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The Wage Impact of the Marielitos: A Reappraisal

George J. Borjas Harvard University

October 2015

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The Wage Impact of the Marielitos: A Reappraisal

George J. Borjas

ABSTRACT

This paper brings a new perspective to the analysis of the Mariel supply shock, revisiting

the question and the data armed with the accumulated insights from the vast literature on

the economic impact of immigration. A crucial lesson from this literature is that any

credible attempt to measure the wage impact of immigration must carefully match the

skills of the immigrants with those of the pre-existing workers. The Marielitos were

disproportionately low-skill; at least 60 percent were high school dropouts. A reappraisal

of the Mariel evidence, specifically examining the evolution of wages in the low-skill group

most likely to be affected, quickly overturns the finding that Mariel did not affect Miami’s

wage structure. The absolute wage of high school dropouts in Miami dropped dramatically,

as did the wage of high school dropouts relative to that of either high school graduates or

college graduates. The drop in the relative wage of the least educated Miamians was

substantial (10 to 30 percent), implying an elasticity of wages with respect to the number

of workers between -0.5 and -1.5. The analysis also documents the sensitivity of the

estimated wage impact to the choice of a placebo. The measured impact is much smaller

when the placebo consists of cities where pre-Mariel employment growth was weak

relative to Miami.

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The Wage Impact of the Marielitos: A Reappraisal

George J. Borjas* I. Introduction

The study of how immigration affects labor market conditions has been a central

concern in labor economics for nearly three decades. The significance of the question arises

not only because of the policy issues involved, but also because the study of how labor

markets respond to supply shocks can teach us much about how labor markets work. In an

important sense, examining how immigration affects the wage structure confronts directly

one of the fundamental questions in economics: What makes prices go up and down?

David Card’s (1990) classic study of the labor market impact of the Mariel supply

shock stands as a landmark in this literature. On April 20, 1980, Fidel Castro declared that

Cuban nationals wishing to move to the United States could leave freely from the port of

Mariel, and around 125,000 Cubans quickly accepted the offer. The Card study was one of

the pioneering attempts to exploit the insight that a careful study of natural experiments,

such as the exogenous supply shock stimulated by Castro’s seemingly random decision to

“let the people go,” can help identify parameters of significant economic interest. In

particular, the Mariel supply shock would let us measure the wage elasticity that shows

how the wage of native workers responds to an exogenous increase in supply.

Card’s empirical analysis of the Miami labor market, when compared to conditions

in other labor markets that served as a control group or “placebo,” indicated that nothing

much happened to Miami despite the very large number of Marielitos. Native wages did not

go down in the short run as would have been predicted by the textbook model of a

competitive labor market. And unemployment, even for groups with low average skills,

remained unchanged relative to what was happening in the placebo cities. Card’s study has

* Harvard University, National Bureau of Economic Research, and IZA. I am particularly grateful to

Alberto Abadie and Larry Katz for very productive discussions of the issues examined in this paper and for many valuable comments and suggestions. I have also benefitted from the reactions and advice of Josh Angrist, Fran Blau, Brian Cadena, Kirk Doran, Richard Freeman, Daniel Hamermesh, Gordon Hanson, Alan Krueger, Joan Llull, Joan Monras, Panu Poutvaara, Marta Tienda, and Steve Trejo. I alone am responsible for all errors.

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been extremely influential, both in terms of its prominent role in policy discussions and its

methodological approach.1

During the 1980s and 1990s, a parallel (but non-experimental) literature attempted

to estimate the labor market impact of immigration by essentially correlating wages and

immigration across cities (Grossman, 1982; Borjas, 1987; Altonji and Card, 1991). These

spatial correlations have been criticized for two reasons: (1) immigrants are more likely to

settle in high-wage cities, so that the endogeneity of supply shocks induces a spurious

positive correlation between immigration and wages; and (2) native workers and firms

respond to supply shocks by resettling in areas that offer better opportunities, effectively

diffusing the impact of immigration across the national labor market.

Card’s Mariel study is impervious to both of these criticisms. The fact that the

Marielitos settled in Miami had little to do with pre-existing wage opportunities, and much

to do with the fact that Castro suddenly decided to allow the boatlift to occur and that the

Cuban-Americans who organized the flotilla lived in South Florida.2 Similarly, the very

short run nature of Card’s empirical exercise, effectively looking at the impact of

immigration just a few years after the supply shock, means that we should be measuring

the short-run elasticity, an elasticity that is not yet contaminated by labor market

adjustments and that economic theory predicts to be negative.

Angrist and Krueger’s (1999) analysis of the “The Mariel Boatlift That Did Not

Happen” provides the most important conceptual criticism of Card’s study to date:3

In the summer of 1994, tens of thousands of Cubans boarded boats destined for Miami in an attempt to emigrate to the United States in a second Mariel Boatlift that promised to be almost as large as the first one...Wishing to avoid the political fallout that accompanied the earlier boatlift, the Clinton Administration interceded and ordered the Navy to divert the would-be

1 Studies that examine exogenous supply shocks that are clearly influenced by the Card analysis

include Hunt (1992), Carrington and de Lima (1996), Friedberg (2001), Saiz (2003), Borjas and Doran (2012), Glitz (2012), Pinotti et al (2013), and Dustmann, Schönberg, and Stuhler (2015).

2 Both the 1990 and 2000 censuses report that almost two-thirds of the Cuban immigrants who likely were part of the Mariel influx still resided in the Miami metropolitan area.

3 There have also been many discussions of the statistical inference difficulties raised by this type of analysis; see Bertrand, Duflo, and Mullainathan (2004), Donald and Lang (2007), and Aydemir and Kırdar (2013).

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immigrants to a base in Guantanamo Bay. Only a small fraction of the Cuban émigrés ever reached the shores of Miami. Hence, we call this event, "The Mariel Boatlift That Did Not Happen" (Angrist and Krueger, 1999, p. 1328; emphasis added).

Angrist and Krueger reproduced the methodological design of Card’s Mariel article

by comparing the labor market in Miami before and after 1994 with the same set of placebo

cities. It turned out that this potential supply shock made things much worse for some

natives. For example, the black unemployment rate in Miami increased from 10 to 14

percent, at a time that the aggregate economy was booming and unemployment was

dropping in the placebo cities.

The usual interpretation would have to be that a “phantom menace” of non-existent

workers harmed Miami’s African-American workforce. It obviously makes no sense to

make such a claim, but this raises an important question: Does the evidence from the

Mariel boatlift that did happen really indicate that immigration had no impact? As Angrist

and Krueger (1999, p. 1329) conclude, “Since there was no immigration shock in 1994, this

illustrates that different labor market trends can generate spurious findings in research of

this type.”

In retrospect, however, the Angrist-Krueger claim that “only a small fraction of the

Cuban émigrés ever reached the shores of Miami,” written before the availability of the

2000 census, was not accurate. As I will show shortly, President Clinton’s decision to

reroute the potential migrants to Guantanamo seemed to only delay a sizable supply shock

of around 50,000 Cubans by only a year or so. As a result, it may be difficult to infer much

from the comparison of the Mariel supply shock to the 1994 event that ended up bringing

many immigrants to the Miami metropolitan area.

This paper provides a reappraisal of the evidence of how the Miami labor market

responded to the influx of Marielitos. The paper is not a replication of the earlier studies.

