in Nov 2014 while the youth civilian noninstitutional population increased from 37.076 million in Nov 2006 to 38.628 million in Nov 2014 or 1.552 million. The youth civilian labor force decreased 1.472 million from 22.136 million in Dec 2006 to 20.664 million in Dec 2014 while the youth civilian noninstitutional population increased from 37.100 million in Dec 2006 to 38.606 million in Dec 2014 or 1.506 million. The youth civilian labor force decreased 0.831 million from 21.368 million in Jan 2006 to 20.555 million in Jan 2015 while the youth noninstitutional population increased from 36.761 million in Jan 2006 to 38.732 million in Jan 2015 or 1.971 million. The youth civilian labor force decreased 0.864 million from 21.615 million in Feb 2006 to 20.751 million in Feb 2015 while the youth noninstitutional population increased 1.914 million from 36.791 million in Feb 2006 to 38.705 million in Feb 2015. The youth civilian labor force decreased 0.907 million from 21.507 million in Mar 2006 to 20.600 million in Mar 2015 while the civilian noninstitutional population increased 1.858 million from 36.821 million in Mar 2006 to 38.679 million in Mar 2015. The youth civilian labor force decreased 1.082 million from 21.498 million in Apr 2006 to 20.416 million in Apr 2015 while the youth civilian noninstitutional population increased 1.800 million from 36.854 million in Apr 2006 to 38.654 million in Apr 2015. The youth civilian labor force decreased 0.681 million from 22.023 million in May 2006 to 21.342 million in May 2015 while the youth civilian noninstitutional population increased 1,733 million from 36.897 million in May 2006 to 38.630 million in May 2015. The youth civilian labor force decreased 1.202 million from 24.128 million in Jun 2006 to 22.926 million in Jun 2015 while the youth civilian noninstitutional population increased 1.666 million from 36.943 million in Jun 2006 to 38.609 million in Jun 2015. Youth in the US abandoned their participation in the labor force because of the frustration that there are no jobs available for them.
Chart I-21B, US, Civilian Labor Force Ages 16 to 24 Years, Thousands NSA, 2001-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-21C provides the ratio of labor force to noninstitutional population or labor force participation of ages 16 to 24 years not seasonally adjusted. The US labor force participation rates for ages 16 to 24 years fell from 66.7 in Jul 2006 to 60.5 in Jul 2013 because of the frustration of young people who believe there may not be jobs available for them. The US labor force participation rate of young people fell from 63.9 in Aug 2006 to 56.9 in Aug 2013. The US labor force participation rate of young people fell from 59.1 percent in Sep 2006 to 54.6 percent in Sep 2013. The US labor force participation rate of young people fell from 59.7 percent in Oct 2006 to 54.1 in Oct 2013. The US labor force participation rate of young people fell from 59.7 percent in Nov 2006 to 53.7 percent in Nov 2013. The US labor force participation rate fell from 57.8 in Dec 2007 to 53.2 in Dec 2013. The youth labor force participation rate fell from 58.4 in Jan 2007 to 52.7 in Jan 2014. The US youth labor force participation rate fell from 58.0 percent in Feb 2007 to 52.6 percent in Feb 2013. The labor force participation rate of ages 16 to 24 years fell from 58.0 in Mar 2007 to 54.0 in Mar 2014. The labor force participation rate of ages 16 to 24 years fell from 57.4 in Apr 2007 to 52.8 in Apr 2014. The labor force participation rate of ages 16 to 24 years fell from 57.9 in May 2007 to 54.6 in May 2014. The labor force participation rate of ages 16 to 24 years fell from 65.3 in Jun 2006 to 59.0 in Jun 2014. The labor force participation rate ages 16 to 24 years fell from 66.7 in Jul 2006 to 60.5 in Jul 2014. The labor force participation rate ages 16 to 24 years fell from 63.9 in Aug 2006 to 56.4 in Aug 2014. The labor force participation rate ages 16 to 24 years fell from 59.1 in Sep 2006 to 54.2 in Sep 2014. The labor force participation rate ages 16 to 24 years fell from 59.7 in Oct 2006 to 55.4 in Oct 2014. The labor force participation rate ages 16 to 24 years fell from 59.7 in Nov 2006 to 54.5 in Nov 2014. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 53.5 in Dec 2014. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 53.1 in Jan 2015. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 53.6 in Feb 2015. The labor force participation rate ages 16 to 64 fell from 58.4 in Mar 2006 to 53.3 in Mar 2015. The labor force participation rate ages 16 to 64 fell from 58.7 in Apr 2005 to 52.8 in Apr 2006. The labor force participation rate ages 16 to 64 fell from 59.7 in May 2006 to 55.2 in May 2015. The labor force participation rates ages 16 to 64 fell from 65.3 in Jun 2006 to 59.4 in Jun 2015. Many young people abandoned searches for employment, dropping from the labor force.
Chart I-21C, US, Labor Force Participation Rate Ages 16 to 24 Years, NSA, 2001-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
An important measure of the job market is the number of people with jobs relative to population available for work (civilian noninstitutional population) or employment/population ratio. Chart I-21D provides the employment population ratio for ages 16 to 24 years. The US employment/population ratio NSA for ages 16 to 24 years collapsed from 59.2 in Jul 2006 to 50.7 in Jul 2013. The employment population ratio for ages 16 to 24 years dropped from 57.2 in Aug 2006 to 48.0 in Aug 2013. The employment population ratio for ages to 16 to 24 years declined from 52.9 in Sep 2006 to 46.5 in Sep 2013. The employment population ratio for ages 16 to 24 years fell from 53.6 in Oct 2006 to 46.3 in Oct 2013. The employment population ratio for ages 16 to 24 years fell from 53.7 in Nov 2007 to 46.7 in Nov 2013. The US employment population ratio for ages 16 to 24 years fell from 51.6 in Dec 2007 to 46.7 in Dec 2013. The US employment population ratio fell from 52.1 in Jan 2007 to 44.8 in Jan 2014. The US employment population ratio for ages 16 to 24 fell from 52.0 in Feb 2007 to 44.8 in Feb 2014. The US employment population ratio for ages 16 to 24 years fell from 52.3 in Mar 2007 to 46.3 in Mar 2014. The US employment population ratio for ages 16 to 24 years fell from 51.9 in Apr 2007 to 46.5 in Apr 2014. The US employment population ratio for ages 16 to 24 years fell from 52.1 in May 2007 to 47.3 in May 2014. The US employment population ratio for ages 16 to 24 years fell from 57.6 in Jun 2006 to 50.1 in Jun 2014. The US employment population ratio for ages 16 to 24 years fell from 59.2 in Jul 2006 to 50.1 in Jul 2014. The employment population ratio for ages 16 to 24 years fell from 57.2 in Aug 2006 to 49.0 in Aug 2014. The employment population ratio for ages 16 to 24 fell from 52.9 in Sep 2006 to 46.8 in Sep 2014. The employment population ratio for ages 16 to 24 fell from 53.6 in Oct 2006 to 48.6 in Oct 2014. The employment population ratio for ages 16 to 24 fell from 53.7 in Nov 2006 to 48.1 in Nov 2014. The employment population ration for ages 16 to 24 fell from 54.3 in Dec 2006 to 47.5 in Dec 2014. The employment population ration for ages 16 to 24 years fell from 51.7 in Jan 2006 to 46.2 in Jan 2015. The employment population ratio for ages 16 to 24 fell from 52.1 in Feb 2006 to 47.1 in Feb 2015. The employment population ratio for ages 16 to 24 years fell from 52.4 in Mar 2006 to 46.7 in Mar 2015. The employment population ratio for ages 16 to 24 years fell from 52.7 in Apr 2006 to 47.2 in Apr 2015. The employment population ratio for ages 16 to 24 fell from 53.6 in May 206 to 48.4 in May 2015. The employment population ratio for ages 16 to 24 fell from 57.6 in Jun 2006 to 51.3 in Jun 2015. Chart I-21D shows vertical drop during the global recession without recovery.
Chart I-21D, US, Employment Population Ratio Ages 16 to 24 Years, Thousands NSA, 2001-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Table I-11 provides US unemployment level ages 16 to 24 years. The number unemployed ages 16 to 24 years increased from 2342 thousand in 2007 to 3634 thousand in 2011 or by 1.292 million and 3451 thousand in 2012 or by 1.109 million. The unemployment level ages 16 to 24 years increased from 2342 in 2007 to 3324 thousand in 2013 or by 0.982 million. The unemployment level ages 16 to 24 years increased from 2342 thousand in 2007 to 2853 thousand in 2014 or by 0.511 million.. The unemployment level ages 16 to 24 years increased from 2.203 million in May 2007 to 2.633 million in May 2015 or increase by 0.430 million. The unemployment level ages 16 to 24 years increased from 2.860 million in Jun 2006 in to 3.138 million in Jun 2015 or increase by 0.278 million. This situation may persist for many years.
Table I-11, US, Unemployment Level 16-24 Years, NSA, Thousands
| Year | Jan | Feb | Mar | Apr | May | Jun | Dec | Annual |
| 2001 | 2250 | 2258 | 2253 | 2095 | 2171 | 2775 | 2412 | 2371 |
| 2002 | 2754 | 2731 | 2822 | 2515 | 2568 | 3167 | 2374 | 2683 |
| 2003 | 2748 | 2740 | 2601 | 2572 | 2838 | 3542 | 2248 | 2746 |
| 2004 | 2767 | 2631 | 2588 | 2387 | 2684 | 3191 | 2294 | 2638 |
| 2005 | 2661 | 2787 | 2520 | 2398 | 2619 | 3010 | 2055 | 2521 |
| 2006 | 2366 | 2433 | 2216 | 2092 | 2254 | 2860 | 2007 | 2353 |
| 2007 | 2363 | 2230 | 2096 | 2074 | 2203 | 2883 | 2323 | 2342 |
| 2008 | 2633 | 2480 | 2347 | 2196 | 2952 | 3450 | 2928 | 2830 |
| 2009 | 3278 | 3457 | 3371 | 3321 | 3851 | 4653 | 3532 | 3760 |
| 2010 | 3983 | 3888 | 3748 | 3803 | 3854 | 4481 | 3352 | 3857 |
| 2011 | 3851 | 3696 | 3520 | 3365 | 3628 | 4248 | 3161 | 3634 |
| 2012 | 3416 | 3507 | 3294 | 3175 | 3438 | 4180 | 3153 | 3451 |
| 2013 | 3674 | 3449 | 3261 | 3129 | 3478 | 4198 | 2536 | 3324 |
| 2014 | 3051 | 3033 | 3002 | 2440 | 2831 | 3429 | 2317 | 2853 |
| 2015 | 2644 | 2529 | 2524 | 2175 | 2633 | 3138 |
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-22 provides the unemployment level for ages 16 to 24 from 2001 to 2015. The level rose sharply from 2007 to 2010 with tepid improvement into 2012 and deterioration into 2013-2014 with recent marginal improvement iin 2015 alternating with deterioration.
Chart I-22, US, Unemployment Level 16-24 Years, Thousands SA, 2001-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Table I-12 provides the rate of unemployment of young peoples in ages 16 to 24 years. The annual rate jumped from 10.5 percent in 2007 to 18.4 percent in 2010, 17.3 percent in 2011 and 16.2 percent in 2012. The rate of youth unemployment fell marginally to 15.5 percent in 2013, declining to 13.4 percent in Dec 2014. During the seasonal peak in Jul, the rate of youth unemployed was 18.1 percent in Jul 2011, 17.1 percent in Jul 2012 and 16.3 percent in Jul 2013 compared with 10.8 percent in Jul 2007. The rate of youth unemployment rose from 11.2 percent in Jul 2006 to 16.3 percent in Jul 2013 and likely higher if adding those who ceased searching for a job in frustration none may be available. The rate of youth unemployment rose from 10.8 in Jul 2007 to 14.3 in Jul 2014. The rate of youth unemployment increased from 9.1 percent in Dec 2006 to 12.3 percent in Dec 2013. The rate of youth unemployment increased from 10.9 percent in Jan 2007 to 14.9 percent in Jan and Feb 2014. The rate of youth unemployment increased from 9.7 percent in Mar 2007 to 14.3 percent in Mar 2014. The rate of youth unemployment increased from 9.7 percent in Apr 2007 to 11.9 percent in Apr 2014. The rate of youth unemployment increased from 10.2 percent in May 2007 to 13.4 percent in May 2014. The rate of youth unemployment increased from 12.0 percent in Jun 2007 to 15.0 percent in Jun 2014. The rate of youth unemployment increased from 10.8 in Jul 2007 to 14.3 in Jul 2014. The rate of youth unemployment increased from 10.5 in Aug 2007 to 13.0 in Aug 2014. The rate of youth unemployment increased from 11.0 in Sep 2007 to 13.6 in Sep 2014. The rate of youth unemployment increased from 10.3 in Oct 2007 to 12.2 in Oct 2014. The rate of youth unemployment increased from 10.3 in Nov 2007 to 11.7 in Nov 2014. The rate of youth unemployment increased from 10.7 in Dec 2007 to 11.2 in Dec 2014. The rate of youth unemployment increased from 10.9 in Jan 2007 to 12.9 in Jan 2015. The rate of youth unemployment increased from 10.3 percent in Feb 2007 to 12.2 percent in Feb 2015. The rate of youth unemployment increased from 9.7 in Mar 2007 to 12.3 in Mar 2015. The rate of youth unemployment increased from 9.7 in Apr 2007 to 10.7 in Apr 2015. The rate of youth unemployment increased from 10.2 in May 2007 to 12.3 in May 2015. The rate of youth unemployment increased from 11.9 in Jun 2007 to 13.7 in Jun 2015. The actual rate is higher because of the difficulty in counting those dropping from the labor force because they believe there are no jobs available for them.
Table I-12, US, Unemployment Rate 16-24 Years, Thousands, NSA
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Annual |
| 2001 | 10.3 | 10.3 | 10.2 | 9.6 | 10.0 | 11.6 | 10.5 | 10.7 | 10.5 | 11.0 | 11.2 | 11.0 | 10.6 |
| 2002 | 12.9 | 12.5 | 12.9 | 11.6 | 11.6 | 13.2 | 12.4 | 11.5 | 11.4 | 11.2 | 11.7 | 10.9 | 12.0 |
| 2003 | 12.7 | 12.7 | 12.2 | 12.0 | 13.0 | 14.8 | 13.3 | 11.9 | 12.5 | 11.6 | 11.6 | 10.5 | 12.4 |
| 2004 | 12.8 | 12.3 | 12.1 | 11.1 | 12.2 | 13.4 | 12.3 | 11.1 | 11.5 | 11.6 | 11.1 | 10.5 | 11.8 |
| 2005 | 12.4 | 13.0 | 11.7 | 11.2 | 11.9 | 12.6 | 11.0 | 10.8 | 10.7 | 10.3 | 10.7 | 9.4 | 11.3 |
| 2006 | 11.1 | 11.3 | 10.3 | 9.7 | 10.2 | 11.9 | 11.2 | 10.4 | 10.5 | 10.2 | 10.1 | 9.1 | 10.5 |
| 2007 | 10.9 | 10.3 | 9.7 | 9.7 | 10.2 | 12.0 | 10.8 | 10.5 | 11.0 | 10.3 | 10.3 | 10.7 | 10.5 |
| 2008 | 12.3 | 11.8 | 11.1 | 10.3 | 13.3 | 14.4 | 14.0 | 13.0 | 13.4 | 13.2 | 13.3 | 13.7 | 12.8 |
| 2009 | 15.8 | 16.4 | 16.1 | 15.8 | 18.0 | 19.9 | 18.5 | 18.0 | 18.2 | 18.5 | 18.1 | 17.5 | 17.6 |
| 2010 | 19.8 | 19.2 | 18.4 | 18.5 | 18.4 | 20.0 | 19.1 | 17.8 | 17.6 | 18.1 | 17.4 | 16.7 | 18.4 |
| 2011 | 18.9 | 18.2 | 17.2 | 16.5 | 17.5 | 18.9 | 18.1 | 17.5 | 17.0 | 16.2 | 15.9 | 15.5 | 17.3 |
| 2012 | 16.8 | 17.0 | 16.0 | 15.4 | 16.3 | 18.1 | 17.1 | 16.8 | 15.2 | 15.5 | 14.8 | 15.2 | 16.2 |
| 2013 | 17.6 | 16.7 | 15.9 | 15.1 | 16.4 | 18.0 | 16.3 | 15.6 | 14.8 | 14.4 | 13.1 | 12.3 | 15.5 |
| 2014 | 14.9 | 14.9 | 14.3 | 11.9 | 13.4 | 15.0 | 14.3 | 13.0 | 13.6 | 12.2 | 11.7 | 11.2 | 13.4 |
| 2015 | 12.9 | 12.2 | 12.3 | 10.7 | 12.3 | 13.7 |
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-23 provides the BLS estimate of the not-seasonally-adjusted rate of youth unemployment for ages 16 to 24 years from 2001 to 2015. The rate of youth unemployment increased sharply during the global recession of 2008 and 2009 but has failed to drop to earlier lower levels because of low growth of GDP. 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.
