CMP Associates Regulation Blog

Chart I-1 shows the annual level of total nonfarm hiring (HNF) that collapsed during the global recession after 2007 in contrast with milder decline in the shallow recession of 2001. Nonfarm hiring has not recovered, remaining at a depressed level. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 253.538 million in 2016 or by 24.723 million. Hiring has not recovered precession levels while needs of hiring multiplied because of growth of population by more than 24 million.

Chart I-1, US, Level Total Nonfarm Hiring (HNF), Annual, 2001-2016

Source: US Bureau of Labor Statistics

Chart I-2 shows the ratio or rate of nonfarm hiring to employment (RNF) that also fell much more in the recession of 2007 to 2009 than in the shallow recession of 2001. Recovery is weak in the current environment of cyclical slow growth.

Chart I-2, US, Rate Total Nonfarm Hiring (HNF), Annual, 2001-2016

Source: US Bureau of Labor Statistics

Yearly percentage changes of total nonfarm hiring (HNF) are provided in Table I-2. There were much milder declines in 2002 of 6.9 percent and 2.6 percent in 2003 followed by strong rebounds of 5.8 percent in 2004 and 4.7 percent in 2005. In contrast, the contractions of nonfarm hiring in the recession after 2007 were much sharper in percentage points: 2.0 in 2007, 12.0 in 2008 and 15.7 percent in 2009. On a yearly basis, nonfarm hiring grew 5.3 percent in 2010 relative to 2009, 3.3 percent in 2011, 4.1 percent in 2012 and 3.8 percent in 2013. Nonfarm hiring grew 8.0 percent in 2014 and increased 5.8 percent in 2015. Nonfarm hiring grew 1.1 percent in 2016. The relatively large length of 27 quarters of the current expansion reduces the likelihood of significant recovery of hiring levels in the United States because lower rates of growth and hiring in the final phase of expansions.

Table I-2, US, Annual Total Nonfarm Hiring (HNF), Annual Percentage Change, 2002-2016

Year	Annual ∆%
2002	-6.9
2003	-2.6
2004	5.8
2005	4.7
2006	0.6
2007	-2.0
2008	-12.0
2009	-15.7
2010	5.3
2011	3.3
2012	4.1
2013	3.8
2014	8.0
2015	5.8
2016	1.1

Source: US Bureau of Labor Statistics

Total private hiring (HP) 12-month percentage changes of annual data are in Chart I-3. There has been sharp contraction of total private hiring in the US and only milder recovery from 2010 to 2016.

Chart I-3, US, Total Nonfarm Hiring Level, Annual, ∆%, 2001-2016

Source: Bureau of Labor Statistics

Total private hiring (HP) annual data are in Chart I-5. There has been sharp contraction of total private hiring in the US and only milder recovery from 2010 to 2016.

Chart I-5, US, Total Private Hiring, Annual, 2001-2016

Source: Bureau of Labor Statistics

Chart I-5A plots the rate of total private hiring relative to employment (RHP). The rate collapsed during the global recession after 2007 with insufficient recovery.

Chart I-5A, US, Rate Total Private Hiring, Annual, 2001-2016

Source: Bureau of Labor Statistics

Total nonfarm hiring (HNF), total private hiring (HP) and their respective rates are in Table I-3 for the month of Feb in the years from 2001 to 2017. Hiring numbers are in thousands. There is recovery in HNF from 3481 thousand (or 3.5 million) in Mar 2009 to 3911 thousand in Mar 2010, 4040 thousand in Mar 2011, 4182 thousand in Mar 2012, 4017 thousand in Mar 2013, 4458 thousand in Mar 2014, 4797 thousand in Mar 2015, 4974 thousand in Mar 2016, and 4952 in Mar 2017 for cumulative gain of 42.3 percent at average rate of 4.5 percent per year. HP rose from 3309 thousand in Mar 2009 to 3657 thousand in Mar 2010, 3866 thousand in Mar 2011, 3971 thousand in Mar 2012, 3814 thousand in Mar 2013, 4217 thousand in Mar 2014, 4542 in Mar 2015, 4287 thousand in Mar 2016, and 4701 thousand in Mar 2017 for cumulative gain of 42.1 percent at the average yearly rate of 4.5 percent. HNF has decreased from 5050 thousand in Mar 2006 to 4952 thousand in Mar 2017 or by 1.9 percent. HP has decreased from 4789 thousand in Mar 2006 to 4701 thousand in Mar 2017 or by 1.8 percent. The civilian noninstitutional population of the US, or those in condition of working, rose from 227.975 million in Mar 2006 to 254.414 million in Mar 2017, by 26.439 million or 11.6 percent. There is often ignored ugly fact that hiring decreased by around 1.8 percent while population available for working increased around 11.6 percent. Private hiring of 59.206 million in 2006 was equivalent to 25.9 percent of the civilian noninstitutional population of 228.815, or those in condition of working, falling to 58.385 million in 2016 or 23.0 percent of the civilian noninstitutional population of 253.538 million in 2016. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 65.666 million of hiring in 2016 (0.259x253.538), which would be 7.281 million higher than actual 58.385 million in 2016. 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. Cyclical slow growth over the entire business cycle from IVQ2007 to the present in comparison with earlier cycles and long-term trend (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html) explains the fact that there are many million fewer hires in the US than before the global recession. The labor market continues to be fractured, failing to provide an opportunity to exit from unemployment/underemployment or to find an opportunity for advancement away from declining inflation-adjusted earnings.

Table I-3, US, Total Nonfarm Hiring (HNF) and Total Private Hiring (HP) in the US in

Thousands and in Percentage of Total Employment Not Seasonally Adjusted

	HNF	Rate RNF	HP	Rate HP
2001 Mar	5264	4.0	5019	4.5
2002 Mar	4326	3.3	4110	3.8
2003 Mar	4140	3.2	3940	3.7
2004 Mar	4902	3.8	4671	4.3
2005 Mar	4913	3.7	4696	4.3
2006 Mar	5050	3.7	4789	4.2
2007 Mar	5036	3.7	4770	4.2
2008 Mar	4444	3.2	4215	3.7
2009 Mar	3481	2.6	3309	3.0
2010 Mar	3911	3.0	3657	3.4
2011 Mar	4040	3.1	3866	3.6
2012 Mar	4182	3.1	3971	3.6
2013 Mar	4017	3.0	3814	3.4
2014 Mar	4458	3.2	4217	3.7
2015 Mar	4797	3.4	4542	3.9
2016 Mar	4974	3.5	4687	3.9
2017 Mar	4952	3.4	4701	3.8

Source: Bureau of Labor Statistics

Chart I-6 provides total nonfarm hiring monthly from 2001 to 2017. Nonfarm hiring rebounded in early 2010 but then fell and stabilized at a lower level than the early peak not-seasonally adjusted (NSA) of 4815 in May 2010 until it surpassed it with 5006 in Jun 2011 but declined to 3097 in Dec 2012. Nonfarm hiring fell to 2997 in Dec 2011 from 3814 in Nov 2011 and to revised 3627 in Feb 2012, increasing to 4182 in Mar 2012, 3097 in Dec 2012 and 4277 in Jan 2013 and declining to 3692 in Feb 2013. Nonfarm hires not seasonally adjusted increased to 4239 in Nov 2013 and 3233 in Dec 2013. Nonfarm hires reached 3729 in Dec 2014, 4057 in Dec 2015 and 3905 in Dec 2016. Nonfarm hires reached 4952 in Mar 2017. Chart I-6 provides seasonally adjusted (SA) monthly data. The number of seasonally-adjusted hires in Oct 2011 was 4239 thousand, increasing to revised 4419 thousand in Feb 2012, or 4.2 percent, moving to 4360 in Dec 2012 for cumulative increase of 3.0 percent from 4234 in Dec 2011 and 4500 in Dec 2013 for increase of 3.2 percent relative to 4360 in Dec 2012. The number of hires not seasonally adjusted was 5006 in Jun 2011, falling to 2997 in Dec 2011 but increasing to 4140 in Jan 2012 and declining to 3097 in Dec 2012. The number of nonfarm hiring not seasonally adjusted fell by 40.1 percent from 5006 in Jun 2011 to 2997 in Dec 2011 and fell 39.9 percent from 5151 in Jun 2012 to 3097 in Dec 2012 in a yearly-repeated seasonal pattern. The number of nonfarm hires not seasonally adjusted fell from 5102 in Jun 2013 to 3233 in Dec 2013, or decline of 36.6 percent, showing strong seasonality. The number of nonfarm hires not seasonally adjusted fell from 5520 in Jun 2014 to 3729 in Dec 2014 or 32.4 percent. The level of nonfarm hires fell from 5885 in Jun 2015 to 4057 in Dec 2015 or 31.1 percent. The level of nonfarm hires not seasonally adjusted fell from 5922 in Jun 2016 to 3905 in Dec 2016 or 34.1 percent.

Chart I-6, US, Total Nonfarm Hiring (HNF), 2001-2017 Month SA

Source: Bureau of Labor Statistics

Similar behavior occurs in the rate of nonfarm hiring in Chart I-7. Recovery in early 2010 was followed by decline and stabilization at a lower level but with stability in monthly SA estimates of 3.2 in Aug 2011 to 3.2 in Jan 2012, increasing to 3.3 in May 2012 and stabilizing to 3.3 in Jun 2012. The rate stabilized at 3.2 in Jul 2012, increasing to 3.3 in Aug 2012 but falling to 3.2 in Dec 2012 and 3.3 in Dec 2013. The rate not seasonally adjusted fell from 3.8 in Jun 2011 to 2.2 in Dec 2011, climbing to 3.8 in Jun 2012 but falling to 2.3 in Dec 2012. The rate of nonfarm hires not seasonally adjusted fell from 3.7 in Jun 2013 to 2.3 in Dec 2013. The NSA rate of nonfarm hiring fell from 3.9 in Jun 2014 to 2.6 in Dec 2014. The NSA rate fell from 4.1 in Jun 2015 to 2.8 in Dec 2015. The NSA rate fell from 4.1 in Jun 2016 to 2.7 in Dec 2016. Rates of nonfarm hiring NSA were in the range of 2.7 (Dec) to 4.4 (Jun) in 2006. The rate of nonfarm hiring SA stood at 3.6 in Mar 2017 and at 3.4 NSA.

Chart I-7, US, Rate Total Nonfarm Hiring, Month SA 2001-2017

Source: Bureau of Labor Statistics

There is only milder improvement in total private hiring shown in Chart I-8. Hiring private (HP) rose in 2010 with stability and renewed increase in 2011 followed by almost stationary series in 2012. The number of private hiring seasonally adjusted fell from 4043 thousand in Sep 2011 to 3933 in Dec 2011 or by 2.7 percent, decreasing to 4014 in Jan 2012 or decline by 0.7 percent relative to the level in Sep 2011. Private hiring fell to 3959 in Sep 2012 or lower by 2.1 percent relative to Sep 2011, moving to 4063 in Dec 2012 for increase of 1.2 percent relative to 4014 in Jan 2012. The number of private hiring not seasonally adjusted fell from 4626 in Jun 2011 to 2817 in Dec 2011 or by 39.1 percent, reaching 3885 in Jan 2012 or decline of 16.0 percent relative to Jun 2011 and moving to 2918 in Dec 2012 or 38.5 percent lower relative to 4745 in Jun 2012. Hires not seasonally adjusted fell from 4743 in Jun 2013 to 3068 in Dec 2013. The level of private hiring NSA fell from 5101 in Jun 2014 to 3530 in Dec 2014 or 30.8 percent. The level of private hiring fell from 5452 in Jun 2015 to 3828 in Dec 2015 or 29.8 percent. The level of private hiring not seasonally adjusted fell from 5456 in Jun 2016 to 3711 in Dec 2016 or 32.0 percent. Companies reduce hiring in the latter part of the year that explains the high seasonality in year-end employment data. For example, NSA private hiring fell from 5614 in Jun 2006 to 3579 in Dec 2006 or by 36.2 percent. Private hiring NSA data are useful in showing the huge declines from the period before the global recession. Hiring in the nonfarm sector (HNF) has declined from 63.491 million in 2006 to 62.719 million in 2016 or by 0.772 million while hiring in the private sector (HP) has declined from 59.206 million in 2006 to 58.385 million in 2016 or by 0.821 million, as shown in Table I-1. The ratio of nonfarm hiring to employment (RNF) has fallen from 47.1 in 2005 to 43.5 in 2016 and in the private sector (RHP) from 52.8 in 2005 to 47.8 in 2016. Hiring has not recovered as in previous cyclical expansions because of the low rate of economic growth in the current cyclical expansion. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 253.538 million in 2016 or by 24.723 million. Hiring has not recovered prerecession levels while needs of hiring multiplied because of growth of population by more than 24 million. Private hiring of 59.206 million in 2006 was equivalent to 25.9 percent of the civilian noninstitutional population of 228.815, or those in condition of working, falling to 58.385 million in 2016 or 23.0 percent of the civilian noninstitutional population of 253.538 million in 2016. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 65.666 million of hiring in 2016 (0.259x253.538), which would be 7.281 million higher than actual 58.385 million in 2016.

Chart I-8, US, Total Private Hiring Month SA 2001-2017

Source: Bureau of Labor Statistics

Chart I-9 shows similar behavior in the rate of private hiring. The rate in 2011 in monthly SA data did not rise significantly above the peak in 2010. The rate seasonally adjusted fell from 3.7 in Sep 2011 to 3.5 in Dec 2011 and reached 3.6 in Dec 2012 and 3.7 in Dec 2013. The rate not seasonally adjusted (NSA) fell from 3.7 in Sep 2011 to 2.5 in Dec 2011, increasing to 3.8 in Oct 2012 but falling to 2.6 in Dec 2012 and 3.4 in Mar 2013. The NSA rate of private hiring fell from 4.8 in Jul 2006 to 3.4 in Aug 2009 but recovery was insufficient to only 3.9 in Aug 2012, 2.6 in Dec 2012 and 2.6 in Dec 2013. The NSA rate increased to 3.1 in Dec 2015 and 3.0 in Dec

2016. The rate NSA reached 3.8 in Mar 2017.

Chart I-9, US, Rate Total Private Hiring Month SA 2001-2017

Source: Bureau of Labor Statistics

The JOLTS report of the Bureau of Labor Statistics also provides total nonfarm job openings (TNF JOB), TNF JOB rate and TNF LD (layoffs and discharges) shown in Table I-4 for the month of Mar from 2001 to 2017. The final column provides annual TNF LD for the years from 2001 to 2016. Nonfarm job openings (TNF JOB) increased from a peak of 4583 in Mar 2007 to 5684 in Mar 2017 or by 24.0 percent while the rate increased from 3.2 to 3.8. This was mediocre performance because the civilian noninstitutional population of the US, or those in condition of working rose from 231.034 million in Mar 2007 to 254.414 million in Mar 2017, by 23.380 million or 10.1 percent. Nonfarm layoffs and discharges (TNF LD) rose from 1358 in Mar 2006 to 2015 in Mar 2009 or by 48.4 percent. The annual data show layoffs and discharges rising from 20.9 million in 2006 to 26.6 million in 2009 or by 27.3 percent. Business pruned payroll jobs to survive the global recession but there has not been hiring because of the low rate of GDP growth. 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.

Table I-4, US, Total Nonfarm Job Openings and Total Nonfarm Layoffs and Discharges, Thousands NSA

	TNF JOB	TNF JOB Rate	TNF LD	TNF LD Annual
Mar 2001	4465	3.3	1735	24271
Mar 2002	3458	2.6	1442	22719
Mar 2003	2981	2.3	1490	23420
Mar 2004	3312	2.5	1578	22584
Mar 2005	3767	2.8	1590	22151
Mar 2006	4409	3.2	1358	20856
Mar 2007	4583	3.2	1487	21997
Mar 2008	3920	2.8	1518	23969
Mar 2009	2429	1.8	2015	26557
Mar 2010	2610	2.0	1484	21703
Mar 2011	3081	2.3	1369	20756
Mar 2012	3796	2.8	1338	20942
Mar 2013	3854	2.8	1365	19888
Mar 2014	4156	2.9	1329	20398
Mar 2015	5116	3.5	1558	20954
Mar 2016	5827	3.9	1439	19911
Mar 2017	5684	3.8	1349

Notes: TNF JOB: Total Nonfarm Job Openings; LD: Layoffs and Discharges

Source: Bureau of Labor Statistics

Chart I-10 shows monthly job openings rising from the trough in 2009 to a high in the beginning of 2010. Job openings then stabilized into 2011 but have surpassed the peak of 3220 seasonally adjusted in Apr 2010 with 3570 seasonally adjusted in Dec 2012, which is higher by 10.9 percent relative to Apr 2010 but higher by 1.4 percent relative to 3521 in Nov 2012 and lower by 6.8 percent than 3831 in Mar 2012. Nonfarm job openings increased from 3570 in Dec 2012 to 3742 in Dec 2013 or by 4.8 percent and to 4795 in Dec 2014 or 28.1 percent relative to 2013. The high of job openings not seasonally adjusted was 3408 in Apr 2010 that was surpassed by 3647 in Jul 2011, increasing to 3906 in Oct 2012 but declining to 3213 in Dec 2012 and increasing to 3371 in Dec 2013. The level of job opening NSA increased to 4961 in Dec 2015. The level of job opening NSA increased to 5116 in Dec 2016, reaching 5685 in Mar 2017. The level of job openings not seasonally adjusted fell to 3213 in Dec 2012 or by 17.5 percent relative to 3893 in Apr 2012. There is here again the strong seasonality of year-end labor data. Job openings fell from 4209 in Apr 2013 to 3371 in Dec 2013 and from 4844 in Apr 2014 to 4398 in Dec 2014, showing strong seasonal effects. The level of nonfarm job openings decreased from 5933 in Apr 2015 to 4961 in Dec 2015 or by 16.4 percent. The level of nonfarm job openings NSA fell from 5951 in Apr 2016 to 5116 in Dec 2016 or 14.0 percent. Nonfarm job openings (TNF JOB) increased from a peak of 4583 in Mar 2007 to 5684 in Mar 2016 or by 24.0 percent while the rate increased from 3.2 to 3.8. This was mediocre performance because the civilian noninstitutional population of the US, or those in condition of working rose from 231.034 million in Mar 2007 to 254.414 million in Mar 2017, by 23.380 million or 10.1 percent. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 31 quarters from IIIQ2009 to IQ2017. 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 first estimate of GDP for IQ2017 (https://www.bea.gov/newsreleases/national/gdp/2017/pdf/gdp1q17_adv.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 dividing 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 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (http://www.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2017 would have accumulated to 31.4 percent. GDP in IQ2017 would be $19,699.2 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2856.8 billion than actual $16,842.4 billion. There are about two trillion dollars of GDP less than at trend, explaining the 21.9 million unemployed or underemployed equivalent to actual unemployment/underemployment of 13.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html). US GDP in IQ2017 is 14.5 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,842.4 billion in IQ2017 or 12.3 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.2 percent per year from Mar 1919 to Mar 2017. Growth at 3.2 percent per year would raise the NSA index of manufacturing output from 108.2393 in Dec 2007 to 144.8512 in Mar 2017. The actual index NSA in Mar 2017 is 103.4144, which is 28.6 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Mar 2017. Using trend growth of 2.1 percent per year, the index would increase to 131.1817 in Mar 2017. The output of manufacturing at 103.4144 in Mar 2017 is 21.2 percent below trend under this alternative calculation.

Chart I-10, US Job Openings, Thousands NSA, 2001-2017

Source: US Bureau of Labor Statistics

The rate of job openings in Chart I-11 shows similar behavior. The rate seasonally adjusted increased from 2.2 in Jan 2011 to 2.5 in Dec 2011, 2.6 in Dec 2012, 2.7 in Dec 2013 and 3.3 in Dec 2014. The rate seasonally adjusted stood at 3.6 in Dec 2015 and 3.7 in Dec 2016. The rate seasonally adjusted reached 3.8 in Mar 2017. The rate not seasonally adjusted rose from the high of 2.6 in Apr 2010 to 3.0 in Apr 2013, easing to 2.4 in Dec 2013. The rate of job openings NSA fell from 3.3 in Jul 2007 to 1.6 in Nov-Dec 2009, recovering to 3.3 in Dec 2015. The rate of job opening NSA stood at 3.4 in Dec 2016, reaching 3.8 in Mar 2017.

Chart I-11, US, Rate of Job Openings, NSA, 2001-2017

Source: US Bureau of Labor Statistics

Total separations are in Chart I-12. Separations are lower in 2012-17 than before the global recession but hiring has not recovered.

Chart I-12, US, Total Nonfarm Separations, Month Thousands SA, 2001-2017

Source: US Bureau of Labor Statistics

Chart I-13 provides annual total separations. Separations fell sharply during the global recession but hiring has not recovered relative to population growth.

Chart I-13, US, Total Separations, Annual, Thousands, 2001-2016

Source: US Bureau of Labor Statistics

Table I-5 provides total nonfarm total separations from 2001 to 2016. Separations fell from 61.3 million in 2006 to 47.6 million in 2010 or by 13.6 million and 48.2 million in 2011 or by 13.1 million. Total separations increased from 48.2 million in 2011 to 51.9 million in 2013 or by 3.7 million and to 55.6 million in 2014 or by 7.4 million relative to 2011. Total separations increased to 59.3 million in 2015 or by 11.1 million relative to 2011. Total separations increased to 60.419 million in 2016 or 12.2 million relative to 2011.

Table I-5, US, Total Nonfarm Total Separations, Thousands, 2001-2016

Year	Annual Thousands
2001	64560
2002	58942
2003	56961
2004	58224
2005	60633
2006	61284
2007	60984
2008	58209
2009	51358
2010	47649
2011	48214
2012	50131
2013	51932
2014	55587
2015	59275
2016	60419

Source: US Bureau of Labor Statistics

Monthly data of layoffs and discharges reach a peak in early 2009, as shown in Chart I-14. Layoffs and discharges dropped sharply with the recovery of the economy in 2010 and 2011 once employers reduced their job count to what was required for cost reductions and loss of business. 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. Growth rates have been unusually low in the expansion of the current economic cycle.

Chart I-14, US, Total Nonfarm Layoffs and Discharges, Monthly Thousands SA, 2001-2017

Source: US Bureau of Labor Statistics

Layoffs and discharges in Chart I-15 rose sharply to a peak in 2009. There was pronounced drop into 2010 and 2011 with mild increase into 2012 and renewed decline into 2013. There is mild increase into 2014-2015 followed by decline in 2016.

Chart I-15, US, Total Nonfarm Layoffs and Discharges, Annual, 2001-2016

Source: US Bureau of Labor Statistics

Annual layoff and discharges are in Table I-6. Layoffs and discharges increased sharply from 20.856 million in 2006 to 26.557 million in 2009 or 27.3 percent. Layoff and discharges fell to 19.888 million in 2013 or 25.1 percent relative to 2009 and increased to 20.398 million in 2014 or 2.6 percent relative to 2013. Layoffs and discharges increased to 20.954 million in 2015 or 2.7 percent relative to 2014. Layoffs and discharges fell to 19.911 in 2016 or 5.0 percent relative to 2015.

Table I-6, US, Total Nonfarm Layoffs and Discharges, Thousands, 2001-2016

Year	Annual Thousands
2001	24271
2002	22719
2003	23420
2004	22584
2005	22151
2006	20856
2007	21997
2008	23969
2009	26557
2010	21703
2011	20756
2012	20942
2013	19888
2014	20398
2015	20954
2016	19911

Source: US Bureau of Labor Statistics

IA2 Labor Underutilization. The Bureau of Labor Statistics also provides alternative measures of labor underutilization shown in Table I-7. The most comprehensive measure is U6 that consists of total unemployed plus total employed part time for economic reasons plus all marginally attached workers as percent of the labor force. U6 not seasonally adjusted has risen from 8.2 percent in 2006 to 8.1 percent in Apr 2017.

Table I-7, US, Alternative Measures of Labor Underutilization NSA %

	U1	U2	U3	U4	U5	U6
2017
Apr	1.8	2.1	4.1	4.4	5.0	8.1
Mar	1.9	2.4	4.6	4.8	5.5	8.9
Feb	1.9	2.6	4.9	5.3	6.0	9.5
Jan	2.0	2.7	5.1	5.5	6.2	10.1
2016
Dec	1.9	2.3	4.5	4.8	5.5	9.1
Nov	1.8	2.1	4.4	4.8	5.6	9.0
Oct	1.9	2.1	4.7	5.0	5.7	9.2
Sep	1.9	2.2	4.8	5.1	5.9	9.3
Aug	1.8	2.4	5.0	5.3	6.0	9.7
Jul	1.9	2.4	5.1	5.5	6.3	10.1
Jun	1.9	2.3	5.1	5.4	6.1	9.9
May	2.0	2.1	4.5	4.9	5.6	9.4
Apr	2.2	2.3	4.7	5.0	5.7	9.3
Mar	2.3	2.6	5.1	5.5	6.1	9.9
Feb	2.2	2.7	5.2	5.6	6.3	10.1
Jan	2.1	2.7	5.3	5.7	6.5	10.5
2015
Dec	2.1	2.4	4.8	5.2	5.9	9.8
Nov	2.1	2.3	4.8	5.2	5.8	9.6
Oct	2.1	2.3	4.8	5.2	6.0	9.5
Sep	2.0	2.2	4.9	5.3	6.0	9.6
Aug	2.1	2.5	5.2	5.6	6.3	10.3
Jul	2.0	2.7	5.6	6.0	6.7	10.7
Jun	2.1	2.5	5.5	5.8	6.6	10.8
May	2.4	2.5	5.3	5.6	6.4	10.4
Apr	2.4	2.5	5.1	5.5	6.4	10.4
Mar	2.6	2.9	5.6	6.0	6.8	11.0
Feb	2.7	3.0	5.8	6.3	7.1	11.4
Jan	2.7	3.1	6.1	6.5	7.4	12.0
2014
Dec	2.5	2.8	5.4	5.8	6.7	11.1
Nov	2.7	2.7	5.5	5.9	6.8	11.0
Oct	2.7	2.6	5.5	6.0	6.8	11.1
Sep	2.7	2.7	5.7	6.2	7.1	11.3
Aug	2.8	3.0	6.3	6.7	7.5	12.0
Jul	2.8	3.1	6.5	7.0	7.8	12.6
Jun	2.8	3.0	6.3	6.7	7.5	12.4
May	3.1	3.0	6.1	6.5	7.3	11.7
Apr	3.3	3.2	5.9	6.3	7.2	11.8
Mar	3.7	3.7	6.8	7.2	8.1	12.8
Feb	3.6	3.9	7.0	7.5	8.4	13.1
Jan	3.5	4.0	7.0	7.5	8.6	13.5
2013
Dec	3.5	3.5	6.5	7.0	7.9	13.0
Nov	3.7	3.5	6.6	7.1	7.9	12.7
Oct	3.7	3.6	7.0	7.4	8.3	13.2
Sep	3.7	3.5	7.0	7.5	8.4	13.1
Aug	3.7	3.8	7.3	7.9	8.7	13.6
Jul	3.7	3.8	7.7	8.3	9.1	14.3
Jun	3.9	3.8	7.8	8.4	9.3	14.6
May	4.1	3.7	7.3	7.7	8.5	13.4
Apr	4.3	3.9	7.1	7.6	8.5	13.4
Mar	4.3	4.3	7.6	8.1	9.0	13.9
Feb	4.3	4.6	8.1	8.6	9.6	14.9
Jan	4.3	4.9	8.5	9.0	9.9	15.4
2012
Dec	4.2	4.3	7.6	8.3	9.2	14.4
Nov	4.2	3.9	7.4	7.9	8.8	13.9
Oct	4.3	3.9	7.5	8.0	9.0	13.9
Sep	4.2	4.0	7.6	8.0	9.0	14.2
Aug	4.3	4.4	8.2	8.7	9.7	14.6
Jul	4.3	4.6	8.6	9.1	10.0	15.2
Jun	4.5	4.4	8.4	8.9	9.9	15.1
May	4.7	4.3	7.9	8.4	9.3	14.3
Apr	4.8	4.3	7.7	8.3	9.1	14.1
Mar	4.9	4.8	8.4	8.9	9.7	14.8
Feb	4.9	5.1	8.7	9.3	10.2	15.6
Jan	4.9	5.4	8.8	9.4	10.5	16.2
2011
Dec	4.8	5.0	8.3	8.8	9.8	15.2
Nov	4.9	4.7	8.2	8.9	9.7	15.0
Oct	5.0	4.8	8.5	9.1	10.0	15.3
Sep	5.2	5.0	8.8	9.4	10.2	15.7
Aug	5.2	5.1	9.1	9.6	10.6	16.1
Jul	5.2	5.2	9.3	10.0	10.9	16.3
Jun	5.1	5.1	9.3	9.9	10.9	16.4
May	5.5	5.1	8.7	9.2	10.0	15.4
Apr	5.5	5.2	8.7	9.2	10.1	15.5
Mar	5.7	5.8	9.2	9.7	10.6	16.2
Feb	5.6	6.0	9.5	10.1	11.1	16.7
Jan	5.6	6.2	9.8	10.4	11.4	17.3
Dec 2010	5.4	5.9	9.1	9.9	10.7	16.6
Annual
2016	2.0	2.3	4.9	5.2	5.9	9.6
2015	2.3	2.6	5.3	5.7	6.4	10.4
2014	3.0	3.1	6.2	6.6	7.5	12.0
2013	3.9	3.9	7.4	7.9	8.8	13.8
2012	4.5	4.4	8.1	8.6	9.5	14.7
2011	5.3	5.3	8.9	9.5	10.4	15.9
2010	5.7	6.0	9.6	10.3	11.1	16.7
2009	4.7	5.9	9.3	9.7	10.5	16.2
2008	2.1	3.1	5.8	6.1	6.8	10.5
2007	1.5	2.3	4.6	4.9	5.5	8.3
2006	1.5	2.2	4.6	4.9	5.5	8.2
2005	1.8	2.5	5.1	5.4	6.1	8.9
2004	2.1	2.8	5.5	5.8	6.5	9.6
2003	2.3	3.3	6.0	6.3	7.0	10.1
2002	2.0	3.2	5.8	6.0	6.7	9.6
2001	1.2	2.4	4.7	4.9	5.6	8.1
2000	0.9	1.8	4.0	4.2	4.8	7.0

Note: LF: labor force; U1, persons unemployed 15 weeks % LF; U2, job losers and persons who completed temporary jobs %LF; U3, total unemployed % LF; U4, total unemployed plus discouraged workers, plus all other marginally attached workers; % LF plus discouraged workers; U5, total unemployed, plus discouraged workers, plus all other marginally attached workers % LF plus all marginally attached workers; U6, total unemployed, plus all marginally attached workers, plus total employed part time for economic reasons % LF plus all marginally attached workers

Source: US Bureau of Labor Statistics

Monthly seasonally adjusted measures of labor underutilization are provided in Table I-8. U6 climbed from 16.1 percent in Aug 2011 to 16.4 percent in Sep 2011 and then fell to 14.5 percent in Mar 2012, reaching 8.6 percent in Apr 2017. Unemployment is an incomplete measure of the stress in US job markets. A different calculation in this blog is provided by using the participation rate in the labor force before the global recession. This calculation shows 21.9 million in job stress of unemployment/underemployment in Apr 2017, not seasonally adjusted, corresponding to 13.0 percent of the labor force (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html).

