The Employment Benefits of Cutting Unemployment Benefits

Does a new NBER study prove that cutting unemployment benefits increases employment?


The National Bureau of Economic Research this month issued a report by economists Marcus Hagedorn of the University of Oslo, Kurt Mitman of Stockholm University, and Iourii Manovskii of University of Pennsylvania, called "The Impact of Unemployment Benefit Extensions on Employment: The 2014 Employment Miracle?"

Senate Democrats / Foter / CC BY

It studies the effects of Congress' cutoff of federal unemployment extension benefits at the end of 2013 on employment. The economists believe they find that "1.8 million additional jobs were created in 2014 due to the benefit cut. Almost 1 million of these jobs were filled by workers from out of the labor force who would not have participated in the labor market had benefit extensions been reauthorized."

It's the phenomenon they quote President Obama crowing about in July 2014 as "the quickest drop in unemployment in 30 years." These economists conclude that a cut in benefits the Democrats did not want deserves a big chunk of the credit.

They spell out that common-sense guesses about human reactions to incentives can push in both directions on the question they want to answer: what effect did cutting the unemployment benefits have on employment?

Basic decision theory suggests that some unemployed may increase their search effort in response to a cut in benefits, while others, who were mainly searching to qualify for benefits, might drop out of the labor force once losing eligibility, leading to offsetting effects on employment. Equilibrium job search theory typically implies a positive effect of a cut in benefit duration on job creation. This makes it easier to find jobs and might induce those previously out-of-labor force to rejoin the labor force, leading to an increase in employment with an ambiguous effect on unemployment since the number of job vacancies and the number of searchers increases at the same time. The empirical micro literature has focused virtually exclusively on measuring the effects of benefit eligibility on the search effort of unemployed workers – a focus that is too narrow to infer the impact of benefit duration on employment

Some thought, the economists note, that ending benefits would hurt aggregate employment by both discouraging some jobhunters from continuing to search (since doing so was a requirement of getting the benefits) and by hurting aggregate national demand—less benefit cash in the unemployed's hands to spur spending, right?

Here's the nub of how the trio think they cut through the buzzing, blooming confusion empirical reality presents us with (where singular causes on always-multivariate effects are not visible to the mere counting naked eye) to pinpoint the employment effect of the unemployment benefit cut:

We perform two simple experiments: First, we partition states into two groups based on benefit durations right before the reform in December 2013. Assuming that the pre-reform employment trends in those states would have continued into 2014 (in absence of the benefit cut), we find that the cut in unemployment benefit duration led to a 2% increase in aggregate employment, accounting for nearly all of the remarkable employment growth in the U.S. in 2014. Second, we refine the measurement of underlying employment trends by comparing only counties that border each other but belong to different states.

As we explain below, the underlying economic fundamentals are expected to evolve similarly across counties bordering each other. Unemployment insurance policies, determined at the state level, however, are discontinuous at the state border. Thus, a comparison of employment growth between border counties in relation to the change in benefit durations in the states to which these border counties belong, provides another way to assess the labor market implications of unemployment benefit durations. We find that employment growth was much higher in 2014 in the border counties that experienced a larger decline in benefit durations relative to the adjacent counties.

What makes this finding even more remarkable is that year after year prior to 2014 the relative employment growth was lower in the high benefit counties. Once again, the analysis based on this simple inference implies that the cut in benefits in 2014 can explain nearly all of the observed aggregate employment growth in 2014. The abrupt reversal in the relative employment growth trend of high benefit states and border counties in December 2013, right at the time when the benefit durations were cut, strongly suggests that our analysis indeed identifies the implications of this particular policy change.

There's more to it, as there always is in technical economics. And they note that not all the employments effects of 2014 are attributable, in their estimation, to the benefit cuts:

our estimates of the interactive effects model attribute some of the observed relative increase in employment growth in high benefit counties to the effects of economic fundamentals. In the aggregate, our estimates imply that the cut in benefit duration accounted for about 61 percent of the aggregate employment growth in 2014.

But in the end, they are confident that although:

unemployment benefit extensions are routinely used for the purposes of macroeconomic stabilization. Yet, the findings in this paper imply that the negative effects of unemployment benefit extensions on employment far outweighs the potential stimulative effects often ascribed to this policy. It appears important to take these effects into account.

Not everyone believes in the conclusions of this NBER report. For more commentary and debate on the NBER reports sanguine assessment of cutting unemployment benefits on the job market, see Max Ehrenfreund in the Washington Post, who notes that:

Jesse Rothstein of the University of California, Berkeley…pointed out that the researchers relied on data for individual counties that was partly estimated based on data from the entire surrounding state. Neighboring counties where conditions for workers were in fact quite similar may have appeared to be very different based on these estimates, because they were in states with divergent economic fortunes.

Since the federal insurance program provided more generous unemployment benefits in states with higher rates of unemployment, Congress reduced benefits most drastically in the states with the worst economies. Data from counties in those states would reflect the poor economic condition of the state as a whole. And since economies tend to bounce back harder the farther they've fallen, the fact that employment improved more quickly in those counties with drastic cuts in unemployment might simply result from the fact that the statewide situation was worse initially.

Mike Konczal of the Roosevelt Institute presents a bevy of complaints about the NBER trio's data, assumptions, and models. A few:

in their study HMM only look at aggregate employment….there should be something in the paper about actual wage data or job openings moving in response to this change. There is not. Indeed, their argument hinges entirely on the idea that the labor market was too tight, with workers having too much bargaining power, in 2010-2013. The end of UI finally relaxed this. If that's the case, then where are the wage declines and corporate profit gains in 2014?….

The model's vagueness is amplified by the control issue. One of the nice things about the standard model is that people without UI make a nice control group for contrast. Here, HMM simply compare high-UI and low-UI duration states and then counties, without looking at individuals. They argue that since the expiration was done by Congress, it is essentially a random change.

But a quick glance shows their high benefits states group had an unemployment rate of 8.4 percent in 2012, while their low benefits states had an unemployment rate of 6.5 percent. Not random. As the economy recovers, we'd naturally expect to see the states with a higher initial unemployment rate recover faster. But that would just be "recovery", not an argument about UI….

As Dean Baker notes in an excellent post, the local area data they use is noisy, confusing based on whether the state is where one works versus lives, and is largely model driven. The fact that much of it is model-driven is problematic for their cross-state county comparisons.

Baker also says, Konczal reports, that if you use Bureau of Labor Statistics (BLS) national Current Employment Statistics rather than the localized data from the Local Area Unemployment Statistics (also a BLS source) the NBER authors use, their effect seems to disappear:

It's not encouraging that you can get the opposite result by changing from one data source to another. Baker isn't the first to question the robustness of these results to even minor changes in the data. The Cleveland Fed, on an earlier version of their argument, found their results collapsed with a longer timeframe and excluding outliers. The fact that the paper doesn't have robustness tests to a variety of data sources and measures also isn't encouraging.

Empiricism in economics is never as cut and dried as policymakers, or those of us who are supposed to have opinions about policy, might hope, and any individual might decide the wisdom of the misery-ameliorating effects for specific individuals of unemployment benefits are more (or less, natch) important than macroeconomic effects, and this data dispute might be less important to you in that case.

Reason has been on this tip for a while: J.D. Tuccille blogged in October about an earlier set of economists' coming to similar conclusions about the employment benefits of killing unemployment benefits: "Are Jobs Returning Because Extended Unemployment Benefits Are Ending?