In a recent post, Tyler Cowen discusses my recent paper on minimum wages and poverty. Cowen acknowledges that “[my] paper, econometrically speaking, is a clear advance over [a 2010 paper by] Sabia and Burkhauser.” However, he is more persuaded by “facts” such as simulation results from Sabia and Burkhuaser’s paper that claims “[o]nly 11.3% of workers who will gain from an increase in the federal minimum wage to $9.50 per hour live in poor households.”
Cowen concludes that my paper “pays little heed to integrating econometric results with common sense facts and observations about the economy.”
I’m pleased that Cowen thinks my paper represents a clear econometric advance. But I strongly disagree that the econometrics are at odds with common sense facts. Simulation studies are not facts, and when we interpret the relationship between wages and poverty properly, the econometric results appear eminently sensible.
Let’s start with the econometrics. First, both the weight of the existing studies, and my own econometric evidence that Cowen considers an advance, suggest that minimum wages have a modest impact on reducing poverty. I find minimum wage elasticities for the poverty rate ranging between -0.12 and -0.37. This range of estimates is based on 16 different models, and the list includes just about every specification (or close to it) used in the literature. That includes specifications in Burkhauser and Sabia (2007), Sabia (2008), Sabia and Burkhauser (2010), and Neumark and Wascher (2011)—except that I use more data.
Now, if this range does not include your preferred estimate, that’s fine. But, then you need to acknowledge that you also do not believe empirical results from regressions in the previous papers using one of the 16 models I do estimate. Another possibility is that you like one of the 16 models, but prefer earlier results that showed a weaker connection between the minimum wage and poverty. In that case, you need to explain why you believe a result from a smaller sample of data used to one that uses more data. This is especially important because many of the estimates in the literature are highly imprecise, in part due to use of small samples. You are not allowed to pick econometric results from a specification when you agree with the outcome, but dismiss results from the same specification when the results are not agreeable to you.
So is this type of econometric evidence—of the sort that has become the hallmark of applied micro-economics in the past few decades—out of touch with the “real world?” Instead, should we turn to simulations like those in Sabia and Burkhauser to get closer to reality? As a believer in the credibility revolution in economics, I’m going to go ahead and say, no. Actually, the type of estimates that I have found both in my own work, and generally from the literature, is what you would expect based on the pattern of wages and family incomes if you also account for the commonsensical—and empirically supported—propositions that: (1) workers above the minimum get some raise too (“spillover effects”), and (2) reported wages in survey data is measured with error which makes the relationship between low wages and low family incomes weaker than it is in reality. The simulation results like Sabia and Burkhauser do not in fact account for these, and they understate the association between minimum wages and low family incomes.
So let us go through these points more carefully. As I report in my paper, poor workers are disproportionately low-wage workers. In 2013, 63 percent of workers under the poverty line reported earning less than $10.10/hour, as compared to 22 percent of workers overall. This is broadly consistent with my econometric findings: that minimum wages raise family incomes relatively more at the bottom of the family income distribution. At the same time, I also explicitly state that the relationship between low wages and low family incomes (e.g., poverty) is indeed imperfect. In 2013, around 18.9 percent of workers who reported earning under $10.10/hour were in families under the poverty line, and around 46.0 percent were below two times the poverty line. So yes, the minimum wage is a “blunt” tool if the only goal were to reduce poverty. (Curious why my estimate of 18.9 percent differs from Sabia and Burkhauser’s estimate of 11.3 percent? Jump to the Postscript at the end.)
But, I also point out in my paper that using the distribution of reported wages to make predictions about minimum wage impact on poverty—like Sabia and Burkhauser do—will almost certainly under-estimate the true effects. To see why, consider the assumptions made by Sabia and Burkhauser in their simulations:
- They assume that most workers reporting a wage under the old minimum will not get a raise when the minimum wage goes up.
- They also assume that no one above the new minimum will see a raise.
These assumptions are inconsistent with a large body of evidence. First, there is almost certainly some wage spillover or “ripple effects”, as shown most recently by Autor Manning and Smith (2010)—a paper Cowen says is of “highest academic pedigree.” A minimum wage increase ripples up to the 20th percentile of the wage, which would simply not happen if there were no spillovers. Interestingly, a recent study by the Hamilton Project uses spillover estimates from a paper by Neumark et al. (2004) to estimate that a total of 35 million workers would get a raise from increasing the minimum wage to $10.10/hour, many more than number of workers directly affected by the policy.