Table I-8, US, Alternative Measures of Labor Underutilization SA %

	U1	U2	U3	U4	U5	U6
Apr 2017	1.7	2.2	4.4	4.7	5.3	8.6
Mar	1.7	2.2	4.5	4.8	5.4	8.9
Feb	1.8	2.3	4.7	5.0	5.7	9.2
Jan	1.9	2.3	4.8	5.1	5.8	9.4
Dec 2016	1.9	2.3	4.7	5.0	5.7	9.2
Nov	1.8	2.2	4.6	5.0	5.8	9.3
Oct	2.0	2.3	4.8	5.1	5.9	9.5
Sep	2.0	2.5	4.9	5.3	6.0	9.7
Aug	1.9	2.4	4.9	5.3	5.9	9.7
Jul	2.0	2.3	4.9	5.2	6.0	9.7
Jun	2.0	2.4	4.9	5.2	6.0	9.6
May	1.9	2.3	4.7	5.0	5.7	9.7
Apr	2.1	2.4	5.0	5.3	6.0	9.7
Mar	2.1	2.4	5.0	5.4	6.0	9.8
Feb	2.1	2.4	4.9	5.3	6.0	9.8
Jan	2.0	2.3	4.9	5.3	6.2	9.9
Dec 2015	2.1	2.4	5.0	5.4	6.1	9.9
Nov	2.1	2.5	5.0	5.4	6.1	9.9
Oct	2.1	2.5	5.0	5.4	6.2	9.8
Sep	2.1	2.4	5.0	5.4	6.2	10.0
Aug	2.2	2.5	5.1	5.5	6.2	10.2
Jul	2.1	2.6	5.2	5.6	6.4	10.3
Jun	2.2	2.6	5.3	5.6	6.4	10.5
May	2.4	2.7	5.5	5.8	6.6	10.7
Apr	2.3	2.6	5.4	5.9	6.7	10.8
Mar	2.4	2.7	5.4	5.9	6.7	10.9
Feb	2.5	2.7	5.5	6.0	6.8	11.0
Jan	2.7	2.7	5.7	6.1	7.0	11.3
Dec 2014	2.6	2.8	5.6	6.0	6.9	11.2
Nov	2.7	2.9	5.8	6.2	7.0	11.4
Oct	2.8	2.8	5.7	6.2	7.0	11.5
Sep	2.8	2.9	5.9	6.3	7.3	11.8
Aug	2.9	3.0	6.2	6.6	7.4	12.0
July	2.9	3.1	6.2	6.6	7.5	12.2
Jun	3.0	3.1	6.1	6.5	7.3	12.0
May	3.1	3.2	6.3	6.7	7.5	12.1
Apr	3.2	3.3	6.2	6.7	7.5	12.3
Mar	3.4	3.5	6.7	7.1	7.9	12.6
Feb	3.4	3.5	6.7	7.1	8.0	12.6
Jan	3.4	3.5	6.6	7.1	8.1	12.7
Dec 2013	3.6	3.5	6.7	7.3	8.1	13.1
Nov	3.7	3.7	6.9	7.4	8.2	13.1
Oct	3.8	4.0	7.2	7.7	8.5	13.6
Sep	3.8	3.8	7.2	7.7	8.6	13.7
Aug	3.9	3.7	7.3	7.8	8.6	13.6
Jul	3.9	3.8	7.3	7.9	8.7	13.8
Jun	4.0	3.9	7.5	8.1	9.0	14.2
May	4.1	3.9	7.5	8.0	8.8	13.8
Apr	4.1	4.1	7.6	8.1	8.9	14.0
Mar	4.1	4.1	7.5	8.0	8.9	13.8
Feb	4.1	4.2	7.7	8.2	9.2	14.4
Jan	4.2	4.3	8.0	8.5	9.4	14.5
Dec 2012	4.3	4.2	7.9	8.5	9.4	14.4
Nov	4.2	4.2	7.7	8.3	9.2	14.4
Oct	4.4	4.2	7.8	8.3	9.2	14.4
Sep	4.4	4.2	7.8	8.3	9.3	14.8
Aug	4.5	4.4	8.1	8.6	9.6	14.6
Jul	4.5	4.6	8.2	8.7	9.6	14.8
Jun	4.7	4.6	8.2	8.7	9.6	14.8
May	4.6	4.5	8.2	8.7	9.6	14.7
Apr	4.6	4.4	8.2	8.8	9.6	14.6
Mar	4.6	4.5	8.2	8.7	9.6	14.5
Feb	4.7	4.6	8.3	8.9	9.8	15.0
Jan	4.8	4.7	8.3	8.9	9.9	15.2
Dec 2011	4.9	4.9	8.5	9.1	10.0	15.2
Nov	5.0	5.0	8.6	9.3	10.1	15.5
Oct	5.1	5.1	8.8	9.4	10.3	15.8
Sep	5.4	5.2	9.0	9.7	10.5	16.4
Aug	5.4	5.2	9.0	9.6	10.5	16.1
Jul	5.3	5.3	9.0	9.6	10.6	15.9
Jun	5.3	5.3	9.1	9.7	10.7	16.1
May	5.3	5.4	9.0	9.5	10.3	15.8
Apr	5.2	5.4	9.1	9.7	10.5	16.1
Mar	5.3	5.4	9.0	9.5	10.4	15.9
Feb	5.3	5.5	9.0	9.6	10.6	16.0
Jan	5.5	5.5	9.1	9.7	10.8	16.2

Source: US Bureau of Labor Statistics

Chart I-16 provides U6 monthly from 2001 to 2017. There was a steep climb from 2007 into 2009 and then this measure of unemployment and underemployment stabilized at that high level but declined into 2012. The low of U6 SA was 8.0 percent in Mar 2007 and the peak was 17.1 percent in Apr 2010. The low NSA was 7.6 percent in Oct 2006 and the peak was 18.0 percent in Jan 2010.

Chart I-16, US, U6, total unemployed, plus all marginally attached workers, plus total employed Part-Time for Economic Reasons, Month, SA, 2001-2017

Source: US Bureau of Labor Statistics

Chart I-17 provides the number employed part-time for economic reasons or who cannot find full-time employment. There are sharp declines at the end of 2009, 2010 and 2011 but an increase in 2012 followed by relative stability in 2013-2017.

Chart I-17, US, Working Part-time for Economic Reasons

Thousands, Month SA 2001-2017

Sources: US Bureau of Labor Statistics

ICA3 Ten Million Fewer Full-time Jobs. There is strong seasonality in US labor markets around the end of the year.

Seasonally adjusted part-time for economic reasons. The number employed part-time for economic reasons because they could not find full-time employment fell from 9.166 million in Sep 2011 to 7.775 million in Mar 2012, seasonally adjusted, or decline of 1.391 million in six months, as shown in Table I-9. The number employed part-time for economic reasons rebounded to 8.671 million in Sep 2012 for increase of 697,000 in one month from Aug to Sep 2012. The number employed part-time for economic reasons declined to 8.203 million in Oct 2012 or by 468,000 again in one month, further declining to 8.166 million in Nov 2012 for another major one-month decline of 37,000 and 7.943 million in Dec 2012 or fewer 223,000 in just one month. The number employed part-time for economic reasons increased to 8.074 million in Jan 2013 or 131,000 more than in Dec 2012 and to 8.119 million in Feb 2013, declining to 7.864 million in May 2013 but increasing to 8.096 million in Jun 2013. The number employed part-time for economic reasons fell to 7.804 million in Aug 2013 for decline of 279,000 in one month from 8.083 million in Jul 2013. The number employed part-time for economic reasons increased 207,000 from 7.804 million in Aug 2013 to 8.011 million in Sep 2013. The number part-time for economic reasons rose to 7.995 million in Oct 2013, falling by 265,000 to 7.730 million in Nov 2013. The number part-time for economic reasons increased to 7.792 million in Dec 2013, decreasing to 7.298 million in Jan 2014. The number employed part-time for economic reasons fell from 7.298 million in Jan 2014 to 7.262 million in Feb 2014. The number employed part-time for economic reasons increased to 7.403 million in Mar 2014 and 7.466 million in Apr 2014. The number employed part-time for economic reasons fell to 7.170 million in May 2014, increasing to 7.469 million in Jun 2014. The level employed part-time for economic reasons fell to 7.430 million in Jul 2014 and 7.173 million in Aug 2014. The level employed part-time for economic reasons fell to 7.123 million in Sep 2014, 7.033 million in Oct 2014 and 6.870 million in Nov 2014. The level employed part-time for economic reasons fell to 6.819 million in Dec 2014, increasing to 6.836 million in Jan 2015. The level employed part-time for economic reasons fell to 6.664 million in Feb 2015, increasing to 6.646 million in Mar 2015. The level of employed part-time for economic reasons fell to 6.563 million in Apr 2015, increasing to 6.544 million in May 2015. The level employed part-time for economic reasons fell to 6.463 million in Jun 2015 and 6.292 million in Jul 2015. The level employed part-time for economic reasons increased to 6.438 million in Aug 2015, declining to 6.031 million in Sep 2015. The level employed part-time for economic reasons fell to 5.734 million in Oct 2015, increasing to 6.113 million in Nov 2015. The level of part-time for economic reasons fell to 6.057 million in Dec 2015, decreasing to 6.035 million in Jan 2016. The level employed part-time for economic reasons decreased to 6.019 million in Feb 2016 and increased to 6.120 million in Mar 2016. The level employed part-time for economic reasons fell to 5.970 million in Apr 2016 and increased to 6.409 million in May 2016. The level of part-time for economic reasons fell to 5.820 million in Jun 2016, increasing to 5.936 million in Jul 2016. The level of part-time for economic reasons increased to 6.027 million in Aug 2016, decreasing to 5.874 million in Sep 2016. The level of part-time for economic reasons reached 5.850 million in Oct 2016, decreasing to 5.659 million in Nov 2016 and 5.598 million in Dec 2016. The level of part-time for economic reasons increased to 5.840 million in Jan 2017, decreasing to 5.704 million in Feb 2017. The level of part-time for economic reasons fell to 5.553 million in Mar 2017 and fell to 5.272 million in Apr 2017.
Seasonally adjusted full-time. The number employed full-time increased from 112.923 million in Oct 2011 to 115.024 million in Mar 2012 or 2.101 million but then fell to 114.233 million in May 2012 or 0.791 million fewer full-time employed than in Mar 2012. The number employed full-time increased from 114.736 million in Aug 2012 to 115.570 million in Oct 2012 or increase of 0.834 million full-time jobs in two months and further to 115.724 million in Jan 2013 or increase of 0.988 million more full-time jobs in five months from Aug 2012 to Jan 2013. The number of full time jobs decreased slightly to 115.674 million in Feb 2013, increasing to 116.247 million in May 2013 and 116.126 million in Jun 2013. Then number of full-time jobs increased to 116.155 million in Jul 2013, 116.435 million in Aug 2013 and 116.895 million in Sep 2013. The number of full-time jobs fell to 116.362 million in Oct 2013 and increased to 117.046 in Nov 2013. The level of full-time jobs increased to 117.351 million in Dec 2013, increasing to 117.504 million in Jan 2014 and 117.747 million in Feb 2014. The level of employment full-time increased to 117.941 million in Mar 2014 and 118.516 million in Apr 2014. The level of full-time employment reached 118.816 million in May 2014, decreasing to 118.238 million in Jun 2014. The level of full-time jobs increased to 118.450 million in Jul 2014 and 118.707 million in Aug 2014. The level of full-time jobs increased to 119.338 million in Sep 2014, 119.763 million in Oct 2014 and 119.645 million in Nov 2014. The level of full-time jobs increased to 120.075 million in Dec 2014 and 120.575 million in Jan 2015. The level of full-time jobs increased to 120.776 million in Feb 2015 and 120.963 million in Mar 2015. The level of full-time jobs decreased to 120.870 million in Apr 2015, increasing to 121.523 million in May 2015 and decreasing to 121.066 million in Jun 2015. The level of full-time jobs increased to 121.629 million in Jul 2015 and increased to 121.934 million in Aug 2015, decreasing to 121.829 million in Sep 2015. The level of full-time jobs increased to 122.071 million in Oct 2015 and increased to 122.110 million in Nov 2015. The level of full-time jobs increased to 122.700 million in Dec 2015 and 123.116 million in Jan 2016. The level of full-time jobs increased to 123.210 million in Feb 2016 and increased to 123.513 million in Mar 2016. The level of full-time jobs decreased to 123.259 million in Apr 2016 and 123.232 million in May 2016. The level of full-time jobs increased to 123.618 million in Jun 2016, increasing to 123.888 million in Jul 2016. The level of full-time jobs increased to 124.256 million in Aug 2016, decreasing to 124.253 million in Sep 2016 and 124.190 million in Oct 2016. The level of full-time jobs increased to 124.213 million in Nov 2016 and 124.248 million in Dec 2016. The level of full-time jobs increased to 124.705 million in Jan 2017, increasing to 125.031 million in Feb 2017. The level of full-time jobs increased to 125.507 million in Mar 2017 and increased to 125.987 million in Apr 2017 Adjustments of benchmark and seasonality-factors at the turn of every year could affect comparability of labor market indicators (http://cmpassocregulationblog.blogspot.com/2016/02/fluctuating-risk-financial-assets-in.html http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html http://cmpassocregulationblog.blogspot.com/2014/02/financial-instability-rules.html http://cmpassocregulationblog.blogspot.com/2013/02/thirty-one-million-unemployed-or.html).
Not seasonally adjusted part-time for economic reasons. The number of employed part-time for economic reasons actually increased without seasonal adjustment from 8.271 million in Nov 2011 to 8.428 million in Dec 2011 or by 157,000 and then to 8.918 million in Jan 2012 or by an additional 490,000 for cumulative increase from Nov 2011 to Jan 2012 of 647,000. The level of employed part-time for economic reasons then fell from 8.918 million in Jan 2012 to 7.867 million in Mar 2012 or by 1.051 million and to 7.694 million in Apr 2012 or 1.224 million fewer relative to Jan 2012. In Aug 2012, the number employed part-time for economic reasons reached 7.842 million NSA or 148,000 more than in Apr 2012. The number employed part-time for economic reasons increased from 7.842 million in Aug 2012 to 8.110 million in Sep 2012 or by 3.4 percent. The number part-time for economic reasons fell from 8.110 million in Sep 2012 to 7.870 million in Oct 2012 or by 240.000 in one month. The number employed part-time for economic reasons NSA increased to 8.628 million in Jan 2013 or 758,000 more than in Oct 2012. The number employed part-time for economic reasons fell to 8.298 million in Feb 2013, which is lower by 330,000 relative to 8.628 million in Jan 2013 but higher by 428,000 relative to 7.870 million in Oct 2012. The number employed part time for economic reasons fell to 7.734 million in Mar 2013 or 564,000 fewer than in Feb 2013 and fell to 7.709 million in Apr 2013. The number employed part-time for economic reasons reached 7.618 million in May 2013. The number employed part-time for economic reasons jumped from 7.618 million in May 2013 to 8.440 million in Jun 2013 or 822,000 in one month. The number employed part-time for economic reasons fell to 8.324 million in Jul 2013 and 7.690 million in Aug 2013. The number employed part-time for economic reasons NSA fell to 7.522 million in Sep 2013, increasing to 7.700 million in Oct 2013. The number employed part-time for economic reasons fell to 7.563 million in Nov 2013 and increased to 7.990 million in Dec 2013. The number employed part-time for economic reasons fell to 7.771 million in Jan 2014 and 7.397 million in Feb 2014. The level of part-time for economic reasons increased to 7.455 million in Mar 2014 and fell to 7.243 million in Apr 2014. The number of part-time for economic reasons fell to 6.960 million in May 2014, increasing to 7.805 million in Jun 2014. The level of part-time for economic reasons fell to 7.665 million in Jul 2014 and 7.083 million in Aug 2014. The level of part-time for economic reasons fell to 6.711 million in Sep 2014 and increased to 6.787 million in Oct 2014. The level of part-time for economic reasons reached 6.713 million in Nov 2014 and 6.970 million in Dec 2014, increasing to 7.269 million in Jan 2015. The level of part-time for economic reasons fell to 6.772 million in Feb 2015 and 6.672 million in Mar 2015, falling to 6.356 million in Apr 2015. The level of part-time for economic reasons increased to 6.363 million in May 2015 and to 6.776 million in Jun 2015, decreasing to 6.511 million in Jul 2015. The level of part-time for economic reasons fell to 6.361 million in Aug 2015 and 5.693 million in Sep 2015. The level of part-time for economic reasons fell to 5.536 million in Oct 2015, increasing to 5.967 million in Nov 2015. The level of part-time for economic reasons increased to 6.179 million in Dec 2015, increasing to 6.406 million in Jan 2016. The level of part-time for economic reasons decreased to 6.106 million in Feb 2016 and increased to 6.138 million in Mar 2016. The level of part-time for economic reasons decreased to 5.771 million in Apr 2016 and increased to 6.238 million in May 2016. The level of part-time for economic reasons decreased to 6.119 million in Jun 2016, increasing to 6.157 million in Jul 2016. The level of part-time for economic reasons fell to 5.963 million in Aug 2016, decreasing to 5.550 million in Sep 2016. The level of part-time for economic reasons increased to 5.648 million in Oct 2016, decreasing to 5.518 million in Nov 2016 and increasing to 5.707 million in Dec 2016. The level of part-time for economic reasons increased to 6.226 million in Jan 2017, decreasing to 5.773 million in Feb 2017. The level of part-time for economic reasons fell to 5.552 million in Mar 2017, decreasing to 5.058 million in Apr 2017.
Not seasonally adjusted full-time. The number employed full time without seasonal adjustment fell from 113.138 million in Nov 2011 to 113.050 million in Dec 2011 or by 88,000 and fell further to 111.879 in Jan 2012 for cumulative decrease of 1.259 million. The number employed full-time not seasonally adjusted fell from 113.138 million in Nov 2011 to 112.587 million in Feb 2012 or by 551.000 but increased to 116.214 million in Aug 2012 or 3.076 million more full-time jobs than in Nov 2011. The number employed full-time not seasonally adjusted decreased from 116.214 million in Aug 2012 to 115.678 million in Sep 2012 for loss of 536,000 full-time jobs and rose to 116.045 million in Oct 2012 or by 367,000 full-time jobs in one month relative to Sep 2012. The number employed full-time NSA fell from 116.045 million in Oct 2012 to 115.515 million in Nov 2012 or decline of 530.000 in one month. The number employed full-time fell from 115.515 in Nov 2012 to 115.079 million in Dec 2012 or decline by 436,000 in one month. The number employed full time fell from 115.079 million in Dec 2012 to 113.868 million in Jan 2013 or decline of 1.211 million in one month. The number of full time jobs increased to 114.191 in Feb 2012 or by 323,000 in one month and increased to 114.796 million in Mar 2013 for cumulative increase from Jan by 928,000 full-time jobs but decrease of 283,000 from Dec 2012. The number employed full time reached 117.400 million in Jun 2013 and increased to 117.688 in Jul 2013 or by 288,000. The number employed full-time reached 117.868 million in Aug 2013 for increase of 180,000 in one month relative to Jul 2013. The number employed full-time fell to 117.308 million in Sep 2013 or by 560,000. The number employed full-time fell to 116.798 million in Oct 2013 or decline of 510.000 in one month. The number employed full-time rose to 116.875 million in Nov 2013, falling to 116.661 million in Dec 2013. The number employed full-time fell to 115.744 million in Jan 2014 but increased to 116.323 million in Feb 2014. The level of full-time jobs increased to 116.985 in Mar 2014 and 118.073 million in Apr 2014. The number of full-time jobs increased to 119.179 million in May 2014, increasing to 119.472 million in Jun 2014. The level of full-time jobs increased to 119.900 million in Jul 2014. Comparisons over long periods require use of NSA data. 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 Apr 2017 is 125.532 million, which is higher by 2.313 million relative to the peak of 123.219 million in Jul 2007.
Loss of full-time jobs. 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 254.588 million in Apr 2017 or by 22.630 million (http://www.bls.gov/data/). The number with full-time jobs in Apr 2017 is 125.532 million, which is higher by 2.313 million relative to the peak of 123.219 million in Jul 2007. 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 135.186 million full-time jobs with population of 254.588 million in Apr 2017 (0.531 x 254.588) or 9.654 million fewer full-time jobs relative to actual 125.532 million. There appear to be around 10 million fewer full-time jobs in the US than before the global recession while population increased around 20 million. Mediocre GDP growth is the main culprit of the fractured US labor market. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 31 quarters from IIIQ2009 to IQ2017. 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 first estimate of GDP for IQ2017 (https://www.bea.gov/newsreleases/national/gdp/2017/pdf/gdp1q17_adv.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 dividing 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 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (http://www.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2017 would have accumulated to 31.4 percent. GDP in IQ2017 would be $19,699.2 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2856.8 billion than actual $16,842.4 billion. There are about two trillion dollars of GDP less than at trend, explaining the 21.9 million unemployed or underemployed equivalent to actual unemployment/underemployment of 13.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html). US GDP in IQ2017 is 14.5 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,842.4 billion in IQ2017 or 12.3 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.2 percent per year from Mar 1919 to Mar 2017. Growth at 3.2 percent per year would raise the NSA index of manufacturing output from 108.2393 in Dec 2007 to 144.8512 in Mar 2017. The actual index NSA in Mar 2017 is 103.4144, which is 28.6 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Mar 2017. Using trend growth of 2.1 percent per year, the index would increase to 131.1817 in Mar 2017. The output of manufacturing at 103.4144 in Mar 2017 is 21.2 percent below trend under this alternative calculation.

Table I-9, US, Employed Part-time for Economic Reasons, Thousands, and Full-time, Millions

	Part-time Thousands	Full-time Millions
Seasonally Adjusted
Apr 2017	5.272	125.987
Mar 2017	5.553	125.507
Feb 2017	5.704	125.031
Jan 2017	5,840	124.705
Dec 2016	5,598	124.248
Nov 2016	5,659	124.213
Oct 2016	5,850	124.190
Sep 2016	5,874	124.253
Aug 2016	6,027	124.256
Jul 2016	5,936	123.888
Jun 2016	5,820	123.618
May 2016	6,409	123.232
Apr 2016	5,970	123.259
Mar 2016	6,120	123.513
Feb 2016	6,019	123.210
Jan 2016	6,035	123.116
Dec 2015	6,057	122.700
Nov 2015	6,113	122.110
Oct 2015	5,734	122.071
Sep 2015	6,031	121.829
Aug 2015	6,438	121.934
Jul 2015	6,292	121.629
Jun 2015	6,463	121.066
May 2015	6,544	121.523
Apr 2015	6,563	120.870
Mar 2015	6,646	120.963
Feb 2015	6,664	120.776
Jan 2015	6,836	120.575
Dec 2014	6,819	120.075
Nov 2014	6,870	119.645
Oct 2014	7,033	119.763
Sep 2014	7,123	119.338
Aug 2014	7,173	118.707
Jul 2014	7,430	118.450
Jun 2014	7,469	118.238
May 2014	7,170	118.816
Apr 2014	7,466	118.516
Mar 2014	7,403	117.941
Feb 2014	7,262	117.747
Jan 2014	7,298	117.504
Dec 2013	7,792	117.351
Nov 2013	7,730	117.046
Oct 2013	7,995	116.362
Sep 2013	8,011	116.895
Aug 2013	7,804	116.435
Jul 2013	8,083	116.155
Jun 2013	8,096	116.126
May 2013	7,864	116.247
Apr 2013	7,936	116.044
Mar 2013	7,658	115.785
Feb 2013	8,119	115.674
Jan 2013	8,074	115.724
Dec 2012	7,943	115.791
Nov 2012	8,166	115.655
Oct 2012	8,203	115.570
Sep 2012	8,671	115.252
Aug 2012	7,974	114.736
Jul 2012	8,082	114.575
Jun 2012	8,072	114.749
May 2012	8,101	114.233
Apr 2012	7,913	114.371
Mar 2012	7,775	115.024
Feb 2012	8,238	114.141
Jan 2012	8,305	113.755
Dec 2011	8,171	113.774
Nov 2011	8,447	113.213
Oct 2011	8,657	112.923
Sep 2011	9,166	112.544
Aug 2011	8,788	112.723
Jul 2011	8,281	112.193
Not Seasonally Adjusted
Apr 2017	5,058	125.532
Mar 2017	5,552	124.566
Feb 2017	5,773	123.610
Jan 2017	6,226	123.015
Dec 2016	5,707	123.570
Nov 2016	5,518	123.960
Oct 2016	5,648	124.588
Sep 2016	5,550	124.728
Aug 2016	5,963	125.892
Jul 2016	6,157	125.507
Jun 2016	6,119	124.903
May 2016	6,238	123.548
Apr 2016	5,771	122.742
Mar 2016	6,138	122.522
Feb 2016	6,106	121.757
Jan 2016	6,406	121.411
Dec 2015	6,179	122.013
Nov 2015	5,967	121.897
Oct 2015	5,536	122.466
Sep 2015	5,693	122.303
Aug 2015	6,361	123.420
Jul 2015	6,511	123.142
Jun 2015	6,776	122.268
May 2015	6,363	121.863
Apr 2015	6,356	120.402
Mar 2015	6,672	119.981
Feb 2015	6,772	119.313
Jan 2015	7,269	118.840
Dec 2014	6,970	119.394
Nov 2014	6,713	119.441
Oct 2014	6,787	120.176
Sep 2014	6,711	119.791
Aug 2014	7,083	120.110
Jul 2014	7,665	119.900
Jun 2014	7,805	119.472
May 2014	6,960	119.179
Apr 2014	7,243	118.073
Mar 2014	7,455	116.985
Feb 2014	7,397	116.323
Jan 2014	7,771	115.774
Dec 2013	7,990	116.661
Nov 2013	7,563	116.875
Oct 2013	7,700	116.798
Sep 2013	7,522	117.308
Aug 2013	7,690	117.868
Jul 2013	8,324	117.688
Jun 2013	8,440	117.400
May 2013	7,618	116.643
Apr 2013	7,709	115.674
Mar 2013	7,734	114.796
Feb 2013	8,298	114.191
Jan 2013	8,628	113.868
Dec 2012	8,166	115.079
Nov 2012	7,994	115.515
Oct 2012	7,870	116.045
Sep 2012	8,110	115.678
Aug 2012	7,842	116.214
Jul 2012	8,316	116.131
Jun 2012	8,394	116.024
May 2012	7,837	114.634
Apr 2012	7,694	113.999
Mar 2012	7,867	113.916
Feb 2012	8,455	112.587
Jan 2012	8,918	111.879
Dec 2011	8,428	113.050
Nov 2011	8,271	113.138
Oct 2011	8,258	113.456
Sep 2011	8,541	112.980
Aug 2011	8,604	114.286
Jul 2011	8,514	113.759
Jun 2011	8,738	113.255
May 2011	8,270	112.618
Apr 2011	8,425	111.844
Mar 2011	8,737	111.186
Feb 2011	8,749	110.731
Jan 2011	9,187	110.373
Dec 2010	9,205	111.207
Nov 2010	8,670	111.348
Oct 2010	8,408	112.342
Sep 2010	8,628	112.385
Aug 2010	8,628	113.508
Jul 2010	8,737	113.974
Jun 2010	8,867	113.856
May 2010	8,513	112.809
Apr 2010	8,921	111.391
Mar 2010	9,343	109.877
Feb 2010	9,282	109.100
Jan 2010	9,290	108.777 (low)
Dec 2009	9,354 (high)	109.875
Nov 2009	8,894	111.274
Oct 2009	8,474	111.599
Sep 2009	8,255	111.991
Aug 2009	8,835	113.863
Jul 2009	9,103	114.184
Jun 2009	9,301	114.014
May 2009	8,785	113.083
Apr 2009	8,648	112.746
Mar 2009	9,305	112.215
Feb 2009	9,170	112.947
Jan 2009	8,829	113.815
Dec 2008	8,250	116.422
Nov 2008	7,135	118.432
Oct 2008	6,267	120.020
Sep 2008	5,701	120.213
Aug 2008	5,736	121.556
Jul 2008	6,054	122.378
Jun 2008	5,697	121.845
May 2008	5,096	120.809
Apr 2008	5,071	120.027
Mar 2008	5,038	119.875
Feb 2008	5,114	119.452
Jan 2008	5,340	119.332
Dec 2007	4,750	121.042
Nov 2007	4,374	121.846
Oct 2007	4,028	122.006
Sep 2007	4,137	121.728
Aug 2007	4,494	122.870
Jul 2007	4,516	123.219 (high)
Jun 2007	4,469	122.150
May 2007	4,315	120.846
Apr 2007	4,205	119.609
Mar 2007	4,384	119.640
Feb 2007	4,417	119.041
Jan 2007	4,726	119.094
Dec 2006	4,281	120.371
Nov 2006	4,054	120.507
Oct 2006	4,010	121.199
Sep 2006	3,735 (low)	120.780
Aug 2006	4,104	121.979
Jul 2006	4,450	121.951
Jun 2006	4,456	121.070
May 2006	3,968	118.925
Apr 2006	3,787	118.559
Mar 2006	4,097	117.693
Feb 2006	4,403	116.823
Jan 2006	4,597	116.395

Source: US Bureau of Labor Statistics

People lose their marketable job skills after prolonged unemployment and face increasing difficulty in finding another job. Chart I-18 shows the sharp rise in unemployed over 27 weeks and stabilization at an extremely high level.

Chart I-18, US, Number Unemployed for 27 Weeks or Over, Thousands SA Month 2001-2017

Sources: US Bureau of Labor Statistics

Another segment of U6 consists of people marginally attached to the labor force who continue to seek employment but less frequently on the frustration there may not be a job for them. Chart I-19 shows the sharp rise in people marginally attached to the labor force after 2007 and subsequent stabilization.

Chart I-19, US, Marginally Attached to the Labor Force, NSA Month, Thousands, 2001-2017

Sources: US Bureau of Labor Statistics

Chart I-20 provides the level of full-time jobs from 2001 to 2017. 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 254.588 million in Apr 2017 or by 22.630 million (http://www.bls.gov/data/). The number with full-time jobs in Apr 2017 is 125.532 million, which is higher by 2.313 million relative to the peak of 123.219 million in Jul 2007. 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 135.186 million full-time jobs with population of 254.588 million in Apr 2017 (0.531 x 254.588) or 9.654 million fewer full-time jobs relative to actual 125.532 million. There appear to be around 10 million fewer full-time jobs in the US than before the global recession while population increased around 20 million. Mediocre GDP growth is the main culprit of the fractured US labor market.

There is current interest in past theories of “secular stagnation.” Alvin H. Hansen (1939, 4, 7; see Hansen 1938, 1941; for an early critique see Simons 1942) argues:

“Not until the problem of full employment of our productive resources from the long-run, secular standpoint was upon us, were we compelled to give serious consideration to those factors and forces in our economy which tend to make business recoveries weak and anaemic (sic) and which tend to prolong and deepen the course of depressions. This is the essence of secular stagnation-sick recoveries which die in their infancy and depressions which feed on them-selves and leave a hard and seemingly immovable core of unemployment. Now the rate of population growth must necessarily play an important role in determining the character of the output; in other words, the com-position of the flow of final goods. Thus a rapidly growing population will demand a much larger per capita volume of new residential building construction than will a stationary population. A stationary population with its larger proportion of old people may perhaps demand more personal services; and the composition of consumer demand will have an important influence on the quantity of capital required. The demand for housing calls for large capital outlays, while the demand for personal services can be met without making large investment expenditures. It is therefore not unlikely that a shift from a rapidly growing population to a stationary or declining one may so alter the composition of the final flow of consumption goods that the ratio of capital to output as a whole will tend to decline.”

