September jobs report: solid, slowing, and not yet at full employment

October 4th, 2019 at 9:37 am

Payrolls rose 136,000 last month and the unemployment rate dipped to 3.5 percent, its lowest rate since the late 1960s. Though the payroll number missed analysts’ expectations (~145,000), the more reliable 3-month average came in at a healthy 157,000, strong enough to put downward pressure on unemployment (the prior two months of payroll data were revised up by 45,000 jobs).

Our monthly smoother takes 3, 6, and 12-month averages of monthly job gains to help pull out the underlying trend out of the noisier monthly data. Over the past 6 months, payroll gains have average 154,000, a deceleration from the 12-month number (179K), but such a pattern is expected in an economy closing in on—though not yet at—full employment.

Wage growth for private-sector workers was up 2.9 percent over the past year, a slightly slower rate than in previous months. The wage pace was stronger for middle-wage workers (production, non-supervisors) at 3.5 percent, but in both cases, as the figures reveal (note especially the 6-month moving averages), the trend in wage growth is not accelerating, even given the low unemployment rate. For the “all” group (first figure), there’s even some evidence of decelerating wages, a possibility that is now on my watch list. I return to these important observations below.

State and local government hiring was important in September, adding 24,000 jobs. Though analysts expected hiring for the decennial Census to be a factor in these data, that was not the case, as the BLS reported such hiring only accounted for 1,000 jobs last month. The factory sector is clearly stressed, with manufacturing losing 2,000 jobs in September. The GM strike is certainly in the mix here, but thus far this year, the factory sector has added an average of fewer than 5,000 jobs per month, compared to 22,000/month last year. That’s much more trade-war than strike.

As noted, the Household survey showed greater signs of job-market strength last month. Along with unemployment at a 50 year low, the underemployment rate (the “u6” rate, which includes part-timers who want full-time work) fell to 6.9 percent, close to its all-time low of 6.8 in October of 2000 (this series only starts in 1994). The closely watched employment rate (“epop,” for employment-to-population ratio) for prime-age workers ticked up one-tenth for both men and women. Women’s prime-age epop–74 percent last month–has handily surpassed its 2007 peak, while men’s–86.4 percent–is still below their 2007 peak of 88 percent.

However, as the next figure shows, since the 1970s, men’s epop’s have moved like a ratchet–highly cyclical, but never quite regaining prior peaks. One conclusion is that men (and women) respond to employment opportunities but, at least for the men, they’ve been losing more in the downturns than they’ve gained in the expansions. My analysis suggests that if the cycle persists, prime-age epop’s will regain their prior peak, pushing back on the long-term ratchet.

Consider the following:

–Wage growth is not speeding up and probably decelerating;

–The pace of job gains has attenuated but remains solid, even this late in the expansion;

–Labor supply continues to grow, as per the epop discussion above.

–Price growth shows little pressure, even at historically low unemployment.

Put these facts together and one, strong conclusion is that even in year 11 of this long expansion, the U.S. labor market is not yet at full employment. Thus, the Federal Reserve has little cause to tap the growth brakes and good reasons to try to keep the recovery going, which in the current context means pushing back on pressures from the trade war, slowing global growth, and political chaos.

[Huge hat-tip to Katie Windham for stepping up and helping with the above!]

Got work? The highly responsive labor supply of low-income, prime-age workers.

October 2nd, 2019 at 8:02 pm

[Note: this is draft of a forthcoming paper for CBPP’s Full Employment Project. I posted it here first as I will be referencing its findings at a Brookings inflation conference on Thurs, Oct 3.]

By Jared Bernstein and Keith Bentele[i]


The benefits to running a hot labor market continue to be evident both in the data and in anecdotal accounts. In our last paper, we examined the monetary policy rationale for allowing high-pressure labor markets to continue to flourish.[ii] We also focused on the benefits of persistently low unemployment to lower income workers, through both higher real pay and more hours of work. In this short paper, we turn back to this evidence, with a closer focus on the benefits of high-pressure labor markets to the labor supply of lower-paid workers.

