Predicting wage growth with measures of labor market slack: It’s complicated
Josh Bivens, Director of Research
Why have wages grown so slowly in recent years despite relatively low unemployment rates? This puzzle has dominated economic commentary.
Figure A below, for example, shows a scatterplot of quarterly nominal wage growth (measured against the same quarter in the previous year) and unemployment rates since 2008. The trendline showing the relationship between these variables demonstrates it’s very weak—both statistically and economically insignificant.
Unemployment does not predict wage growth after 2007 : Unemployment rate and annual change in nominal wage growth, 2008–2018
| UR | NWC | |
|---|---|---|
| 2008-Q1 | 5.00% | 3.81% |
| 2008-Q2 | 5.33% | 3.65% |
| 2008-Q3 | 6.00% | 3.73% |
| 2008-Q4 | 6.87% | 3.88% |
| 2009-Q1 | 8.27% | 3.61% |
| 2009-Q2 | 9.30% | 3.10% |
| 2009-Q3 | 9.63% | 2.72% |
| 2009-Q4 | 9.93% | 2.62% |
| 2010-Q1 | 9.83% | 2.49% |
| 2010-Q2 | 9.63% | 2.50% |
| 2010-Q3 | 9.47% | 2.32% |
| 2010-Q4 | 9.50% | 2.20% |
| 2011-Q1 | 9.03% | 2.15% |
| 2011-Q2 | 9.07% | 2.09% |
| 2011-Q3 | 9.00% | 2.08% |
| 2011-Q4 | 8.63% | 1.82% |
| 2012-Q1 | 8.27% | 1.52% |
| 2012-Q2 | 8.20% | 1.55% |
| 2012-Q3 | 8.03% | 1.42% |
| 2012-Q4 | 7.80% | 1.45% |
| 2013-Q1 | 7.73% | 1.92% |
| 2013-Q2 | 7.53% | 1.90% |
| 2013-Q3 | 7.23% | 2.13% |
| 2013-Q4 | 6.93% | 2.32% |
| 2014-Q1 | 6.67% | 2.35% |
| 2014-Q2 | 6.20% | 2.39% |
| 2014-Q3 | 6.07% | 2.36% |
| 2014-Q4 | 5.70% | 2.13% |
| 2015-Q1 | 5.53% | 1.89% |
| 2015-Q2 | 5.43% | 2.06% |
| 2015-Q3 | 5.10% | 2.03% |
| 2015-Q4 | 5.03% | 2.33% |
| 2016-Q1 | 4.93% | 2.45% |
| 2016-Q2 | 4.90% | 2.45% |
| 2016-Q3 | 4.90% | 2.53% |
| 2016-Q4 | 4.77% | 2.42% |
| 2017-Q1 | 4.60% | 2.33% |
| 2017-Q2 | 4.37% | 2.30% |
| 2017-Q3 | 4.30% | 2.38% |
| 2017-Q4 | 4.13% | 2.33% |
| 2018-Q1 | 4.07% | 2.50% |
| 2018-Q2 | 3.90% | 2.71% |
| 2018-Q3 | 3.80% | 2.83% |
| 2018-Q4 | 3.80% | 3.25% |
Note: Data are quarterly, with nominal wage changes measured from the same quarter in the previous year.
Source: Unemployment rates are from the Bureau of Labor Statistics (BLS) Current Population Survey and wages are the average hourly earnings of production and nonsupervisory workers from the BLS Current Employment Statistics.
In this newsletter, I address a number of questions raised by this weak relationship between unemployment rates and wage growth since 2008. My key conclusions are:
- Since 2008, the share of adults between the ages of 25 and 54 who are employed (or the “prime-age EPOP”) has predicted wage growth better than the unemployment rate.
- But even the prime-age EPOP has done a poor job at predicting wage growth since 2008 compared with both its own predictive power pre-2008 and the predictive power of the unemployment rate in earlier periods.
- The prime-age EPOP’s advantage in predicting wage growth seems to have started even a bit before the Great Recession, around 2001.
- Because both the unemployment rate and the prime-age EPOP have seen a large reduction in their predictive power regarding wage growth since 2008, efforts to explain this decline in predictive power should involve looking to the unique features of the Great Recession: very high rates of unemployment combined with very low rates of inflation.
- While both the unemployment rate and the prime-age EPOP are likely to be fine statistical predictors of wage growth moving forward, there has been a steady decline in how responsive wage growth is to a given change in either. In short, workers have seemingly needed ever-tighter labor markets (measured by quantity-side variables like the unemployment rate and the prime-age EPOP) to generate a given amount of wage growth.
If unemployment fails, does anything predict post-2008 wage growth?
The weak connection between unemployment rates and wage growth since 2008 has led to many analysts looking for signs of labor market slack in other measures. For example, to be classified as unemployed, someone must be first classified as in the labor force, which requires that person to be actively engaged in a job search. If an extended period of weak hiring leads significant numbers of potential workers to despair of finding a job, then they might cease to look for work and drop out of the labor force, which lowers the measured unemployment rate. In early parts of the recovery from the Great Recession (until roughly 2015), this reduction in labor force participation actually explained about a third of the overall decline in unemployment.
A measure of labor market health uninfected by sometimes-arbitrary labor force participation decisions is the employment-to-population ratio (EPOP). This simply measures what share of adults have jobs, regardless of whether the jobless are classified as “actively” searching for work or not. Because large waves of baby boomers are currently reaching retirement age, the overall EPOP has trended down in recent years for demographic reasons. A measure that circumvents these demographic effects is the “prime age” EPOP—the EPOP measuring only adults between the ages of 25 to 54. At EPI we have often highlighted the importance of using the prime-age EPOP to supplement other measures of labor market health. In The Upshot from The New York Times, Ernie Tedeschi showed that a related measure (an “adjusted” EPOP that accounted for the effect of demography) predicted nominal wage growth quite well between 1990 and 2017.
This backstory raises the question of whether the prime-age EPOP has done a significantly better job since 2008 of predicting wage growth. Figure B shows a scatterplot like Figure A but with the prime-age EPOP substituted for unemployment.
Prime-age employment-to-population ratio predicts wage growth after 2007 : Prime-age EPOP and annual change in nominal wage growth, 2008–2018
| PAEPOP | NWC | |
|---|---|---|
| 2008-Q1 | 79.7% | 3.8% |
| 2008-Q2 | 79.4% | 3.7% |
| 2008-Q3 | 79.0% | 3.7% |
| 2008-Q4 | 78.1% | 3.9% |
| 2009-Q1 | 76.7% | 3.6% |
| 2009-Q2 | 75.8% | 3.1% |
| 2009-Q3 | 75.4% | 2.7% |
| 2009-Q4 | 75.1% | 2.6% |
| 2010-Q1 | 75.2% | 2.5% |
| 2010-Q2 | 75.2% | 2.5% |
| 2010-Q3 | 75.1% | 2.3% |
| 2010-Q4 | 75.0% | 2.2% |
| 2011-Q1 | 75.2% | 2.2% |
| 2011-Q2 | 75.0% | 2.1% |
| 2011-Q3 | 75.0% | 2.1% |
| 2011-Q4 | 75.3% | 1.8% |
| 2012-Q1 | 75.6% | 1.5% |
| 2012-Q2 | 75.7% | 1.5% |
| 2012-Q3 | 75.7% | 1.4% |
| 2012-Q4 | 75.9% | 1.4% |
| 2013-Q1 | 75.8% | 1.9% |
| 2013-Q2 | 75.9% | 1.9% |
| 2013-Q3 | 76.0% | 2.1% |
| 2013-Q4 | 76.0% | 2.3% |
| 2014-Q1 | 76.4% | 2.4% |
| 2014-Q2 | 76.6% | 2.4% |
| 2014-Q3 | 76.8% | 2.4% |
| 2014-Q4 | 77.0% | 2.1% |
| 2015-Q1 | 77.2% | 1.9% |
| 2015-Q2 | 77.2% | 2.1% |
| 2015-Q3 | 77.3% | 2.0% |
| 2015-Q4 | 77.4% | 2.3% |
| 2016-Q1 | 77.8% | 2.4% |
| 2016-Q2 | 77.9% | 2.4% |
| 2016-Q3 | 77.9% | 2.5% |
| 2016-Q4 | 78.2% | 2.4% |
| 2017-Q1 | 78.3% | 2.3% |
| 2017-Q2 | 78.5% | 2.3% |
| 2017-Q3 | 78.6% | 2.4% |
| 2017-Q4 | 79.0% | 2.3% |
| 2018-Q1 | 79.2% | 2.5% |
| 2018-Q2 | 79.3% | 2.7% |
| 2018-Q3 | 79.4% | 2.8% |
| 2018-Q4 | 79.7% | 3.3% |
Note: Data are quarterly, with nominal wage changes measured from the same quarter in the previous year.
Source: Prime-age unemployment-to-population ratio is from the Bureau of Labor Statistics (BLS) Current Population Survey and wages are the average hourly earnings of production and nonsupervisory workers from the BLS Current Employment Statistics.
As the figure shows (and statistical tests confirm), the prime-age EPOP has done better at predicting wage growth than the unemployment rate has since 2008. The prime-age EPOP’s effect on wage growth is both economically and statistically significant, as it explains roughly 30 percent of the variation in wage growth over this period (this statistic is reported later, in Table 1).
But that’s actually a pretty poor performance! In earlier periods, the unemployment rate predicted a far higher share of variation in wage growth than this.
Figure C shows a scatterplot of the relationship between real wage growth and unemployment from 1979 to 2007. Over this longer period price inflation varied a lot, szo we can no longer use nominal wages but rather must account for price changes. The reasoning is simple: When inflation is hovering around 2 percent (like it has since the early 1990s), nominal wage growth of 4 percent would be pretty healthy—generating inflation-adjusted wage increases well in excess of productivity. But when inflation is hovering around 8 percent (like it did in the early 1980s), nominal wage growth of 4 percent is a disaster, leading to steep inflation-adjusted wage cuts for workers. In short, against the backdrop of unstable inflation that characterizes a long period like 1979 to 2007, a given nominal wage growth figure can mean very different things regarding the health of the labor market.
To calculate real wages, I generated my own measure of expected inflation. The idea is that workers bargain over nominal wages with their employers with some idea of future expected price inflation in the backs of their minds. If workers assume prices will rise 5 percent over the next year, they know that nominal wage growth of less than 5 percent constitutes a cut in real living standards. Workers’ expectations of price growth can be reasonably represented by a rolling average of the recent inflation experience, with more-recent inflation rates weighted more heavily. My expected inflation measure incorporates data from the previous 12 quarters. Year-over-year inflation rates lagged one to four quarters are each given a weight of 12.5 percent. Inflation rates lagged five to eight quarters are given a weight of 10 percent, and inflation rates lagged nine to 12 quarters are given a weight of 2.5 percent. None of the results that follow are sensitive to precise weighting decisions.
