Labor Market Weakness Is Still not due to Workers Lacking the Right Skills

The figure below shows the number of unemployed workers and the number of job openings by industry. This figure is useful for diagnosing what’s behind our sustained high unemployment. If our current elevated unemployment were due to skills shortages or mismatches, we would expect to find some sectors where there are more unemployed workers than job openings, and some where there are more job openings than unemployed workers. What we find, however, is that unemployed workers dramatically outnumber job openings across the board. There are between 1.1 and 6.5 times as many unemployed workers as job openings in every industry. In other words, even in the industry with the most favorable ratio of unemployed workers to job openings (health care and social assistance), there are still about 10 percent more unemployed workers than job openings. This demonstrates that the main problem in the labor market is a broad-based lack of demand for workers—not, as is often claimed, available workers lacking the skills needed for the sectors with job openings.

Figure A

Unemployed and job openings, by industry (in millions)

Industry Unemployed Job openings
Professional and business services 1.151667 0.792083
Health care and social assistance 0.719667 0.665667
Retail trade 1.179833 0.473667
Accommodation and food services 0.975167 0.544
Government 0.734333 0.421333
Finance and insurance 0.27225 0.216333
Durable goods manufacturing 0.518667 0.174417
Other services 0.40575 0.143417
Wholesale trade 0.171167 0.145917
Transportation, warehousing, and utilities 0.38375 0.157833
Information 0.163667 0.103083
Construction 0.815333 0.126167
Nondurable goods manufacturing 0.343667 0.10575
Educational services 0.23275 0.073333
Real estate and rental and leasing 0.126 0.052
Arts, entertainment, and recreation 0.226167 0.074583
Mining and logging 0.05425 0.026833
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Note: Because the data are not seasonally adjusted, these are 12-month averages, September 2013–August 2014.

Source: EPI analysis of data from the Job Openings and Labor Turnover Survey and the Current Population Survey

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Job Seekers Outnumber Jobs by 2-to-1

In August, the total number of job openings was 4.8 million, up from a revised 4.6 million in July. In August, there were 9.6 million job seekers (unemployment data are from the Current Population Survey), meaning that there were 2.0 times as many job seekers as job openings. Put another way, job seekers so outnumbered job openings that about half of the unemployed were not going to find a job in August no matter what they did. In a labor market with strong job opportunities, there would be roughly as many job openings as job seekers.

The decline of the job seekers to job openings ratio to 2.0 continues the overall downward trend since the high of 6.8 to 1 in July 2009 (see Figure A). The ratio has steadily declined, falling by about 1.0 over the last year.

While this is clearly a move in the right direction, the 9.6 million unemployed workers understate how many job openings will be needed when a robust jobs recovery finally begins, due to the existence of 5.9 million would-be workers  (in August) who are currently not in the labor market, but who would be if job opportunities were strong. Many of these “missing workers” will become job seekers when we enter a robust jobs recovery, so job openings will be needed for them, too.

Figure A

The job-seekers ratio, December 2000–August 2014

Month Unemployed job seekers per job opening
Dec-2000 1.1
Jan-2001 1.1
Feb-2001 1.3
Mar-2001 1.3
Apr-2001 1.3
May-2001 1.4
Jun-2001 1.5
Jul-2001 1.5
Aug-2001 1.7
Sep-2001 1.8
Oct-2001 2.1
Nov-2001 2.3
Dec-2001 2.3
Jan-2002 2.3
Feb-2002 2.4
Mar-2002 2.3
Apr-2002 2.6
May-2002 2.4
Jun-2002 2.5
Jul-2002 2.5
Aug-2002 2.4
Sep-2002 2.5
Oct-2002 2.4
Nov-2002 2.4
Dec-2002 2.8
Jan-2003 2.3
Feb-2003 2.5
Mar-2003 2.8
Apr-2003 2.8
May-2003 2.8
Jun-2003 2.8
Jul-2003 2.8
Aug-2003 2.7
Sep-2003 2.9
Oct-2003 2.7
Nov-2003 2.6
Dec-2003 2.5
Jan-2004 2.5
Feb-2004 2.4
Mar-2004 2.5
Apr-2004 2.4
May-2004 2.2
Jun-2004 2.4
Jul-2004 2.1
Aug-2004 2.2
Sep-2004 2.1
Oct-2004 2.1
Nov-2004 2.3
Dec-2004 2.1
Jan-2005 2.2
Feb-2005 2.1
Mar-2005 2.0
Apr-2005 1.9
May-2005 2.0
Jun-2005 1.9
Jul-2005 1.8
Aug-2005 1.8
Sep-2005 1.8
Oct-2005 1.8
Nov-2005 1.7
Dec-2005 1.7
Jan-2006 1.7
Feb-2006 1.7
Mar-2006 1.6
Apr-2006 1.6
May-2006 1.6
Jun-2006 1.6
Jul-2006 1.8
Aug-2006 1.6
Sep-2006 1.5
Oct-2006 1.5
Nov-2006 1.5
Dec-2006 1.5
Jan-2007 1.6
Feb-2007 1.5
Mar-2007 1.4
Apr-2007 1.5
May-2007 1.5
Jun-2007 1.5
Jul-2007 1.6
Aug-2007 1.6
Sep-2007 1.6
Oct-2007 1.7
Nov-2007 1.7
Dec-2007 1.8
Jan-2008 1.8
Feb-2008 1.9
Mar-2008 1.9
Apr-2008 2.0
May-2008 2.1
Jun-2008 2.3
Jul-2008 2.4
Aug-2008 2.6
Sep-2008 3.0
Oct-2008 3.1
Nov-2008 3.4
Dec-2008 3.7
Jan-2009 4.4
Feb-2009 4.6
Mar-2009 5.4
Apr-2009 6.1
May-2009 6.0
Jun-2009 6.2
Jul-2009 6.8
Aug-2009 6.5
Sep-2009 6.2
Oct-2009 6.5
Nov-2009 6.3
Dec-2009 6.1
Jan-2010 5.5
Feb-2010 6.0
Mar-2010 5.8
Apr-2010 5.0
May-2010 5.1
Jun-2010 5.3
Jul-2010 5.0
Aug-2010 5.0
Sep-2010 5.2
Oct-2010 4.8
Nov-2010 4.9
Dec-2010 5.0
Jan-2011 4.8
Feb-2011 4.6
Mar-2011 4.4
Apr-2011 4.5
May-2011 4.5
Jun-2011 4.3
Jul-2011 4.0
Aug-2011 4.3
Sep-2011 3.9
Oct-2011 4.0
Nov-2011 4.2
Dec-2011 3.7
Jan-2012 3.5
Feb-2012 3.7
Mar-2012 3.3
Apr-2012 3.5
May-2012 3.4
Jun-2012 3.3
Jul-2012 3.5
Aug-2012 3.4
Sep-2012 3.4
Oct-2012 3.2
Nov-2012 3.2
Dec-2012 3.4
Jan-2013 3.3
Feb-2013 3.0
Mar-2013 3.0
Apr-2013 3.1
May-2013 3.0
Jun-2013 3.0
Jul-2013 3.0
Aug-2013 2.9
Sep-2013 2.8
Oct-2013 2.8
Nov-2013 2.6
Dec-2013 2.6
Jan-2014 2.6
Feb-2014 2.5
Mar-2014 2.5
Apr-2014 2.2
May-2014 2.1
Jun-2014 2.0
Jul-2014 2.1
Aug-2014 2.0
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Note: Shaded areas denote recessions.

Source: EPI analysis of Bureau of Labor Statistics Job Openings and Labor Turnover Survey and Current Population Survey

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Even further, a job opening when the labor market is weak often does not mean the same thing as a job opening when the labor market is strong. There is a wide range of “recruitment intensity” with which a company can deal with a job opening. For example, if a company is trying hard to fill an opening, it may increase the compensation package and/or scale back the required qualifications. Conversely, if it is not trying very hard, it may hike up the required qualifications and/or offer a meager compensation package. Perhaps unsurprisingly, research shows that recruitment intensity is cyclical; it tends to be stronger when the labor market is strong, and weaker when the labor market is weak. This means that when a job opening goes unfilled when the labor market is weak, as it is today, companies may very well be holding out for an overly qualified candidate at a cheap price.

Job Openings Are Up, but the Hires Rate Is Down

The August Job Openings and Labor Turnover Survey (JOLTS) data release this morning from the Bureau of Labor Statistics showed mixed results. While the job openings rose, the hires rate fell. Layoffs continue to trend downwards, while the quits rate remained flat—it’s been flat now since February.