The most basic labor market theories generally lack the necessary nuance to shed much light on this question. The textbook 101 model assumes full employment and an equilibrium wage where employers’ demands’ and undifferentiated workers’ supply perfectly match. A wage set too high will lead to more job seekers than jobs; a wage set too low will cause the opposite problem: too few workers willing to fill available slots. In the real world, however, there are of course periods of slack labor markets, along with factors such as racial and gender discrimination. Some particularly disadvantaged workers may face uniquely high barriers to labor market entry. Also, recent research has identified large sectors in our economy, like retail, tech, and health care, where few employers dominate. In such markets, employers can become wage makers, not wage takers, i.e., they can use their dominance to set wages below the theoretical equilibrium.

In this note, we ask a simple, specific question related to this more realistic version of the labor market: do low-wage workers respond to high-pressure labor markets by increasing their labor supply? What evidence is there that tight job markets pull in such workers?

We find highly cyclical responses to both the extensive and intensive margins of labor supply for low-income, prime-age persons, especially for first-quintile African Americans and for women. A simple decomposition finds, for example, that the earnings of low-income Black people doubled in the high-pressure labor market of the 1990s, with gains in their share working (extensive margin) explaining half of the increase. Conversely, under low-pressure conditions, the decline in working shares dominates sharp income losses for these groups of people. We also find the extensive margin to be particularly important for low-income women, and a simple simulation suggests that in the hot labor market of the 1990s, most of the gains in the gender gap were due to women’s relative (to men) gains along the extensive margin. We offer policy implications of these findings on both macro and micro levels.

Previous Literature

Arthur Okun is widely credited with pioneering research into the benefits of very low unemployment for marginalized groups. In the context of a “high-pressure economy” he hypothesized that employers are more likely to lower formal hiring standards in order to fill vacancies, and that this would benefit  less-advantaged job seekers in the labor market[iii]. In a 1973 paper Okun found that in such periods, women and teenagers experienced disproportionately large increases in employment. Since this initial confirmation of a rather straightforward hypothesis, many studies have reinforced this finding. The uniquely strong economy in the late-1990s prompted a body of work evaluating the impacts of these conditions. Roberts and Rodgers (2000) examined the impacts of low unemployment on earnings and employment in metro labor markets and found that less educated men, and young African American men in particular, experienced the greatest improvements[iv]. Similarly, Wilson (2015) explored the positive impacts of strong economic growth in the late-1990s on both the employment and earnings of African American people[v]. Katz and Krueger (1999) found that the tight labor market of the late-1990s contributed to a significant increase in the both the incomes of lower-income families and falling poverty rates in those years[vi]. Jargowsky’s (2003) research captured a 24% decline in the number of people living in high-poverty neighborhoods, census tracts where 40% or more of residents are in poverty, between 1990 and 2000[vii]. This study found that the strong economy reduced the concentration of poverty for all racial and ethnic groups, but had a particularly pronounced effect on African American communities. The share of poor African American people living in high poverty neighborhoods fell from 30% in 1990 to 19% in 2000. Such impacts have informed William J Wilson’s assertion that the “ideal solution” to addressing a root cause of concentrated poverty, inner-city joblessness, “would be economic policies that produce tight labor markets” (Wilson 2008:568)[viii].

The specific findings of greater cyclical variation in wages, employment, and labor market participation for economically vulnerable groups have been widely documented. Hoynes (2000) found this to be the case for the employment and earnings of less educated workers, people of color, and women with lower skill levels. All experienced more variation over the course of the business cycle relative to higher skilled men, a finding that was particularly pronounced in the context of one’s probability of being employed full-time year round[ix]. Jefferson (2008) found that trends in the employment-to-population ratio for workers with less education are substantially more volatile compared to those with more educated workers [x]. And more recent research, including that which examines the current strong economy, has only further bolstered these findings. For example, Aaronson et al. (2019:3) state that their research,

“reaffirm[s] the earlier finding of other authors that the labor market outcomes of blacks, Hispanics, and those with less education are more cyclically sensitive than the outcomes of whites and those with more education.”[xi]

And consistent with the work of Roberts and Rodgers (2000), they find that cyclical variability in labor market outcomes is particularly pronounced for young African American workers. Further, Aaronson et al. (2019) find suggestive evidence that further strengthening in the context of an already very strong economy is particularly beneficial to some disadvantaged groups.

