User’s Guide to NAS Report, 2: Labor Market Impact

Chapter 5 of the report, entitled “Employment and Wage Impacts of Immigration,” weighs in at over 32,000 words (for context, that’s over half the length of my new book, We Wanted Workers). I am cynical enough to know that most of the people who will bother to wade through the verbiage are fishing for “talking points” that will support their ideological point of view. But they’ll be missing something. This is, by far, the best and most extensive survey of a difficult and voluminous literature. The report’s emphasis on the diversity of findings, and the many caveats that go along with those findings, reflects the doubts and uncertainty in the existing academic literature.

Having said that, we still need “stylized facts” to help us think about this issue. The very long chapter only has two tables (in the main text). And those two tables summarize the key insights from the literature. So let me describe what those two tables do, and note the take-away points.

Table 5-1, copied verbatim from tables that I published in my technical book Immigration Economics, summarizes the evidence from “structural models” of the labor market. In plain English. Let’s assume that all the immigrants who arrived between 1990 and 2010 entered the country all at once. We are then going to stream these data through a mathematical model that purports to describe how the labor market works. This mathematical exercise then lets us “see” how the market reacts in the “short run” (the instant after the immigrants arrive) and the “long run” (after the market has fully adjusted to their entry).


Although I am personally responsible for introducing this type of structural simulation in the second half of my 2003 Quarterly Journal of Economics paper, I’m not a big fan of it. Why? Because the mathematical model builds in many assumptions, and assumptions matter. This opens up the door for a lot of mischief and obfuscation, as different researchers play with different assumptions and end up producing different answers. Let me quote the report on what it is we learn from this type of structural analysis:

The key differences in the structural studies literature can be linked back to the studies’ modeling assumptions. Allowing capital to adjust (i.e., moving from a short-run to a long-run scenario) reduces the estimated negative effects across the board [Going from Panel A to Panel B of the table]…The simulations also show that allowing for imperfect substitution between immigrants and natives [going from Scenario 1 to Scenario 2] does not greatly attenuate the wage impact of immigration on high school dropouts. There is still a 2 to 5 percent wage loss, depending on whether one looks at the long run or short run…The scenario that does lead to a much lower negative or even positive impact of immigration on the lowest skilled workers is the one that also incorporates the possibility that high school dropouts and high school graduates are perfect substitutes [going from Scenario 1 to Scenario 4].

Let me translate all this. Two assumptions have been used to claim that immigrants have only a trivial wage effect on low-skill natives. The first is that low-skill immigrants are not productive “clones” of low-skill natives–so that the entry of low-skill immigrants may actually be making the low-skill natives more productive. This is precisely the claim first made by Ottaviano and Peri a decade ago. We now know, as Peri-coauthor Ethan Lewis concludes in footnote 7 of his survey, that this type of complementarity is, at best, “very modest.” Not surprisingly, the NAS reports that accounting for this issue “does not greatly attenuate the wage impact of immigration on high school dropouts.” What really matters is adding in the other assumption: that high school dropouts and high school graduates are productive clones. This, as the report acknowledges, is the assumption one needs to get the data to finally “confess” that low-skill workers are not harmed by immigration.

(For the geeky reader. Scenario 2 in Table 5-1 assumes low-skill immigrants complement low-skill natives; Scenario 3 assumes high school dropouts and high school graduates are productive clones; and Scenario 4 assumes both).

The other table in the chapter (Table  5-2) skips all that math and all those assumptions, and instead summarizes what we find when we simply correlate wages with immigration (across cities or skill groups).


This table is a “let-the-data-decide” kind of table (in Panels A and B). I think this is a far more credible approach. And this is what the NAS report says about those correlations:

Some notable patterns emerge…Native dropouts tend to be more negatively affected than better-educated natives (as indicated by comparing results for dropouts with the overall results for all workers or all men or women). The results in the table also suggest that this negative effect may be compounded for native minorities. Altonji and Card (1991) found more-negative results for low-education blacks than low-education whites…Cortés examined a number of groups and found the largest negative effects for Hispanic dropouts with poor English, as well as larger negative effects for Hispanic dropouts than for all dropouts. This could be because native dropout minorities are the closest native substitutes for immigrants.

In plain English: the actual data indicate that those natives who are most likely to be affected by the immigrants because they share similar skills are, in fact, the natives most affected by those immigrants. There is a delicious irony in Table 5-2 that I cannot resist pointing out. Look and see which economist has produced the most negative impact of immigration on the wage of low-skill workers. It happens to be none other than David Card. [Full Disclosure: Another panel member was responsible for the construction of the table].

And, after everything is said and done, the NAS report concludes:

When measured over a period of 10 years or more, the impact of immigration on the overall native wage may be small and close to zero. However, estimates for subgroups span a wider range and suggest some revisions in understanding of the wage impact of immigration since the 1990s…The intensive research on this topic over the past two decades, summarized in Table 5-2, displays a much wider variation in the estimates of the wage impact on natives who are most likely to compete with immigrants, with some studies suggesting sizable negative wage effects on native high school dropouts…Thus, the evidence suggests that groups comparable to the immigrants in terms of their skill may experience a wage reduction as a result of immigration-induced increases in labor supply, although there are still a number of studies that suggest small to zero effects.

Let me add an important caveat to this quote. The zero average wage effect in the long run (“10 years or more”) is based on the structural estimates reported in Table 5-1. Take a look at the last column of that table and note that the long-run impact of immigration on the average wage of workers is always exactly equal to 0.0 percent, regardless of which scenario we look at. What a remarkable statistical coincidence!

As the panel itself acknowledges, however, this zero wage effect is built in by the mathematics of the model: “In the case of structural studies, when capital is assumed to be perfectly flexible, [average] wage effects on natives are zero, although this result is built in by theoretical assumptions.” Put bluntly, claims that the long-run effect of immigration on the average wage is “small and close to zero” have nothing to do with the data. That result is instead a by-product of a mathematical assumption used to construct the model of the labor market.

And, to make matters worse, this mathematical assumption cascades over to every other number reported in Table 5-1. After all, the wage effects for the various skill groups must average out to zero. This means that each particular wage impact needs to “align itself” around zero so that the weighted average of the relevant numbers indeed adds up to the mathematically built-in 0.0 long run wage effect. Put bluntly: Table 5-1 should come stamped with a big Users Beware sign.

Author: George Borjas

I am a Professor of Economics and Social Policy at the Harvard Kennedy School.

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