The figure below shows the hires rate, the quits rate, and the layoffs rate. The first thing to note is that layoffs, which shot up during the recession, recovered quickly once the recession officially ended. Layoffs have been at prerecession levels for more than three years. This makes sense—the economy is in a recovery and businesses are no longer shedding workers at an elevated rate. And the continued trend downward in August is a good sign.

But for a full recovery in the labor market to occur, two key things need to happen: Layoffs need to come down, and hiring needs to pick up. Hiring is the side of that equation that, while generally improving, has not yet come close to a full recovery. The hires rate remains well below its prerecession level.

Another piece of the puzzle is voluntary quits (shown by the quits rate in the figure below). A larger number of people voluntarily quitting their job indicates a labor market in which hiring is prevalent and workers are able to leave jobs that are not right for them, and find new ones. The voluntary quits rate, which has been flat for the last seven months, is also nowhere near a full recovery. There are 14 percent percent fewer voluntary quits each month than there were before the recession began, and the quits rate is the same as it was last October. Low voluntary quits indicate that there are a large number of workers who are locked into jobs who would leave if they could.


Hires, quits, and layoff rates, December 2000–August 2014

Month Hires rate Layoffs rate Quits rate
Dec-2000 4.1% 1.4% 2.3%
Jan-2001 4.4% 1.6% 2.6%
Feb-2001 4.1% 1.4% 2.5%
Mar-2001 4.2% 1.6% 2.4%
Apr-2001 4.0% 1.5% 2.4%
May-2001 4.0% 1.5% 2.4%
Jun-2001 3.8% 1.5% 2.3%
Jul-2001 3.9% 1.5% 2.2%
Aug-2001 3.8% 1.4% 2.1%
Sep-2001 3.8% 1.6% 2.1%
Oct-2001 3.8% 1.7% 2.2%
Nov-2001 3.7% 1.6% 2.0%
Dec-2001 3.7% 1.4% 2.0%
Jan-2002 3.7% 1.4% 2.2%
Feb-2002 3.7% 1.5% 2.0%
Mar-2002 3.5% 1.4% 1.9%
Apr-2002 3.8% 1.5% 2.1%
May-2002 3.8% 1.5% 2.1%
Jun-2002 3.7% 1.4% 2.0%
Jul-2002 3.8% 1.5% 2.1%
Aug-2002 3.7% 1.4% 2.0%
Sep-2002 3.7% 1.4% 2.0%
Oct-2002 3.7% 1.4% 2.0%
Nov-2002 3.8% 1.5% 1.9%
Dec-2002 3.8% 1.5% 2.0%
Jan-2003 3.8% 1.5% 1.9%
Feb-2003 3.6% 1.5% 1.9%
Mar-2003 3.4% 1.4% 1.9%
Apr-2003 3.6% 1.6% 1.8%
May-2003 3.5% 1.5% 1.8%
Jun-2003 3.7% 1.6% 1.8%
Jul-2003 3.6% 1.6% 1.8%
Aug-2003 3.6% 1.5% 1.8%
Sep-2003 3.7% 1.5% 1.9%
Oct-2003 3.8% 1.4% 1.9%
Nov-2003 3.6% 1.4% 1.9%
Dec-2003 3.8% 1.5% 1.9%
Jan-2004 3.7% 1.5% 1.9%
Feb-2004 3.6% 1.4% 1.9%
Mar-2004 3.9% 1.4% 2.0%
Apr-2004 3.9% 1.5% 2.0%
May-2004 3.8% 1.4% 1.9%
Jun-2004 3.8% 1.4% 2.0%
Jul-2004 3.7% 1.4% 2.0%
Aug-2004 3.9% 1.5% 2.0%
Sep-2004 3.8% 1.4% 2.0%
Oct-2004 3.9% 1.4% 2.0%
Nov-2004 3.9% 1.5% 2.1%
Dec-2004 4.0% 1.5% 2.1%
Jan-2005 3.9% 1.4% 2.1%
Feb-2005 3.9% 1.4% 2.0%
Mar-2005 3.9% 1.5% 2.1%
Apr-2005 4.0% 1.4% 2.1%
May-2005 3.9% 1.4% 2.1%
Jun-2005 3.9% 1.5% 2.1%
Jul-2005 3.9% 1.4% 2.0%
Aug-2005 4.0% 1.4% 2.2%
Sep-2005 4.0% 1.4% 2.3%
Oct-2005 3.8% 1.3% 2.2%
Nov-2005 3.9% 1.2% 2.2%
Dec-2005 3.7% 1.3% 2.1%
Jan-2006 3.9% 1.3% 2.1%
Feb-2006 3.9% 1.3% 2.2%
Mar-2006 3.9% 1.2% 2.2%
Apr-2006 3.8% 1.3% 2.1%
May-2006 4.0% 1.4% 2.2%
Jun-2006 3.9% 1.2% 2.2%
Jul-2006 3.9% 1.3% 2.2%
Aug-2006 3.8% 1.2% 2.2%
Sep-2006 3.8% 1.3% 2.1%
Oct-2006 3.8% 1.3% 2.1%
Nov-2006 4.0% 1.3% 2.3%
Dec-2006 3.8% 1.3% 2.2%
Jan-2007 3.8% 1.2% 2.2%
Feb-2007 3.8% 1.3% 2.2%
Mar-2007 3.8% 1.3% 2.2%
Apr-2007 3.7% 1.3% 2.1%
May-2007 3.8% 1.3% 2.2%
Jun-2007 3.8% 1.3% 2.0%
Jul-2007 3.7% 1.3% 2.1%
Aug-2007 3.7% 1.3% 2.1%
Sep-2007 3.7% 1.5% 1.9%
Oct-2007 3.8% 1.4% 2.1%
Nov-2007 3.7% 1.4% 2.0%
Dec-2007 3.6% 1.3% 2.0%
Jan-2008 3.5% 1.3% 2.0%
Feb-2008 3.5% 1.4% 2.0%
Mar-2008 3.4% 1.3% 1.9%
Apr-2008 3.5% 1.3% 2.1%
May-2008 3.3% 1.3% 1.9%
Jun-2008 3.5% 1.5% 1.9%
Jul-2008 3.3% 1.4% 1.8%
Aug-2008 3.3% 1.6% 1.7%
Sep-2008 3.1% 1.4% 1.8%
Oct-2008 3.3% 1.6% 1.8%
Nov-2008 2.9% 1.6% 1.5%
Dec-2008 3.2% 1.8% 1.6%
Jan-2009 3.1% 1.9% 1.5%
Feb-2009 3.0% 1.9% 1.5%
Mar-2009 2.8% 1.8% 1.4%
Apr-2009 2.9% 2.0% 1.3%
May-2009 2.8% 1.6% 1.3%
Jun-2009 2.8% 1.6% 1.3%
Jul-2009 2.9% 1.7% 1.3%
Aug-2009 2.9% 1.6% 1.3%
Sep-2009 3.0% 1.6% 1.3%
Oct-2009 2.9% 1.5% 1.3%
Nov-2009 3.1% 1.4% 1.4%
Dec-2009 2.9% 1.5% 1.3%
Jan-2010 3.0% 1.4% 1.3%
Feb-2010 2.9% 1.4% 1.3%
Mar-2010 3.2% 1.4% 1.4%
Apr-2010 3.1% 1.3% 1.5%
May-2010 3.4% 1.3% 1.4%
Jun-2010 3.1% 1.5% 1.5%
Jul-2010 3.2% 1.6% 1.4%
Aug-2010 3.0% 1.4% 1.4%
Sep-2010 3.1% 1.4% 1.4%
Oct-2010 3.1% 1.3% 1.4%
Nov-2010 3.2% 1.4% 1.4%
Dec-2010 3.2% 1.4% 1.5%
Jan-2011 3.0% 1.3% 1.4%
Feb-2011 3.1% 1.3% 1.4%
Mar-2011 3.2% 1.3% 1.5%
Apr-2011 3.2% 1.3% 1.5%
May-2011 3.1% 1.3% 1.5%
Jun-2011 3.3% 1.4% 1.5%
Jul-2011 3.1% 1.3% 1.5%
Aug-2011 3.2% 1.3% 1.5%
Sep-2011 3.3% 1.3% 1.5%
Oct-2011 3.2% 1.3% 1.5%
Nov-2011 3.2% 1.3% 1.5%
Dec-2011 3.2% 1.3% 1.5%
Jan-2012 3.2% 1.2% 1.5%
Feb-2012 3.3% 1.3% 1.6%
Mar-2012 3.3% 1.2% 1.6%
Apr-2012 3.2% 1.4% 1.6%
May-2012 3.3% 1.4% 1.6%
Jun-2012 3.2% 1.3% 1.6%
Jul-2012 3.2% 1.2% 1.6%
Aug-2012 3.3% 1.4% 1.6%
Sep-2012 3.1% 1.3% 1.4%
Oct-2012 3.2% 1.3% 1.5%
Nov-2012 3.3% 1.3% 1.6%
Dec-2012 3.2% 1.2% 1.6%
Jan-2013 3.2% 1.2% 1.7%
Feb-2013 3.4% 1.2% 1.7%
Mar-2013 3.2% 1.3% 1.6%
Apr-2013 3.3% 1.3% 1.6%
May-2013 3.3% 1.3% 1.6%
Jun-2013 3.2% 1.2% 1.6%
Jul-2013 3.3% 1.2% 1.7%
Aug-2013 3.4% 1.2% 1.7%
Sep-2013 3.4% 1.3% 1.7%
Oct-2013 3.3% 1.1% 1.8%
Nov-2013 3.3% 1.1% 1.8%
Dec-2013 3.3% 1.2% 1.8%
Jan-2014 3.3% 1.2% 1.7%
Feb-2014 3.4% 1.2% 1.8%
Mar-2014 3.4% 1.2% 1.8%
Apr-2014 3.5% 1.2% 1.8%
May-2014 3.4% 1.2% 1.8%
Jun-2014 3.5% 1.2% 1.8%
Jul-2014 3.6% 1.2% 1.8%
Aug-2014 3.3% 1.1% 1.8%
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The data below can be saved or copied directly into Excel.