The argument that anemic population growth causes “secular stagnation” in the US (Hansen 1938, 1939, 1941) is as misplaced currently as in the late 1930s (for early dissent see Simons 1942). This is merely another case of theory without reality with dubious policy proposals.

Inferior performance of the US economy and labor markets, during cyclical slow growth not secular stagnation, is the critical current issue of analysis and policy design.

Chart I-20, US, Full-time Employed, Thousands, NSA, 2001-2017

Sources: US Bureau of Labor Statistics

Chart I-20A provides the noninstitutional civilian population of the United States from 2001 to 2017. There is clear trend of increase of the population while the number of full-time jobs collapsed after 2008 without sufficient recovery as shown in the preceding Chart I-20.

Chart I-20A, US, Noninstitutional Civilian Population, Thousands, 2001-2017

Sources: US Bureau of Labor Statistics

Chart I-20B provides number of full-time jobs in the US from 1968 to 2017. There were multiple recessions followed by expansions without contraction of full-time jobs and without recovery as during the period after 2008. The problem is specific of the current cycle and not secular.

Chart I-20B, US, Full-time Employed, Thousands, NSA, 1968-2017

Sources: US Bureau of Labor Statistics

Chart I-20C provides the noninstitutional civilian population of the United States from 1968 to 2017. Population expanded at a relatively constant rate of increase with the assurance of creation of full-time jobs that has been broken since 2008.

Chart I-20C, US, Noninstitutional Civilian Population, Thousands, 1968-2017

Sources: US Bureau of Labor Statistics

IA4 Theory and Reality of Secular Stagnation: Youth and Middle-Age Unemployment. Table I-9A provides percentage change of real GDP in the United States in the 1930s, 1980s and 2000s. The recession in 1981-1982 is quite similar on its own to the 2007-2009 recession. In contrast, during the Great Depression in the four years of 1930 to 1933, GDP in constant dollars fell 26.4 percent cumulatively and fell 45.3 percent in current dollars (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 150-2, Pelaez and Pelaez, Globalization and the State, Vol. II (2009b), 205-7 and revisions in http://bea.gov/iTable/index_nipa.cfm). Data are available for the 1930s only on a yearly basis. US GDP fell 4.7 percent in the two recessions (1) from IQ1980 to IIIQ1980 and (2) from III1981 to IVQ1982 and 4.2 percent cumulatively in the recession from IVQ2007 to IIQ2009. It is instructive to compare the first years of the expansions in the 1980s and the current expansion. GDP grew at 4.6 percent in 1983, 7.3 percent in 1984, 4.2 percent in 1985, 3.5 percent in 1986, 3.5 percent in 1987, 4.2 percent in 1988 and 3.7 percent in 1989. In contrast, GDP grew 2.5 percent in 2010, 1.6 percent in 2011, 2.2 percent in 2012, 1.7 percent in 2013, 2.4 percent in 2014 and 2.6 percent in 2015. GDP grew 1.6 percent in 2016. Actual annual equivalent GDP growth in the twenty quarters from 2012 to 2016 is 2.1 percent and 2.0 percent in the four quarters ending in IVQ2016. GDP grew at 4.2 percent in 1985, 3.5 percent in 1986, 3.5 percent in 1987, 4.2 percent in 1988 and 3.7 percent in 1989. The forecasts of the central tendency of participants of the Federal Open Market Committee (FOMC) are in the range of 2.0 to 2.2 percent in 2017 (https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20170315.pdf) with less reliable forecast of 1.8 to 2.3 percent in 2018 (https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20170315.pdf). Growth of GDP in the expansion from IIIQ2009 to IVQ2016 has been at average 2.1 percent in annual equivalent.

Table I-9A, US, Percentage Change of GDP in the 1930s, 1980s and 2000s, ∆%

Year	GDP ∆%	Year	GDP ∆%	Year	GDP ∆%
1930	-8.5	1980	-0.2	2000	4.1
1931	-6.4	1981	2.6	2001	1.0
1932	-12.9	1982	-1.9	2002	1.8
1933	-1.3	1983	4.6	2003	2.8
1934	10.8	1984	7.3	2004	3.8
1935	8.9	1985	4.2	2005	3.3
1936	12.9	1986	3.5	2006	2.7
1937	5.1	1987	3.5	2007	1.8
1938	-3.3	1988	4.2	2008	-0.3
1939	8.0	1989	3.7	2009	-2.8
1940	8.8	1990	1.9	2010	2.5
1941	17.7	1991	-0.1	2011	1.6
1942	18.9	1992	3.6	2012	2.2
1943	17.0	1993	2.7	2013	1.7
1944	8.0	1994	4.0	2014	2.4
1945	-1.0	1995	2.7	2015	2.6
1946	-11.6	1996	3.8	2016	1.6

Source: US Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm

Characteristics of the four cyclical contractions are in Table I-9B with the first column showing the number of quarters of contraction; the second column the cumulative percentage contraction; and the final column the average quarterly rate of contraction. There were two contractions from IQ1980 to IIIQ1980 and from IIIQ1981 to IVQ1982 separated by three quarters of expansion. The drop of output combining the declines in these two contractions is 4.7 percent, which is almost equal to the decline of 4.2 percent in the contraction from IVQ2007 to IIQ2009. In contrast, during the Great Depression in the four years of 1930 to 1933, GDP in constant dollars fell 26.4 percent cumulatively and fell 45.3 percent in current dollars (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 150-2, Pelaez and Pelaez, Globalization and the State, Vol. II (2009b), 205-7 and revisions in http://bea.gov/iTable/index_nipa.cfm). The comparison of the global recession after 2007 with the Great Depression is entirely misleading. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (http://www.bea.gov/iTable/index_nipa.cfm).

Table I-9B, US, Number of Quarters, GDP Cumulative Percentage Contraction and Average Percentage Annual Equivalent Rate in Cyclical Contractions

	Number of Quarters	Cumulative Percentage Contraction	Average Percentage Rate
IIQ1953 to IIQ1954	3	-2.4	-0.8
IIIQ1957 to IIQ1958	3	-3.0	-1.0
IVQ1973 to IQ1975	5	-3.1	-0.6
IQ1980 to IIIQ1980	2	-2.2	-1.1
IIIQ1981 to IVQ1982	4	-2.5	-0.64
IVQ2007 to IIQ2009	6	-4.2	-0.72

Sources: Source: Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm

Table I-9C shows the mediocre average annual equivalent growth rate of 2.1 percent of the US economy in the thirty-one quarters of the current cyclical expansion from IIIQ2009 to IQ2017. In sharp contrast, the average growth rate of GDP was:

5.7 percent in the first thirteen quarters of expansion from IQ1983 to IQ1986
5.4 percent in the first fifteen quarters of expansion from IQ1983 to IIIQ1986
5.2 percent in the first sixteen quarters of expansion from IQ1983 to IVQ1986
5.0 percent in the first seventeen quarters of expansion from IQ1983 to IQ1987
5.0 percent in the first eighteen quarters of expansion from IQ1983 to IIQ1987
4.9 percent in the first nineteen quarters of expansion from IQ1983 to IIIQ1987
5.0 percent in the first twenty quarters of expansion from IQ1983 to IVQ1987
4.9 percent in the first twenty-first quarters of expansion from IQ1983 to IQ1988
4.9 percent in the first twenty-two quarters of expansion from IQ1983 to IIQ1988
4.8 percent in the first twenty-three quarters of expansion from IQ1983 to IIIQ1988
4.8 percent in the first twenty-four quarters of expansion from IQ1983 to IVQ1988
4.8 percent in the first twenty-five quarters of expansion from IQ1983 to IQ1989
4.7 percent in the first twenty-six quarters of expansion from IQ1983 to IIQ1989
4.7 percent in the first twenty-seven quarters of expansion from IQ1983 to IIIQ1989
4.5 percent in the first twenty-eight quarters of expansion from IQ1983 to IVQ1989
4.5 percent in the first twenty-nine quarters of expansion from IQ1983 to IQ1990
4.4 percent in the first thirty quarters of expansion from IQ1983 to IIQ1990
4.3 percent in the first thirty-one quarters of expansion from IQ1983 to IIIQ1990

The line “average first four quarters in four expansions” provides the average growth rate of 7.7 percent with 7.8 percent from IIIQ1954 to IIQ1955, 9.2 percent from IIIQ1958 to IIQ1959, 6.1 percent from IIIQ1975 to IIQ1976 and 7.8 percent from IQ1983 to IVQ1983. The United States missed this opportunity of high growth in the initial phase of recovery. BEA data show the US economy in standstill relative to historical experience with annual growth of 2.5 percent in 2010 decelerating to 1.6 percent annual growth in 2011, 2.2 percent in 2012, 1.7 percent in 2013, 2.4 percent in 2014, 2.6 percent in 2015 and 1.6 percent in 2016 (http://www.bea.gov/iTable/index_nipa.cfm). The expansion from IQ1983 to IQ1986 was at the average annual growth rate of 5.7 percent, 5.2 percent from IQ1983 to IVQ1986, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IQ1988, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988. 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989. 4.5 percent from IQ1983 to IVQ1989, 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990 and at 7.8 percent from IQ1983 to IVQ1983. The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (http://www.bea.gov/iTable/index_nipa.cfm). GDP grew 2.7 percent in the first four quarters of the expansion from IIIQ2009 to IIQ2010. GDP growth in the twenty-one quarters from 2012 to 2017 accumulated to 10.9 percent. This growth is equivalent to 2.0 percent per year, obtained by dividing GDP in IQ2017 of $16,842.4 billion by GDP in IVQ2011 of $15,190.3 billion and compounding by 4/21: {[($16,842.4/$15,190.3)^4/21 -1]100 = 2.0 percent}.

Table I-9C shows that GDP grew 17.1 percent in the first thirty quarters of expansion from IIIQ2009 to IVQ2016 at the annual equivalent rate of 2.1 percent.

Table I-9C, US, Number of Quarters, Cumulative Growth and Average Annual Equivalent Growth Rate in Cyclical Expansions

	Number of Quarters	Cumulative Growth ∆%	Average Annual Equivalent Growth Rate
IIIQ 1954 to IQ1957	11	12.8	4.5
First Four Quarters IIIQ1954 to IIQ1955	4	7.8
IIQ1958 to IIQ1959	5	10.0	7.9
First Four Quarters IIIQ1958 to IIQ1959	4	9.2
IIQ1975 to IVQ1976	8	8.3	4.1
First Four Quarters IIIQ1975 to IIQ1976	4	6.1
IQ1983-IQ1986 IQ1983-IIIQ1986 IQ1983-IVQ1986 IQ1983-IQ1987 IQ1983-IIQ1987 IQ1983 to IIIQ1987 IQ1983 to IVQ1987 IQ1983 to IQ1988 IQ1983 to IIQ1988 IQ1983 to IIIQ1988 IQ1983 to IVQ1988 IQ1983 to IQ1989 IQ1983 to IIQ1989 IQ1983 to IIIQ1989 IQ1983 to IVQ1989 IQ1983 to IQ1990 IQ1983 to IIQ1990 IQ1983 to III1990	13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31	19.9 21.6 22.3 23.1 24.5 25.6 27.7 28.4 30.1 30.9 32.6 34.0 35.0 36.0 36.3 37.8 38.3 38.4	5.7 5.4 5.2 5.0 5.0 4.9 5.0 4.9 4.9 4.8 4.8 4.8 4.7 4.7 4.5 4.5 4.4 4.3
First Four Quarters IQ1983 to IVQ1983	4	7.8
Average First Four Quarters in Four Expansions*		7.7
IIIQ2009 to IQ2017	31	17.3	2.1
First Four Quarters IIIQ2009 to IIQ2010		2.7

*First Four Quarters: 7.8% IIIQ1954-IIQ1955; 9.2% IIIQ1958-IIQ1959; 6.1% IIIQ1975-IQ1976; 7.8% IQ1983-IVQ1983

Source: Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm

Table EMP provides the comparison between the labor market in the current whole cycle from 2007 to 2016 and the whole cycle from 1979 to 1989. In the entire cycle from 2007 to 2016, the number employed increased 5.389 million, full-time employed increased 2.670 million, part-time for economic reasons increased 1.542 million and population increased 21.491 million. The number employed increased 3.7 percent, full-time employed increased 2.2 percent, part-time for economic reasons increased 35.0 percent and population increased 9.3 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 1989, the number employed increased 18.518 million, full-time employed increased 14.715 million, part-time for economic reasons increased 1.317 million and population increased 21.530 million. In the entire cycle from 1979 to 1989, the number employed increased 18.7 percent, full-time employed increased 17.8 percent, part-time for economic reasons increased 36.8 percent and population increased 13.1 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
2015	148.834	121.492	6.371	250.801
2016	151.436	123.761	5.943	253.358
∆2007-2016	5.389	2.670	1.542	21.491
∆% 2007-2016	3.7	2.2	35.0	9.3
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
∆1979-1989	18.518	14.715	1.317	21.530
∆% 1979-1989	18.7	17.8	36.8	13.1

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 21.9 million or 13.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.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
2015	250.8	121.5	148.8	157.1	62.7	59.3	8.3
2016	253.5	123.8	151.4	159.2	62.8	59.7	7.8
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
4/17	254.6	125.5	153.3	159.8	62.8	60.2	6.6

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
2015	38.6	18.8	21.2	55.0	48.6	2.5	11.6
2016	38.4	19.0	21.2	55.2	49.4	2.2	10.4
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
4/17	38.2	18.9	20.7	54.2	49.6	1.8	8.5

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. Youth employment fell from 20.041 million in 2006 to 18.756 million in 2015 or 1.285 million. Youth employment fell from 20.041 million in 2006 to 18.992 million in 2016 or 1.049 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 20.129 million in Dec 2006 to 18.720 million in Dec 2015 or 1.409 million fewer jobs. Youth jobs fell from 20.129 million in Dec 2006 to 18.830 million in Dec 2016 or 1.299 million. Youth jobs fell from 19.003 million in Jan 2016 to 18.311 million in Jan 2017 or 0.692 million. 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 youth civilian noninstitutional population increased 1.600 million from 36.989 million in Jul 2006 to 38.589 million in Jul 2015 while the number of youth jobs fell 1.581 million. The youth civilian noninstitutional population increased 1.548 million from 37.008 million in Aug 2006 to 38.556 million in Aug 2015 while the number of youth jobs fell 1.590 million. The youth civilian noninstitutional population increased 1,498 million from 37.027 million in Sep 2006 to 38.525 million in Sep 2015 while the number of youth jobs fell 1.249 million. The youth civilian noninstitutional population increased 1.444 million from 37.047 million in Oct 2006 to 38.491 million in Oct 2015 while the number of youth jobs fell 1.199 million. The youth civilian noninstitutional population increased 1.392 million from 37.076 million in Nov 2006 to 38.468 million in Nov 2015 while the number of youth jobs fell 1.418 million. The youth civilian noninstitutional population increased 1.341 million from 37.100 million in Dec 2006 to 38.441 million in Dec 2015 while the level of youth jobs 1.409 million. The youth civilian noninstitutional population increased 1.734 million from 36.761 million in Jan 2006 to 38.495 million in Jan 2016 while the level of youth jobs fell 0.844 million. The youth civilian noninstitutional population increased 1.698 million from 36.791 million in Feb 2006 to 38.489 million in Feb 2016 while the number of youth jobs fell 0.726 million. The youth civilian noninstitutional population increased 1,662 million from 36.821 million in Mar 2006 to 38.483 million in Mar 2016 while the number of youth jobs fell 0.711 million. The youth civilian noninstitutional population increased 1.626 million from 36.854 million in Apr 2006 to 38.480 million in Apr 2016 while the number of youth jobs fell 0.895 million. The youth civilian noninstitutional population increased 1.571 million from 36.897 million in May 2006 to 38.468 million in May 2016 while the number of youth jobs fell 0.894 million. The youth civilian noninstitutional population increased 1.516 million from 36.943 million in Jun 2006 to 38.459 million in Jun 2016 while the number of youth jobs fell 1.301 million. The youth civilian noninstitutional population increased 1.461 million from 36.989 million in Jul 2006 to 38.450 million in Jul 2016 while the number of youth jobs fell 1.458 million. The youth civilian noninstitutional population increased 1.414 million from 37.008 million in Aug 2006 to 38.422 million in Aug 2016 while the number of youth jobs fell 1.291 million. The youth civilian noninstitutional population increased 1.368 million from 37.027 million in Sep 2006 to 38.395 million in Sep 2016 while the number of youth jobs fell 0.911 million. The youth civilian noninstitutional population increased 1.320 million from 37.047 million in Oct 2006 to 38.367 million in Oct 2016 while the number of youth jobs fell 1.158 million. The youth civilian noninstitutional population increased 1.283 million from 37.076 million in Nov 2006 to 38.359 million in Nov 2016 while the number of youth jobs fell 1.102 million. The youth civilian noninstitutional population increased 1.248 million from 37.100 million in Dec 2006 to 38.348 million in Dec 2016 while the number of youth jobs fell 1.299 million. The youth civilian noninstitutional population increased 1.488 million from 36.761 million in Jan 2006 to 38.249 million in Jan 2017 while the number of youth jobs decreased 0.692 million. The youth civilian noninstitutional population increased 1.440 million from 36.791 million in Feb 2006 to 38.231 million in Feb 2017 while the number of youth jobs decreased 0.578 million. The youth civilian noninstitutional population increased 1.393 million from 36.821 million in Mar 2006 to 38.214 million in Mar 2017 while the number of youth jobs decreased 0.377 million. The youth civilian noninstitutional population increased 1.343 million from 36.854 million in Apr 2006 to 38.197 million in Mar 2017 while the number of youth jobs decreased 0.458 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

Source: US Bureau of Labor Statistics http://www.bls.gov/data/

Year	Jan	Feb	Mar	Apr	Dec	Annual
2001	19678	19745	19800	19778	19547	20088
2002	18653	19074	19091	19108	19394	19683
2003	18811	18880	18709	18873	19136	19351
2004	18852	18841	18752	19184	19619	19630
2005	18858	18670	18989	19071	19733	19770
2006	19003	19182	19291	19406	20129	20041
2007	19407	19415	19538	19368	19361	19875
2008	18724	18546	18745	19161	18378	19202
2009	17467	17606	17564	17739	16615	17601
2010	16166	16412	16587	16764	16727	17077
2011	16512	16638	16898	16970	17234	17362
2012	16944	17150	17301	17387	17604	17834
2013	17183	17257	17271	17593	18106	18057
2014	17372	17357	17939	18021	18347	18442
2015	17912	18222	18076	18241	18720	18756
2016	18159	18456	18580	18511	18830	18992
2017	18311	18604	18914	18948

Source: Bureau of Labor Statistics

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 for ages 16 to 24 increased from 2342 thousand in 2007 to 2467 thousand in 2015, decreasing to 2.211 million in 2016. The unemployment level ages 16 to 24 years decreased from 2.092 million in Apr 2006 to 1.759 million in Apr 2017 or decrease by 0.333 million. This situation may persist for many years.

Chart I-21 provides US employment level ages 16 to 24 years from 2002 to 2017. Employment level is sharply lower in Apr 2017 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, 2001-2017

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 2017. 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 youth civilian noninstitutional population increased 1.600 million from 36.989 million in Jul 2006 to 38.589 million in Jul 2015 while the number of youth jobs fell 1.581 million. The youth civilian noninstitutional population increased 1.548 million from 37.008 million in Aug 2006 to 38.556 million in Aug 2015 while the number of youth jobs fell 1.590 million. The youth civilian noninstitutional population increased 1,498 million from 37.027 million in Sep 2006 to 38.525 million in Sep 2015 while the number of youth jobs fell 1.249 million. The youth civilian noninstitutional population increased 1.444 million from 37.047 million in Oct 2006 to 38.491 million in Oct 2015 while the number of youth jobs fell 1.199 million. The youth civilian noninstitutional population increased 1.392 million from 37.076 million in Nov 2006 to 38.468 million in Nov 2015 while the number of youth jobs fell 1.418 million. The youth civilian noninstitutional population increased 1.341 million from 37.100 million in Dec 2006 to 38.441 million in Dec 2015 while the level of youth jobs 1.409 million. The youth civilian noninstitutional population increased 1.734 million from 36.761 million in Jan 2006 to 38.495 million in Jan 2016 while the level of youth jobs fell 0.844 million. The youth civilian noninstitutional population increased 1.698 million from 36.791 million in Feb 2006 to 38.489 million in Feb 2016 while the number of youth jobs fell 0.726 million. The youth civilian noninstitutional population increased 1,662 million from 36.821 million in Mar 2006 to 38.483 million in Mar 2016 while the number of youth jobs fell 0.711 million. The youth civilian noninstitutional population increased 1.626 million from 36.854 million in Apr 2006 to 38.480 million in Apr 2016 while the number of youth jobs fell 0.895 million. The youth civilian noninstitutional population increased 1.571 million from 36.897 million in May 2006 to 38.468 million in May 2016 while the number of youth jobs fell 0.894 million. The youth civilian noninstitutional population increased 1.516 million from 36.943 million in Jun 2006 to 38.459 million in Jun 2016 while the number of youth jobs fell 1.301 million. The youth civilian noninstitutional population increased 1.461 million from 36.989 million in Jul 2006 to 38.450 million in Jul 2016 while the number of youth jobs fell 1.458 million. The youth civilian noninstitutional population increased 1.414 million from 37.008 million in Aug 2006 to 38.422 million in Aug 2016 while the number of youth jobs fell 1.291 million. The youth civilian noninstitutional population increased 1.368 million from 37.027 million in Sep 2006 to 38.395 million in Sep 2016 while the number of youth jobs fell 0.911 million. The youth civilian noninstitutional population increased 1.320 million from 37.047 million in Oct 2006 to 38.367 million in Oct 2016 while the number of youth jobs fell 1.158 million. The youth civilian noninstitutional population increased 1.283 million from 37.076 million in Nov 2006 to 38.359 million in Nov 2016 while the number of youth jobs fell 1.102 million. The youth civilian noninstitutional population increased 1.248 million from 37.100 million in Dec 2006 to 38.348 million in Dec 2016 while the number of youth jobs fell 1.299 million. The youth civilian noninstitutional population increased 1.488 million from 36.761 million in Jan 2006 to 38.249 million in Jan 2017 while the number of youth jobs decreased 0.692 million. The youth civilian noninstitutional population increased 1.440 million from 36.791 million in Feb 2006 to 38.231 million in Feb 2017 while the number of youth jobs decreased 0.578 million. The youth civilian noninstitutional population increased 1.393 million from 36.821 million in Mar 2006 to 38.214 million in Mar 2017 while the number of youth jobs decreased 0.377 million. The youth civilian noninstitutional population increased 1.343 million from 36.854 million in Apr 2006 to 38.197 million in Mar 2017 while the number of youth jobs decreased 0.458 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, 2001-2017

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 2017. 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. The youth civilian labor force decreased 1.502 million from 24.664 million in Jul 2007 to 23.162 million in Jul 2015 while the youth civilian noninstitutional population increased 1.600 million from 36.989 million in Jul 2006 to 38.589 million in Jul 2015. The youth civilian labor force decreased 1.667 million from 23.634 million in Aug 2006 to 21.967 million in Aug 2015 while the youth civilian noninstitutional population increased 1.548 million from 37.008 in Aug 2006 to 38.556 million in Aug 2015. The youth civilian labor force decreased 1.290 million from 21.901 million in Sep 2006 to 20.611 in Sep 2015 while the youth civilian noninstitutional population increased 1.498 million from 37.027 million in Sep 2006 to 38.525 million in Sep 2015. The youth civilian labor force decreased 1.228 million from 22.105 million in Oct 2006 to 20.877 million in Oct 2015 while the youth civilian noninstitutional population increased 1.444 million from 37.047 million in Oct 2006 to 38.491 million in Oct 2015. The youth civilian labor force decreased 1.513 million from 22.145 million in Nov 2006 to 20.632 million in Nov 2015 while the youth civilian noninstitutional population increased 1.392 million from 37.076 million in Nov 2006 to 38.468 million in Nov 2015. The youth civilian labor force decreased 1.301 million from 22.136 million in Dec 2006 to 20.835 million in Dec 2015 while the youth civilian noninstitutional population increased 1.341 million from 37.100 million in Dec 2006 to 38.441 million in Dec 2015. The youth civilian labor force decreased 1.004 million from 21.368 million in Jan 2006 to 20.364 million in Jan 2016 while the youth civilian noninstitutional population increased 1.734 million from 36.761 million in Jan 2006 to 38.495 million in Jan 2016. The youth civilian labor force decreased 0.930 million from 21.615 million in Feb 2006 to 20.685 million in Feb 2016 while the youth civilian noninstitutional population increased 1.698 million from 36.791 million in Feb 2006 to 38.489 million in Feb 2016. The youth civilian labor force decreased 0.767 million from 21.507 million in Mar 2006 to 20.740 million in Mar 2016 while the youth civilian noninstitutional population increased 1.662 million from 36.821 million in Mar 2006 to 38.483 million in Mar 2016. The youth civilian labor force decreased 0.950 million from 21.498 million in Apr 2006 to 20.548 million in Apr 2016 while the youth civilian noninstitutional population increased 1.626 million from 36.854 million in Apr 2006 to 38.480 million in Apr 2016. The youth civilian labor force decreased 0.921 million from 22.023 million in May 2006 to 21.102 million in May 2016 while the youth civilian noninstitutional population increased 1.571 million from 36.897 million in May 2006 to 38.468 million in May 2016. The youth civilian labor force decreased 1.373 million from 24.128 million in Jun 2006 to 22.755 million in Jun 2016 while the youth civilian noninstitutional population increased 1.516 million from 36.943 million in Jun 2006 to 38.459 million in Jun 2016. The youth civilian labor force decreased 1.560 million from 24.664 million in Jul 2006 to 23.104 million in Jul 2016 while the youth civilian noninstitutional population increased 1,461 million from 36.989 million in Jul 2006 to 38.450 million in Jul 2016. The youth civilian labor force decreased 1.536 million from 23.634 million in Aug 2006 to 22.098 million in Aug 2016 while the youth civilian noninstitutional population increased 1.414 million from 37.008 million in Aug 2006 to 38.422 million in Aug 2016. The youth civilian labor force decreased 1.082 million from 21.901 million in Sep 2006 to 20.891 million in Sep 2016 while the youth civilian noninstitutional population increased 1.368 million from 37.027 million in Sep 2006 to 38.395 million in Sep 2016. The youth civilian labor force decreased 1.315 million from 22.105 million in Oct 2006 to 20.790 million in Oct 2016 while the youth civilian noninstitutional population increased 1.320 million from 37.047 million in Oct 2006 to 38.367 million in Oct 2016. The youth civilian labor force decreased 1.410 million from 22.145 million in Nov 2006 to 20.735 million in Nov 2016 while the youth civilian noninstitutional population increased 1.283 million from 37.076 million in Nov 2006 to 38.359 million in Nov 2016. The youth civilian labor force decreased 1.447 million from 22.136 million in Dec 2006 to 20.689 million in Dec 2016 while the youth civilian noninstitutional population increased 1.248 million from 37.100 million in Dec 2006 to 38.348 million in Dec 2016. The youth civilian labor force decreased 0.861 million from 21.368 million in Jan 2006 to 20.507 million in Jan 2017 while the youth civilian noninstitutional population increased 1.488 million from 36.761 million in Jan 2006 to 38.249 million in Jan 2017. The youth civilian labor force decreased 0.918 million from 21.615 million in Feb 2006 to 20.697 million in Feb 2017 while the youth civilian noninstitutional population increased 1.440 million from 36.791 million in Feb 2006 to 38.231 million in Feb 2017. The youth civilian labor force decreased 0.751 million from 21.507 million in Mar 2006 to 20.756 million in Mar 2017 while the youth civilian noninstitutional population increased 1.393 million from 36.821 million in Mar 2006 to 38.214 million in Mar 2017. The youth civilian labor force decreased 0.790 million from 21.498 million in Apr 2006 to 20.708 million in Apr 2017 while the youth civilian noninstitutional population increased 1.343 million from 36.854 million in Apr 2006 to 38.197 million in Apr 2017. 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, 2001-2017

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 rate ages 16 to 64 fell from 65.3 in Jun 2006 to 59.4 in Jun 2015. The labor force participation rate ages 16 to 24 fell from 66.7 in Jul 2006 to 60.0 in Jul 2014. The labor force participation rate ages 16 to 24 fell from 63.9 in Aug 2006 to 57.0 in Aug 2015. The labor force participation rate ages 16 to 24 fell from 59.1 in Sep 2006 to 53.5 in Sep 2015. The labor force participation rate ages 16 to 24 fell from 59.7 in Oct 2006 to 54.2 in Oct 2015. The labor force participation rate ages 16 to 24 fell from 59.7 in Nov 2006 to 53.6 in Nov 2015. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 54.2 in Dec 2015. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 52.9 in Jan 2016. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 53.7 in Feb 2016. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 53.9 in Mar 2016. The labor force participation rate ages 16 to 24 fell from 58.3 in Apr 2006 to 53.4 in Apr 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in May 2006 to 54.9 in May 2016. The labor force participation rate ages 16 to 24 fell from 65.3 in Jun 2006 to 59.2 in Jun 2016. The labor force participation rate ages 16 to 24 fell from 66.7 in Jul 2006 to 60.1 in Jul 2016. The labor force participation rate ages 16 to 24 fell from 63.9 in Aug 2006 to 57.5 in Aug 2016. The labor force participation rate ages 16 to 24 fell from 59.1 in Sep 2006 to 54.2 in Sep 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in Oct 2006 to 54.2 in Oct 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in Nov 2006 to 54.1 in Nov 2016. The labor force participation rate ages 16 to 24 fell from 59.7 in Dec 2006 to 54.0 in Dec 2016. The labor force participation rate ages 16 to 24 fell from 58.1 in Jan 2006 to 53.6 in Jan 2017. The labor force participation rate ages 16 to 24 fell from 58.8 in Feb 2006 to 54.1 in Feb 2017. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 54.3 in Mar 2017. The labor force participation rate ages 16 to 24 fell from 58.4 in Mar 2006 to 54.3 in Mar 2017. 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, 2001-2017

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. The employment population ratio for ages 16 to 24 fell from 59.2 in Jul 2006 to 52.7 in Jul 2015. The employment population ratio for ages 16 to 24 fell from 57.2 in Aug 2006 to 50.8 in Aug 2015. The employment population ratio for ages 16 to 24 years fell from 52.9 in Sep 2006 to 47.6 in Sep 2015. The employment population ratio for ages 16 to 24 years fell from 53.6 in Oct 2006 to 48.5 in Oct 2015. The employment population ratio for ages 16 to 24 years fell from 53.7 in Nov 2006 to 48.1 in Nov 2015. The employment population ratio for ages 16 to 24 years fell from 54.3 in Dec 2006 to 48.7 in Dec 2015. The employment population ratio for ages 16 to 24 years fell from 51.7 in Jan 2006 to 47.2 in Jan 2016. The employment population ration for ages 16 to 24 years fell from 52.1 in Feb 2006 to 48.0 in Feb 2016. The employment population ratio for ages 16 to 24 fell from 52.4 in Mar 2006 to 48.3 in Mar 2016. The employment population ratio for ages 16 to 24 fell from 52.7 in Apr 2006 to 48.1 in Apr 2016. The employment population ratio for ages 16 to 24 fell from 53.6 in May 2006 to 49.1 in May 2016. The employment population ratio for ages 16 to 24 fell from 57.6 in Jun 2006 to 51.9 in Jun 2016. The employment population ratio for ages 16 to 24 fell from 59.2 in Jul 2006 to 53.2 in Jul 2016. The employment population ratio for ages 16 to 24 fell from 57.2 in Aug 2006 to 51.7 in Aug 2016. The employment population ratio for ages 16 to 24 fell from 52.9 in Sep 2006 to 48.7 in Sep 2016. The employment population ratio for ages 16 to 24 fell from 53.6 in Oct 2006 to 48.7 in Oct 2016. The employment population ratio for ages 16 to 24 fell from 53.7 in Nov 2006 to 49.0 in Nov 2016. The employment population ratio for ages 16 to 24 fell from 54.3 in Dec 2006 to 49.1 in Dec 2016. The employment population ratio for ages 16 to 24 fell from 51.7 in Jan 2006 to 47.9 in Jan 2017. The employment population ratio for ages 16 to 24 fell from 52.1 in Feb 2006 to 48.7 in Feb 2017. The employment population ratio for ages 16 to 24 fell from 52.4 in Mar 2006 to 49.5 in Mar 2017. The employment population ratio for ages 16 to 24 fell from 52.7 in Apr 2006 to 49.6 in Apr 2017. 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, 2001-2017

Source: US Bureau of Labor Statistics http://www.bls.gov/data/

Table I-11, US, Unemployment Level 16-24 Years, NSA, Thousands

Year	Jan	Feb	Mar	Apr	Nov	Dec	Annual
2001	2250	2258	2253	2095	2470	2412	2371
2002	2754	2731	2822	2515	2570	2374	2683
2003	2748	2740	2601	2572	2522	2248	2746
2004	2767	2631	2588	2387	2448	2294	2638
2005	2661	2787	2520	2398	2369	2055	2521
2006	2366	2433	2216	2092	2242	2007	2353
2007	2363	2230	2096	2074	2250	2323	2342
2008	2633	2480	2347	2196	2833	2928	2830
2009	3278	3457	3371	3321	3699	3532	3760
2010	3983	3888	3748	3803	3561	3352	3857
2011	3851	3696	3520	3365	3287	3161	3634
2012	3416	3507	3294	3175	3102	3153	3451
2013	3674	3449	3261	3129	2721	2536	3324
2014	3051	3033	3002	2440	2458	2317	2853
2015	2644	2529	2524	2175	2147	2114	2467
2016	2205	2229	2160	2037	1934	1859	2211
2017	2195	2093	1842	1759

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 2017. The level rose sharply from 2007 to 2010 with tepid improvement into 2012 and deterioration into 2013-2014 with recent marginal improvement in 2015-17 alternating with deterioration.