Chart I-23, US, Unemployment Rate 16-24 Years, Percent, NSA, 2001-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-24 provides longer perspective with the rate of youth unemployment in ages 16 to 24 years from 1948 to 2015. The rate of youth unemployment rose to 20 percent during the contractions of the early 1980s and also during the contraction of the global recession in 2008 and 2009. The data illustrate again the argument in this blog that the contractions of the early 1980s are the valid framework for comparison with the global recession of 2008 and 2009 instead of misleading comparisons with the 1930s. During the initial phase of recovery, the rate of youth unemployment 16 to 24 years NSA fell from 18.9 percent in Jun 1983 to 14.5 percent in Jun 1984. In contrast, the rate of youth unemployment 16 to 24 years was nearly the same during the expansion after IIIQ2009: 17.5 percent in Dec 2009, 16.7 percent in Dec 2010, 15.5 percent in Dec 2011, 15.2 percent in Dec 2012, 17.6 percent in Jan 2013, 16.7 percent in Feb 2013, 15.9 percent in Mar 2013, 15.1 percent in Apr 2013. The rate of youth unemployment was 16.4 percent in May 2013, 18.0 percent in Jun 2013, 16.3 percent in Jul 2013 and 15.6 percent in Aug 2013. In Sep 2006, the rate of youth unemployment was 10.5 percent, increasing to 14.8 percent in Sep 2013. The rate of youth unemployment was 10.3 in Oct 2007, increasing to 14.4 percent in Oct 2013. The rate of youth unemployment was 10.3 percent in Nov 2007, increasing to 13.1 percent in Nov 2013. The rate of youth unemployment was 10.7 percent in Dec 2013, increasing to 12.3 percent in Dec 2013. The rate of youth unemployment was 10.9 percent in Jan 2007, increasing to 14.9 percent in Jan 2014. The rate of youth unemployment was 10.3 percent in Feb 2007, increasing to 14.9 percent in Feb 2014. The rate of youth unemployment was 9.7 percent in Mar 2007, increasing to 14.3 percent in Mar 2014. The rate of youth unemployment was 9.7 percent in Apr 2007, increasing to 11.9 percent in Apr 2014. The rate of youth unemployment was 10.2 percent in May 2007, increasing to 13.4 percent in May 2014. The rate of youth unemployment was 12.0 percent in Jun 2007, increasing to 15.0 percent in Jun 2014. The rate of youth unemployment was 10.8 percent in Jul 2007, increasing to 14.3 percent in Jul 2014. The rate of youth unemployment was 10.5 percent in Aug 2007, increasing to 13.0 percent in Aug 2014. The rate of youth unemployment was 11.0 percent in Sep 2007, increasing to 13.6 percent in Sep 2014. The rate of youth unemployment increased from 10.3 in Oct 2007 to 12.2 in Oct 2014. The rate of youth unemployment increased from 10.3 percent in Nov 2007 to 11.7 percent in Nov 2014. The rate of youth unemployment increased from 10.7 in Dec 2007 to 11.2 in Dec 2014. The rate of youth unemployment increased from 9.7 in Mar 2007 to 12.3 in Mar 2015. The rate of youth unemployment increased from 9.7 in Apr 2007 to 10.7 in Apr 2015. The rate of youth unemployment increased from 10.2 in May 2007 to 12.3 in May 2015. The rate of youth unemployment increased from 12.0 in Jun 2007 to 13.7 in Jun 2015. The actual rate is higher because of the difficulty in counting those dropping from the labor force because they believe there are no jobs available for them. The difference originates in the vigorous seasonally-adjusted annual equivalent average rate of GDP growth of 5.9 percent during the recovery from IQ1983 to IVQ1985 and 4.8 percent from IQ1983 to IIIQ1988 compared with 2.2 percent on average during the first 23 quarters of expansion from IIIQ2009 to IQ2015. US economic growth has been at only 2.2 percent on average in the cyclical expansion in the 23 quarters from IIIQ2009 to IQ2015. 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 IQ2015 (http://www.bea.gov/newsreleases/national/gdp/2015/pdf/gdp1q15_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/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.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 IQ2015 would have accumulated to 23.9 percent. GDP in IQ2015 would be $18,574.8 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,287.1 billion than actual $16,287.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 25.0 million unemployed or underemployed equivalent to actual unemployment/underemployment of 15.1 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html). US GDP in IQ2015 is 12.3 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,287.7 billion in IQ2015 or 8.6 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 Jun 1919 to Jun 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 126.6006 in Jun 2015. The actual index NSA in Jun 2015 is 104.0319, which is 17.8 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014. Using trend growth of 2.4 percent per year, the index would increase to 118.5586 in Jun 2015. The output of manufacturing at 104.0319 in Jun 2015 is 12.3 percent below trend under this alternative calculation.
Chart I-24, US, Unemployment Rate 16-24 Years, Percent NSA, 1948-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
It is more difficult to move to other jobs after a certain age because of fewer available opportunities for mature individuals than for new entrants into the labor force. Middle-aged unemployed are less likely to find another job. Table I-13 provides the unemployment level ages 45 years and over. The number unemployed ages 45 years and over rose from 1.607 million in Oct 2006 to 4.576 million in Oct 2010 or by 184.8 percent. The number of unemployed ages 45 years and over declined to 3.800 million in Oct 2012 that is still higher by 136.5 percent than in Oct 2006. The number unemployed age 45 and over increased from 1.704 million in Nov 2006 to 3.861 million in Nov 2012, or 126.6 percent. The number unemployed age 45 and over is still higher by 98.5 percent at 3.383 million in Nov 2013 than 1.704 million in Nov 2006. The number unemployed age 45 and over jumped from 1.794 million in Dec 2006 to 4.762 million in Dec 2010 or 165.4 percent. At 3.927 million in Dec 2012, mature unemployment is higher by 2.133 million or 118.9 percent higher than 1.794 million in Dec 2006. The level of unemployment of those aged 45 year or more of 3.632 million in Oct 2013 is higher by 2.025 million than 1.607 million in Oct 2006 or higher by 126.0 percent. The number of unemployed 45 years and over increased from 1.794 million in Dec 2006 to 3.378 million in Nov 2013 or 88.3 percent. The annual number of unemployed 45 years and over increased from 1.848 million in 2006 to 3.719 million in 2013 or 101.2 percent. The number of unemployed 45 years and over increased from 2.126 million in Jan 2006 to 4.394 million in Jan 2013, by 2.618 million or 106.7 percent. The number of unemployed 45 years and over rose from 2.126 million in Jan 2006 to 3.508 million in Jan 2014, by 1.382 million or 65.0 percent. The level of unemployed 45 years or older increased 2.051 million or 99.8 percent from 2.056 million in Feb 2006 to 4.107 million in Feb 2013 and at 3.490 million in Feb 2014 is higher by 69.7 percent than in Feb 2006. The number of unemployed 45 years and over increased 2.048 million or 108.9 percent from 1.881 million in Mar 2006 to 3.929 million in Mar 2013 and at 3.394 million in Mar 2014 is higher by 80.4 percent than in Mar 2006. The number of unemployed 45 years and over increased 1.846 million or 100.2 percent from 1.843 million in Apr 2006 to 3.689 million in Apr 2013 and at 3.006 million in Apr 2014 is higher by 1.163 million or 63.1 percent. The number of unemployed ages 45 years and over increased 102.1 percent from 1.784 million in May 2006 to 3.605 million in May 2014 and at 2.913 million in May 2014 is higher by 63.3 percent than in May 2007.
The number of unemployed ages 45 years and over increased 102.1 percent from 1.805 million in Jun 2007 to 3.648 million in Jun 2013 and at 2.832 million in Jun 2014 is higher by 56.9 percent than in Jun 2007. The number of unemployed ages 45 years and over increased 81.5 percent from 2.053 million in Jul 2007 to 3.727 million in Jul 2013 and at 3.083 million in Jul 2014 is higher by 50.2 percent than in Jul 2007. The level unemployed ages 45 years and over increased 84.4 percent from 1.956 million in Aug 2007 to 3.607 million in Aug 2013 and at 3.037 million in Aug 2014 is 55.2 percent higher than in Aug 2007. The level unemployed ages 45 years and over increased 90.7 percent from 1.854 million in Sep 2007 to 3.535 million in Sep 2013 and at 2.640 million in Sep 2014 is 42.4 percent higher than in Sep 2007. The level unemployed ages 45 years and over increased 1.747 million from 1.885 million in Oct 2007 to 3.632 million in Oct 2013 and at 2.606 million in Oct 2014 is 38.2 percent higher than in Oct 2007. The level unemployed ages 45 years and over increased 1.458 million from 1.925 million in Nov 2007 to 3.383 million in Nov 2013 and at 2.829 million in Nov 2014 is 47.0 percent higher than in Nov 2007. The level of unemployed ages 45 years and over increased 1.258 million from Dec 2007 to Dec 2013 and at 2.667 million in Dec 2014 is 25.8 higher than in Dec 2007. The level unemployed ages 45 years and over increased 1.353 million from Jan 2007 to Jan 2015 and at 3.077 million in Jan 2015 is 42.8 percent higher than in Jan 2007. The level unemployed ages 45 years and over increased 1.352 million from 2.138 million in Feb 2007 to 3.490 million in Feb 2014 and at 2.991 million in Feb 2015 is 39.9 percent higher than in Feb 2007. The level of unemployed ages 45 years and over increased 1.363 million from 2.031 million in Mar 2007 to 3.394 million in Mar 2014 and at 2.724 million in Mar 2015 is 34.1 percent higher than in Mar 2007. The level of unemployed ages 45 years and over increased from 1.871 million in Apr 2007 to 3.006 million in Apr 2014 and at 2.579 million in Apr 2015 is 37.8 higher than in Apr 2007. The level of unemployed ages 45 years and over increased from 1.803 million in May 2007 to 2.913 million in Jun 2014 and at 2.457 million in May 2015 is 36.3 percent higher than in May 2007. The level of unemployed ages 45 years and over increased from 1.805 million in Jun 2007 to 2.832 million in Jun 2014 and at 2.359 million in Jun 2015 is 30.7 percent higher than in Jun 2007. The actual number unemployed is likely much higher because many are not accounted who abandoned job searches in frustration there may not be a job for them. Recent improvements may be illusory.
Table I-13, US, Unemployment Level 45 Years and Over, Thousands NSA
| Year | Jan | Feb | Mar | Apr | May | Jun | Dec | Annual |
| 2000 | 1498 | 1392 | 1291 | 1062 | 1074 | 1163 | 1217 | 1249 |
| 2001 | 1572 | 1587 | 1533 | 1421 | 1259 | 1371 | 1901 | 1576 |
| 2002 | 2235 | 2280 | 2138 | 2101 | 1999 | 2190 | 2210 | 2114 |
| 2003 | 2495 | 2415 | 2485 | 2287 | 2112 | 2212 | 2130 | 2253 |
| 2004 | 2453 | 2397 | 2354 | 2160 | 2025 | 2182 | 2086 | 2149 |
| 2005 | 2286 | 2286 | 2126 | 1939 | 1844 | 1868 | 1963 | 2009 |
| 2006 | 2126 | 2056 | 1881 | 1843 | 1784 | 1813 | 1794 | 1848 |
| 2007 | 2155 | 2138 | 2031 | 1871 | 1803 | 1805 | 2120 | 1966 |
| 2008 | 2336 | 2336 | 2326 | 2104 | 2095 | 2211 | 3485 | 2540 |
| 2009 | 4138 | 4380 | 4518 | 4172 | 4175 | 4505 | 4960 | 4500 |
| 2010 | 5314 | 5307 | 5194 | 4770 | 4565 | 4564 | 4762 | 4879 |
| 2011 | 5027 | 4837 | 4748 | 4373 | 4356 | 4559 | 4182 | 4537 |
| 2012 | 4458 | 4472 | 4390 | 4037 | 4083 | 4084 | 3927 | 4133 |
| 2013 | 4394 | 4107 | 3929 | 3689 | 3605 | 3648 | 3378 | 3719 |
| 2014 | 3508 | 3490 | 3394 | 3006 | 2913 | 2832 | 2667 | 3000 |
| 2015 | 3077 | 2991 | 2724 | 2579 | 2457 | 2359 |
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
Chart I-25 provides the level unemployed ages 45 years and over. There was an increase in the recessions of the 1980s, 1991 and 2001 followed by declines to earlier levels. The current expansion of the economy after IIIQ2009 has not been sufficiently vigorous to reduce significantly middle-age unemployment. Recent improvements could be illusory because many abandoned job searches in frustration that there may not be jobs for them and are not counted as unemployed.
Chart I-25, US, Unemployment Level Ages 45 Years and Over, Thousands, NSA, 1976-2015
Source: US Bureau of Labor Statistics http://www.bls.gov/data/
The analysis by Kydland (http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2004/kydland-bio.html) and Prescott (http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2004/prescott-bio.html) (1977, 447-80, equation 5) uses the “expectation augmented” Phillips curve with the natural rate of unemployment of Friedman (1968) and Phelps (1968), which in the notation of Barro and Gordon (1983, 592, equation 1) is:
Ut = Unt – α(πt – πe) α > 0 (1)
Where Ut is the rate of unemployment at current time t, Unt is the natural rate of unemployment, πt is the current rate of inflation and πe is the expected rate of inflation by economic agents based on current information. Equation (1) expresses unemployment net of the natural rate of unemployment as a decreasing function of the gap between actual and expected rates of inflation. The system is completed by a social objective function, W, depending on inflation, π, and unemployment, U:
W = W(πt, Ut) (2)
The policymaker maximizes the preferences of the public, (2), subject to the constraint of the tradeoff of inflation and unemployment, (1). The total differential of W set equal to zero provides an indifference map in the Cartesian plane with ordered pairs (πt, Ut - Un) such that the consistent equilibrium is found at the tangency of an indifference curve and the Phillips curve in (1). The indifference curves are concave to the origin. The consistent policy is not optimal. Policymakers without discretionary powers following a rule of price stability would attain equilibrium with unemployment not higher than with the consistent policy. The optimal outcome is obtained by the rule of price stability, or zero inflation, and no more unemployment than under the consistent policy with nonzero inflation and the same unemployment. Taylor (1998LB) attributes the sustained boom of the US economy after the stagflation of the 1970s to following a monetary policy rule instead of discretion (see Taylor 1993, 1999). It is not uncommon for effects of regulation differing from those intended by policy. Professors Edward C. Prescott and Lee E. Ohanian (2014Feb), writing on “US productivity growth has taken a dive,” on Feb 3, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303942404579362462611843696?KEYWORDS=Prescott), argue that impressive productivity growth over the long-term constructed US prosperity and wellbeing. Prescott and Ohanian (2014Feb) measure US productivity growth at 2.5 percent per year since 1948. Average US productivity growth has been only 1.1 percent since 2011. Prescott and Ohanian (2014Feb) argue that living standards in the US increased at 28 percent in a decade but with current slow growth of productivity will only increase 12 percent by 2024. There may be collateral effects on productivity growth from policy design similar to those in Kydland and Prescott (1977). The Bureau of Labor Statistics important report on productivity and costs released on Jun 4, 2015 (http://www.bls.gov/lpc/) supports the argument of decline of productivity growth in the US analyzed by Prescott and Ohanian (2014Feb). Table II-2 provides the annual percentage changes of productivity, real hourly compensation and unit labor costs for the entire economic cycle from 2007 to 2014. The data confirm the argument of Prescott and Ohanian (2014Feb): productivity increased cumulatively 2.8 percent from 2011 to 2014 at the average annual rate of 0.7 percent. The situation is direr by excluding growth of 1.0 percent in 2012, which leaves an average of 0.6 percent for 2011, 2012 and 2013. Average productivity growth for the entire economic cycle from 2007 to 2014 is only 1.5 percent. The argument by Prescott and Ohanian (2014Feb) is proper in choosing the tail of the business cycle because the increase in productivity in 2009 of 3.2 percent and 3.3 percent in 2010 consisted of reducing labor hours.