Instead, I approach and examine these questions from a fresh perspective, building on what

we have learned from the 30 years of research on the labor market impact of immigration.

One crucial insight from this research is that any credible attempt to measure the impact

must carefully match the skills of the immigrants with the skills of the pre-existing

workforce. Borjas (2003), in the study that introduced the approach of correlating wages

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and immigration across skill groups in the national labor market, found a significant

negative correlation between the wage growth of specific skill groups, defined by education

and age, and the size of the immigration-induced supply shock into those groups.

The analysis of the available microdata using this new perspective provides a very

different picture of what happened after Mariel. As is well known, the Marielitos were

disproportionately low-skill; around 60 percent were high school dropouts and only 10

percent were college graduates. At the time, about a quarter of Miami’s pre-existing

workers lacked a high school diploma. As a result, even though the Mariel supply shock

increased the number of workers in Miami by 8 percent, it increased the number of high

school dropouts by almost 20 percent.

The unbalanced nature of this supply shock obviously suggests that we should look

at what happened to the wage of high school dropouts in Miami before and after Mariel.

Remarkably, this trivial comparison was not made in Card’s (1990) study and, to the best of

my knowledge, has not yet been conducted.4 By focusing on this very specific skill group,

the finding that the Mariel supply shock did not have any consequences for pre-existing

workers immediately disappears. In fact, the absolute wage of high school dropouts in

Miami dropped dramatically, as did the wage of high school dropouts relative to that of

either high school graduates or college graduates. The drop in the low-skill wage between

1979 and 1985 was substantial, perhaps as much as 30 percent.

The evidence reported in this paper provides an entirely new perspective of how

the Miami labor market responded to an exogenous supply shock. At least in the short run,

the labor market responded precisely in the way that the “textbook” model predicts: an

increase in the number of potential workers lowered the wage of those workers who faced

the most competition from the new immigrants. It seems that the short-run labor demand

curve, even in the Miami of the early 1980s, was downward sloping after all.

4 Table 7 in Card (1980) reports wage and employment changes for the subsample of black high

school dropouts, but does not report any other pre-post Mariel differences for the least educated workers. Card’s finding that the black wage in Miami declined after Mariel, which he attributes to cyclical fluctuations, will be discussed below. In an unpublished online appendix, Monras (2014) attempts to replicate some of Card’s results and also examines wage trends in the sample of workers who have at most a high school diploma. Monras’s evidence is very suggestive of the findings reported in this paper.

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II. Data The migration of large numbers of Cubans to the United States began shortly after

Fidel Castro’s communist takeover on January 1, 1959. By the year 2010, over 1.3 million

Cubans had emigrated.

The first large-scale data set that precisely identifies an immigrant’s year of arrival

is the 2000 decennial census. Prior to 2000, the census microdata reported the year of

arrival in intervals (e.g., 1960-1964). I merged the data from various censuses and the

American Community Surveys (ACS) to construct a mortality-adjusted number of Cuban

immigrants for each arrival year between 1955 and 2010.5 For example, I used the 1970

census to estimate the number of Cuban immigrants who arrived in the United States

between 1960 and 1964, and then used the detailed year-of-migration information in the

2000 census to allocate those early immigrants to specific years within the 5-year band.

Figure 1 shows the trend in the number of Cubans migrating to the United States.

Several patterns emerge from the time series. First, it is easy to see the immediate

impact of the communist takeover of the island. In 1958, only 8,000 Cubans migrated to the

United States. By 1961 and 1962, 52,000 Cubans were migrating annually.6 The Cuban

Missile Crisis abruptly stopped this flow in October 1962, and it took several years for

other escape routes to open up. By the late 1960s, the number of Cubans moving to the

United States was again near the level reached before the Missile Crisis.

The huge spike in 1980, of course, is the Mariel supply shock. Between 1978 and

1980, the number of new Cuban immigrants increased 17-fold, from 6,500 to 110,000. The

figure shows yet another spike in 1994 and 1995, coinciding with the period of Angrist and

Krueger’s (1999) “Mariel Boatlift That Did Not Happen.” The census data clearly indicates

that somehow the “phantom” Cubans from that boatlift ended up in the United States,

making this supply shock a Little Mariel. Although the number of “Little Marielitos” pales in

comparison to the number of actual Marielitos, it is still quite large; the number of migrants

arriving in 1995 was similar to that of the early Cuban waves in the 1960s. It is also evident

5 In principle, the calculation also adjusts for potential out-migration of Cuban immigrants. I suspect,

however, that the number of Cubans who chose to return is trivially small (although a larger number might have migrated elsewhere).

6 Full disclosure: I am a data point in the 1962 flow.

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that there has been a steady increase in the number of Cuban migrants since the early

1980s. By 2010, about 40,000 Cubans were arriving annually.

One last detail is worth noting about the Cuban migration: A disproportionately

large number of the immigrants ended up residing in the Miami metropolitan area. The

fraction of Cuban immigrants residing in Miami was 50 percent in the 1980 census, 58

percent in the 1990 census, and 60 percent in the 2000 census. Regarding the Marielitos

themselves, 62.6 percent of the Marielitos resided in Miami in 1990 and 63.4 percent still

resided there in 2000.

The main data sets used in the empirical analysis are the 1977-1993 March

Supplements of the Current Population Surveys (CPS).7 These surveys report the annual

wage and salary income as well as the number of weeks worked by a respondent in the

previous calendar year. The wage analysis will be restricted to men aged 25-59, who are

not self-employed, who are not enrolled in school, and who report positive annual earnings,

positive weeks worked, and positive usual hours worked.8 The age restriction ensures that

a worker’s observed earnings are not contaminated by transitory fluctuations that occur

during the transitions from school to work and from work to retirement.

The 1977-1993 period that will be analyzed throughout much of the paper is

selected for two reasons. First, although the March CPS data files are available since 1962,

the Miami metropolitan area can only be consistently identified in the 1973-2004 surveys.

Beginning with the 1977 survey, the CPS began to identify 44 metropolitan areas (including

Miami) that can be used in the empirical analysis.9 Second, my analysis of wage trends will

7 The March surveys are known as the Annual Social and Economic Supplements (ASEC). The data

was downloaded from the Integrated Public Use Microdata Series (IPUMS) website on August 22, 2015. Card (1990) used the CPS Outgoing Rotation Groups (ORG). I will show below that the evidence from the ORG data leads to a similar inference: something did indeed happen to the low-skill labor market in post-Mariel Miami.

8 In addition, I exclude persons who reside in group quarters or have a negative sample weight. It is tempting to increase sample size by including working women in the study, but female labor force participation was increasing very rapidly in the 1980s, so that wage trends are likely to be affected by the selection that marks women’s entry into the labor market. The labor force participation rate of women (aged 18-64) increased from 52.1 to 72.5 percent between 1980 and 1990 in Miami, and from 49.2 to 71.2 percent in all other urban areas.

9 The Miami-Hialeah metropolitan area is not identified at all before 1973, and is combined with the Fort Lauderdale metropolitan area after 2004. The 1973-1976 surveys identify only 34 metropolitan areas, and one of them (New York City) is not consistently defined throughout the period; the Nassau-Suffolk metropolitan area is pooled with the New York City metro area in 1976.

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stop with the 1993 survey to avoid contamination from the Little Mariel supply shock of

1994 and 1995.