Chart I-22, US, Unemployment Level 16-24 Years, Thousands SA, 2001-2017

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 2006 to 13.7 in Jun 2015. The rate of youth unemployment increased from 10.8 in Jul 2007 to 12.2 in Jul 2015. The rate of youth unemployment increased from 10.5 in Aug 2007 to 10.9 in Aug 2015. The rate of youth unemployment decreased from 11.0 in Sep 2007 to 10.9 in Sep 2015. The rate of youth unemployment increased from 10.3 in Oct 2007 to 10.6 in Oct 2015. The rate of youth unemployment increased from 10.3 in Nov 2007 to 10.4 in Nov 2015. The rate of youth unemployment decreased from 10.7 in Dec 2007 to 10.1 in Dec 2015. The rate of youth unemployment decreased from 10.9 in Jan 2007 to 10.8 in Jan 2016. The rate of youth unemployment increased from 10.3 in Feb 2007 to 10.8 in Feb 2016. The rate of youth unemployment increased from 9.7 in Mar 2007 to 10.4 in Mar 2016. The rate of youth unemployment increased from 9.7 in Apr 2007 to 9.9 in Apr 2016. The rate of youth unemployment increased from 10.2 in May 2007 to 10.6 in May 2016. The rate of youth unemployment increased from 12.0 in Jun 2007 to 12.3 in Jun 2016. The rate of youth unemployment increased from 10.8 in Jul 2007 to 11.5 in Jul 2016. The rate of youth unemplopyment fell from 10.5 in Aug 2007 to 10.1 in Aug 2016. The rate of youth unemployment fell from 11.0 in Sep 2007 to 10.2 in Sep 2016. The rate of youth unemployment fell from 10.3 in Oct 2007 to 10.1 in Oct 2016. The rate of youth unemployment fell from 10.3 in Nov 2007 to 9.3 in Nov 2016. The rate of youth unemployment fell from 10.7 in Dec 2007 to 9.0 in Dec 2016. The rate of youth unemployment fell from 10.9 in Jan 2007 to 10.7 in Jan 2017. The rate of youth unemployment fell from 10.3 in Feb 2007 to 10.1 in Feb 2017. The rate of youth unemployment fell from 9.7 in Mar 2007 to 8.9 in Mar 2017. The rate of youth unemployment fell from 9.7 in Apr 2007 to 8.5 in Apr 2017. 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
2015	12.9	12.2	12.3	10.7	12.3	13.7	12.2	10.9	10.9	10.6	10.4	10.1
2016	10.8	10.8	10.4	9.9	10.6	12.3	11.5	10.1	10.2	10.1	9.3	9.0
2017	10.7	10.1	8.9	8.5

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 2017. 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, 2001-2017

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 2017. 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 rate of youth unemployment increased from 10.8 in Jul 2007 to 12.2 in Jul 2015. The rate of youth unemployment increased from 10.5 in Aug 2007 to 10.9 in Aug 2015. The rate of youth unemployment decreased from 11.0 in Sep 2007 to 10.9 in Sep 2015. The rate of youth unemployment increased from 10.3 in Oct 2007 to 10.6 in Oct 2015, decreasing to 10.4 in Nov 2015. The rate of youth unemployment decreased to 10.1 in Dec 2015. The rate of youth unemployment stood at 10.8 in Jan 2016, 10.8 in Feb 2016, 10.4 in Mar 2016 and 9.9 in Apr 2016. The rate of youth unemployment increased to 10.6 in May 2016 and 12.3 in Jun 2016. The rate of youth unemployment fell to 11.5 in Jul 2016, decreasing to 10.1 in Aug 2016. The rate of youth unemployment increased to 10.2 in Sep 2016, decreasing to 10.1 in Oct 2016 and 9.3 in Nov 2016. The rate of youth unemployment decreased to 9.0 in Dec 2016, increasing to 10.7 in Jan 2017. The rate of youth unemployment fell to 10.1 in Feb 2017, decreasing to 8.9 in Mar 2017. The rate of youth unemployment fell to 8.5 in Apr 2017. 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 4.4 percent from IQ1983 to IIQ1990 compared with 2.1 percent on average during the first 30 quarters of expansion from IIIQ2009 to IVQ2016. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 30 quarters from IIIQ2009 to IVQ2016. 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 IVQ2016 (https://www.bea.gov/newsreleases/national/gdp/2017/pdf/gdp4q16_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 dividing 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 (https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/rising-valuations-of-risk-financial.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/rising-valuations-of-risk-financial.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 IVQ2016 would have accumulated to 30.5 percent. GDP in IVQ2016 would be $19,564.3 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2751.0 billion than actual $16,813.3 billion. There are about two trillion dollars of GDP less than at trend, explaining the 22.9 million unemployed or underemployed equivalent to actual unemployment/underemployment of 13.6 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/03/increasing-interest-rates-twenty-four.html). US GDP in IVQ2016 is 14.1 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,813.3 billion in IVQ2016 or 12.1 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.2 percent per year from Feb 1919 to Feb 2017. Growth at 3.2 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 144.4892 in Feb 2017. The actual index NSA in Feb 2017 is 103.4436, which is 28.4 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Feb 2017. Using trend growth of 2.0 percent per year, the index would increase to 130.9602 in Feb 2017. The output of manufacturing at 103.4436 in Feb 2017 is 21.0 percent below trend under this alternative calculation.

Chart I-24, US, Unemployment Rate 16-24 Years, Percent NSA, 1948-2017

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 level of unemployed ages 45 years and over increased from 2.053 million in Jul 2007 to 3.083 million in Jul 2014 and at 2.666 million in Jul 2015 is 30.0 percent higher than in Jul 2007. The level of unemployed ages 45 years and over increased from 1.956 million in Aug 2007 to 3.037 million in Aug 2014 and at 2.693 million in Aug 2015 is 37.7 higher than in Aug 2007. The level of unemployed ages 45 years and over increased from 1.854 million in Sep 2007 to 2.640 million in Sep 2015 and at 2.388 million in Sep 2015 is 28.8 percent higher than in Sep 2007. The level of unemployment ages 45 years and over increased from 1.885 million in Oct 2007 to 2.606 million in Oct 2014 and at 2.290 million in Oct 2015 is 21.5 percent higher than in Oct 2007. The level of unemployment ages 45 years and over increased from 1.925 million in Nov 2007 to 2.829 million in Nov 2014 and at 2.349 million in Nov 2015 is 22.0 percent higher than in Nov 2007. The level of unemployment ages 45 years and over increased from 2.120 million in Dec 2007 to 2.667 million in Dec 2014 and at 2.317 million in Dec 2015 is 9.3 percent higher than in Dec 2007. The level of unemployment ages 45 and over increased from 2.155 million in Jan 2007 to 3.077 million in Jan 2015 and at 2.736 million in Jan 2016 is 27.0 percent higher than in Jan 2007. The level of unemployment ages 45 and over increased from 2.138 million in Feb 2007 to 2.991 million in Feb 2015 and at 2.744 million in Feb 2016 is 28.3 percent higher than in Feb 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.724 million in Mar 2015 and at 2.747 million in Mar 2016 is 35.3 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 1.871 million in Apr 2007 to 2.579 million in Apr 2015 and at 2.410 million in Apr 2016 is 28.8 percent higher than in Apr 2007. The level of unemployment ages 45 and over increased from 1.803 million in May 2007 to 2.457 million in May 2015 and at 2.190 million in May 2016 is 21.5 percent higher than in May 2007. The level of unemployment ages 45 and over increased from 1.805 million in Jun 2007 to 2.359 million in Jun 2015 and at 2.345 million in Jun 2016 is 29.9 percent higher than in Jun 2007. The level of unemployment ages 45 and over increased from 2.053 million in Jul 2007 to 2.666 million in Jul 2015 and at 2.619 million in Jul 2016 is 27.6 percent higher than in Jul 2007. The level of unemployment ages 45 and over increased from 1.956 million in Aug 2007 to 2.693 million in Aug 2015 and at 2.565 million in Aug 2016 is 31.1 percent higher than in Aug 2007. The level of unemployment ages 45 and over increased from 1.854 million in Sep 2007 to 2.388 million in Sep 2015 and at 2.414 million in Sep 2016 is 30.2 percent higher than in Sep 2007. The level of unemployment ages 45 and over increased from 1.885 million in Oct 2007 to 2.290 million in Oct 2015 and at 2.337 million in Oct 2016 is 24.0 percent higher than in Oct 2007. The level of unemployment ages 45 and over increased from 1.925 million in Nov 2007 to 2.349 million in Nov 2015 and at 2.355 million in Nov 2016 is 22.3 percent higher than in Nov 2007. The level of unemployment ages 45 and over increased from 2.120 million in Dec 2007 to 2.317 million in Dec 2015 and at 2.360 million in Dec 2016 is 11.3 percent higher than in Dec 2007. The level of unemployment ages 45 and over increased from 2.155 million in Jan 2007 to 2.736 million in Jan 2016 and at 2.585 million in Jan 2017 is 20.0 percent higher than in Jan 2007. The level of unemployment ages 45 and over increased from 2.138 million in Feb 2007 to 2.744 million in Feb 2016 and at 2.493 million in Feb 2017 is 16.6 percent higher than in Feb 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.747 million in Mar 2016 and at 2.413 million in Mar 2017 is 18.8 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 2.031 million in Mar 2007 to 2.747 million in Mar 2016 and at 2.413 million in Mar 2017 is 18.8 percent higher than in Mar 2007. The level of unemployment ages 45 and over increased from 1.871 million in Apr 2007 to 2.410 million in Apr 2016 and at 2.202 million in Apr 2017 is 17.7 percent higher than in Apr 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. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 31 quarters from IIIQ2009 to IQ2017. 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 first estimate of GDP for IQ2017 (https://www.bea.gov/newsreleases/national/gdp/2017/pdf/gdp1q17_adv.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 dividing 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 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (http://www.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2017 would have accumulated to 31.4 percent. GDP in IQ2017 would be $19,699.2 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2856.8 billion than actual $16,842.4 billion. There are about two trillion dollars of GDP less than at trend, explaining the 21.9 million unemployed or underemployed equivalent to actual unemployment/underemployment of 13.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html). US GDP in IQ2017 is 14.5 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,842.4 billion in IQ2017 or 12.3 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.2 percent per year from Mar 1919 to Mar 2017. Growth at 3.2 percent per year would raise the NSA index of manufacturing output from 108.2393 in Dec 2007 to 144.8512 in Mar 2017. The actual index NSA in Mar 2017 is 103.4144, which is 28.6 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Mar 2017. Using trend growth of 2.1 percent per year, the index would increase to 131.1817 in Mar 2017. The output of manufacturing at 103.4144 in Mar 2017 is 21.2 percent below trend under this alternative calculation.

Table I-13, US, Unemployment Level 45 Years and Over, Thousands NSA

Year	Jan	Feb	Mar	Apr	Nov	Dec	Annual
2000	1498	1392	1291	1062	1242	1217	1249
2001	1572	1587	1533	1421	1786	1901	1576
2002	2235	2280	2138	2101	2013	2210	2114
2003	2495	2415	2485	2287	2132	2130	2253
2004	2453	2397	2354	2160	2053	2086	2149
2005	2286	2286	2126	1939	1920	1963	2009
2006	2126	2056	1881	1843	1704	1794	1848
2007	2155	2138	2031	1871	1925	2120	1966
2008	2336	2336	2326	2104	3078	3485	2540
2009	4138	4380	4518	4172	4655	4960	4500
2010	5314	5307	5194	4770	4909	4762	4879
2011	5027	4837	4748	4373	4195	4182	4537
2012	4458	4472	4390	4037	3861	3927	4133
2013	4394	4107	3929	3689	3383	3378	3719
2014	3508	3490	3394	3006	2829	2667	3000
2015	3077	2991	2724	2579	2349	2317	2574
2016	2736	2744	2747	2410	2355	2360	2485
2017	2585	2493	2413	2202

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, 1976-2017

Source: US Bureau of Labor Statistics http://www.bls.gov/data/

IIA United States International Trade. Table IIA-1 provides the trade balance of the US and monthly growth of exports and imports seasonally adjusted with the latest release and revisions (http://www.census.gov/foreign-trade/). Because of heavy dependence on imported oil, fluctuations in the US trade account originate largely in fluctuations of commodity futures prices caused by carry trades from zero interest rates into commodity futures exposures in a process similar to world inflation waves (https://cmpassocregulationblog.blogspot.com/2017/04/world-inflation-waves-united-states.html). The Census Bureau revised data for 2017, 2016, 2015, 2014 and 2013. Exports decreased 0.9 percent in Mar 2017 while imports decreased 0.7 percent. The trade deficit decreased from $43,760 million in Feb 2017 to $43,706 million in Mar 2017. The trade deficit deteriorated to $48,189 million in Mar 2015. The trade deficit improved to $40,885 million in Apr 2015 and $40,170 million in May 2015. The trade deficit deteriorated to $42,973 million in Jun 2015 and improved to $39,900 million in Jul 2015, deteriorating to $44,639 million in Aug 2015. The trade deficit improved to $41,072 million in Sep 2015, deteriorating to $41,600 million in Oct 2015 and improving to $41,122 million in Nov 2015. The trade deficit deteriorated to $45,588 million in Mar 2016, improving to $37,259 million in Mar 2016. The trade deficit deteriorated to $38,544 million in Apr 2016, deteriorating to $42,189 million in May 2016 and $45,073 million in Jun 2016. The trade deficit improved to $39,691 million in Jul 2016, deteriorating to $40,513 million in Aug 2016. The trade deficit improved to $36,026 million in Sep 2016, deteriorating to $42,577 million in Oct 2016. The trade deficit deteriorated to $45,484 million in Nov 2016, improving to $44,259 million in Dec 2016. The trade deficit deteriorated to $48,173 million in Jan 2017, improving to $43,760 million in Feb 2017. The trade deficit improved to $43,706 million in Mar 2017.

Table IIA-1, US, Trade Balance of Goods and Services Seasonally Adjusted Millions of Dollars and ∆%

	Trade Balance	Exports	Month ∆%	Imports	Month ∆%
Mar 2017	-43,706	190,986	-0.9	234,692	-0.7
Feb	-43,760	192,655	0.1	236,414	-1.8
Jan	-48,173	192,512	0.8	240,685	2.3
Dec 2016	-44,259	191,014	2.7	235,273	1.5
Nov	-45,484	185,995	-0.2	231,478	1.1
Oct	-42,577	186,398	-2.0	228,975	1.2
Sep	-36,026	190,210	1.0	226,236	-1.1
Aug	-40,513	188,326	0.9	228,839	1.2
Jul	39,691	186,444	2.0	226,134	-0.7
Jun	-45,073	182,723	0.8	227,796	1.9
May	-42,189	181,297	-0.2	223,485	1.5
Apr	-38,544	181,570	1.7	220,115	2.0
Mar	-37,259	178,543	-1.2	215,802	-4.7
Feb	-45,588	180,747	1.1	226,336	1.9
Jan	-43,356	178,813	-2.3	222,170	-1.1
Dec 2015	-41,487	183,074	-0.3	224,561	-0.1
Nov	-41,122	183,576	-1.1	224,698	-1.1
Oct	-41,600	185,587	-1.0	227,186	-0.6
Sep	-41,072	187,550	0.5	228,622	-1.1
Aug	-44,639	186,620	-1.8	231,259	0.5
Jul	-39,900	190,106	-0.1	230,006	-1.4
Jun	-42,973	190,347	0.0	233,320	1.2
May	-40,170	190,361	-0.7	230,531	-0.9
Apr	-40,885	191,675	0.6	232,560	-2.5
Mar	-48,189	190,448	0.3	238,637	5.5
Feb	-36,268	189,852	-1.1	226,121	-3.4
Jan	-42,057	191,968	-2.8	234,024	-3.0
Jan-Dec 2016	-500,560	2,212,079	-2.2	2,712,639	-1.8

Note: Trade Balance of Goods = Exports of Goods less Imports of Goods. Trade balance may not add exactly because of errors of rounding and seasonality. Source: US Census Bureau, Foreign Trade Division

Table IIA-1B provides US exports, imports and the trade balance of goods. The US has not shown a trade surplus in trade of goods since 1976. The deficit of trade in goods deteriorated sharply during the boom years from 2000 to 2007. The deficit improved during the contraction in 2009 but deteriorated in the expansion after 2009. The deficit could deteriorate sharply with growth at full employment.

Table IIA-1B, US, International Trade Balance of Goods, Exports and Imports of Goods, Millions of Dollars, Census Basis

	Balance	∆%	Exports	∆%	Imports	∆%
1960	4,608	(X)	19,626	(X)	15,018	(X)
1961	5,476	18.8	20,190	2.9	14,714	-2.0
1962	4,583	-16.3	20,973	3.9	16,390	11.4
1963	5,289	15.4	22,427	6.9	17,138	4.6
1964	7,006	32.5	25,690	14.5	18,684	9.0
1965	5,333	-23.9	26,699	3.9	21,366	14.4
1966	3,837	-28.1	29,379	10.0	25,542	19.5
1967	4,122	7.4	30,934	5.3	26,812	5.0
1968	837	-79.7	34,063	10.1	33,226	23.9
1969	1,289	54.0	37,332	9.6	36,043	8.5
1970	3,224	150.1	43,176	15.7	39,952	10.8
1971	-1,476	-145.8	44,087	2.1	45,563	14.0
1972	-5,729	288.1	49,854	13.1	55,583	22.0
1973	2,389	-141.7	71,865	44.2	69,476	25.0
1974	-3,884	-262.6	99,437	38.4	103,321	48.7
1975	9,551	-345.9	108,856	9.5	99,305	-3.9
1976	-7,820	-181.9	116,794	7.3	124,614	25.5
1977	-28,352	262.6	123,182	5.5	151,534	21.6
1978	-30,205	6.5	145,847	18.4	176,052	16.2
1979	-23,922	-20.8	186,363	27.8	210,285	19.4
1980	-19,696	-17.7	225,566	21.0	245,262	16.6
1981	-22,267	13.1	238,715	5.8	260,982	6.4
1982	-27,510	23.5	216,442	-9.3	243,952	-6.5
1983	-52,409	90.5	205,639	-5.0	258,048	5.8
1984	-106,702	103.6	223,976	8.9	330,678	28.1
1985	-117,711	10.3	218,815	-2.3	336,526	1.8
1986	-138,279	17.5	227,159	3.8	365,438	8.6
1987	-152,119	10.0	254,122	11.9	406,241	11.2
1988	-118,526	-22.1	322,426	26.9	440,952	8.5
1989	-109,399	-7.7	363,812	12.8	473,211	7.3
1990	-101,719	-7.0	393,592	8.2	495,311	4.7
1991	-66,723	-34.4	421,730	7.1	488,453	-1.4
1992	-84,501	26.6	448,164	6.3	532,665	9.1
1993	-115,568	36.8	465,091	3.8	580,659	9.0
1994	-150,630	30.3	512,626	10.2	663,256	14.2
1995	-158,801	5.4	584,742	14.1	743,543	12.1
1996	-170,214	7.2	625,075	6.9	795,289	7.0
1997	-180,522	6.1	689,182	10.3	869,704	9.4
1998	-229,758	27.3	682,138	-1.0	911,896	4.9
1999	-328,821	43.1	695,797	2.0	1,024,618	12.4
2000	-436,104	32.6	781,918	12.4	1,218,022	18.9
2001	-411,899	-5.6	729,100	-6.8	1,140,999	-6.3
2002	-468,263	13.7	693,103	-4.9	1,161,366	1.8
2003	-532,350	13.7	724,771	4.6	1,257,121	8.2
2004	-654,830	23.0	814,875	12.4	1,469,704	16.9
2005	-772,373	18.0	901,082	10.6	1,673,455	13.9
2006	-827,971	7.2	1,025,967	13.9	1,853,938	10.8
2007	-808,763	-2.3	1,148,199	11.9	1,956,962	5.6
2008	-816,199	0.9	1,287,442	12.1	2,103,641	7.5
2009	-503,582	-38.3	1,056,043	-18.0	1,559,625	-25.9
2010	-635,362	26.2	1,278,495	21.1	1,913,857	22.7
2011	-725,447	14.2	1,482,508	16.0	2,207,954	15.4
2012	-730,446	0.7	1,545,821	4.3	2,276,267	3.1
2013	-689,470	-5.6	1,578,517	2.1	2,267,987	-0.4
2014	-735,194	6.6	1,621,172	2.7	2,356,366	3.9
2015	-745,660	1.4	1,502,572	-7.3	2,248,232	-4.6
2016	-734,331	-1.5	1,454,607	-3.2	2,188,938	-2.6

Source: US Census Bureau, Foreign Trade Division

http://www.census.gov/briefrm/esbr/www/esbr042.html

Chart IIA-1 of the US Census Bureau of the Department of Commerce shows that the trade deficit (gap between exports and imports) fell during the economic contraction after 2007 but has grown again during the expansion. The low average rate of growth of GDP of 2.1 percent during the expansion beginning since IIIQ2009 does not deteriorate further the trade balance. Higher rates of growth may cause sharper deterioration.

Chart IIA-1, US, International Trade Balance, Exports and Imports of Goods and Services USD Billions

Source: US Census Bureau

Table IIA-2B provides the US international trade balance, exports and imports of goods and services on an annual basis from 1992 to 2016. The trade balance deteriorated sharply over the long term. The US has a large deficit in goods or exports less imports of goods but it has a surplus in services that helps to reduce the trade account deficit or exports less imports of goods and services. The current account deficit of the US not seasonally adjusted decreased from $137.2 billion in IIIQ2015 to $133.2 billion in IIIQ2016 (http://www.bea.gov/international/index.htm). The current account deficit seasonally adjusted at annual rate did not change from 2.5 percent of GDP in IVQ2015 to 2.5 percent of GDP in IIIQ2016, decreasing to 2.4 percent of GDP in IVQ2016 (http://www.bea.gov/international/index.htm http://www.bea.gov/iTable/index_nipa.cfm). The ratio of the current account deficit to GDP has stabilized around 3 percent of GDP compared with much higher percentages before the recession (see Pelaez and Pelaez, The Global Recession Risk (2007), Globalization and the State, Vol. II (2008b), 183-94, Government Intervention in Globalization (2008c), 167-71). The final rows of Table IIA-2B show marginal improvement of the trade deficit from $548,625 million in 2011 to lower $536,773 million in 2012 with exports growing 4.3 percent and imports 3.0 percent. The trade balance improved further to deficit of $461,876 million in 2013 with growth of exports of 3.4 percent while imports virtually stagnated. The trade deficit deteriorated in 2014 to $490,176 million in 2014 with growth of exports of 3.6 percent and of imports of 4.0 percent. The trade deficit deteriorated in 2015 to $500,361 million with decrease of exports of 4.9 percent and decrease of imports of 3.7 percent. The trade deficit deteriorated in 2016 to $500,560 million with decrease of exports of 2.2 percent and decrease of imports of 1.8 percent. Growth and commodity shocks under alternating inflation waves (https://cmpassocregulationblog.blogspot.com/2017/04/world-inflation-waves-united-states.html) have deteriorated the trade deficit from the low of $383,774 million in 2009.

Table IIA-2B, US, International Trade Balance of Goods and Services, Exports and Imports of Goods and Services, SA, Millions of Dollars, Balance of Payments Basis

	Balance	Exports	∆%	Imports	∆%
1960	3,508	25,940	NA	22,432	NA
1961	4,195	26,403	1.8	22,208	-1.0
1962	3,370	27,722	5.0	24,352	9.7
1963	4,210	29,620	6.8	25,410	4.3
1964	6,022	33,341	12.6	27,319	7.5
1965	4,664	35,285	5.8	30,621	12.1
1966	2,939	38,926	10.3	35,987	17.5
1967	2,604	41,333	6.2	38,729	7.6
1968	250	45,543	10.2	45,293	16.9
1969	91	49,220	8.1	49,129	8.5
1970	2,254	56,640	15.1	54,386	10.7
1971	-1,302	59,677	5.4	60,979	12.1
1972	-5,443	67,222	12.6	72,665	19.2
1973	1,900	91,242	35.7	89,342	23.0
1974	-4,293	120,897	32.5	125,190	40.1
1975	12,404	132,585	9.7	120,181	-4.0
1976	-6,082	142,716	7.6	148,798	23.8
1977	-27,246	152,301	6.7	179,547	20.7
1978	-29,763	178,428	17.2	208,191	16.0
1979	-24,565	224,131	25.6	248,696	19.5
1980	-19,407	271,834	21.3	291,241	17.1
1981	-16,172	294,398	8.3	310,570	6.6
1982	-24,156	275,236	-6.5	299,391	-3.6
1983	-57,767	266,106	-3.3	323,874	8.2
1984	-109,072	291,094	9.4	400,166	23.6
1985	-121,880	289,070	-0.7	410,950	2.7
1986	-138,538	310,033	7.3	448,572	9.2
1987	-151,684	348,869	12.5	500,552	11.6
1988	-114,566	431,149	23.6	545,715	9.0
1989	-93,141	487,003	13.0	580,144	6.3
1990	-80,864	535,233	9.9	616,097	6.2
1991	-31,135	578,344	8.1	609,479	-1.1
1992	-39,212	616,882	6.7	656,094	7.6
1993	-70,311	642,863	4.2	713,174	8.7
1994	-98,493	703,254	9.4	801,747	12.4
1995	-96,384	794,387	13.0	890,771	11.1
1996	-104,065	851,602	7.2	955,667	7.3
1997	-108,273	934,453	9.7	1,042,726	9.1
1998	-166,140	933,174	-0.1	1,099,314	5.4
1999	-258,617	969,867	3.9	1,228,485	11.8
2000	-372,517	1,075,321	10.9	1,447,837	17.9
2001	-361,511	1,005,654	-6.5	1,367,165	-5.6
2002	-418,955	978,706	-2.7	1,397,660	2.2
2003	-493,890	1,020,418	4.3	1,514,308	8.3
2004	-609,883	1,161,549	13.8	1,771,433	17.0
2005	-714,245	1,286,022	10.7	2,000,267	12.9
2006	-761,716	1,457,642	13.3	2,219,358	11.0
2007	-705,375	1,653,548	13.4	2,358,922	6.3
2008	-708,726	1,841,612	11.4	2,550,339	8.1
2009	-383,774	1,583,053	-14.0	1,966,827	-22.9
2010	-494,658	1,853,606	17.1	2,348,263	19.4
2011	-548,625	2,127,021	14.8	2,675,646	13.9
2012	-536,773	2,218,989	4.3	2,755,762	3.0
2013	-461,876	2,293,457	3.4	2,755,334	0.0
2014	-490,176	2,376,577	3.6	2,866,754	4.0
2015	-500,361	2,261,163	-4.9	2,761,525	-3.7
2016	-500,560	2,212,079	-2.2	2,712,639	-1.8

Source: US Census Bureau

Chart IIA-2 of the US Census Bureau provides the US trade account in goods and services SA from Jan 1992 to Mar 2017. There is long-term trend of deterioration of the US trade deficit shown vividly by Chart IIA-2. The global recession from IVQ2007 to IIQ2009 reversed the trend of deterioration. Deterioration resumed together with incomplete recovery and was influenced significantly by the carry trade from zero interest rates to commodity futures exposures (these arguments are elaborated in Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 157-66, Regulation of Banks and Finance (2009b), 217-27, International Financial Architecture (2005), 15-18, The Global Recession Risk (2007), 221-5, Globalization and the State Vol. II (2008b), 197-213, Government Intervention in Globalization (2008c), 182-4 http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html http://cmpassocregulationblog.blogspot.com/2011/01/professor-mckinnons-bubble-economy.html http://cmpassocregulationblog.blogspot.com/2011/01/world-inflation-quantitative-easing.html http://cmpassocregulationblog.blogspot.com/2011/01/treasury-yields-valuation-of-risk.html http://cmpassocregulationblog.blogspot.com/2010/11/quantitative-easing-theory-evidence-and.html http://cmpassocregulationblog.blogspot.com/2010/12/is-fed-printing-money-what-are.html). Earlier research focused on the long-term external imbalance of the US in the form of trade and current account deficits (Pelaez and Pelaez, The Global Recession Risk (2007), Globalization and the State Vol. II (2008b) 183-94, Government Intervention in Globalization (2008c), 167-71). US external imbalances have not been fully resolved and tend to widen together with improving world economic activity and commodity price shocks. There are additional effects for revaluation of the dollar with the Fed orienting interest rate increases while the European Central Bank and the Bank of Japan determine negative nominal interest rates.

