Still More On Mariel

After posting my reaction to the new critique of my Mariel paper yesterday, a few friends and many other people contacted me to ask if I had done any additional statistical work to back up my claim that the key message in the Clemens-Hunt paper was, as I put in the title of my blog post, “fake news.” I obviously had, but chose to write a post that summarized my take in a way that would be easiest to explain to a broad audience. Yesterday I presented some simple graphs showing that wages for low-skill workers fell after Mariel even when blacks are excluded from the sample. The point of the exercise is easy to grasp, but the samples, as I emphasized, are very small.

So several people asked me exactly the same question: What would happen to the regression results in the key Table 5 of my paper if one were to redo the entire analysis using the age- and race-adjusted wage of workers? This approach has the huge advantage that we do not need to cut down on sample size. I can still use my original sample (which was small to begin with) and simply include a variable in the wage regressions indicating whether a particular worker was black or white. We can then use the regression results to calculate the average age- and race-adjusted wage in a particular city in a particular year and see if there is any Mariel effect in those trends. Here is what those regression coefficients look like. (Here are the programs for those who want to replicate; all I’ve done is add a “black” indicator variable to the individual-level regressions):

Race Adjusted Table 5

In rough terms, the regression coefficients give the percent wage difference between Miami and the placebo cities at some time after 1980 relative to what the difference was prior to 1980. So for example, the -0.117 statistic in the last column of the first row tells us that the low-skill wage in Miami relative to all other cities fell by 11.7 percent between 1977-1979 and 1981-1983.

It’s pretty obvious that the regression coefficients–which account for the changing black share of the workforce in Miami and elsewhere–still show a significant wage drop in Miami relative to any placebo one cares to pick. And this is true both in the March CPS data as well as in the ORG. So the key inference from the key regression table in my paper is unchanged. Something happened to Miami’s low-skill wage after 1980.

Several people also asked me how I could be so sure that there is no relation between the very strange increase in the fraction of black workers in Miami and the wage drop exhibited by Miami’s low-skill workforce between 1980 and 1985. Because black workers tend to have lower wages (even after adjusting for education), a higher fraction of blacks in the sample would mechanically reduce the average wage of the population. So it is certainly possible that the wage drop could be attributed to the change in sample composition.

It is trivially easy to show that this cannot possibly be the explanation by simply looking at the year-by-year data in either the March CPS or ORG. Let’s look at the March CPS first. The figure below shows the trend in the age-adjusted wage used in my original paper–which includes blacks–and plots it alongside the trend in the black share of the workforce.

March Year to Year.png

To emphasize my point, I’ve shaded in the period 1979 through 1983. It is obvious that nothing whatsoever happened to the black share of the workforce (as measured by the March CPS) in this particular period. But it is also obvious that this is the period where the average wage of Miami’s low-skill workers fell most. In short, it is impossible to explain that steep wage drop in terms of a rising black share. And this leads to an obvious inference: the Clemens-Hunt argument is not consistent with the timing of the increase in the black share and the drop in the average low-skill wage.

(A geeky point about the March CPS graph. The March CPS data in a particular year gives earnings in the previous calendar year. So, for example, the 1980 wage data comes from the 1981 survey. To make sure everything is consistently timed, I’ve lined up the graph so that the 1980 data for both earnings and percent black come from the 1981 survey).

The ORG data in this next graph is equally striking. Again, the average low-skill wage in Miami (including blacks) fell dramatically between 1980 and 1984 while the black share rose slightly and then declined slightly over the period–ending up pretty much at the same place it started. So how could the change in the black share possibly account for the drop in the average low-skill wage? It can’t.

ORG Year to Year.png

Finally, several people wanted me to opine on where things stand and where we go from here. Well, let me give credit where credit is due. Clemens and Hunt discovered a really weird thing about the racial composition of Miami’s low-skill workforce as measured by the March CPS, with a somewhat similar trend in the ORG data. This is something that future work must take into account. I think we would all agree that the ideal exercise is to track the average person over time to see what happens as a result of the Mariel shock–and we definitely don’t want that “average” to change as a result of changes in sample composition.