Unemployment accurately predicted wage growth before 2008: Unemployment rate and annual change in real wage growth, 1979–2007
| UR | RWC_2 | |
|---|---|---|
| 1979-Q1 | 5.9% | 2.0% |
| 1979-Q2 | 5.7% | 0.9% |
| 1979-Q3 | 5.9% | 0.7% |
| 1979-Q4 | 6.0% | -0.2% |
| 1980-Q1 | 6.3% | -1.0% |
| 1980-Q2 | 7.3% | -1.0% |
| 1980-Q3 | 7.7% | -1.5% |
| 1980-Q4 | 7.4% | -1.1% |
| 1981-Q1 | 7.4% | -1.0% |
| 1981-Q2 | 7.4% | -1.4% |
| 1981-Q3 | 7.4% | -1.5% |
| 1981-Q4 | 8.2% | -2.7% |
| 1982-Q1 | 8.8% | -3.3% |
| 1982-Q2 | 9.4% | -3.7% |
| 1982-Q3 | 9.9% | -3.8% |
| 1982-Q4 | 10.7% | -3.8% |
| 1983-Q1 | 10.4% | -3.4% |
| 1983-Q2 | 10.1% | -2.8% |
| 1983-Q3 | 9.4% | -2.8% |
| 1983-Q4 | 8.5% | -1.9% |
| 1984-Q1 | 7.9% | -1.8% |
| 1984-Q2 | 7.4% | -1.6% |
| 1984-Q3 | 7.4% | -1.2% |
| 1984-Q4 | 7.3% | -1.5% |
| 1985-Q1 | 7.2% | -1.4% |
| 1985-Q2 | 7.3% | -1.2% |
| 1985-Q3 | 7.2% | -1.2% |
| 1985-Q4 | 7.0% | -1.0% |
| 1986-Q1 | 7.0% | -1.1% |
| 1986-Q2 | 7.2% | -1.5% |
| 1986-Q3 | 7.0% | -1.4% |
| 1986-Q4 | 6.8% | -1.2% |
| 1987-Q1 | 6.6% | -0.7% |
| 1987-Q2 | 6.3% | -0.2% |
| 1987-Q3 | 6.0% | 0.1% |
| 1987-Q4 | 5.8% | 0.3% |
| 1988-Q1 | 5.7% | 0.0% |
| 1988-Q2 | 5.5% | 0.4% |
| 1988-Q3 | 5.5% | 0.4% |
| 1988-Q4 | 5.3% | 0.3% |
| 1989-Q1 | 5.2% | 0.7% |
| 1989-Q2 | 5.2% | 0.2% |
| 1989-Q3 | 5.2% | 0.1% |
| 1989-Q4 | 5.4% | -0.1% |
| 1990-Q1 | 5.3% | 0.1% |
| 1990-Q2 | 5.3% | 0.1% |
| 1990-Q3 | 5.7% | -0.1% |
| 1990-Q4 | 6.1% | -0.6% |
| 1991-Q1 | 6.6% | -1.3% |
| 1991-Q2 | 6.8% | -1.4% |
| 1991-Q3 | 6.9% | -1.6% |
| 1991-Q4 | 7.1% | -1.7% |
| 1992-Q1 | 7.4% | -1.6% |
| 1992-Q2 | 7.6% | -1.5% |
| 1992-Q3 | 7.6% | -1.4% |
| 1992-Q4 | 7.4% | -1.0% |
| 1993-Q1 | 7.1% | -0.5% |
| 1993-Q2 | 7.1% | -0.5% |
| 1993-Q3 | 6.8% | -0.4% |
| 1993-Q4 | 6.6% | -0.2% |
| 1994-Q1 | 6.6% | -0.1% |
| 1994-Q2 | 6.2% | 0.0% |
| 1994-Q3 | 6.0% | 0.2% |
| 1994-Q4 | 5.6% | 0.3% |
| 1995-Q1 | 5.5% | 0.2% |
| 1995-Q2 | 5.7% | 0.4% |
| 1995-Q3 | 5.7% | 0.6% |
| 1995-Q4 | 5.6% | 0.6% |
| 1996-Q1 | 5.5% | 0.8% |
| 1996-Q2 | 5.5% | 1.1% |
| 1996-Q3 | 5.3% | 1.0% |
| 1996-Q4 | 5.3% | 1.2% |
| 1997-Q1 | 5.2% | 1.2% |
| 1997-Q2 | 5.0% | 1.0% |
| 1997-Q3 | 4.9% | 1.3% |
| 1997-Q4 | 4.7% | 1.7% |
| 1998-Q1 | 4.6% | 1.8% |
| 1998-Q2 | 4.4% | 2.0% |
| 1998-Q3 | 4.5% | 1.9% |
| 1998-Q4 | 4.4% | 1.7% |
| 1999-Q1 | 4.3% | 1.8% |
| 1999-Q2 | 4.3% | 1.9% |
| 1999-Q3 | 4.2% | 2.1% |
| 1999-Q4 | 4.1% | 1.9% |
| 2000-Q1 | 4.0% | 1.9% |
| 2000-Q2 | 3.9% | 1.8% |
| 2000-Q3 | 4.0% | 1.6% |
| 2000-Q4 | 3.9% | 1.7% |
| 2001-Q1 | 4.2% | 1.4% |
| 2001-Q2 | 4.4% | 1.1% |
| 2001-Q3 | 4.8% | 0.6% |
| 2001-Q4 | 5.5% | 0.3% |
| 2002-Q1 | 5.7% | 0.0% |
| 2002-Q2 | 5.8% | -0.1% |
| 2002-Q3 | 5.7% | 0.4% |
| 2002-Q4 | 5.9% | 0.7% |
| 2003-Q1 | 5.9% | 0.9% |
| 2003-Q2 | 6.1% | 0.7% |
| 2003-Q3 | 6.1% | 0.5% |
| 2003-Q4 | 5.8% | -0.1% |
| 2004-Q1 | 5.7% | -0.3% |
| 2004-Q2 | 5.6% | 0.0% |
| 2004-Q3 | 5.4% | 0.0% |
| 2004-Q4 | 5.4% | 0.2% |
| 2005-Q1 | 5.3% | 0.1% |
| 2005-Q2 | 5.1% | 0.1% |
| 2005-Q3 | 5.0% | 0.1% |
| 2005-Q4 | 5.0% | 0.2% |
| 2006-Q1 | 4.7% | 0.4% |
| 2006-Q2 | 4.6% | 0.7% |
| 2006-Q3 | 4.6% | 0.7% |
| 2006-Q4 | 4.4% | 0.7% |
| 2007-Q1 | 4.5% | 0.8% |
| 2007-Q2 | 4.5% | 0.9% |
| 2007-Q3 | 4.7% | 0.9% |
| 2007-Q4 | 4.8% | 0.8% |
Note: Data are quarterly, with wage changes measured from the same quarter in the previous year. Nominal wages are deflated with a measure of expected inflation. This expected inflation uses the Consumer Price Index Research Series (CPI-U-RS). It uses 12 quarters of lagged inflation rates to obtain the expected inflation series. The most recent 4 quarters’ inflation rates are weighted at 12.5 percent each, inflation rates lagged five to eight quarters are weighted at 10 percent each, while inflation rates lagged nine through 12 quarters are weighted at 2.5 percent each. This expected inflation allows lagged inflation to be used to deflate nominal wages to represent “adaptive expectations” to inflation, but smooths out quarterly spikes.
Source: Unemployment rates are from the Bureau of Labor Statistics (BLS) Current Population Survey and wages are the average hourly earnings of production and nonsupervisory workers from the BLS Current Employment Statistics.
The relationship between unemployment and wage growth is striking in comparison with the similar scatterplot in Figure A. Before 2008, the unemployment rate predicted wage growth quite well; far more accurately, for example, than the prime-age EPOP has since 2008.
What was the better predictor of wage growth before the Great Recession?
The predictive power of unemployment before 2008 raises the question of whether the superior performance of the prime-age EPOP in predicting wage growth is only a recent phenomenon. The short answer is that the prime-age EPOP seems to be doing better than the unemployment rate as a wage growth predictor only since around 2001. Table 1 below simply reports the coefficient and r-squared values of regressions of nominal wage growth on the unemployment rate or the prime-age EPOP and our measure of expected inflation (where r-squared in this case is the share of total variation in wage growth that is explained by each regression model).
Coefficients and r-squareds from wage Phillips curves in different time periods
| Unemployment rate | Prime-age EPOP | ||||||
|---|---|---|---|---|---|---|---|
| Coefficient | r-squared | Coefficient | r-squared | ||||
| 1979–2000 | -0.904 | *** | 0.940 | 0.505 | *** | 0.759 | |
| 2001–2007 | -0.341 | * | 0.745 | 0.441 | *** | 0.802 | |
| 2008–2013 | -0.018 | 0.320 | 0.372 | *** | 0.523 | ||
| 2014–2018 | -0.211 | 0.383 | 0.196 | *** | 0.480 | ||
*** indicates significant at 1 percent
* indicates significant at 10 percent
Note: Regression coefficients are from a regression of nominal wage changes on the measure of labor market slack (either the unemployment rate or the prime-age employment-to-population ratio) and a measure of expected inflation. This inflation measure is a weighted average of the previous 12 quarters’ inflation rates, with a weight of 12.5 percent on the previous 4 quarters’ inflation rates, 10 percent on each the inflation rates between four and eight quarters ago, and a weight of 2.5 percent on inflation rates between nine and 12 quarters ago.
Source: Unemployment rates and prime-age employment-to-population ratios are from the Bureau of Labor Statistics (BLS) Current Population Survey and wages are the average hourly earnings of production and nonsupervisory workers from the BLS Current Employment Statistics.
Before 2000, the regression with the unemployment rate outperforms the prime-age EPOP, by a relatively clear margin, judged on the basis of the r-squared. Between 2001 and 2007, the performance is very similar, with the prime-age EPOP seemingly having a slight advantage. It is only in the post-2007 period that the prime-age EPOP is clearly superior. And its advantage is starting to erode post-2013.
The reason the prime-age EPOP seemed to be the inferior predictor of wage growth pre-2001 seems clear: it had a strong structural (upward) trend that was largely unrelated to the overall health of the labor market. This trend was driven by the rising share of women entering the workforce (even as there was a very mild downward trend in male labor force participation). But since 2001, there has been no upward trend in women’s prime-age EPOP.
Lessons going forward
Where does this leave us? First, while the unemployment rate failed to predict wage growth accurately after the Great Recession, this doesn’t necessarily mean that we should throw it out forever as a predictive tool. Table 1 shows that the really poor performance mostly occurred between 2008 and 2013. Figure D depicts this. It shows actual wage growth along with predicted wage growth based on the relationship between wage growth and unemployment that prevailed between 1979 and 2007. The unemployment rate (along with expected inflation) does a fine job predicting wages until 2007—meaning that the actual and predicted wage growth lines are very close together. After 2007, the lines diverge, and radically so between 2009 and 2013. Basically, in the first few years into the recovery, inflation plummeted and the unemployment rate rose to some of its highest levels in the postwar period. These two influences lead our model to predict steeply negative nominal wage growth for a spell. But, as much research has pointed out, nominal wage growth seems rigid at zero and rarely goes outright negative.
Immediate post–Great Recession period explains the declining predictive power of unemployment on wages : Predicted nominal wage changes and actual nominal wage changes, 1979–2018
| Predicted nominal wage change | Actual nominal wage change | |
|---|---|---|
| 1979-Q1 | 6.4568% | 8.5480% |
| 1979-Q2 | 6.9673% | 7.7275% |
| 1979-Q3 | 7.1740% | 7.9820% |
| 1979-Q4 | 7.5579% | 7.5949% |
| 1980-Q1 | 7.8323% | 7.3355% |
| 1980-Q2 | 7.5601% | 7.8640% |
| 1980-Q3 | 7.6828% | 7.8605% |
| 1980-Q4 | 8.2987% | 8.6445% |
| 1981-Q1 | 8.6399% | 9.0955% |
| 1981-Q2 | 8.8435% | 8.9655% |
| 1981-Q3 | 8.9093% | 8.8803% |
| 1981-Q4 | 8.1953% | 7.6742% |
| 1982-Q1 | 7.5155% | 6.9093% |
| 1982-Q2 | 6.5770% | 6.0127% |
| 1982-Q3 | 5.6151% | 5.3635% |
| 1982-Q4 | 4.3919% | 4.7661% |
| 1983-Q1 | 4.0328% | 4.5239% |
| 1983-Q2 | 3.6313% | 4.4350% |
| 1983-Q3 | 3.6747% | 3.8704% |
| 1983-Q4 | 3.8395% | 4.1736% |
| 1984-Q1 | 3.7962% | 3.7098% |
| 1984-Q2 | 3.8859% | 3.5933% |
| 1984-Q3 | 3.6103% | 3.6857% |
| 1984-Q4 | 3.4368% | 3.0849% |
| 1985-Q1 | 3.3082% | 3.0207% |
| 1985-Q2 | 3.0360% | 2.9957% |
| 1985-Q3 | 2.9615% | 2.7734% |
| 1985-Q4 | 3.0002% | 2.9149% |
| 1986-Q1 | 2.9136% | 2.7006% |
| 1986-Q2 | 2.5933% | 2.1814% |
| 1986-Q3 | 2.4426% | 1.8624% |
| 1986-Q4 | 2.3243% | 1.8505% |
| 1987-Q1 | 2.1802% | 1.9534% |
| 1987-Q2 | 2.2492% | 2.2472% |
| 1987-Q3 | 2.5366% | 2.5746% |
| 1987-Q4 | 2.8056% | 2.9663% |
| 1988-Q1 | 3.0181% | 2.7634% |
| 1988-Q2 | 3.2665% | 3.2601% |
| 1988-Q3 | 3.4062% | 3.3830% |
| 1988-Q4 | 3.7379% | 3.4930% |
| 1989-Q1 | 4.0190% | 4.0875% |
| 1989-Q2 | 4.2318% | 3.7602% |
| 1989-Q3 | 4.4209% | 3.8705% |
| 1989-Q4 | 4.3500% | 3.8274% |
| 1990-Q1 | 4.5140% | 4.0303% |
| 1990-Q2 | 4.6829% | 4.2051% |
| 1990-Q3 | 4.3946% | 4.1328% |
| 1990-Q4 | 4.2089% | 3.7869% |
| 1991-Q1 | 4.0041% | 3.2450% |
| 1991-Q2 | 3.8937% | 3.2152% |
| 1991-Q3 | 3.7944% | 3.0579% |
| 1991-Q4 | 3.4960% | 2.8092% |
| 1992-Q1 | 2.9904% | 2.6620% |
| 1992-Q2 | 2.5399% | 2.4476% |
| 1992-Q3 | 2.3386% | 2.3674% |
| 1992-Q4 | 2.2722% | 2.4812% |
| 1993-Q1 | 2.2684% | 2.6867% |
| 1993-Q2 | 2.0688% | 2.5132% |
| 1993-Q3 | 2.1733% | 2.4360% |
| 1993-Q4 | 2.2161% | 2.5437% |
| 1994-Q1 | 2.1219% | 2.5555% |
| 1994-Q2 | 2.3637% | 2.5121% |
| 1994-Q3 | 2.4079% | 2.6189% |
| 1994-Q4 | 2.6998% | 2.6300% |
| 1995-Q1 | 2.7405% | 2.5215% |
| 1995-Q2 | 2.5620% | 2.6867% |
| 1995-Q3 | 2.5743% | 2.9334% |
| 1995-Q4 | 2.6462% | 2.9121% |
| 1996-Q1 | 2.7328% | 3.0961% |
| 1996-Q2 | 2.7614% | 3.4503% |
| 1996-Q3 | 2.9853% | 3.4198% |
| 1996-Q4 | 3.0281% | 3.5937% |
| 1997-Q1 | 3.2039% | 3.7609% |
| 1997-Q2 | 3.4235% | 3.6131% |
| 1997-Q3 | 3.4660% | 3.8027% |
| 1997-Q4 | 3.5928% | 4.2065% |
| 1998-Q1 | 3.5966% | 4.1655% |
| 1998-Q2 | 3.6223% | 4.2918% |
| 1998-Q3 | 3.4048% | 4.0350% |
| 1998-Q4 | 3.3602% | 3.6697% |
| 1999-Q1 | 3.2924% | 3.6095% |
| 1999-Q2 | 3.1843% | 3.6008% |
| 1999-Q3 | 3.2572% | 3.7765% |
| 1999-Q4 | 3.3716% | 3.6662% |
| 2000-Q1 | 3.5472% | 3.7594% |
| 2000-Q2 | 3.8250% | 3.8481% |
| 2000-Q3 | 3.9408% | 3.8358% |
| 2000-Q4 | 4.2454% | 4.1463% |
| 2001-Q1 | 4.1994% | 4.0580% |
| 2001-Q2 | 4.2207% | 3.9684% |
| 2001-Q3 | 4.0310% | 3.6704% |
| 2001-Q4 | 3.4834% | 3.3489% |
| 2002-Q1 | 3.2336% | 2.9944% |
| 2002-Q2 | 2.9470% | 2.6903% |
| 2002-Q3 | 2.8163% | 2.9237% |
| 2002-Q4 | 2.4633% | 3.0591% |
| 2003-Q1 | 2.3758% | 3.2004% |
| 2003-Q2 | 2.1979% | 2.9333% |
| 2003-Q3 | 2.1004% | 2.6187% |
| 2003-Q4 | 2.3025% | 1.9569% |
| 2004-Q1 | 2.3464% | 1.7908% |
| 2004-Q2 | 2.4058% | 2.0231% |
| 2004-Q3 | 2.6931% | 2.1194% |
| 2004-Q4 | 2.7882% | 2.4585% |
| 2005-Q1 | 3.0226% | 2.5531% |
| 2005-Q2 | 3.2727% | 2.6226% |
| 2005-Q3 | 3.4576% | 2.7319% |
| 2005-Q4 | 3.6522% | 3.0309% |
| 2006-Q1 | 4.0823% | 3.4310% |
| 2006-Q2 | 4.3377% | 3.9061% |
| 2006-Q3 | 4.4906% | 4.0404% |
| 2006-Q4 | 4.7206% | 4.1267% |
| 2007-Q1 | 4.4964% | 4.0858% |
| 2007-Q2 | 4.4399% | 4.0592% |
| 2007-Q3 | 4.2121% | 4.0618% |
| 2007-Q4 | 3.9904% | 3.7865% |
| 2008-Q1 | 3.9029% | 3.8088% |
| 2008-Q2 | 3.7446% | 3.6510% |
| 2008-Q3 | 3.2556% | 3.7319% |
| 2008-Q4 | 2.7425% | 3.8752% |
| 2009-Q1 | 1.4262% | 3.6129% |
| 2009-Q2 | 0.2150% | 3.0960% |
| 2009-Q3 | -0.5686% | 2.7166% |
| 2009-Q4 | -1.3954% | 2.6206% |
| 2010-Q1 | -1.5087% | 2.4932% |
| 2010-Q2 | -1.4088% | 2.4996% |
| 2010-Q3 | -1.4654% | 2.3231% |
| 2010-Q4 | -1.7253% | 2.1990% |
| 2011-Q1 | -1.3960% | 2.1505% |
| 2011-Q2 | -1.3722% | 2.0877% |
| 2011-Q3 | -0.9381% | 2.0782% |
| 2011-Q4 | -0.1843% | 1.8220% |
| 2012-Q1 | 0.2929% | 1.5186% |
| 2012-Q2 | 0.4972% | 1.5467% |
| 2012-Q3 | 0.7122% | 1.4200% |
| 2012-Q4 | 0.9508% | 1.4485% |
| 2013-Q1 | 1.0609% | 1.9208% |
| 2013-Q2 | 1.1572% | 1.8954% |
| 2013-Q3 | 1.2468% | 2.1255% |
| 2013-Q4 | 1.3511% | 2.3182% |
| 2014-Q1 | 1.3599% | 2.3516% |
| 2014-Q2 | 1.6594% | 2.3916% |
| 2014-Q3 | 1.7429% | 2.3621% |
| 2014-Q4 | 2.0478% | 2.1343% |
| 2015-Q1 | 2.1240% | 1.8902% |
| 2015-Q2 | 1.9794% | 2.0600% |
| 2015-Q3 | 2.0349% | 2.0332% |
| 2015-Q4 | 1.9391% | 2.3308% |
| 2016-Q1 | 1.9165% | 2.4468% |
| 2016-Q2 | 1.9033% | 2.4476% |