The flip side of higher cyclical variability is that recessions hit low-wage workers and members of marginalized groups particularly hard. Decreases in earnings and employment during recessions are consistently disproportionately larger for low-wage earners, people of color, and low-income female-headed households[xii]. Kenworthy (2011) has stressed the devastating impact of recessions on hours worked by very low-income households, an issue compounded when followed by a weak recovery[xiii]. While the evidence is admittedly thin at this point, high-pressure labor markets may potentially play a small protective role in regards to this cyclical vulnerability. Aaronson et al. (2019) find that the gains made during high-pressure periods for Black people and women are “somewhat persistent”. Similarly, Hotchkiss and Moore (2018) find that high-pressure periods lead to higher employment, wages, and earnings in subsequent downturns for young men and Black people. However, they stress that these specific positive benefits are largely confined to these groups and may be short lived.


For a full description of the data used below, see our earlier paper. We use the same procedures and inclusion criteria to generate the estimates used here with only two important changes. First, previously we used household income from all sources to determine quintile thresholds, here we have used a measure of total household income adjusted for household size (household income/number of household members). Second, our estimates of annual hours worked are based on the average of individual-level data on hours worked (hours per week*weeks per year), as opposed to a quintile-level estimate of annual hours. We found that our inclusion of individuals with $0 earnings in our estimates of quintile level hours and weeks produced an underestimation of annual hours that was not present in the averaged individual-level estimate of annual hours.


While it is common for labor analysts to look at employment rates, such rates are generally overall averages, often by gender or race. Because our data is broken out by income fifth (and by gender and race), we can look at the share of prime-age people with positive annual work hours, meaning any reported paid work last year, by quintile.[xiv] As Figure 1 shows, consistent with prior research, the series for persons in the lowest fifth is much more cyclical than that for the middle or top fifth.

Figure 1.

In fact, simply using the unemployment rate (logged, with one lag) and the lagged first-quintile series in the above figure, we can derive a dynamic prediction that tracks the series well (See Figure 2).[xv] The share working exceeded the forecast in the latter 1990s, but this was a period when many “pro-work” policy changes affected low-income workers, including work requirements in TANF, a large expansion in the Earned Income Tax Credit, and an increase in the minimum wage. But it was also a high-pressure labor market period. Using the 2018 unemployment rate of 3.9 percent, the model forecasts a jump of about 5 percentage points in 2018.[xvi]

Figure 2.

Figure 3 examines the share of working African American people. Because of the smaller sample size, we plot the bottom 40 percent. These workers appear to be particularly elastic to high-pressure labor markets, with large employment gains in the 1990s and in the current recovery.

Figure 3.

In the appendix, we include similar figures for prime-aged men and women in the first quintile of adjusted household incomes. The cyclical patterns are roughly similar to the total figure above (the simple model tracks the series well), though as earlier research has shown, the women’s series tends to trend up while that of men trends down due in part to structural challenges like the loss of production jobs.

Because we have earnings data and annual hours, we can decompose the earnings of low-income people into the share working (the “extensive margin”), annual hours among workers, and hourly wages (see Table 1).[xvii] By decomposing these changes over different time periods, we can observe how this group has fared in both high- and low-pressure labor markets and which factors have the greatest impact on real earnings growth.