Chart IIA-2, US, Balance of Trade SA, Monthly, Millions of Dollars, Jan 1992-Mar 2017

Source: US Census Bureau

Chart IIA-3 of the US Census Bureau provides US exports SA from Jan 1992 to Mar 2017. There was sharp acceleration from 2003 to 2007 during worldwide economic boom and increasing inflation. Exports fell sharply during the financial crisis and global recession from IVQ2007 to IIQ2009. Growth picked up again together with world trade and inflation but stalled in the final segment with less rapid global growth and inflation.

Chart IIA-3, US, Exports SA, Monthly, Millions of Dollars Jan 1992-Mar 2017

Source: US Census Bureau

Chart IIA-4 of the US Census Bureau provides US imports SA from Jan 1992 to Mar 2017. Growth was stronger between 2003 and 2007 with worldwide economic boom and inflation. There was sharp drop during the financial crisis and global recession. There is stalling import levels in the final segment resulting from weaker world economic growth and diminishing inflation because of risk aversion and portfolio reallocations from commodity exposures to equities.

Chart IIA-4, US, Imports SA, Monthly, Millions of Dollars Jan 1992-Mar 2017

Source: US Census Bureau

There is deterioration of the US trade balance in goods in Table IIA-3 from deficit of $57,654 million in Mar 2016 to deficit of $65,509 million in Mar 2017. The nonpetroleum deficit increased by $3284 million while the petroleum deficit increased $4613 million. Total exports of goods increased 7.9 percent in Mar 2017 relative to a year earlier while total imports increased 9.9 percent. Nonpetroleum exports increased 6.3 percent from Mar 2016 to Mar 2017 while nonpetroleum imports increased 6.3 percent. Petroleum imports increased 70.6 percent.

Table IIA-3, US, International Trade in Goods Balance, Exports and Imports $ Millions and ∆% SA

	Mar 2017	Mar 2016	∆%
Total Balance	-65,509	-57,654
Petroleum	-7,884	-3,271
Non Petroleum	-56,345	-53,061
Total Exports	126,282	116,999	7.9
Petroleum	8,836	6,529	35.3
Non Petroleum	116,988	110,067	6.3
Total Imports	191,972	174,653	9.9
Petroleum	16,719	9,800	70.6
Non Petroleum	173,333	163,127	6.3

Details may not add because of rounding and seasonal adjustment

Source: US Census Bureau

US exports and imports of goods not seasonally adjusted in Jan-Mar 2017 and Jan-Mar 2016 are in Table IIA-4. The rate of growth of exports was 7.3 percent and 7.3 percent for imports. The US has partial hedge of commodity price increases in exports of agricultural commodities that increased 16.0 percent and of mineral fuels that increased 55.0 percent both because prices of raw materials and commodities increase and fall recurrently because of shocks of risk aversion and portfolio reallocations. The US exports an insignificant but growing amount of crude oil, increasing 38.6 percent in cumulative Jan-Mar 2017 relative to a year earlier. US exports and imports consist mostly of manufactured products, with less rapidly increasing prices. US manufactured exports increased 3.7 percent while manufactured imports increased 4.6 percent. Significant part of the US trade imbalance originates in imports of mineral fuels increasing 60.7 percent and petroleum increasing 63.6 percent with wide oscillations in oil prices. The limited hedge in exports of agricultural commodities and mineral fuels compared with substantial imports of mineral fuels and crude oil results in waves of deterioration of the terms of trade of the US, or export prices relative to import prices, originating in commodity price increases caused by carry trades from zero interest rates. These waves are similar to those in worldwide inflation.

Table IIA-4, US, Exports and Imports of Goods, Not Seasonally Adjusted Millions of Dollars and %, Census Basis

	Jan-Mar 2017 $ Millions	Jan-Mar 2016 $ Millions	∆%
Exports	372,890	347,559	7.3
Manufactured	263,825	254,490	3.7
Agricultural Commodities	36,051	31,067	16.0
Mineral Fuels	31,059	20,036	55.0
Petroleum	22,745	16,410	38.6
Imports	550,103	512,777	7.3
Manufactured	469,642	448,782	4.6
Agricultural Commodities	30,351	29,365	3.4
Mineral Fuels	49,586	30,864	60.7
Petroleum	45,797	27,993	63.6

Source: US Census Bureau

http://www.bea.gov/international/index.htm#bop

The current account of the US balance of payments is in Table VI-3A for IVQ2015 and IVQ2016. The Bureau of Economic Analysis analyzes as follows (https://www.bea.gov/newsreleases/international/transactions/2017/pdf/trans416.pdf):

“The U.S. current-account deficit decreased to $112.4 billion (preliminary) in the fourth quarter of 2016 from $116.0 billion (revised) in the third quarter of 2016, according to statistics released by the Bureau of Economic Analysis (BEA). The deficit decreased to 2.4 percent of current-dollar gross domestic product (GDP) from 2.5 percent in the third quarter. The $3.6 billion decrease in the current-account deficit mostly reflected a $19.9 billion increase in the surplus on primary income that was largely offset by a $17.5 billion increase in the deficit on goods. The changes in the surplus on services and the deficit on secondary income were relatively small.”

The US has a large deficit in goods or exports less imports of goods but it has a surplus in services that helps to reduce the trade account deficit or exports less imports of goods and services. The current account deficit of the US not seasonally adjusted decreased from $114.4 billion in IVQ2015 to $109.2 billion in IVQ2016. The current account deficit seasonally adjusted at annual rate did not change from 2.5 percent of GDP in IVQ2015 to 2.5 percent of GDP in IIIQ2016, decreasing to 2.4 percent of GDP in IVQ2016. The ratio of the current account deficit to GDP has stabilized below 3 percent of GDP compared with much higher percentages before the recession but is combined now with much higher imbalance in the Treasury budget (see Pelaez and Pelaez, The Global Recession Risk (2007), Globalization and the State, Vol. II (2008b), 183-94, Government Intervention in Globalization (2008c), 167-71).

Table VI-3A, US, Balance of Payments, Millions of Dollars NSA

	IVQ2015	IVQ2016	Difference
Goods Balance	-190,399	-192,671	-2,272
X Goods	372,466	379,869	2.0 ∆%
M Goods	-562,865	-572,540	1.7 ∆%
Services Balance	63,719	63,254	-465
X Services	186,124	190,525	2.4 ∆%
M Services	-122,406	-127,271	4.0 ∆%
Balance Goods and Services	-126,680	-129,417	-2,737
Exports of Goods and Services and Income Receipts	784,456	811,393
Imports of Goods and Services and Income Payments	-898,881	-920,543
Current Account Balance	-114,425	-109,150	5,275
% GDP	IVQ2015	IVQ2016	IIIQ2016
	2.5	2.4	2.5

X: exports; M: imports

Balance on Current Account = Exports of Goods and Services – Imports of Goods and Services and Income Payments

Source: Bureau of Economic Analysis

In their classic work on “unpleasant monetarist arithmetic,” Sargent and Wallace (1981, 2) consider a regime of domination of monetary policy by fiscal policy (emphasis added):

“Imagine that fiscal policy dominates monetary policy. The fiscal authority independently sets its budgets, announcing all current and future deficits and surpluses and thus determining the amount of revenue that must be raised through bond sales and seignorage. Under this second coordination scheme, the monetary authority faces the constraints imposed by the demand for government bonds, for it must try to finance with seignorage any discrepancy between the revenue demanded by the fiscal authority and the amount of bonds that can be sold to the public. Suppose that the demand for government bonds implies an interest rate on bonds greater than the economy’s rate of growth. Then if the fiscal authority runs deficits, the monetary authority is unable to control either the growth rate of the monetary base or inflation forever. If the principal and interest due on these additional bonds are raised by selling still more bonds, so as to continue to hold down the growth of base money, then, because the interest rate on bonds is greater than the economy’s growth rate, the real stock of bonds will growth faster than the size of the economy. This cannot go on forever, since the demand for bonds places an upper limit on the stock of bonds relative to the size of the economy. Once that limit is reached, the principal and interest due on the bonds already sold to fight inflation must be financed, at least in part, by seignorage, requiring the creation of additional base money.”

The alternative fiscal scenario of the CBO (2012NovCDR, 2013Sep17) resembles an economic world in which eventually the placement of debt reaches a limit of what is proportionately desired of US debt in investment portfolios. This unpleasant environment is occurring in various European countries.

The current real value of government debt plus monetary liabilities depends on the expected discounted values of future primary surpluses or difference between tax revenue and government expenditure excluding interest payments (Cochrane 2011Jan, 27, equation (16)). There is a point when adverse expectations about the capacity of the government to generate primary surpluses to honor its obligations can result in increases in interest rates on government debt.

First, Unpleasant Monetarist Arithmetic. Fiscal policy is described by Sargent and Wallace (1981, 3, equation 1) as a time sequence of D(t), t = 1, 2,…t, …, where D is real government expenditures, excluding interest on government debt, less real tax receipts. D(t) is the real deficit excluding real interest payments measured in real time t goods. Monetary policy is described by a time sequence of H(t), t=1,2,…t, …, with H(t) being the stock of base money at time t. In order to simplify analysis, all government debt is considered as being only for one time period, in the form of a one-period bond B(t), issued at time t-1 and maturing at time t. Denote by R(t-1) the real rate of interest on the one-period bond B(t) between t-1 and t. The measurement of B(t-1) is in terms of t-1 goods and [1+R(t-1)] “is measured in time t goods per unit of time t-1 goods” (Sargent and Wallace 1981, 3). Thus, B(t-1)[1+R(t-1)] brings B(t-1) to maturing time t. B(t) represents borrowing by the government from the private sector from t to t+1 in terms of time t goods. The price level at t is denoted by p(t). The budget constraint of Sargent and Wallace (1981, 3, equation 1) is:

D(t) = {[H(t) – H(t-1)]/p(t)} + {B(t) – B(t-1)[1 + R(t-1)]} (1)

Equation (1) states that the government finances its real deficits into two portions. The first portion, {[H(t) – H(t-1)]/p(t)}, is seigniorage, or “printing money.” The second part,

{B(t) – B(t-1)[1 + R(t-1)]}, is borrowing from the public by issue of interest-bearing securities. Denote population at time t by N(t) and growing by assumption at the constant rate of n, such that:

N(t+1) = (1+n)N(t), n>-1 (2)

The per capita form of the budget constraint is obtained by dividing (1) by N(t) and rearranging:

B(t)/N(t) = {[1+R(t-1)]/(1+n)}x[B(t-1)/N(t-1)]+[D(t)/N(t)] – {[H(t)-H(t-1)]/[N(t)p(t)]} (3)

On the basis of the assumptions of equal constant rate of growth of population and real income, n, constant real rate of return on government securities exceeding growth of economic activity and quantity theory equation of demand for base money, Sargent and Wallace (1981) find that “tighter current monetary policy implies higher future inflation” under fiscal policy dominance of monetary policy. That is, the monetary authority does not permanently influence inflation, lowering inflation now with tighter policy but experiencing higher inflation in the future.

Second, Unpleasant Fiscal Arithmetic. The tool of analysis of Cochrane (2011Jan, 27, equation (16)) is the government debt valuation equation:

(M_t + B_t)/P_t = E_t∫(1/R_t_{, t+τ})s_t_+τdτ (4)

Equation (4) expresses the monetary, M_t, and debt, B_t, liabilities of the government, divided by the price level, P_t, in terms of the expected value discounted by the ex-post rate on government debt, R_t_{, t+τ}, of the future primary surpluses s_t_+τ, which are equal to T_t_+τ – G_t_+τ or difference between taxes, T, and government expenditures, G. Cochrane (2010A) provides the link to a web appendix demonstrating that it is possible to discount by the ex post R_t_{, t+τ}. The second equation of Cochrane (2011Jan, 5) is:

M_tV(i_t, ·) = P_tY_t (5)

Conventional analysis of monetary policy contends that fiscal authorities simply adjust primary surpluses, s, to sanction the price level determined by the monetary authority through equation (5), which deprives the debt valuation equation (4) of any role in price level determination. The simple explanation is (Cochrane 2011Jan, 5):

“We are here to think about what happens when [4] exerts more force on the price level. This change may happen by force, when debt, deficits and distorting taxes become large so the Treasury is unable or refuses to follow. Then [4] determines the price level; monetary policy must follow the fiscal lead and ‘passively’ adjust M to satisfy [5]. This change may also happen by choice; monetary policies may be deliberately passive, in which case there is nothing for the Treasury to follow and [4] determines the price level.”

An intuitive interpretation by Cochrane (2011Jan 4) is that when the current real value of government debt exceeds expected future surpluses, economic agents unload government debt to purchase private assets and goods, resulting in inflation. If the risk premium on government debt declines, government debt becomes more valuable, causing a deflationary effect. If the risk premium on government debt increases, government debt becomes less valuable, causing an inflationary effect.

There are multiple conclusions by Cochrane (2011Jan) on the debt/dollar crisis and Global recession, among which the following three:

(1) The flight to quality that magnified the recession was not from goods into money but from private-sector securities into government debt because of the risk premium on private-sector securities; monetary policy consisted of providing liquidity in private-sector markets suffering stress

(2) Increases in liquidity by open-market operations with short-term securities have no impact; quantitative easing can affect the timing but not the rate of inflation; and purchase of private debt can reverse part of the flight to quality

(3) The debt valuation equation has a similar role as the expectation shifting the Phillips curve such that a fiscal inflation can generate stagflation effects similar to those occurring from a loss of anchoring expectations.

This analysis suggests that there may be a point of saturation of demand for United States financial liabilities without an increase in interest rates on Treasury securities. A risk premium may develop on US debt. Such premium is not apparent currently because of distressed conditions in the world economy and international financial system. Risk premiums are observed in the spread of bonds of highly indebted countries in Europe relative to bonds of the government of Germany.

The issue of global imbalances centered on the possibility of a disorderly correction (Pelaez and Pelaez, The Global Recession Risk (2007), Globalization and the State Vol. II (2008b) 183-94, Government Intervention in Globalization (2008c), 167-71). Such a correction has not occurred historically but there is no argument proving that it could not occur. The need for a correction would originate in unsustainable large and growing United States current account deficits (CAD) and net international investment position (NIIP) or excess of financial liabilities of the US held by foreigners net relative to financial liabilities of foreigners held by US residents. The IMF estimated that the US could maintain a CAD of two to three percent of GDP without major problems (Rajan 2004). The threat of disorderly correction is summarized by Pelaez and Pelaez, The Global Recession Risk (2007), 15):

“It is possible that foreigners may be unwilling to increase their positions in US financial assets at prevailing interest rates. An exit out of the dollar could cause major devaluation of the dollar. The depreciation of the dollar would cause inflation in the US, leading to increases in American interest rates. There would be an increase in mortgage rates followed by deterioration of real estate values. The IMF has simulated that such an adjustment would cause a decline in the rate of growth of US GDP to 0.5 percent over several years. The decline of demand in the US by four percentage points over several years would result in a world recession because the weakness in Europe and Japan could not compensate for the collapse of American demand. The probability of occurrence of an abrupt adjustment is unknown. However, the adverse effects are quite high, at least hypothetically, to warrant concern.”

The United States could be moving toward a situation typical of heavily indebted countries, requiring fiscal adjustment and increases in productivity to become more competitive internationally. The CAD and NIIP of the United States are not observed in full deterioration because the economy is well below trend. There are two complications in the current environment relative to the concern with disorderly correction in the first half of the past decade. In the release of Jun 14, 2013, the Bureau of Economic Analysis (http://www.bea.gov/newsreleases/international/transactions/2013/pdf/trans113.pdf) informs of revisions of US data on US international transactions since 1999:

“The statistics of the U.S. international transactions accounts released today have been revised for the first quarter of 1999 to the fourth quarter of 2012 to incorporate newly available and revised source data, updated seasonal adjustments, changes in definitions and classifications, and improved estimating methodologies.”

The BEA introduced new concepts and methods (http://www.bea.gov/international/concepts_methods.htm) in comprehensive restructuring on Jun 18, 2014 (http://www.bea.gov/international/modern.htm):

“BEA introduced a new presentation of the International Transactions Accounts on June 18, 2014 and will introduce a new presentation of the International Investment Position on June 30, 2014. These new presentations reflect a comprehensive restructuring of the international accounts that enhances the quality and usefulness of the accounts for customers and bring the accounts into closer alignment with international guidelines.”

Table IIA2-3 provides data on the US fiscal and balance of payments imbalances incorporating all revisions and methods. In 2007, the federal deficit of the US was $161 billion corresponding to 1.1 percent of GDP while the Congressional Budget Office estimates the federal deficit in 2012 at $1087 billion or 6.8 percent of GDP. The estimate of the deficit for 2013 is $680 billion or 4.1 percent of GDP. The combined record federal deficits of the US from 2009 to 2012 are $5094 billion or 31.6 percent of the estimate of GDP for fiscal year 2012 implicit in the CBO (CBO 2013Sep11) estimate of debt/GDP. The deficits from 2009 to 2012 exceed one trillion dollars per year, adding to $5.094 trillion in four years, using the fiscal year deficit of $1087 billion for fiscal year 2012, which is the worst fiscal performance since World War II. Federal debt in 2007 was $5035 billion, slightly less than the combined deficits from 2009 to 2012 of $5094 billion. Federal debt in 2012 was 70.4 percent of GDP (CBO 2015Jan26) and 72.6 percent of GDP in 2013 (http://www.cbo.gov/). This situation may worsen in the future (CBO 2013Sep17):

“Between 2009 and 2012, the federal government recorded the largest budget deficits relative to the size of the economy since 1946, causing federal debt to soar. Federal debt held by the public is now about 73 percent of the economy’s annual output, or gross domestic product (GDP). That percentage is higher than at any point in U.S. history except a brief period around World War II, and it is twice the percentage at the end of 2007. If current laws generally remained in place, federal debt held by the public would decline slightly relative to GDP over the next several years, CBO projects. After that, however, growing deficits would ultimately push debt back above its current high level. CBO projects that federal debt held by the public would reach 100 percent of GDP in 2038, 25 years from now, even without accounting for the harmful effects that growing debt would have on the economy. Moreover, debt would be on an upward path relative to the size of the economy, a trend that could not be sustained indefinitely.

The gap between federal spending and revenues would widen steadily after 2015 under the assumptions of the extended baseline, CBO projects. By 2038, the deficit would be 6½ percent of GDP, larger than in any year between 1947 and 2008, and federal debt held by the public would reach 100 percent of GDP, more than in any year except 1945 and 1946. With such large deficits, federal debt would be growing faster than GDP, a path that would ultimately be unsustainable.

Incorporating the economic effects of the federal policies that underlie the extended baseline worsens the long-term budget outlook. The increase in debt relative to the size of the economy, combined with an increase in marginal tax rates (the rates that would apply to an additional dollar of income), would reduce output and raise interest rates relative to the benchmark economic projections that CBO used in producing the extended baseline. Those economic differences would lead to lower federal revenues and higher interest payments. With those effects included, debt under the extended baseline would rise to 108 percent of GDP in 2038.”

The most recent CBO long-term budget on Mar 27, 2017, projects US federal debt at 150.0 percent of GDP in 2047 (Congressional Budget Office, The 2017 Long-term Budget Outlook. Washington, DC, Mar 30, 2017 https://www.cbo.gov/publication/52480).

Table VI-3B, US, Current Account, NIIP, Fiscal Balance, Nominal GDP, Federal Debt and Direct Investment, Dollar Billions and %

	2007	2008	2009	2010	2011
Goods & Services	-705	-709	-384	-495	-549
Primary Income	101	146	124	178	221
Secondary Income	-114	-128	-124	-125	-133
Current Account	-719	-691	-384	-442	-460
NGDP	14478	14719	14419	14964	15518
Current Account % GDP	-5.0	-4.7	-2.7	-3.0	-3.0
NIIP	-1279	-3995	-2628	-2512	-4455
US Owned Assets Abroad	20705	19423	19426	21767	22209
Foreign Owned Assets in US	21984	23418	22054	24279	26664
NIIP % GDP	-8.8	-27.1	-18.2	-16.8	-28.7
Exports Goods, Services and Income	2569	2751	2286	2631	2988
NIIP % Exports Goods, Services and Income	-50	-145	-115	-95	-149
DIA MV	5858	3707	4945	5486	5215
DIUS MV	4134	3091	3619	4099	4199
Fiscal Balance	-161	-459	-1413	-1294	-1300
Fiscal Balance % GDP	-1.1	-3.1	-9.8	-8.7	-8.5
Federal Debt	5035	5803	7545	9019	10128
Federal Debt % GDP	35.2	39.3	52.3	60.9	65.9
Federal Outlays	2729	2983	3518	3457	3603
∆%	2.8	9.3	17.9	-1.7	4.2
% GDP	19.1	20.2	24.4	23.4	23.4
Federal Revenue	2568	2524	2105	2163	2303
∆%	6.7	-1.7	-16.6	2.7	6.5
% GDP	17.9	17.1	14.6	14.6	15.0

	2012	2013	2014	2015	2016
Goods & Services	-537	-462	-490	-500	-501
Primary Income	216	219	224	182	181
Secondary Income	-126	-124	-126	-145	-161
Current Account	-447	-366	-392	-463	-481
NGDP	16155	16692	17393	18037	18566
Current Account % GDP	-2.8	-2.2	-2.3	-2.6	-2.6
NIIP	-4518	-5373	-7046	-7281	-8110
US Owned Assets Abroad	22562	24145	24718	23341	23916
Foreign Owned Assets in US	27080	29517	31764	30621	32026
NIIP % GDP	-28.0	-32.2	-40.5	-40.4	-43.7
Exports Goods, Services and Income	3097	3215	3339	3173	3142
NIIP % Exports Goods, Services and Income	-146	-167	-211	-229	-258
DIA MV	5969	7121	7133	6978	7412
DIUS MV	4662	5815	6350	6544	7419
Fiscal Balance	-1087	-680	-485	-439	-587
Fiscal Balance % GDP	-6.8	-4.1	-2.8	-2.4	-3.2
Federal Debt	11281	11983	12780	13117	14168
Federal Debt % GDP	70.4	72.6	74.2	73.3	77.0
Federal Outlays	3537	3455	3506	3688	3854
∆%	-1.8	-2.3	1.5	5.2	4.5
% GDP	22.1	20.9	20.4	20.6	20.9
Federal Revenue	2450	2775	3022	3250	3267
∆%	6.4	13.3	8.9	7.6	0.5
% GDP	15.3	16.8	17.6	18.2	17.8

Sources:

Notes: NGDP: nominal GDP or in current dollars; NIIP: Net International Investment Position; DIA MV: US Direct Investment Abroad at Market Value; DIUS MV: Direct Investment in the US at Market Value. There are minor discrepancies in the decimal point of percentages of GDP between the balance of payments data and federal debt, outlays, revenue and deficits in which the original number of the CBO source is maintained. See Bureau of Economic Analysis, US International Economic Accounts: Concepts and Methods. 2014. Washington, DC: BEA, Department of Commerce, Jun 2014 http://www.bea.gov/international/concepts_methods.htm These discrepancies do not alter conclusions. Budget http://www.cbo.gov/

https://www.cbo.gov/about/products/budget-economic-data#6

https://www.cbo.gov/about/products/budget_economic_data#3 https://www.cbo.gov/about/products/budget_economic_data#2 Balance of Payments and NIIP http://www.bea.gov/international/index.htm#bop Gross Domestic Product, Bureau of Economic Analysis (BEA) http://www.bea.gov/iTable/index_nipa.cfm

Table VI-3C provides quarterly estimates NSA of the external imbalance of the United States. The current account deficit seasonally adjusted increases from 2.5 percent of GDP in IVQ2015 to 2.9 percent in IQ2016. The current account deficit decreases to 2.6 percent of GDP in IIQ2016. The deficit decreases to 2.5 percent in IIIQ2016 and decreases to 2.4 percent in IVQ2016. The absolute value of the net international investment position increases from minus $7.3 trillion in IVQ2015 to minus $7.6 trillion in IQ2016, increasing at minus $8.0 trillion in IIQ2016. The absolute value of the net international investment position decreases to minus $7.8 trillion in IIIQ2016 and increases to minus $8.1 trillion in IVQ2016. The BEA explains as follows (https://www.bea.gov/newsreleases/international/intinv/2017/pdf/intinv416.pdf):

“The U.S. net international investment position decreased to -$8,109.7 billion (preliminary) at the end of the fourth quarter of 2016 from -$7,807.3 billion (revised) at the end of the third quarter, according to statistics released today by the Bureau of Economic Analysis (BEA). The $302.3 billion decrease reflected a $954.8 billion decrease in U.S. assets and a $652.5 billion decrease in U.S. liabilities.”

The BEA explains further (https://www.bea.gov/newsreleases/international/intinv/2017/pdf/intinv416.pdf): “The net investment position decreased 3.9 percent in the fourth quarter, compared with an increase of 2.7 percent in the third quarter and an average quarterly decrease of 6.0 percent from the first quarter of 2011 through the second quarter of 2016. U.S. assets decreased $954.8 billion to $23,916.7 billion at the end of the fourth quarter. Financial derivatives decreased $566.1 billion to $2,209.0 billion, reflecting a decrease in single-currency interest rate contracts that was partly offset by an increase in foreign exchange contracts. Assets excluding financial derivatives decreased $388.7 billion to $21,707.7 billion, mostly reflecting decreases in portfolio investment and other investment. The $388.7 billion decrease resulted from other changes in position of -$285.6 billion and financial transactions of -$103.1 billion (table A). Other changes in position reflected decreases from exchange-rate changes, as depreciation of major foreign currencies against the U.S. dollar lowered the value of assets in dollar terms. These decreases were partly offset by price increases on equity assets of portfolio and direct investments. U.S. liabilities decreased $652.5 billion to $32,026.3 billion at the end of the fourth quarter. Financial derivatives decreased $572.2 billion to $2,147.7 billion, reflecting a decrease in single-currency interest rate contracts that was partly offset by an increase in foreign exchange contracts. Liabilities excluding financial derivatives decreased $80.3 billion to $29,878.6 billion, reflecting decreases in portfolio investment and other investment that were partly offset by an increase in direct investment. The $80.3 billion decrease was driven by other changes in position of -$89.9 billion that reflected decreases from exchange-rate changes, as depreciation of major foreign currencies lowered the value of foreign-currency-denominated liabilities in dollar terms.”

Table VI-3C, US, Current Account, NIIP, Fiscal Balance, Nominal GDP, Federal Debt and Direct Investment, Dollar Billions and % NSA

	IVQ2015	IQ2016	IIQ2016	IIIQ2016	IVQ2016
Goods & Services	-127	-103	-134	-134	-129
Primary Income	48	34	43	43	61
Secondary Income	-35	-41	-36	-43	-40
Current Account	-114	-110	-127	-135	-109
Current Account % GDP	-2.5	-2.9	-2.6	-2.5	-2.4
NIIP	-7281	-7582	-8027	-7807	-8110
US Owned Assets Abroad	23341	24062	24515	24871	23916
Foreign Owned Assets in US	-30621	-31644	-32542	-32679	-32026
DIA MV	6978	6993	6980	7365	74118
DIA MV Equity	5811	5838	5797	6165	6211
DIUS MV	6544	6665	6955	7247	7419
DIUS MV Equity	4979	5070	5272	5515	5691

Notes: NIIP: Net International Investment Position; DIA MV: US Direct Investment Abroad at Market Value; DIUS MV: Direct Investment in the US at Market Value. See Bureau of Economic Analysis, US International Economic Accounts: Concepts and Methods. 2014. Washington, DC: BEA, Department of Commerce, Jun 2014 http://www.bea.gov/international/concepts_methods.htm

Chart VI-3C of the US Bureau of Economic Analysis provides the quarterly and annual US net international investment position (NIIP) NSA in billion dollars. The NIIP deteriorated in 2008, improving in 2009-2011 followed by deterioration after 2012.

Chart VI-3C, US Net International Investment Positon, NSA, Billion US Dollars

Source: Bureau of Economic Analysis

http://www.bea.gov/newsreleases/international/intinv/intinvnewsrelease.htm

Chart VI-10 of the Board of Governors of the Federal Reserve System provides the overnight Fed funds rate on business days from Jul 1, 1954 at 1.13 percent through Jan 10, 1979, at 9.91 percent per year, to May 11, 2017, at 0.91 percent per year. US recessions are in shaded areas according to the reference dates of the NBER (http://www.nber.org/cycles.html). In the Fed effort to control the “Great Inflation” of the 1970s (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 Great Inflation; see Taylor 1993, 1997, 1998LB, 1999, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB and http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html), the fed funds rate increased from 8.34 percent on Jan 3, 1979 to a high in Chart VI-10 of 22.36 percent per year on Jul 22, 1981 with collateral adverse effects in the form of impaired savings and loans associations in the United States, emerging market debt and money-center banks (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 72-7; Pelaez 1986, 1987). Another episode in Chart VI-10 is the increase in the fed funds rate from 3.15 percent on Jan 3, 1994, to 6.56 percent on Dec 21, 1994, which also had collateral effects in impairing emerging market debt in Mexico and Argentina and bank balance sheets in a world bust of fixed income markets during pursuit by central banks of non-existing inflation (Pelaez and Pelaez, International Financial Architecture (2005), 113-5). Another interesting policy impulse is the reduction of the fed funds rate from 7.03 percent on Jul 3, 2000, to 1.00 percent on Jun 22, 2004, in pursuit of equally non-existing deflation (Pelaez and Pelaez, International Financial Architecture (2005), 18-28, The Global Recession Risk (2007), 83-85), followed by increments of 25 basis points from Jun 2004 to Jun 2006, raising the fed funds rate to 5.25 percent on Jul 3, 2006 in Chart VI-10. 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 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 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 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). A final episode in Chart VI-10 is the reduction of the fed funds rate from 5.41 percent on Aug 9, 2007, to 2.97 percent on October 7, 2008, to 0.12 percent on Dec 5, 2008 and close to zero throughout a long period with the final point at 0.91 percent on May 11, 2017. Evidently, this behavior of policy would not have occurred had there been theory, measurements and forecasts to avoid these violent oscillations that are clearly detrimental to economic growth and prosperity without inflation. 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 FOMC (Federal Open Market Committee) raised the fed funds rate to ¼ to ½ percent at its meeting on Dec 16, 2015 (http://www.federalreserve.gov/newsevents/press/monetary/20151216a.htm).