The CPS did not report a person’s country of birth before 1994, so that it is not

possible to measure the wage impact of the Mariel supply shock on the native-born

population. I instead examine the impact on non-Hispanic men (where Hispanic

background is determined by a person’s answer to the Hispanic ethnicity question), a

sample restriction that comes close to identifying Miami’s native-born population at the

time. For example, the 1980 census, conducted days before the Mariel supply shock,

reports that 40.7 percent of Miami’s male workforce was foreign-born, with 65.1 percent of

the immigrants born in Cuba and another 11.2 percent born in other Latin American

countries.

The labor market outcome examined throughout the study will be the worker’s log

weekly earnings, where weekly earnings are defined by the ratio of annual income in the

previous calendar year to the number of weeks worked. I use the Consumer Price Index

(CPI) for all urban consumers to deflate the earnings data (1980 = 100).10 For expositional

consistency and unless otherwise noted, whenever I refer to a particular calendar year

hereafter, it will be the year in which earnings were actually received by a worker, as

opposed to the CPS survey year.11

Before proceeding to an examination of wage trends, it is important to document

what we know about the skill distribution of the Marielitos. As noted earlier, the Mariel

supply shock began a few days after the 1980 census enumeration, so that the first large

survey that contains a large sample of the Marielitos themselves is the 1990 census.

Nevertheless, a few CPS supplements conducted in the 1980s (including April 1983, June

1986, and June 1988) provide information on a (very) small sample of Cuban immigrants

who arrived at the time of Mariel.

10 To minimize the problem of outlying observations, I exclude all workers who earn less than $1.50

an hour or more than $40 an hour (in 1980 dollars). This restriction approximately drops workers in the top and bottom 1 percent of the earnings distribution. I replicated the analysis using the log hourly wage as an alternative measure of a worker’s income, and the results are similar to those reported in this paper.

11 For example, a discussion of the earnings of workers in 1985 refers to the data drawn from the 1986 March CPS.

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Table 1 presents the education distribution of the sample of adult Cuban immigrants

who arrived in 1980 (or in 1980-1981, depending on the data set) and who were

enumerated in various surveys sometime between 1983 and 2000. The calculation includes

the entire population of Marielitos (workers and non-workers, as well as men and women)

who were at least 18 years old as of 1980.

The crucial implication of the table is that the Mariel supply shock consisted of

workers who were very unskilled, with a remarkably large fraction of the Marielitos being

high school dropouts.12 Despite the variation in sample size and the almost 20-year span in

the surveys reported in the table, the fraction of Marielitos who lacked a high school

diploma hovers around 60 percent. Table 1 also shows that a very small fraction of these

immigrants were college graduates (around 10 percent).

It is insightful to compare the education distribution of the Marielitos with that of

the pre-existing workforce. The last row of Table 1 shows that “only” 26.7 percent of labor

force participants in the Miami metropolitan area were high school dropouts. In fact,

Miami’s workforce was remarkably balanced in terms of its skill distribution, with 20 to 30

percent of workers in each of the four education groups.13

Table 2 summarizes what we know about the magnitude of the Mariel supply shock.

There were 176,300 high school dropouts in Miami’s labor force just prior to Mariel (out of

a total of 659,400). According to the 1990 census, 60,100 Cuban workers migrated (as

adults) either in 1980 or 1981. If we make a slight adjustment for the small number who

entered the country in 1981, Mariel increased the size of the labor force by 55,700 persons,

of which almost 60 percent were high school dropouts.14 Although the Mariel supply shock

12 The fact that most of the adult Marielitos lacked a high school diploma does not necessarily imply

that they did not complete compulsory schooling in Cuba. There is also a possibility that the skills of the Marielitos were further “downgraded” upon arrival, as in Dustmann, Frattini, and Preston (2013), so that even those immigrants with a high school diploma were still competing with the least educated workers in the pre-existing Miami workforce.

13 The pre-existing workforce includes all labor force participants in Miami, regardless of where they were born or their ethnicity. The fraction of non-Hispanic workers who were high school dropouts was also very high (19.8 percent).

14 The 2000 census indicates that approximately 92.8 percent of the Cuban immigrants who entered the country in either 1980 or 1981 actually entered in 1980. It is also important to note that the supply shock was probably slightly larger than indicated in Table 2 because the calculation does not account for mortality through 1990.

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increased Miami’s workforce by 8.4 percent and increased the number of the most

educated workers by 3 to 5 percent, the size of the low-skill labor force rose by a

remarkable 18 percent. Moreover, this supply shock occurred almost overnight (Stabile

and Scheina, 2015). The first Marielitos arrived in Florida on April 23, 1980. The Coast

Guard reports that over 100,000 refugees had reached the Florida shores by June 3.

III. Descriptive Evidence The very low skills of the Marielitos indicates that we should perhaps focus our

attention on the labor market outcomes of the least educated workers in Miami to get a

first-order sense of whether the supply shock had any impact on Miami’s wage structure. In

fact, the literature sparked by Borjas (2003) suggests that it is important to “match” the

immigrants to corresponding native workers by skill groups. Educational attainment is a

skill category that would seem to be extremely relevant in an examination of the Mariel

supply shock.

Any empirical study of the impact of Mariel encounters an immediate data problem:

The number of workers enumerated by the CPS in the Miami labor market is small,

introducing a lot of random noise into any calculation. In particular, the number of non-

Hispanic men who satisfy the sample restrictions and who are employed in the Miami area

was around 90-100 per CPS cross-section in the 1980s, with about a quarter consisting of

high school dropouts. The sample size problem, however, becomes particularly acute with

the 1991 survey, when the number of non-Hispanic men sampled in Miami falls abruptly

(by almost a third), and the number of high school dropouts drops to the single digits. This

change in sample size suggests that the evidence is probably most credible when we

examine outcomes during the first decade after Mariel.

Nevertheless, it is instructive to start by reporting the evidence from the most

straightforward calculation of the potential wage impact that uses all the available data. It

turns out that even the most cursory examination of the wage trends reveals a remarkable

pattern that immediately overturns the conventional wisdom about Mariel: Something

indeed did happen to the wage structure in Miami after 1980. It seems, in fact, as if the

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Marielitos may have had a large and adverse wage impact on the wage of comparable

Miamians after all.

To easily illustrate the key finding of this paper, I simply calculate the average log

weekly wage of high school dropouts in Miami each year between 1972 and 2003, the

period for which the March CPS has a consistent time series for the Miami metropolitan

area. Figure 2 illustrates the wage trend, using a 3-year moving average to smooth out the

noise in the time series. The figure also illustrates the trend for similarly educated non-

Hispanic men working outside Miami. It is important to emphasize that this simple exercise

does not adjust the CPS data in any way whatsoever (other than taking a moving average),

so that it provides a very transparent indication of what happened to wages in Miami pre-

and post-Mariel.

It is obvious that despite the similarity in wage trends between Miami and the rest

of the United States prior to 1980, something happened in 1980 that caused the two wage

series to diverge. Before Mariel, the log wage of high school dropouts in Miami was 0.10 log

points below that of workers in the rest of the country. By 1985, the gap had widened to

0.42 log points, implying that whatever caused the divergence had lowered the relative

wage of low-skill workers in Miami by around 30 percent. Note that the low-skill wage in

Miami fully recovered by 1990, only to be “hammered” again in 1995, coincidentally the

time of the Little Mariel supply shock. By 2002, the wage gap between high school dropouts

in Miami and elsewhere had returned to its pre-Mariel normal of around 0.11 log points.