Table 1. Real Earnings Contributions in High- and Low-Pressure Labor Markets

The first few panels look at high-pressure labor markets. Between 1993-2000, when the unemployment rate fell from 6.9 to 4 percent, the real, annual earnings for all low-income persons rose just under 50 percent (log points), from about $8,200 to $13,400. Importantly, we include those with zero hours, and thus zero earnings, in these calculations, so as to capture the impact on earnings of their crossing the extensive margin (going from zero to positive hours worked). As the first column shows, the share working increase 23 percent, and since this is an additive decomposition (the first three columns of the row “ln change” sum to the fourth column), this added labor supply explains almost half (23/49) of the earnings gain over this high-pressure period.

The second panel show the more recent period, 2011-2017, when unemployment fell from 8.9 percent to 4.4 percent. Real earnings were up about 30 percent over this period, meaning that on an annualized basis, real earnings grew 2 points faster in the 1990s high-pressure labor market (7 versus 5 percent per year) through 2017. The extensive and intensive margins, entry into employment and increased hours respectively, explain about one-third each, as does the growth in real hourly wages.

The next panel drills down into the experience of first-quintile African Americans in the high-pressure labor market of the 1990s, when Black unemployment fell from 13 percent to 7.6 percent and their real, annual earnings remarkably doubled, for an annual real growth rate of just under 15 percent per year. Fully half of that gain was due to an almost 50 percent increase in the share working, with the other half split between more annual hours by workers and higher real hourly wages.

It is important to turn to low-pressure labor markets to see how this process shifts into reverse. From 2007-11, the jobless rate rose from 4.6 to 8.9 percent, and first quintile real earnings fell almost 30 percent, with half the decline attributable to the fall in the share of workers from 67 to 58 percent. Compare this fall to the absence of change (not shown) for the top quintile over these years. In both 2007 and 2011, their prime-age share at work was 95 percent, a stark reminder of who bears the brunt of recessions.

African American unemployment about doubled, 2007-11, from about 8 to 16 percent. Black people in the first quintile lost almost half of their real earnings in this downturn, with three-fifths of the decline coming from the decline in the share working.

Turning back to the top fifth, the bottom panel of Table 1 shows how affluent, prime-age people are extremely inelastic regarding changes in labor supply. In fact, they’re largely topped out in terms of annual hours worked and share working. Their average share working is 95 percent and the standard deviation around that mean over these years is  three-fifths of a percentage point, compared to a standard deviation of 6 points for the first quintile of all prime-age people (10 times that of the top fifth) and 10 points for the African American first quintile. These are profound differences in cyclical variability in employment.

Table 2 presents the same decomposition for first quintile, prime-age people by gender, looking at the high-pressure 1993-2000 period, the Great Recession, and the slow recovery of 2007-11. As shown in the appendix figures on share working by gender, low-income women are more cyclically responsive in these data, at least until around 2000, when both genders appear somewhat more elastic to the business cycle. The findings show that low-income women’s extensive margin gains were particularly important in the 1990s, explaining 60 percent of their significant real earnings’ gains (45/75). For men, however, that margin contributed little compared to more hours worked and higher real wages.

Table 2. Real Earnings Contributions in High- and Low-Pressure Labor Markets, by Gender


Table 3 does a simple simulation to parse out the role of crossing the extensive margin in reducing the gender wage gap in the high-pressure labor market of the 1990s. Note that the gender gap compressed by 23 percentage points in these years, from 39 percent in 1993 to 62 percent in 2000. A simple simulation (see footnote and table note) suggests that almost all of that closure was a function of the increase in women’s share working.[xviii]

Table 3. Gender Inequality (Q1) and the Extensive Margin

Note: The 2000 simulated value (in bold) is the product of the 1993 share of women working and their 2000 hours and hourly wage.

In the low-pressure labor market panel, real earnings fell sharply for both genders, and in this case, the extensive margin dominates in both cases.


Our findings and those of other researchers, in tandem with extensive recent anecdotes from the media regarding new opportunities for left-behind workers, show that the labor supply of low-income workers, especially women and Black people, is highly elastic to labor market conditions, while that of high-income workers is much less so. In the high-pressure labor market of the latter 1990s, real annual earnings grew by half for all first quintile workers (including “zeros”) and doubled for African American people. In both cases, the increase in the share working explained about half these gains.