It is a forecast mandate because of the lags in effect of monetary policy impulses on income and prices (Romer and Romer 2004). The intention is to reduce unemployment close to the “natural rate” (Friedman 1968, Phelps 1968) of around 5 percent and inflation at or below 2.0 percent. If forecasts were reasonably accurate, there would not be policy errors. A commonly analyzed risk of zero interest rates is the occurrence of unintended inflation that could precipitate an increase in interest rates similar to the Himalayan rise of the fed funds rate from 9.91 percent on Jan 10, 1979, at the beginning in Chart VI-10, to 22.36 percent on Jul 22, 1981. There is a less commonly analyzed risk of the development of a risk premium on Treasury securities because of the unsustainable Treasury deficit/debt of the United States (https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html and earlier http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html http://cmpassocregulationblog.blogspot.com/2016/07/unresolved-us-balance-of-payments.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/proceeding-cautiously-in-reducing.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/weakening-equities-and-dollar.html and earlier http://cmpassocregulationblog.blogspot.com/2015/09/monetary-policy-designed-on-measurable.html and earlier http://cmpassocregulationblog.blogspot.com/2015/06/fluctuating-financial-asset-valuations.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/03/irrational-exuberance-mediocre-cyclical.html and earlier http://cmpassocregulationblog.blogspot.com/2014/12/patience-on-interest-rate-increases.html

and earlier http://cmpassocregulationblog.blogspot.com/2014/09/world-inflation-waves-squeeze-of.html and earlier (http://cmpassocregulationblog.blogspot.com/2014/02/theory-and-reality-of-cyclical-slow.html and earlier (http://cmpassocregulationblog.blogspot.com/2013/02/united-states-unsustainable-fiscal.html). There is not a fiscal cliff or debt limit issue ahead but rather free fall into a fiscal abyss. The combination of the fiscal abyss with zero interest rates could trigger the risk premium on Treasury debt or Himalayan hike in interest rates.

Chart VI-10, US, Fed Funds Rate, Business Days, Jul 1, 1954 to May 11, 2017, Percent per Year

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/datadownload/Choose.aspx?rel=H15

There is a false impression of the existence of a monetary policy “science,” measurements and forecasting with which to steer the economy into “prosperity without inflation.” Market participants are remembering the Great Bond Crash of 1994 shown in Table VI-7G when monetary policy pursued nonexistent inflation, causing trillions of dollars of losses in fixed income worldwide while increasing the fed funds rate from 3 percent in Jan 1994 to 6 percent in Dec. The exercise in Table VI-7G shows a drop of the price of the 30-year bond by 18.1 percent and of the 10-year bond by 14.1 percent. CPI inflation remained almost the same and there is no valid counterfactual that inflation would have been higher without monetary policy tightening because of the long lag in effect of monetary policy on inflation (see Culbertson 1960, 1961, Friedman 1961, Batini and Nelson 2002, Romer and Romer 2004). The pursuit of nonexistent deflation during the past ten years has resulted in the largest monetary policy accommodation in history that created the 2007 financial market crash and global recession and is currently preventing smoother recovery while creating another financial crash in the future. The issue is not whether there should be a central bank and monetary policy but rather whether policy accommodation in doses from zero interest rates to trillions of dollars in the fed balance sheet endangers economic stability.

Table VI-7G, Fed Funds Rates, Thirty and Ten Year Treasury Yields and Prices, 30-Year Mortgage Rates and 12-month CPI Inflation 1994

1994	FF	30Y	30P	10Y	10P	MOR	CPI
Jan	3.00	6.29	100	5.75	100	7.06	2.52
Feb	3.25	6.49	97.37	5.97	98.36	7.15	2.51
Mar	3.50	6.91	92.19	6.48	94.69	7.68	2.51
Apr	3.75	7.27	88.10	6.97	91.32	8.32	2.36
May	4.25	7.41	86.59	7.18	88.93	8.60	2.29
Jun	4.25	7.40	86.69	7.10	90.45	8.40	2.49
Jul	4.25	7.58	84.81	7.30	89.14	8.61	2.77
Aug	4.75	7.49	85.74	7.24	89.53	8.51	2.69
Sep	4.75	7.71	83.49	7.46	88.10	8.64	2.96
Oct	4.75	7.94	81.23	7.74	86.33	8.93	2.61
Nov	5.50	8.08	79.90	7.96	84.96	9.17	2.67
Dec	6.00	7.87	81.91	7.81	85.89	9.20	2.67

Notes: FF: fed funds rate; 30Y: yield of 30-year Treasury; 30P: price of 30-year Treasury assuming coupon equal to 6.29 percent and maturity in exactly 30 years; 10Y: yield of 10-year Treasury; 10P: price of 10-year Treasury assuming coupon equal to 5.75 percent and maturity in exactly 10 years; MOR: 30-year mortgage; CPI: percent change of CPI in 12 months

Sources: yields and mortgage rates http://www.federalreserve.gov/releases/h15/data.htm CPI ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.t

Chart VI-14 provides the overnight fed funds rate, the yield of the 10-year Treasury constant maturity bond, the yield of the 30-year constant maturity bond and the conventional mortgage rate from Jan 1991 to Dec 1996. In Jan 1991, the fed funds rate was 6.91 percent, the 10-year Treasury yield 8.09 percent, the 30-year Treasury yield 8.27 percent and the conventional mortgage rate 9.64 percent. Before monetary policy tightening in Oct 1993, the rates and yields were 2.99 percent for the fed funds, 5.33 percent for the 10-year Treasury, 5.94 for the 30-year Treasury and 6.83 percent for the conventional mortgage rate. After tightening in Nov 1994, the rates and yields were 5.29 percent for the fed funds rate, 7.96 percent for the 10-year Treasury, 8.08 percent for the 30-year Treasury and 9.17 percent for the conventional mortgage rate.

Chart VI-14, US, Overnight Fed Funds Rate, 10-Year Treasury Constant Maturity, 30-Year Treasury Constant Maturity and Conventional Mortgage Rate, Monthly, Jan 1991 to Dec 1996

Source: Board of Governors of the Federal Reserve System

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

Chart VI-15 of the Bureau of Labor Statistics provides the all items consumer price index from Jan 1991 to Dec 1996. There does not appear acceleration of consumer prices requiring aggressive tightening.

Chart VI-15, US, Consumer Price Index All Items, Jan 1991 to Dec 1996

Source: Bureau of Labor Statistics

http://www.bls.gov/cpi/data.htm

Chart IV-16 of the Bureau of Labor Statistics provides 12-month percentage changes of the all items consumer price index from Jan 1991 to Dec 1996. Inflation collapsed during the recession from Jul 1990 (III) and Mar 1991 (I) and the end of the Kuwait War on Feb 25, 1991 that stabilized world oil markets. CPI inflation remained almost the same and there is no valid counterfactual that inflation would have been higher without monetary policy tightening because of the long lag in effect of monetary policy on inflation (see Culbertson 1960, 1961, Friedman 1961, Batini and Nelson 2002, Romer and Romer 2004). Policy tightening had adverse collateral effects in the form of emerging market crises in Mexico and Argentina and fixed income markets worldwide.

Chart VI-16, US, Consumer Price Index All Items, Twelve-Month Percentage Change, Jan 1991 to Dec 1996

Source: Bureau of Labor Statistics

http://www.bls.gov/cpi/data.htm

The Congressional Budget Office (CBO 2017Jan24) 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 2016 at 3.2 percent per year. The projected path is significantly lower at 1.8 percent per year from 2017 to 2027. The legacy of the economic cycle expansion from IIIQ2009 to IQ2017 at 2.1 percent on average is in contrast with 4.3 percent on average in the expansion from IQ1983 to IIIQ1990 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). Subpar economic growth may perpetuate unemployment and underemployment estimated at 21.9 million or 13.0 percent of the effective labor force in Apr 2017 (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html) with much lower hiring than in the period before the current cycle (Section I and earlier https://cmpassocregulationblog.blogspot.com/2017/04/world-inflation-waves-united-states.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	3.2	2.5	0.6
1982-1990	3.4	1.7	1.7
1991-2001	3.3	1.2	2.0
2002-2007	2.4	1.0	1.4
2008-2016	1.4	0.5	0.9
Total 1950-2016	3.2	1.4	1.7
Projected Average Annual ∆%
2017-2020	1.7	0.5	1.2
2021-2027	1.9	0.5	1.4
2017-2027	1.8	0.5	1.3

*Ratio of potential GDP to potential labor force

Source: CBO, The budget and economic outlook: 2017-2027. Washington, DC, Jan 24, 2017 https://www.cbo.gov/publication/52370 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. Aug 2016

Chart IB1-A1 of the Congressional Budget Office provides historical and projected annual growth of United States potential GDP. There is sharp decline of growth of United States potential GDP.

Chart IB-1A1, Congressional Budget Office, Projections of Annual Growth of United States Potential GDP

Source: CBO, The budget and economic outlook: 2017-2027. Washington, DC, Jan 24, 2017 https://www.cbo.gov/publication/52370

https://www.cbo.gov/about/products/budget-economic-data#6

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 2.1 percent on average from IIIQ2009 to IQ2017 during the current economic expansion in contrast with 4.3 percent on average in the cyclical expansion from IQ1983 to IIIQ1990 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.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 21.9 million or 13.0 percent of the labor force as estimated for Apr 2017 (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html). There is no exit from unemployment/underemployment and stagnating real wages because of the collapse of hiring (Section I and earlier https://cmpassocregulationblog.blogspot.com/2017/04/world-inflation-waves-united-states.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.

Chart IIA2-3 of the Bureau of Economic Analysis of the Department of Commerce shows on the lower negative panel the sharp increase in the deficit in goods and the deficits in goods and services from 1960 to 2012. The upper panel shows the increase in the surplus in services that was insufficient to contain the increase of the deficit in goods and services. The adjustment during the global recession has been in the form of contraction of economic activity that reduced demand for goods.

Chart IIA2-3, US, Balance of Goods, Balance on Services and Balance on Goods and Services, 1960-2013, Millions of Dollars

Source: Bureau of Economic Analysis http://www.bea.gov/iTable/index_ita.cfm

Chart IIA2-4 of the Bureau of Economic Analysis shows exports and imports of goods and services from 1960 to 2012. Exports of goods and services in the upper positive panel have been quite dynamic but have not compensated for the sharp increase in imports of goods. The US economy apparently has become less competitive in goods than in services.

Chart IIA2-4, US, Exports and Imports of Goods and Services, 1960-2013, Millions of Dollars

Source: Bureau of Economic Analysis http://www.bea.gov/iTable/index_ita.cfm

Chart IIA2-5 of the Bureau of Economic Analysis shows the US balance on current account from 1960 to 2012. The sharp devaluation of the dollar resulting from unconventional monetary policy of zero interest rates and elimination of auctions of 30-year Treasury bonds did not adjust the US balance of payments. Adjustment only occurred after the contraction of economic activity during the global recession.

Chart IIA2-5, US, Balance on Current Account, 1960-2013, Millions of Dollars

Source: Bureau of Economic Analysis http://www.bea.gov/iTable/index_ita.cfm

Chart IIA2-6 of the Bureau of Economic Analysis provides real GDP in the US from 1960 to 2014. The contraction of economic activity during the global recession was a major factor in the reduction of the current account deficit as percent of GDP.

Chart IIA2-6, US, Real GDP, 1960-2015, Billions of Chained 2009 Dollars

Source: Bureau of Economic Analysis

Chart IIA-7 provides the US current account deficit on a quarterly basis from 1980 to IQ1983. The deficit is at a lower level because of growth below potential not only in the US but worldwide. The combination of high government debt and deficit with external imbalance restricts potential prosperity in the US.

Chart IIA-7, US, Balance on Current Account, Quarterly, 1980-2013

Source: Bureau of Economic Analysis

https://www.treasury.gov/resource-center/data-chart-center/tic/Pages/ticpress.aspx

Risk aversion channels funds toward US long-term and short-term securities that finance the US balance of payments and fiscal deficits benefitting from risk flight to US dollar denominated assets. There are now temporary interruptions because of fear of rising interest rates that erode prices of US government securities because of mixed signals on monetary policy and exit from the Fed balance sheet of four trillion dollars of securities held outright. Net foreign purchases of US long-term securities (row C in Table VA-4) strengthened from $0.6 billion in Jan 2017 to $14.8 billion in Mar 2017. Foreign residents’ purchases minus sales of US long-term securities (row A in Table VA-4) in Jan 2017 of $14.8 billion strengthened to $35.9 billion in Feb 2017. Net US (residents) purchases of long-term foreign securities (row B in Table VA-4) strengthened from minus $8.9 billion in Jan 2017 to $17.5 billion in Feb 2017. Other transactions (row C2 in Table VA-4) changed from minus $5.3 billion in Jan 2017 to minus $38.6 billion in Feb 2017. In Feb 2017,

C = A + B + C2 = $35.9 billion + $17.5 billion -$38.6 billion = $14.8 billion

There are minor rounding errors. There is weakening demand in Table VA-4 in Feb 2017 in A1 private purchases by residents overseas of US long-term securities of $41.1 billion of which weakening in A11 Treasury securities of minus $2.7 billion, weakening in A12 of $8.4 billion in agency securities, strengthening of

$18.0 billion of corporate bonds and strengthening of $17.4 billion in equities. Worldwide risk aversion causes flight into US Treasury obligations with significant oscillations. Official purchases of securities in row A2 decreased $5.2 billion with decrease of Treasury securities of $10.7 billion in Feb 2017. Official purchases of agency securities increased $4.3 billion in Feb 2017. Row D shows increase in Feb 2017 of $13.5 billion in purchases of short-term dollar denominated obligations. Foreign private holdings of US Treasury bills decreased $16.6 billion (row D11) with foreign official holdings decreasing $3.6 billion while the category “other” increased $6.7 billion. Foreign private holdings of US Treasury bills decreased $16.6 billion in what could be arbitrage of duration exposures and international risks. Risk aversion of default losses in foreign securities dominates decisions to accept zero interest rates in Treasury securities with no perception of principal losses. In the case of long-term securities, investors prefer to sacrifice inflation and possible duration risk to avoid principal losses with significant oscillations in risk perceptions.

Table VA-1, Net Cross-Borders Flows of US Long-Term Securities, Billion Dollars, NSA

	Jan 2016 12 Months	Jan 2017 12 Months	Jan 2017	Feb 2017
A Foreign Purchases less Sales of US LT Securities	158.6	109.7	14.8	35.9
A1 Private	445.3	356.0	63.4	41.1
A11 Treasury	307.5	-25.5	37.7	-2.7
A12 Agency	125.8	230.6	13.6	8.4
A13 Corporate Bonds	138.5	126.0	-4.8	18.0
A14 Equities	-126.6	24.9	16.9	17.4
A2 Official	-286.6	-246.3	-48.7	-5.2
A21 Treasury	-306.7	-281.0	-44.9	-10.7
A22 Agency	37.7	35.7	-1.2	4.3
A23 Corporate Bonds	-2.3	-6.7	-1.1	-0.4
A24 Equities	-15.3	5.7	-1.5	1.7
B Net US Purchases of LT Foreign Securities	205.7	132.8	-8.9	17.5
B1 Foreign Bonds	307.7	214.2	0.3	26.7
B2 Foreign Equities	-102.0	-81.4	-9.2	-9.3
C1 Net Transactions	364.3	242.4	5.9	53.4
C2 Other	-222.7	-271.8	-5.3	-38.6
C Net Foreign Purchases of US LT Securities	141.6	-29.3	0.6	14.8
D Increase in Foreign Holdings of Dollar Denominated Short-term	42.2	20.0	13.2	-13.5
D1 US Treasury Bills	35.2	-63.3	2.9	-20.2
D11 Private	67.7	-41.9	0.1	-16.6
D12 Official	-32.4	-21.4	2.8	-3.6
D2 Other	6.9	83.3	10.3	6.7

C1 = A + B; C = C1+C2

A = A1 + A2

A1 = A11 + A12 + A13 + A14

A2 = A21 + A22 + A23 + A24

B = B1 + B2

D = D1 + D2

Sources: United States Treasury

http://www.treasury.gov/press-center/press-releases/Pages/jl2609.aspx

Table VA-5 provides major foreign holders of US Treasury securities. China is the second largest holder with $1059.7 billion in Feb 2017, increasing 0.8 percent from $1051.1 billion in Jan 2017 while decreasing $192.6 billion from Feb 2016 or 15.4 percent. The United States Treasury estimates US government debt held by private investors at $11,360 billion in Dec 2016. China’s holding of US Treasury securities represents 9.3 percent of US government marketable interest-bearing debt held by private investors (http://www.fms.treas.gov/bulletin/index.html). Min Zeng, writing on “China plays a big role as US Treasury yields fall,” on Jul 16, 2014, published in the Wall Street Journal (http://online.wsj.com/articles/china-plays-a-big-role-as-u-s-treasury-yields-fall-1405545034?tesla=y&mg=reno64-wsj), finds that acceleration in purchases of US Treasury securities by China has been an important factor in the decline of Treasury yields in 2014. Japan decreased its holdings from $1133.2 billion in Feb 2016 to $1115.1 billion in Feb 2017 or 1.6 percent. The combined holdings of China and Japan in Feb 2017 add to $2174.8 billion, which is equivalent to 19.1 percent of US government marketable interest-bearing securities held by investors of $11,360 billion in Dec 2016 (http://www.fms.treas.gov/bulletin/index.html). Total foreign holdings of Treasury securities decreased from $6242.0 billion in Feb 2016 to $6012.0 billion in Feb 2017, or 3.7 percent. The US continues to finance its fiscal and balance of payments deficits with foreign savings (see Pelaez and Pelaez, The Global Recession Risk (2007)). A point of saturation of holdings of US Treasury debt may be reached as foreign holders evaluate the threat of reduction of principal by dollar devaluation and reduction of prices by increases in yield, including possibly risk premium. Shultz et al (2012) find that the Fed financed three-quarters of the US deficit in fiscal year 2011, with foreign governments financing significant part of the remainder of the US deficit while the Fed owns one in six dollars of US national debt. Concentrations of debt in few holders are perilous because of sudden exodus in fear of devaluation and yield increases and the limit of refinancing old debt and placing new debt. In their classic work on “unpleasant monetarist arithmetic,” Sargent and Wallace (1981, 2) consider a regime of domination of monetary policy by fiscal policy (emphasis added):

Table VA-5, US, Major Foreign Holders of Treasury Securities $ Billions at End of Period

	Feb 2017	Jan 2017	Feb 2016
Total	6012.0	5953.0	6242.0
Japan	1115.1	1102.5	1133.2
China	1059.7	1051.1	1252.3
Ireland	308.7	293.7	255.2
Cayman Islands	258.0	256.9	255.4
Brazil	257.7	257.7	247.3
Switzerland	236.2	224.0	236.2
Luxembourg	216.6	218.9	208.7
United Kingdom	216.6	214.1	230.6
Hong Kong	200.2	189.4	201.5
Taiwan	183.6	183.6	182.7
Saudi Arabi	113.8	112.3	119.9
India	112.3	113.7	118.8
Foreign Official Holdings	3811.3	3785.5	4081.8
A. Treasury Bills	297.6	301.1	319.0
B. Treasury Bonds and Notes	3513.7	3484.4	3762.8

Source: United States Treasury

http://www.treasury.gov/resource-center/data-chart-center/tic/Pages/ticpress.aspx

http://www.treasury.gov/resource-center/data-chart-center/tic/Pages/index.aspx

http://ticdata.treasury.gov/Publish/mfh.txt

II Rules, Discretionary Authorities and Slow Productivity Growth. The Bureau of Labor Statistics (BLS) of the Department of Labor provides the quarterly report on productivity and costs. The operational definition of productivity used by the BLS is (http://www.bls.gov/news.release/pdf/prod2.pdf 1): “Labor productivity, or output per hour, is calculated by dividing an index of real output by an index of hours worked of all persons, including employees, proprietors, and unpaid family workers.” The BLS has revised the estimates for productivity and unit costs. Table II-1 provides the first estimate for IQ2017 and revision of the estimates for IVQ2016 and IIIQ2016 together with data for nonfarm business sector productivity and unit labor costs in seasonally adjusted annual equivalent (SAAE) rate and the percentage change from the same quarter a year earlier. Reflecting increase in output at 1.0 percent and increase at 1.6 percent in hours worked, nonfarm business sector labor productivity increased at the SAAE rate of minus 0.6 percent in IQ2017, as shown in column 2 “IQ2017 SAEE.” The increase of labor productivity from IQ2016 to IQ2017 was 1.1 percent, reflecting increases in output of 2.4 percent and of hours worked of 1.3 percent, as shown in column 3 “IQ2017 YoY.” Hours worked increased from 0.8 percent in IIIQ2016 at SAAE to 1.0 percent in IVQ2016 and increased to 1.6 percent in IQ2017 while output growth decreased from 4.2 percent in IIIQ2016 at SAAE to 2.7percent in IVQ2016, decreasing to 1.0 percent in IQ2017. The BLS defines unit labor costs as (http://www.bls.gov/news.release/pdf/prod2.pdf 1): “BLS calculates unit labor costs as the ratio of hourly compensation to labor productivity. Increases in hourly compensation tend to increase unit labor costs and increases in output per hour tend to reduce them.” Unit labor costs increased at the SAAE rate of 3.0 percent in IQ2017 and increased 2.8 percent in IQ2017 relative to IQ2016. Hourly compensation increased at the SAAE rate of 2.4 percent in IQ2017, which deflating by the estimated consumer price increase SAAE rate in IQ20167results in decrease of real hourly compensation at 0.8 percent. Real hourly compensation increased 1.3 percent in IQ2017 relative to IQ2016.

Table II-1, US, Nonfarm Business Sector Productivity and Costs %

	IQ 2017 SAAE	IQ 2017 YoY	IVQ 2016 SAAE	IVQ 2016 YoY	III 2016 SAAE	III 2016 YOY
Productivity	-0.6	1.1	1.8	1.1	3.3	0.1
Output	1.0	2.4	2.7	2.3	4.2	1.8
Hours	1.6	1.3	1.0	1.2	0.8	1.7
Hourly Comp.	2.4	3.9	3.1	3.0	4.1	3.2
Real Hourly Comp.	-0.8	1.3	0.0	1.2	2.2	2.0
Unit Labor Costs	3.0	2.8	1.3	1.9	0.7	3.0
Unit Nonlabor Payments	-0.4	0.3	2.9	0.9	1.6	-1.4
Implicit Price Deflator	1.5	1.7	1.9	1.5	1.1	1.1

Notes: SAAE: seasonally adjusted annual equivalent; Comp.: compensation; YoY: Quarter on Same Quarter Year Earlier

http://www.bls.gov/lpc/

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:

U_t = Uⁿ_t – α(π_t – π^e) α > 0 (1)

Where U_t is the rate of unemployment at current time t, Uⁿ_t is the natural rate of unemployment, π_t is the current rate of inflation and π^e 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(π_t, U_t) (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 (π_t, U_t- Uⁿ) such that the consistent equilibrium is found at the tangency of an indifference curve and the Phillips curve in (1). The indifference curves are concave to the origin. The consistent policy is not optimal. Policymakers without discretionary powers following a rule of price stability would attain equilibrium with unemployment not higher than with the consistent policy. The optimal outcome is obtained by the rule of price stability, or zero inflation, and no more unemployment than under the consistent policy with nonzero inflation and the same unemployment. Taylor (1998LB) attributes the sustained boom of the US economy after the stagflation of the 1970s to following a monetary policy rule instead of discretion (see Taylor 1993, 1999). Professor John B. Taylor (2014Jul15, 2014Jun26) building on advanced research (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB, 2015, 2012 Oct 25; 2013Oct28, 2014 Jan01, 2014Jan3, 2014Jun26, 2014Jul15, 2015, 2016Dec7, 2016Dec20 http://www.johnbtaylor.com/) finds that a monetary policy rule would function best in promoting an environment of low inflation and strong economic growth with stability of financial markets. There is strong case for using rules instead of discretionary authorities in monetary policy (http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). It is not uncommon for effects of regulation differing from those intended by policy. Professors Edward C. Prescott and Lee E. Ohanian (2014Feb), writing on “US productivity growth has taken a dive,” on Feb 3, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303942404579362462611843696?KEYWORDS=Prescott), argue that impressive productivity growth over the long-term constructed US prosperity and wellbeing. Prescott and Ohanian (2014Feb) measure US productivity growth at 2.5 percent per year since 1948. Average US productivity growth has been only 1.1 percent since 2011. Prescott and Ohanian (2014Feb) argue that living standards in the US increased at 28 percent in a decade but with current slow growth of productivity will only increase 12 percent by 2024. There may be collateral effects on productivity growth from policy design similar to those in Kydland and Prescott (1977). Professor Edward P. Lazear (2017Feb27), writing in the Wall Street Journal, on Feb 27, 2017 (https://www.wsj.com/articles/how-trump-can-hit-3-growthmaybe-1488239746), finds that productivity growth was 7 percent between 2009 and 2016 at annual equivalent 1 percent. Lazear measures productivity growth at 2.3 percent per year from 2001 to 2008. The Bureau of Labor Statistics important report on productivity and costs released on Mar 8, 2017 (http://www.bls.gov/lpc/) supports the argument of decline of productivity growth in the US analyzed by Prescott and Ohanian (2014Feb) and Lazear (2017Feb27). 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 2016. The estimates incorporate the yearly revision of the US national accounts (http://www.bea.gov/national/an1.htm#2016annualupdate). The data confirm the argument of Prescott and Ohanian (2014Feb) and Lazear (2017Feb27): productivity increased cumulatively 3.2 percent from 2011 to 2016 at the average annual rate of 0.5 percent. The situation is direr by excluding growth of 0.9 percent in 2012, which leaves an average of 0.5 percent for 2011-2016. Average productivity growth for the entire economic cycle from 2007 to 2016 is only 1.2 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 3.1 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

	2016 ∆%	2015 ∆%	2014 ∆%	2013 ∆%	2012 ∆%	2011 ∆%
Productivity	0.2	0.9	0.8	0.3	0.9	0.1
Real Hourly Compensation	1.6	2.8	1.1	-0.3	0.6	-1.0
Unit Labor Costs	2.6	2.0	2.0	0.9	1.7	2.1

	2010 ∆%	2009 ∆%	2008 ∆%	2007∆%
Productivity	3.3	3.1	0.8	1.6
Real Hourly Compensation	0.3	1.4	-1.0	1.4
Unit Labor Costs	-1.3	-2.0	2.0	2.7

Source: US Bureau of Labor Statistics

http://www.bls.gov/lpc/

Productivity jumped in the recovery after the recession from Mar IQ2001 to Nov IVQ2001 (http://www.nber.org/cycles.html). Table II-3 provides quarter on quarter and annual percentage changes in nonfarm business output per hour, or productivity, from 1999 to 2017. The annual average jumped from 2.7 percent in 2001 to 4.4 percent in 2002. Nonfarm business productivity increased at the SAAE rate of 9.3 percent in the first quarter after the recession in IQ2002. Productivity increases decline later in the expansion period. Productivity increases were mediocre during the recession from Dec IVQ2007 to Jun IIIQ2009 (http://www.nber.org/cycles.html) and increased during the first phase of expansion from IIQ2009 to IQ2010, trended lower and collapsed in 2011 and 2012 with sporadic jumps and declines. Productivity increased at 4.5 percent in IVQ2013 and contracted at 3.7 percent in IQ2014. Productivity increased at 1.7 percent in IIQ2014 and at 4.1 percent in IIIQ2014. Productivity contracted at 1.4 percent in IVQ2014 and increased at 1.2 percent in IQ2015. Productivity grew at 1.1 percent in IIQ2015 and increased at 1.8 percent in IIIQ2015. Productivity contracted at 2.0 percent in IVQ2015 and contracted at 0.7 percent in IQ2016. Productivity contracted at 0.1 percent in IIQ2016 and expanded at 3.3 percent in IIIQ2016. Productivity grew at 1.8 percent in IVQ2016 and contracted at 0.6 percent in IQ2017.

Table II-3, US, Nonfarm Business Output per Hour, Percent Change from Prior Quarter at Annual Rate, 1999-2017

Year	Qtr1	Qtr2	Qtr3	Qtr4	Annual
1999	4.5	1.2	3.6	6.6	3.7
2000	-1.9	8.2	-0.2	4.1	3.0
2001	-1.6	7.0	2.1	5.2	2.7
2002	9.3	0.3	3.1	-0.7	4.4
2003	4.2	5.5	9.0	3.9	3.7
2004	-0.1	3.8	1.4	1.3	3.1
2005	4.5	-0.4	3.0	0.1	2.1
2006	2.4	-0.3	-1.8	3.0	0.9
2007	0.4	2.5	4.9	1.7	1.6
2008	-3.8	4.0	1.0	-2.5	0.8
2009	3.1	7.9	5.9	4.9	3.1
2010	2.1	1.4	2.0	1.6	3.3
2011	-3.3	1.3	-0.7	2.8	0.1
2012	0.6	2.3	-0.7	-1.8	0.9
2013	0.9	-0.7	1.7	4.5	0.3
2014	-3.7	1.7	4.1	-1.4	0.8
2015	1.2	1.1	1.8	-2.0	0.9
2016	-0.7	-0.1	3.3	1.8	0.2
2017	-0.6

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Chart II-1 of the Bureau of Labor Statistics (BLS) provides SAAE rates of nonfarm business productivity from 1999 to 2017. There is a clear pattern in both episodes of economic cycles in 2001 and 2007 of rapid expansion of productivity in the transition from contraction to expansion followed by more subdued productivity expansion. Part of the explanation is the reduction in labor utilization resulting from adjustment of business to the sudden shock of collapse of revenue. Productivity rose briefly in the expansion after 2009 but then collapsed and moved to negative change with some positive changes recently at lower rates. Contractions in the cycle from 2007 to 2016 have been more frequent and sharper.

Chart II-1, US, Nonfarm Business Output per Hour, Percent Change from Prior Quarter at Annual Rate, 1999-2017

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Percentage changes from prior quarter at SAAE rates and annual average percentage changes of nonfarm business unit labor costs are provided in Table II-4. Unit labor costs fell during the contractions with continuing negative percentage changes in the early phases of the recovery. Weak labor markets partly explain the decline in unit labor costs. As the economy moves toward full employment, labor markets tighten with increase in unit labor costs. The expansion beginning in IIIQ2009 has been characterized by high unemployment and underemployment. Table II-4 shows continuing subdued increases in unit labor costs in 2011 but with increase at 8.9 percent in IQ2012 followed by decrease at 0.1 percent in IIQ2012, increase at 1.1 percent in IIIQ2012 and increase at 13.2 percent in IVQ2012. Unit labor costs decreased at 9.7 percent in IQ2013 and increased at 6.5 percent in IIQ2013. Unit labor costs decreased at 0.5 percent in IIIQ2013 and decreased at 1.9 percent in IVQ2013. Unit labor costs increased at 10.2 percent in IQ2014 and at minus 3.7 percent in IIQ2014. Unit labor costs decreased at 0.2 percent in IIIQ2014 and increased at 5.2 percent in IVQ2014. Unit labor costs increased at 0.7 percent in IQ2015 and increased at 3.5 percent in IIQ2015. Unit labor costs increased at 0.8 percent in IIIQ2015 and increased at 5.7 percent in IVQ2015. Unit labor costs decreased at 0.3 percent in IQ2016 and increased at 6.2 percent in IIQ2016. Unit labor costs increased at 0.7 percent in IIIQ2016 and increased at 1.3 percent in IVQ2016. Unit labor costs increased at 3.0 percent in IQ2017.