It would not surprise me if the weird pattern in the black share of the low-skill workforce as measured by the March surveys is the result of a data glitch or imputation problem that lies undetected in the vaults of the BLS or IPUMS offices. But I also suspect that the less weird ORG pattern of a gradually increasing black share (although with ups and downs through 1987) is not something we should altogether dismiss. This trend may contain valuable information. Could it be that, for reasons maybe related to Mariel or maybe not, the Miami of the 1980s increasingly became a place that did not reward whatever it is that low-skill whites bring into the workplace? And that is something worth investigating.

 

 

More Fake News On Mariel

It seems that the tremors set off by my Mariel paper (which first circulated privately almost two years ago; here is the published version) are still reverberating. I’m quickly losing track of all the rebuttals. But those critiques– including an early reaction written about a month after the public release of my NBER working paper by David Roodman, the Peri-Yasenov paper that appeared three months after the NBER release, and a recent exercise by Alex Nowrasteh at Cato–have not been able to demolish my evidence.

As anyone involved in the immigration debate well knows, the narrative that immigration is good for everyone must live on. Each time one of these critical appraisals comes out, the reaction is the same. A lot of gloating from the usual suspects in the interwebs about my original paper being proved wrong, etc. But, somehow, the paper refuses to retire peacefully to that burial ground populated by tens of thousands of forgotten and useless academic studies, as additional rebuttals keep appearing to beat up what the gloaters have repeatedly declared to be a dead horse.

So it is not surprising that my inbox is again cluttered with messages about yet another paper that questions my results. And this time the paper comes along with the appearance of paid-for empirical research. This new exercise was funded by a Silicon Valley “philanthropic” organization, Good Ventures. It’s hard to make this stuff up, but Good Ventures, run by Facebook co-founder Dustin Moskovitz, actually lists “love” as its first value. And, as we all know, such organizations, just like pharmaceutical and energy companies, will never fund research that offers anything but a balanced and objective appraisal of their missions.

The main criticism that Michael Clemens and Jennifer Hunt make of my Mariel paper is succinctly stated in their abstract:

We show that conflicting findings on the effects of the Mariel Boatlift can be explained by a sudden change in the race composition of the Current Population Survey extracts in 1980, specific to Miami but unrelated to the Boatlift.

I have not had the time–and most definitely do not have the desire–to go line-by-line through their code. But I can very easily dismiss their entire criticism by simply looking at what happens if I excluded all blacks from my analysis, so that the post-1980 increase in the relative number of blacks could not possibly play any role in generating the wage drop in Miami. Curiously enough, the evidence resulting from this trivially simple exercise is not reported in the Clemens-Hunt paper.

One crucial caveat: By excluding blacks, the sample size in the March CPS becomes even smaller than it was in my original Mariel analysis. Nevertheless, the results from the larger ORG samples seem similar.

This exercise is extremely easy to do with the programs and data that I put online last year. You only need to add one line to the code–a line that drops blacks from the sample (and here are the new programs). To my surprise, and despite the very small sample sizes, not much happens. Just look at the graph of the three-year moving average of the wage of non-black, non-Hispanic high school dropouts in Miami and in all other cities.

March wage

And here’s the same graph with the larger ORG sample:

ORG wage

And for those interested in regressions, these are the regression coefficients and standard errors that go along with those reported in the last column of Table 5 in my paper. As in the original paper, the regression coefficients are smaller and less significant in the ORG, but I showed that some of that arises because the ORG sample excludes many people who happened not to work in the survey’s reference week.

Revised regression table

In short, using the increase in the relative size of Miami’s black workforce after 1980 to dismiss my Mariel evidence performs the job of obfuscating the debate further, but does little to clarify.

There is no doubt that the racial composition of the sampled low-skill workforce in Miami changed beginning in 1980 (at least in the March CPS). These are the trends in both the March and ORG samples. (As an aside, there seems to be a problem with Table 1 of the Clemens-Hunt paper that confuses the survey and earnings year for the ORG. You can tell their data are wrong because they show the sample size for the ORG increasing in 1980. That increase should have been observed in 1979, the first year of the ORG files).

Percent black

But the claim that the rising proportion of blacks explains the observed decline in Miami’s low-skill wage is more than just a little misleading–it is downright false. Most obviously, note that the fraction of blacks in Miami’s low-skill workforce is relatively constant between the 1980 and 1984 survey years (representing earnings between 1979 and 1983), which just happen to be the years when the wage of high school dropouts fell most in my original paper! Here’s the graph showing the original year-by-year wage trend in the March CPS including blacks, rather than the 3-year moving average. It is trivially easy to see that the timing is off. There’s no connection between the 1979-83 large drop in the low-skill wage and the black share of the workforce. (And here’s the comparable graph in the ORG for the curious geek).