The policy implications of these findings invoke both macro and micro policies. At the macro level, as we stressed in our earlier paper, running the labor market hotter-for-longer returns economically significant benefits to those who need them the most. From the perspective of the Federal Reserve’s monetary policy, the additional fact that inflation has proven to be quite insensitive to low unemployment seals the deal: as long as inflation remains “well-anchored,” for vulnerable workers to get ahead, we can and should pursue full employment.

At the micro level, our findings speak strongly against the notion that receipt of anti-poverty benefits should be conditional on the work requirements that have recently surfaced in various states, for example, through federal waivers to the Medicaid program. Low-income workers have already been highly responsive to job opportunities; the problem is that those opportunities either don’t exist in slack labor markets, or they face internal (skill deficits) or external (discrimination) barriers to getting into the job market. Hassling them off of the benefit rolls by making them jump through administration hoops will not lead them to work more, but it will surely diminish their income and their health.


Table A.1

Table A.2



[i] We thank Jesse Rothstein for comments and Kathleen Bryant for technical assistance. Any mistakes are our own.

[ii]  Jared Bernstein and Keith Bentele, “The Increasing Benefits and Diminished Costs of Running a High-Pressure Labor Market,” Center on Budget and Policy Priorities: Full Employment Project, May 15, 2019,

[iii] Okun, Arthur M. 1973.“Upward Mobility in a High-pressure Economy.” Brookings Papers on Economic Activity. 1:207-261.

[iv] Cherry, Robert and William M. Rodgers III (eds.) 2000. Prosperity for All? The Economic Boom and African Americans. New York: Russell Sage Foundation.

[v] Wilson, Valerie. 2015. “The Impact of Full Employment on African American Employment and

Wages,” Economic Policy Institute.

[vi] Katz, Lawrence, and Alan Krueger. 1999. “The High-Pressure U.S. Labor Market of the 1990s.” Brookings Papers on Economic Activity. 1:1-87.

[vii] Paul Jargowsky. 2003.  “Stunning Progress, Hidden Problems: The Dramatic Decline of Concentrated Poverty in the 1990s.” The Brookings Institution.

[viii] William Julius Wilson. 2008-09. “The Political and Economic Forces Shaping Concentrated Poverty.” Political Science Quarterly. 123(4):555-571.

[ix] Hoynes, Hilary. 2000. “The Employment and Earnings of Less Skilled Workers Over the Business Cycle.” in Finding Jobs: Work and Welfare Reform, edited by Rebecca Blank and David Card. New York: Russell Sage Foundation.

[x] Jefferson, Philip N. 2008. “Educational Attainment and the Cyclical Sensitivity of

Employment.” Journal of Business and Economic Statistics 26(4):526-35.3

[xi] Aaronson, Stephanie R., Mary C. Daly, William Wascher &  David W. Wilcox. 2019. “Okun Revisited: Who Benefits Most From a Strong Economy?” Brookings Papers on Economic Activity. BPEA Conference Drafts.

[xii] Aaronson, Stephanie R., Mary C. Daly, William Wascher &  David W. Wilcox. 2019. “Okun Revisited: Who Benefits Most From a Strong Economy?” Brookings Papers on Economic Activity. BPEA Conference Drafts.

Bentele, Keith G. 2012. “Evaluating the Performance of the U.S. Social Safety Net in the Great Recession.” Center for Social Policy Publications. Paper 62.

Cajner, Tomaz, Tyler Radler, David Ratner, and Ivan Vidangos. 2017. “Racial Gaps in Labor Market Outcomes in the Last Four Decades and over the Business Cycle.” Working Paper 2017-071. Finance and Economics Discussion Series. Washington, D.C.: Federal Reserve Board.

Zavodny, Madeline, and Tao Zha. 2000. “Monetary Policy and Racial Unemployment Rates.” Federal Reserve Bank of Atlanta Economic Review 85(4):1–59.