Table II-4, US, Nonfarm Business Unit Labor Costs, Percent Change from Prior Quarter at Annual Rate 1999-2017

Year	Qtr1	Qtr2	Qtr3	Qtr4	Annual
1999	2.9	0.3	0.0	1.6	0.9
2000	17.4	-6.8	8.2	-1.7	4.0
2001	11.4	-5.4	-1.7	-1.4	1.6
2002	-6.6	3.3	-1.1	1.7	-2.0
2003	-1.5	1.6	-2.6	1.5	0.1
2004	-0.5	3.9	5.6	0.5	1.4
2005	-1.3	2.6	2.0	2.3	1.6
2006	6.1	0.5	2.3	4.0	3.0
2007	9.8	-2.7	-3.2	2.6	2.7
2008	8.3	-3.6	2.4	7.1	2.0
2009	-12.3	2.1	-3.0	-2.3	-2.0
2010	-4.8	3.2	-0.2	0.2	-1.3
2011	11.0	-3.5	3.3	-7.7	2.1
2012	8.9	-0.1	1.1	13.2	1.7
2013	-9.7	6.5	-0.5	-1.9	0.9
2014	10.2	-3.7	-0.2	5.2	2.0
2015	0.7	3.5	0.8	5.7	2.0
2016	-0.3	6.2	0.7	1.3	2.6
2017	3.0

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Chart II-2 provides change of unit labor costs at SAAE from 1999 to 2016. There are multiple oscillations recently with negative changes alternating with positive changes.

Chart II-2, US, Nonfarm Business Unit Labor Costs, Percent Change from Prior Quarter at Annual Rate 1999-2017

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Table II-5 provides percentage change from prior quarter at annual rates for nonfarm business real hourly worker compensation. The expansion after the contraction of 2001 was followed by strong recovery of real hourly compensation. Real hourly compensation increased at the rate of 2.9 percent in IQ2011 but fell at annual rates of 6.5 percent in IIQ2011 and 6.8 percent in IVQ2011. Real hourly compensation increased at 7.1 percent in IQ2012, increasing at 1.4 percent in IIQ2012, declining at 1.4 percent in IIIQ2012 and increasing at 8.3 percent in IVQ2012. Real hourly compensation fell at 1.0 percent in 2011 and increased at 0.6 percent in 2012. Real hourly compensation fell at 10.4 percent in IQ2013 and increased at 6.3 percent in IIQ2013, falling at 1.0 percent in IIIQ2013. Real hourly compensation increased at 0.9 percent in IVQ2013 and at 3.4 percent in IQ2014. Real hourly compensation decreased at 3.9 percent in IIQ2014. Real hourly compensation increased at 2.8 percent in IIIQ2014. The annual rate of increase of real hourly compensation for 2013 is minus 0.3 percent. Real hourly compensation increased at 4.5 percent in IVQ2014. The annual rate of increase of real hourly compensation in 2014 is 1.1 percent. Real hourly compensation increased at 4.6 percent in IQ2015 and increased at 2.2 percent in IIQ2015. Real hourly compensation increased at 1.1 percent in IIIQ2015 and increased at 3.2 percent in IVQ2015. Real hourly compensation increased at 2.8 percent in 2015. Real hourly compensation decreased at 1.1 percent in IQ2016 and increased at 3.7 percent in IIQ2016. Real hourly compensation increased at 2.2 percent in IIIQ2016 and changed at 0.0 percent in IVQ2016. Real hourly compensation increased 1.6 percent in 2016. Real hourly compensation decreased at 0.8 percent in IQ2017.

Table II-5, Nonfarm Business Real Hourly Compensation, Percent Change from Prior Quarter at Annual Rate 1999-2017

Year	Qtr1	Qtr2	Qtr3	Qtr4	Annual
1999	6.0	-1.5	0.5	5.1	2.5
2000	10.6	-2.2	4.1	-0.5	3.5
2001	5.4	-1.7	-0.7	4.1	1.4
2002	0.7	0.3	-0.2	-1.3	0.7
2003	-1.5	7.8	3.0	3.9	1.5
2004	-3.8	4.5	4.5	-2.5	1.8
2005	1.1	-0.6	-1.0	-1.3	0.3
2006	6.4	-3.5	-3.1	8.8	0.6
2007	6.0	-4.7	-1.0	-0.5	1.4
2008	-0.3	-4.7	-2.6	14.6	-1.0
2009	-7.1	7.8	-0.7	-0.6	1.4
2010	-3.4	4.8	0.6	-1.4	0.3
2011	2.9	-6.5	-0.1	-6.8	-1.0
2012	7.1	1.4	-1.4	8.3	0.6
2013	-10.4	6.3	-1.0	0.9	-0.3
2014	3.4	-3.9	2.8	4.5	1.1
2015	4.6	2.2	1.1	3.2	2.8
2016	-1.1	3.7	2.2	0.0	1.6
2017	-0.8

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Chart II-3 provides percentage change from prior quarter at annual rate of nonfarm business real hourly compensation. There have been multiple negative percentage quarterly changes in the current cycle since IVQ2007.

Chart II-3, US, Nonfarm Business Real Hourly Compensation, Percent Change from Prior Quarter at Annual Rate 1999-2017

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Chart II-4 provides percentage change of nonfarm business output per hour in a quarter relative to the same quarter a year earlier. As in most series of real output, productivity increased sharply in 2010 but the momentum was lost after 2011 as with the rest of the real economy.

Chart II-4, US, Nonfarm Business Output per Hour, Percent Change from Same Quarter a Year Earlier 1999-2017

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Chart II-5 provides percentage changes of nonfarm business unit labor costs relative to the same quarter a year earlier. Softening of labor markets caused relatively high yearly percentage changes in the recession of 2001 repeated in the recession in 2009. Recovery was strong in 2010 but then weakened.

Chart II-5, US, Nonfarm Business Unit Labor Costs, Percent Change from Same Quarter a Year Earlier 1999-2017

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

Chart II-6 provides percentage changes in a quarter relative to the same quarter a year earlier for nonfarm business real hourly compensation. Labor compensation eroded sharply during the recession with brief recovery in 2010 and another fall until recently.

Chart II-6, US, Nonfarm Business Real Hourly Compensation, Percent Change from Same Quarter a Year Earlier 1999-2017

2005=100

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

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.

U_t = Uⁿ_t – α(π_t – π^e) α > 0 (1)

W = W(π_t, U_t) (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 (π_t, U_t- Uⁿ) such that the consistent equilibrium is found at the tangency of an indifference curve and the Phillips curve in (1). The indifference curves are concave to the origin. The consistent policy is not optimal. Policymakers without discretionary powers following a rule of price stability would attain equilibrium with unemployment not higher than with the consistent policy. The optimal outcome is obtained by the rule of price stability, or zero inflation, and no more unemployment than under the consistent policy with nonzero inflation and the same unemployment. Taylor (1998LB) attributes the sustained boom of the US economy after the stagflation of the 1970s to following a monetary policy rule instead of discretion (see Taylor 1993, 1999). Professor John B. Taylor (2014Jul15, 2014Jun26) building on advanced research (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB, 2015, 2012 Oct 25; 2013Oct28, 2014 Jan01, 2014Jan3, 2014Jun26, 2014Jul15, 2015, 2016Dec7, 2016Dec20 http://www.johnbtaylor.com/) finds that a monetary policy rule would function best in promoting an environment of low inflation and strong economic growth with stability of financial markets. There is strong case for using rules instead of discretionary authorities in monetary policy (http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). It is not uncommon for effects of regulation differing from those intended by policy. Professors Edward C. Prescott and Lee E. Ohanian (2014Feb), writing on “US productivity growth has taken a dive,” on Feb 3, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303942404579362462611843696?KEYWORDS=Prescott), argue that impressive productivity growth over the long-term constructed US prosperity and wellbeing. Prescott and Ohanian (2014Feb) measure US productivity growth at 2.5 percent per year since 1948. Average US productivity growth has been only 1.1 percent since 2011. Prescott and Ohanian (2014Feb) argue that living standards in the US increased at 28 percent in a decade but with current slow growth of productivity will only increase 12 percent by 2024. There may be collateral effects on productivity growth from policy design similar to those in Kydland and Prescott (1977). Professor Edward P. Lazear (2017Feb27), writing in the Wall Street Journal, on Feb 27, 2017 (https://www.wsj.com/articles/how-trump-can-hit-3-growthmaybe-1488239746), finds that productivity growth was 7 percent between 2009 and 20016 at annual equivalent 1 percent. Lazear measures productivity growth at 2.3 percent per year from 2001 to 2008. The Bureau of Labor Statistics important report on productivity and costs released on Mar 8, 2017 (http://www.bls.gov/lpc/) supports the argument of decline of productivity growth in the US analyzed by Prescott and Ohanian (2014Feb) and Lazear (2017Feb27). 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 2017. The estimates incorporate the yearly revision of the US national accounts (http://www.bea.gov/national/an1.htm#2016annualupdate). The data confirm the argument of Prescott and Ohanian (2014Feb) and Lazear (2017Feb27): productivity increased cumulatively 3.2 percent from 2011 to 2016 at the average annual rate of 0.5 percent. The situation is direr by excluding growth of 0.9 percent in 2012, which leaves an average of 0.5 percent for 2011-2016. Average productivity growth for the entire economic cycle from 2007 to 2016 is only 1.2 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 3.1 percent and 3.3 percent in 2010 consisted of 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 s_i of a demographic group in an industry i and unemployment rate y_i of that demographic group (Lazear and Spletzer 2012JHJul22, 5-6):

Y = ∑_is_iy_i (1)

This equation can be decomposed for analysis as (Lazear and Spletzer 2012JHJul22, 6):

∆Y = ∑_i∆s_iy*_i+ ∑_i∆y_is*_i (2)

The first term in (2) captures changes in the demographic and industrial composition of the economy ∆s_i 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 ∆y_i, 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 2017. 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, 1947-2017, Index 2009=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 2016. Labor productivity increased 3.3 percent in 2010 and 3.1 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 3.1 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 2016 of productivity growth at average 0.5 percent per year.

Table II-6, US, Productivity and Costs, Annual Percentage Changes 2007-2016

	2016	2015	2014	2013	2012
Productivity	0.2	0.9	0.8	0.3	0.9
Output	1.7	3.1	3.0	2.0	3.1
Hours Worked	1.5	2.1	2.2	1.7	2.2
Employment	1.8	2.2	2.0	1.8	2.0
Average Weekly Hours Worked	-0.3	-0.1	0.2	-0.1	0.2
Unit Labor Costs	2.6	2.0	2.0	0.9	1.7
Hourly Compensation	2.9	3.0	2.8	1.2	2.6
Consumer Price Inflation	1.6	0.1	3.2	1.5	2.1
Real Hourly Compensation	1.6	2.8	1.1	-0.3	0.6
Non-labor Payments	1.0	2.7	4.4	4.4	5.3
Output per Job	-0.1	0.8	1.0	0.2	1.1

	2011	2010	2009	2008	2007
Productivity	0.1	3.3	3.1	0.8	1.6
Output	2.2	3.2	-4.3	-1.3	2.3
Hours Worked	2.1	-0.1	-7.2	-2.1	0.7
Employment	1.6	-1.2	-5.7	-1.5	0.9
Average Weekly Hours Worked	0.5	1.1	-1.6	-0.6	-0.2
Unit Labor Costs	2.1	-1.3	-2.0	2.0	2.7
Hourly Compensation	2.2	1.9	1.0	2.8	4.3
Consumer Price Inflation	3.2	1.6	-0.4	3.8	2.8
Real Hourly Compensation	-1.0	0.3	1.4	-1.0	1.4
Non-labor Payments	3.7	7.5	0.0	-0.4	3.4
Output per Job	0.6	4.4	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 2.1 percent per year on average from 1947 to 2016 to 1.2 percent per year on average in the whole cycle from 2007 to 2016. 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 2016 to 1.4 percent from 2007 to 2016. Output grew at 3.7 percent per year on average from 1947 to 2007. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 31 quarters from IIIQ2009 to IQ2017. 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 first estimate of GDP for IQ2017 (https://www.bea.gov/newsreleases/national/gdp/2017/pdf/gdp1q17_adv.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 dividing 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 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990, 4.4 percent from IQ1983 to IIQ1990, 4.3 percent from IQ1983 to IIIQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html). The National Bureau of Economic Research (NBER) dates a contraction of the US from IQ1990 (Jul) to IQ1991 (Mar) (http://www.nber.org/cycles.html). The expansion lasted until another contraction beginning in IQ2001 (Mar). US GDP contracted 1.3 percent from the pre-recession peak of $8983.9 billion of chained 2009 dollars in IIIQ1990 to the trough of $8865.6 billion in IQ1991 (http://www.bea.gov/iTable/index_nipa.cfm). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IQ2017 would have accumulated to 31.4 percent. GDP in IQ2017 would be $19,699.2 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2856.8 billion than actual $16,842.4 billion. There are about two trillion dollars of GDP less than at trend, explaining the 21.9 million unemployed or underemployed equivalent to actual unemployment/underemployment of 13.0 percent of the effective labor force (https://cmpassocregulationblog.blogspot.com/2017/05/twenty-two-million-unemployed-or.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/twenty-three-million-unemployed-or.html). US GDP in IQ2017 is 14.5 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,842.4 billion in IQ2017 or 12.3 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.2 percent per year from Mar 1919 to Mar 2017. Growth at 3.2 percent per year would raise the NSA index of manufacturing output from 108.2393 in Dec 2007 to 144.8512 in Mar 2017. The actual index NSA in Mar 2017 is 103.4144, which is 28.6 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Mar 2017. Using trend growth of 2.1 percent per year, the index would increase to 131.1817 in Mar 2017. The output of manufacturing at 103.4144 in Mar 2017 is 21.2 percent below trend under this alternative calculation.

Table II-7, US, Productivity and Costs, Average Annual Percentage Changes 2007-2016 and 1947-2016

	Average Annual Percentage Rate 2007-2016	Average Annual Percentage Rate 1947-2007	Average Annual Percentage Rate 1947-2016
Productivity	1.2	2.3	2.1
Output	1.4	3.7	3.4
Hours	0.2	1.4	1.2
Employment	0.3	1.6	1.5
Average Weekly Hours	-0.7*	-14.4*	-15.0*
Hourly Compensation	2.3	5.4	5.0
Consumer Price Inflation	1.6	3.8	3.5
Real Hourly Compensation	0.6	1.7	1.6
Unit Labor Costs	1.1	3.0	2.8
Unit Non-labor Payments	1.7	3.5	3.2
Output per Job	1.1	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, 1947-2017, 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, 1947-2017, Index 2009=100

Source: US Bureau of Labor Statistics http://www.bls.gov/lpc/

II C United States Producer Prices. Unconventional monetary policy of zero interest rates and large-scale purchases of long-term securities for the balance sheet of the central bank is proposed to prevent deflation. The data of CPI inflation of all goods and CPI inflation excluding food and energy for the past six decades does not show even one negative change, as shown in Table CPIEX.

Table CPIEX, Annual Percentage Changes of the CPI All Items Excluding Food and Energy

Year	Annual ∆%
1958	2.4
1959	2.0
1960	1.3
1961	1.3
1962	1.3
1963	1.3
1964	1.6
1965	1.2
1966	2.4
1967	3.6
1968	4.6
1969	5.8
1970	6.3
1971	4.7
1972	3.0
1973	3.6
1974	8.3
1975	9.1
1976	6.5
1977	6.3
1978	7.4
1979	9.8
1980	12.4
1981	10.4
1982	7.4
1983	4.0
1984	5.0
1985	4.3
1986	4.0
1987	4.1
1988	4.4
1989	4.5
1990	5.0
1991	4.9
1992	3.7
1993	3.3
1994	2.8
1995	3.0
1996	2.7
1997	2.4
1998	2.3
1999	2.1
2000	2.4
2001	2.6
2002	2.4
2003	1.4
2004	1.8
2005	2.2
2006	2.5
2007	2.3
2008	2.3
2009	1.7
2010	1.0
2011	1.7
2012	2.1
2013	1.8
2014	1.7
2015	1.8
2016	2.2

Source: Bureau of Labor Statistics

http://www.bls.gov/cpi/

The history of producer price inflation in the past five decades does not provide evidence of deflation. The finished core PPI does not register even one single year of decline, as shown in Table PPIEX.

Table PPIEX, Annual Percentage Changes of the PPI Finished Goods Excluding Food and Energy

Year	Annual ∆%
1974	11.4
1975	11.4
1976	5.7
1977	6.0
1978	7.5
1979	8.9
1980	11.2
1981	8.6
1982	5.7
1983	3.0
1984	2.4
1985	2.5
1986	2.3
1987	2.4
1988	3.3
1989	4.4
1990	3.7
1991	3.6
1992	2.4
1993	1.2
1994	1.0
1995	2.1
1996	1.4
1997	0.3
1998	0.9
1999	1.7
2000	1.3
2001	1.4
2002	0.1
2003	0.2
2004	1.5
2005	2.4
2006	1.5
2007	1.9
2008	3.4
2009	2.6
2010	1.2
2011	2.4
2012	2.6
2013	1.5
2014	1.9
2015	2.0
2016	1.6

Source: Bureau of Labor Statistics

Chart I-1 provides US nominal GDP from 1929 to 2016. The chart disguises the decline of nominal GDP during the 1930s from $104.6 billion in 1929 to $57.2 billion in 1933 or by 45.3 percent (data from the US Bureau of Economic Analysis at http://www.bea.gov/iTable/index_nipa.cfm). The level of nominal GDP reached $102.9 billion in 1940 and exceeded the $104.6 billion of 1929 only with $129.4 billion in 1941. The only major visible bump in the chart occurred in the recession of IVQ2007 to IIQ2009 with revised cumulative decline of real GDP of 4.2 percent. US nominal GDP fell from $14,718.6 billion in 2008 to $14,418.7 billion in 2009 or by 2.0 percent. US nominal GDP rose to $14,964.4 billion in 2010 or by 3.8 percent and to $15,517.9 billion in 2011 for an additional 3.7 percent for cumulative increase of 7.6 percent relative to 2009 and to $16,155.3 billion in 2012 for an additional 4.1 percent and cumulative increase of 12.0 percent relative to 2009. US nominal GDP increased from $14,477.6 in 2007 to $18,569.1 billion in 2016 or by 28.3 percent at the average annual rate of 2.8 percent per year (http://www.bea.gov/iTable/index_nipa.cfm). Tendency for deflation would be reflected in persistent bumps. In contrast, during the Great Depression in the four years of 1929 to 1933, GDP in constant dollars fell 26.3 percent cumulatively and fell 45.3 percent in current dollars (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 150-2, Pelaez and Pelaez, Globalization and the State, Vol. II (2009b), 205-7). The comparison of the global recession after 2007 with the Great Depression is entirely misleading (https://cmpassocregulationblog.blogspot.com/2017/04/dollar-devaluation-mediocre-cyclical.html and earlier https://cmpassocregulationblog.blogspot.com/2017/04/mediocre-cyclical-economic-growth-with.html).

Chart I-1, US, Nominal GDP 1929-2016

Source: US Bureau of Economic Analysis

Chart I-2 provides US real GDP from 1929 to 2016. The chart also disguises the Great Depression of the 1930s. In the four years of 1929 to 1933, GDP in constant dollars fell 26.3 percent cumulatively and fell 45.3 percent in current dollars (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 150-2, Pelaez and Pelaez, Globalization and the State, Vol. II (2009b), 205-7; data from the US Bureau of Economic Analysis at http://www.bea.gov/iTable/index_nipa.cfm). Persistent deflation threatening real economic activity would also be reflected in the series of long-term growth of real GDP. There is no such behavior in Chart I-2 except for periodic recessions in the US economy that have occurred throughout history.

Chart I-2, US, Real GDP 1929-2016

Source: US Bureau of Economic Analysis

Deflation would also be in evidence in long-term series of prices in the form of bumps. The GDP implicit deflator series in Chart I-3 from 1929 to 2016 shows sharp dynamic behavior over time. There is decline of the implicit price deflator of GDP by 25.8 percent from 1929 to 1933 (data from the US Bureau of Economic Analysis at http://www.bea.gov/iTable/index_nipa.cfm). In contrast, the implicit price deflator of GDP of the US increased from 97.337 (2009 =100) in 2007 to 100.00 in 2009 or by 2.7 percent and increased to 111.445 in 2016 or by 11.4 percent relative to 2009 and 14.5 percent relative to 2007. The implicit price deflator of US GDP increased in every quarter from IVQ2007 to IVQ2012 with only two declines from 100.062 in IQ2009 to 99.895 in IIQ2009 or by 0.2 percent and to 99.873 in IIIQ2009 for cumulative 0.2 percent relative to IQ2009 and -0.02 percent relative to IIQ2009 (http://www.bea.gov/iTable/index_nipa.cfm). Wars are characterized by rapidly rising prices followed by declines when peace is restored. The US economy is not plagued by deflation but by long-run inflation.

Chart I-3, US, GDP Implicit Price Deflator 1929-2016

Source: US Bureau of Economic Analysis

Chart I-4 provides percent change from preceding quarter in prices of GDP at seasonally adjusted annual rates (SAAR) from 1980 to 2016. There is one case of negative change by 0.6 percent in IIQ2009 that was adjustment from 2.8 percent in IIIQ2008 following 2.3 percent in IQ2008 and 1.8 percent IIQ2008 caused by carry trades from policy interest rates being moved to zero into commodity futures. These positions were reversed because of the fear of toxic assets in banks in the proposal of TARP in late 2008 (Cochrane and Zingales 2009). Prices of GDP increased at 0.5 percent in IVQ2014. GDP prices decreased at 0.1 percent in IQ2015, increasing at 2.3 percent in IIQ015 and at 1.3 percent in IIIQ2015. Prices of GDP increased at 0.8 percent in IVQ2015 and at 0.5 percent in IQ2016. Prices of GDP increased at 2.3 percent in IIQ2016 and increased at 1.4 percent in IIIQ2016. Prices of GDP increased at 2.1 percent in IVQ2016 and increased at 2.3 percent in IQ2017. There has not been actual deflation or risk of deflation threatening depression in the US that would justify unconventional monetary policy.

Chart I-4, Percent Change from Preceding Period in Prices for GDP Seasonally Adjusted at Annual Rates 1980-2017

Source: US Bureau of Economic Analysis

Chart I-5 provides percent change from preceding year in prices of GDP from 1929 to 2015. There are four consecutive years of declines of prices of GDP during the Great Depression: 3.8 percent in 1930, 9.9 percent in 1931, 11.4 percent in 1932 and 2.7 percent in 1933. There were two consecutive declines of 1.8 percent in 1938 and 1.3 percent in 1939. Prices of GDP fell 0.1 percent in 1949 after increasing 12.6 percent in 1946, 11.2 percent in 1947 and 5.6 percent in 1948, which is similar to experience with wars in other countries. There are no other negative changes of annual prices of GDP in 74 years from 1939 to 2016.

Chart I-5, Percent Change from Preceding Year in Prices for Gross Domestic Product 1930-2016

The producer price index of the US from 1947 to 2017 in Chart I-6 shows various periods of more rapid or less rapid inflation but no bumps. The major event is the decline in 2008 when risk aversion because of the global recession caused the collapse of oil prices from $148/barrel to less than $80/barrel with most other commodity prices also collapsing. The event had nothing in common with explanations of deflation but rather with the concentration of risk exposures in commodities after the decline of stock market indexes. Eventually, there was a flight to government securities because of the fears of insolvency of banks caused by statements supporting proposals for withdrawal of toxic assets from bank balance sheets in the Troubled Asset Relief Program (TARP), as explained by Cochrane and Zingales (2009). The bump in 2008 with decline in 2009 is consistent with the view that zero interest rates with subdued risk aversion induce carry trades into commodity futures.

Chart I-6, US, Producer Price Index, Finished Goods, NSA, 1947-2017

Source: US Bureau of Labor Statistics

Chart I-17 provides 12-month percentage changes of the producer price index from 1948 to 2017. The distinguishing even in Chart I-7 is the Great Inflation of the 1970s. The shape of the two-hump Bactrian camel of the 1970’s resembles the double hump from 2007 to 2017.

Chart I-7, US, Producer Price Index, Finished Goods, 12-Month Percentage Change, NSA, 1948-2017

Source: US Bureau of Labor Statistics

Annual percentage changes of the producer price index from 1948 to 2016 are shown in Table I-1A. The producer price index fell 2.8 percent in 1949 following the adjustment to World War II and fell 0.6 percent in 1952 and 1.0 percent in 1953 around the Korean War. There are two other mild declines of 0.3 percent in 1959 and 0.3 percent in 1963. There are only few subsequent and isolated declines of the producer price index of 1.4 percent in 1986, 0.8 percent in 1998, 1.3 percent in 2002 and 2.6 percent in 2009. The decline of 2009 was caused by unwinding of carry trades in 2008 that had lifted oil prices to $140/barrel during deep global recession because of the panic of probable toxic assets in banks that would be removed with the Troubled Asset Relief Program (TARP) (Cochrane and Zingales 2009). Producer prices fell 3.2 percent in 2015 and declined 1.0 percent in 2016 during collapse of commodity prices form high prices induced by zero interest rates. There is no evidence in this history of 65 years of the US producer price index suggesting that there is frequent and persistent deflation shock requiring aggressive unconventional monetary policy. The design of such anti-deflation policy could provoke price and financial instability because of lags in effect of monetary policy, model errors, inaccurate forecasts and misleading analysis of current economic conditions.

Table I-1A, US, Annual PPI Inflation ∆% 1948-2016

Year	Annual ∆%
1948	8.0
1949	-2.8
1950	1.8
1951	9.2
1952	-0.6
1953	-1.0
1954	0.3
1955	0.3
1956	2.6
1957	3.8
1958	2.2
1959	-0.3
1960	0.9
1961	0.0
1962	0.3
1963	-0.3
1964	0.3
1965	1.8
1966	3.2
1967	1.1
1968	2.8
1969	3.8
1970	3.4
1971	3.1
1972	3.2
1973	9.1
1974	15.4
1975	10.6
1976	4.5
1977	6.4
1978	7.9
1979	11.2
1980	13.4
1981	9.2
1982	4.1
1983	1.6
1984	2.1
1985	1.0
1986	-1.4
1987	2.1
1988	2.5
1989	5.2
1990	4.9
1991	2.1
1992	1.2
1993	1.2
1994	0.6
1995	1.9
1996	2.7
1997	0.4
1998	-0.8
1999	1.8
2000	3.8
2001	2.0
2002	-1.3
2003	3.2
2004	3.6
2005	4.8
2006	3.0
2007	3.9
2008	6.3
2009	-2.6
2010	4.2
2011	6.1
2012	1.9
2013	1.2
2014	1.9
2015	-3.2
2016	-1.0

Source: US Bureau of Labor Statistics

The producer price index excluding food and energy from 1973 to 2017, the first historical date of availability in the dataset of the Bureau of Labor Statistics (BLS), shows similarly dynamic behavior as the overall index, as shown in Chart I-8. There is no evidence of persistent deflation in the US PPI.

Chart I-8, US Producer Price Index, Finished Goods Excluding Food and Energy, NSA, 1973-2017

Source: US Bureau of Labor Statistics

Chart I-9 provides 12-month percentage rates of change of the finished goods index excluding food and energy. The dominating characteristic is the Great Inflation of the 1970s. The double hump illustrates how inflation may appear to be subdued and then returns with strength.

Chart I-9, US Producer Price Index, Finished Goods Excluding Food and Energy, 12-Month Percentage Change, NSA, 1974-2017

Source: US Bureau of Labor Statistics

The producer price index of energy goods from 1974 to 2017 is in Chart I-10. The first jump occurred during the Great Inflation of the 1970s analyzed in various comments of this blog (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 in Appendix I. There is relative stability of producer prices after 1986 with another jump and decline in the late 1990s into the early 2000s. The episode of commodity price increases during a global recession in 2008 could only have occurred with interest rates dropping toward zero, which stimulated the carry trade from zero interest rates to leveraged positions in commodity futures. Commodity futures exposures were dropped in the flight to government securities after Sep 2008. Commodity future exposures were created again when risk aversion diminished around Mar 2010 after the finding that US bank balance sheets did not have the toxic assets that were mentioned in proposing TARP in Congress (see Cochrane and Zingales 2009). Fluctuations in commodity prices and other risk financial assets originate in carry trade when risk aversion ameliorates. There are also fluctuations originating in shifts in preference for asset classes such as between commodities and equities.

Chart I-10, US, Producer Price Index, Finished Energy Goods, NSA, 1974-2017

Source: US Bureau of Labor Statistics

http://www.bls.gov/cpi/data.htm

Chart I-11 shows 12-month percentage changes of the producer price index of finished energy goods from 1975 to 2017. This index is only available after 1974 and captures only one of the humps of energy prices during the Great Inflation. Fluctuations in energy prices have occurred throughout history in the US but without provoking deflation. Two cases are the decline of oil prices in 2001 to 2002 that has been analyzed by Barsky and Kilian (2004) and the collapse of oil prices from over $140/barrel with shock of risk aversion to the carry trade in Sep 2008.

