Minimum Wages and Job Growth: a Statistical Artifact

In a recent paper, Jonathan Meer and Jeremy West argue that it takes time for employment to adjust in response to a minimum wage hike, making it more difficult to detect an impact by looking at employment levels. In contrast, they argue, impact is easier to discern when considering employment growth. They find that a 10 percent increase in minimum wage is associated with as much as 0.5 percentage point lower aggregate employment growth. These estimates are very large, as John Schmitt explains in a recent post, and far outside the range in the existing literature. But are they right?

As I show in a new paper, the short answer is: no. The negative association between job growth and minimum wages is in the wrong place: it shows up in a sector like manufacturing that has few minimum wage workers, but is absent in low-wage sectors like food services and retail. In other words, it is likely a statistical artifact, and not a causal relationship.

Meer and West do not study the impact across sectors. The Business Dynamics Statistics (BDS) dataset that they use only allows them to study aggregate employment. This is a problem because there are many factors affecting overall job growth like sectoral shifts, demographic changes, etc., that vary across states with high versus low minimum wages. For this reason, most scholars in the literature control for overall employment, while Meer and West choose to use it as their key outcome.

The good news is that we can use richer datasets to study the relationship between minimum wages and employment growth. In my paper, I use data from the Quarterly Census of Employment and Wages between 1990 and 2011 to look at not only aggregate employment growth, but also growth in different sectors.

First, I show that the negative association between aggregate employment growth and minimum wages can also be found using the QCEW data, especially since mid-1990s. (I do find that controlling for population growth, which Meer and West do not, diminishes the estimates by around a third). This means there isn’t anything special about the BDS data or the sample that they use. But here is the surprising finding: this negative association is particularly strong in manufacturing, a sector with virtually no minimum wage workers. And yet, the negative association is absent in both retail and accommodation and food services, two low wage sectors that together account for nearly 2/3 of all minimum wage workers. In other words, minimum wages are indeed associated with lower employment growth, but exactly in the wrong places for the correlation to reflect a causal impact.

Why would we expect a statistical artifact like this to contaminate the study? In a recent IZA Discussion Paper (written with Sylvia Allegretto, Michael Reich and Ben Zipperer), we show that states with higher minimum wages have had deeper recessions, and greater reduction in routine-task jobs—factors that could explain the spurious manufacturing results. Complicating things further, minimum wage hikes are much more frequent in the latter part of economic expansions, making the timing non-random as well. Secular and cyclical differences across states with different minimum wage policies makes it particularly important to have reliable control groups. In our previous work, we have shown that contiguous counties make for good controls. And it turns out that the negative association between aggregate employment growth and minimum wages indeed disappears when I compare bordering counties with different minimum wages.

Together, the results indicate that the statistical association reported in Meer and West does not represent a causal effect of the policy. Rather, the correlation reflects the kind of heterogeneity between high and low minimum wage areas that we have documented elsewhere. The findings here also provide added external validity for our argument that a credible research design like comparing bordering counties can filter out such artifacts, and produce reliable estimates.

PS. If you want to a learn about employment dynamics and minimum wages, especially how hiring, separation, and turnover respond, ready my paper here.

Broken Job Ladders and the Great Recession: Fast Food Edition

One of the features of the current labor market is that the usual mechanisms by which people find better quality jobs have broken down, as net job creation in manufacturing sector, larger establishments and more established firms—all of which tend to pay better—have fallen relatively more. A key avenue for mobility, transitions between jobs, took a dive with the onset of the Great Recession, and has not recovered. In a recent paper, Giuseppe Moscarini and Fabien Postel-Vinay call this the failure of the job ladder.

One of the consequences of a broken job ladder is that workers take—and stick around in—less than ideal jobs like fast food.  But the failure of the job ladder does not impact all workers equally.  Higher-credentialed workers have more opportunities to get some job—any job—than their lower-credentialed counterparts.  A college graduate can get a job at McDonald’s if she wants to, but a high school grad will have a hard time getting a job in finance. So as the labor market is stuck in a low gear, an increasing share of fast food vacancies are filled with people who may not have taken such jobs in a healthy labor market, or may have climbed up the job ladder to better opportunities.  This is a form of skills-mismatch, but one that is induced by demand conditions.