Table II-2, US, Revised Nonfarm Business Sector Productivity and Costs Annual Average, ∆% Annual Average
| 2014 ∆% | 2013 ∆% | 2012 ∆% | 2011 ∆% | 2010 ∆% | 2009 ∆% | 2008 ∆% | 2007 ∆% | |
| Productivity | 0.7 | 0.9 | 1.0 | 0.2 | 3.3 | 3.2 | 0.8 | 1.6 |
| Real Hourly Compensation | 0.9 | -0.3 | 0.6 | -0.9 | 0.3 | 1.4 | -1.1 | 1.4 |
| Unit Labor Costs | 1.8 | 0.2 | 1.7 | 2.1 | -1.3 | -2.0 | 2.0 | 2.7 |
Source: US Bureau of Labor Statistics
In the analysis of Hansen (1939, 3) of secular stagnation, economic progress consists of growth of real income per person driven by growth of productivity. The “constituent elements” of economic progress are “(a) inventions, (b) the discovery and development of new territory and new resources, and (c) the growth of population” (Hansen 1939, 3). Secular stagnation originates in decline of population growth and discouragement of inventions. According to Hansen (1939, 2), US population grew by 16 million in the 1920s but grew by one half or about 8 million in the 1930s with forecasts at the time of Hansen’s writing in 1938 of growth of around 5.3 million in the 1940s. Hansen (1939, 2) characterized demography in the US as “a drastic decline in the rate of population growth.” Hansen’s plea was to adapt economic policy to stagnation of population in ensuring full employment. In the analysis of Hansen (1939, 8), population caused half of the growth of US GDP per year. Growth of output per person in the US and Europe was caused by “changes in techniques and to the exploitation of new natural resources.” In this analysis, population caused 60 percent of the growth of capital formation in the US. Declining population growth would reduce growth of capital formation. Residential construction provided an important share of growth of capital formation. Hansen (1939, 12) argues that market power of imperfect competition discourages innovation with prolonged use of obsolete capital equipment. Trade unions would oppose labor-savings innovations. The combination of stagnating and aging population with reduced innovation caused secular stagnation. Hansen (1939, 12) concludes that there is role for public investments to compensate for lack of dynamism of private investment but with tough tax/debt issues.
The current application of Hansen’s (1938, 1939, 1941) proposition argues that secular stagnation occurs because full employment equilibrium can be attained only with negative real interest rates between minus 2 and minus 3 percent. Professor Lawrence H. Summers (2013Nov8) finds that “a set of older ideas that went under the phrase secular stagnation are not profoundly important in understanding Japan’s experience in the 1990s and may not be without relevance to America’s experience today” (emphasis added). Summers (2013Nov8) argues there could be an explanation in “that the short-term real interest rate that was consistent with full employment had fallen to -2% or -3% sometime in the middle of the last decade. Then, even with artificial stimulus to demand coming from all this financial imprudence, you wouldn’t see any excess demand. And even with a relative resumption of normal credit conditions, you’d have a lot of difficulty getting back to full employment.” The US economy could be in a situation where negative real rates of interest with fed funds rates close to zero as determined by the Federal Open Market Committee (FOMC) do not move the economy to full employment or full utilization of productive resources. Summers (2013Oct8) finds need of new thinking on “how we manage an economy in which the zero nominal interest rates is a chronic and systemic inhibitor of economy activity holding our economies back to their potential.”
Former US Treasury Secretary Robert Rubin (2014Jan8) finds three major risks in prolonged unconventional monetary policy of zero interest rates and quantitative easing: (1) incentive of delaying action by political leaders; (2) “financial moral hazard” in inducing excessive exposures pursuing higher yields of risker credit classes; and (3) major risks in exiting unconventional policy. Rubin (2014Jan8) proposes reduction of deficits by structural reforms that could promote recovery by improving confidence of business attained with sound fiscal discipline.
Professor John B. Taylor (2014Jan01, 2014Jan3) provides clear thought on the lack of relevance of Hansen’s contention of secular stagnation to current economic conditions. The application of secular stagnation argues that the economy of the US has attained full-employment equilibrium since around 2000 only with negative real rates of interest of minus 2 to minus 3 percent. At low levels of inflation, the so-called full-employment equilibrium of negative interest rates of minus 2 to minus 3 percent cannot be attained and the economy stagnates. Taylor (2014Jan01) analyzes multiple contradictions with current reality in this application of the theory of secular stagnation:
- Secular stagnation would predict idle capacity, in particular in residential investment when fed fund rates were fixed at 1 percent from Jun 2003 to Jun 2004. Taylor (2014Jan01) finds unemployment at 4.4 percent with house prices jumping 7 percent from 2002 to 2003 and 14 percent from 2004 to 2005 before dropping from 2006 to 2007. GDP prices doubled from 1.7 percent to 3.4 percent when interest rates were low from 2003 to 2005.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the application of secular stagnation based on low interest rates because of savings glut and lack of investment opportunities. Taylor (2009) shows that there was no savings glut. The savings rate of the US in the past decade is significantly lower than in the 1980s.
- Taylor (2014Jan01, 2014Jan3) finds another contradiction in the low ratio of investment to GDP currently and reduced investment and hiring by US business firms.
- Taylor (2014Jan01, 2014Jan3) argues that the financial crisis and global recession were caused by weak implementation of existing regulation and departure from rules-based policies.
- Taylor (2014Jan01, 2014Jan3) argues that the recovery from the global recession was constrained by a change in the regime of regulation and fiscal/monetary policies.
The analysis by Kydland (http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2004/kydland-bio.html) and Prescott (http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2004/prescott-bio.html) (1977, 447-80, equation 5) uses the “expectation augmented” Phillips curve with the natural rate of unemployment of Friedman (1968) and Phelps (1968), which in the notation of Barro and Gordon (1983, 592, equation 1) is:
Ut = Unt – α(πt – πe) α > 0 (1)
Where Ut is the rate of unemployment at current time t, Unt is the natural rate of unemployment, πt is the current rate of inflation and πe is the expected rate of inflation by economic agents based on current information. Equation (1) expresses unemployment net of the natural rate of unemployment as a decreasing function of the gap between actual and expected rates of inflation. The system is completed by a social objective function, W, depending on inflation, π, and unemployment, U:
W = W(πt, Ut) (2)
The policymaker maximizes the preferences of the public, (2), subject to the constraint of the tradeoff of inflation and unemployment, (1). The total differential of W set equal to zero provides an indifference map in the Cartesian plane with ordered pairs (πt, Ut - Un) such that the consistent equilibrium is found at the tangency of an indifference curve and the Phillips curve in (1). The indifference curves are concave to the origin. The consistent policy is not optimal. Policymakers without discretionary powers following a rule of price stability would attain equilibrium with unemployment not higher than with the consistent policy. The optimal outcome is obtained by the rule of price stability, or zero inflation, and no more unemployment than under the consistent policy with nonzero inflation and the same unemployment. Taylor (1998LB) attributes the sustained boom of the US economy after the stagflation of the 1970s to following a monetary policy rule instead of discretion (see Taylor 1993, 1999). It is not uncommon for effects of regulation differing from those intended by policy. Professors Edward C. Prescott and Lee E. Ohanian (2014Feb), writing on “US productivity growth has taken a dive,” on Feb 3, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303942404579362462611843696?KEYWORDS=Prescott), argue that impressive productivity growth over the long-term constructed US prosperity and wellbeing. Prescott and Ohanian (2014Feb) measure US productivity growth at 2.5 percent per year since 1948. Average US productivity growth has been only 1.1 since 2011. Prescott and Ohanian (2014Feb) argue that living standards in the US increased at 28 percent in a decade but with current slow growth of productivity will only increase 12 percent by 2024. There may be collateral effects on productivity growth from policy design similar to those in Kydland and Prescott (1977). The Bureau of Labor Statistics important report on productivity and costs released on Jun 4, 2015 (http://www.bls.gov/lpc/) supports the argument of decline of productivity in the US analyzed by Prescott and Ohanian (2014Feb). Table II-2 provides the annual percentage changes of productivity, real hourly compensation and unit labor costs for the entire economic cycle from 2007 to 2014. The data confirm the argument of Prescott and Ohanian (2014Feb): productivity increased cumulatively 2.8 percent from 2011 to 2014 at the average annual rate of 0.7 percent. The situation is direr by excluding growth of 1.0 percent in 2012, which leaves an average of 0.6 percent for 2011, 2013 and 2014. Average productivity growth for the entire economic cycle from 2007 to 2014 is only 1.5 percent. The argument by Prescott and Ohanian (2014Feb) is proper in choosing the tail of the business cycle because the increase in productivity in 2009 of 3.2 percent and 3.3 percent in 2013 consisted on reducing labor hours.
In revealing research, Edward P. Lazear and James R. Spletzer (2012JHJul22) use the wealth of data in the valuable database and resources of the Bureau of Labor Statistics (http://www.bls.gov/data/) in providing clear thought on the nature of the current labor market of the United States. The critical issue of analysis and policy currently is whether unemployment is structural or cyclical. Structural unemployment could occur because of (1) industrial and demographic shifts and (2) mismatches of skills and job vacancies in industries and locations. Consider the aggregate unemployment rate, Y, expressed in terms of share si of a demographic group in an industry i and unemployment rate yi of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):
Y = ∑isiyi (1)
This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):
∆Y = ∑i∆siy*i + ∑i∆yis*i (2)
The first term in (2) captures changes in the demographic and industrial composition of the economy ∆si multiplied by the average rate of unemployment y*i , or structural factors. The second term in (2) captures changes in the unemployment rate specific to a group, or ∆yi, multiplied by the average share of the group s*i, or cyclical factors. There are also mismatches in skills and locations relative to available job vacancies. A simple observation by Lazear and Spletzer (2012JHJul22) casts intuitive doubt on structural factors: the rate of unemployment jumped from 4.4 percent in the spring of 2007 to 10 percent in October 2009. By nature, structural factors should be permanent or occur over relative long periods. The revealing result of the exhaustive research of Lazear and Spletzer (2012JHJul22) is:
“The analysis in this paper and in others that we review do not provide any compelling evidence that there have been changes in the structure of the labor market that are capable of explaining the pattern of persistently high unemployment rates. The evidence points to primarily cyclic factors.”
The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. The theory of secular stagnation departs from an aggregate production function in which output grows with the use of labor, capital and technology (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), 11-6). Simon Kuznets (1971) analyzes modern economic growth in his Lecture in Memory of Alfred Nobel:
“The major breakthroughs in the advance of human knowledge, those that constituted dominant sources of sustained growth over long periods and spread to a substantial part of the world, may be termed epochal innovations. And the changing course of economic history can perhaps be subdivided into economic epochs, each identified by the epochal innovation with the distinctive characteristics of growth that it generated. Without considering the feasibility of identifying and dating such economic epochs, we may proceed on the working assumption that modern economic growth represents such a distinct epoch - growth dating back to the late eighteenth century and limited (except in significant partial effects) to economically developed countries. These countries, so classified because they have managed to take adequate advantage of the potential of modern technology, include most of Europe, the overseas offshoots of Western Europe, and Japan—barely one quarter of world population.”
Chart II-7 provides nonfarm-business labor productivity, measured by output per hour, from 1947 to 2015. The rate of productivity increase continued in the early part of the 2000s but then softened and fell during the global recession. The interruption of productivity increases occurred exclusively in the current business cycle. Lazear and Spletzer (2012JHJul22) find “primarily cyclic” factors in explaining the frustration of currently depressed labor markets in the United States. Stagnation of productivity is another cyclic event and not secular trend. The theory and application of secular stagnation to current US economic conditions is void of reality.
Chart II-7, US, Nonfarm Business Labor Productivity, Output per Hour, 1947-2015, Index 2005=100
Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/
Table II-6 expands Table II-2 providing more complete measurements of the Productivity and Cost research of the Bureau of Labor Statistics. The proper emphasis of Prescott and Ohanian (2014Feb) is on the low productivity increases from 2011 to 2014. Labor productivity increased 3.3 percent in 2010 and 3.2 percent in 2009. There is much stronger yet not sustained performance in 2010 with productivity growing 3.3 percent because of growth of output of 3.2 percent with decline of hours worked of 0.1 percent. Productivity growth of 3.2 percent in 2009 consists of decline of output by 4.3 percent while hours worked collapsed 7.2 percent, which is not a desirable route to progress. The expansion phase of the economic cycle concentrated in one year, 2010, with underperformance in the remainder of the expansion from 2011 to 2014 of productivity growth at average 0.7 percent per year.
Table II-6, US, Productivity and Costs, Annual Percentage Changes 2007-2014
| 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | |
| Productivity | 0.7 | 0.9 | 1.0 | 0.2 | 3.3 | 3.2 | 0.8 | 1.6 |
| Output | 3.0 | 2.6 | 3.2 | 2.2 | 3.2 | -4.3 | -1.3 | 2.3 |
| Hours Worked | 2.3 | 1.7 | 2.2 | 2.0 | -0.1 | -7.2 | -2.0 | 0.7 |
| Employment | 2.1 | 1.9 | 2.0 | 1.6 | -1.2 | -5.7 | -1.5 | 0.9 |
| Average Weekly Hours Worked | 0.2 | -0.1 | 0.2 | 0.5 | 1.1 | -1.6 | -0.6 | -0.2 |
| Unit Labor Costs | 1.8 | 0.2 | 1.7 | 2.1 | -1.3 | -2.0 | 2.0 | 2.7 |
| Hourly Compensation | 2.6 | 1.1 | 2.7 | 2.2 | 2.0 | 1.1 | 2.7 | 4.3 |
| Consumer Price Inflation | 1.6 | 1.5 | 2.1 | 3.2 | 1.6 | -0.4 | 3.8 | 2.8 |
| Real Hourly Compensation | 0.9 | -0.3 | 0.6 | -0.9 | 0.3 | 1.4 | -1.1 | 1.4 |
| Non-labor Payments | 3.8 | 5.5 | 5.3 | 3.7 | 7.5 | 0.0 | -0.4 | 3.4 |
| Output per Job | 0.9 | 0.7 | 1.2 | 0.6 | 4.5 | 1.5 | 0.2 | 1.4 |
Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/
Productivity growth can bring about prosperity while productivity regression can jeopardize progress. Cobet and Wilson (2002) provide estimates of output per hour and unit labor costs in national currency and US dollars for the US, Japan and Germany from 1950 to 2000 (see Pelaez and Pelaez, The Global Recession Risk (2007), 137-44). The average yearly rate of productivity change from 1950 to 2000 was 2.9 percent in the US, 6.3 percent for Japan and 4.7 percent for Germany while unit labor costs in USD increased at 2.6 percent in the US, 4.7 percent in Japan and 4.3 percent in Germany. From 1995 to 2000, output per hour increased at the average yearly rate of 4.6 percent in the US, 3.9 percent in Japan and 2.6 percent in Germany while unit labor costs in USD fell at minus 0.7 percent in the US, 4.3 percent in Japan and 7.5 percent in Germany. There was increase in productivity growth in Japan and France within the G7 in the second half of the 1990s but significantly lower than the acceleration of 1.3 percentage points per year in the US. Table II-7 provides average growth rates of indicators in the research of productivity and growth of the US Bureau of Labor Statistics. There is dramatic decline of productivity growth from 2.2 percent per year on average from 1947 to 2014 to 1.4 percent per year on average in the whole cycle from 2007 to 2014. Productivity increased at the average rate of 2.3 percent from 1947 to 2007. There is profound drop in the average rate of output growth from 3.4 percent on average from 1947 to 2014 to 1.2 percent from 2007 to 2014. Output grew at 3.7 percent per year on average from 1947 to 2007. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.2 percent on average in the cyclical expansion in the 23 quarters from IIIQ2009 to IQ2015. 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 IQ2015 (http://www.bea.gov/newsreleases/national/gdp/2015/pdf/gdp1q15_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/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.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 IQ2015 would have accumulated to 23.9 percent. GDP in IQ2015 would be $18,574.8 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,287.1 billion than actual $16,287.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 25.0 million unemployed or underemployed equivalent to actual unemployment/underemployment of 15.1 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html). US GDP in IQ2015 is 12.3 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,287.7 billion in IQ2015 or 8.6 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 Jun 1919 to Jun 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 126.6006 in Jun 2015. The actual index NSA in Jun 2015 is 104.0319, which is 17.8 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014. Using trend growth of 2.4 percent per year, the index would increase to 118.5586 in Jun 2015. The output of manufacturing at 104.0319 in Jun 2015 is 12.3 percent below trend under this alternative calculation.
Table II-7, US, Productivity and Costs, Average Annual Percentage Changes 2007-2014 and 1947-2014
| Average Annual Percentage Rate 2007-2014 | Average Annual Percentage Rate 1947-2007 | Average Annual Percentage Rate 1947-2014 | |
| Productivity | 1.4 | 2.3 | 2.2 |
| Output | 1.2 | 3.7 | 3.4 |
| Hours | -1.5* | 1.4 | 1.2 |
| Employment | -1.2* | 1.6 | 1.5 |
| Average Weekly Hours | -0.3* | -14.6* | -14.8* |
| Hourly Compensation | 1.9 | 5.4 | 5.0 |
| Consumer Price Inflation | 1.9 | 3.8 | 3.6 |
| Real Hourly Compensation | 0.1 | 1.7 | 1.5 |
| Unit Labor Costs | 0.6 | 3.0 | 2.8 |
| Unit Non-labor Payments | 2.4 | 3.5 | 3.4 |
| Output per Job | 1.4 | 2.0 | 1.9 |
* Percentage Change
Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/
Unit labor costs increased sharply during the Great Inflation from the late 1960s to 1981 as shown by sharper slope in Chart II-8. Unit labor costs continued to increase but at a lower rate because of cyclic factors and not because of imaginary secular stagnation.