[xiii] Kenworthy, Lane. 2011. Progress for the Poor. New York: Oxford University Press.

[xiv] Note that this is a somewhat different metric than the oft-cited prime-age employment rate from BLS. The measure asks if people are working in the reference week of the month; this one asks if people had any positive work hours over the course of the prior year. The latter tends to have a higher level, but the trends are roughly similar.

[xv] “Dynamic” in this context means the lagged dependent variable is predicted (versus plugging in the actual value) for each observation in the predicted series.

[xvi] We plan to shortly update this analysis with the recently released 2018 data.

[xvii] Annual earnings for all prime-age persons in the quintile (assigning zeros to non-workers) is the product of the
share working*annual hours*hourly wage. Taking log changes facilitates the decomposition.

[xviii] We make this calculation by simulating women’s 2000 earnings using their actual hours and wage but using their 1993 share working (45%). The difference between this simulated gender gap and the actual gender gap can thus be assigned to their large increase in share working.

The King of the Blues Birthday!

September 16th, 2019 at 3:45 pm

Google tells me that today would have been BB King’s 94’th birthday, so I got my booty over to YouTube to queue up one of my fav BB jams–Let’s Get Down to Business! BB crushes it, of course, but also dig busy-yet-funky electric bass playing by Jerry Jemmott.

“Whatever made us breakup baby
I don’t know til today
But if it was my fault
I swear I’ll change my ways!”



How hot labor markets can lead to misleading median comparisons.

September 10th, 2019 at 3:07 pm

The Census income and earnings data sometimes have a confusing characteristic that is not uncommon in these sorts of data, especially in periods of tight labor markets, as was 2018. The issue has to do with changes in medians from one year to the next.

For example, the data that came out this morning showed that for both men and women full-time, full-year workers, real annual earnings rose 3.4 and 3.3 percent, respectively, 2017-18. But for all ft/fy workers, combining both genders, earnings fell 0.6 percent. The decline was statistically insignificant, but jeez–that’s confusing, right? Why would earnings fall, overall, in a year with a clearly solid job market, especially when both genders did pretty well?

A number have folks have asked me about this today–a similar dynamic is in the data for median household income: the real median went up  significantly last year for the two household types–family and non-family (individual) households–but not for all households (i.e., it rose slightly, by 0.9 percent, but the change was insignificant).

One way this often occurs, especially in hotter labor markets, is that the composition of workers or families change in ways that can make it hard to figure out what’s up and what’s down. Consider the first two columns below, arrayed from lowest to highest, assuming no change in the composition of people or HHs in the data. The median goes up in ways that we expect in positive economies.

No comp change                   Comp change

Year 1 Year 2 Year 1 Year 2
1 2 1 1
2 3 2 1
3 4 3 2
4 5 4 3
5 6 5 4

Numbers in bold italics are medians.

But now look at columns 3 and especially 4. What happens there, relative to column 2, is that two people with very low incomes or earnings enter the distribution. Imagine, for example, that these two “1’s” were sitting it out on the sidelines of the labor market, but got pulled in, hoping to take advantage of the tight conditions. This shifts everybody else up that year such that median slips from 4 to 3. A comparison of the two years would suggest no change at all in incomes and earnings. In fact, the number of ft/fy women grew more than twice that of men last year (0.7 million men vs. 1.6 million women).

At any rate, I suspect something like this is going on in these data.

The 2018 Poverty, Income, and health coverage results: a tale of three forces.

September 10th, 2019 at 12:51 pm

This morning, the Census Bureau released new data on health insurance coverage, poverty, and middle-class incomes. While the data are for last year, they shine an important light on key aspects of families’ living standards that we don’t get from the more up-to-date macro-indicators, like GDP and unemployment.

As the economic recovery that began over a decade ago persisted through 2018, poverty once again fell, by half-a-percentage point, from 12.3 percent to 11.8 percent. Other results from the report show that anti-poverty and income support programs lifted millions of people out of poverty, including 27 million through Social Security alone. Though the real median household income—the income of the household right in the middle of the income scale—increased slightly less than 1 percent last year, the increase was not statistically significant. Median earnings of full-time men and women workers both rose significantly, by over 3 percent for each (for reasons discussed below, sometimes earnings rise significantly but income does not).