Of course, the distinctive wage trend in Miami may not appear quite as distinctive

when contrasted with what happened in other specific cities. The comparison of Miami to

an aggregate of the U.S. labor market may be masking a lot of the variation that influences

particular places and that disappears when averaged out. It is, therefore, important to

create a control group of comparable cities unaffected by the Mariel supply shock to

determine if the wage trends evident in Miami were due to macroeconomic factors that

affected other similar communities as well. Beginning with the 1977 survey, the March CPS

data identifies 43 metropolitan areas that can be combined in some fashion to construct a

sort of placebo. Card (1980, p. 249; emphasis added) describes the construction of his

control group as follows:

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For comparative purposes, I have assembled similar data…in four other cities: Atlanta, Los Angeles, Houston, and Tampa-St. Petersburg. These four cities were selected both because they had relatively large populations of blacks and Hispanics and because they exhibited a pattern of economic growth similar to that in Miami over the late 1970s and early 1980s. A comparison of employment growth rates…suggests that economic conditions were very similar in Miami and the average of the four comparison cities between 1976 and 1984.

It is important to emphasize that the four cities in the Card placebo were chosen

partly based on employment trends observed after the Mariel supply shock. Put differently,

if Mariel worsened employment conditions in Miami, the Card placebo is comparing the

poorer outcomes of workers in Miami to the outcomes of workers in cities where some

other factor worsened their opportunities as well. It is obviously far preferable to

exogenize the choice of a placebo by comparing cities that were roughly similar prior to the

treatment, rather than being similar after one of them was “injected” with a very large

supply shock.

The various panels of Figure 3 illustrate the wage trends in Miami and several

potential placebos between 1976 and 1992. The top panel shows that the log wage of high

school dropouts declined dramatically after 1980 when compared to what happened in the

cities that make up the Card placebo. Of course, trends in absolute wages reflect many

factors that are specific to local labor markets, so that it is possible that these ups and

downs capture idiosyncratic shifts that affected all workers in Miami. The Mariel supply

shock, however, specifically targeted the least educated workers and the bottom two

panels of the figure show that the relative wage of high school dropouts in Miami—relative

to either college graduates or high school graduates—also declined dramatically after

Mariel, and also recovered by 1990.15 In sum, the wage trends observed in Miami—relative

to those seen in the cities that make up the Card placebo—consistently indicate that the

15 The time series of the wage of high school dropouts in Miami relative to high school graduates has

a data quirk that is worth noting: High school dropouts earned slightly more than high school graduates prior to Mariel. This anomaly arises because the average wage of high school graduates sampled by the CPS in Miami in 1979 is unusually low, and this data anomaly carries over to neighboring years because of the moving average calculation. All the findings in this paper are invariant to dropping the 1979 observation. The figure also shows that the wage of high school dropouts again exceeds that of high school graduates after 1990. As noted earlier, however, there is a precipitous drop in the number of high school dropouts sampled in Miami after 1990.

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economic well-being of the least educated workers in Miami took a downward turn shortly

after 1980, reached its nadir around 1985-1986, and did not recover fully until 1990.

As noted above, the cities in the Card placebo do not make up a proper control

group because they were chosen, in part, so that post-Mariel employment conditions in the

placebo cities resembled those in Miami. To determine the set of cities that had comparable

employment growth prior to Mariel, I pooled the 1977 and 1978 cross-sections of the CPS,

and also pooled the 1979 and 1980 cross-sections.16 Note that the 1980 CPS data, collected

in March, is not affected by the supply shock, as the Marielitos did not begin to arrive until

late April. I then used the two pooled cross-sections to calculate the log of the ratio of the

total number of workers in 1979-1980 to the number of workers in 1977-1978. Column 1

of Table 3 reports the employment growth rate for each of the 44 metropolitan areas,

ranked by the growth rate.

It is evident that Miami’s pre-Mariel employment conditions were quite robust,

ranking 6th in the rate of employment growth. Note that all the cities that make up the Card

placebo had lower growth rates than Miami between 1977 and 1980. In fact, the average

employment growth rate in those four cities (weighted by average employment) was 6.9

percent, less than half the 15.3 percent growth rate in Miami.

I use the rankings reported in Table 3 to construct a new placebo, which I call the

“employment placebo,” by simply choosing the four cities that were most similar to Miami

prior to 1980. Specifically, the employment placebo consists of the four cities (Anaheim,

Rochester, Nassau-Suffolk, and San Jose) ranked just above and just below Miami.

Figure 3 clearly shows that the relative decline in the wage of low-educated workers

in Miami is much larger when we compare Miami to cities that had comparable

employment growth than to the cities that make up the Card placebo. Between 1979 and

1985, for instance, the wage of high school dropouts in Miami relative to the Card placebo

fell by about 0.28 log points (or 24 percent), but the decline was about 0.48 log points (38

percent) when compared to the cities in the employment placebo. This difference, of course,

is not surprising. The comparison of post-Mariel economic conditions in Miami to that of

16 These years refer to the survey years and not the calendar years where earnings are observed. A

person is employed if he or she works in the CPS reference week.

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cities where employment conditions are also poor by construction inevitably hides some of

the impact of the Marielitos.

In short, the choice of a placebo plays a crucial role in determining the wage impact

of Mariel. The fact that there are 43 metropolitan areas from which to select a 4-city control

group (a number that is itself arbitrary) implies that there are a total of 123,410 potential

placebos. In view of the very large number of choices, it might be reasonable to expect a

huge dispersion in the estimated wage effect of the Marielitos across the 123,410 potential

comparisons that can be made. I will report below the distribution of estimated wage

impacts across all potential four-city placebos and show that the wage impact of Mariel is

significantly larger when the placebo contains cities that better resembled Miami’s

economic conditions before 1980.

An alternative way of choosing a placebo is to employ the “synthetic control”

statistical method developed by Abadie, Diamond, and Hainmueller (2010). The method

essentially “searches” across all potential placebo cities and derives a weight that averages

cities to create a new synthetic city. This synthetic city is the one that best resembles the

pre-Mariel Miami labor market. The synthetic control approach has two beneficial

properties. First, it precludes the researcher from making arbitrary decisions about what

the proper placebo should be. Second, the weights attached to the potential placebo cities

can be based on several economic characteristics.17

I defined a “synthetic placebo” by using three such characteristics: the rate of

employment growth in the 4-year period prior to Mariel (i.e., the variable used to define the

employment placebo); the concurrent rate of employment growth for high school

dropouts; and the concurrent rate of wage growth for high school dropouts. The last two

columns of Table 3 report these additional characteristics, showing that Miami also had a

robust low-skill labor market prior to Mariel. Miami ranked sixth in the growth of low-skill

employment and 13th in the rate of wage growth.

Figure 3 also illustrates the wage trends in the “city” that makes up the synthetic

placebo. As will be seen throughout the paper, sometimes the trends from the synthetic

17 The synthetic control method still requires the researcher to specify the vector of variables (in

addition to the labor market outcome of interest) that should be similar between Miami and the synthetic city in the pre-treatment period.

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placebo resemble those from the Card placebo; sometimes they resemble those from the

employment placebo, and sometimes they resemble neither. Much depends on the labor

market characteristic being examined.