Chart II-8, US, Nonfarm Business, Unit Labor Costs, 1947-2015, Index 2009=100
Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/
Real hourly compensation increased at relatively high rates after 1947 to the early 1970s but reached a plateau that lasted until the early 1990s, as shown in Chart II-9. There were rapid increases until the global recession. Cyclic factors and not alleged secular stagnation explain the interruption of increases in real hourly compensation.
Chart II-9, US, Nonfarm Business, Real Hourly Compensation, 1947-2015, Index 2009=100
Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/
There are collateral effects of unconventional monetary policy. Chart VIII-1 of the Board of Governors of the Federal Reserve System provides the rate on the overnight fed funds rate and the yields of the 10-year constant maturity Treasury and the Baa seasoned corporate bond. Table VIII-3 provides the data for selected points in Chart VIII-1. There are two important economic and financial events, illustrating the ease of inducing carry trade with extremely low interest rates and the resulting financial crash and recession of abandoning extremely low interest rates.
- The Federal Open Market Committee (FOMC) lowered the target of the fed funds rate from 7.03 percent on Jul 3, 2000, to 1.00 percent on Jun 22, 2004, in pursuit of non-existing deflation (Pelaez and Pelaez, International Financial Architecture (2005), 18-28, The Global Recession Risk (2007), 83-85). Central bank commitment to maintain the fed funds rate at 1.00 percent induced adjustable-rate mortgages (ARMS) linked to the fed funds rate. Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment. The exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at interest rates close to zero, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV). The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper with the objective of purchasing default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever-increasing house prices. The suspension of auctions of 30-year Treasuries was designed to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity by the penalty in the form of low interest rates and unsound credit decisions. The put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4). The FOMC implemented increments of 25 basis points of the fed funds target from Jun 2004 to Jun 2006, raising the fed funds rate to 5.25 percent on Jul 3, 2006, as shown in Chart VIII-1. The gradual exit from the first round of unconventional monetary policy from 1.00 percent in Jun 2004 (http://www.federalreserve.gov/boarddocs/press/monetary/2004/20040630/default.htm) to 5.25 percent in Jun 2006 (http://www.federalreserve.gov/newsevents/press/monetary/20060629a.htm) caused the financial crisis and global recession.
- On Dec 16, 2008, the policy determining committee of the Fed decided (http://www.federalreserve.gov/newsevents/press/monetary/20081216b.htm): “The Federal Open Market Committee decided today to establish a target range for the federal funds rate of 0 to 1/4 percent.” Policymakers emphasize frequently that there are tools to exit unconventional monetary policy at the right time. At the confirmation hearing on nomination for Chair of the Board of Governors of the Federal Reserve System, Vice Chair Yellen (2013Nov14 http://www.federalreserve.gov/newsevents/testimony/yellen20131114a.htm), states that: “The Federal Reserve is using its monetary policy tools to promote a more robust recovery. A strong recovery will ultimately enable the Fed to reduce its monetary accommodation and reliance on unconventional policy tools such as asset purchases. I believe that supporting the recovery today is the surest path to returning to a more normal approach to monetary policy.” Perception of withdrawal of $2671 billion, or $2.7 trillion, of bank reserves (http://www.federalreserve.gov/releases/h41/current/h41.htm#h41tab1), would cause Himalayan increase in interest rates that would provoke another recession. There is no painless gradual or sudden exit from zero interest rates because reversal of exposures created on the commitment of zero interest rates forever.
In his classic restatement of the Keynesian demand function in terms of “liquidity preference as behavior toward risk,” James Tobin (http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1981/tobin-bio.html) identifies the risks of low interest rates in terms of portfolio allocation (Tobin 1958, 86):
“The assumption that investors expect on balance no change in the rate of interest has been adopted for the theoretical reasons explained in section 2.6 rather than for reasons of realism. Clearly investors do form expectations of changes in interest rates and differ from each other in their expectations. For the purposes of dynamic theory and of analysis of specific market situations, the theories of sections 2 and 3 are complementary rather than competitive. The formal apparatus of section 3 will serve just as well for a non-zero expected capital gain or loss as for a zero expected value of g. Stickiness of interest rate expectations would mean that the expected value of g is a function of the rate of interest r, going down when r goes down and rising when r goes up. In addition to the rotation of the opportunity locus due to a change in r itself, there would be a further rotation in the same direction due to the accompanying change in the expected capital gain or loss. At low interest rates expectation of capital loss may push the opportunity locus into the negative quadrant, so that the optimal position is clearly no consols, all cash. At the other extreme, expectation of capital gain at high interest rates would increase sharply the slope of the opportunity locus and the frequency of no cash, all consols positions, like that of Figure 3.3. The stickier the investor's expectations, the more sensitive his demand for cash will be to changes in the rate of interest (emphasis added).”
Tobin (1969) provides more elegant, complete analysis of portfolio allocation in a general equilibrium model. The major point is equally clear in a portfolio consisting of only cash balances and a perpetuity or consol. Let g be the capital gain, r the rate of interest on the consol and re the expected rate of interest. The rates are expressed as proportions. The price of the consol is the inverse of the interest rate, (1+re). Thus, g = [(r/re) – 1]. The critical analysis of Tobin is that at extremely low interest rates there is only expectation of interest rate increases, that is, dre>0, such that there is expectation of capital losses on the consol, dg<0. Investors move into positions combining only cash and no consols. Valuations of risk financial assets would collapse in reversal of long positions in carry trades with short exposures in a flight to cash. There is no exit from a central bank created liquidity trap without risks of financial crash and another global recession. The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Friedman 1957). According to a subsequent statement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:
W = Y/r (1)
Equation (1) shows that as r goes to zero, r→0, W grows without bound, W→∞. Unconventional monetary policy lowers interest rates to increase the present value of cash flows derived from projects of firms, creating the impression of long-term increase in net worth. An attempt to reverse unconventional monetary policy necessarily causes increases in interest rates, creating the opposite perception of declining net worth. As r→∞, W = Y/r →0. There is no exit from unconventional monetary policy without increasing interest rates with resulting pain of financial crisis and adverse effects on production, investment and employment.
Dan Strumpf and Pedro Nicolaci da Costa, writing on “Fed’s Yellen: Stock Valuations ‘Generally are Quite High,’” on May 6, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/feds-yellen-cites-progress-on-bank-regulation-1430918155?tesla=y ), quote Chair Yellen at open conversation with Christine Lagarde, Managing Director of the IMF, finding “equity-market valuations” as “quite high” with “potential dangers” in bond valuations. The DJIA fell 0.5 percent on May 6, 2015, after the comments and then increased 0.5 percent on May 7, 2015 and 1.5 percent on May 8, 2015.
| Fri May 1 | Mon 4 | Tue 5 | Wed 6 | Thu 7 | Fri 8 |
| DJIA 18024.06 -0.3% 1.0% | 18070.40 0.3% 0.3% | 17928.20 -0.5% -0.8% | 17841.98 -1.0% -0.5% | 17924.06 -0.6% 0.5% | 18191.11 0.9% 1.5% |
There are two approaches in theory considered by Bordo (2012Nov20) and Bordo and Lane (2013). The first approach is in the classical works of Milton Friedman and Anna Jacobson Schwartz (1963a, 1987) and Karl Brunner and Allan H. Meltzer (1973). There is a similar approach in Tobin (1969). Friedman and Schwartz (1963a, 66) trace the effects of expansionary monetary policy into increasing initially financial asset prices: “It seems plausible that both nonbank and bank holders of redundant balances will turn first to securities comparable to those they have sold, say, fixed-interest coupon, low-risk obligations. But as they seek to purchase these they will tend to bid up the prices of those issues. Hence they, and also other holders not involved in the initial central bank open-market transactions, will look farther afield: the banks, to their loans; the nonbank holders, to other categories of securities-higher risk fixed-coupon obligations, equities, real property, and so forth.”
The second approach is by the Austrian School arguing that increases in asset prices can become bubbles if monetary policy allows their financing with bank credit. Professor Michael D. Bordo provides clear thought and empirical evidence on the role of “expansionary monetary policy” in inflating asset prices (Bordo2012Nov20, Bordo and Lane 2013). Bordo and Lane (2013) provide revealing narrative of historical episodes of expansionary monetary policy. Bordo and Lane (2013) conclude that policies of depressing interest rates below the target rate or growth of money above the target influences higher asset prices, using a panel of 18 OECD countries from 1920 to 2011. Bordo (2012Nov20) concludes: “that expansionary money is a significant trigger” and “central banks should follow stable monetary policies…based on well understood and credible monetary rules.” Taylor (2007, 2009) explains the housing boom and financial crisis in terms of expansionary monetary policy.
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 44.7 percent relative to the dollar from the high on Jul 15, 2008 to Jul 24, 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% |
| Fri Apr 24 | Mon 27 | Tue 28 | Wed 29 | Thu 30 | May Fri 1 |
| USD/ EUR 1.0874 -0.6% -0.4% | 1.0891 -0.2% -0.2% | 1.0983 -1.0% -0.8% | 1.1130 -2.4% -1.3% | 1.1223 -3.2% -0.8% | 1.1199 -3.0% 0.2% |
In a speech at Brown University on May 22, 2015, Chair Yellen stated (http://www.federalreserve.gov/newsevents/speech/yellen20150522a.htm):
“For this reason, if the economy continues to improve as I expect, I think it will be appropriate at some point this year to take the initial step to raise the federal funds rate target and begin the process of normalizing monetary policy. To support taking this step, however, I will need to see continued improvement in labor market conditions, and I will need to be reasonably confident that inflation will move back to 2 percent over the medium term. After we begin raising the federal funds rate, I anticipate that the pace of normalization is likely to be gradual. The various headwinds that are still restraining the economy, as I said, will likely take some time to fully abate, and the pace of that improvement is highly uncertain.”
The US dollar appreciated 3.8 percent relative to the euro in the week of May 22, 2015:
| Fri May 15 | Mon 18 | Tue 19 | Wed 20 | Thu 21 | Fri 22 |
| USD/ EUR 1.1449 -2.2% -0.3% | 1.1317 1.2% 1.2% | 1.1150 2.6% 1.5% | 1.1096 3.1% 0.5% | 1.1113 2.9% -0.2% | 1.1015 3.8% 0.9% |
The Managing Director of the International Monetary Fund (IMF), Christine Lagarde, warned on Jun 4, 2015, that: (http://blog-imfdirect.imf.org/2015/06/04/u-s-economy-returning-to-growth-but-pockets-of-vulnerability/):
“The Fed’s first rate increase in almost 9 years is being carefully prepared and telegraphed. Nevertheless, regardless of the timing, higher US policy rates could still result in significant market volatility with financial stability consequences that go well beyond US borders. I weighing these risks, we think there is a case for waiting to raise rates until there are more tangible signs of wage or price inflation than are currently evident. Even after the first rate increase, a gradual rise in the federal fund rates will likely be appropriate.”
The President of the European Central Bank (ECB), Mario Draghi, warned on Jun 3, 2015 that (http://www.ecb.europa.eu/press/pressconf/2015/html/is150603.en.html):
“But certainly one lesson is that we should get used to periods of higher volatility. At very low levels of interest rates, asset prices tend to show higher volatility…the Governing Council was unanimous in its assessment that we should look through these developments and maintain a steady monetary policy stance.”
The Chair of the Board of Governors of the Federal Reserve System, Janet L. Yellen, stated on Jul 10, 2015 that (http://www.federalreserve.gov/newsevents/speech/yellen20150710a.htm):
“Based on my outlook, I expect that it will be appropriate at some point later this year to take the first step to raise the federal funds rate and thus begin normalizing monetary policy. But I want to emphasize that the course of the economy and inflation remains highly uncertain, and unanticipated developments could delay or accelerate this first step. I currently anticipate that the appropriate pace of normalization will be gradual, and that monetary policy will need to be highly supportive of economic activity for quite some time. The projections of most of my FOMC colleagues indicate that they have similar expectations for the likely path of the federal funds rate. But, again, both the course of the economy and inflation are uncertain. If progress toward our employment and inflation goals is more rapid than expected, it may be appropriate to remove monetary policy accommodation more quickly. However, if progress toward our goals is slower than anticipated, then the Committee may move more slowly in normalizing policy.”
There is essentially the same view in the Testimony of Chair Yellen in delivering the Semiannual Monetary Policy Report to the Congress on Jul 15, 2015 (http://www.federalreserve.gov/newsevents/testimony/yellen20150715a.htm).