Health coverage, however, significantly deteriorated last year, as the share of the uninsured rose for the first time since 2009, from 7.9 percent to 8.5 percent. In total, 27.5 million lacked coverage in 2018, an increase of 1.9 million over 2017. This result is partially driven by actions of the Trump administration to undermine the Affordable Care Act (note that Medicaid coverage was down by 0.7 percentage points), and in this regard, it should be taken as a powerful signal of the impact of conservative policy on U.S. health coverage.

Taken together, the poverty, income, and health coverage results tell a tale of three powerful forces: the strong economy, effective anti-poverty programs, and the Trump administration’s ongoing attack on affordable health coverage. A strong labor market is an essential asset for working-age families, and the data are clear that poor people respond to the opportunities associated with a labor market closing in on full employment. Anti-poverty programs are lifting millions of economically vulnerable persons, including seniors and children, out of poverty. But while a strong labor market and a responsive safety net help to solve a lot of problems, the history of both U.S. and other countries shows that it takes national health care policy to ensure families have access to affordable coverage. The ACA was and is playing that role, but efforts to undermine its effectiveness are evident in the Census data.

Poverty, Income, Inequality

The Census provides two measures of poverty: the official poverty measure (OPM) and the Supplement Poverty Measure (SPM). The latter is a more accurate metric as it uses an updated and more realistic income threshold to determine poverty status, and it counts important benefits that the OPM leaves out. While the two measures often track each other, year-to-year, that wasn’t the case last year, as the SPM rose an insignificant one-tenth of a percent, from 13.0 to 13.1 percent, while the OPM fell a significant half-a-percent, from 12.3 to 11.8 percent. Because the SPM has a higher income threshold than the OPM, 4.4 million more people were poor by that more accurate measure.

Because it counts anti-poverty policies that the official measure leaves out, one particularly useful characteristic of the SPM data is that it breaks out the millions of people lifted out of poverty by specific anti-poverty programs. For example, refundable tax credits, such as the Earned Income Tax Credit and the Child Tax Credit lifted about 8 million people out of poverty in 2018; SNAP (food stamps) lifted 3 million more out each, and Social Security was the most powerful poverty reducer, lifting 27 million out of poverty in 2018, 18 million of whom were elderly (65 and older).

As noted, median household income, inflation-adjusted, rose less than a percent last year, a statistically insignificant change (meaning a change that is statistically indistinguishable from no change at all). Yet, real median earnings of full-time, full-year workers rose more than 3 percent for both men and women. It is hard to square these results, but they are not that unusual and probably have something to do with the changing composition of households and the fact that the median male worker is different from the median female worker and neither are necessarily in the median household. Note, for example, that family households (basically, two or more related people) and non-family households (people living alone) both rose significantly last year. But when the Census smushes them together, we get an insignificant increase.

I conclude from this and other information in the report, like the fact that the number of full-year workers rose 2.3 million, or the evidence showing real wage gains last year for middle and low-wage worker, that the strong labor market helped to boost family incomes in 2018 (though as I show below, these gains are slowing over time). Another key factor pushing up wage growth at the low end of the pay scale were the minimum wage hikes that occurred in 18 states in 2018, affecting 4.5 million workers, according to EPI.

Here’s one way to look at this relationship between labor markets and, in this case, poverty outcomes. It’s a scatterplot of unemployment against the change in poverty rates (using the OPM for which we have a long, consistent time series). It shows how low unemployment correlates with declines in the poverty rate and vice-versa. Why? Because able-bodied, poor people respond to tight labor markets, an important fact that pushes back on the alleged need for work requirements.