Note: Shaded areas denote recessions. The hires rate is the number of hires during the entire month as a percent of total employment. The layoff rate is the number of layoffs and discharges during the entire month as a percent of total employment. The quits rate is the number of quits during the entire month as a percent of total employment.

Source: EPI analysis of Bureau of Labor Statistics Job Openings and Labor Turnover Survey

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How Do U.S. Retirees Compare with Those in Other Countries?

In Monday’s Wall Street Journal, Andrew Biggs and Sylvester Schieber cited these statistics from the Organisation for Economic Co-operation and Development (OECD):

“Despite a supposedly stingy Social Security program and ineffective retirement-savings vehicles, the average U.S. retiree has an income equal to 92% of the average American income, handily outpacing the Scandinavian countries (81%), Germany (85%), Belgium (77%) and many others.”

Meanwhile, in its Global AgeWatch Index released Tuesday, HelpAge International ranked the United States #8 among the best countries to grow old in, ahead of France (#18) but trailing Norway, Sweden, Switzerland, Canada, Germany, Netherlands, and Iceland (#1-7). Afghanistan (#96) was in last place.

It’s not hard to imagine how wealthy countries like Norway and the United States outrank poor and war-torn countries like Afghanistan. But the relative ranking of the wealthy countries comes as a surprise. How did the United States and other English-speaking countries like the United Kingdom and Australia, not known for their generous social insurance programs or employee benefits, come close to the Nordic cradle-to-grave welfare states and handily beat out France, with its famously generous pensions and high-quality affordable healthcare? Are older Americans really living in a retiree paradise?

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Strong Jobs Numbers for Teachers in September, but Large Jobs Gap Remains

In September, public-sector employment increased by 12,000, with the majority of that growth coming from local government education—an increase of 6,700 jobs. Local government education is largely jobs in public K-12 education (the majority of which are teachers, but also teacher aides, librarians, guidance counselors, administrators, support staff, etc.).

While this is clearly a positive sign, unfortunately, the number of teachers and related education staffers fell dramatically in the recession and has failed to get anywhere near its pre-recession level, let alone the level that would be required to keep up with the expanding student population. The figure below breaks down the teacher gap. The dark blue line illustrates the level of teacher employment. While the most recent positive trend is obvious, the longer term losses are also readily apparently.

Along with dismal trends in public sector employment in general, about a quarter million public education jobs were lost in the great recession and its aftermath. If we add to that the number of public education jobs that should have been added simply to keep up with growing enrollment, then we are currently experiencing a 377,000 job shortfall in local public education. The costs of a significant teacher gap are measurable: larger class sizes, fewer teacher aides, fewer extracurricular activities, and changes to the curriculum.

The Unemployment Rate Fails to Take into Account Missing Workers

Let’s put the pieces of the puzzle together. The unemployment rate fell in September by 0.2 percent points, from 6.1 to 5.9 percent. There was also a decrease in the sheer number of unemployed people—down 329,000 from August. On its face, this sounds like good news.

At the same time, the employment-to-population ratio has remained 59.0 percent for four months running. If the unemployment rate dropped and the employment-to-population ratio remained the same, the missing part of the puzzle is the labor force participation rate. In September, the labor force participation rate dropped to 62.7 percent. The last time the labor force participation rate was this low was February 1978. And, the biggest drop in labor force participation was among prime-age workers, 25-54 years old.

Over the last year, the labor force participation rate fell 0.5 percentage points. Therefore, it’s not surprising that missing workers—potential workers who are neither working nor actively seeking work due to the weak labor market—are at an all-time-high of 6.3 million. The vast majority of them (3.4 million) are 25 to 54 years old.

To put the official unemployment rate in perspective, the figure below shows the actual unemployment rate and the unemployment rate if the missing workers were in the labor force looking for work and thus counted as unemployed. The unemployment rate including the missing workers sits at 9.6 percent, the same rate for the last four months. Perhaps, this is a better indication of the slack in the labor market and the reason why wage growth has remained so sluggish even with a falling unemployment rate.

Missing Workers

The unemployment rate is vastly understating weakness in today’s labor market: Unemployment rate, actual and if missing workers* were looking for work, January 2006–November 2014

Date Actual If missing workers were looking for work
2006-01-01 4.7% 5.0%
2006-02-01 4.8% 4.8%
2006-03-01 4.7% 4.8%
2006-04-01 4.7% 4.9%
2006-05-01 4.6% 4.8%
2006-06-01 4.6% 4.7%
2006-07-01 4.7% 4.8%
2006-08-01 4.7% 4.6%
2006-09-01 4.5% 4.6%
2006-10-01 4.4% 4.4%
2006-11-01 4.5% 4.4%
2006-12-01 4.4% 4.1%
2007-01-01 4.6% 4.4%
2007-02-01 4.5% 4.4%
2007-03-01 4.4% 4.3%
2007-04-01 4.5% 4.9%
2007-05-01 4.4% 4.8%
2007-06-01 4.6% 4.8%
2007-07-01 4.7% 4.9%
2007-08-01 4.6% 5.1%
2007-09-01 4.7% 4.9%
2007-10-01 4.7% 5.2%
2007-11-01 4.7% 4.9%
2007-12-01 5.0% 5.1%
2008-01-01 5.0% 4.8%
2008-02-01 4.9% 5.0%
2008-03-01 5.1% 5.1%
2008-04-01 5.0% 5.2%
2008-05-01 5.4% 5.4%
2008-06-01 5.6% 5.6%
2008-07-01 5.8% 5.7%
2008-08-01 6.1% 6.0%
2008-09-01 6.1% 6.3%
2008-10-01 6.5% 6.5%
2008-11-01 6.8% 7.1%
2008-12-01 7.3% 7.5%
2009-01-01 7.8% 8.2%
2009-02-01 8.3% 8.7%
2009-03-01 8.7% 9.3%
2009-04-01 9.0% 9.4%
2009-05-01 9.4% 9.7%
2009-06-01 9.5% 9.9%
2009-07-01 9.5% 10.1%
2009-08-01 9.6% 10.4%
2009-09-01 9.8% 10.9%
2009-10-01 10.0% 11.3%
2009-11-01 9.9% 11.2%
2009-12-01 9.9% 11.7%
2010-01-01 9.7% 11.3%
2010-02-01 9.8% 11.4%
2010-03-01 9.9% 11.3%
2010-04-01 9.9% 11.0%
2010-05-01 9.6% 11.1%
2010-06-01 9.4% 11.1%
2010-07-01 9.5% 11.3%
2010-08-01 9.5% 11.1%
2010-09-01 9.5% 11.3%
2010-10-01 9.5% 11.5%
2010-11-01 9.8% 11.7%
2010-12-01 9.4% 11.6%
2011-01-01 9.1% 11.4%
2011-02-01 9.0% 11.4%
2011-03-01 9.0% 11.3%
2011-04-01 9.1% 11.4%
2011-05-01 9.0% 11.4%
2011-06-01 9.1% 11.5%
2011-07-11 9.0% 11.7%
2011-08-20 9.0% 11.4%
2011-09-01 9.0% 11.3%
2011-10-11 8.8% 11.2%
2011-11-20 8.6% 11.0%
2011-12-30 8.5% 11.0%
2012-01-12 8.2% 10.8%
2012-02-12 8.3% 10.7%
2012-03-12 8.2% 10.7%
2012-04-12 8.2% 10.9%
2012-05-12 8.2% 10.6%
2012-06-12 8.2% 10.5%
2012-07-12 8.2% 10.8%
2012-08-12 8.1% 10.8%
2012-09-12 7.8% 10.4%
2012-10-12 7.8% 10.0%
2012-11-12 7.8% 10.3%
2012-12-12 7.9% 10.3%
2013-01-12 7.9% 10.4%
2013-02-12 7.7% 10.5%
2013-03-12 7.5% 10.6%
2013-04-12 7.5% 10.5%
2013-05-12 7.5% 10.3%
2013-06-12 7.5% 10.3%
2013-07-12 7.3% 10.2%
2013-08-12 7.2% 10.3%
2013-09-12 7.2% 10.3%
2013-10-12 7.2% 10.7%
2013-11-12 7.0% 10.3%
2013-12-12 6.7% 10.2%
2014-01-12 6.6% 10.0%
2014-02-12 6.7% 10.0
2014-03-12 6.7% 9.8%
2014-04-12 6.3% 9.9%
2014-05-12 6.3% 9.7%
2014-06-12 6.1% 9.6%
2014-07-12 6.2% 9.6%
2014-08-12 6.1% 9.6%
2014-09-12 5.9% 9.6%
2014-10-12 5.8% 9.1%
2014-11-12 5.8% 9.2%