Chart VIII-1, Fed Funds Rate and Yields of Ten-year Treasury Constant Maturity and Baa Seasoned Corporate Bond, Jan 2, 2001 to Jul 16, 2015
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/h15/
Table VIII-3, Selected Data Points in Chart VIII-1, % per Year
| Fed Funds Overnight Rate | 10-Year Treasury Constant Maturity | Seasoned Baa Corporate Bond | |
| 1/2/2001 | 6.67 | 4.92 | 7.91 |
| 10/1/2002 | 1.85 | 3.72 | 7.46 |
| 7/3/2003 | 0.96 | 3.67 | 6.39 |
| 6/22/2004 | 1.00 | 4.72 | 6.77 |
| 6/28/2006 | 5.06 | 5.25 | 6.94 |
| 9/17/2008 | 2.80 | 3.41 | 7.25 |
| 10/26/2008 | 0.09 | 2.16 | 8.00 |
| 10/31/2008 | 0.22 | 4.01 | 9.54 |
| 4/6/2009 | 0.14 | 2.95 | 8.63 |
| 4/5/2010 | 0.20 | 4.01 | 6.44 |
| 2/4/2011 | 0.17 | 3.68 | 6.25 |
| 7/25/2012 | 0.15 | 1.43 | 4.73 |
| 5/1/13 | 0.14 | 1.66 | 4.48 |
| 9/5/13 | 0.089 | 2.98 | 5.53 |
| 11/21/2013 | 0.09 | 2.79 | 5.44 |
| 11/26/13 | 0.09 | 2.74 | 5.34 (11/26/13) |
| 12/5/13 | 0.09 | 2.88 | 5.47 |
| 12/11/13 | 0.09 | 2.89 | 5.42 |
| 12/18/13 | 0.09 | 2.94 | 5.36 |
| 12/26/13 | 0.08 | 3.00 | 5.37 |
| 1/1/2014 | 0.08 | 3.00 | 5.34 |
| 1/8/2014 | 0.07 | 2.97 | 5.28 |
| 1/15/2014 | 0.07 | 2.86 | 5.18 |
| 1/22/2014 | 0.07 | 2.79 | 5.11 |
| 1/30/2014 | 0.07 | 2.72 | 5.08 |
| 2/6/2014 | 0.07 | 2.73 | 5.13 |
| 2/13/2014 | 0.06 | 2.73 | 5.12 |
| 2/20/14 | 0.07 | 2.76 | 5.15 |
| 2/27/14 | 0.07 | 2.65 | 5.01 |
| 3/6/14 | 0.08 | 2.74 | 5.11 |
| 3/13/14 | 0.08 | 2.66 | 5.05 |
| 3/20/14 | 0.08 | 2.79 | 5.13 |
| 3/27/14 | 0.08 | 2.69 | 4.95 |
| 4/3/14 | 0.08 | 2.80 | 5.04 |
| 4/10/14 | 0.08 | 2.65 | 4.89 |
| 4/17/14 | 0.09 | 2.73 | 4.89 |
| 4/24/14 | 0.10 | 2.70 | 4.84 |
| 5/1/14 | 0.09 | 2.63 | 4.77 |
| 5/8/14 | 0.08 | 2.61 | 4.79 |
| 5/15/14 | 0.09 | 2.50 | 4.72 |
| 5/22/14 | 0.09 | 2.56 | 4.81 |
| 5/29/14 | 0.09 | 2.45 | 4.69 |
| 6/05/14 | 0.09 | 2.59 | 4.83 |
| 6/12/14 | 0.09 | 2.58 | 4.79 |
| 6/19/14 | 0.10 | 2.64 | 4.83 |
| 6/26/14 | 0.10 | 2.53 | 4.71 |
| 7/2/14 | 0.10 | 2.64 | 4.84 |
| 7/10/14 | 0.09 | 2.55 | 4.75 |
| 7/17/14 | 0.09 | 2.47 | 4.69 |
| 7/24/14 | 0.09 | 2.52 | 4.72 |
| 7/31/14 | 0.08 | 2.58 | 4.75 |
| 8/7/14 | 0.09 | 2.43 | 4.71 |
| 8/14/14 | 0.09 | 2.40 | 4.69 |
| 8/21/14 | 0.09 | 2.41 | 4.69 |
| 8/28/14 | 0.09 | 2.34 | 4.57 |
| 9/04/14 | 0.09 | 2.45 | 4.70 |
| 9/11/14 | 0.09 | 2.54 | 4.79 |
| 9/18/14 | 0.09 | 2.63 | 4.91 |
| 9/25/14 | 0.09 | 2.52 | 4.79 |
| 10/02/14 | 0.09 | 2.44 | 4.76 |
| 10/09/14 | 0.08 | 2.34 | 4.68 |
| 10/16/14 | 0.09 | 2.17 | 4.64 |
| 10/23/14 | 0.09 | 2.29 | 4.71 |
| 11/13/14 | 0.09 | 2.35 | 4.82 |
| 11/20/14 | 0.10 | 2.34 | 4.86 |
| 11/26/14 | 0.10 | 2.24 | 4.73 |
| 12/04/14 | 0.12 | 2.25 | 4.78 |
| 12/11/14 | 0.12 | 2.19 | 4.72 |
| 12/18/14 | 0.13 | 2.22 | 4.78 |
| 12/23/14 | 0.13 | 2.26 | 4.79 |
| 12/30/14 | 0.06 | 2.20 | 4.69 |
| 1/8/15 | 0.12 | 2.03 | 4.57 |
| 1/15/15 | 0.12 | 1.77 | 4.42 |
| 1/22/15 | 0.12 | 1.90 | 4.49 |
| 1/29/15 | 0.11 | 1.77 | 4.35 |
| 2/05/15 | 0.12 | 1.83 | 4.43 |
| 2/12/15 | 0.12 | 1.99 | 4.53 |
| 2/19/15 | 0.12 | 2.11 | 4.64 |
| 2/26/15 | 0.11 | 2.03 | 4.47 |
| 3/5/215 | 0.11 | 2.11 | 4.58 |
| 3/12/15 | 0.11 | 2.10 | 4.56 |
| 3/19/15 | 0.12 | 1.98 | 4.48 |
| 3/26/15 | 0.11 | 2.01 | 4.56 |
| 4/03/15 | 0.12 | 1.92 | 4.47 |
| 4/9/15 | 0.12 | 1.97 | 4.50 |
| 4/16/15 | 0.13 | 1.90 | 4.45 |
| 4/23/15 | 0.13 | 1.96 | 4.50 |
| 5/1/15 | 0.08 | 2.05 | 4.65 |
| 5/7/15 | 0.13 | 2.18 | 4.82 |
| 5/14/15 | 0.13 | 2.23 | 4.97 |
| 5/21/15 | 0.12 | 2.19 | 4.94 |
| 5/28/15 | 0.12 | 2.13 | 4.88 |
| 6/04/15 | 0.13 | 2.31 | 5.03 |
| 6/11/15 | 0.13 | 2.39 | 5.10 |
| 6/18/15 | 0.14 | 2.35 | 5.17 |
| 6/25/15 | 0.13 | 2.40 | 5.20 |
| 7/1/15 | 0.13 | 2.43 | 5.26 |
| 7/9/15 | 0.13 | 2.32 | 5.20 |
| 7/16/2015 | 0.14 | 2.36 | 5.24 |
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/h15/
Chart VIII-2 of the Board of Governors of the Federal Reserve System provides the rate of US dollars (USD) per euro (EUR), USD/EUR. The rate appreciated from USD 1.3600/EUR on Jul 10, 2014 to USD 1.1150/EUR on Jul 10, 2015 or 18.0 percent. The euro has devalued 44.7 percent relative to the dollar from the high on Jul 15, 2008 to Jul 24, 2015. US corporations with foreign transactions and net worth experience losses in their balance sheets in converting revenues from depreciated currencies to the dollar. Corporate profits with IVA and CCA fell at $110.8 billion in IQ2015 with decrease of domestic industries at $81.8 billion, mostly because of decline of nonfinancial business at $79.6 billion, and decrease of profits from operations in the rest of the world at $29.0 billion. Receipts from the rest of the world fell at $40.0 billion. Total corporate profits with IVA and CCA were $2029.5 billion in IQ2015 of which $1684.2 billion from domestic industries, or 83.0 percent of the total, and $345.3 billion, or 17.0 percent, from the rest of the world. Nonfinancial corporate profits of $1230.7 billion account for 60.6 percent of the total. There is increase in corporate profits from devaluing the dollar with unconventional monetary policy of zero interest rates and decrease of corporate profits in revaluing the dollar with attempts at “normalization” or increases in interest rates. Conflicts arise while other central banks differ in their adjustment process
Chart VIII-2, Exchange Rate of US Dollars (USD) per Euro (EUR), Jul 10, 2014 to Jul 10, 2015
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/H10/default.htm
Chart VIII-3 of the Board of Governors of the Federal Reserve System provides the yield of the 10-year Treasury constant maturity note from 1.90 percent on Apr 16, 2015 to 2.36 percent on Apr 16, 2015. There is turbulence in financial markets originating in a combination of intentions of normalizing or increasing US policy fed funds rate, quantitative easing in Europe and Japan and increasing perception of financial/economic risks.
Chart VIII-3, Yield of Ten-year Constant Maturity Treasury, Apr 16, 2015 to Jul 16, 2015
Source: Board of Governors of the Federal Reserve System
http://www.federalreserve.gov/releases/h15/
(4) Counterfactual of Policies Causing the Financial Crisis and Global Recession. The counterfactual of avoidance of deeper and more prolonged contraction by fiscal and monetary policies is not the critical issue. As Professor John B. Taylor (2012Oct25) argues, the critically important counterfactual is that the financial crisis and global recession would have not occurred in the first place if different economic policies had been followed. The counterfactual intends to verify that a combination of housing policies and discretionary monetary policies instead of rules (Taylor 1993) caused, deepened and prolonged the financial crisis (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB; see http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html) and that the experience resembles that of the Great Inflation of the 1960s and 1970s with stop-and-go growth/inflation that coined the term stagflation (http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html http://cmpassocregulationblog.blogspot.com/2011/04/new-economics-of-rose-garden-turned.html http://cmpassocregulationblog.blogspot.com/2011/03/is-there-second-act-of-us-great.html and Appendix I).
The explanation of the sharp contraction of United States housing 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 (Ingersoll 1987, 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).
Dan Strumpf and Pedro Nicolaci da Costa, writing on “Fed’s Yellen: Stock Valuations ‘Generally are Quite High,’” on May 6, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/feds-yellen-cites-progress-on-bank-regulation-1430918155?tesla=y ), quote Chair Yellen at open conversation with Christine Lagarde, Managing Director of the IMF, finding “equity-market valuations” as “quite high” with “potential dangers” in bond valuations. The DJIA fell 0.5 percent on May 6, 2015, after the comments and then increased 0.5 percent on May 7, 2015 and 1.5 percent on May 8, 2015.
| Fri May 1 | Mon 4 | Tue 5 | Wed 6 | Thu 7 | Fri 8 |
| DJIA 18024.06 -0.3% 1.0% | 18070.40 0.3% 0.3% | 17928.20 -0.5% -0.8% | 17841.98 -1.0% -0.5% | 17924.06 -0.6% 0.5% | 18191.11 0.9% 1.5% |
There are two approaches in theory considered by Bordo (2012Nov20) and Bordo and Lane (2013). The first approach is in the classical works of Milton Friedman and Anna Jacobson Schwartz (1963a, 1987) and Karl Brunner and Allan H. Meltzer (1973). There is a similar approach in Tobin (1969). Friedman and Schwartz (1963a, 66) trace the effects of expansionary monetary policy into increasing initially financial asset prices: “It seems plausible that both nonbank and bank holders of redundant balances will turn first to securities comparable to those they have sold, say, fixed-interest coupon, low-risk obligations. But as they seek to purchase these they will tend to bid up the prices of those issues. Hence they, and also other holders not involved in the initial central bank open-market transactions, will look farther afield: the banks, to their loans; the nonbank holders, to other categories of securities-higher risk fixed-coupon obligations, equities, real property, and so forth.”
The second approach is by the Austrian School arguing that increases in asset prices can become bubbles if monetary policy allows their financing with bank credit. Professor Michael D. Bordo provides clear thought and empirical evidence on the role of “expansionary monetary policy” in inflating asset prices (Bordo2012Nov20, Bordo and Lane 2013). Bordo and Lane (2013) provide revealing narrative of historical episodes of expansionary monetary policy. Bordo and Lane (2013) conclude that policies of depressing interest rates below the target rate or growth of money above the target influences higher asset prices, using a panel of 18 OECD countries from 1920 to 2011. Bordo (2012Nov20) concludes: “that expansionary money is a significant trigger” and “central banks should follow stable monetary policies…based on well understood and credible monetary rules.” Taylor (2007, 2009) explains the housing boom and financial crisis in terms of expansionary monetary policy.
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 44.7 percent relative to the dollar from the high on Jul 15, 2008 to Jul 24, 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% |
| Fri Apr 24 | Mon 27 | Tue 28 | Wed 29 | Thu 30 | May Fri 1 |
| USD/ EUR 1.0874 -0.6% -0.4% | 1.0891 -0.2% -0.2% | 1.0983 -1.0% -0.8% | 1.1130 -2.4% -1.3% | 1.1223 -3.2% -0.8% | 1.1199 -3.0% 0.2% |
In a speech at Brown University on May 22, 2015, Chair Yellen stated (http://www.federalreserve.gov/newsevents/speech/yellen20150522a.htm):
“For this reason, if the economy continues to improve as I expect, I think it will be appropriate at some point this year to take the initial step to raise the federal funds rate target and begin the process of normalizing monetary policy. To support taking this step, however, I will need to see continued improvement in labor market conditions, and I will need to be reasonably confident that inflation will move back to 2 percent over the medium term. After we begin raising the federal funds rate, I anticipate that the pace of normalization is likely to be gradual. The various headwinds that are still restraining the economy, as I said, will likely take some time to fully abate, and the pace of that improvement is highly uncertain.”
The US dollar appreciated 3.8 percent relative to the euro in the week of May 22, 2015:
| Fri May 15 | Mon 18 | Tue 19 | Wed 20 | Thu 21 | Fri 22 |
| USD/ EUR 1.1449 -2.2% -0.3% | 1.1317 1.2% 1.2% | 1.1150 2.6% 1.5% | 1.1096 3.1% 0.5% | 1.1113 2.9% -0.2% | 1.1015 3.8% 0.9% |
The Managing Director of the International Monetary Fund (IMF), Christine Lagarde, warned on Jun 4, 2015, that: (http://blog-imfdirect.imf.org/2015/06/04/u-s-economy-returning-to-growth-but-pockets-of-vulnerability/):
“The Fed’s first rate increase in almost 9 years is being carefully prepared and telegraphed. Nevertheless, regardless of the timing, higher US policy rates could still result in significant market volatility with financial stability consequences that go well beyond US borders. I weighing these risks, we think there is a case for waiting to raise rates until there are more tangible signs of wage or price inflation than are currently evident. Even after the first rate increase, a gradual rise in the federal fund rates will likely be appropriate.”
The President of the European Central Bank (ECB), Mario Draghi, warned on Jun 3, 2015 that (http://www.ecb.europa.eu/press/pressconf/2015/html/is150603.en.html):
“But certainly one lesson is that we should get used to periods of higher volatility. At very low levels of interest rates, asset prices tend to show higher volatility…the Governing Council was unanimous in its assessment that we should look through these developments and maintain a steady monetary policy stance.”
The Chair of the Board of Governors of the Federal Reserve System, Janet L. Yellen, stated on Jul 10, 2015 that (http://www.federalreserve.gov/newsevents/speech/yellen20150710a.htm):
“Based on my outlook, I expect that it will be appropriate at some point later this year to take the first step to raise the federal funds rate and thus begin normalizing monetary policy. But I want to emphasize that the course of the economy and inflation remains highly uncertain, and unanticipated developments could delay or accelerate this first step. I currently anticipate that the appropriate pace of normalization will be gradual, and that monetary policy will need to be highly supportive of economic activity for quite some time. The projections of most of my FOMC colleagues indicate that they have similar expectations for the likely path of the federal funds rate. But, again, both the course of the economy and inflation are uncertain. If progress toward our employment and inflation goals is more rapid than expected, it may be appropriate to remove monetary policy accommodation more quickly. However, if progress toward our goals is slower than anticipated, then the Committee may move more slowly in normalizing policy.”
There is essentially the same view in the Testimony of Chair Yellen in delivering the Semiannual Monetary Policy Report to the Congress on Jul 15, 2015 (http://www.federalreserve.gov/newsevents/testimony/yellen20150715a.htm).
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 used in purchasing default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever-increasing house prices. The suspension of auctions of 30-year Treasuries intended 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 of skills of the relationship banker converts 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 caused by 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 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. Bank charters were the source of profits 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 based on 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. Congress established the Federal Home Loan Bank (FHLB) system 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 Germany. 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.
(5) Historically Sharper Recoveries from Deeper Contractions and Financial Crises. Professor Michael D. Bordo (2012Sep27), at Rutgers University, is providing clear thought on the correct comparison of the current business cycles in the United States with those in United States history. There are two issues raised by Professor Bordo: (1) lumping together countries with different institutions, economic policies and financial systems; and (2) the conclusion that growth is mediocre after financial crises and deep recessions, which is repeated daily in the media, but that Bordo and Haubrich (2012DR) persuasively demonstrate to be inconsistent with United States experience.
Depriving economic history of institutions is perilous as is illustrated by the economic history of Brazil. Douglass C. North (1994) emphasized the key role of institutions in explaining economic history. Rondo E. Cameron (1961, 1967, 1972) applied institutional analysis to banking history. Friedman and Schwartz (1963) analyzed the relation of money, income and prices in the business cycle and related the monetary policy of an important institution, the Federal Reserve System, to the Great Depression. Bordo, Choudhri and Schwartz (1995) analyze the counterfactual of what would have been economic performance if the Fed had used during the Great Depression the Friedman (1960) monetary policy rule of constant growth of money (for analysis of the Great Depression see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 198-217). Alan Meltzer (2004, 2010a,b) analyzed the Federal Reserve System over its history. The reader would be intrigued by Figure 5 in Reinhart and Rogoff (2010FCDC, 15) in which Brazil is classified in external default for seven years between 1828 and 1834 but not again until 64 years later in 1989, above the 50 years of incidence for “serial default”. William R. Summerhill, Jr. (2007SC, 2007IR) has filled this void in scholarly research on nineteenth-century Brazil. There are important conclusions by Summerhill on the exceptional sample of institutional change or actually lack of change, public finance and financial repression in Brazil between 1822 and 1899, combining tools of economics, political science and history. During seven continuous decades, Brazil did not miss a single interest payment with government borrowing without repudiation of debt or default. What is surprising is that Brazil borrowed by means of long-term bonds and, even more surprising, interest rates fell over time. The external debt of Brazil in 1870 was ₤41,275,961 and the domestic debt in the internal market was ₤25,708,711, or 62.3 percent of the total (Summerhill 2007IR, 73).
The experience of Brazil differed from that of Latin America (Summerhill 2007IR). During the six decades when Brazil borrowed without difficulty, Latin American countries becoming independent after 1820 engaged in total defaults, suffering hardship in borrowing abroad. The countries that borrowed again fell again in default during the nineteenth century. Venezuela defaulted in four occasions. Mexico defaulted in 1827, rescheduling its debt eight different times and servicing the debt sporadically. About 44 percent of Latin America’s sovereign debt was in default in 1855 and approximately 86 percent of total government loans defaulted in London originated in Spanish American borrowing countries.
External economies of commitment to secure private rights in sovereign credit would encourage development of private financial institutions, as postulated in classic work by North and Weingast (1989), Summerhill (2007IR, 22). This is how banking institutions critical to the Industrial Revolution were developed in England (Cameron 1967). The obstacle in Brazil found by Summerhill (2007IR) is that sovereign debt credibility was combined with financial repression. There was a break in Brazil of the chain of effects from protecting public borrowing, as in North and Weingast (1989), to development of private financial institutions.
Professor Stephen Haber (2011, 115) analyzes research in various fields of inquiry that lead to seminal conclusions full of implications for current social and economy policy and institutional organization:
“This chapter has looked at the political and economic histories of three New World economies in order to assess how the distribution of power across society shaped the institutions that governed entry into banking. The results are broadly consistent with the view that the distribution of human capital and the ability to project power exert an effect on an economy’s economic institutions. One clear pattern that emerges from these case studies is that representative institutions alone—such as Brazil’s parliament in the nineteenth century—are necessary but not sufficient conditions to generate economic institutions that give rise to broadly based financial development. Financial incumbents can either capture the representative institutions or form coalitions with their members; effective suffrage is necessary in order to align the incentives of political elites with the end users of credit.
