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”

Earnings of Undocumented Immigrants

I have a new paper that looks at how undocumented workers perform in the U.S. labor market. Here are some of the main findings:

First, the age-earnings profile of undocumented workers lies far below that of legal immigrants and of native workers, and is almost perfectly flat during the prime working years. Second, the unadjusted gap in the log hourly wage between undocumented workers and natives is very large (around 40 percent), but half of this gap disappears once the calculation adjusts for differences in observable socioeconomic characteristics, particularly educational attainment. Finally, the adjusted wage of undocumented workers rose rapidly in the past decade. As a result, there was a large decline in the wage penalty associated with undocumented status.

It is this last result that I find particularly intriguing. Define the “wage penalty” to undocumented status as the difference in wages between observationally equivalent legal and undocumented immigrants. Here’s the graph that illustrates just how noticeable the decline in the wage penalty has been:

Wage Penalty

As I conclude in the paper, as long as we take these trends at face value, it seems that “a regularization program may only have a modest impact on the wage of undocumented workers.”

This paper is a follow-up to my earlier work on the labor supply of undocumented immigrants, which showed that undocumented men have very high labor force participation rates and inelastic labor supply. That paper is now finished and forthcoming in Labour Economics. Click here if you’d like to access the code that I used to conduct the calculations reported in that paper, including the code that imputes undocumented status in the post-1994 Current Population Surveys. As soon as I have a little extra time (we’re in the midst of downsizing and moving), I’ll clean up and post the code for the new paper as well.



Immigration And Poverty: Updated Facts

Last year, I published this paper discussing the link between immigrant poverty and the 1996 welfare reform legislation. It showed that although the legislation cut the number of immigrants receiving assistance, their poverty rate actually fell. Many of the affected immigrants resorted to good, old-fashioned employment as a means of support.

There have been some rumblings recently about how those results translate in today’s immigration debate. Although the paper was published last year, I actually wrote it years ago, focusing on the period around welfare reform and examining data through 2001. But then I got sidetracked into various other things. (FYI: there are two or three other papers in my computer hard drive awaiting resurrection in the same way).

The current discussion sparked my curiosity: What do immigrant poverty rates look like now?


So I took out the Current Population Survey (CPS) data from 1994 to 2016, the period over which this exercise can actually be done, and proceeded to calculate the poverty rate of immigrants aged 20+. I dropped the children to avoid the complication of whether the US-born children of immigrants should be counted as “immigrants” or “natives.” In any case, a few minutes later I ended up with the graph above.

It is easy to see the post-1996 decline in poverty rates that motivated my paper. But it is also easy to see the very adverse impact that the Great Recession seems to have had on immigrant poverty. Their poverty rates rose substantially, from 14.1 percent in 2007 to 18.7 percent in 2011. The increase was much smaller for natives, from 9.7 to 11.5 percent. It is also interesting that the economic recovery reduced poverty rates much more for immigrants than for natives. I am sure there’s a paper waiting to be written that explains the “excess sensitivity” of immigrant poverty rates to outside shocks.

A number of readers have indicated they like these types of fact-based blog posts, where I use available data to easily compute relevant statistics. So I’m creating a new tag for this type of post simply called “Factoids,” and I’ll try to go back in the archives and recategorize past posts. And for the true geeks, the STATA code that creates the graph is, as they used to say, below the fold.

Continue reading “Immigration And Poverty: Updated Facts”

Op-Ed In New York Times

Today’s New York Times published my op-ed on “The Immigration Debate We Need.” Having worked on this for far too many years, I’m not holding my breath that this is, in fact, the immigration debate we’ll have.

Who Is A Public Charge?

According to the Washington Post, the Trump administration is considering a number of changes in current immigration policy, focusing more on the economic side of things this time around. That WP article is already cluttered with half-truths (spouted by the usual suspects at Cato and the like), so I thought it’d be a good idea to clarify the muddied waters regarding one particular proposal that is being considered to reduce welfare use in the immigrant population.

Since 1882, the United States has banned the entry of anyone who has the potential of becoming a “public charge.” This is how current law reads:

Any alien who, in the opinion of the consular officer at the time of application for a visa, or in the opinion of the Attorney General at the time of application for admission or adjustment of status, is likely at any time to become a public charge is inadmissible.

Since 1903, the United States has allowed for the deportation of immigrants who became a public charge after they entered the country, and this is how the law now reads:

Any alien who, within five years after the date of entry, has become a public charge from causes not affirmatively shown to have arisen since entry is deportable.

Given these very straightforward–and very old–restrictions, it seems puzzling that we would find many immigrants on welfare. But, as always, the devil is in the details. The law is often not enforced, and the common-sense definition of a public charge that we carry in our heads has little to do with how the immigration regulators have defined it. This is how that definition now reads:

For purposes of determining inadmissibility, “public charge” means an individual who is likely to become primarily dependent on the government for subsistence, as demonstrated by either the receipt of public cash assistance for income maintenance or institutionalization for long-term care at government expense.

Note the big elephant in the room. Immigrants who receive non-cash benefits–including the most expensive benefit of all, Medicaid–are not considered to be public charges. In the words of DHS: “Non-cash benefits (other than institutionalization for long-term care) are generally not taken into account for purposes of a public charge determination.”

If we began to actually enforce the law with a common-sense interpretation of the century-old statutes, the policy shift will affect an awful lot of people. I took data from the March Current Population Surveys (CPS) from 1994 through 2016 to calculate the fraction of immigrant-headed households who receive some type of assistance (either cash, food stamps, or Medicaid). I then divided the foreign-born households into 2 categories–those where the household head is naturalized, and those where the household head is not. Any proposed shift in policy would affect the non-naturalized households. And this is what the trend in the fraction of households receiving assistance looks like:


In 2016, there were 8.9 million households headed by a non-citizen. Almost 42 percent of those households received some type of assistance. Put bluntly, taking the public charge provisions of immigration law seriously could potentially affect 3.7 million households, making the recent kerfuffle over a relatively small number of refugees look like small potatoes.

The policy challenge is obvious, and the economic and social ramifications will be dramatic. Before we start envisioning deportations by the hundreds of thousands, however, let’s remember that we all respond to incentives. Few economists would be surprised if some of the affected households begin to find other ways of providing for their needs.

Some additional information:

  1. No need to take my word for the graph. The trends are very easy to reproduce by anyone who is willing to spend a little time looking at the publicly available CPS data. Here is the program, the data can be downloaded here; and click here if you are really geeky and want to see the computer output and detailed statistics.
  2. The recent immigration report of the National Academy of Sciences (NAS) has similar statistics on the number of immigrant households on welfare. Table 3-15 shows the fraction of households with children receiving some type of assistance (here’s a screenshot of the table). According to the NAS, 41.8 percent of native households and 55.8 percent of immigrant households receive assistance (but they do not break up the immigrant households according to citizenship status).
  3. The NAS report also calculated the size of the fiscal burden implied by these numbers; that discussion is in Chapters 8 and 9 of the report. See here and here for a User’s Guide to the NAS fiscal impact discussion.
  4. The CPS data are notorious for understating the extent of welfare participation in the population. The Survey of Income and Program Participation (SIPP) is supposed to provide much better measures of welfare use, but it is a much harder data set to manipulate. As I note in We Wanted Workers (Table 9.1), the welfare participation rate of immigrant households implied by the SIPP is far higher than what the CPS suggests (at least 10 percentage points higher).