| 2016-Q3 | 1.8453% | 2.5304% |
| 2016-Q4 | 1.8932% | 2.4191% |
| 2017-Q1 | 2.1424% | 2.3259% |
| 2017-Q2 | 2.5573% | 2.2960% |
| 2017-Q3 | 2.7936% | 2.3754% |
| 2017-Q4 | 3.1166% | 2.3313% |
| 2018-Q1 | 3.2593% | 2.5019% |
| 2018-Q2 | 3.5560% | 2.7146% |
| 2018-Q3 | 3.8445% | 2.8326% |
| 2018-Q4 | 4.0281% | 3.2524% |
Note: The predicted nominal wage change is obtained from a regression of nominal wage changes on expected inflation and the unemployment rate for the years 1979–2007. The predicted nominal wage changes from this regression fit very tightly with actual nominal changes until the immediate post–Great Recession period (shaded in the figure).
Source: Unemployment rates are from the Bureau of Labor Statistics (BLS) Current Population Survey and wages are the average hourly earnings of production and nonsupervisory workers from the BLS Current Employment Statistics.
This leads to one clear lesson from this examination. Because historical relationships can go haywire when the economy becomes deeply depressed for a spell, we must make sure that the portfolio of indicators used to measure labor market health and to predict future wage growth is large. Relying on any single measure can lead to bad policymaking choices.
Finally, just because a statistically detectable relationship between unemployment rates and wage growth seems to be reasserting itself post-2013, this doesn’t mean policymakers should be on a hair-trigger impulse to slow growth for fear of inflation. Modest wage growth will not necessarily feed directly into price inflation because much of the post-recession decline in labor share of income has yet to rebound, and because wage pressure might actually induce some faster productivity growth.
More importantly, the reemergence of any statistical relationship at all between lower unemployment and wage growth is interesting—but the magnitude of this relationship is important to note. This magnitude has been falling steadily over time, as shown in the regression coefficients on unemployment in Table 1. Essentially, a given rate of wage growth requires ever-lower unemployment, a finding we’ve highlighted before. This is what you’d expect if structural determinants of wage growth were pushing down on workers’ bargaining power and leverage in the labor market. But this downward structural pressure on wages is also something that macroeconomic policymakers, including Federal Reserve officials, should keep in mind as they decide whether inflationary pressure is building in the economy.
Higher returns on education can’t explain growing wage inequality
Steep and rising wage inequality is too often blamed on growing demand for workers with higher levels of educational attainment—the more schooling you have, the more you’ll be paid. But our research shows the rising gulf in pay has little to do with rising returns to education.
A prevalent story explains wage inequality as a simple consequence of growing employer demand for skills and education—often thought to be driven by advances in technology. According to this explanation, because there is a shortage of college-educated workers, the wage gap between those with and without college degrees is widening. The expected boost to workers’ pay from a four-year college degree is known as the “college premium.”
Despite its great popularity and intuitive appeal, this story about recent wage trends driven more and more by a race between education and technology does not fit the facts well, especially since the mid-1990s. The growing inequality of note is that between the top (or very top) and everyone else. The pulling away of the very top cannot be explained by education differences, but rather the escalation of executive and financial sector pay.
Even when looking at the relative changes in the 95th percentile of wage earners compared to the 50th percentile of wage earners, and comparing that gap with the college wage premium from 2000 to 2018, it is clear that gains in the college wage premium have been very modest and far less than the continued steady growth of the 95/50 wage gap. Therefore, it is highly implausible that the growth of unmet employer needs for college graduates has driven wage inequality.
The evidence suggests the demand for college graduates has grown far less in the period since the mid-1990s than it did before then. This is difficult to square with contentions that automation or changes in the types of skills employers require have been more rapid in the 2000s than in earlier decades. Rather, automation has been slower in the recent period than in earlier decades as seen in the pace of productivity, capital, information equipment, and software investment—and in the speed of changes in occupational employment patterns.
A close look at recent increases in the black unemployment rate
Everything from weather to furloughs made it hard to draw any major conclusions from this month’s employment report, but one recent worrisome trend persisted—a continued increase in unemployment for black workers.
The Labor Department’s February employment report showed job growth effectively stalled last month, rising just 20,000. That was much lower than anticipated and substantially weaker than the prevailing trend of the last few years. The average over the last three months came in at a more solid 186,000, likely a better reflection of underlying trends, given the unusually harsh weather in February. At the same time, wages grew 3.4 percent over the year, the highest so far in the economic recovery from the Great Recession.
Turning to the separate household survey, the unemployment rate ticked down to 3.8 percent, while the labor force participation rate and the employment-to-population ratio (EPOP) held steady. The overall unemployment rate has sat at or below 4.0 percent for the last 12 months, averaging 3.9 percent over the year. The black unemployment rate, on the other hand, averaged 6.4 percent over the last year and has been increasing in recent months. For comparison, white unemployment tracked the drop in overall unemployment in February and has averaged 3.4 percent over the last year.
What to Watch on Jobs Day: Stronger wage growth as prime-age labor force participation continues to climb
Wage growth has continued to be the number one indicator to track in the monthly jobs report. Nominal wage growth has been slowly climbing over the last several months. Over the last three months, year-over-year wage growth averaged 3.3 percent, up from 3.2 percent the prior three months, and 2.8 percent the six months before that. Wage growth has still yet to reach levels fast enough—and for long enough—to reach full employment and restore labor’s share of corporate-sector income. At the pace of growth we’ve seen in recent months, however, I’m optimistic that the economy will continue on track toward genuine full employment.
One of the reasons I’m optimistic is that more and more workers are returning to the labor force. And, the vast majority of the newly employed are coming from out of the labor force, so lots of those workers who have (re)entered the labor force are getting jobs. I’m unconcerned by the slight increase in the unemployment rate over the last couple of months. The unemployment rate has sat at or below 4.0 percent for nearly a year. As the labor force participation rate continues to recover, the unemployment rate may rise, but those increases will be for the right reasons as more workers grow optimistic about their chances in the labor market.
In the figures below, I take a closer look at the labor force participation rate and the share of the population with a job. I’m going to focus on trends in the prime-age population, with attention to 25- to 54-year-olds to remove any possible confounding factors due to retiring baby boomers at the top end or longer years of schooling at the bottom end. The figure below shows the prime-age labor force participation rate (LFPR) in blue and the prime-age employment-to-population ratio (EPOP) in green. The prime-age LFPR is the share of the prime-age population either with a job (employed) or actively looking for work (unemployed). The prime-age EPOP is the share of the prime-age population with a job (employed). The denominator is the prime-age population for both lines and the space in between can be roughly thought of as the unemployment rate. (Technically, the unemployment rate is 1 – EPOP/LFPR and the space between the lines is the number of unemployed people as a share of the population, but they track each other well.)
Record U.S. trade deficit in 2018 reflects failure of Trump’s trade policies
The U.S. Census Bureau reported that the U.S. goods trade deficit reached a record of $891.3 billion in 2018, an increase of $83.8 billion (10.4 percent). The broader goods and services deficit reached $621.0 billion in 2018, an increase of $68.8 billion (12.5 percent). The rapid growth of U.S. trade deficits reflect the failure of Trump administration trade policies, as well as the negative impacts of tax cuts and spending increases, which have sharply increased the federal budget deficit, and tightening of U.S. monetary policy, resulting in upward pressure on interest rates and the real value of the dollar.
The IMF predicts that the U.S. current account deficit—the broadest measure of U.S. trade in goods, services, and income—will nearly double between 2016 and 2022. Unless these trends are offset by a rapid decline in the value of the U.S. dollar, rapidly rising trade deficits could be devastating for U.S. manufacturing, likely giving rise to massive job loss on the scale experienced in the 2000–2007 period, when 3.5 million U.S. manufacturing jobs were lost.
The U.S. goods trade deficit with China reached a new record of $419.2 billion in 2018, up from $375.6 billion in 2017, an increase of $43.6 billion (11.6 percent). United States trade with China is dominated by the deficit in manufactured products. Although the United States has imposed tariffs of 10 to 25 percent on $250 billion in imports from China (about half of total U.S. imports from that country), China has played its ‘ace-in-the-hole’ by allowing it’s currency to fall by roughly 10 percent against the dollar. As a result, the U.S. trade deficit with China increased faster (11.6 percent) than the U.S. deficit with the world as a whole (10.4 percent). While the United States and China are poised to negotiate a deal to end their trade dispute, the proposed deal amounts “much ado about nothing much,” as Paul Krugman puts it. It will do little to reduce the massive imbalance in U.S.–China trade flows.
When will ‘Buy American’ really mean buy American?
Our government’s procurement policy falls far short of its potential to encourage and support good jobs in domestic manufacturing. We need to strengthen domestic sourcing requirements for publicly funded programs, including those intended to repair our failing infrastructure. While the recently issued “Executive Order on Strengthening Buy-American Preferences for Infrastructure Projects” is an improvement over the status quo, it falls far short of making the substantive improvements that are needed to make sure that “Buy American” actually means buying American.
To begin, the EO does nothing to strengthen domestic content requirements that agencies use to determine if a good is “domestically sourced”—that is, actually made in the United States. Many Americans would be startled to learn that a product requires only 51 percent U.S. content to be considered domestically made under the Buy American Act, which applies to federal government procurement. This does not even take into account the substantial transformation test, when a product is deemed domestic even if it is “made at least in part from materials manufactured in another country,” a special concern for the federal government’s procurement of the equipment and construction materials that are required for infrastructure projects.
In contrast, the Federal Trade Commission requires that the entire product be made substantially domestically in order to satisfy its definition of “Made in the U.S.A.,” although much more must be done to ensure that the FTC rules are effectively enforced.
There’s nothing radical about Elizabeth Warren’s proposal for universal childcare
The right-wing punditry machine has gone into full spin cycle as Democratic presidential candidates throw their hats into the ring, ready to brand any initiative that might ameliorate the lot of working families as radical or, worse in American political parlance, “socialist.”
That has certainly been the case with Senator Elizabeth Warren’s proposal for universal child care. But there’s nothing radical or socialist about her plan, which represents a sensible, evidence-based, practical, and much-needed strategy that tackles several critical national crises in one neat package. And it doesn’t even take money away from the GOP’s sacred cows of military spending and border security.
What crises do Warren’s proposal address? Let’s review:
First, and perhaps foremost, the majority of American families currently struggle to get their young children into child care that is decent, let alone of high quality. And for a substantial subgroup in the bottom quintile of the wage distribution, “struggle” is an understatement. For example, a 2015 EPI study showed that a single parent with one child who worked full-time for the minimum wage would not be able to sustain a modest but adequate lifestyle due to the high cost of child care.