Are these results generalizable? Obviously, more detailed case studies beyond the three studied here are necessary before any firm conclusions should be drawn, but the available evidence from large- N studies is broadly consistent with the patterns we find in Mexico, Brazil, and the United States.”
Banking was important in facilitating economic growth in historical periods (Cameron 1961, 1967, 1972; Cameron et al. 1992). Banking is also important currently because small- and medium-size business may have no other form of financing than banks in contrast with many options for larger and more mature companies that have access to capital markets. Calomiris and Haber (2014) find that broad voting rights and institutions restricting coalitions of bankers and populists ensure stable banking systems and access to credit. Barth, Caprio, and Levine (2006) analyze a cross section of sixty-five countries in 2003 and find that democratic political institutions are associated with greater ease in obtaining a bank charter and fewer restrictions on the operation of banks. They also find that the tight regulatory restrictions on banks created by autocratic political institutions are associated with lower credit market development and less bank stability, as well as with more corruption in lending. Bordo and Rousseau (2006) analyze a panel of seventeen countries over the period 1880 to 1997, and produce similar results: “there is a strong, independent effect of proportional representation, frequent elections, female suffrage, and political stability on the size of the financial sector.”
The first sample of Barth, Caprio and Levine (2006) includes 200 regulatory and supervisory practices in 100 countries. The second sample of Barth, Caprio and Levine (2006) increases coverage for 50 more countries and 100 new queries. The conclusions are quite powerful in favor of the private interest view, which explains regulation on motivation of promoting self-interest, in contrast with the public interest view, explaining regulation on the motive of improving public interest. Barth, Caprio and Levine (2006) conclude that disclosure of information would promote sound bank governance by empowering investors in enforcing such governance. Powerful government regulation does not ameliorate bank fragility or promote bank efficiency. The contrast of the private interest view and the public interest view is an important foundation of analysis of bank and financial regulation (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), Regulation of Banks and Finance (2008b)).
Nicia Vilela Luz and Carlos Manuel Peláez (1972, 276) find that:
“The lack of interest on historical moments by economists may explain their emphasis on secular trends in their research on the past instead of changes in the historical process. This may be the origin of why they fill gaps in documentation with their extrapolations.”
Vilela Luz (1960) provides classic research on the struggle for industrialization of Brazil from 1808 to 1930. According to Pelaez 1976, 283) following Cameron:
“The banking law of 1860 placed severe restrictions on two basic modern economic institutions—the corporation and the commercial bank. The growth of the volume of bank credit was one of the most significant factors of financial intermediation and economic growth in the major trading countries of the gold standard group. But Brazil placed strong restrictions on the development of banking and intermediation functions, preventing the channeling of coffee savings into domestic industry at an earlier date.”
Brazil actually abandoned the gold standard during multiple financial crises in the nineteenth century, as it should have to protect domestic economic activity. Pelaez (1975, 447) finds similar experience in the first half of nineteenth-century Brazil:
“Brazil’s experience is particularly interesting in that in the period 1808-1851 there were three types of monetary systems. Between 1808 and 1829, there was only one government-related Bank of Brazil, enjoying a perfect monopoly of banking services. No new banks were established in the 1830s after the liquidation of the Bank of Brazil in 1829. During the coffee boom in the late 1830s and 1840s, a system of banks of issue, patterned after similar institutions in the industrial countries [Cameron 1967], supplied the financial services required in the first stage of modernization of the export economy.”
Financial crises in the advanced economies transmitted to nineteenth-century Brazil by the arrival of a ship (Pelaez and Suzigan 1981). The explanation of those crises and the economy of Brazil requires knowledge and roles of institutions, economic policies and the financial system chosen by Brazil, in agreement with Bordo (2012Sep27).
The departing theoretical framework of Bordo and Haubrich (2012DR) is the plucking model of Friedman (1964, 1988). Friedman (1988, 1) recalls “I was led to the model in the course of investigating the direction of influence between money and income. Did the common cyclical fluctuation in money and income reflect primarily the influence of money on income or of income on money?” Friedman (1964, 1988) finds useful for this purpose to analyze the relation between expansions and contractions. Analyzing the business cycle in the United States between 1870 and 1961, Friedman (1964, 15) found that “a large contraction in output tends to be followed on the average by a large business expansion; a mild contraction, by a mild expansion.” The depth of the contraction opens up more room in the movement toward full employment (Friedman 1964, 17):
“Output is viewed as bumping along the ceiling of maximum feasible output except that every now and then it is plucked down by a cyclical contraction. Given institutional rigidities and prices, the contraction takes in considerable measure the form of a decline in output. Since there is no physical limit to the decline short of zero output, the size of the decline in output can vary widely. When subsequent recovery sets in, it tends to return output to the ceiling; it cannot go beyond, so there is an upper limit to output and the amplitude of the expansion tends to be correlated with the amplitude of the contraction.”
Kim and Nelson (1999) test the asymmetric plucking model of Friedman (1964, 1988) relative to a symmetric model using reference cycles of the NBER and find evidence supporting the Friedman model. Bordo and Haubrich (2012DR) analyze 27 cycles beginning in 1872, using various measures of financial crises while considering different regulatory and monetary regimes. The revealing conclusion of Bordo and Haubrich (2012DR, 2) is that:
“Our analysis of the data shows that steep expansions tend to follow deep contractions, though this depends heavily on when the recovery is measured. In contrast to much conventional wisdom, the stylized fact that deep contractions breed strong recoveries is particularly true when there is a financial crisis. In fact, on average, it is cycles without a financial crisis that show the weakest relation between contraction depth and recovery strength. For many configurations, the evidence for a robust bounce-back is stronger for cycles with financial crises than those without.”
The average rate of growth of real GDP in expansions after recessions with financial crises was 8 percent but only 6.9 percent on average for recessions without financial crises (Bordo 2012Sep27). Real GDP declined 12 percent in the Panic of 1907 and increased 13 percent in the recovery, consistent with the plucking model of Friedman (Bordo 2012Sep27). Bordo (2012Sep27) finds two probable explanations for the weak recovery during the current economic cycle: (1) collapse of United States housing; and (2) uncertainty originating in fiscal policy, regulation and structural changes. There are serious doubts if monetary policy is adequate to recover the economy under these conditions.
Lucas (2011May) estimates US economic growth in the long-term at 3 percent per year and about 2 percent per year in per capita terms. There are displacements from this trend caused by events such as wars and recessions but the economy grows much faster during the expansion, compensating for the contraction and maintaining trend growth over the entire cycle. Historical US GDP data exhibit remarkable growth: Lucas (2011May) estimates an increase of US real income per person by a factor of 12 in the period from 1870 to 2010. The explanation by Lucas (2011May) of this remarkable growth experience is that government provided stability and education while elements of “free-market capitalism” were an important driver of long-term growth and prosperity. Lucas sharpens this analysis by comparison with the long-term growth experience of G7 countries (US, UK, France, Germany, Canada, Italy and Japan) and Spain from 1870 to 2010. Countries benefitted from “common civilization” and “technology” to “catch up” with the early growth leaders of the US and UK, eventually growing at a faster rate. Significant part of this catch up occurred after World War II. Lucas (2011May) finds that the catch up stalled in the 1970s. The analysis of Lucas (2011May) is that the 20-40 percent gap that developed originated in differences in relative taxation and regulation that discouraged savings and work incentives in comparison with the US. A larger welfare and regulatory state, according to Lucas (2011May), could be the cause of the 20-40 percent gap. Cobet and Wilson (2002) provide estimates of output per hour and unit labor costs in national currency and US dollars for the US, Japan and Germany from 1950 to 2000 (see Pelaez and Pelaez, The Global Recession Risk (2007), 137-44). The average yearly rate of productivity change from 1950 to 2000 was 2.9 percent in the US, 6.3 percent for Japan and 4.7 percent for Germany while unit labor costs in USD increased at 2.6 percent in the US, 4.7 percent in Japan and 4.3 percent in Germany. From 1995 to 2000, output per hour increased at the average yearly rate of 4.6 percent in the US, 3.9 percent in Japan and 2.6 percent in Germany while unit labor costs in USD fell at minus 0.7 percent in the US, 4.3 percent in Japan and 7.5 percent in Germany. There was increase in productivity growth in Japan and France within the G7 in the second half of the 1990s but significantly lower than the acceleration of 1.3 percentage points per year in the US. The key indicator of growth of real income per capita, which is what a person earns after inflation, measures long-term economic growth and prosperity. A refined concept would include real disposable income per capita, which is what a person earns after inflation and taxes.
Table IB-1 provides the data required for broader comparison of long-term and cyclical performance of the United States economy. Revisions and enhancements of United States GDP and personal income accounts by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) provide important information on long-term growth and cyclical behavior. First, Long-term performance. Using annual data, US GDP grew at the average rate of 3.3 percent per year from 1929 to 2014 and at 3.2 percent per year from 1947 to 2014. Real disposable income grew at the average yearly rate of 3.2 percent from 1929 to 2014 and at 3.7 percent from 1947 to 1999. Real disposable income per capita grew at the average yearly rate of 2.0 percent from 1929 to 2014 and at 2.3 percent from 1947 to 1999. US economic growth was much faster during expansions, compensating contractions in maintaining trend growth for whole cycles. Using annual data, US real disposable income grew at the average yearly rate of 3.5 percent from 1980 to 1989 and real disposable income per capita at 2.6 percent. The US economy has lost its dynamism in the current cycle: real disposable income grew at the yearly average rate of 1.5 percent from 2006 to 2014 and real disposable income per capita at 0.7 percent. Real disposable income grew at the average rate of 1.4 percent from 2007 to 2014 and real disposable income per capita at 0.6 percent. Table IB-1 illustrates the contradiction of long-term growth with the proposition of secular stagnation (Hansen 1938, 1938, 1941 with early critique by Simons (1942). Secular stagnation would occur over long periods. Table IB-1 also provides the corresponding rates of population growth that is only marginally lower at 0.8 to 0.9 percent recently from 1.1 percent over the long-term. GDP growth fell abruptly from 2.6 percent on average from 2000 to 2006 to 1.2 percent from 2006 to 2014 and 1.1 percent from 2007 to 2014 and real disposable income growth fell from 2.9 percent on average from 2000 to 2006 to 1.5 percent from 2006 to 2014. The decline of growth of real per capita disposable income is even sharper from average 2.0 percent from 2000 to 2006 to 0.7 percent from 2006 to 2014 and 0.6 percent from 2007 to 2014 while population growth was 0.8 percent on average. Lazear and Spletzer (2012JHJul122) provide theory and measurements showing that cyclic factors explain currently depressed labor markets. This is also the case of the overall economy. Second, first four quarters of expansion. Growth in the first four quarters of expansion is critical in recovering loss of output and employment occurring during the contraction. In the first four quarters of expansion from IQ1983 to IVQ1983: GDP increased 7.8 percent, real disposable personal income 5.3 percent and real disposable income per capita 4.4 percent. In the first four quarters of expansion from IIIQ2009 to IIQ2010: GDP increased 2.7 percent, real disposable personal income 0.2 percent and real disposable income per capita decreased 0.7 percent. Third, first 23 quarters of expansion. In the expansion from IQ1983 to IIIQ1988: GDP grew 30.9 percent at the annual equivalent rate of 4.8 percent; real disposable income grew 26.3 percent at the annual equivalent rate of 4.1 percent; and real disposable income per capita grew 20.0 percent at the annual equivalent rate of 3.2 percent. In the expansion from IIIQ2009 to IQ2015: GDP grew 13.5 percent at the annual equivalent rate of 2.2 percent; real disposable income grew 12.3 percent at the annual equivalent rate of 2.0 percent; and real disposable personal income per capita grew 6.3 percent at the annual equivalent rate of 1.1 percent. Fourth, entire quarterly cycle. In the entire cycle combining contraction and expansion from IQ1980 to IIIQ1988: GDP grew 30.7 percent at the annual equivalent rate of 3.0 percent; real disposable personal income grew 33.6 percent at the annual equivalent rate of 3.3 percent; and real disposable personal income per capita 23.2 percent at the annual equivalent rate of 2.3 percent. In the entire cycle combining contraction and expansion from IVQ2007 to IQ2015: GDP grew 8.6 percent at the annual equivalent rate of 1.2 percent; real disposable personal income 12.9 percent at the annual equivalent rate of 1.7 percent; and real disposable personal income per capita 6.7 percent at the annual equivalent rate of 0.9 percent. The United States grew during its history at high rates of per capita income that made its economy the largest in the world. That dynamism is disappearing. Bordo (2012 Sep27) and Bordo and Haubrich (2012DR) provide strong evidence that recoveries have been faster after deeper recessions and recessions with financial crises, casting serious doubts on the conventional explanation of weak growth during the current expansion allegedly because of the depth of the contraction of 4.2 percent from IVQ2007 to IIQ2009 and the financial crisis. The proposition of secular stagnation should explain a long-term process of decay and not the actual abrupt collapse of the economy and labor markets currently.
Table IB-1, US, GDP, Real Disposable Personal Income, Real Disposable Income per Capita and Population Long-term and in 1983-88 and 2007-2014, %
| Long-term Average ∆% per Year | GDP | Population | |
| 1929-2014 | 3.3 | 1.1 | |
| 1947-2014 | 3.2 | 1.2 | |
| 1947-1999 | 3.6 | 1.3 | |
| 1980-1989 | 3.5 | 0.9 | |
| 2000-2014 | 1.8 | 0.9 | |
| 2000-2006 | 2.6 | 0.9 | |
| 2006-2014 | 1.2 | 0.8 | |
| 2007-2014 | 1.1 | 0.8 | |
| Long-term Average ∆% per Year | Real Disposable Income | Real Disposable Income per Capita | Population |
| 1929-2014 | 3.2 | 2.0 | 1.1 |
| 1947-1999 | 3.7 | 2.3 | 1.3 |
| 2000-2014 | 2.1 | 1.2 | 0.9 |
| 2000-2006 | 2.9 | 2.0 | 0.9 |
| 2006-2014 | 1.5 | 0.7 | 0.8 |
| 2007-2014 | 1.4 | 0.6 | 0.8 |
| Whole Cycles Average ∆% per Year | |||
| 1980-1989 | 3.5 | 2.6 | 0.9 |
| 2006-2014 | 1.5 | 0.7 | 0.8 |
| 2007-2014 | 1.4 | 0.6 | 0.8 |
| Comparison of Cycles | # Quarters | ∆% | ∆% Annual Equivalent |
| GDP | |||
| I83 to IV83 I83 to IQ87 I83 to II87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 IQ1983 to IIIQ1988 | 4 17 18 19 20 21 22 23 | 7.8 23.1 24.5 25.6 27.7 28.4 30.1 30.9 | 7.8 5.0 5.0 4.9 5.0 4.9 4.9 4.8 |
| RDPI | |||
| I83 to IV83 I83 to I87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 | 4 17 19 20 21 22 23 | 5.3 19.5 20.5 22.1 23.8 25.1 26.3 | 5.3 4.3 4.0 4.1 4.2 4.2 4.1 |
| RDPI Per Capita | |||
| I83 to IV83 I83 to I87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 | 4 17 19 20 21 22 23 | 4.4 15.1 15.5 16.7 18.2 19.2 20.0 | 4.4 3.4 3.1 3.1 3.2 3.2 3.2 |
| Whole Cycle IQ1980 to IIIQ1988 | |||
| GDP | 36 | 30.7 | 3.0 |
| RDPI | 36 | 33.6 | 3.3 |
| RDPI per Capita | 36 | 23.2 | 2.3 |
| Population | 36 | 8.5 | 0.9 |
| GDP | |||
| III09 to II10 III09 to I15 | 4 23 | 2.7 13.5 | 2.7 2.2 |
| RDPI | |||
| III09 to II10 III09 to I15 | 4 23 | 0.2 12.3 | 0.2 2.0 |
| RDPI per Capita | |||
| III09 to II10 III09 to I15 | 4 23 | -0.7 6.3 | -0.7 1.1 |
| Population | |||
| III09 to II010 III09 to I15 | 4 23 | 0.8 4.5 | 0.8 0.8 |
| IVQ2007 to IQ2015 | 29 | ||
| GDP | 29 | 8.6 | 1.2 |
| RDPI | 29 | 12.9 | 1.7 |
| RDPI per Capita | 29 | 6.7 | 0.9 |
| Population | 29 | 5.9 | 0.8 |
RDPI: Real Disposable Personal Income
Source: US Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm
There are seven basic facts illustrating the current economic disaster of the United States:
- GDP maintained trend growth in the entire business cycle from IQ1980 to IIIQI988, including contractions and expansions. GDP is well below trend in the entire business cycle from IVQ2007 to IQ2015, including contractions and expansions
- Per capita real disposable income exceeded trend growth in the 1980s but is substantially below trend in IQ2015
- Level of employed persons increased in the 1980s but declined/stagnated into IQ2015
- Level of full-time employed persons increased in the 1980s but declined/stagnated into IQ2015
- Level unemployed, unemployment rate and employed part-time for economic reasons fell in the recovery from the recessions in the 1980s but not substantially in the recovery since IVQ2009
- Wealth of households and nonprofit organizations soared in the 1980s but stagnated in real terms into IVQ2014
- Gross private domestic investment increased sharply from IQ1980 to IIIQ1988 but gross private domestic investment stagnated and private fixed investment stagnated from IVQ2007 into IQ2015
There is a critical issue of the United States economy will be able in the future to attain again the level of activity and prosperity of projected trend growth. Growth at trend during the entire business cycles built the largest economy in the world but there may be an adverse, permanent weakness in United States economic performance and prosperity. Table IB-2 provides data for analysis of these seven basic facts. The seven blocks of Table IB-2 are separated initially after individual discussion of each one followed by the full Table IB-2.