Chart I-11, US, Producer Price Index, Finished Energy Goods, 12-Month Percentage Change, NSA, 1974-2017

Source: US Bureau of Labor Statistics

Headline and core producer price indexes are in Table I-6. The headline PPI SA increased 0.6 percent in Mar 2017 and increased 3.9 percent NSA in the 12 months ending in Apr 2017. The core PPI SA increased 0.3 percent in Apr 2017 and increased 1.9 percent in 12 months. Analysis of annual equivalent rates of change shows inflation waves similar to those worldwide. In the first wave, the absence of risk aversion from the sovereign risk crisis in Europe motivated the carry trade from zero interest rates into commodity futures that caused the annual equivalent rate of 11.1 percent in the headline PPI in Jan-Apr 2011 and 3.7 percent in the core PPI. In the second wave, commodity futures prices collapsed in Jun 2011 with the return of risk aversion originating in the sovereign risk crisis of Europe. The annual equivalent rate of headline PPI inflation collapsed to 0.6 percent in May-Jun 2011 but the core annual equivalent inflation rate was higher at 2.4 percent. In the third wave, headline PPI inflation resuscitated with annual equivalent at 4.1 percent in Jul-Sep 2011 and core PPI inflation at 3.2 percent. Core PPI inflation was persistent throughout 2011, jumping from annual equivalent at 2.0 percent in the first three months of 2010 to 3.0 percent in 12 months ending in Dec 2011. Unconventional monetary policy is based on the proposition that core rates reflect more fundamental inflation and are thus better predictors of the future. In practice, the relation of core and headline inflation is as difficult to predict as future inflation (see IIID Supply Shocks in http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html). In the fourth wave, risk aversion originating in the lack of resolution of the European debt crisis caused unwinding of carry trades with annual equivalent headline PPI inflation of 0.0 percent in Oct-Dec 2011 and 2.0 percent in the core annual equivalent. In the fifth wave from Jan to Mar 2012, annual equivalent inflation was 3.2 percent for the headline index but 3.2 percent for the core index excluding food and energy. In the sixth wave, annual equivalent inflation in Apr-May 2012 during renewed risk aversion was minus 4.1 percent for the headline PPI and 1.8 percent for the core. In the seventh wave, continuing risk aversion caused reversal of carry trades into commodity exposures with annual equivalent headline inflation of minus 1.2 percent in Jun-Jul 2012 while core PPI inflation was at annual equivalent 3.7 percent. In the eighth wave, relaxed risk aversion because of the announcement of the impaired bond buying program or Outright Monetary Transactions (OMT) of the European Central Bank (http://www.ecb.int/press/pr/date/2012/html/pr120906_1.en.html) induced carry trades that drove annual equivalent inflation of producer prices of the United States at 13.4 percent in Aug-Sep 2012 and 1.2 percent in the core index. In the ninth wave, renewed risk aversion caused annual equivalent inflation of minus 2.4 percent in Oct 2012-Dec 2012 in the headline index and 1.2 percent in the core index. In the tenth wave, annual equivalent inflation was 7.4 percent in the headline index in Jan-Feb 2013 and 1.8 percent in the core index. In the eleventh wave, annual equivalent inflation was minus 6.4 percent in Mar-Apr 2012 and 1.2 percent for the core index. In the twelfth wave, annual equivalent inflation returned at 2.4 percent in May-Aug 2013 and 1.2 percent in the core index. In the thirteenth wave, portfolio reallocations away from commodities and into equities reversed commodity carry trade with annual equivalent inflation of 1.2 percent in Sep-Nov 2013 in the headline PPI and 1.6 percent in the core. In the fourteenth wave, annual equivalent inflation returned at 5.7 percent annual equivalent for the headline index in Dec 2013-Feb 2014 and 3.7 percent for the core index. In the fifteenth wave, annual equivalent inflation was 1.2 percent for the general PPI index in Mar 2014 and 0.0 percent for the core PPI index. In the sixteenth wave, annual equivalent headline PPI inflation increased at 1.8 percent in Apr-Jul 2014 and 1.8 percent for the core PPI. In the seventeenth wave, annual equivalent inflation in Aug-Nov 2014 was minus 3.0 percent and 1.8 percent for the core index. In the eighteenth wave, annual equivalent inflation fell at 17.1 percent for the general index in Dec 2014 to Jan 2015 and increased at 3.0 percent in the core index. In the nineteenth wave, annual equivalent inflation increased at 3.7 percent in Feb 2015 and increased at 3.7 percent for the core index. In the twentieth wave, annual equivalent producer prices increased at 1.2 percent in Mar 2015 and the core at 1.2 percent. In the twenty-first wave, producer prices fell at 4.7 percent annual equivalent in Apr 2015 while the core index changed at 0.0 percent. In the twenty-second wave, producer prices increased at annual equivalent 10.7 percent in May-Jun 2015 and core producer prices at 4.3 percent. In the twenty-third wave, producer prices fell at 3.5 percent in Jul 2015 and the core index increased at 2.4 percent. In the twenty-fourth wave, annual equivalent inflation fell at 7.4 percent in Aug-Oct 2015 and the core index changed at 0.0 percent annual equivalent. In the twenty-fifth wave, annual equivalent inflation was 2.4 percent in Nov 2015 with the core at 1.2 percent. In the twenty-sixth wave, the headline PPI fell at annual equivalent 6.2 percent and the core increased at 2.0 percent in Dec 2015-Feb 2016. In the twenty-seventh wave, annual equivalent inflation was 3.7 percent for the central index in Mar-May 2016 and 2.0 percent for the core. In the twenty-eighth wave, annual equivalent inflation was 7.4 percent for the headline index in Jun 2016 and 3.7 percent for the core. In the twenty-ninth wave, producer prices fell at annual equivalent 2.4 percent in Jul 2016 and core producer prices fell at 1.2 percent. In the thirtieth wave, producer prices fell at 2.4 percent annual equivalent in Aug 2016 while core producer prices increased at 1.2 percent. In the thirty-first wave, producer prices increased at annual equivalent 5.5 percent in Sep-Oct 2016 while core prices increased at 1.8 percent. In the thirty-second wave, producer prices increased at 1.2 percent annual equivalent in Nov 2016 and the core index increased at 1.2 percent. In the thirty-third wave, producer prices increased at 7.4 percent in Dec 2016 and the core index increased at 3.7 percent. In the thirty-fourth wave, producer prices increased at 14.0 percent in Jan 2017 while the core increased at 2.4 percent. In the thirty-fifth wave, producer prices increased at 1.2 percent in Feb 2017 while the core index changed at 0.0 percent. In the thirty-sixth wave, producer prices fell at annual equivalent 2.4 percent in Mar 2017 while core producer prices increased at 3.7 percent. In the thirty-seventh wave, annual equivalent inflation of the headline index was at 7.4 percent and 3.7 percent for the core. It is almost impossible to forecast PPI inflation and its relation to CPI inflation. “Inflation surprise” by monetary policy could be proposed to climb along a downward sloping Phillips curve, resulting in higher inflation but lower unemployment (see Kydland and Prescott 1977, Barro and Gordon 1983 and past comments of this blog http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html http://cmpassocregulationblog.blogspot.com/2011/04/new-economics-of-rose-garden-turned.html http://cmpassocregulationblog.blogspot.com/2011/03/is-there-second-act-of-us-great.html http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). The architects of monetary policy would require superior inflation forecasting ability compared to forecasting naivety by everybody else. In practice, we are all naïve in forecasting inflation and other economic variables and events.

Table I-6, US, Headline and Core PPI Inflation Monthly SA and 12-Month NSA ∆%

	Finished Goods SA Month	Finished Goods NSA 12 months	Finished Core SA Month	Finished Core NSA 12 months
Apr 2017	0.6	3.9	0.3	1.9
AE Apr	7.4		3.7
Mar	-0.2	3.7	0.3	1.8
AE Mar	-2.4		3.7
Feb	0.1	3.7	0.0	1.6
AE Feb	1.2		0.0
Jan	1.1	3.0	0.2	1.7
AE Jan	14.0		2.4
Dec 2016	0.6	1.9	0.3	1.7
AE Dec	7.4		3.7
Nov	-0.1	0.4	0.2	1.6
AE Nov	-1.2		2.4
Oct	0.4	0.7	0.0	1.6
Sep	0.5	-0.1	0.2	1.4
AE Sep-Oct	5.5		1.8
Aug	-0.2	-1.9	0.1	1.4
AE Aug	-2.4		1.2
Jul	-0.2	-2.0	-0.1	1.2
AE Jul	-2.4		-1.2
Jun	0.6	-2.0	0.3	1.5
AE Jun	7.4		3.7
May	0.5	-2.2	0.2	1.6
Apr	0.4	-1.5	0.2	1.6
Mar	0.0	-2.3	0.1	1.5
AE Mar-May	3.7		2.0
Feb	-0.6	-2.0	0.2	1.5
Jan	-0.1	-1.2	0.2	1.7
Dec 2015	-0.9	-2.7	0.1	1.8
AE Dec-Feb	-6.2		2.0
Nov	0.2	-3.3	0.1	1.7
AE Nov	2.4		1.2
Oct	-0.3	-4.0	-0.2	1.8
Sep	-1.3	-4.1	0.2	2.1
Aug	-0.3	-3.1	0.0	2.1
AE ∆% Aug-Oct	-7.4		0.0
Jul	-0.3	-2.8	0.2	2.3
AE ∆% Jul	-3.5		2.4
Jun	0.5	-2.6	0.5	2.3
May	1.2	-2.9	0.2	2.0
AE ∆% May-Jun	10.7		4.3
Apr	-0.4	-4.5	0.0	2.0
AE ∆% Apr	-4.7		0.0
Mar	0.1	-3.3	0.1	2.1
AE ∆% Mar	1.2		1.2
Feb	0.3	-3.2	0.3	1.9
AE ∆% Feb	3.7		3.7
Jan	-1.6	-3.0	0.4	1.7
Dec 2014	-1.5	-0.6	0.1	1.7
AE ∆% Dec-Jan	-17.1		3.0
Nov	-0.4	1.1	0.1	2.0
Oct	-0.3	1.8	0.2	2.2
Sep	-0.2	2.2	0.2	2.1
Aug	-0.1	2.3	0.1	1.9
AE ∆% Aug-Nov	-3.0		1.8
July	0.0	2.9	0.1	1.9
Jun	0.2	2.8	0.2	1.9
May	-0.2	2.5	0.2	1.8
Apr	0.6	3.1	0.1	1.7
AE ∆% Apr-Jul	1.8		1.8
Mar	0.1	1.8	0.0	1.7
AE ∆% Mar	1.2		0.0
Feb	0.3	1.3	0.1	1.9
Jan	0.8	1.6	0.4	2.0
Dec 2013	0.3	1.4	0.4	1.6
AE ∆% Dec-Feb	5.7		3.7
Nov	0.2	0.8	0.3	1.3
Oct	0.2	0.3	0.1	1.2
Sep	-0.1	0.2	0.0	1.2
AE ∆% Sep-Nov	1.2		1.6
Aug	0.4	1.2	0.1	1.2
Jul	-0.1	2.1	0.1	1.3
Jun	0.1	2.3	0.1	1.6
May	0.4	1.6	0.1	1.7
AE ∆% May-Aug	2.4		1.2
Apr	-0.6	0.5	0.1	1.7
Mar	-0.5	1.1	0.1	1.7
AE ∆% Mar-Apr	-6.4		1.2
Feb	0.7	1.8	0.2	1.8
Jan	0.5	1.5	0.1	1.8
AE ∆% Jan-Feb	7.4		1.8
Dec 2012	-0.2	1.4	0.0	2.1
Nov	-0.5	1.4	0.2	2.2
Oct	0.1	2.3	0.1	2.2
AE ∆% Oct-Dec	-2.4		1.2
Sep	0.9	2.1	0.0	2.4
Aug	1.2	1.9	0.2	2.6
AE ∆% Aug-Sep	13.4		1.2
Jul	0.2	0.5	0.4	2.6
Jun	-0.4	0.7	0.2	2.6
AE ∆% Jun-Jul	-1.2		3.7
May	-0.6	0.6	0.1	2.7
Apr	-0.1	1.8	0.2	2.7
AE ∆% Apr-May	-4.1		1.8
Mar	0.1	2.7	0.2	2.9
Feb	0.3	3.4	0.2	3.1
Jan	0.4	4.1	0.4	3.1
AE ∆% Jan-Mar	3.2		3.2
Dec 2011	-0.1	4.7	0.2	3.0
Nov	0.3	5.7	0.1	3.0
Oct	-0.2	5.9	0.2	2.9
AE ∆% Oct-Dec	0.0		2.0
Sep	0.9	7.1	0.3	2.8
Aug	-0.3	6.6	0.2	2.7
Jul	0.4	7.2	0.3	2.7
AE ∆% Jul-Sep	4.1		3.2
Jun	-0.4	7.0	0.3	2.3
May	0.5	7.1	0.1	2.1
AE ∆% May-Jun	0.6		2.4
Apr	0.9	6.7	0.3	2.3
Mar	0.7	5.7	0.3	2.0
Feb	1.1	5.5	0.2	1.8
Jan	0.8	3.7	0.4	1.6
AE ∆% Jan-Apr	11.1		3.7
Dec 2010	0.9	3.8	0.2	1.4
Nov	0.4	3.4	0.0	1.2
Oct	0.8	4.3	0.0	1.6
Sep	0.3	3.9	0.2	1.6
Aug	0.6	3.3	0.1	1.3
Jul	0.1	4.1	0.1	1.5
Jun	-0.3	2.7	0.1	1.1
May	0.0	5.1	0.3	1.3
Apr	0.0	5.4	0.0	0.9
Mar	0.7	5.9	0.2	0.9
Feb	-0.7	4.1	0.1	1.0
Jan	1.0	4.5	0.2	1.0

Note: Core: excluding food and energy; AE: annual equivalent

Source: US Bureau of Labor Statistics http://www.bls.gov/ppi/data.htm

The US producer price index NSA from 2000 to 2017 is in Chart I-24. There are two episodes of decline of the PPI during recessions in 2001 and in 2008. Barsky and Kilian (2004) consider the 2001 episode as one in which real oil prices were declining when recession began. Recession and the fall of commodity prices instead of generalized deflation explain the behavior of US inflation in 2008. There is similar collapse of producer prices into 2015 as in 2009 caused by the drop of

commodity prices.

Chart I-24, US, Producer Price Index, NSA, 2000-2017

Source: US Bureau of Labor Statistics

Twelve-month percentage changes of the PPI NSA from 2000 to 2017 are in Chart I-25. It may be possible to forecast trends a few months in the future under adaptive expectations but turning points are almost impossible to anticipate especially when related to fluctuations of commodity prices in response to risk aversion. In a sense, monetary policy has been tied to behavior of the PPI in the negative 12-month rates in 2001 to 2003 and then again in 2009 to 2010. There is similar sharp decline of inflation into 2015 caused by the drop of commodities. Monetary policy following deflation fears caused by commodity price fluctuations would introduce significant volatility and risks in financial markets and eventually in consumption and investment.

Chart I-25, US, Producer Price Index, 12-Month Percentage Change NSA, 2000-2017

Source: US Bureau of Labor Statistics

The US PPI excluding food and energy from 2000 to 2017 is in Chart I-26. There is here again a smooth trend of inflation instead of prolonged deflation as in Japan.

Chart I-26, US, Producer Price Index Excluding Food and Energy, NSA, 2000-2017

Source: US Bureau of Labor Statistics

Twelve-month percentage changes of the producer price index excluding food and energy are in Chart I-27. Fluctuations replicate those in the headline PPI. There is an evident trend of increase of 12-month rates of core PPI inflation in 2011 but lower rates in 2012-2014. Prices rose less rapidly into 2015-2017 as during earlier fluctuations.

Chart I-27, US, Producer Price Index Excluding Food and Energy, NSA, 12-Month Percentage Changes, 2000-2017

Source: US Bureau of Labor Statistics

The US producer price index of energy goods from 2000 to 2017 is in Chart I-28. There is a clear upward trend with fluctuations, which would not occur under persistent deflation.

Chart I-28, US, Producer Price Index Finished Energy Goods, NSA, 2000-2017

Source: US Bureau of Labor Statistics

Chart I-29 provides 12-month percentage changes of the producer price index of energy goods from 2000 to 2017. Barsky and Killian (2004) relate the episode of declining prices of energy goods in 2001 to 2002 to the analysis of decline of real oil prices. Interest rates dropping to zero during the global recession in 2008 induced carry trades that explain the rise of the PPI of energy goods toward 30 percent. Bouts of risk aversion with policy interest rates held close to zero explain the fluctuations in the 12-month rates of the PPI of energy goods in the expansion phase of the economy. Symmetric inflation targets induce significant instability in inflation and interest rates with adverse effects on financial markets and the overall economy.

Chart I-29, US, Producer Price Index Energy Goods, 12-Month Percentage Change, NSA, 2000-2017

Source: US Bureau of Labor Statistics

Effective with the January 2014 Producer Price Index (PPI) data release in February 2014 (https://www.bls.gov/news.release/archives/ppi_02192014.pdf 8), “BLS transitions from the Stage of Processing (SOP) to the Final Demand-Intermediate Demand (FD-ID) aggregation system. This shift results in significant changes to the PPI news release, as well as other documents available from PPI. The transition to the FD-ID system is the culmination of a long-standing PPI objective to improve the current SOP aggregation system by incorporating PPIs for services, construction, government purchases, and exports. In comparison to the SOP system, the FD-ID system more than doubles PPI coverage of the United States economy to over 75 percent of in-scope domestic production. The FD-ID system was introduced as a set of experimental indexes in January 2011. Nearly all new FD-ID goods, services, and construction indexes provide historical data back to either November 2009 or April 2010, while the indexes for goods that correspond with the historical SOP indexes go back to the 1970s or earlier.”

Headline and core final demand producer price indexes are in Table I-6B. The headline FD PPI SA increased 0.5 percent in Mar 2017 and increased 2.5 percent NSA in the 12 months ending in Mar 2017. The core FD PPI SA increased 0.4 percent in Mar 2017 and increased 1.9 percent in 12 months. Analysis of annual equivalent rates of change shows inflation waves similar to those worldwide. In the first wave, the absence of risk aversion from the sovereign risk crisis in Europe motivated the carry trade from zero interest rates into commodity futures that caused the average equivalent rate of 7.4 percent in the headline FD PPI in Jan-Apr 2011 and 4.6 percent in the core FD PPI. In the second wave, commodity futures prices collapsed in Jun 2011 with the return of risk aversion originating in the sovereign risk crisis of Europe. The annual equivalent rate of headline FD PPI inflation collapsed to 2.4 percent in May-Jun 2011 but the core annual equivalent inflation rate was at 2.4 percent. In the third wave, headline FD PPI inflation resuscitated with annual equivalent at 3.2 percent in Jul-Sep 2011 and core PPI inflation at 3.2 percent. Core FD PPI inflation was persistent throughout 2011, from annual equivalent at 4.6 percent in the first four months of 2011 to 2.6 percent in 12 months ending in Dec 2011. Unconventional monetary policy is based on the proposition that core rates reflect more fundamental inflation and are thus better predictors of the future. In practice, the relation of core and headline inflation is as difficult to predict as future inflation (see IIID Supply Shocks in http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html). In the fourth wave, risk aversion originating in the lack of resolution of the European debt crisis caused unwinding of carry trades with annual equivalent headline FD PPI inflation of minus 0.8 percent in Oct-Dec 2011 and minus 0.4 percent in the core annual equivalent. In the fifth wave from Jan to Mar 2012, annual equivalent inflation was 3.7 percent for the headline index and 3.7 percent for the core index excluding food and energy. In the sixth wave, annual equivalent inflation in Apr-May 2012 during renewed risk aversion was 1.2 percent for the headline FD PPI and 3.0 percent for the core. In the seventh wave, continuing risk aversion caused reversal of carry trades into commodity exposures with annual equivalent headline inflation of minus 2.4 percent in Jun-Jul 2012 while core FD PPI inflation was at annual equivalent minus 1.2 percent. In the eighth wave, relaxed risk aversion because of the announcement of the impaired bond buying program or Outright Monetary Transactions (OMT) of the European Central Bank (http://www.ecb.int/press/pr/date/2012/html/pr120906_1.en.html) induced carry trades that drove annual equivalent inflation of final demand producer prices of the United States at 6.2 percent in Aug-Sep 2012 and 1.2 percent in the core index. In the ninth wave, renewed risk aversion caused annual equivalent inflation of 0.8 percent in Oct 2011-Dec 2012 in the headline index and 2.8 percent in the core index. In the tenth wave, annual equivalent inflation was 3.0 percent in the headline index in Jan-Feb 2013 and 0.6 percent in the core index. In the eleventh wave, annual equivalent price change was minus 1.2 percent in Mar-Apr 2012 and 2.4 percent for the core index. In the twelfth wave, annual equivalent inflation returned at 1.8 percent in May-Aug 2013 and 1.2 percent in the core index. In the thirteenth wave, portfolio reallocations away from commodities and into equities reversed commodity carry trade with annual equivalent inflation of 1.6 percent in Sep-Nov 2013 in the headline FD PPI and 2.0 percent in the core. In the fourteenth wave, annual equivalent inflation was 2.8 percent annual equivalent for the headline index in Dec 2013-Feb 2014 and 1.6 percent for the core index. In the fifteenth wave, annual equivalent inflation increased to 2.2 percent in the headline FD PPI and 2.7 percent in the core in Mar-Jul 2014. In the sixteenth wave, annual equivalent inflation was minus 1.2 percent for the headline FD index and minus 1.2 percent for the core FD index in Aug-Sep 2014. In the seventeenth wave, annual equivalent inflation was 2.4 percent for the headline FD and 6.2 percent for the core FD in Oct 2014. In the eighteenth wave, annual equivalent inflation was minus 3.5 percent for the headline FDI and 1.2 percent for the core in Nov-Dec 2014. In the nineteenth wave, annual equivalent inflation was minus 5.3 percent for the general index and minus 2.4 percent for the core in Jan-Feb 2015. In the twentieth wave, annual equivalent inflation was 0.0 percent for the general index in Mar 2015 and 0.0 percent for the core. In the twenty-first wave, final demand prices changed at annual equivalent 0.0 percent for the headline index in Apr 2015 and increased at 2.4 percent for the core index. In the twenty-second wave, annual equivalent inflation returned at 3.2 percent for the headline index in May-Jul 2015 and at 2.0 percent for the core index. In the twenty-third wave, the headline final demand index fell at 2.4 percent annual equivalent in Aug 2015 and the core decreased at 1.2 percent annual equivalent. In the twenty-fourth wave, FD prices fell at annual equivalent 4.1 percent in Sep-Oct 2015. In the twenty-fifth wave, FD prices increased at 1.2 percent annual equivalent in Nov 2015. In the twenty-sixth wave, FD prices decreased at 2.4 percent annual equivalent in Dec 2015. In the twenty-seventh wave, FD prices increased at 6.2 percent annual equivalent in Jan 2016 and the core FD increased at 8.7 percent. In the twenty-eighth wave, FD prices fell at annual equivalent 2.4 percent in Feb-Mar 2016 while the core decreased at 1.2 percent. In the twenty-ninth wave, FD prices increased at 4.1 percent annual equivalent in Apr-Jun 2016 and core FD increased at 2.4 percent. In the thirtieth wave, final demand prices fell at 1.2 percent in annual equivalent in Jul 2016 while the core changed at 0.0 percent. In the thirty-first wave, final demand prices decreased at annual equivalent 2.4 percent in Aug 2016 and the core decreased at 1.2 percent. In the thirty-second wave, final demand prices increased at annual equivalent 3.7 percent in Sep 2016 while core final demand increased at 2.4 percent. In the thirty-third wave, final demand prices increased at 3.7 percent and core final demand prices increased at 1.2 percent in Oct 2016. In the thirty-fourth wave, final demand producer prices increased at 2.4 percent annual equivalent in Nov-Dec 2016 while the core increased at 2.4 percent. In the thirty-fifth wave, final demand producer prices increased at 6.2 percent in Jan 2017 while core prices increased at 3.7 percent. In the thirty-sixth wave, final demand prices increased at 3.7 percent annual equivalent in Feb 2017 while the core index increased at 3.7 percent. In the thirty-seventh wave, final demand prices fell at 1.2 percent annual equivalent in Mar 2017 while the core index changed at 0.0 percent. In the thirty-eighth wave, final demand prices increased at 6.2 percent in Apr 2017 while the core increased at 4.9 percent. It is almost impossible to forecast PPI inflation and its relation to CPI inflation. “Inflation surprise” by monetary policy could be proposed to climb along a downward sloping Phillips curve, resulting in higher inflation but lower unemployment (see Kydland and Prescott 1977, Barro and Gordon 1983 and past comments of this blog http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html http://cmpassocregulationblog.blogspot.com/2011/04/new-economics-of-rose-garden-turned.html http://cmpassocregulationblog.blogspot.com/2011/03/is-there-second-act-of-us-great.html http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). The architects of monetary policy would require superior inflation forecasting ability compared to forecasting naivety by everybody else. In practice, we are all naïve in forecasting inflation and other economic variables and events.

Table I-6B, US, Headline and Core Final Demand Producer Price Inflation Monthly SA and 12-Month NSA ∆%

	Final Demand SA Month	Final Demand NSA 12 months	Final Demand Core SA Month	Final Demand Core NSA 12 months
Apr 2017	0.5	2.5	0.4	1.9
AE ∆% Apr	6.2		4.9
Mar	-0.1	2.3	0.0	1.6
AE ∆% Mar	-1.2		0.0
Feb	0.3	2.2	0.3	1.5
AE ∆% Feb	3.7		3.7
Jan	0.5	1.6	0.3	1.2
AE ∆% Jan	6.2		3.7
Dec 2016	0.2	1.7	0.1	1.7
Nov	0.2	1.3	0.3	1.7
AE ∆% Nov-Dec	2.4		2.4
Oct	0.3	1.1	0.1	1.5
AE ∆% Oct	3.7		1.2
Sep	0.3	0.6	0.2	1.2
AE ∆% Sep	3.7		2.4
Aug	-0.2	0.0	-0.1	1.0
AE ∆% Aug	-2.4		-1.2
July	-0.1	0.0	0.0	0.9
AE ∆% Jul	-1.2		0.0
Jun	0.5	0.2	0.3	1.2
May	0.2	0.0	0.1	1.2
Apr	0.3	0.2	0.2	1.1
AE ∆% Apr-Jun	4.1		2.4
Mar	-0.2	-0.1	-0.1	1.1
Feb	-0.2	0.1	-0.1	1.3
AE ∆% Mar-Feb	-2.4		-1.2
Jan	0.5	0.0	0.7	0.8
AE ∆% Jan	6.2		8.7
Dec 2015	-0.2	-1.1	0.1	0.2
AE ∆% Dec	-2.4		1.2
Nov	0.1	-1.3	0.1	0.3
AE ∆% Nov	1.2		1.2
Oct	-0.2	-1.4	-0.2	0.2
Sep	-0.5	-1.1	0.0	0.7
AE ∆% Sep-Oct	-4.1		-1.2
Aug	-0.2	-1.0	-0.1	0.6
AE ∆% Aug	-2.4		-1.2
Jul	0.1	-0.7	0.2	0.8
Jun	0.3	-0.5	0.3	1.1
May	0.4	-0.8	0.0	0.7
AE ∆% May-Jul	3.2		2.0
Apr	0.0	-1.1	0.2	1.0
AE ∆% Apr	0.0		2.4
Mar	0.0	-0.9	0.0	0.8
AE ∆% Mar	0.0		0.0
Feb	-0.4	-0.5	-0.4	1.0
Jan	-0.5	0.0	0.0	1.7
AE ∆% Jan-Feb	-5.3		-2.4
Dec 2014	-0.4	0.9	0.2	2.0
Nov	-0.2	1.3	0.0	1.7
AE ∆% Nov-Dec	-3.5		1.2
Oct	0.2	1.5	0.5	1.9
AE ∆% Oct	2.4		6.2
Sep	-0.2	1.6	-0.2	1.6
Aug	0.0	1.9	0.0	1.9
AE ∆% Aug-Sep	-1.2		-1.2
Jul	0.3	1.9	0.5	1.9
Jun	0.0	1.8	0.0	1.6
May	0.2	2.1	0.3	2.1
Apr	0.1	1.8	0.0	1.5
Mar	0.3	1.6	0.3	1.6
AE ∆% Mar-Jul	2.2		2.7
Feb	0.3	1.2	0.2	1.6
Jan	0.4	1.3	0.2	1.4
Dec 2013	0.0	1.2	0.0	1.2
AE ∆% Dec-Feb	2.8		1.6
Nov	0.2	1.1	0.2	1.4
Oct	0.1	1.3	0.2	1.7
Sep	0.1	1.1	0.1	1.6
AE ∆% Sep-Nov	1.6		2.0
Aug	0.1	1.7	0.0	1.8
Jul	0.2	2.0	0.2	1.7
Jun	0.4	1.7	0.5	1.3
May	-0.1	0.9	-0.3	0.9
AE ∆% May-Aug	1.8		1.2
Apr	-0.2	0.9	0.2	1.3
Mar	0.0	1.3	0.2	1.5
AE ∆% Mar-Apr	-1.2		2.4
Feb	0.2	1.6	0.0	1.4
Jan	0.3	1.6	0.1	1.7
AE ∆% Jan-Feb	3.0		0.6
Dec 2012	0.0	1.9	0.1	2.0
Nov	0.1	1.7	0.5	1.8
Oct	0.1	1.9	0.1	1.6
AE ∆% Oct-Dec	0.8		2.8
Sep	0.7	1.5	0.3	1.4
Aug	0.3	1.2	-0.1	1.2
AE ∆% Aug-Sep	6.2		1.2
Jul	-0.1	1.0	-0.1	1.7
Jun	-0.3	1.3	-0.1	1.9
AE ∆% Jun-Jul	-2.4		-1.2
May	-0.1	1.6	0.2	2.2
Apr	0.3	2.0	0.3	2.1
AE ∆% Apr-May	1.2		3.0
Mar	0.2	2.4	0.2	2.3
Feb	0.3	2.8	0.3	2.6
Jan	0.4	3.1	0.4	2.5
AE ∆% Jan-Mar	3.7		3.7
Dec 2011	-0.1	3.2	0.0	2.6
Nov	0.3	3.7	0.2	2.7
Oct	-0.4	3.7	-0.3	2.7
AE ∆% Oct-Dec	-0.8		-0.4
Sep	0.4	4.5	0.2	2.9
Aug	0.2	4.4	0.4	3.0
Jul	0.2	4.5	0.2	2.7
AE ∆% Jul-Sep	3.2		3.2
Jun	0.1	4.3	0.2	2.6
May	0.3	4.2	0.2	2.3
AE ∆% May-Jun	2.4		2.4
Apr	0.5	4.2	0.3	2.5
Mar	0.7	4.0	0.5	NA
Feb	0.6	3.3	0.3	NA
Jan	0.6	2.4	0.4	NA
AE ∆% Jan-Apr	7.4		4.6
Dec 2010	0.3	2.8	0.1	NA
Nov	0.3	2.6	0.1	NA
Oct	0.4	NA	0.1	NA
Sep	0.3	NA	0.2	NA
Aug	0.2	NA	0.0	NA
Jul	0.2	NA	0.2	NA
Jun	-0.2	NA	-0.1	NA
May	0.2	NA	0.3	NA
Apr	0.3	NA	NA	NA
Mar	0.1	NA	NA	NA
Feb	-0.2	NA	NA	NA
Jan	0.9	NA	NA	NA
Dec 2009	0.1

Note: Core: excluding food and energy; AE: annual equivalent

Source: US Bureau of Labor Statistics http://www.bls.gov/ppi/data.htm

Chart I-24B provides the FD PPI NSA from 209 to 2017. There is persistent inflation with periodic declines in inflation waves similar to those worldwide.

Chart I-24B, US, Final Demand Producer Price Index, NSA, 2009-2017

Source: US Bureau of Labor Statistics

Twelve-month percentage changes of the FD PPI from 2010 to 2017 are in Chart I-25B. There are fluctuations in the rates with evident trend of decline to more subdued inflation. Reallocations of investment portfolios of risk financial assets from commodities to stocks explain much lower FD PPI inflation.

Chart I-25B, US, Final Demand Producer Price Index, 12-Month Percentage Change NSA, 2010-2017

Source: US Bureau of Labor Statistics

The core FD PPI NSA is in Chart I-26B. The behavior is similar to the headline index but with less fluctuation.

Chart I-26B, US, Final Demand Producer Price Index Excluding Food and Energy, NSA, 2009-2017

Source: US Bureau of Labor Statistics

Percentage changes in 12 months of the core FD PPI are in Chart I-27B. There are fluctuations in 12-month percentage changes but with evident declining trend to more moderate inflation.

Chart I-27B, US, Final Demand Producer Price Index Excluding Food and Energy, 12-Month Percentage Change, NSA, 2010-2017

Source: US Bureau of Labor Statistics

The energy FD PPI NSA is in Chart I-28B. The index increased during the reposition of carry trades after the discovery of lack of toxic assets in banks that caused flight away from risk financial assets into government obligations of the US (Cochrane and Zingales 2009). Alternating risk aversion and appetite with reallocations among classes of risk financial assets explain the behavior of the index after late 2010.

Chart I-28B, US, Final Demand Energy Producer Price Index, NSA, 2009-2017

Source: US Bureau of Labor Statistics

Twelve-month percentage changes of the FD energy PPI are in Chart I-29B. Rates moderated from late 2010 to the present. There are multiple negative rates. Investors create and reverse carry trades from zero interest rates to derivatives of commodities in accordance with relative risk evaluations of classes of risk financial assets.

Chart I-29B, US, Final Demand Energy Producer Price Index, 12-Month Percentage Change, NSA, 2010-2017

Source: US Bureau of Labor Statistics