There are a lot of news stories and anecdotal evidence that support this argument.  But is there hard evidence on how hiring patterns have responded in low-wage service jobs?  To test this story, I went to one of my favorite data sources, the Quarterly Workforce Indicators (QWI). The QWI gives rich sectoral and demographic data on jobs and earnings at the state (or county) level. It is based on a near census of payroll employment that comes from employer filings for the Unemployment Insurance program.  The Census Bureau then matches data from Social Security records and other sources to glean demographic information, before aggregating it by demographic and geographic cells. The QWI reports both employment counts, and also information on hires.  This last part is important because sometimes it is easier to detect changes by looking at flows (hires) than stocks (employment.)

As a starting point, I plot the hiring trends for limited-service eating places (NAICS code 7222) which comprises of “establishments primarily engaged in providing food services where patrons generally order or select items and pay before eating.”  These are what we colloquially call fast food restaurants. The QWI provides educational breakdown for hires, for those who are at least 25 years old. (It makes sense to exclude those who are usually too young to have a college degree.)

Share of Fast Food Hires with College Experience

Share of Fast Food Hires with College Experience

Between 2000 and 2007, the share of hires with at least some college experience (including associate or bachelor’s degree) clocked around 43.5%. However, it jumped up by 2 percentage points to 45.5% at the onset of the Great Recession and did not decline during the recovery through 2012q2 (latest QWI data available).  While 2 percentage points may seem small, it is likely just the tip of the iceberg when it comes to the set of workers who are in these jobs only because of a weak labor market.

The pattern above suggests that overall joblessness nudged more higher-credentialed workers towards low-quality jobs, making it even harder for lower-credentialed workers to find jobs. Does this show up when we compare across different states?  The answer is yes.  To do this, I looked at the correlation between the higher-credentialed (some college experience) share of fast-food hires in a state and the quarterly state unemployment rate between 2000q1 and 2012q2, after de-meaning the data by state and time. (Actually very little changes if you don’t de-mean the data, but doing so provides a stronger test of the theory.)

Relationship between Unemployment Rate and Share of Fast Food Hires with College Experience

Relationship between Unemployment Rate and Share of Fast Food Hires with College Experience

As the graph above shows, there is a strong relationship between overall unemployment rate in the state in a given quarter and the higher-credentialed share of fast food hires. The regression slope of 0.25 says a 5 percentage point increase in the unemployment rate is associated with a 1.3 percentage point gain in the higher-credentialed share of fast food hires.  The relationship is highly statistically significant (at the 1 percent level). The cross sectional relationship and time series relationship match up fairly well—the regression estimate can rationalize most of the time-series change in the picture above. Similarly, the green colored markers show that the data points for 2012q2 follow a similar pattern as the full period.

Overall, the evidence suggests that we have a stock of misallocated workers in jobs due to weak demand. When hiring kicks into a higher gear, it won’t just be the new job creation for low-credentialed workers that will help reduce their unemployment rate. When the job ladder starts working again, it will also pull out over-qualified workers from fast food checkout counters which will help their lesser-credentialed counterparts.  A final observation: if we want to understand what moves the low-wage service job share of employment for non-college-educated workers, it’s not just technology and polarization that matter; pulls and pushes due to labor market slack matter as well. While I am looking at somewhat different questions, Dean Baker recently made a similar argument as well.

Windows into the past and the future … Guest post @ Rortybomb (Jun 1, 2013)

Recently, we have seen a number of explorations of the timing of growth around episodes of high debt as a way to discern the likely direction of causality in that relationship.  This is important, because we doobserve that there is a negative correlation between contemporaneous debt and growth. For instance, this is true when using the corrected data from Reinhart and Rogoff, and equal weighting of country-year observations. Although there is no evidence of tipping points, a negative relationship remains.

In a blog post in April, I showed that the timing of this negative relationship went against an interpretation where high debt caused low growth.

Continue reading here.

Growth in a Time Before Debt ….Guest post @Rortybomb (Apr 15, 2013)

Recent work by my colleagues at UMass Thomas Herndon, Michael Ash and Robert Pollin (2013)—hereafter HAP—has demonstrated that in contrast to the apparent results in Reinhart and Rogoff (2010), there is no real discontinuity or “tipping point” around 90 percent of debt-to-GDP ratio.

In their response, Reinhart and Rogoff—hereafter RR—admit to the arithmetic mistakes, but argue that the negative correlation between debt-to-GDP ratio and growth in the corrected data still supports their original contention.

Continue reading the post here.