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* Potential workers who, due to weak job opportunities, are neither employed nor actively seeking work

Source: EPI analysis of Mitra Toossi, “Labor Force Projections to 2016: More Workers in Their Golden Years,” Bureau of Labor Statistics Monthly Labor Review, November 2007; and Current Population Survey public data series

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Wage Growth Continues to be Sluggish

While the BLS reported positive overall jobs numbers for September, one notable downside of this morning’s release is that wages grew not at all in the last month. Average hourly earnings of all employees on nonfarm payrolls were little changed (a one cent decline) and average hourly earnings of production and nonsupervisory employees on private nonfarm payrolls saw zero growth. That said, I’d caution reading too much into one month’s numbers because monthly changes can be volatile and longer term trends are more indicative of the overall health of the economy. The fact is that wage growth for both series has been hovering just above 2 percent over the last year.

As shown in the figure below, wage growth is far below the 3.5 percent rate consistent with the Federal Reserve Board’s inflation target of 2 percent. It’s clear that Fed policymakers should abandon notions of slowing the economy. (For a longer analysis of what to watch for in upcoming months on wage growth, see this explainer.)

Walton Family Net Worth is a Case Study Why Growing Wealth Concentration Isn’t Just an Academic Worry

Earlier this year, economist Thomas Piketty caused a stir with a book arguing that the future in advanced economies could see a relentless concentration of wealth among a small sliver of families, whose fortunes would increasingly dwarf those of the typical citizen. The last couple of weeks have seen the release of a couple of key barometers of wealth inequality in America, and combining them, it’s easy to see that this hypothesis of ever-concentrating wealth seems likely indeed. In the past month, the Federal Reserve released its triennial Survey of Consumer Finance (SCF) for 2013, while Forbes magazine released their annual list of the 400 wealthiest Americans.

The SFC is the most comprehensive and high-quality measure of Americans’ wealth up and down the distribution. It makes a special effort to sample very high wealth American households, but actually explicitly excludes listed members of the Forbes 400 (for reasons of confidentiality). The Forbes 400, as is well known, puts a dollar value on the net worth of the 400 wealthiest Americans. There is plenty of material in these releases to assess the current state of wealth inequality in America.

Take one example, that we’ve calculated before: comparing the family wealth of six of the wealthiest members of the Walton family (reported at just under $145 billion in 2013) with the number of American families that you could add together and still have their net worth come in less than the 6 Walton heirs: 52.5 million, or 42.9 percent of American families.

Some have objected to this statistic on the grounds that the negative net worth families (11.5 percent of all American families) somehow shouldn’t count in this calculation. So, try another statistic: how many families that held the median wealth would you need to add together to equal the holdings of the six Walton heirs: more than 1.7 million. The median wealthholder in the United States, remember, has more wealth than half of all American families and less wealth than half (around $81,200 in 2013).

So, what this statistic means is that you’d essentially need a large city’s worth of these typical American families to equal the wealth of the six Walton heirs. And this number has grown steadily over time, as the figure below shows. The falling wealth of the median family (driven largely by the housing bubble burst) and the steadily rising wealth at the very top—including the Walton heirs—have combined to make the gap between them larger and larger over time.



Myths and Facts about Corporate Taxes, Part 1: Do American Corporations Pay the Highest Taxes in the World?

It’s become conventional wisdom that American corporate tax rates are the highest in the developed world, leaving American businesses at a competitive disadvantage—and that the only solution is fundamental tax reform (a phrase used by both Republicans and Democrats). Just yesterday, the Washington Post reported offhandedly that U.S. businesses “currently labor under the highest corporate tax rate in the developed world.” The fact is there are a lot of myths about the corporate tax code—myths that are repeated by corporations that stand to benefit from them. So, let’s look at the facts.

Myth: American corporations pay more in taxes than their competitors in any other country.

Fact: Any claim that the United States has the highest corporate tax rate in the world should be accompanied by a clarification that the rate American companies actually pay, on average, is comparable to what their foreign competitors pay.

Yes, the tax rates on the books (the “statutory” rates) in the United States are high relative to our international peers, but the U.S. corporate tax code has become so riddled with loopholes—and American corporations so adept at exploiting them—that the total amount of taxes actually paid by U.S. corporations (the “effective” corporate tax rate) is far less.

The Government Accountability Office found that large, profitable American corporations pay an effective rate of less than 13 percent in U.S. federal taxes; when state and foreign taxes are included, the rate only increases to 17 percent—a far cry from the statutory 39 percent. Meanwhile, the Congressional Research Service found that the effective rate here is nearly identical to the weighted average of corporate taxes in the world’s other most developed economies. (EPI’s Thomas Hungerford found the same thing.)

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What to Watch on Jobs Day: Nominal Wages, Teacher Gap, and Upward Revisions

Tomorrow, the Bureau of Labor Statistics will release the September numbers on employment, unemployment, and nominal wages. While the report contains a host of data, there are three particular numbers I’m going to be watching closely.

First, the overall employment numbers from the payroll survey were lower than expected last month. Consensus estimates had projected job growth of about 230,000, but they came in at only 142,000. The consensus so far for September is again in the low 200,000s. So, two key things to watch: whether there are any upward revisions to the August employment numbers and whether the September numbers come in below consensus two months in a row. Last month, I suggested that slow job growth should make those arguing that policymakers need to worry about an overheating economy and inflationary pressures reconsider. Tomorrow, we will get some more information that can inform the question of whether we are at a new lower trend, which I hope not, or whether last month was a blip in a jobs picture that has otherwise been consistent for much of this year.

Second, with kids heading back to the classroom, it’s worth re-examining the teacher gap—the gap between actual local public education employment and what is needed to keep up with growth in the student population. During the recession, thousands of local public education jobs were lost, and those losses continued deep into the official economic recovery (as did public sector jobs in general). The costs of a significant teacher gap are measurable: larger class sizes, fewer teacher aides, fewer extracurricular activities, and changes to the curriculum. And, in sheer numbers, the teacher gap can explain a non-trivial part of the overall jobs gap. On Friday, I will compare where jobs in public education should be, using the precession ratio, student population growth, and the most recent jobs numbers.

Third, I’ll continue to track nominal wages. Last month, Josh Bivens and I explained how very far we are from the kind of wage growth that would suggest that the Federal Reserve can put the brakes on the economy. On Friday, we will put the latest nominal wage trends in perspective, both historically and against target level wage growth. These numbers on nominal wage growth are likely to be the single most important indicator in coming months driving Federal Reserve decisions.

What’s Up (or Down) With the Boomers’ Retirement Savings?!

The recent release of the Federal Reserve’s triennial Survey of Consumer Finances has many retirement researchers scratching their heads. As expected, GenXers’ savings (shaded blue lines in Figure 1) benefited from the rebound in stock prices and the economic recovery. Meanwhile, Silent Generation retirees (dashed red and yellow lines) saw a surprisingly large bounce in retirement savings. But Baby Boomers (solid purple, black and green lines) who were approaching retirement when the housing bubble burst saw weak gains or even losses between 2010 and 2013. Those who were born between 1949 and 1954, for example, saw a decline in mean retirement account savings from $176,000 in 2010 to $167,000 in 2013 (values are in 2013 dollars rounded to the nearest $1,000). This is far below the $199,000 their predecessors—older Boomers born between 1943 and 1948—had accumulated at the same age in 2007.