1. Trend Growth.
i. As shown in Table IB-2, actual GDP grew cumulatively 30.2 percent from IQ1980 to IIIQ1988, which is relatively close to what trend growth would have been at 30.5 percent. Real GDP grew 30.7 percent from IVQ1979 to IIIQ1988. Rapid growth at the average annual rate of 4.8 percent per quarter during the expansion from IQ1983 to IIIQ1988 erased the loss of GDP of 4.7 percent during the contractions and maintained trend growth at 3.0 percent for GDP and 3.3 percent for real disposable personal income over the entire cycle.
ii. In contrast, cumulative growth from IVQ2007 to IQ2015 was 8.6 percent while trend growth would have been 23.9 percent. GDP in IQ2015 would be $18,574.8 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,287.1 billion than actual $16,287.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 25.0 million unemployed or underemployed equivalent to actual unemployment/underemployment of 15.1 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html). US GDP in IQ2015 is 12.3 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,287.7 billion in IQ2015 or 8.6 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 May 1919 to May 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 126.2585 in May 2015. The actual index NSA in May 2015 is 101.5858, which is 19.5 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014. Using trend growth of 2.4 percent per year, the index would increase to 118.3245 in May 2015. The output of manufacturing at 101.5858 in May 2015 is 14.1 percent below trend under this alternative calculation.
The civilian labor force consists of people who are available and willing to work and who have searched for employment recently. The labor force of the US grew 9.4 percent from 142.828 million in Jan 2001 to 156.255 million in Jul 2009. The civilian labor force is 1.3 percent higher at 158.283 million in Jun 2015 than in Jul 2009, all numbers not seasonally adjusted. Chart I-3 shows the flattening of the curve of expansion of the labor force and its decline in 2010 and 2011. The ratio of the labor force of 154.871 million in Jul 2007 to the noninstitutional population of 231.958 million in Jul 2007 was 66.8 percent while the ratio of the labor force of 158.823 million in Jun 2015 to the noninstitutional population of 250.663 million in Jun 2015 was 63.1 percent. The labor force of the US in Jun 2015 corresponding to 66.8 percent of participation in the population would be 167.443 million (0.668 x 250.663). The difference between the measured labor force in Jun 2015 of 158.823 million and the labor force in Jun 2015 with participation rate of 66.8 percent (as in Jul 2007) of 167.443 million is 8.620 million. The level of the labor force in the US has stagnated and is 8.620 million lower than what it would have been had the same participation rate been maintained. Millions of people have abandoned their search for employment because they believe there are no jobs available for them. The key issue is whether the decline in participation of the population in the labor force is the result of people giving up on finding another job.
| Period IQ1980 to IIIQ1988 | |
| GDP SAAR USD Billions | |
| IQ1980 | 6,524.9 |
| IIIQ1988 | 8,498.3 |
| ∆% IQ1980 to IIIQ1988 (30.7 percent from IVQ1979 $6503.9 billion) | 30.2 |
| ∆% Trend Growth IQ1980 to IIIQ1988 | 30.5 |
| Period IVQ2007 to IQ2015 | |
| GDP SAAR USD Billions | |
| IVQ2007 | 14,991.8 |
| IQ2015 | 16,287.7 |
| ∆% IVQ2007 to IQ2015 Actual | 8.6 |
| ∆% IVQ2007 to IQ2015 Trend Growth | 23.9 |
2. Stagnating Per Capita Real Disposable Income
i. In the entire business cycle from IQ1980 to IIIQ1988, as shown in Table IB-2, per capita real disposable income, or what is left per person after inflation and taxes, grew cumulatively 23.2 percent, which is close to what would have been trend growth of 19.5 percent.
ii. In contrast, in the entire business cycle from IVQ2007 to IQ2015, per capita real disposable income increased 6.7 percent while trend growth would have been 15.4 percent. Income available after inflation and taxes is about the same as before the contraction after 23 consecutive quarters of GDP growth at mediocre rates relative to those prevailing during historical cyclical expansions. Growth of personal income during the expansion has been tepid even with the new revisions. In IVQ2012, nominal disposable personal income grew at the SAAR of 13.8 percent and real disposable personal income at 11.8 percent (Table 2.1 http://bea.gov/iTable/index_nipa.cfm). The BEA explains as follows: “Personal income in November and December was boosted by accelerated and special dividend payments to persons and by accelerated bonus payments and other irregular pay in private wages and salaries in anticipation of changes in individual income tax rates. Personal income in December was also boosted by lump-sum social security benefit payments” (page 2 at http://www.bea.gov/newsreleases/national/pi/2013/pdf/pi1212.pdf pages 1-2 at http://www.bea.gov/newsreleases/national/pi/2013/pdf/pi0113.pdf). The Bureau of Economic Analysis explains as (http://www.bea.gov/newsreleases/national/pi/2013/pdf/pi0213.pdf 2-3): “The January estimate of employee contributions for government social insurance reflected the expiration of the “payroll tax holiday,” that increased the social security contribution rate for employees and self-employed workers by 2.0 percentage points, or $114.1 billion at an annual rate. For additional information, see FAQ on “How did the expiration of the payroll tax holiday affect personal income for January 2013?” at www.bea.gov. The January estimate of employee contributions for government social insurance also reflected an increase in the monthly premiums paid by participants in the supplementary medical insurance program, in the hospital insurance provisions of the Patient Protection and Affordable Care Act, and in the social security taxable wage base.”
The increase was provided in the “fiscal cliff” law H.R. 8 American Taxpayer Relief Act of 2012 (http://www.gpo.gov/fdsys/pkg/BILLS-112hr8eas/pdf/BILLS-112hr8eas.pdf).
In IQ2013, personal income fell at the SAAR of minus 8.6 percent; real personal income excluding current transfer receipts at minus 11.9 percent; and real disposable personal income at minus 12.6 percent (Table 6 at http://www.bea.gov/newsreleases/national/pi/2014/pdf/pi1014.pdf). The BEA explains as follows (page 3 at http://www.bea.gov/newsreleases/national/pi/2013/pdf/pi0313.pdf):
“The February and January changes in disposable personal income (DPI) mainly reflected the effect of special factors in January, such as the expiration of the “payroll tax holiday” and the acceleration of bonuses and personal dividends to November and to December in anticipation of changes in individual tax rates.”
In IIQ2013, personal income grew at 4.5 percent, real personal income excluding current transfer receipts at 4.6 percent and real disposable income at 3.8 percent (http://www.bea.gov/newsreleases/national/pi/2014/pdf/pi1114.pdf). In IIIQ2013, personal income grew at 3.3 percent, real personal income excluding current transfers at 1.5 percent and real disposable income at 2.0 percent (Table 6 at http://www.bea.gov/newsreleases/national/pi/2015/pdf/pi0215.pdf). In IVQ2013, personal income grew at 1.8 percent and real disposable income at 0.2 percent (Table 6 at http://www.bea.gov/newsreleases/national/pi/2015/pdf/pi0515.pdf). In IQ2014, personal income grew at 4.9 percent in nominal terms and 3.2 percent in real terms excluding current transfer receipts while nominal disposable income grew at 4.8 percent and real disposable income at 3.4 percent (http://www.bea.gov/newsreleases/national/pi/2015/pdf/pi0515.pdf). In IIQ2014, personal income grew at 4.9 percent and 2.2 percent in real terms excluding current transfers. Nominal disposable income grew at 5.5 percent and at 3.1 percent in real terms (http://www.bea.gov/newsreleases/national/pi/2015/pdf/pi0515.pdf). In IIIQ2014, personal income grew at 4.2 percent, real personal income excluding current transfers at 2.7 percent and real disposable personal income at 2.4 percent (http://www.bea.gov/newsreleases/national/pi/2015/pdf/pi0515.pdf). In IVQ2014, personal income grew at 4.6 percent in nominal terms and at 5.5 percent in real terms excluding current transfers while nominal disposable income grew at 3.7 percent in nominal terms and at 4.1 percent in real terms (http://www.bea.gov/newsreleases/national/pi/2015/pdf/pi0515.pdf). In IQ2015, nominal personal income grew at 4.2 percent and at 5.4 percent in real terms excluding current transfer receipts while nominal disposable income grew at 3.2 percent and at 5.3 percent in real terms (http://www.bea.gov/newsreleases/national/pi/2015/pdf/pi0515.pdf).
| Period IQ1980 to IIIQ1988 |
| Real Disposable Personal Income per Capita IQ1980 Chained 2009 USD | 20,241 |
| Real Disposable Personal Income per Capita IIIQ1988 Chained 2009 USD | 24,917 |
| ∆% IQ1980 to IIIQ1988 (21.3 percent from IVQ1979 $20,230) | 23.2 |
| ∆% Trend Growth | 19.5 |
| Period IVQ2007 to IQ2015 |
| Real Disposable Personal Income per Capita IVQ2007 Chained 2009 USD | 35,819 |
| Real Disposable Personal Income per Capita IQ2015 Chained 2009 USD | 38,210 |
| ∆% IVQ2007 to IQ2015 | 6.7 |
| ∆% Trend Growth | 15.4 |
3. Number of Employed Persons
i. As shown in Table IB-2, the number of employed persons increased over the entire business cycle from 98.527 million not seasonally adjusted (NSA) in IQ1980 to 115.474 million NSA in IIIQ1988 or by 17.2 percent.
ii. In contrast, during the entire business cycle the number employed stagnated from 146.334 million in IVQ2007 to 147.635 million in IQ2015 or by 0.9 percent higher. There are 25.0 million persons unemployed or underemployed, which is 15.1 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html). The number employed in Jun 2015 was 149.645 million (NSA) or 2.330 million more people with jobs relative to the peak of 147.315 million in Jul 2007 while the civilian noninstitutional population of ages 16 years and over increased from 231.958 million in Jul 2007 to 250.663 million in Jun 2015 or by 18.705 million. The number employed increased 1.6 percent from Jul 2007 to Jun 2015 while the noninstitutional civilian population of ages of 16 years and over, or those available for work, increased 8.1 percent. The ratio of employment to population in Jul 2007 was 63.5 percent (147.315 million employment as percent of population of 231.958 million). The same ratio in Jun 2015 would result in 159.171 million jobs (0.635 multiplied by noninstitutional civilian population of 250.663 million). There are effectively 9.526 million fewer jobs in Jun 2015 than in Jul 2007, or 159.171 million minus 149.645 million. There is actually not sufficient job creation in merely absorbing new entrants in the labor force because of those dropping from job searches, worsening the stock of unemployed or underemployed in involuntary part-time jobs.
| Period IQ1980 to IIIQ1988 |
| Employed Millions IQ1980 NSA End of Quarter | 98.527 |
| Employed Millions IIIQ1988 NSA End of Quarter | 115.474 |
| ∆% Employed IQ1980 to IIQ1988 | 17.2 |
| Period IVQ2007 to IQ2015 |
| Employed Millions IVQ2007 NSA End of Quarter | 146.334 |
| Employed Millions IQ2015 NSA End of Quarter | 147.635 |
| ∆% Employed IVQ2007 to IQ2015 | 0.9 |
4. Number of Full-Time Employed Persons
i. As shown in Table IB-2, during the entire business cycle in the 1980s, including contractions and expansion, the number of employed full-time rose from 81.280 million NSA in IQ1980 to 96.033 million NSA in IIIQ1988 or 18.2 percent.
ii. In contrast, during the entire current business cycle, including contraction and expansion, the number of persons employed full-time fell from 121.042 million in IVQ2007 to 119.981 million in IQ2015 or by minus 0.9 percent. The number with full-time jobs fell from a high of 123.219 million in Jul 2007 to 108.777 million in Jan 2010 or by 14.442 million. The number with full-time jobs in Jun 2015 is 122.268 million, which is lower by 0.951 million relative to the peak of 123.219 million in Jul 2007. The magnitude of the stress in US labor markets is magnified by the increase in the civilian noninstitutional population of the United States from 231.958 million in Jul 2007 to 250.663 million in Jun 2015 or by 18.705 million (http://www.bls.gov/data/) while in the same period the number of full-time jobs fell 0.951 million. The ratio of full-time jobs of 123.219 million in Jul 2007 to civilian noninstitutional population of 231.958 million was 53.1 percent. If that ratio had remained the same, there would be 133.102 million full-time jobs with population of 250.663 million in Jun 2015 (0.531 x 250.663) or 10.834 million fewer full-time jobs relative to actual 122.268 million. There appear to be around 10 million fewer full-time jobs in the US than before the global recession while population increased around 18 million. Mediocre GDP growth is the main culprit of the fractured US labor market.
4. Number of Full-time Employed Persons
| Period IQ1980 to IIIQ1988 |
| Employed Full-time Millions IQ1980 NSA End of Quarter | 81.280 |
| Employed Full-time Millions IIIQ1988 NSA End of Quarter | 96.033 |
| ∆% Full-time Employed IQ1980 to IIIQ1988 | 18.2 |
| Period IVQ2007 to IQ2015 |
| Employed Full-time Millions IVQ2007 NSA End of Quarter | 121.042 |
| Employed Full-time Millions IQ2015 NSA End of Quarter | 119.981 |
| ∆% Full-time Employed IVQ2007 to IQ2015 | -0.9 |
5. Unemployed, Unemployment Rate and Employed Part-time for Economic Reasons.
i. As shown in Table IB-2 and in the following block, in the cycle from IQ1980 to IIQ1988: (a) The rate of unemployment was lower at 5.2 percent in IIIQ1988 relative to 6.6 percent in IQ1980. (b) The number unemployed decreased from 6.983 million in IQ1980 to 6.368 million in IIIQ1988 or 8.8 percent. (c) The number employed part-time for economic reasons increased 29.8 percent from 3.624 million in IQ1980 to 4.704 million in IIIQ1988.
ii. In contrast, in the economic cycle from IVQ2007 to IQ2015: (a) The rate of unemployment increased from 4.8 percent in IVQ2007 to 5.6 percent in IQ2015. (b) The number unemployed increased 17.8 percent from 7.371 million in IVQ2007 to 8.682 million in IQ2015. (c) The number employed part-time for economic reasons because they could not find any other job increased 40.5 percent from 4.750 million in IVQ2007 to 6.672 million in IQ2015. (d) U6 Total Unemployed plus all marginally attached workers plus total employed part time for economic reasons as percent of all civilian labor force plus all marginally attached workers NSA increased from 8.7 percent in IVQ2007 to 11.0 percent in IQ2015.
| Period IQ1980 to IIIQ1988 |
| Unemployment Rate IQ1980 NSA End of Quarter | 6.6 |
| Unemployment Rate IIIQ1988 NSA End of Quarter | 5.2 |
| Unemployed IQ1980 Millions End of Quarter | 6.983 |
| Unemployed IIIQ1988 Millions End of Quarter | 6.368 |
| ∆% | -8.8 |
| Employed Part-time Economic Reasons Millions IQ1980 End of Quarter | 3.624 |
| Employed Part-time Economic Reasons Millions IIIQ1988 End of Quarter | 4.704 |
| ∆% | 29.8 |
| Period IVQ2007 to IQ2015 |
| Unemployment Rate IVQ2007 NSA End of Quarter | 4.8 |
| Unemployment Rate IQ2015 NSA End of Quarter | 5.6 |
| Unemployed IVQ2007 Millions End of Quarter | 7.371 |
| Unemployed IQ2015 Millions End of Quarter | 8.682 |
| ∆% | 17.8 |
| Employed Part-time Economic Reasons IVQ2007 Millions End of Quarter | 4.750 |
| Employed Part-time Economic Reasons Millions IQ2015 End of Quarter | 6.672 |
| ∆% | 40.5 |
| U6 Total Unemployed plus all marginally attached workers plus total employed part time for economic reasons as percent of all civilian labor force plus all marginally attached workers NSA | |
| IVQ2007 | 8.7 |
| IQ2015 | 11.0 |
6. Wealth of Households and Nonprofit Organizations.
The comparison of net worth of households and nonprofit organizations in the entire economic cycle from IQ1980 (and from IVQ1979) to IIIQ1988 and from IVQ2007 to IQ2015 is provided in Table IIA-5. The data reveal the following facts for the cycles in the 1980s:
- IVQ1979 to IIIQ1988. Net worth increased 112.3 percent from IVQ1979 to IIIQ1988, the all items CPI index increased 56.2 percent from 76.7 in Dec 1979 to 119.8 in Sep 1988 and real net worth increased 35.9 percent.