Warren’s plan, which would make high-quality care free for families living at up to 200 percent of the federal poverty line and institute a sliding scale above that, with no family paying more than seven percent of their income, would do away entirely with that problem. Families that currently must choose among rent, food, and keeping their toddlers safe can now have all three, and middle-class families can invest in other child development activities and resources. Sounds a lot better than a wall already.
Stark black–white divide in wages is widening further
One of the most striking features of U.S. racial inequality is just how stubborn the wage gap between black and white workers has remained over the last four decades.
That trend was evident in EPI’s new State of Working America (SWA) Wages report, which highlights trends in wages across the wage distribution, by education, as well as by gender, race, and ethnicity.
Overall, the findings indicate wages are slowly improving with the growing economy, but wage inequality has grown and wage gaps have persisted, and in some cases, worsened. In this post, I will highlight one particular worsening wage gap and look at it from multiple dimensions. Since 2000, by any way it’s measured, the wage gap between black and white workers has grown significantly.
The findings here support the important research by Valerie Wilson and William M. Rodgers III, which shows that black–white wage gaps expanded with rising wage inequality from 1979 to 2015. Where their report is incredibly comprehensive, the trends outlined here are rudimentary, but reinforce the same basic truths.
In the figure below, I’ve collected some of the main findings on the black–white wage gap found both in the latest SWA report as well as the SWA data library. Using various measures, I compare wages for black and white workers over the last 18 years, highlighting the gaps in wages in 2000, the last time the economy was closest to full employment, 2007, the last business cycle peak before the Great Recession, and 2018, the latest data available.
Against these benchmarks, I illustrated the growth in the average gap; the gap for low-, middle-, and high-wage workers; the gap for workers with a high school diploma, a college degree, and an advanced degree; and a regression-adjusted wage gap (controlling for age, gender, education, and region).
Black women’s labor market history reveals deep-seated race and gender discrimination
The black woman’s experience in America provides arguably the most overwhelming evidence of the persistent and ongoing drag from gender and race discrimination on the economic fate of workers and families.
Black women’s labor market position is the result of employer practices and government policies that disadvantaged black women relative to white women and men. Negative representations of black womanhood have reinforced these discriminatory practices and policies. Since the era of slavery, the dominant view of black women has been that they should be workers, a view that contributed to their devaluation as mothers with caregiving needs at home. African-American women’s unique labor market history and current occupational status reflects these beliefs and practices.
Compared with other women in the United States, black women have always had the highest levels of labor market participation regardless of age, marital status, or presence of children at home. In 1880, 35.4 percent of married black women and 73.3 percent of single black women were in the labor force compared with only 7.3 percent of married white women and 23.8 percent of single white women. Black women’s higher participation rates extended over their lifetimes, even after marriage, while white women typically left the labor force after marriage.
Differences in black and white women’s labor participation were due not only to the societal expectation of black women’s gainful employment but also to labor market discrimination against black men which resulted in lower wages and less stable employment compared to white men. Consequently, married black women have a long history of being financial contributors—even co-breadwinners—to two-parent households because of black men’s precarious labor market position.
Black women’s main jobs historically have been in low-wage agriculture and domestic service.1 Even after migration to the north during the 20th century, most employers would only hire black women in domestic service work.2 Revealingly, although whites have devalued black women as mothers to their own children, black women have been the most likely of all women to be employed in the low-wage women’s jobs that involve cooking, cleaning, and caregiving even though this work is associated with mothering more broadly.
Trump’s national emergency declaration over the border wall is dangerous and not justified by the facts
Where, exactly, is the national emergency?
Both houses of Congress have now passed appropriations legislation to fund the government for the fiscal year, and it’s become clear they will not provide President Donald Trump with the full $5.7 billion he requested to fund construction of a wall on the southern border.
Trump has been so desperate to fund his signature campaign promise that he’s gone so far as to shut down the government over it for over a month, which caused federal employees to suffer and billions of dollars-worth of economic losses. In response to failing to get the money from Congress, Trump is declaring a national emergency to achieve the same ends by different means.
An emergency declaration, the president hopes, will allow him to access alternative streams of funding that will go towards funding the wall’s construction. Reuters reports the Trump administration expects to be able to allocate about $7 billion from two defense funds and a Treasury forfeiture fund as a result of an emergency declaration. This would be an extreme step that is unjustified by the facts because there is no ongoing national emergency at the southern border under any reasonable definition of the term.
Many on the right and left are unhappy with the legislation passed by Congress. Some conservative pundits and advocates are unhappy that it does not provide larger funding increases for immigration enforcement, going as far as saying that it will lead to “open borders.”
Some progressive legislators and advocates are opposing the legislation because it funds too much immigration enforcement and does little to rein in Immigration and Customs Enforcement (ICE) and Customs and Border Patrol (CBP), especially in light of recent internal criticisms of mismanagement, abuses of detainees, and policies that are being challenged in the courts for being inconsistent with domestic and international law. (The Democratic legislators in opposition, however, do not support another shutdown, and prefer that Congress pass a continuing resolution that would keep the government funded at current levels.) Trump expressed displeasure even before the legislation passed, noting earlier this week that the provisions included were “not doing the trick.”
The state of American manufacturing: The failure of Trump’s trade and economic policies
Separating fact from fiction is always tough when listening to President Trump, who lives in his own, fact-free fantasy-world. This is particularly so when it comes to trade and manufacturing. Here are a few key points on that topic to keep in mind when listening to his State of the Union address. There’s a lot that can be done to create millions of good manufacturing jobs for working Americans, but not the way this president is going about it.
- The trade deficit is growing more than twice as fast as the overall economy, because of the Trump administration’s trade and economic policies. The total U.S. goods trade deficit has increased 18.1 percent since 2016, and our trade deficit with China has grown even faster, 20.5 percent in the same period (annual estimates, based on year to date trade through October). Growing trade deficits over the past two decades are the single largest cause of the loss of roughly 5 million U.S. manufacturing jobs.
- The Trump administration has failed to end currency manipulation and dollar misalignment, despite Trump’s promise to name China a currency manipulator on day one upon taking office. Currency misalignment is the single largest cause of growing U.S. trade deficits. U.S. trade can be rebalanced, creating millions of good manufacturing jobs, by lowering the value of the dollar by about 25 percent. The failure to end currency misalignment is a major cause of GM’s recent decision to close 5 manufacturing plants and outsource production, eliminating 12,000 jobs, and Ford’s plan to reduce its workforce by 12 percent, eliminating 24,000 jobs.
- Trump’s massive tax cuts (for corporations and the wealthy) and spending increases are expanding the federal budget deficit, pushing up the value of the dollar and the U.S. trade deficit. The dollar has increased 20 percent since 2013, including 5 percent in 2018 alone. As a result, the IMF now predicts that the broadest measure of the U.S. trade deficit will nearly double between 2017 and 2022. Growing trade deficits will decimate manufacturing over the next few years, and could push the United States into a recession.
Trump’s hateful border wall fantasy would do nothing to address the real immigration crisis
For well over a month, President Donald Trump has demanded Congress pass appropriations legislation to fund the government that includes $5.7 billion for his administration to build additional miles of wall and fencing on the southern border. The president claims that the result of building a border wall will be that “CRIME WILL FALL,” and is threatening to declare a national state of emergency in order to get the funds he wants to begin construction.
When Democrats refused to give in to the president’s demands in December 2018, he caused the longest government shutdown in history before ultimately signing legislation on January 25 to reopen the government for three weeks, until February 15. This is intended to give a bipartisan committee of members of Congress time to agree to fiscal year 2019 appropriations legislation that includes new border security funds. Today the president is scheduled to give his annual State of the Union speech, and it’s been widely reported he will again threaten to declare a state of emergency to get what he wants—or even make a definitive statement about an emergency declaration.
The president’s focus on the wall and border security is misguided, and does little to address today’s realities on the border. There the U.S. immigration system certainly faces challenges, but the president isn’t proposing valid solutions. Instead, he’s trying to scare the public by muddling the issue with alarmist language and false statistics.
The reality at the border is this: The overall size of the unauthorized immigrant population has declined to its lowest level in a decade. And while a wall is mainly designed to keep out unauthorized border crossers, the number of migrants being apprehended for attempting to enter the United States without authorization is lower than it has been in decades. The numbers that are growing, however, are the share of total apprehensions by U.S. Customs and Border Protection (CBP) that consist of families and unaccompanied minors, as well as the number of families who present themselves voluntarily before CBP. Many then go on to request asylum once in CBP custody.
Before the State of the Union, a fact check on black unemployment
The historically low black unemployment rate has become one of Donald Trump’s favorite statistical claims, one he is likely to tout again at the upcoming State of the Union address.
The fallacy of touting this as a genuine accomplishment of the Trump administration rather than fortuitous timing has been noted by me and others on multiple occasions. Still, at the start of Black History Month, it’s useful to provide some facts about the African American labor force that you will probably not hear during the presidential address to Congress.
To begin with, it is true that the 2018 black unemployment rate was the lowest it has been since the Bureau of Labor Statistics began reporting it in 1972. But little, if any, credit for that belongs to the Trump administration. As the graph below clearly shows, the black unemployment rate had been steadily falling since 2011, well before Trump was sworn into office, and the rate of decline has not gained momentum since. Arguably, the decline of the black unemployment rate to its current level has more to do with the Fed’s decision to keep interest rates at or near zero for an extended period of time—decisions led by the two previous Federal Reserve chairpersons.
Unemployment rate of workers age 16 and older by race, 1995–2018
| Year | White | Black |
|---|---|---|
| 1994 | 4.84% | 11.54% |
| 1995 | 4.46% | 10.53% |
| 1996 | 4.22% | 10.59% |
| 1997 | 3.82% | 10.13% |
| 1998 | 3.45% | 8.95% |
| 1999 | 3.30% | 7.98% |
| 2000 | 3.14% | 7.59% |
| 2001 | 3.79% | 8.65% |
| 2002 | 4.73% | 10.23% |
| 2003 | 4.85% | 10.77% |
| 2004 | 4.49% | 10.39% |
| 2005 | 4.11% | 10.03% |
| 2006 | 3.84% | 8.97% |
| 2007 | 3.85% | 8.29% |
| 2008 | 4.75% | 10.11% |
| 2009 | 7.80% | 14.79% |
| 2010 | 8.01% | 15.92% |
| 2011 | 7.25% | 15.84% |
| 2012 | 6.58% | 13.95% |
| 2013 | 5.99% | 13.16% |
| 2014 | 4.88% | 11.51% |
| 2015 | 4.16% | 9.66% |
| 2016 | 3.96% | 8.50% |
| 2017 | 3.53% | 7.57% |
| 2018 | 3.19% | 6.57% |
Source: EPI analysis of BLS Current Population Survey microdata
However, even at an annual rate of 6.6 percent, the black unemployment rate was still more than double the white unemployment rate of 3.2 percent in 2018. In fact, the graph also shows that the last time the white unemployment rate was 6.6 percent was six years earlier in 2012, when the black unemployment rate was 14 percent. If you’re starting to see a pattern emerge, then the shortsightedness of Trump’s boasting about the black unemployment rate should also be apparent. The black unemployment rate has been about double the white unemployment rate for more than four decades, making this relationship more historically significant than any single unemployment rate. And, it is the durability of this gap that allows blind celebration of an unemployment rate that is higher than that of any other race or ethnicity reported by BLS, when the more appropriate response would be to focus on solutions for closing the gap.
The Fed shouldn’t give up on restoring labor’s share of income—and measure it correctly
U.S. workers’ wages have climbed modestly but noticeably over the past year. EPI’s nominal wage tracker shows that in 2015 and 2016, this growth averaged 2.4 percent, in 2017 it averaged 2.5 percent, but in 2018 it accelerated to 2.85 percent—and it surpassed 3 percent growth in the last quarter of the year. This uptick has been long-coming and it took a longer spell of low unemployment to spur it than most would have thought.
Three percent growth for a quarter, however, should not constitute “mission accomplished” in the minds of macroeconomic policymakers like the Federal Reserve. In the long run, nominal wage growth should run at a rate equal to the Fed’s inflation target (2 percent) plus the long-trend growth in potential productivity (let’s call this 1.5 percent).1 This indicates that even the recent accelerations in wage growth leave us failing to meet these long-run goals.
Even more importantly, wage growth should run substantially above these long-run targets for a spell of time after long periods of labor market slack. The arithmetic reasoning for this is straightforward: any time wage growth runs slower than current rates of inflation plus productivity, the result will be labor compensation shrinking as a share of the economy. The economic intuition is simply that extended periods of labor market slack sap workers’ ability to secure wage increases from employers.
This undermining of labor’s leverage shows up clearly in the data. EPI’s nominal wage tracker, besides charting wage growth over time, also tracks a measure of labor’s share of income, precisely to highlight the accumulated shortfall of labor income that policymakers should aim to restore.
What to Watch on Jobs Day: Furloughs and month-to-month volatility
The government shutdown adds several layers of complexity to interpreting January employment figures set for release on Friday. While I still plan to look at wage growth and the prime-age employment-to-population ratio to measure the slack that remains in the labor market, the effect of the extended partial government shutdown on the top-line employment numbers will get a lot of attention too. In this post, I will explore how the shutdown likely will and will not affect the numbers, along with a reminder about typical month-to-month volatility.
The reference period for the upcoming Current Employment Statistics (CES) and the Current Population Survey (CPS) is the pay period or week, respectively, including January 12, 2019. The 12th of January marks roughly the middle of the 35-day partial government shutdown. The shutdown directly affected three types of workers and their paychecks: government employees working without pay, government employees not working, and government contractors not working. Indirectly, the shutdown also affected workers who typically service government employees who were not working, but who are not directly paid for by government contracts—think restaurants, taxi cabs, and other services purchased by the government workforce.
Let’s start with the CES. Government workers, furloughed or not, will be counted as employed in the CES. As soon as the Government Fair Treatment Act of 2019 was signed into law, those furloughed workers were guaranteed their back pay and therefore count as employed workers. In other words, the effects of the furlough will not affect the count of federal jobs in the CES. The loss of private sector work, on the other hand, either as direct federal contractors or indirect employment that services government work, could register as a fall in private-sector employment or private-sector hours, or both.