Wage and percent black

From my perspective, the increased proportion of low-skill black workers beginning in the 1980 survey raises even more questions. Could it be more than just a sampling glitch or a coding problem with the original CPS data? Where did all the white low-skill workers go? Might there be a link between their gradual disappearance in the ORG and the post-Mariel labor market dislocations? What do we make of the very different trends in the racial composition of the workforce in the March and ORG surveys? What does it say about the sacred statistics derived from the CPS?

In the meantime, however, the narrative must live on. And if there are funds to ensure its survival (and there seem to be an awful lot of “charity” organizations out in Silicon Valley trying to reenact the Summer of Love), there will surely be a large supply of researchers with an incentive to use every trick in the book to throw noise into the discussion, and further confuse and obfuscate the issue “with a little help from their friends.” Hopefully, this new Summer of Love will not come crashing down in Altamont.

Update, 5-23-17. About an hour after the blog post went online, I discovered that the specification I used in the ORG regression was not identical to the one I used in the Mariel paper. I have updated the regression table using the same specification, and also updated the programs.

Update, 5-24-17. And here are some more results.

The Weekly Standard On We Wanted Workers

The Weekly Standard just published Peter Hansen’s careful review of We Wanted Workers. Hansen did a great job, neatly capturing the essence of what my book is about. My favorite part:

it’s hard to imagine a more suitable book if you’re genuinely seeking information about what may well be today’s most politically charged issue.

Hansen also grasped the significance of the “elementary error” I point out in David Card’s Mariel study. Believe it or not, Card actually used post-1980 (that is, post-Mariel) data to create the set of placebo cities that Miami should be compared to. (The quote from the Card article describing what he actually did appears at the end of this post). As I write in my book,

This elementary error is akin to a medical researcher choosing the placebo by looking for patients who were not injected with a harmful dosage of an experimental drug but somehow got sick anyway.

This is one of those things that is universally swept under the rug when describing Card’s work. Just imagine the reaction to a young economist (or medical researcher) today if he/she published a paper where the placebo group was deliberately chosen to resemble the post-treatment outcomes of the treated group!

This blog has not been active for a few weeks. It’s been very hectic, as I’ve been downsizing and moving. We sold our big old house in Lexington and moved to a condo in Cambridge within walking distance of my office. Valuable advice for the young ones. Throw out all that junk now before it starts to accumulate and overwhelm. It’s way too much work to take care of it when you are trying to downsize.

Continue reading “The Weekly Standard On We Wanted Workers”

Cato On Mariel

The Cato Institute’s Alex Nowrasteh posted a very interesting piece that reproduces and expands the findings in my Mariel paper. Although the title, “The Mariel Boatlift Raised the Wages of Low-Skilled Miamians,” is very misleading (making it a good example of Cato publicists gone wild), I actually liked the essay and recommend it to anyone interested in the subject.

My paper showed that the wage of high-school dropouts fell significantly after Mariel, but recovered by 1990. Here’s the graph that goes with that conclusion (where the shaded area indicates the margin of error):

Mariel_Figure_2

In early drafts of my Mariel paper, as well as in We Wanted Workers (Figure 7.5 on p. 148), I documented that the wage drop was not experienced by high school graduates, the group of workers on the next rung up the skill distribution. In contrast to the dropouts, high school graduates actually saw their wages rise. Here’s the graph from my book:

mariel-high-school-graduates

A few months ago, Joan Monras and I followed up on this insight to document a pattern common to many refugee supply shocks: refugees have harmful wage/employment effects on the workers that they most resemble, but beneficial effects on the workers that are different.

Nowrasteh’s conclusion that “low-skilled Miamians” gained comes about because he pools the two groups of workers (high school dropouts and high school graduates) into a large “low-skilled” workforce, and he shows that the wage of the average worker in this group increased as a result of Mariel. He concludes: “The Marielitos redistributed wages from dropouts to workers with only a high school degree with a net positive effect on all low-skill workers.”