It is interesting to examine the weights implied by the synthetic control method (see

Appendix Table A-1 for a listing). When the labor market interest of outcome is the log

wage of high school dropouts, the synthetic control method assigns the largest weights to

Kansas City (with a weight of 0.56), Anaheim (0.27), Sacramento (0.041), and San Diego

(0.013). The appendix table also reports the weights assigned by the synthetic control

method in the regression analysis reported below. The metropolitan areas with the largest

weights are Anaheim (0.372), San Diego (0.239), Rochester (0.159), and San Jose (0.043).

By looking at the ranking of all these cities in Table 3, it is obvious that the synthetic control

method consistently selects metropolitan areas that had robust labor markets prior to

Mariel (generating some overlap between the cities that make up the synthetic control and

the cities in the employment placebo). Figure 3 documents that a comparison of wage

trends between Miami and the synthetic placebo again suggests that the Miami experience

was unique.

The wage comparisons between Miami and the various placebos, however, do not

preclude the possibility that other cities in other time periods have experienced equally

steep wage cuts. Perhaps there are many documented cases of similar transitory and

numerically large wage reductions in other cities that are attributable to sampling error or

to factors that have nothing to do with Mariel.

It is easy to establish that the steep drop in the low-skill wage in post-Mariel Miami

was a very unusual event. The average wage of high-school dropouts in Miami fell by

around 35 percent between 1976-1979 and 1981-1986. We can calculate the comparable

wage change in every other metropolitan area for all equivalent time periods between

1976 and 2003 and see if the Miami experience at the time of Mariel stands out.18

Obviously, if 35-percent wage cuts happen frequently, it would be harder to claim that

18 Garthwaite, Gross, and Notowidigdo (2014) conduct a similar exercise to examine the distribution of the impact of an experiment in health insurance availability on employment lock. Note that I extend the sample period through 2003 to determine how the steep wage drop observed among low-skill Miamians in the early 1980s compares to the experience of comparable workers in all other metropolitan areas over a two-decade period.

16

Miami’s experience had much to do with the Marielitos. Perhaps something else was going

on—a “something else” that occurs frequently enough in local labor markets—that just

happened to coincide with the timing of Castro’s decision.

To assess how Miami’s post-Mariel experience compares to that of the entire

distribution of wage changes, I calculated the wage change between every single “pre-

treatment” period τ (1976-1979, 1977-1980,…,1993-1996) and the corresponding “post-

treatment” period τ′ (1981-1986, 1982-1987,…,1998-2003). Note that to replicate the

Mariel experiment, I skip a year between the 4-year pre-treatment span and the 6-year

post-treatment span. I conducted this calculation for each metropolitan area, leading to a

total of 774 possible “events” outside Miami (43 metropolitan areas and 18 potential

treatment years between 1980 and 1997). I also calculated the change in the log wage of

the other education groups for all city-year permutations.

The top panel of Figure 4 illustrates the frequency distribution of all observed

changes in the wage of high school dropouts outside Miami. Table 4 reports summary

statistics from the various distributions created by this empirical exercise.

Between 1977-1979 and 1981-1986, the log wage of high school dropouts in Miami

fell by 0.439 log points (or 35.5 percent). It is visually obvious from Figure 4 that such a

large wage drop was an unusual event. The mean observed wage change across all city-

year permutations was only about -0.10 log points. The Mariel experience ranks in the 1.8th

percentile of the distribution of all observed wage changes between 1976 and 2003 across

all metropolitan areas. Similarly, the frequency distribution of observed wage changes for

high school dropouts in the 1980 treatment year shows that the wage drop observed in

Miami was the largest wage drop observed among all metropolitan areas.

Equally important, this exercise reveals that more educated workers in Miami did

not experience a substantial wage decline (see the bottom panel of Figure 4). Although

there have been recent claims that perhaps high school dropouts and high school graduates

are perfect substitutes and should be pooled to form the “low skill” workforce (more on

this in the next section), the data clearly contradicts this conjecture. The mean wage change

in the log wage of high school graduates across all city-year permutations in the years 1977

through 2001 was -0.061, and Miami’s Mariel experience ranked in the 66th percentile. The

17

value observed in the Miami metropolitan area at the time of Mariel was -0.021, ranking

42nd out of the 44 metropolitan areas in the distribution for treatment year 1980.

In short, something unique happened to the economic well being of high school

dropouts in Miami in the early 1980s, but not to high school graduates or to workers with

even more education. The event that shocked the wage structure in Miami at the time of

Mariel, whatever it happened to be, happens rarely and its adverse consequences were

targeted very narrowly on workers who lacked a high school diploma.

IV. Robustness of the Descriptive Evidence

Given the striking picture that the raw data gives about the labor market impact of

the Marielitos, and given the very contentious debate over immigration policy both in the

United States and abroad, it is important to establish that the evidence presented in the

previous section is robust.

This section addresses several distinct questions to evaluate the sensitivity of the

results. For example, was the decline in the wage of high school dropouts in the Miami of

the early 1980s recorded by other contemporaneous data sets, such as the CPS Outgoing

Rotation Groups (ORG)? After all, the trends in wage inequality observed in the ORG

sometimes differ markedly from those observed in the March CPS.

Similarly, is the evidence robust to alternative definitions of the low-skill

workforce? The descriptive analysis, motivated by the education distribution of the

Marielitos, used the sample of high school dropouts to define the low-skill workforce. Are

the wage trends similar if we defined a low-skill worker differently or if we examined the

shape of Miami’s wage distribution?

1. Results from the CPS-ORG

It is well known (Autor, Katz, and Kearney, 2008; Lemieux, 2006) that wage trends

recorded by the March CPS sometimes differ from the “comparable” wage trends recorded

by the CPS Outgoing Rotation Groups. Unlike the March CPS, which measures annual

earnings in the calendar year prior to the survey, the ORG gives a measure of the hourly

wage for respondents who are paid by the hour and of the usual weekly wage for all other

18

workers. The ORG time series begins in 1979, so that the pre-treatment period only

contains one year of data. Following Autor, Katz, and Kearney (2008), I extend the pre-

treatment period by using the roughly comparable (though smaller) May CPS supplements

for 1977 and 1978. 19

It is important to emphasize that the differences in wage trends between the March

CPS and the ORG arise partly because the two surveys measure different concepts of

income. The March CPS reports total earnings from all jobs held in the previous calendar

year. The ORG measures the wage in the main job held by a person in the week prior to the

survey (if working). The ORG does not provide any earnings information for persons who

happen not to be working on that particular week, whereas the March CPS would capture

the earnings losses associated with jobless periods. From the perspective of determining

the labor market impact of the Mariel supply shock, it would seem that the more

encompassing measure of labor market outcomes in the March CPS is far preferable.

Before proceeding to examine the potential disparities in wage trends across the

two surveys, it is convenient to first adjust the data for differences in the age distribution of

workers in different time periods and in different metropolitan areas. To make the analysis

transparent, I used a simple regression model to calculate the age-adjusted mean wage of a

skill group in a particular market. Specifically, I estimated the following individual-level

earnings regression separately in each CPS cross-section:20

(1) log wirst = θr + Ai γt + ε,

where wirst is the weekly wage of worker i in city r in education group s at time t; θr is a

vector of fixed effects indicating city of residence; and Ai is a vector of fixed effects giving

19 I use the 1979-2001 ORG files archived at the National Bureau of Economic Research. Because the

May supplements before 1977 provide limited information on metropolitan area of residence, the wage series in the ORG begins in 1977 while the comparable series in the March CPS begins in 1976. The wage measure used in the ORG analysis is the recoded usual earnings per week (earnwke).