It’s not news that the Boomers’ retirement savings took a hit during the downturn. What’s more surprising is that they have fared so poorly in the recovery compared to younger workers and retirees. One explanation is simply that the Boomers, unlike older retirees, were hit by both the stock and labor market downturns and didn’t benefit as much from the subsequent rebound in stock prices as younger workers who were heavily invested in stocks through target date funds.

Figure 1

Mean retirement account balances by birth cohort , 1989–2013

1931–1936 1937–1942 1943–1948   1949–1954  1955–1960  1961–1966 1967–1972 1973–1978
1989 $56,383 $44,937 $30,369 $27,428 $7,419
1992 $68,060 $75,678 $44,749 $22,920 $12,552 $7,176
1995 $64,254 $90,781 $75,262 $50,090 $22,642 $16,480
1998 $113,886 $114,908 $91,373 $64,166 $48,084 $29,458 $11,059
2001 $124,634 $162,156 $155,809 $91,203 $75,722 $40,706 $18,841
2004 $111,834 $149,322 $158,827 $128,848 $81,818 $54,205 $27,100 $11,640
2007 $109,709 $164,654 $199,218 $161,187 $114,482 $69,036 $41,928 $18,436
2010 $80,091 $138,102 $209,317 $175,697 $138,713 $85,454 $48,472 $25,864
2013 $88,944 $168,828 $214,277 $166,597 $154,630 $103,838 $75,433 $46,593
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TIP:  For apples-to-apples comparisons, look at how successive 6-year birth cohorts fared at 6-year intervals (2013, 2007, and 2001), ignoring intervening surveys. 

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LA Hotel Workers Win $15.37 Minimum Wage: a New Day for Labor in the United States?

The Los Angeles City Council’s vote to raise the minimum wage for hotel workers is another herald of big changes coming in the way the United States deals with low wages and inequality. The Council voted 12 to 3 to raise the minimum wage for workers at large hotels to $15.37 an hour by 2017, which is more than the national median wage for women ($15.10 in 2013). Mayor Eric Garcetti will sign the bill after it receives a confirming second vote next week.

The LA County AFL-CIO, UNITE HERE Local 11 (the LA area union of hospitality workers), and the Los Angeles Alliance for a New Economy, which led the campaign, don’t intend to rest on their laurels and will push for an across-the-board minimum wage increase to $13.25 an hour, far above the national minimum wage of $7.25 an hour. Mayor Garcetti strongly supports that bill, too.

As in Seattle, where a union-led coalition won a $15 minimum wage, the people of Los Angeles realize that many businesses will not share revenues fairly with their workers unless they are required to do so. Even businesses that want to pay their employees a living wage feel constrained by their competitors: How can they compete with a competitor paying its workers $5.00 an hour less? The only way to break through these constraints is to reset labor standards to a level that provides a decent living. As Franklin Roosevelt said when he first sent minimum wage legislation to Congress in 1933: “No business which depends for existence on paying less than living wages to its workers has any right to continue in this country… By living wages, I mean more than a bare subsistence level. I mean the wages of decent living.”

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Now It’s Explicit: Fighting Inflation Is a War to Ensure That Real Wages for the Vast Majority Never Grow

Remember that episode of The West Wing when Josh Lyman announced a secret plan to fight inflation? That was great. Turns out that Dallas Federal Reserve Bank President Richard Fisher has a secret paper telling us how to fight inflation: stop progress in reducing unemployment so that nominal wages never grow fast enough to actually boost living standards (or, never grow fast enough to boost real wages).

Last week, Fisher argued that a so-far unpublished (i.e. secret) paper by his staff showed that “declines in the unemployment rate below 6.1 percent exert significantly higher wage pressures than if the rate is above 6.1 percent.”

In the interview, Fisher mostly characterized this as a Phillips curve that is flat at unemployment rates higher than 6.1 percent, but which starts to have a negative slope below this rate, meaning that future declines in unemployment should be associated with higher rates of wage-growth. However, if you’re really thinking in terms of a stable Phillips Curve, this means that we can simply choose what unemployment/wage-inflation combination we’d like without worrying about accelerating inflation. Currently, nominal wage-growth is running around 2-2.5 percent. But as we’ve shown before, even the Fed’s too-conservative 2 percent inflation target is consistent with nominal wage growth of closer to 4 percent. So we have plenty of room to move “up” Fisher’s Phillips Curve before hitting even conservative inflation targets.

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2013 ACS Shows Depth of Native American Poverty and Different Degrees of Economic Well-Being for Asian Ethnic Groups

Thursday’s release of 2013 American Community Survey (ACS) data allows us to fill in the blanks for minority populations that were not covered in Tuesday’s Census Bureau report on income, poverty, and health insurance coverage in 2013. ACS is an annual nationwide survey that provides detailed demographic, social, and economic data for smaller populations like Native Americans and the thirteen distinct ethnic groups that make up the Asian population.

Together with the 2013 Income, Poverty, and Health Insurance Coverage report, the 2013 ACS data provide a more complete picture of the economic status of America’s various racial and ethnic groups. This information helps to address the sense of “invisibility” felt by many of these groups, provides critical information for the states and local communities where these populations are concentrated and expands the scope for evaluating the impact of national policies.

Between 2012 and 2013, the real median household income for Native Americans increased 2.3 percent to $36,641. This was 70 percent of the national average in 2013 and $3,066 (-7.7 percent) lower than the group’s 2007 pre-recession level.

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ACS Data Show Almost No Improvement in State Poverty Rates

The American Community Survey (ACS) poverty data that were released by the Census Bureau earlier today showed that poverty rates were essentially unchanged from 2012 to 2013 in virtually every state.1  Only six states had significant changes in their poverty rates: Colorado (-0.7 percentage points), New Hampshire (-1.3 percentage points), New Jersey (+0.6 percentage points), New Mexico (+1.1 percentage points), Texas (-0.4 percentage points), and Wyoming (-1.7 percentage points). All other states had no significant change from their 2012 poverty rates.

The increases in poverty in New Jersey and New Mexico are the most troubling, although the lack of any significant decrease in most other states is also deeply frustrating. As shown in the figure below, North Dakota is the only state where the poverty rate has fallen back down to pre-recession levels. In every other state nationwide, poverty rates remain significantly above their 2007 levels.

The failure to see any significant reduction in poverty over the last several years is a direct consequence of the continued weakness in the labor market. (It’s not surprising that poverty has fallen in North Dakota given that the state’s unemployment rate has averaged 3.3 percent from the start of the recession to today.) At the same time, however, policymakers have directly stymied poverty reduction by cutting back on unemployment insurance. If we want to start bringing poverty rates down, we need to restore the labor market back to full health, lift wages, and start sharing economic growth more broadly.

1. The ACS data also showed no significant change in the national poverty rate. This differs from the official national poverty rate generated from the Current Population Survey (CPS) that was released earlier this week, which did show a significant decrease in the share of families in poverty. This discrepancy is due to differences in the way the two surveys treat household members not related to the head of household, and the fact that the ACS data reflect a slightly different timeframe than the CPS. See here for further explanation.

Across the States, Some Modest Improvements, But Incomes are Still Below Where They Were at the Start of the Millenium

This morning the Census Bureau released its annual report on income and poverty within states, with data from the American Community Survey (ACS). This report follows the release earlier this week of national income and poverty statistics. Not surprisingly, the state report tells much the same story as the national data: for the typical U.S. family, incomes in most states were largely unchanged from where they were the year before—and still well below their levels from over a decade ago.

Between 2012 and 2013, median household income rose significantly in 14 states, while the remaining 36 states, plus the District of Columbia, had no significant change. The table below shows the states that had statistically significant year-over-year increases in median household income. The ACS data, which reflect a slightly different time period than the national income data gathered from the Current Population Survey, also showed a small, but significant increase in median income for the nation as a whole.

Table 1

States with significant year-over-year changes in median household income, 2013 to 2012

State 2012 2013 Change
United States $51,915 $52,250 0.6%
Alaska $68,577 $72,237 5.3%
California $59,184 $60,190 1.7%
Colorado $57,430 $58,823 2.4%
Florida $45,578 $46,036 1.0%
Kentucky $42,230 $43,399 2.8%
Michigan $47,447 $48,273 1.7%
Minnesota $59,747 $60,702 1.6%
Missouri $45,919 $46,931 2.2%
Ohio $47,454 $48,081 1.3%
Oklahoma $44,903 $45,690 1.8%
Tennessee $43,504 $44,297 1.8%
Texas $51,198 $51,704 1.0%
Utah $57,841 $59,770 3.3%
Wyoming $55,569 $58,752 5.7%

Source: Adapted from Noss, Amanda. 2014. Household Income: 2013. U.S. Census Bureau.