Reliable data is one of the many victims of the government shutdown
One of the many things we rely on the federal government for is timely, accurate, independent, and publicly available data that is used by households, businesses, and policymakers to make informed economic decisions. Of the many negative effects of the government shutdown, there have been some “data casualties,” including the release of Current Population Survey (CPS) public microdata files. These files are the source data for monthly reports on the unemployment and labor force participation rates, key timely barometers of the nation’s economic health. This microdata also forms the basis of much of the research that EPI and other research organizations and academics conduct. EPI urges the president to end the government shutdown so that federal employees and contractors can receive their paychecks again, researchers at EPI and elsewhere can continue to provide current economic analysis, businesses and households can make economic plans with fuller information, and the new Congress can make well-researched policy choices.
The Current Population Survey (aka the Household Survey), which is used to measure the nation’s unemployment rate (along with many other measures of labor market health), is collected and analyzed through a partnership between the Bureau of Labor Statistics (BLS) and the Census Bureau. Every month, a few weeks after BLS releases the monthly jobs report with CPS-derived estimates of unemployment and labor force participation, Census releases a version of the CPS microdata for public use. These public-use extracts allow for analysis beyond what’s available in tables published by BLS. For example, the jobs report includes an enormous amount of information on employment status by race/ethnicity, gender, age, education, and a variety of other demographic characteristics. However, to look at how the employment status of young people with a high school degree differs by race, you need to use the CPS microdata to run your own analysis. The CPS microdata is also a key source for assessing trends in wage growth for workers at different points in the wage distribution—an assessment we make annually at EPI and which will be delayed by this week’s announcement.
Au pair lawsuit reveals collusion and large-scale wage theft from migrant women through State Department’s J-1 visa program
Last week, the Associated Press (AP) reported on a proposed settlement agreement for $65.5 million between a dozen former au pairs from Colombia, Australia, Germany, South Africa, and Mexico who were brave enough to bring a lawsuit against the companies that recruited them to work the United States. Thanks to the former au pairs and the tireless efforts of the smart lawyers at Towards Justice, a nonprofit organization in Denver, nearly 100,000 young migrant workers (mostly women) will finally receive some portion of the wages they should have been paid while working in the United States providing low-cost child care to Americans.
The migrant au pairs doing this work as in-home caretakers were employed in the United States through the U.S. State Department’s Au Pair program, one of 15 programs in State’s J-1 visa Exchange Visitor Program. Each year about 20,000 au pairs are hired by American families, assisted by J-1 “sponsors,” which can be either for-profit companies or nonprofit organizations that act as labor recruiters for families looking to hire foreign au pairs, and to which the State Department has mostly outsourced the management and oversight of the J-1 visa program. The sponsors make money by charging the au pairs to participate in the program, as well as by charging fees to families in order to connect them to au pairs. According to the AP, in the lawsuit the au pairs claimed that the:
15 companies authorized to bring au pairs to the United States colluded to keep their wages low, ignoring overtime and state minimum wage laws and treating the federal minimum wage for au pairs as a maximum amount they can earn. In some cases, the lawsuit said, families pushed the limits of their duties, requiring au pairs to do things like feed backyard chickens, help families move and do gardening, and not allowing them to eat with the family.
The economy has made great strides since the recession, but some weakness lingers
With today’s Bureau of Labor Statistics (BLS) jobs report we can look at the entirety of 2018—putting the year as a whole in perspective and comparing 2018 with other years. Yesterday, I provided a fairly broad overview of the first 11 months of 2018 including context since the last business cycle peak before the Great Recession (2007) and the last time the U.S. economy was at full employment (2000).
As the recovery has strengthened we’ve seen improvements in all measures of employment, unemployment, and wage growth. These measures tell a consistent story—an economy on its way to full employment, but not there yet. Taking a data-driven approach to policymaking would mean continuing to push to reduce slack, keeping interest rates from rising further and letting the economy recover for Americans across races, ethnicities, ages, levels of educational attainment, and areas of the country.
Payroll employment growth in December was 312,000, bringing average job growth in 2018 up to 220,000. As shown in the figure below, job growth during this time period was a bit higher than in 2017. This can be attributed to the shift in federal policy from austerity to stimulus in the form of both tax cuts and a nearly $300 billion increase in government spending.
Average monthly total nonfarm employment growth, 2006–2018
| Year | Average monthly total nonfarm employment growth |
|---|---|
| 2006 | 175 |
| 2007 | 96 |
| 2008 | -297 |
| 2009 | -422 |
| 2010 | 88 |
| 2011 | 174 |
| 2012 | 179 |
| 2013 | 192 |
| 2014 | 250 |
| 2015 | 226 |
| 2016 | 195 |
| 2017 | 182 |
| 2018 | 220 |
Source: Data are from the Current Employment Statistics (CES) series of the Bureau of Labor Statistics and are subject to occasional revisions.
What to Watch on Jobs Day: An assessment of the 2018 labor market, 11 years since the start of the Great Recession
The last Bureau of Labor Statistics (BLS) jobs report of 2018 comes out on Friday, giving us a chance to step back and look at how working people fared over the entire year. The report also marks the 11th anniversary of the official start of the Great Recession. My expectation is that the December data will confirm that, while by some measures the economy has nearly recovered its immediate pre-Great Recession health, by other measures it is still somewhat weaker than in 2007—the last year before the Great Recession hit. Further, as I have often noted, 2007 should not be considered a benchmark for a fully healthy economy for America’s workers. Almost all labor market measures were notably weaker in 2007 than they were at the previous business cycle peak in 2000. There was very little reason to think that the U.S. economy in 2007 was at full employment. If one looks at the stronger business cycle peak of 2000 as a more appropriate benchmark, the economy in 2018 looks even further from full employment. Many working people are still not seeing the recovery reflected in their paychecks—and the economy will not be at genuine full employment until employers are consistently offering workers meaningfully higher wages.
In this blog post—and Friday when the December numbers come out—I’m going to look at average payroll employment growth over the last several years. Because there is always a bit of volatility in the monthly data—especially in the household series that has a smaller sample size—taking a year-long approach allows us to smooth out the bumps and take stock of the key measures: payroll employment growth, the unemployment rate, the employment-to-population ratio, and nominal wage growth.
Over 5 million workers will have higher pay on January 1 thanks to state minimum wage increases
On January 1, 2019, 19 states will raise their minimum wages, lifting pay for 5.2 million workers across the country.1 The increases, which range from a $0.05 inflation adjustment in Alaska to a $2.00 per hour increase in New York City, will give affected workers approximately $5.3 billion in increased wages over the course of 2019. Affected workers who work year-round will see their annual pay go up between $90 and $1,300, on average, depending on the size of the minimum wage change in their state.
The map below describes the impacts of each state increase, which are also summarized in Table 1. Note that these estimates do not account for changes in local minimum wages.2 There are 24 cities and counties with higher local minimum wages taking effect on January 1, all of which can be found in EPI’s Minimum Wage Tracker. They also do not include any “indirectly affected workers” already making more than the new minimum wage who receive raises as employers adjust their overall pay scales.
State minimum wage increases will raise pay for 5.2 million workers on January 1: States with minimum wage increases effective January 1, 2019, by type of increase
| State | Share of workforce directly benefiting | Type of increase | New minimum wage as of Jan. 1, 2019 | Amount of increase | Total workers directly benefiting | Total increase in annual wages | Average increase in annual earnings of year-round workers | |
|---|---|---|---|---|---|---|---|---|
| Alabama | -1 | 0.00% | ||||||
| Connecticut | 0 | 0.00% | ||||||
| Georgia | -1 | 0.00% | ||||||
| Idaho | -1 | 0.00% | ||||||
| Illinois | 0 | 0.00% | ||||||
| Indiana | -1 | 0.00% | ||||||
| Iowa | -1 | 0.00% | ||||||
| Kansas | -1 | 0.00% | ||||||
| Kentucky | -1 | 0.00% | ||||||
| Louisiana | -1 | 0.00% | ||||||
| Maryland | 0 | 0.00% | ||||||
| Mississippi | -1 | 0.00% | ||||||
| Nebraska | 0 | 0.00% | ||||||
| Nevada | 0 | 0.00% | ||||||
| New Hampshire | -1 | 0.00% | ||||||
| New Mexico | 0 | 0.00% | ||||||
| North Carolina | -1 | 0.00% | ||||||
| North Dakota | -1 | 0.00% | ||||||
| Oklahoma | -1 | 0.00% | ||||||
| Oregon | 0 | 0.00% | ||||||
| Pennsylvania | -1 | 0.00% | ||||||
| South Carolina | -1 | 0.00% | ||||||
| Tennessee | -1 | 0.00% | ||||||
| Texas | -1 | 0.00% | ||||||
| Utah | -1 | 0.00% | ||||||
| Virginia | -1 | 0.00% | ||||||
| Washington D.C. | 0 | 0.00% | ||||||
| West Virginia | 0 | 0.00% | ||||||
| Wisconsin | -1 | 0.00% | ||||||
| Wyoming | -1 | 0.00% | ||||||
| New York | 2 | 5.49% | Legislation | $11.10–$15.00 | $0.70–$2.00 | 464,200 | $282,159,000 | $610 |
| Hawaii | 0 | 0.00% | ||||||
| Montana | 1 | 1.19% | Inflation adjustment | $8.50 | $0.20 | 5,000 | $1,636,300 | $330 |
| Ohio | 1 | 1.33% | Inflation adjustment | $8.55 | $0.25 | 67,300 | $19,569,500 | $290 |
| South Dakota | 1 | 1.58% | Inflation adjustment | $9.10 | $0.25 | 6,000 | $1,598,100 | $270 |
| New Jersey | 1 | 1.72% | Inflation adjustment | $8.85 | $0.25 | 67,300 | $26,135,200 | $390 |
| Florida | 1 | 1.82% | Inflation adjustment | $8.46 | $0.21 | 159,200 | $59,849,800 | $380 |
| Michigan | 0 | 0.00% | ||||||
| Minnesota | 1 | 3.43% | Inflation adjustment | $9.86 | $0.21 | 92,600 | $24,791,400 | $270 |
| Delaware | 2 | 3.51% | Legislation | $8.75 | $0.50 | 14,900 | $10,010,700 | $670 |
| Vermont | 1 | 3.58% | Inflation adjustment | $10.77 | $0.27 | 10,300 | $3,388,000 | $330 |
| Alaska | 1 | 3.80% | Inflation adjustment | $9.89 | $0.05 | 11,500 | $1,005,800 | $90 |
| Missouri | 3 | 4.06% | Ballot measure | $8.60 | $0.75 | 107,100 | $66,261,900 | $620 |
| Rhode Island | 2 | 4.10% | Legislation | $10.50 | $0.40 | 19,800 | $12,476,600 | $630 |
| Arkansas | 3 | 6.71% | Ballot measure | $9.25 | $0.75 | 81,000 | $74,720,300 | $920 |
| Colorado | 3 | 9.87% | Ballot measure | $11.10 | $0.90 | 254,600 | $273,050,500 | $1070 |
| Washington | 3 | 10.70% | Ballot measure | $12.00 | $0.50 | 337,100 | $258,186,300 | $770 |
| Massachusetts | 2 | 11.46% | Legislation | $12.00 | $1.00 | 372,300 | $440,491,000 | $1180 |
| Maine | 3 | 15.22% | Ballot measure | $11.00 | $1.00 | 87,200 | $90,755,900 | $1040 |
| Arizona | 3 | 15.52% | Ballot measure | $11.00 | $0.50 | 443,400 | $329,781,700 | $740 |
| California | 2 | 15.85% | Legislation | $12.00 | $1.00 | 2,560,100 | $3,345,224,400 | $1310 |
Notes: “Legislation” indicates that the new rate was established by the legislature. “Ballot measure” indicates the new rate was set by a ballot initiative passed by voters. “Inflation adjustment” indicates that the new rate was established by a formula, reflecting the change in prices over the preceding year.
Directly affected workers will see their wages rise because the new minimum wage rate exceeds their current hourly pay. This does not include additional workers who may receive a wage increase through “spillover” effects, as employers adjust overall pay scales.
In September 2018, the Michigan Legislature adopted, as legislation, a ballot initiative that was scheduled to be on the November ballot that would have raised the state minimum wage to $10.00 on January 1, 2019, with subsequent increases raising it to $12 by 2022 with automatic adjustment for inflation every year thereafter. By adopting the initiative, the legislature removed the measure from the ballot. After the November election, the legislature amended the legislation so that the minimum wage will reach $12 by 2030—eight years more slowly—with no further automatic inflation adjustments. As a result of the amended legislation, the increase to Michigan's minimum wage will take effect in April, not January 1.
The New York minimum wage changes take effect on December 31, 2018. Population growth between the data period and January 2019 estimated using state-specific projections for growth in the total population or the population ages 15—69, where available. Nominal wage growth between the data period and January 2019 estimated using the mid-point between national inflation as measured by the CPI-U from the first three quarters of 2017 to the first three quarters of 2018, and the 3-year average of nominal wage growth of the bottom 20 percent of wage earners in each state from 2014 to 2017. A full methodology is available in Appendix B of Raising the Minimum Wage to $15 by 2024 Would Lift Wages for 41 Million American Workers.
Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata 2017 and state population projections from various state agencies and demography centers
Increases in eight states are the result of automatic adjustments for inflation. In Alaska, Florida, Minnesota, Montana, New Jersey, Ohio, South Dakota, and Vermont, the minimum wage is adjusted each year to reflect changes in prices over the preceding year—thereby ensuring that the minimum wage supports the same level of spending year after year.
In five states—California, Delaware, Massachusetts, New York, and Rhode Island—the increases reflect new minimum wage levels set by state legislatures. Several of these increases, such as those in California, Massachusetts, and New York, are intermediate steps as these states gradually raise their minimum wages to $15 per hour. In 2017, congressional Democrats proposed raising the federal minimum wage to $15 by 2024, which would lift pay for an estimated 41 million U.S. workers, but the bill was never allowed to come to a vote. Lawmakers in Congress have not raised the federal minimum wage since 2007, and since the last federal increase took effect, the purchasing power of the federal minimum wage has declined by over 12 percent.