Second, many workers who report earning lower than the current minimum wage are in reality earning at or slightly higher than the minimum wage, and they will also see their wages rise. In general, the more measurement error there is in wages and other sources of incomes, the weaker the relationship between poverty and low wages will appear to be. Measurement errors don’t just “wash out”—they weaken correlations in what economists call an “attenuation bias.” The concern with measurement error in reported wages is also discussed in Autor, Manning and Smith 2010. And while measurement errors may lead to problems inferring exactly why we see spillover effects, they mean the kind of assumptions used by Sabia and Burkhauser will understate the true relationship between minimum wages and poverty.
Third, many of the workers who are truly being paid lower than the statutory minimum in the informal sector will see an increase, a phenomenon is sometimes called the “lighthouse effect.” There are different explanations for this, but consider the following analogy. There are people who drive at 70 miles an hour even when the speed limit is 65; now if the speed limit is lowered to 55, most people who were previously driving at 70 miles an hour will also lower their speed, while continuing to speed a bit. A similar logic applies to cases like the minimum wage where the probability of detection likely varies with the extent of violation.
So to take stock, if you consider the Sabia and Burkhauser simulation results as “facts” you also are claiming that no worker reporting a wage below the old minimum will get a raise, and no one above the new minimum will get a raise. These are not very good assumptions, and they certainly are not facts.
Of course, you don’t have to make these assumptions. You could allow for spillovers. You could allow for wages to rise below the minimum. You could allow for measurement error in reported wages and other sources of income. But then you are not in a world where tabulating survey data gives you simple facts that are beyond reproach. You need to make additional assumptions to make causal claims. And we have not even begun to talk about behavioral effects—be they on labor demand side, or on labor supply side such worker search effort, etc. (And by the way those do not all go in the same direction.) So you could add a lot more assumptions and continue with the simulation route, or you could use quasi-experimental approach used in almost all of applied micro-economics to empirically estimate the effect of minimum wages on poverty and other outcomes. Of course, you would want to subject your identifying assumptions to specification checks and falsification tests to ensure you have reliable control groups; and you would account for possibly confounding policies such as state EITCs. And when you do all of that, and some more, you would probably end up with a paper like this one.
So where does this leave us? As I said in my paper, policies like cash transfers, food stamps, and EITC are better targeted to help the poor, although even there minimum wages are better thought of as complements and not substitutes. More generally, however, motivations behind minimum wage policies go beyond reducing poverty. The popular support for minimum wages is in part fueled by a desire to raise earnings of low and moderate income families more broadly, and by fairness concerns that seek to limit the extent of wage inequality, or employers’ exercise of market power. And the evidence suggests is that attaining such goals through increasing minimum wages is also consistent with a modest reduction in poverty, and moderate increases in family incomes at the bottom.
Postscript. If you were paying close attention, you would have noticed a potential discrepancy. Sabia and Burkhauser argue that 11.3 percent of workers who will gain from an increase in the federal minimum wage to $9.50 per hour live in poor households, and 36.8 percent are under twice the poverty line. I find that 18.9 percent of workers who will gain from an increase to $10.10 an hour are in families below the poverty line, and 46.0 percent under twice the poverty line. So what is going on here? Two things. First, Sabia and Burkhauser use the “household” as the unit to calculate poverty status, while I use “family.” While neither is clearly superior to the other, you should know that official Census definition of poverty uses the family as the unit. And for whatever reason, Sabia and Burkhuaser use a family based poverty measure when they run regressions, but use households when the do simulations. Second, low wage workers actually appear to be somewhat poorer today than they were in 2008, which is the period Sabia and Burkhauser were analyzing. I will have more to say on this in a future post, but the upshot is that even on this narrow count, it is more accurate to say the 19 (and not 11) percent of workers who report wages below the proposed $10.10/hour are in families that would be officially designated as being poor. And as I explained above, this number is almost certainly too small due to measurement error in both wages and other incomes. Return to the post.