Sources: Census, BLS

Unfortunately, over the past few decades, labor markets have not consistently provided the job and earnings opportunities that help to support income growth for families in the bottom half of the income scale and longer-term comparisons show real median income not too far above its pre-recession peaks in 2000 and 2007. Moreover, as inequality has increased, we cannot blithely extrapolate from positive macro-indicators, like unemployment and GDP, to indicators like poverty and median income that will often reflect less improvement in periods when growth disproportionately accrues higher up the income and wealth scale. Though these Census data are less comprehensive than some other sources of inequality data, they do show that in 2018, the highest fifth of households held more income (52 percent of it) than the bottom 80 percent. Though, as noted, the survey has changed over the years such that long-term comparisons should be made with care, in 1967, this share was 44 percent, meaning the bottom 80 percent controlled more income than the top fifth. This increase in inequality is solidly confirmed in much other data.

The table below brings the critical dimension of race into the analysis (note: none of the income changes shown for 2018 are statistically significant). Median household income growth was slower in 2018 relative to earlier years, particularly for Hispanic families. Note also how poverty rates for blacks and Hispanics are multiples of those of whites. The scatterplot shows that lower unemployment correlates with lower poverty, and the table shows this effect to be greater for non-whites, who, over this period, experienced larger declines in unemployment accompanied by bigger drops in poverty. For example, over this period both white unemployment and poverty fell about 1 percentage point. For blacks, the comparable declines are 3 points for both variables. Hispanic poverty was down almost 4 percentage points.

Sources: Census, BLS.

Health Coverage

As noted, as soon as the ACA passed, the expansion of Medicaid coverage and premium subsidies through the exchanges quickly reduced the share of people without coverage. The discussion above—the one noting the increase in the uninsured rate—focused on the main national survey featured by the Census today (the ASEC). But due to its many discontinuities, to compare changes over time it is better to use the other survey results released by Census today, from the American Community Survey (ACS).

This figure clearly shows the historical coverage gains made by the ACA, but it also shows those gains fading in 2017 and this year, in 2018 (the 0.2 point increase in the uninsured rate last year is statistically significant).

Source: ACS

In recent years, gridlock, dysfunction, government shutdowns, and the general unwillingness of Congress to deal with our fundamental challenges has led to a justified skepticism of our federal system. But it’s worth remembering that not too far back, this system passed and implemented the largest and most consequential change in national health policy since the advent of Medicaid and Medicare in the 1960s. And the results, in terms of increased coverage, were equally dramatic.

This insight makes today’s health coverage results extremely concerning, as they reveal the impact of policies to reverse those gains. This attack on affordable coverage, according to my CBPP colleagues, “began on President Trump’s first day in office, with an executive order calling on federal agencies to waive and delay ACA provisions “to the maximum extent permitted by law.”’ They include repealing the individual mandate, anti-immigrant measures that are likely leading immigrants to avoid publicly-provided coverage, cuts in ACA outreach and enrollment assistance, work requirements that hassle people off of the Medicaid rolls, and a wide variety of waivers and eligibility barriers designed to shrink public coverage and shift medical costs onto consumers.

What’s it all mean?

The Census report is a tale of three powerful forces. First, the momentum from the strong economy continues to boost work and wages for low- and middle-income people. Second, anti-poverty programs are reliably helping to lift millions out of poverty. Third, such gains can be reversed by policies hostile to them. It is thus extremely worrisome to consider actions the Trump administration is taking to reduce government support of poor households, especially those with immigrants. Such actions include work requirements that ramp-up administrative demand to hassle low-income people off of Medicaid and SNAP; the “public charge” changes that threaten to block legal immigrants from seeking support they and their children need, changes in poverty measurement designed to make it look like fewer people are poor (and thus reduce their eligibility for assistance), and changes to nutritional support also designed to kick currently eligible persons off the roles.

The economy and complementary work supports are helping many low- and moderate income get ahead. Significant gaps persist, especially with regard to race. But the underlying trends of poverty and income have been favorable. Health coverage tells a different story and we must be vigilant not to let these same political forces do to anti-poverty programs what they’re doing to health programs.