The failure of Trump’s trade and manufacturing policy
A shorter version of this post appeared in the Detroit News on 12/2/2018: GM Cutbacks a result of overvalued dollar.
Last month, General Motors announced plant closures in the U.S. that could lead to roughly 14,700 layoffs by the end of 2019. The shutdowns will have the biggest impact in industrial states like Ohio and Michigan, where key plants in Detroit-Hamtramck, Lordstown, and Warren are being closed. But the closures also have wider implications for American industry—and not just the machine shops and fabricators that produce rubber, steel, and glass components for auto assembly. America’s manufacturers are all struggling with the same issue—an overvalued dollar that puts them at risk from rising trade deficits. And it all derives from flawed Trump administration economic policies.
Trump’s tax cuts and increased government spending for defense and nondefense needs are widening the U.S. budget deficit, which will top $1 trillion in 2020 (5 percent of GDP). On top of that, Trump’s tariffs on China have backfired. China has reduced the value of the yuan 10 percent this year, and its trade surplus with the United States has increased 10 percent over the same period last year—even faster than the overall U.S. goods trade deficit, which is up 9.4 percent in the same period. The IMF projects that the overall U.S. current account deficit (the broadest measure of trade in goods, services and income) will nearly double over the next four years.
As a result of the rising dollar and increasing current account deficit, the U.S. goods trade deficit will increase to between $1.2 trillion and $2 trillion by 2020, an increase of $400 billion to $1.2 trillion above the $807 billion U.S. goods trade deficit in 2017, as shown below. This will directly eliminate between 2.5 and 7.5 million U.S. jobs, mostly in manufacturing (because 85 percent of U.S. goods trade consists of manufactured products). The collapse in output, especially in the capital intensive manufacturing sector, will decimate investment—and taken together, both will result in large additional job losses as income and spending collapse, resulting in a steep recession if nothing is done to reduce the over-valued dollar. The dollar must fall by at least 25 to 30 percent (on a real, trade-weighted basis) to rebalance U.S. trade and avert the coming trade tsunami that’s baked into the economy as a result of the rising trade deficit.
The bad economics of PAYGO swamp any strategic gain from adopting it
The obscure Congressional budget rule known as PAYGO (“pay as you go”) has burst into the news lately. A PAYGO rule means that any tax cut or spending increase passed into law needs to be offset in the same spending cycle with tax increases or spending cuts elsewhere in the budget. Incoming House Speaker Nancy Pelosi has indicated that the House of Representatives will abide by PAYGO in the next Congress, and this decision has sparked much controversy.
Many Washington insiders assert forcefully that committing to PAYGO rules in the House for the next Congress is good politics. The argument is that it assuages fears of politicians who believe they must make public commitments to lower deficits to avoid being punished by voters who care deeply about this issue. If voters do indeed have strong preferences for reducing deficits, then policymakers—even those who want to use fiscal policy to reduce inequality by expanding public spending and investment—must first commit to PAYGO to convince these voters that budget measures can both reduce inequality and be fiscally “responsible.”
The strength of evidence supporting this political claim is debatable. What’s less debatable is that PAYGO really has hindered progressive policymaking in the not-so-recent past. For example, it was commitments to adhere to PAYGO that led to the Affordable Care Act (ACA) having underpowered subsidies for purchasing insurance and, even more importantly, having a long lag in implementation; the law passed in January 2010 yet the exchanges with subsidies only were up and running by 2014. This implementation lag meant that the ACA’s benefits were not as sunk into Americans’ economic lives by the time a hostile Republican Congress and administration began launching attacks on it following the 2016 elections. It is a real testament to how much better the ACA made life for Americans that it has been stubbornly resistant to these attacks. But it would have been helpful to have a couple more years to have it running smoothly, but that didn’t happen largely because the ACA’s architects wanted to meet PAYGO rules over the 10-year budget window.
Bonuses are up $0.02 since the GOP tax cuts passed
Newly available data from the Bureau of Labor Statistics’ Employer Costs for Employee Compensation data allows an update of the trends of worker bonuses through September 2018, to gauge the impact of the GOP’s Tax Cuts and Jobs Act of 2017. The tax cutters claimed that their bill would raise the wages of rank-and-file workers, with congressional Republicans and members of the Trump administration promising raises of many thousands of dollars within ten years. The Trump administration’s chair of the Council of Economic Advisers argued in April that we were already seeing the positive wage impact of the tax cuts:
A flurry of corporate announcements provide further evidence of tax reform’s positive impact on wages. As of April 8, nearly 500 American employers have announced bonuses or pay increases, affecting more than 5.5 million American workers.
Following the bill’s passage, a number of corporations made conveniently-timed announcements that their workers would be getting raises or bonuses (some of which were in the works well before the tax cuts passed). But as Josh Bivens and Hunter Blair have shown there are many reasons to be skeptical of the claim that the TCJA, particularly corporate tax cuts, will produce significant wage gains.
Millions of working women of childbearing age are not included in protections for nursing mothers
The federal Break Time for Nursing Mothers provision of the Fair Labor Standards Act (FLSA) requires employers to provide reasonable unpaid break time, as needed, for an employee to express breast milk for her nursing child for one year after the child’s birth. Further, employers are required to provide a place for the employee to express milk—other than a bathroom—that is shielded from view and free from intrusion from coworkers and the public. These requirements were signed into law in 2010 as part of the Affordable Care Act and were a landmark step toward securing pumping accommodations for countless nursing mothers in the workplace.
These provisions were designed to prevent harmful outcomes that can occur without basic workplace accommodations for expressing breast milk, such as negative health consequences, the inability to breastfeed, and economic harm including job loss (documented in the upcoming report Exposed: Discrimination against Breastfeeding Workers from the Center for WorkLife Law). However, the law has several significant problems that leave nursing mothers at risk. One key issue is that due to where these provisions are placed in the FLSA—in the section that requires employers to pay overtime compensation if an employee works more than 40 hours in a week—all those workers who are exempt (i.e. excluded) from the overtime protections of the FLSA are also exempt from the break time protections for nursing mothers. These exemptions affect roughly one out of every four working women of childbearing age (between the ages of 16 and 44). There are a total of 37.8 million working women of childbearing age in the United States, and more than 9 million of them are excluded from the Break Time for Nursing Mothers protections. That includes more than 1 million black women, 976,000 Hispanic women, 825,000 Asian women, more than 6 million white women, and 185,000 women of other races. The below table shows further breakdowns by state and industry.
What to Watch on Jobs Day: Will we see signs of stronger wage growth?
Friday is the last Bureau of Labor Statistics (BLS) Jobs Report before the final meeting of the year for the Federal Open Market Committee (FOMC) meeting. The FOMC has a dual mandate to pursue both maximum employment as well as stable inflation around their 2 percent target. Current forecasts signal that it’s more likely than not that the FOMC will raise interest rates in their December meeting, on pace with their behavior this year so far. However, the data, should give them pause to hold off and let the economy continue to recover—and the data they should really be paying attention to comes from the labor market, not the stock market.
While the economy is experiencing continued low unemployment, there’s other evidence to suggest that recent levels of unemployment are overstating the strength of the economy. The share of the population with a job continues to be softer than recent labor market peaks. The figure below shows the share of the 25–54 year old population with a job, removing any issues of a shrinking labor force due to retiring baby boomers. This prime-age employment-to-population ratio most recently came in at 79.7 percent, a huge improvement since the depths of the aftermath of the Great Recession, but still more than 2 percentage points lower than when the economy was closest to full employment back in 2000.
Employment-to-population ratio of workers ages 25–54, 1989–2018
| date | Employment to population ratio |
|---|---|
| Jan-1989 | 80% |
| Feb-1989 | 79.9 |
| Mar-1989 | 79.9 |
| Apr-1989 | 79.8 |
| May-1989 | 79.8 |
| Jun-1989 | 79.8 |
| Jul-1989 | 79.8 |
| Aug-1989 | 79.9 |
| Sep-1989 | 80 |
| Oct-1989 | 79.9 |
| Nov-1989 | 80.2 |
| Dec-1989 | 80.1 |
| Jan-1990 | 80.2 |
| Feb-1990 | 80.2 |
| Mar-1990 | 80.1 |
| Apr-1990 | 79.9 |
| May-1990 | 79.9 |
| Jun-1990 | 79.8 |
| Jul-1990 | 79.6 |
| Aug-1990 | 79.5 |
| Sep-1990 | 79.4 |
| Oct-1990 | 79.4 |
| Nov-1990 | 79.2 |
| Dec-1990 | 79 |
| Jan-1991 | 78.9 |
| Feb-1991 | 78.9 |
| Mar-1991 | 78.7 |
| Apr-1991 | 79 |
| May-1991 | 78.6 |
| Jun-1991 | 78.7 |
| Jul-1991 | 78.6 |
| Aug-1991 | 78.5 |
| Sep-1991 | 78.6 |
| Oct-1991 | 78.5 |
| Nov-1991 | 78.4 |
| Dec-1991 | 78.3 |
| Jan-1992 | 78.4 |
| Feb-1992 | 78.2 |
| Mar-1992 | 78.2 |
| Apr-1992 | 78.4 |
| May-1992 | 78.4 |
| Jun-1992 | 78.5 |
| Jul-1992 | 78.4 |
| Aug-1992 | 78.4 |
| Sep-1992 | 78.3 |
| Oct-1992 | 78.2 |
| Nov-1992 | 78.2 |
| Dec-1992 | 78.2 |
| Jan-1993 | 78.2 |
| Feb-1993 | 78.1 |
| Mar-1993 | 78.2 |
| Apr-1993 | 78.2 |
| May-1993 | 78.5 |
| Jun-1993 | 78.6 |
| Jul-1993 | 78.6 |
| Aug-1993 | 78.8 |
| Sep-1993 | 78.6 |
| Oct-1993 | 78.7 |
| Nov-1993 | 79 |
| Dec-1993 | 79 |
| Jan-1994 | 78.9 |
| Feb-1994 | 78.9 |
| Mar-1994 | 78.9 |
| Apr-1994 | 79 |
| May-1994 | 79.2 |
| Jun-1994 | 78.8 |
| Jul-1994 | 79.1 |
| Aug-1994 | 79.2 |
| Sep-1994 | 79.6 |
| Oct-1994 | 79.6 |
| Nov-1994 | 79.8 |
| Dec-1994 | 79.8 |
| Jan-1995 | 79.7 |