The exercise illustrates two very important points that Nowrasteh does not emphasize. First, it shows just how easy it is to hide the adverse wage impact of immigration by redefining skill groups. This is a trick that, unfortunately, is used much too often to “prove” that immigration is good for everyone. As I wrote in We Wanted Workers (p. 196): “The more one aggregates skill groups, the more likely one hides away the specific group of affected workers–making it harder to document whether immigration made anyone worse off. The more laser-focused the group of native workers examined, the easier it is to detect that immigration affected the targeted group.”

Second, Nowrasteh (perhaps unwittingly) blows up the cornerstone underlying the Card-Peri argument that immigration has not made low-skill Americans worse off. That cornerstone is the assumption that high school dropouts and high school graduates are productive clones (or “perfect substitutes”). That assumption is what gives the researcher “permission” to pool those two groups. Because there are many fewer dropouts, the wage trend will essentially reflect whatever happened to the wage of high school graduates.

Nowrasteh’s documentation that Mariel had very different effects on high school dropouts and high school graduates flatly contradicts the assumption that the two groups are productive clones. If the two groups were clones, they should have reacted in exactly the same way to Mariel. But they didn’t. Instead, they are complements, implying that the two groups should be studied separately. Those who buy into the Card-Peri argument need to go back to the drawing board if they want to salvage the conclusion that immigration didn’t really harm the least skilled Americans.

New Paper on Refugees

Joan Monras and I have been working on a paper that presents a comprehensive documentation of the labor market consequences of refugee supply shocks; the working paper version is here. We examine four episodes:

  1. The Mariel supply shock in 1980.
  2. The Soviet émigrés who moved to Israel in the early 1990s after the collapse of the Soviet Union
  3. The influx of French repatriates and Algerian nationals into France at the end of the Algerian War of Independence in 1962.
  4. The flow of refugees into several European countries from the many conflicts that made up the Yugoslav Wars of the 1990s.

The paper differs in two key ways from what’s been done before. First, rather than “pick and choose” a different methodological approach to examine each of the four shocks, we use the same regression model, derived from economic theory, to measure the labor market impact. Second, we estimate not only the “own effect” of the refugees on competing natives, but also the “cross effects” of the refugees on complementary natives. So, for example, existing studies of the impact of the very low-skill Marielitos look at what happened to the earnings of native high school dropouts. But what about the earnings of more skilled Miamians? Similarly, existing studies of the impact of the very high-skill Soviet émigrés in Israel look at what happened to the earnings of Israeli college graduates. But what about the earnings of lower-skilled Israelis?

Here’s what we find:

The evidence reveals a common thread that confirms key insights of the canonical model of a competitive labor market: Exogenous supply shocks adversely affect the labor market opportunities of competing natives in the receiving countries, and often have a favorable impact on complementary workers. In short, refugee flows can have large distributional consequences.

We will be presenting the paper in Florence at the 64th Panel Meeting of Economic Policy in October 2016.

 

The Economist on Mariel

The Economist just published a very nice writeup of my Mariel paper.The article captures the essence of the paper very nicely. Over 60 percent of the Marielitos were high school dropouts. It seems more than obvious today that if we want to find out what Mariel did, perhaps we should look at what happened to the wage of similarly educated workers who were living in Miami at the time. Remarkably, that had not been done until I wrote my Mariel reappraisal. As The Economist puts it: “Mr Borjas’s paper shows that empirical results may depend on exactly where researchers look.”

There is a lot of wisdom in those words. Just keep looking in all the wrong places, and one will never discover what the impact of immigration really is. For example, one empirical trick that is often used to “hide” the impact is to define the population of low-skill workers as the aggregate of high school graduates and high school dropouts (click here for a technical discussion, and pages 14-17 here for an English translation). Because there are tens of millions of high school graduates, the impact of immigration on the smaller group of the least skilled workers gets diluted. And it’s usually too late, only after the inevitable political reaction occurs, that we find out that some people were really harmed.

Here’s a quick link to a description of my Mariel analysis, to the paper itself, and to the data.

Odds and Ends on Mariel

Warning: Very geeky post.

Since I posted the final version of my Mariel paper earlier this week, I have heard from a number of people asking for all kinds of details about the paper. One of the nice things about having this blog is that I can quickly address these reactions/questions/doubts without having to resort to writing yet another paper. So here are some responses for those who are really into the minutiae of this stuff.

Continue reading “Odds and Ends on Mariel”