20 Of course, the regressions are estimated separately in the March CPS and the ORG.

19

the worker’s age.21 The fixed effects θr deflate the log weekly wage for regional wage

differences. The average residual from this regression for cell (r, s, t) gives the age-adjusted

mean wage of that cell. Unless otherwise specified, I use age-adjusted wages for the

remainder of the paper.22

Figure 5 illustrates the wage trends calculated in the ORG data for Miami and for

the three placebos defined in the previous section.23 It is again visually evident that

something happened to the low-skill labor market in Miami in the early 1980s, particularly

when the Miami trend is compared to either the employment or synthetic placebos. The

use of the Card placebo in the ORG data often masks much of what went on in post-Mariel

Miami.

For example, the wage of high school dropouts in Miami fell by 0.22 log points

between 1979 and 1985. Figure 5 indicates that the comparable wage fell by 0.17 log

points in the Card placebo, and by -0.10 log points in either the employment or synthetic

placebos. The use of the Card placebo would imply that Mariel lowered the wage of high

school dropouts in Miami by only about 5 percent, while both the employment and

synthetic placebos would imply an impact of around 12 percent. Even more so than the

March CPS data, the ORG shows the crucial role that the choice of a placebo plays in any

measurement of the wage impact of the Marielitos.

2. Other measures of skills

Some recent studies contend that much of the wage impact of immigration

disappears when the low-skill group is defined in an alternative way. Card (2009), for

example, argues that high school dropouts and high school graduates are perfect

substitutes.24 The pooling of these two groups into a very large low-skill workforce

21 I used seven age groups to create the fixed effects (25-29, 30-34, 35-39, 40-44, 45-49, 50-54, and

55-59).

22 It is worth noting that the wage trends in the age-adjusted data implied by the March CPS look almost identical to the raw trends documented in Figure 2.

23 The last column of Appendix Table A-1 shows the weights attached to the different metropolitan areas by the synthetic placebo method in the ORG data. The largest weights were attached to Anaheim (0.396), San Diego (0.234), and Rochester (0.164).

24 See also Ottaviano and Peri (2012) and Manacorda and Manning (2012).

20

inevitably dilutes the disparate impact of low-skill immigration on the least skilled workers,

and helps to “build in” a conclusion that recent immigration could not have had much of an

impact on the wage structure (Borjas, Freeman, and Katz, 1997).

Putting aside whether the two groups are or are not perfect substitutes for the

moment, it is nonetheless important to ascertain if the evidence that Mariel seems to have

had a substantial wage impact disappears when such an aggregation is conducted. Before

proceeding, however, it is worth emphasizing that the raw data in Figure 4 indicated that

while the wage of high school dropouts in Miami fell dramatically after Mariel, the wage of

high school graduates did not (in fact, it rose relative to what happened elsewhere). The

two education groups were almost equally sized in pre-Mariel Miami, so that the

aggregation will again inevitably dilute the impact of Mariel on the least-educated workers.

Figure 6 uses the March CPS data to illustrate the basic trends in the log weekly

wage of the pooled group of high school dropouts and high school graduates. Despite the

fact that the aggregation attenuates some of the wage effect, the figure again shows a

difference between what happened to this aggregated low-skill workforce in Miami and

elsewhere. For example, the wage of the pooled group of high school dropouts and

graduates fell by 12 percent in Miami in the early 1980s, but by only 5 percent in the cities

that make up the employment placebo and 7 percent in the synthetic placebo.

It is important to note that the observed wage trends reject the conjecture that the

two education groups should be pooled. If we start with a nested CES production function

and if we also assume that wages are equal to the value of marginal product, it is well

known that the elasticity of substitution between high school dropouts (group 1) and high

school graduates (group 2) can be estimated by the regression:

(2) log

w1 w2

⎛ ⎝⎜

⎞ ⎠⎟ = λ−

1 σ

log L1 L2

⎛ ⎝⎜

⎞ ⎠⎟

,

where wi is the wage of group i; Li gives the number of workers in that group; and σ is the

elasticity of substitution. The typical study exploits variation in factor prices and factor

21

quantities across regions or over time (or both) to estimate σ. The intercept λ is a function

of technological parameters, and need not be either region- or time-invariant.

The visual evidence (as well as the regression evidence presented in subsequent

sections) suggests that there is little need to take the “detour” of estimating equation (2) to

determine if high school dropouts and high school graduates are perfect substitutes. If we

take the CES framework seriously, equation (2) implies that the wage ratio of the two

groups will be uncorrelated with the quantity ratio only if σ equals infinity. However, the

data consistently indicates that the wage of high school dropouts in Miami relative to that

of high school graduates fell dramatically after the Mariel supply shock. This drop in the

relative wage of high school dropouts is obviously inconsistent with the hypothesis that the

two groups are perfect substitutes. In fact, as we have seen and will see again below, the

impact of Mariel on the wage of high school dropouts is consistently negative, while the

impact on high school graduates is mostly positive.

The “experimental” way of showing that the two groups are not productive clones is

far more convincing than the typical regression approach used to estimate σ. It is well

known that the relative demand for low-skill labor fell in recent decades, so that the

intercept λ in equation (2) is not constant over time. We obviously do not know how to net

out this demand shift in a time-series data set (such as the one that could be constructed

from the CPS), so that assumptions must be made about the shape of the unobserved trend

in relative demand.25 Borjas, Grogger, and Hanson (2012) show that estimates of the slope

coefficient in equation (2) are extremely sensitive to these extraneous assumptions. The

estimate of σ can be made positive, zero, or even negative by assuming different functional

forms for the unobserved trend, regardless of whether the underlying wage data are a time

25 See Katz and Murphy (1992) and Autor, Katz, and Kearney (2008). Goldin and Katz (2010) argue

that the “preferred” specification for the regression model should include a linear trend as well as a post- 1992 spline to account for these unobserved shifts in the relative demand of high school dropouts and high school graduates. If one “buys into” these functional form assumptions, the estimate of (-1/σ) using the Goldin-Katz annual CPS data from 1963 through 2005 is -0.135 (with a standard error of 0.027), rejecting the hypothesis that the two groups are perfect substitutes (see Borjas, Grogger, and Hanson, 2012).

22

series or exploit geographic variation.26 The Mariel evidence that suggests the two groups

are not perfect substitutes is not vulnerable to this criticism.

Finally, it is instructive to show that the wage of Miami’s most disadvantaged

workers behaved differently in the early 1980s even if we dispense completely with the use

of educational attainment to define the low-skill workforce. It turns out that Miami also

experienced a widening of its wage distribution at the time. The most transparent way of

documenting this widening is by examining what happened to the spread of the

distribution of log weekly earnings in the various cities.

Figure 7 uses the March CPS to illustrate the trend in both the wage of the worker at

the 20th percentile as well as the interquantile range, which I define as the difference in the

log weekly wage between the worker at the 20th percentile and the worker at the 80th

percentile. The trends are visually striking. It is evident that the economic well being of

Miamians in the bottom tail of the wage distribution took a beating post-Mariel. Much of

the decline seemed to occur in the first few years after Mariel, at which point both the

absolute and relative position of low-skill workers began to recover.