| Feb-1995 | 80 |
| Mar-1995 | 79.9 |
| Apr-1995 | 79.8 |
| May-1995 | 79.7 |
| Jun-1995 | 79.5 |
| Jul-1995 | 79.7 |
| Aug-1995 | 79.6 |
| Sep-1995 | 79.8 |
| Oct-1995 | 79.8 |
| Nov-1995 | 79.7 |
| Dec-1995 | 79.7 |
| Jan-1996 | 79.8 |
| Feb-1996 | 79.9 |
| Mar-1996 | 79.9 |
| Apr-1996 | 79.9 |
| May-1996 | 80 |
| Jun-1996 | 80.1 |
| Jul-1996 | 80.4 |
| Aug-1996 | 80.5 |
| Sep-1996 | 80.4 |
| Oct-1996 | 80.6 |
| Nov-1996 | 80.5 |
| Dec-1996 | 80.5 |
| Jan-1997 | 80.5 |
| Feb-1997 | 80.4 |
| Mar-1997 | 80.6 |
| Apr-1997 | 80.7 |
| May-1997 | 80.6 |
| Jun-1997 | 80.9 |
| Jul-1997 | 81.1 |
| Aug-1997 | 81.3 |
| Sep-1997 | 81.1 |
| Oct-1997 | 81.1 |
| Nov-1997 | 81 |
| Dec-1997 | 81 |
| Jan-1998 | 81 |
| Feb-1998 | 81 |
| Mar-1998 | 81 |
| Apr-1998 | 81.1 |
| May-1998 | 81 |
| Jun-1998 | 81 |
| Jul-1998 | 81.1 |
| Aug-1998 | 81.2 |
| Sep-1998 | 81.3 |
| Oct-1998 | 81.1 |
| Nov-1998 | 81.2 |
| Dec-1998 | 81.3 |
| Jan-1999 | 81.8 |
| Feb-1999 | 81.5 |
| Mar-1999 | 81.3 |
| Apr-1999 | 81.3 |
| May-1999 | 81.4 |
| Jun-1999 | 81.4 |
| Jul-1999 | 81.2 |
| Aug-1999 | 81.3 |
| Sep-1999 | 81.3 |
| Oct-1999 | 81.5 |
| Nov-1999 | 81.6 |
| Dec-1999 | 81.5 |
| Jan-2000 | 81.8 |
| Feb-2000 | 81.8 |
| Mar-2000 | 81.7 |
| Apr-2000 | 81.9 |
| May-2000 | 81.5 |
| Jun-2000 | 81.5 |
| Jul-2000 | 81.3 |
| Aug-2000 | 81.1 |
| Sep-2000 | 81.1 |
| Oct-2000 | 81.1 |
| Nov-2000 | 81.3 |
| Dec-2000 | 81.4 |
| Jan-2001 | 81.4 |
| Feb-2001 | 81.3 |
| Mar-2001 | 81.3 |
| Apr-2001 | 80.9 |
| May-2001 | 80.8 |
| Jun-2001 | 80.6 |
| Jul-2001 | 80.5 |
| Aug-2001 | 80.2 |
| Sep-2001 | 80.2 |
| Oct-2001 | 79.9 |
| Nov-2001 | 79.7 |
| Dec-2001 | 79.8 |
| Jan-2002 | 79.6 |
| Feb-2002 | 79.8 |
| Mar-2002 | 79.6 |
| Apr-2002 | 79.5 |
| May-2002 | 79.4 |
| Jun-2002 | 79.2 |
| Jul-2002 | 79.1 |
| Aug-2002 | 79.3 |
| Sep-2002 | 79.4 |
| Oct-2002 | 79.2 |
| Nov-2002 | 78.8 |
| Dec-2002 | 79 |
| Jan-2003 | 78.9 |
| Feb-2003 | 78.9 |
| Mar-2003 | 79 |
| Apr-2003 | 79.1 |
| May-2003 | 78.9 |
| Jun-2003 | 78.9 |
| Jul-2003 | 78.8 |
| Aug-2003 | 78.7 |
| Sep-2003 | 78.6 |
| Oct-2003 | 78.6 |
| Nov-2003 | 78.7 |
| Dec-2003 | 78.8 |
| Jan-2004 | 78.9 |
| Feb-2004 | 78.8 |
| Mar-2004 | 78.7 |
| Apr-2004 | 78.9 |
| May-2004 | 79 |
| Jun-2004 | 79.1 |
| Jul-2004 | 79.2 |
| Aug-2004 | 79 |
| Sep-2004 | 79 |
| Oct-2004 | 79 |
| Nov-2004 | 79.1 |
| Dec-2004 | 78.9 |
| Jan-2005 | 79.2 |
| Feb-2005 | 79.2 |
| Mar-2005 | 79.2 |
| Apr-2005 | 79.4 |
| May-2005 | 79.5 |
| Jun-2005 | 79.2 |
| Jul-2005 | 79.4 |
| Aug-2005 | 79.6 |
| Sep-2005 | 79.4 |
| Oct-2005 | 79.3 |
| Nov-2005 | 79.2 |
| Dec-2005 | 79.3 |
| Jan-2006 | 79.6 |
| Feb-2006 | 79.7 |
| Mar-2006 | 79.8 |
| Apr-2006 | 79.6 |
| May-2006 | 79.7 |
| Jun-2006 | 79.8 |
| Jul-2006 | 79.8 |
| Aug-2006 | 79.8 |
| Sep-2006 | 79.9 |
| Oct-2006 | 80.1 |
| Nov-2006 | 80 |
| Dec-2006 | 80.1 |
| Jan-2007 | 80.3 |
| Feb-2007 | 80.1 |
| Mar-2007 | 80.2 |
| Apr-2007 | 80 |
| May-2007 | 80 |
| Jun-2007 | 79.9 |
| Jul-2007 | 79.8 |
| Aug-2007 | 79.8 |
| Sep-2007 | 79.7 |
| Oct-2007 | 79.6 |
| Nov-2007 | 79.7 |
| Dec-2007 | 79.7 |
| Jan-2008 | 80 |
| Feb-2008 | 79.9 |
| Mar-2008 | 79.8 |
| Apr-2008 | 79.6 |
| May-2008 | 79.5 |
| Jun-2008 | 79.4 |
| Jul-2008 | 79.2 |
| Aug-2008 | 78.8 |
| Sep-2008 | 78.8 |
| Oct-2008 | 78.4 |
| Nov-2008 | 78.1 |
| Dec-2008 | 77.6 |
| Jan-2009 | 77 |
| Feb-2009 | 76.7 |
| Mar-2009 | 76.2 |
| Apr-2009 | 76.2 |
| May-2009 | 75.9 |
| Jun-2009 | 75.9 |
| Jul-2009 | 75.8 |
| Aug-2009 | 75.6 |
| Sep-2009 | 75.1 |
| Oct-2009 | 75 |
| Nov-2009 | 75.2 |
| Dec-2009 | 74.8 |
| Jan-2010 | 75.1 |
| Feb-2010 | 75.1 |
| Mar-2010 | 75.1 |
| Apr-2010 | 75.4 |
| May-2010 | 75.1 |
| Jun-2010 | 75.2 |
| Jul-2010 | 75.1 |
| Aug-2010 | 75 |
| Sep-2010 | 75.1 |
| Oct-2010 | 75 |
| Nov-2010 | 74.8 |
| Dec-2010 | 75 |
| Jan-2011 | 75.2 |
| Feb-2011 | 75.1 |
| Mar-2011 | 75.3 |
| Apr-2011 | 75.1 |
| May-2011 | 75.2 |
| Jun-2011 | 75 |
| Jul-2011 | 75 |
| Aug-2011 | 75.1 |
| Sep-2011 | 74.9 |
| Oct-2011 | 74.9 |
| Nov-2011 | 75.3 |
| Dec-2011 | 75.4 |
| Jan-2012 | 75.5 |
| Feb-2012 | 75.5 |
| Mar-2012 | 75.7 |
| Apr-2012 | 75.7 |
| May-2012 | 75.7 |
| Jun-2012 | 75.6 |
| Jul-2012 | 75.6 |
| Aug-2012 | 75.7 |
| Sep-2012 | 76 |
| Oct-2012 | 76.1 |
| Nov-2012 | 75.8 |
| Dec-2012 | 76 |
| Jan-2013 | 75.6 |
| Feb-2013 | 75.8 |
| Mar-2013 | 75.8 |
| Apr-2013 | 75.8 |
| May-2013 | 76 |
| Jun-2013 | 75.9 |
| Jul-2013 | 76 |
| Aug-2013 | 76 |
| Sep-2013 | 76 |
| Oct-2013 | 75.6 |
| Nov-2013 | 76.1 |
| Dec-2013 | 76.1 |
| Jan-2014 | 76.4 |
| Feb-2014 | 76.4 |
| Mar-2014 | 76.5 |
| Apr-2014 | 76.5 |
| May-2014 | 76.4 |
| Jun-2014 | 76.9 |
| Jul-2014 | 76.7 |
| Aug-2014 | 76.9 |
| Sep-2014 | 76.8 |
| Oct-2014 | 76.9 |
| Nov-2014 | 76.9 |
| Dec-2014 | 77.1 |
| Jan-2015 | 77.1 |
| Feb-2015 | 77.2 |
| Mar-2015 | 77.1 |
| Apr-2015 | 77.2 |
| May-2015 | 77.2 |
| Jun-2015 | 77.4 |
| Jul-2015 | 77.1 |
| Aug-2015 | 77.3 |
| Sep-2015 | 77.2 |
| Oct-2015 | 77.3 |
| Nov-2015 | 77.4 |
| Dec-2015 | 77.4 |
| Jan-2016 | 77.7 |
| Feb-2016 | 77.8 |
| Mar-2016 | 77.9 |
| Apr-2016 | 77.7 |
| May-2016 | 77.8 |
| Jun-2016 | 77.9 |
| Jul-2016 | 77.9 |
| Aug-2016 | 77.9 |
| Sep-2016 | 78 |
| Oct-2016 | 78.2 |
| Nov-2016 | 78.1 |
| Dec-2016 | 78.1 |
| Jan-2017 | 78.2 |
| Feb-2017 | 78.3 |
| Mar-2017 | 78.5 |
| Apr-2017 | 78.6 |
| May-2017 | 78.4 |
| Jun-2017 | 78.6 |
| Jul-2017 | 78.6 |
| Aug-2017 | 78.5 |
| Sep-2017 | 78.9 |
| Oct-2017 | 78.9 |
| Nov-2017 | 79 |
| Dec-2017 | 79.1 |
| Jan-2018 | 79 |
| Feb-2018 | 79.3 |
| Mar-2018 | 79.2 |
| Apr-2018 | 79.2 |
| May-2018 | 79.2 |
| Jun-2018 | 79.3 |
| Jul-2018 | 79.5 |
| Aug-2018 | 79.4 |
| Sep-2018 | 79.3 |
| Oct-2018 | 79.7 |
| Nov-2018 | 79.7 |
| Dec-2018 | 79.7 |
| Jan-2019 | 79.9 |
| Feb-2019 | 79.9 |
Source: EPI analysis of Bureau of Labor Statistics’ Current Population Survey public data
As the economy continued to improve, not only did unemployment fall precipitously, but the share of workers (re)entering the labor force continued to rise. As it turns out (and what we’ve long argued), workers have not been permanently sidelined from the Great Recession, but have systematically been returning to the labor market in search of opportunities. Over the last few years, the newly employed have been coming both from the ranks of the unemployed as well as from outside the labor force, those who were not actively seeking work the month prior to finding a job. In fact, the share of newly employed workers who did not look for work the previous month is at a historic high. Over 7-in-10 newly employed workers are coming from out of the labor force. Clearly, these sidelined workers wanted jobs, yet another indication that the unemployment rate is understating the extent of slack or job searchers compared to previous periods.
Share of newly employed workers who said that they were not actively searching for work in the previous month
| Share of newly employed workers who said that they were not actively searching for work in the previous month | |
|---|---|
| Apr-1990 | 61.9% |
| May-1990 | 62.6% |
| Jun-1990 | 62.0% |
| Jul-1990 | 62.0% |
| Aug-1990 | 61.6% |
| Sep-1990 | 62.3% |
| Oct-1990 | 61.0% |
| Nov-1990 | 61.2% |
| Dec-1990 | 60.4% |
| Jan-1991 | 59.9% |
| Feb-1991 | 59.0% |
| Mar-1991 | 58.5% |
| Apr-1991 | 57.7% |
| May-1991 | 57.6% |
| Jun-1991 | 57.2% |
| Jul-1991 | 58.0% |
| Aug-1991 | 57.8% |
| Sep-1991 | 57.7% |
| Oct-1991 | 57.3% |
| Nov-1991 | 56.9% |
| Dec-1991 | 57.0% |
| Jan-1992 | 56.8% |
| Feb-1992 | 57.1% |
| Mar-1992 | 57.1% |
| Apr-1992 | 57.2% |
| May-1992 | 57.3% |
| Jun-1992 | 56.6% |
| Jul-1992 | 56.4% |
| Aug-1992 | 56.1% |
| Sep-1992 | 55.9% |
| Oct-1992 | 55.7% |
| Nov-1992 | 55.8% |
| Dec-1992 | 56.1% |
| Jan-1993 | 56.6% |
| Feb-1993 | 57.7% |
| Mar-1993 | 58.3% |
| Apr-1993 | 58.4% |
| May-1993 | 58.2% |
| Jun-1993 | 58.1% |
| Jul-1993 | 57.5% |
| Aug-1993 | 57.5% |
| Sep-1993 | 58.0% |
| Oct-1993 | 58.9% |
| Nov-1993 | 58.5% |
| Dec-1993 | 58.3% |
| Jan-1994 | 58.8% |
| Feb-1994 | 59.2% |
| Mar-1994 | 59.1% |
| Apr-1994 | 58.7% |
| May-1994 | 58.3% |
| Jun-1994 | 58.5% |
| Jul-1994 | 58.6% |
| Aug-1994 | 59.0% |
| Sep-1994 | 59.1% |
| Oct-1994 | 59.8% |
| Nov-1994 | 60.1% |
| Dec-1994 | 60.3% |
| Jan-1995 | 60.4% |
| Feb-1995 | 59.5% |
| Mar-1995 | 59.7% |
| Apr-1995 | 59.7% |
| May-1995 | 59.2% |
| Jun-1995 | 59.5% |
| Jul-1995 | 59.5% |
| Aug-1995 | 60.0% |
| Sep-1995 | 60.2% |
| Oct-1995 | 59.9% |
| Nov-1995 | 60.6% |
| Dec-1995 | 59.9% |
| Jan-1996 | 59.8% |
| Feb-1996 | 60.3% |
| Mar-1996 | 60.7% |
| Apr-1996 | 61.0% |
| May-1996 | 60.7% |
| Jun-1996 | 60.8% |
| Jul-1996 | 61.5% |
| Aug-1996 | 60.8% |
| Sep-1996 | 60.9% |
| Oct-1996 | 60.2% |
| Nov-1996 | 60.6% |
| Dec-1996 | 59.6% |
| Jan-1997 | 59.1% |
| Feb-1997 | 58.9% |
| Mar-1997 | 60.3% |
| Apr-1997 | 61.4% |
| May-1997 | 61.8% |
| Jun-1997 | 61.1% |
| Jul-1997 | 60.4% |
| Aug-1997 | 61.3% |
| Sep-1997 | 61.9% |
| Oct-1997 | 62.5% |
| Nov-1997 | 62.7% |
| Dec-1997 | 62.8% |
| Jan-1998 | 63.3% |
| Feb-1998 | 62.7% |
| Mar-1998 | 62.9% |
| Apr-1998 | 62.4% |
| May-1998 | 63.5% |
| Jun-1998 | 63.2% |
| Jul-1998 | 64.2% |
| Aug-1998 | 64.0% |
| Sep-1998 | 65.2% |
| Oct-1998 | 65.1% |
| Nov-1998 | 65.1% |
| Dec-1998 | 64.9% |
| Jan-1999 | 65.6% |
| Feb-1999 | 65.5% |
| Mar-1999 | 64.2% |
| Apr-1999 | 65.3% |
| May-1999 | 66.1% |
| Jun-1999 | 67.4% |
| Jul-1999 | 66.4% |
| Aug-1999 | 65.7% |
| Sep-1999 | 65.3% |
| Oct-1999 | 65.5% |
| Nov-1999 | 65.3% |
| Dec-1999 | 65.1% |
| Jan-2000 | 64.4% |
| Feb-2000 | 65.4% |
| Mar-2000 | 65.7% |
| Apr-2000 | 65.9% |
| May-2000 | 65.6% |
| Jun-2000 | 65.9% |
| Jul-2000 | 65.4% |
| Aug-2000 | 65.5% |
| Sep-2000 | 65.6% |
| Oct-2000 | 66.5% |
| Nov-2000 | 67.4% |
| Dec-2000 | 68.1% |
| Jan-2001 | 69.0% |
| Feb-2001 | 68.6% |
| Mar-2001 | 67.9% |
| Apr-2001 | 66.9% |
| May-2001 | 65.8% |
| Jun-2001 | 65.3% |
| Jul-2001 | 65.7% |
| Aug-2001 | 66.2% |
| Sep-2001 | 66.6% |
| Oct-2001 | 65.5% |
| Nov-2001 | 64.4% |
| Dec-2001 | 62.9% |
| Jan-2002 | 62.6% |
| Feb-2002 | 62.3% |
| Mar-2002 | 61.7% |
| Apr-2002 | 61.9% |
| May-2002 | 62.8% |
| Jun-2002 | 64.4% |
| Jul-2002 | 64.5% |
| Aug-2002 | 64.0% |
| Sep-2002 | 63.1% |
| Oct-2002 | 63.1% |
| Nov-2002 | 63.7% |
| Dec-2002 | 64.1% |
| Jan-2003 | 64.2% |
| Feb-2003 | 64.2% |
| Mar-2003 | 64.5% |
| Apr-2003 | 64.3% |
| May-2003 | 63.7% |
| Jun-2003 | 63.5% |
| Jul-2003 | 63.1% |
| Aug-2003 | 63.2% |
| Sep-2003 | 63.4% |
| Oct-2003 | 64.3% |
| Nov-2003 | 64.7% |
| Dec-2003 | 63.7% |
| Jan-2004 | 63.6% |
| Feb-2004 | 63.4% |
| Mar-2004 | 64.9% |
| Apr-2004 | 64.3% |
| May-2004 | 64.3% |
| Jun-2004 | 63.7% |
| Jul-2004 | 64.2% |
| Aug-2004 | 64.5% |
| Sep-2004 | 64.1% |
| Oct-2004 | 64.2% |
| Nov-2004 | 64.0% |
| Dec-2004 | 64.4% |
| Jan-2005 | 64.6% |
| Feb-2005 | 64.8% |
| Mar-2005 | 64.9% |
| Apr-2005 | 65.1% |
| May-2005 | 65.8% |
| Jun-2005 | 66.1% |
| Jul-2005 | 66.6% |
| Aug-2005 | 65.9% |
| Sep-2005 | 66.5% |
| Oct-2005 | 66.2% |
| Nov-2005 | 66.0% |
| Dec-2005 | 65.9% |
| Jan-2006 | 65.8% |
| Feb-2006 | 67.3% |
| Mar-2006 | 67.3% |
| Apr-2006 | 67.6% |
| May-2006 | 67.3% |