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While these modest improvements are welcome, the reality is that household incomes have yet to recover from the recession virtually anywhere. Only four states (Alaska, Wyoming, North Dakota, and South Dakota), plus the District of Columbia have regained their levels from 2007, and most states has not seen income growth in over a decade. The figure below shows median household incomes, by state, in 2000 and 2013. The overlapping bars show that in the vast majority of states, median incomes are still well below where at the start of the millennium. Only Maryland, Alaska, Wyoming, North Dakota, Iowa, South Dakota, Montana, Louisiana, West Virginia, and the District of Columbia have managed to regain or surpass their median income levels from 2000.

As my colleagues Larry Mishel and Josh Bivens explain, “to get household incomes rising, we need to get real wages of the typical worker to rise, something we haven’t seen for more than a decade.” The policies that can help do this are not solely the province of federal lawmakers. A case in point: the only states that saw any wage growth over the past year where those that raised their state minimum wages.

The Fed’s Interest Rate Decisions, Census Data on Income and Poverty… and Occupy Wall Street

It’s been a busy week already for people who think about the economy. On Tuesday, the Census Bureau released its estimates of household income, poverty, and health insurance coverage for 2013. And on Wednesday, the Federal Reserve released its statement on monetary policy, projections of economic growth, and activity for the next year, and Federal Reserve Chair Janet Yellen held a press conference. Wednesday also marked the informal three-year anniversary of Occupy Wall Street (OWS). To incorrectly paraphrase Neil DeGrasse Tyson: it’s all connected, man.

First, the Fed. The debate swirling around the Fed these days is how soon they should start raising short-term interest rates to slow economic growth and forestall excessively high wage and price inflation. The answer to this should be simple: not soon at all. Wage and price inflation remain extraordinarily low, with no evidence that they’re accelerating. In fact, wage growth could effectively double from its current pace before really becoming inconsistent with even the Fed’s too-conservative 2 percent overall price inflation target.

So if this is what the evidence says, why is there a growing chorus arguing for the Fed to tighten?

Here’s where Occupy Wall Street comes in. Tightening now would keep unemployment higher than it would be under genuinely full employment, and stopping job growth short of full employment is a powerful tool to shift bargaining power away from low- and middle-wage workers and keep them from realizing inflation-adjusted wage increases. This tolerance of sub-full employment is a big reason why inflation-adjusted wages for the vast majority have failed to rise at all  for most of the time since 1979, and have certainly not risen anywhere near the pace of overall productivity growth. This wasn’t always the case, but starting in the late 1970s a number of policy decisions made on behalf of corporate managers and owners of capital helped tilt the playing field away from low- and middle-wage workers, and this has been a prime source of the rise in income inequality since. A key part of this inequality by design was having macroeconomic policymakers—particularly the Fed—slow the economy down before full employment could spur across-the-board wage growth. The one time the Fed did not slow the economy “in time”—the late 1990s—led to the first across-the-board wage growth in a generation. If OWS had a grand organizing theme, it was certainly along the lines of the idea that economic policy has helped generate the rising inequality we’ve seen over the past generation. They’re right, and macroeconomic policy that has privileged very low rates of inflation over very low rates of unemployment is part of how policy did it.

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Poverty Reduction Stalled by Policy, Once Again: Unemployment Insurance Edition

As EPI’s Elise Gould pointed out back in January, a key barrier to translating overall economic growth in recent decades into rapid poverty reduction has been the rise in income inequality. Were economic growth more broadly shared, the poverty rate would be much lower. Here we make the case that this rise in inequality has large policy fingerprints all over it. Today’s data on income and poverty from the Census Bureau shows how a recent policy choice—specifically cutting back on unemployment insurance (UI) in recent years—has stalled poverty reduction.

Unemployment insurance is a key plank of the American social insurance system. During the ferocious period of job loss and historically high unemployment during and immediately after the Great Recession, policymakers responded by significantly expanding the duration of benefits, and the American Recovery and Reinvestment Act (ARRA) included boosts to the generosity of benefits as well. The result was that in 2009, UI benefits kept 3.3 million people out of poverty.

However, since 2010, this poverty-fighting impact has eroded, and the share of unemployed workers receiving UI benefits has fallen: Both of these trends are shown in the figure below. This is due to both the extended duration of unemployment for some workers outstripping the UI eligibility period as well as intentional policy changes that reduced UI recipiency. The federal government reduced total weeks available in 2012 and then all long-term benefits (those lasting longer than 27 weeks) were cut off at the end of 2013. (The impact of the long-term benefits cut won’t be seen until next year’s poverty figures are released.) Further, several states have also restricted eligibility. The result is that by 2013 only 1.2 million Americans were kept out of poverty by UI benefits.


Unemployment insurance (UI) recipiency rate* and the number of persons UI lifted out of poverty, 1987–2014


UI recipiency rate* (right axis) Persons lifted above poverty (left axis)
1987 31.2% 0.684
1988 31.8% 0.518
1989 34.0% 0.481
1990 36.5% 0.668
1991 41.2% 1.006
1992 51.3% 1.468
1993 47.7% 1.208
1994 37.2% 0.905
1995 36.3% 0.716
1996 36.8% 0.633
1997 35.4% 0.601
1998 36.7% 0.572
1999 38.1% 0.602
2000 38.0% 0.563
2001 44.3% 0.726
2002 53.1% 1.177
2003 50.3% 1.257
2004 38.1% 0.7
2005 35.9% 0.656
2006 36.0% 0.573
2007 36.7% 0.488
2008 43.7% 0.905
2009 64.3% 3.322
2010 66.5% 3.21
2011 56.4% 2.306
2012 48.5% 1.7
2013 40.8% 1.2
2014** 28.9%
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* Recipiency rate is defined as the number of people receiving any form of unemployment insurance (regular program and extended benefits) as a share of the total number of unemployed.

** 2014 UI recipiency rate value is based off of January–August data.

Source: EPI analysis of Current Population Survey basic monthly microdata; U.S. Department of Labor, "Persons Claiming UI Benefits in State and Federal UI Programs [Excel spreadsheet],” updated August 2014; Thomas Gabe and Julie M. Whittaker, Antipoverty Effects of Unemployment Insurance, Congressional Research Service, October 16, 2012; and Carmen DeNavas-Walt and Bernadette D. Proctor, "Income and Poverty in the United States: 2013," U.S. Census Bureau Current Population Reports, September 2014.

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Real Median Household Incomes for all Racial Groups Remain Well Below Their 2007 Levels

Today’s Census Bureau report on income, poverty and health insurance coverage in 2013 shows that real median household income increased more among Latino (+$1,391) and African American (+$793) households than white households (+$433), but declined for Asian households (-$2,568). Between 2012 and 2013, the black-white income gap has narrowed from 58.4 cents for every dollar of white median household income to 59.4 cents for every dollar of white median household income. The Hispanic-white income gap has also narrowed from 68.4 to 70.3 cents on the dollar. This is fairly consistent with the modest labor market gains made by African Americans and Latinos in 2013. According to the Bureau of Labor Statistics, between 2012 and 2013, the share of employed adults increased for each of these populations while the share for whites remained unchanged. Despite these relative improvements, real median household incomes for all groups remain well below their 2007 levels. Between 2007 and 2013, median household incomes declined by 9.2 percent (-$3,506) for African Americans, 5.7 percent (-$2,492) for Latinos, 5.6 percent (-$3,432) for whites and 9.7 percent (-$7,201) for Asians. Asian households continue to have the highest median income in spite of large income losses in the wake of the recession.