3. Implications for the black-white wage gap

Just days prior to the Mariel supply shock, the 1980 census reported that 25.2

percent of Miami’s (male) workforce was African-American. There was, however, a sizable

disparity in the black share among education groups; it was 42.5 percent for high school

dropouts, but only 6.0 percent for college graduates. This imbalance in the skill

distributions of black and white workers in pre-Mariel Miami suggests that a large supply

shock of low-skill immigrants would likely have a disproportionately larger effect on the

black workforce, and could widen the average wage gap between black and white workers.

The impact of Mariel on Miami’s black workforce is of particular interest because

racial riots ravaged parts of the city within a month after the Mariel boatlift began, leaving

18 dead and 400 injured. The conditions on the ground were volatile, and the riots were

26 The typical regression approach also faces a serious conceptual difficulty: What exactly is the

exogenous force that generates changes in relative quantities that somehow cause changes in relative wages? Despite the classic supply-demand endogeneity problem with this regression framework, the issue has been almost universally ignored in the literature.

23

the consequence of a long list of accumulated grievances, particularly the acquittal of four

white police officers charged with manslaughter when an African-American man died after

a high-speed chase. But, notably, one of the grievances cited by a history of those riots was

“the displacement of blacks by Cubans from jobs and other opportunities” (Vogel and

Stowers, 1991, p. 120).

Figure 8 illustrates the trend in the black-white wage gap in Miami and the placebos

using both the March CPS and the ORG files. It is obvious that the relative black wage

declined sharply after Mariel.27 The March CPS data, for example, indicates that the black

relative wage in Miami fell by almost 20 percentage points between 1979 and 1985,

showing a very different trend than what occurred elsewhere.

It is interesting to note that the original Card study, which used the ORG files,

suggests the possibility that the African-American workforce in Miami was particularly

affected by the Mariel supply shock. Card (1990, Table 3, p. 250) reports that the black

wage in Miami fell by 11 percentage points between 1979 and 1983, as compared to a drop

of only 5 percentage points in the comparison cities. This suggestive evidence, however,

was dismissed: “The data do suggest a relative downturn in black wages in Miami during

1982-83. It seems likely, however, that this downturn reflects an unusually severe cyclical

effect associated with the 1982-83 recession.” Figure 7 shows that the downturn in black

wages was not a transitory cyclical deviation. In fact, the bottom panel of the figure shows

that the ORG data would have revealed a continuing decline in the economic fortunes of

Miami’s black workers had the Card study examined the data beyond its stopping point of

mid-1985.

In short, it seems as if the impact of the Marielitos on relative wages across

education groups substantially worsened the relative economic status of the typical

African-American in Miami relative to his counterpart in the placebo cities. This

disproportionate impact of low-skill immigration on the African-American workforce is

consistent with the evidence reported in Borjas, Grogger, and Hanson (2010).

27 The calculation of the synthetic placebo was not conducted for the black-white wage gap in the March CPS because the methodology requires a perfectly balanced panel over the relevant sample period. Unfortunately, there were over 10 city-year permutations in the data that did not sample any black workers. There were only 3 city-year permutations without black workers in the ORG (and they were all in 1977 or 1978). I used adjacent-year data to impute the missing information for the ORG.

24

It would be of great interest to also examine the relative trends in Hispanic wages,

but the nature of the available data would make that comparison uninformative. A

replication of the analysis illustrated in Figure 8 for the Hispanic population (not shown for

the sake of brevity) would show steady wage declines for Hispanic workers throughout the

entire period in Miami and in the various placebos. The 1980s and 1990s were a period of

substantial Hispanic immigration into many areas of the country, and that influx included

millions of undocumented immigrants who are also disproportionately likely to be high

school dropouts. Many of the placebo cities also received large numbers of low-skill

Hispanic immigrants, diluting their effectiveness as a control group. Moreover, the CPS data

does not allow us to create a sample of “pre-existing” Hispanic workers, so that the

observed trend in the Hispanic wage is largely reflecting the changing composition of the

Hispanic workforce due to the persistent inflow of large numbers of immigrants.

V. Regression Results To estimate the post-treatment effect of the Mariel supply shock relative to the

various placebos, I use the mean age-adjusted wage of high school dropouts in city r at time

t, denoted by logwrt . This wage becomes the dependent variable in a traditional difference-

in-differences regression model:

(3) logwrt = θr + θt +β(Miami× Post-Mariel)+ ε,

where θr is a vector of city fixed effects; θt is a vector of year fixed effects; “Miami”

obviously represents a dummy variable indicating the Miami-Hialeah metropolitan area;

and “Post-Mariel” indicates if time t occurs after 1980.

The regression uses annual observations between t=1977 and t=1992, but excludes

1980, the year of the supply shock.28 The cities r included in the regression are Miami and

the cities in a specific placebo. For example, if the Miami experience is being compared to

that of cities in the employment placebo, there would be five cities in the data, and each of

28 This time span allows me to estimate the identical regression model in both the March CPS and ORG samples.

25

these cities would be observed 15 times between 1977 and 1992, for a total of 75

observations. The regression comparing Miami to the synthetic placebo is similar in spirit,

but there are only two “cities” in this regression: Miami and the synthetic city, for a total of

30 observations.

To allow the wage impact of Mariel to vary over time, the “post-Mariel” variable in

equation (3) is a vector of fixed effects indicating whether the observation refers to 1981-

1983, 1984-1986, 1987-1989, or 1990-1992. Table 5 reports the estimated coefficients in

the vector β for various specifications of the regression model using the March CPS data.

The table also reports robust standard errors that correct for heteroscedasticity. It is likely

that there is serial correlation in outcomes at the city level that would require further

adjustments for valid statistical inference, but it is well known (Cameron and Miller, 2015)

that clustered standard errors are downward biased when there are few clusters in the

data.

Consider initially the regressions reported in Panel A of the table, where the

dependent variable is the age-adjusted log weekly wage of high school dropouts in city r at

time t. The various columns of the table use alternative placebos: the Card placebo, the

employment placebo, the synthetic placebo, as well as an aggregate placebo composed of

all other 43 metropolitan areas. The various rows report the coefficients in the vector

β indicating how the wage impact varies during the post-Mariel period. The trend in these

coefficients presumably captures the wage effect as the Miami labor market adjusts, and

moves from the “short” to the “long” run.

It is evident that the coefficient β estimated immediately after Mariel is negative,

indicating an absolute decline in the wage of low-skill workers in the aftermath of the

supply shock. However, the effect is much smaller when I use the Card placebo than when I

use either the employment or synthetic placebo. The immediate wage cut using the original

Card placebo is -0.137 (0.093), while the wage cut implied by the employment placebo is

twice as large, with a point estimate of -0.289 (0.090), and the wage cut implied by the

synthetic placebo is -0.210 (0.086). It seems, therefore, that the wage of high school

dropouts in Miami fell by 20 to 25 percent in the immediate short run (1981-1983).

Remarkably, this wage effect increases in the next three years, so that the wage for high

26

school dropouts fell by 40 percent within 5 years (using either the employment or

synthetic placebos). The wage effect then begins to weaken, and essentially disappears by

the 1990-1992 period, when the coefficient in the synthetic placebo regression is 0.021

(0.096).

Panels B and C of the table replicate the analysis using the two alternative measures

of relative wages. Both panels suggest that the relative wage of the least educated workers

typically fell immediately after the supply shock, with the wage of high school dropouts

falling by as much as 30 percent relative to high school graduates. As with the absolute

wage results, the relative wage effect also disappears by the early 1990s.