| Jun-2006 | 67.2% |
| Jul-2006 | 66.7% |
| Aug-2006 | 66.4% |
| Sep-2006 | 65.9% |
| Oct-2006 | 66.9% |
| Nov-2006 | 67.6% |
| Dec-2006 | 68.3% |
| Jan-2007 | 68.0% |
| Feb-2007 | 67.0% |
| Mar-2007 | 66.5% |
| Apr-2007 | 65.8% |
| May-2007 | 66.2% |
| Jun-2007 | 67.6% |
| Jul-2007 | 67.7% |
| Aug-2007 | 67.6% |
| Sep-2007 | 66.9% |
| Oct-2007 | 67.0% |
| Nov-2007 | 67.5% |
| Dec-2007 | 66.6% |
| Jan-2008 | 66.4% |
| Feb-2008 | 65.4% |
| Mar-2008 | 65.6% |
| Apr-2008 | 64.7% |
| May-2008 | 65.1% |
| Jun-2008 | 64.9% |
| Jul-2008 | 65.3% |
| Aug-2008 | 64.2% |
| Sep-2008 | 62.9% |
| Oct-2008 | 62.1% |
| Nov-2008 | 61.9% |
| Dec-2008 | 62.3% |
| Jan-2009 | 62.2% |
| Feb-2009 | 61.6% |
| Mar-2009 | 60.8% |
| Apr-2009 | 59.8% |
| May-2009 | 59.6% |
| Jun-2009 | 58.3% |
| Jul-2009 | 57.5% |
| Aug-2009 | 57.0% |
| Sep-2009 | 56.8% |
| Oct-2009 | 57.6% |
| Nov-2009 | 56.7% |
| Dec-2009 | 57.7% |
| Jan-2010 | 57.9% |
| Feb-2010 | 58.8% |
| Mar-2010 | 58.7% |
| Apr-2010 | 57.4% |
| May-2010 | 56.4% |
| Jun-2010 | 56.6% |
| Jul-2010 | 57.2% |
| Aug-2010 | 58.4% |
| Sep-2010 | 58.8% |
| Oct-2010 | 58.9% |
| Nov-2010 | 58.9% |
| Dec-2010 | 58.4% |
| Jan-2011 | 59.1% |
| Feb-2011 | 59.5% |
| Mar-2011 | 60.1% |
| Apr-2011 | 60.4% |
| May-2011 | 60.2% |
| Jun-2011 | 59.7% |
| Jul-2011 | 59.8% |
| Aug-2011 | 59.6% |
| Sep-2011 | 60.6% |
| Oct-2011 | 59.8% |
| Nov-2011 | 59.8% |
| Dec-2011 | 59.1% |
| Jan-2012 | 59.2% |
| Feb-2012 | 59.1% |
| Mar-2012 | 59.4% |
| Apr-2012 | 60.3% |
| May-2012 | 60.9% |
| Jun-2012 | 61.6% |
| Jul-2012 | 61.8% |
| Aug-2012 | 62.3% |
| Sep-2012 | 62.3% |
| Oct-2012 | 62.0% |
| Nov-2012 | 61.8% |
| Dec-2012 | 62.5% |
| Jan-2013 | 62.2% |
| Feb-2013 | 61.5% |
| Mar-2013 | 61.6% |
| Apr-2013 | 63.1% |
| May-2013 | 63.5% |
| Jun-2013 | 63.2% |
| Jul-2013 | 62.3% |
| Aug-2013 | 62.9% |
| Sep-2013 | 63.5% |
| Oct-2013 | 64.3% |
| Nov-2013 | 64.1% |
| Dec-2013 | 63.6% |
| Jan-2014 | 63.9% |
| Feb-2014 | 63.6% |
| Mar-2014 | 63.9% |
| Apr-2014 | 62.8% |
| May-2014 | 64.2% |
| Jun-2014 | 64.5% |
| Jul-2014 | 66.0% |
| Aug-2014 | 65.5% |
| Sep-2014 | 65.3% |
| Oct-2014 | 64.9% |
| Nov-2014 | 65.3% |
| Dec-2014 | 65.8% |
| Jan-2015 | 67.2% |
| Feb-2015 | 67.8% |
| Mar-2015 | 68.4% |
| Apr-2015 | 68.0% |
| May-2015 | 68.5% |
| Jun-2015 | 68.2% |
| Jul-2015 | 69.0% |
| Aug-2015 | 68.6% |
| Sep-2015 | 68.8% |
| Oct-2015 | 68.6% |
| Nov-2015 | 68.6% |
| Dec-2015 | 69.0% |
| Jan-2016 | 68.6% |
| Feb-2016 | 69.8% |
| Mar-2016 | 70.5% |
| Apr-2016 | 70.7% |
| May-2016 | 69.7% |
| Jun-2016 | 69.1% |
| Jul-2016 | 68.9% |
| Aug-2016 | 69.4% |
| Sep-2016 | 69.2% |
| Oct-2016 | 68.2% |
| Nov-2016 | 67.7% |
| Dec-2016 | 68.8% |
| Jan-2017 | 69.4% |
| Feb-2017 | 69.1% |
| Mar-2017 | 68.8% |
| Apr-2017 | 69.4% |
| May-2017 | 70.2% |
| Jun-2017 | 70.7% |
| Jul-2017 | 70.2% |
| Aug-2017 | 70.7% |
| Sep-2017 | 70.4% |
| Oct-2017 | 70.7% |
| Nov-2017 | 70.8% |
| Dec-2017 | 70.7% |
| Jan-2018 | 71.2% |
| Feb-2018 | 71.0% |
| Mar-2018 | 70.6% |
| Apr-2018 | 70.7% |
| May-2018 | 71.2% |
| Jun-2018 | 72.6% |
| Jul-2018 | 73.1% |
| Aug-2018 | 73.0% |
| Sep-2018 | 72.9% |
| Oct-2018 | 73.0% |
| Nov-2018 | 73.3% |
| Dec-2018 | 73.1% |
| Jan-2019 | 72.5% |
| Feb-2019 | 72.4% |
Note: Because of volatility in these data, the line reflects three month moving averages
Source: Bureau of Labor Statistics, Labor Force Flows: Unemployed to Employed (16 Years and Over) [LNS17100000], and Not in Labor Force to Employed (16 years and over) [LNS17200000], retrieved from FRED (Federal Reserve Bank of St. Louis).
By banning mandatory arbitration clauses and class and collective action waivers, Congress could restore a fundamental workers right
Last term, the Supreme Court dealt a significant blow to the fundamental right of workers in this country to join together to address workplace disputes. In Epic Systems v. Lewis, the Court, by a 5-4 majority, held that an employer may lawfully require its employees to agree, as a condition of employment, to resolve all workplace disputes on an individual basis in arbitration. Siding with employers and the Trump administration, the Court’s decision paves the way for the majority of workers in this country to be forced to sign away their right to pursue workplace disputes on a collective or class basis. Available data suggests that, unless Congress acts, more than 80 percent of workplaces will subject their workers to mandatory arbitration with class and collective action waivers within six years.
Mandatory arbitration clauses rob workers of their right to take their employer to court for all types of employment-related claims, forcing workers into a process that overwhelmingly favors employers. Class and collective action waivers go one step further, forcing workers to manage this process alone, even though these issues are rarely confined to one single worker.
Workers depend on collective and class actions to enforce many workplace rights. Employment class actions have helped to combat race and sex discrimination and are fundamental to the enforcement of wage and hour standards. Without the ability to aggregate claims, it is very difficult, if not impossible, for workers to find legal representation in these matters. This is particularly true for low-wage workers, whose cases are unlikely to involve large enough awards to attract attorneys to invest time in the case. Class and collective action suits allow workers to pool their claims, making it possible for an attorney to earn enough to make the case worth pursuing.
The new Democratic House should make worker empowerment a priority
For the first time in nearly a decade, Democrats will hold the majority in the House when Congress convenes in January. The results of yesterday’s election are encouraging and represent historic progress—with a record number of women winning seats in the house, including key victories by diverse candidates across faiths and ethnicities. And importantly, Democrats won the popular vote in the House by a 9.2 percent margin despite today’s 3.7 percent unemployment rate, which should have provide great advantage to the incumbent party.
It is nevertheless important to note that with Republicans in control of the Senate and the White House, it is unlikely that policies that promote a just economy for working people will become law. Still, House Democrats have the opportunity to advance long overdue reforms. It is critical that they focus on an agenda that serves our nation’s workers. This must include House Democrats working to raise workers’ wages, restore workers’ access to justice on the job, and promote workers’ right to collectively bargain.
Workers deserve a fair minimum wage. At $7.25 per hour, the federal minimum wage is now more than 25 percent below where it was in real terms half a century ago. House Democrats must advance legislation to raise the federal minimum wage to $15 per hour by 2024, indexing it to the national median wage thereafter, and phasing out the tipped minimum wage and other subminimum wages. Given inflation expectations, $15 in 2024 would be around $13.00 in 2018 dollars, an appropriate level for the federal floor. The Raise the Wage Act introduced this Congress included all of these reforms. The House must work to pass similar legislation in the new Congress.
Workers should not be forced to sign away their rights as a condition of employment. The use of mandatory arbitration and collective and class action waivers—under which workers are forced to handle workplace disputes as individuals through arbitration, rather than being able to resolve these matters together in court—makes it more difficult for workers to enforce their rights. These agreements bar access to the courts for all types of employment-related claims, including those based on the Fair Labor Standards Act, Title VII of the Civil Rights Act, and the Family Medical Leave Act. This means that a worker who is not paid fairly, discriminated against, or sexually harassed, is forced into a process that overwhelmingly favors the employer—and forced to manage this process alone, even though these issues are rarely confined to one single worker. Congress must act to ban mandatory arbitration agreements and class and collective action waivers. The Restoring Justice for Workers Act introduced this Congress includes all of these reforms. The House should work to pass this important reform in the new Congress.
Voters in Missouri and Arkansas just lifted pay for 1 million workers
In yesterday’s election, voters in Missouri and Arkansas gave overwhelming approval to ballot measures that will raise their state’s minimum wage over the next several years, lifting pay for a combined 1 million workers. In Missouri, 62 percent of voters elected to raise the state minimum wage from its current $7.85 to $12 an hour in 2023. In Arkansas, 68 percent of voters supported a measure that will raise the state minimum wage to $11 per hour in 2021 from its current value of $8.50.
The increase in Arkansas will raise pay for an estimated 300,000 workers (about a quarter of the state’s wage-earning workforce). The Missouri increase will lift pay for 677,000 workers (also about a quarter of wage-earners in the state.) In both cases, the majority of workers who will get a raise are women, most work full time, and they come from families with modest incomes. Analyses of the measures estimate that the raise in Arkansas will put over $400 million into the pockets of low-wage workers there over the course of the increases. In Missouri, low-wage workers will receive nearly $870 million in additional wages over the course of the measure’s implementation.
In voting to raise their state minimum wages, voters in Arkansas and Missouri are making long-overdue corrections to policy failures that political leaders in those states, and at the federal level, should have fixed a long time ago. Raising the minimum wage in Arkansas to $11 by 2021 and $12 by 2023 in Missouri will bring the minimum wage in those states, in both cases, roughly back to where the federal minimum wage was in 1968, when it equaled roughly $10 an hour in today’s dollars. According to the Congressional Budget Office’s (CBO’s) projections for inflation, $11 in 2021 is $9.98 in 2017 dollars, $12 in 2023 is $10.40 in 2017 dollars.
Updating key labor standards, like the minimum wage, is critical if policymakers want to do something about the enormous stagnation in wages that has plagued the country for decades. The recent modest uptick in wage growth is not nearly enough to undo the damage that has been done over the past 70 years. Since the mid-1970s, as the U.S. economy has grown and productivity has risen, hourly pay has barely budged after adjusting for inflation. Since 1973, average labor productivity has grown 77 percent; yet hourly compensation for the typical U.S. worker—and this includes both wages and benefits, such as payments for healthcare premiums and retirement accounts—has grown only 12.4 percent. For low-wage workers, the trends are even worse.