Figure A

Real median household income, by race and ethnicity, 1972–2013

Year White Black Hispanic Asian
Jan-1972 $51,380 $29,569 $38,229
Jan-1973 $52,084 $30,391 $38,165
Jan-1974 $50,314 $29,669 $37,942
Jan-1975 $48,945 $29,163 $34,899
Jan-1976 $50,477 $29,415 $35,621
Jan-1977 $50,965 $29,490 $37,281
Jan-1978 $52,282 $30,838 $38,676
Jan-1979 $52,338 $30,302 $39,001
Jan-1980 $51,180 $28,972 $36,743
Jan-1981 $50,243 $27,793 $37,602
Jan-1982 $49,764 $27,739 $35,179
Jan-1983 (NA) $27,628 $35,357
Jan-1984 $51,546 $28,767 $36,286
Jan-1985 $52,581 $30,595 $36,058
Jan-1986 $54,286 $30,580 $37,215
Jan-1987 $55,342 $30,742 $37,929
Jan-1988 $55,958 $31,044 $38,522
Jan-1989 $56,339 $32,801 $39,762
Jan-1990 $55,194 $32,268 $38,581
Jan-1991 $53,914 $31,369 $37,848
Jan-1992 $54,154 $30,509 $36,759
Jan-1993 $54,249 $31,008 $36,331
Jan-1994 $54,596 $32,682 $36,403
Jan-1995 $56,427 $33,987 $34,696
Jan-1996 $57,342 $34,716 $36,821
Jan-1997 $58,720 $36,250 $38,534
Jan-1998 $60,569 $36,181 $40,433
Jan-1999 $61,733 $39,019 $42,984
Jan-2000 $61,715 $40,131 $44,867
Jan-2001 $60,927 $38,776 $44,164
Jan-2002 $60,729 $37,584 $42,863 $68,143
Jan-2003 $60,513 $37,547 $41,793 $70,547
Jan-2004 $60,318 $37,114 $42,264 $70,916
Jan-2005 $60,597 $36,821 $42,917 $72,899
Jan-2006 $60,567 $36,936 $43,650 $74,218
Jan-2007 $61,702 $38,104 $43,455 $74,266
Jan-2008 $60,079 $37,021 $41,018 $71,013
Jan-2009 $59,146 $35,387 $41,312 $71,101
Jan-2010 $58,185 $34,321 $40,205 $68,654
Jan-2011 $57,392 $33,380 $40,004 $67,456
Jan-2012 $57,837 $33,805 $39,572 $69,633
Jan-2013 $58,270 $34,598 $40,963 $67,065
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Note: White refers to non-Hispanic whites, black refers to blacks alone, Asian refers to Asians alone, and Hispanic refers to Hispanics of any race. Comparable data are not available prior to 2002 for Asians. Data for non-Hispanic whites are unavailable for the year 1983. Shaded areas denote recessions.

Source: EPI analysis of Current Population Survey Annual Social and Economic Supplement Historical Poverty Tables (Table H-5 and H-9)

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By the Numbers: Income and Poverty, 2013

Key numbers from today’s new Census report, Income and Poverty in the United States: 2013. All dollar values are adjusted for inflation (2013 dollars).


  • -$7,337  (-11.2%)
     The decline in median non-elderly household income from 2000 to 2013 in level terms and percentage terms, respectively
  • $52,419 vs. $50,033
    Median earnings for a man working full time, full year in 1973 and 2013, respectively
  • $29,687 vs. $39,157
    Median earnings for a woman working full time, full year in 1973 and 2013, respectively
  • -6.8% vs. -0.5%
    The decline since 2000 in median earnings for full time, full year workers age 25 or older with a college degree, men and women, respectively
  • 0.5% ($1,542)
    Income gains for the top 5 percent over 2009–2013 (this was the only income group to experience gains)
  • -$3,445  (-5.6%)
     The decline in median white, non-Hispanic household income from 2000 to 2013, in level terms and percentage terms, respectively
  • -$5,533  (-13.8%)
     The decline in median African American household income from 2000 to 2013, in level terms and percentage terms, respectively
  • -$3,904  (-8.7%)
     The decline in median Hispanic household income from 2000 to 2013, in level terms and percentage terms, respectively

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The Generation-Long Trend Towards Ever-Greater Income Inequality Continues

Today’s release of data on family income from the Census Bureau reinforces the fact that the generation-long trend towards ever-greater income inequality seems to be firmly underway again, after only the briefest interruption caused by the Great Recession.

Several economic commentators noted the decline in income inequality (mostly driven by steep but temporary falls in income at the very top of the distribution) that accompanied the aftermath of both the early 2000s recession and the Great Recession, some even going so far as to suggest that the recessions had somehow solved the problem of rising income inequality. Yet the evidence is clear that this isn’t the case—recessions seem to only suspend the growth of inequality temporarily. This, of course, should not be a shock—declines at the top of the income distribution are driven largely by stock market movements, and the steep stock market declines of the early 2000s and 2008 bottomed out quickly, and stock prices rose relatively quickly thereafter.

Figure 1 below shows the long-run rise in family income inequality. It tracks growth in average family income by various income groupings since 1947. A key feature of this figure is the extraordinarily tight distribution of income growth from 1947 to 1979 (all lines move upward in a tight bunch), and the rapid pulling apart of income growth thereafter (the lines start pulling apart from each other).

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Modest Income Growth in 2013 Puts Slight Dent in More than a Decade of Income Losses

Wage trends greatly determine how fast incomes at the middle and bottom grow, as well as the overall path of income inequality, as we argued in Raising America’s Pay. This is for the simple reason that most households, including those with low incomes, rely on labor earnings for the vast majority of their income. That is why my initial look at the data from the newly released Census Bureau report on income and, poverty in 2013 will look at wages and the incomes of working age households.

The Census data show that from 2012 to 2013, median household income for non-elderly households (those with a head of household younger than 65 years old) increased 0.4 percent from $58,186 to $58,448. However, that modest growth barely begins to offset the losses incurred during the Great Recession or the losses that prevailed in the prior business cycle from 2000 to 2007. Between 2007 and 2013, median household income for non-elderly households dropped from $63,527 to $58,448, a decline of $5,079, or 8.0 percent. Furthermore, the disappointing trends of the Great Recession and its aftermath come on the heels of the weak labor market from 2000-2007, where the median income of non-elderly households fell significantly, from $65,785 to $63,527, the first time in the post-war period that incomes failed to grow over a business cycle. Altogether, from 2000 to 2013, median income for non-elderly households fell from $65,785 to $58,448, a decline of $7,337, or 11.2 percent.


Real median household income, all and non-elderly, 1979–2013

All households Non-elderly households
Jan-1979 $49,225
Jan-1980 $47,668
Jan-1981 $46,876
Jan-1982 $46,752
Jan-1983 $46,425
Jan-1984 $47,867
Jan-1985 $48,761
Jan-1986 $50,487
Jan-1987 $51,121
Jan-1988 $51,514
Jan-1989 $52,432
Jan-1990 $51,735
Jan-1991 $50,249
Jan-1992 $49,836
Jan-1993 $49,594
Jan-1994 $50,147 $57,893
Jan-1995 $51,719 $59,417
Jan-1996 $52,472 $60,527
Jan-1997 $53,551 $61,307
Jan-1998 $55,497 $63,792
Jan-1999 $56,895 $65,435
Jan-2000 $56,801 $65,785
Jan-2001 $55,562 $64,772
Jan-2002 $54,914 $64,108
Jan-2003 $54,865 $63,545
Jan-2004 $54,674 $62,801
Jan-2005 $55,278 $62,391
Jan-2006 $55,690 $63,228
Jan-2007 $56,435 $63,527
Jan-2008 $54,424 $61,443
Jan-2009 $54,059 $60,623
Jan-2010 $52,646 $59,057
Jan-2011 $51,843 $57,627
Jan-2012 $51,758 $58,186
Jan-2013  $51,939  $58,448
ChartData Download data

The data below can be saved or copied directly into Excel.

Note: Non-elderly households are those in which the head of household is younger than age 65. Data for non-elderly households are not available prior to 1994. Shaded areas denote recessions.

Source: EPI analysis of Current Population Survey Annual Social and Economic Supplement Historical Income Tables (Tables H-5 and HINC-02)

Copy the code below to embed this chart on your website.

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What to Look for in next Week’s Census Income Data: How Long Will It Take to Claw Back Lost Years of Income Growth?

Next week will see the release of Census data on family income (as well as poverty and health insurance coverage) for 2013. Before the data are released, it’s worth reminding ourselves of one thing that last year’s data showed clearly: economic recoveries in recent recessions have been increasingly unequal, largely mirroring the generation-long upwards march of income inequality more generally. And this pattern seems poised to continue in the recovery from the Great Recession.

The figure below shows these unequal recoveries from recessions in a potentially new way. It essentially looks at just how many years of income growth were lost by each income grouping in various recessions. It measures this by simply counting how many years it took after a recession for each group to regain its previous income peak. For example, incomes for the middle fifth saw a peak in 1989 at $62,212. The recession in the following year led average income for these middle-fifth families to fall for a time, and the 1989 peak of $62,212 was not re-gained for these families until 1996, meaning that essentially seven years (from 1989 to 1996) of income growth for this group was stalled by the recession of the early 1990s. The figure below shows this number of lost years of income growth by income grouping across a range of recessions.

income curtain raiser1

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NAM’s “Cost of Regulations” Estimate: An Exercise in How Not to Do Convincing Empirics

The latest effort to scaremonger about a rising regulatory burden on U.S. business was released yesterday by the National Association of Manufacturers (NAM). The report, by W. Mark Crain and Nicole V. Crain (C&C, henceforth) purports to (among other things) estimate the total cost of U.S. regulations. The claimed price tag is enormous—$2.1 trillion. The bulk of these costs (75 percent) are estimated using a cross-country regression analysis. This cross-country analysis, however, is completely unconvincing and should be ignored.