- IQ1980 to IVQ1985. Net worth increased 65.4 percent, the all items CPI index increased 36.5 percent from 80.1 in Mar 1980 to 109.3 in Dec 1985 and real net worth increased 21.2 percent.
- IVQ1979 to IVQ1985. Net worth increased 68.8 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 IIIQ1988. Net worth increased 107.9 percent, the all items CPI index increased 49.6 percent from 80.1 in Mar 1980 to 119.8 in Sep 1988 and real net worth increased 39.0 percent.
There is disastrous performance in the current economic cycle:
- IVQ2007 to IQ2015. Net worth increased 27.3 percent, the all items CPI increased 12.4 percent from 210.036 in Dec 2007 to 236.119 in Mar 2015 and real or inflation adjusted net worth increased 13.2 percent. Real estate assets adjusted for inflation fell 8.1 percent.
The explanation is partly in the sharp decline of wealth of households and nonprofit organizations and partly in the mediocre growth rates of the cyclical expansion beginning in IIIQ2009. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.2 percent on average in the cyclical expansion in the 23 quarters from IIIQ2009 to IQ2015. 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 IQ2015 (http://www.bea.gov/newsreleases/national/gdp/2015/pdf/gdp1q15_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/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.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 IQ2015 would have accumulated to 23.9 percent. GDP in IQ2015 would be $18,574.8 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2,287.1 billion than actual $16,287.7 billion. There are about two trillion dollars of GDP less than at trend, explaining the 25.0 million unemployed or underemployed equivalent to actual unemployment/underemployment of 15.1 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html). US GDP in IQ2015 is 12.3 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,287.7 billion in IQ2015 or 8.6 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 Jun 1919 to Jun 2015. Growth at 3.3 percent per year would raise the NSA index of manufacturing output from 99.2392 in Dec 2007 to 126.6006 in Jun 2015. The actual index NSA in Jun 2015 is 104.0319, which is 17.8 percent below trend. Manufacturing output grew at average 2.4 percent between Dec 1986 and Dec 2014. Using trend growth of 2.4 percent per year, the index would increase to 118.5586 in Jun 2015. The output of manufacturing at 104.0319 in Jun 2015 is 12.3 percent below trend under this alternative calculation.
| 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 III1988 | 15,277.2 16,290.7 16,840.2 17,494.6 17,784.0 18,195.2 18,021.9 18,459.1 18,900.1 19,209.2 |
| ∆ USD Billions IVQ1985 IVQ1979 to IIIQ1988 IQ1980-IVQ1985 IQ1980-IIIQ1986 IQ1980-IVQ1986 IQ1980-IQ1987 IQ1980-IIQ1987 IQ1980-IIIQ1987 IQ1980-IVQ1987 IQ1980-IQ1988 IQ1980-IIQ1988 IQ1980-IIIQ1988 | +6,229.4 ∆%68.8 R∆%18.5 +10161.4 ∆%112.3 R∆%35.9 +6,038.6 ∆%65.4 R∆%21.2 +7,052.1 ∆%76.3 R∆%28.2 +7,601.6 ∆%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 +8783.2 ∆%95.1 R∆%35.4 +9256.5 ∆%100.2 R∆%37.6 +9661.5 ∆%104.6 R∆%38.9 +9970.6 ∆%107.9 R∆%39.0 |
| Period IVQ2007 to IQ2015 | |
| Net Worth of Households and Nonprofit Organizations USD Millions | |
| IVQ2007 | 66,721.8 |
| IQ2015 | 84,924.6 |
| ∆ USD Billions | +18,202.8 ∆%27.3 R∆%13.2 |
Net Worth = Assets – Liabilities. R∆% real percentage change or adjusted for CPI percentage change.
Source: Board of Governors of the Federal Reserve System. 2015. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2015. Washington, DC, Federal Reserve System, Jun 11. http://www.federalreserve.gov/releases/z1/.
7. Gross Private Domestic Investment.
i. The comparison of gross private domestic investment in the entire economic cycles from IQ1980 to IIIQ1988 and from IVQ2007 to IQ2015 is in the following block and in Table IB-2. Gross private domestic investment increased from $951.6 billion in IQ1980 to $1,229.7 billion in IIIQ1988 or by 29.2 percent.
ii In the current cycle, gross private domestic investment increased from $2,605.2 billion in IVQ2007 to $2,792.8 billion in IQ2015, or 7.2 percent. Private fixed investment edged from $2,586.3 billion in IVQ2007 to $2,670.7 billion in IQ2015, or increase by 3.3 percent.
| Period IQ1980 to IIIQ1988 | |
| Gross Private Domestic Investment USD 2009 Billions | |
| IQ1980 | 951.6 |
| IIIQ1988 | 1,229.7 |
| ∆% | 29.2 |
| Period IVQ2007 to IQ2015 | |
| Gross Private Domestic Investment USD Billions | |
| IQ2007 | 2,605.2 |
| IQ2015 | 2,792.8 |
| ∆% | 7.2 |
| Private Fixed Investment USD 2009 Billions | |
| IVQ2007 | 2,586.3 |
| IQ2015 | 2,670.7 |
| ∆% | 3.3 |
Table IB-2, US, GDP and Real Disposable Personal Income per Capita Actual and Trend Growth and Employment, 1980-1985 and 2007-2012, SAAR USD Billions, Millions of Persons and ∆%
| Period IQ1980 to IIIQ1988 | |
| GDP SAAR USD Billions | |
| IQ1980 | 6,524.9 |
| IIIQ1988 | 8,498.3 |
| ∆% IQ1980 to IIIQ1988 (30.7 percent from IVQ1979 $6503.9 billion) | 30.2 |
| ∆% Trend Growth IQ1980 to IIIQ1988 | 30.5 |
| Real Disposable Personal Income per Capita IQ1980 Chained 2009 USD | 20,241 |
| Real Disposable Personal Income per Capita IIIQ1988 Chained 2009 USD | 24,917 |
| ∆% IQ1980 to IIIQ1988 (22.3 percent from IVQ1979 $20,230 billion) | 23.2 |
| ∆% Trend Growth | 19.5 |
| Employed Millions IQ1980 NSA End of Quarter | 98.527 |
| Employed Millions IIIQ1988 NSA End of Quarter | 115.474 |
| ∆% Employed IQ1980 to IIIQ1988 | 17.2 |
| Employed Full-time Millions IQ1980 NSA End of Quarter | 81.280 |
| Employed Full-time Millions IIIQ1988 NSA End of Quarter | 96.033 |
| ∆% Full-time Employed IQ1980 to IQ1II988 | 18.2 |
| Unemployment Rate IQ1980 NSA End of Quarter | 6.6 |
| Unemployment Rate IIIQ1988 NSA End of Quarter | 5.2 |
| Unemployed IQ1980 Millions NSA End of Quarter | 6.983 |
| Unemployed IIIQ1988 Millions NSA End of Quarter | 6.368 |
| ∆% | -8.8 |
| Employed Part-time Economic Reasons IQ1980 Millions NSA End of Quarter | 3.624 |
| Employed Part-time Economic Reasons Millions IIIQ1988 NSA End of Quarter | 4.704 |
| ∆% | 29.8 |
| Net Worth of Households and Nonprofit Organizations USD Billions | |
| IVQ1979 | 9,047.8 |
| IIIQ1988 | 19,209.2 |
| ∆ USD Billions | +10,161.4 |
| ∆% CPI Adjusted | 35.9 |
| Gross Private Domestic Investment USD 2009 Billions | |
| IQ1980 | 951.6 |
| IIIQ1988 | 1229.7 |
| ∆% | 29.2 |
| Period IVQ2007 to IQ2015 | |
| GDP SAAR USD Billions | |
| IVQ2007 | 14,991.8 |
| IQ2015 | 16,264.1 |
| ∆% IVQ2007 to IQ2015 | 8.5 |
| ∆% IVQ2007 to IQ2015 Trend Growth | 23.9 |
| Real Disposable Personal Income per Capita IVQ2007 Chained 2009 USD | 35,819 |
| Real Disposable Personal Income per Capita IQ2015 Chained 2009 USD | 38,210 |
| ∆% IVQ2007 to IQ2015 | 6.7 |
| ∆% Trend Growth | 15.4 |
| Employed Millions IVQ2007 NSA End of Quarter | 146.334 |
| Employed Millions IQ2015 NSA End of Quarter | 147.635 |
| ∆% Employed IVQ2007 to IQ2015 | 0.9 |
| Employed Full-time Millions IVQ2007 NSA End of Quarter | 121.042 |
| Employed Full-time Millions IQ2015 NSA End of Quarter | 119.981 |
| ∆% Full-time Employed IVQ2007 to IQ2015 | -0.9 |
| Unemployment Rate IVQ2007 NSA End of Quarter | 4.8 |
| Unemployment Rate IQ2015 NSA End of Quarter | 5.6 |
| Unemployed IVQ2007 Millions NSA End of Quarter | 7.371 |
| Unemployed IQ2015 Millions NSA End of Quarter | 8.682 |
| ∆% | 17.8 |
| Employed Part-time Economic Reasons IVQ2007 Millions NSA End of Quarter | 4.750 |
| Employed Part-time Economic Reasons Millions IQ2015 NSA End of Quarter | 6.672 |
| ∆% | 40.5 |
| U6 Total Unemployed plus all marginally attached workers plus total employed part time for economic reasons as percent of all civilian labor force plus all marginally attached workers NSA | |
| IVQ2007 | 8.7 |
| IQ2015 | 11.0 |
| Net Worth of Households and Nonprofit Organizations USD Billions | |
| IVQ2007 | 66,721.8 |
| IQ2015 | 84,924.6 |
| ∆ USD Billions | +18,202.8 ∆%27.3 R∆%13.2 |
| Gross Private Domestic Investment USD Billions | |
| IVQ2007 | 2,605.2 |
| IQ2015 | 2,790.1 |
| ∆% | 7.1 |
| Private Fixed Investment USD 2009 Billions | |
| IVQ2007 | 2,586.3 |
| IQ2015 | 2,664.2 |
| ∆% | 3.0 |
Note: GDP trend growth used is 3.0 percent per year and GDP per capita is 2.0 percent per year as estimated by Lucas (2011May) on data from 1870 to 2010.
Source: US Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm US Bureau of Labor Statistics http://www.bls.gov/data/. Board of Governors of the Federal Reserve System. 2015. Flow of funds, balance sheets and integrated macroeconomic accounts: first quarter 2015. Washington, DC, Federal Reserve System, Jun 11. http://www.federalreserve.gov/releases/z1/.
The Congressional Budget Office (CBO 2014BEOFeb4) estimates potential GDP, potential labor force and potential labor productivity provided in Table IB-3. The CBO estimates average rate of growth of potential GDP from 1950 to 2014 at 3.3 percent per year. The projected path is significantly lower at 2.1 percent per year from 2015 to 2025. The legacy of the economic cycle expansion from IIIQ2009 to IQ2015 at 2.2 percent on average is in contrast with 4.8 percent on average in the expansion from IQ1983 to IIIQ1988 (http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.html). Subpar economic growth may perpetuate unemployment and underemployment estimated at 25.0 million or 15.1 percent of the effective labor force in Jun 2015 (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html. There is much lower hiring than in the period before the current cycle (http://cmpassocregulationblog.blogspot.com/2015/07/oscillating-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/volatility-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/volatility-of-financial-asset.html).
Table IB-3, US, Congressional Budget Office History and Projections of Potential GDP of US Overall Economy, ∆%
| Potential GDP | Potential Labor Force | Potential Labor Productivity* | |
| Average Annual ∆% | |||
| 1950-1973 | 4.0 | 1.6 | 2.4 |
| 1974-1981 | 3.3 | 2.5 | 0.8 |
| 1982-1990 | 3.2 | 1.6 | 1.6 |
| 1991-2001 | 3.2 | 1.3 | 1.9 |
| 2002-2007 | 2.8 | 0.9 | 1.9 |
| 2008-2014 | 1.4 | 0.5 | 0.9 |
| Total 1950-2014 | 3.3 | 1.5 | 1.8 |
| Projected Average Annual ∆% | |||
| 2015-2019 | 2.1 | 0.5 | 1.6 |
| 2019-2025 | 2.2 | 0.6 | 1.6 |
| 2015-2025 | 2.1 | 0.5 | 1.6 |
*Ratio of potential GDP to potential labor force
Source: CBO (2014BEOFeb4), CBO, Key assumptions in projecting potential GDP—February 2014 baseline. Washington, DC, Congressional Budget Office, Feb 4, 2014. CBO, The budget and economic outlook: 2015 to 2025. Washington, DC, Congressional Budget Office, Jan 26, 2015.
Chart IB-1A of the Congressional Budget Office provides historical and projected potential and actual US GDP. The gap between actual and potential output closes by 2017. Potential output expands at a lower rate than historically. Growth is even weaker relative to trend.
Chart IB-1A, Congressional Budget Office, Estimate of Potential GDP and Gap
Source: Congressional Budget Office
https://www.cbo.gov/publication/49890
Chart IB-1 of the Congressional Budget Office (CBO 2013BEOFeb5) provides actual and potential GDP of the United States from 2000 to 2011 and projected to 2024. Lucas (2011May) estimates trend of United States real GDP of 3.0 percent from 1870 to 2010 and 2.2 percent for per capita GDP. The United States successfully returned to trend growth of GDP by higher rates of growth during cyclical expansion as analyzed by Bordo (2012Sep27, 2012Oct21) and Bordo and Haubrich (2012DR). Growth in expansions following deeper contractions and financial crises was much higher in agreement with the plucking model of Friedman (1964, 1988). The unusual weakness of growth at 2.2 percent on average from IIIQ2009 to IQ2015 during the current economic expansion in contrast with 4.8 percent on average in the cyclical expansion from IQ1983 to IIIQ1988 (http://cmpassocregulationblog.blogspot.com/2015/06/international-valuations-of-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/dollar-revaluation-squeezing-corporate.html) cannot be explained by the contraction of 4.2 percent of GDP from IVQ2007 to IIQ2009 and the financial crisis. Weakness of growth in the expansion is perpetuating unemployment and underemployment of 25.0 million or 15.1 percent of the labor force as estimated for Jun 2015 (http://cmpassocregulationblog.blogspot.com/2015/07/turbulence-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/higher-volatility-of-asset-prices-at.html). There is no exit from unemployment/underemployment and stagnating real wages because of the collapse of hiring (http://cmpassocregulationblog.blogspot.com/2015/07/oscillating-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/volatility-of-financial-asset.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/volatility-of-financial-asset.html). The US economy and labor markets collapsed without recovery. Abrupt collapse of economic conditions can be explained only with cyclic factors (Lazear and Spletzer 2012Jul22) and not by secular stagnation (Hansen 1938, 1939, 1941 with early dissent by Simons 1942).
Chart IB-1, US, Congressional Budget Office, Actual and Projections of Potential GDP, 2000-2024, Trillions of Dollars
Source: Congressional Budget Office, CBO (2013BEOFeb5). The last year in common in both projections is 2017. The revision lowers potential output in 2017 by 7.3 percent relative to the projection in 2007.
Chart IB-2 provides differences in the projections of potential output by the CBO in 2007 and more recently on Feb 4, 2014, which the CBO explains in CBO (2014Feb28).
Chart IB-2, Congressional Budget Office, Revisions of Potential GDP
Source: Congressional Budget Office, 2014Feb 28. Revisions to CBO’s Projection of Potential Output since 2007. Washington, DC, CBO, Feb 28, 2014.
Chart IB-3 provides actual and projected potential GDP from 2000 to 2024. The gap between actual and potential GDP disappears at the end of 2017 (CBO2014Feb4). GDP increases in the projection at 2.5 percent per year.
Chart IB-3, Congressional Budget Office, GDP and Potential GDP
Source: CBO (2013BEOFeb5), CBO, Key assumptions in projecting potential GDP—February 2014 baseline. Washington, DC, Congressional Budget Office, Feb 4, 2014.
© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015.
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