An earlier C&C study used a similar methodology as yesterday’s release—that study was shown to be deeply flawed by an EPI analysis. Yet, the methodology of the current study is largely the same. In fact, if anything, the current analysis is less robust and convincing than the previous one.

C&C undertake a cross-country regression analysis across 34 OECD countries for the years 2006–2013. This obviously leads to a first reason for being wary of results—one would expect the economic performance of rich countries during the 2006–2013 period to be utterly dominated by the Great Recession and its aftermath. C&C use dummy variables to control for the years 2008 and 2009, presumably to capture the official years of the Great Recession. But most of the OECD remains operating far below potential even in 2013 due simply to a shortfall of demand. Unless one is making the case that regulations impede recovery from recessions (a claim that they do not make) then it is extraordinarily hard to make large inferences about the effects of regulations on long-run economic performance in this short sample period.

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NAM Publishes Bogus Regulatory Cost Estimates

The National Association of Manufacturers (NAM) is a cynical organization. It knows that few journalists will read a lengthy paper on the cost of regulation and realize that it is dressed-up junk economics, so it has published a re-run of the truly meretricious report that Mark Crain and Nicole Crain issued four years ago. The new report is even worse than its predecessor, in the sense that the authors have chosen not to respond to any of the criticism of their earlier work—even though it has been shown to be based on bad research, unreviewable and probably biased data, and faulty assumptions about the relationship between regulation and GDP.

EPI’s Josh Bivens and others will deal with the main methodological problems with the Crains’ analysis. I want to focus just on the Crains’ re-use of the same indefensible research concerning the cost of OSHA regulation, which we first exposed in 2011. The Crains claim that OSHA regulations cost businesses $71 billion a year, even though the cost for new regulations since 2001 is only $733 million. How is it that the previous years’ regulation cost nearly 100 times as much? The Crains don’t have an explanation—they simply rely on someone else’s discredited work.

Joseph M. Johnson published “A Review and Synthesis of the Cost of Workplace Regulations” in 2005. Johnson’s paper makes many serious mistakes, but the biggest is the application of a cost “multiplier” derived from yet another analyst’s work. Harvey S. James, Jr. estimated that the true cost of OSHA rules is not the cost estimated by the agency at the time of rulemaking (which often turns out, in reality, to be too high), but a cost 5.5 times greater because of “fines for violations and the costs of the many non-major regulations for which no cost estimates exist.” This multiplier is ludicrous on its face, both because OSHA fines have never amounted to very much (even today the maximum fine that can be assessed for a willful or repeat violation is only $70,000, and the amount paid is usually far less than what is initially assessed) and because the costs of non-compliance should not be double-counted as compliance costs.

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Here’s Why We Need to Legalize the Undocumented Immigrant Workforce

The Tennessean reported yesterday on the miserable work life of a 17-year old migrant worker named Ivan Alvarez, who lost three fingers when a tobacco farmer’s makeshift shearing machine sliced them off. How did the farmer treat him? He gave him a check for $100 and fired him. No worker’s compensation, no disability insurance, and no compassion.

Young Alvarez was one of six migrant teenagers working at Marty Coley Farms in Macon County, Tennessee. He lived with 13 adult men in a vermin-infested three-bedroom house, and was paid less than minimum wage for six days a week of work. Why did Alvarez and the others put up with such mistreatment? As undocumented immigrants, they were trapped.

A recorded conversation between the farm’s owner and one of the employees after the amputation shows how employers use the threat of deportation to oppress their workers and drive labor standards to the bottom. When the worker said he was leaving to take a better-paying job at another farm, the farmer, Marty Coley (one of the largest tobacco growers in the county), threatened him with deportation.

“I’ll tell you what,” Coley said. “You all go there and I’m going to call immigration and clean the whole damn bunch out.”

It adds insult to injury to learn that, as The Tennessean reported, Marty Coley Farms has received more than half a million dollars in federal tobacco price support subsidies over the past ten years.

One often hears that employers hire undocumented migrants because no American wants to do the kind of work they’re hired to do. Clearly, no American wants to live in overcrowded and disgusting quarters, be paid a subminimum wage, and have his fingers cut off. The answer isn’t to let this kind of exploitation continue—it’s to improve pay and working conditions enough that Americans will do the work, and to give immigrants the right to reject a job that degrades rather than rewards their labor. As long as the undocumented workforce is subjected to the threat of deportation, Marty Coley Farms and other low-road employers will continue to abuse and exploit them, to the detriment of every American.

The Leisure and Hospitality Sector has the Largest Gap between CEO and Worker Pay

Last week, fast food workers across the nation went on strike to demand higher wages, more regular schedules, and the right to collectively bargain. These fast food workers are a part of the restaurant industry, which Heidi Shierholz recently investigated and found to be characterized by low wages, few benefits, and high rates of poverty. But how does the experience of workers in the restaurant industry compare to its CEOs?

In 2013, CEOs of top restaurant chains in the United States made an average of $10.9 million, which is 721 times more than the minimum wage workers they employ. Restaurant are a part of the wider Leisure and Hospitality sector, which is also characterized by high rates of CEO pay and low rates of worker pay. In fact, CEOs at Leisure and Hospitality companies made, on average, 370 times the pay of a “typical” worker in that sector in 2013—making Leisure and Hospitality the worst sector in terms of disparity between CEO and worker pay. This ratio is far above the average CEO-to-worker pay ratio at all top companies, which in 2013 was 295.9-to-1. It’s also much higher than the next most disparate industry, Information, which has a CEO-to-worker pay ratio of 180-to-1, and the one after that, Trade, Transportation, and Utilities, which has a ratio of 178-to-1.

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Blockbuster Report on Construction Industry Tax and Wage Cheating

For 12 months, McClatchy reporters have been carefully digging into a pit of corruption, gathering payroll records in 28 states and interviewing hundreds of workers and business owners about an epidemic of tax cheating, wage theft, and exploitation in the construction industry. The extraordinary report of their investigation was published Thursday, and it’s hair-raising. More than one-third of the employees working on federally-funded projects in Texas and Florida, overseen by public housing authorities and monitored by the U.S. Department of Labor, were improperly classified as independent contractors. The contractors misclassified them in order to escape paying worker’s compensation premiums, unemployment insurance taxes, and FICA taxes, to avoid complying with immigration document requirements, and to avoid liability for labor law violations. In just Florida, Texas, and North Carolina, McClatchy estimates that half a million workers were misclassified, and that the state and federal governments were cheated out of approximately $2 billion in taxes as result.

The stories make the damage this does to the labor market utterly clear. Construction wages were lower in 2012 than they were in 1980, despite rising productivity and huge profits in the industry. Even skilled tradesmen like plumbers and electricians earned 12 to 14 percent less than thirty years ago. Labor law offers no protection to independent contractors, who are not entitled to the minimum wage, overtime pay, or the right to join a union and bargain collectively. Exploited workers—many of them undocumented immigrants who live in fear of deportation—work without adequate safety protections, sometimes receive far less than the pay they were promised, and are deprived of the safety net’s protections if they lose their jobs, are injured or disabled, or reach retirement age. They often live in slum conditions, even while working on luxurious and glitzy new housing.

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Unemployment Rate Continues To Be Elevated Across the Board

The jobs numbers today, along with a closer look at unemployment rates by demographic groups, point to considerable slack in the labor market across the board. A couple weeks back, I examined real (inflation adjusted) wages across the wage distribution. One of my findings was that real wages fell for all education groups between the first half of 2013 and the first half of 2014. As you can see in the figure below, real hourly wages fell even for those with a college or advanced degree.

And, when we look at these declining real wages hand in hand with the unemployment rates of those with a college or advanced degree, it is obvious that the existence of any sort of skill mismatch is a myth.

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Slow Job Growth Should Give Us Pause

Today’s jobs report showed the economy added 142,000 jobs in August, far below expectations of job growth closer to 230,000. Prior to August, monthly job growth averaged 226,000 this year. The figure below charts monthly job growth since the start of the recovery in July 2009. While the general trend has gone up, this month’s job growth was disappointingly below trend. We haven’t seen job growth this slow since December of last year.

While it’s yet to be seen whether this slower job growth is an anomaly or a new trend, these numbers should give us pause. Adding in this month’s disappointing numbers, job growth this year is still above last year’s average at this time. Job growth has averaged 215,000 jobs a month thus far in 2014, compared to 197,000 in the first eight months of 2013. We need to be consistently adding jobs at a much faster rate to return to the labor market conditions before the recession began. Arguably, that standard is a low bar as the labor market at that time still had considerable slack.