Table 6 reports the coefficients from comparable regressions using the ORG data.

The immediate effect on the wage of high school dropouts implied by the ORG is roughly

similar to that implied by the March CPS when I use either the employment placebo or the

synthetic placebo. The log wage of high school dropouts fell by 20 to 25 percent in the

March CPS data and by 15 to 20 percent in the ORG. There is one interesting difference in

the ORG regression results: The wage effect of the Marielitos does not eventually disappear.

Both the employment and synthetic placebos indicate that the log wage of high school

dropouts in Miami is 10 to 20 percent below that of comparable workers in the placebo

cities even a decade years after Mariel (although the effect is not significant with the

employment placebo).

Despite the regression finding that the Mariel supply shock harmed low-skill

workers in the short run, the overall evidence may not be consistent with the textbook

model of factor demand. The evidence consistently suggests that the adverse wage effect of

the Marielitos initially increased over time before eventually disappearing. This is hard to

square with the theoretical prediction that the wage effect would be largest right after the

supply shock and would weaken as the capital stock adjusted over time. One possible

explanation may be that employers are reluctant to cut wages for pre-existing workers, so

that the immigration-induced wage cuts come into play “slowly” as turnover in the low skill

labor market allows firms to take advantage of the changed situation.

Equally important, the adjustments induced by the Mariel supply shock probably

involved much more than the increase in the capital stock that plays the central role in the

neoclassical model of labor demand. As suggested by the racial unrest that shook Miami

27

soon after Mariel, the political and social upheaval created by Castro’s decision to open up

the port of Mariel affected Miami’s economy in ways that extend far beyond what our

models capture (Portes and Stepick, 1994). Put differently, the ceteris paribus assumption

does not really apply. Given these undocumented and unknown reactions, it is difficult to

say much about the dynamics of the wage effect from the evidence generated by the Mariel

supply shock.

We also do not fully understand the factors responsible for the eventual

disappearance of the relative wage effect (at least in the March CPS). Economic theory

implies that it is the average wage in the labor market that will return to its pre-Mariel

level if the production function is linear homogeneous (Borjas, 2014). The relative wage

effect will not go away unless there has also been a change in the relative quantities of low-

and high-skill labor. Card (1990, p. 255) cites evidence that insinuates a possible supply

response: “The Boatlift may have actually held back long-run population growth in

Miami…the population of Dade County in 1986 was about equal to the pre-Boatlift

projection of the University of Florida Bureau of Economic and Business.” Although

suggestive, this slowdown in population growth cannot explain the absence of a long-term

relative wage effect unless the slowdown also resulted in a relative “exodus” of low-skill

workers from the Miami labor market.

Finally, Table 7 summarizes regression coefficients from models that define the low-

skill workforce in alternative ways. To simplify the presentation, I estimated the regression

model in equation (3) using only the years between 1977 and 1986 (excluding 1980), and

the short-run wage effect reported in the table is simply the interaction between the

indicator for the Miami metropolitan area and the indicator for a post-1980 observation.

Although there is obviously a lot of variation in the estimated coefficients (and statistical

significance), the thrust of the evidence suggests a negative short-run impact regardless of

whether we look at the log wage of high school dropouts, the log wage of the pooled group

of high school dropouts and high school graduates, the interquantile range, or the black-

28

white wage gap. The Mariel supply shock typically harmed workers at the bottom end of

the wage distribution.29

The use of either the employment placebo or the synthetic placebo indicates that the

wage of high school dropouts in Miami fell by 10 to 30 percent (depending on the data set

used) during the first 6 years after Mariel. As I noted earlier, the supply shock increased the

number of high school dropouts by around 20 percent, so that the implied wage elasticity

(d log w/d log L) is between -0.5 and -1.5.

Either of these elasticity estimates is far higher than the typical wage effect

estimated in (non-experimental) cross-city regressions that link wages to immigration, an

effect that often clusters around a negligible number. They are also higher than the wage

elasticities estimated by correlating wages and immigration across skill groups in the

national labor market (Borjas 2003), an elasticity that clusters around -0.3 to -0.4.

Interestingly, the estimates are close to those reported in Monras (2015) and Llull (2015),

who use new instruments (including the Peso Crisis in Mexico, natural disasters, armed

conflicts, and changes in political conditions) to correct for the endogeneity of migration

flows. Monras reports a wage elasticity of -0.7 and Llull’s estimates cluster around -1.2.

There are obviously many caveats that need to be considered regarding the

specification of the regression models and the small samples in the CPS data before we fully

buy into an elasticity estimate of between -0.5 and -1.5. Nevertheless, the key implication of

the evidence is unambiguous. The wage of high school dropouts in the Miami labor market

fell significantly after the Mariel supply shock. Any attempt at rationalizing this fact as due

to something other than the Marielitos will need to specify precisely what those other

factors were.

29 I also estimated specifications of the regression model that used different vectors of variables to

predict the synthetic placebo. A very general specification, for example, included the total growth rate of employment in the metropolitan area, the employment and wage growth rates for each of the four education groups, the percent of the workforce that was black, and the percent that was Hispanic. The estimated short- run wage effect was -0.148 (0.059) in the March CPS and -0.134 (0.079) in the ORG. The weighting algorithm in these expanded regressions, particularly when including the percent Hispanic variable, often assigned very large weights to San Diego.

29

VI. The Choice of a Placebo The evidence reported in the previous sections suggests that the choice of a placebo

matters. The short-run impact of the Mariel supply shock (i.e., the impact on wages

between 1981 and 1986) was generally more negative and statistically significant when I

used either the employment or synthetic placebo than when I used the Card placebo. It is

useful to document in a very simple way how it is possible to “cherry pick” placebos to

build in a particular empirical finding. I illustrate this variation by estimating the short-run

wage effect using the difference-in-differences regression model in equation (3) in each of

the 123,410 possible four-city placebos. Because I am focusing on the short-run wage

impact, the regressions only employ the observations between 1977 and 1986 (with the

1980 observation excluded throughout).

The two panels of Figure 9 illustrate the frequency distribution of estimated effects

when the dependent variable is the log wage of high school dropouts, while Table 8 reports

summary statistics for the various distributions. For comparison purposes, the bottom

rows of the table report the actual estimated wage impact (and standard error) when using

the Card, employment, and synthetic placebos.

Consider the distribution of estimated effects on the wage of high school dropouts in

the March CPS data. The mean effect is -0.243, which is far smaller than the estimate

obtained from either the employment placebo (-0.374) or the synthetic placebo (-0.335).

Nevertheless, most of the potential placebos would still suggest that the wage effect is

significant: over 98 percent of the estimated effects have a t-statistic above 1.6.

Note, however, that if the set of placebos were restricted so that the average

employment growth in the four placebo cities was roughly similar to that of pre-Mariel

Miami, the mean wage effect rises to -0.282. Similarly, if we look at the still smaller subset

of placebos where each city in the placebo had a similar pre-Mariel employment growth as

Miami, the estimated wage effect becomes even stronger; the mean coefficient is -0.333,

and all of those coefficients are statistically significant. Put differently, the closer we get to a

placebo that seems to replicate the pre-existing employment conditions in Miami, the more

likely we are to find that the Marielitos had a numerically sizable and a statistically

significant wage effect on low-skill Miamians. As Table 8 shows, the same general trend is

implied by the frequency distribution of wage effects computed in the ORG data.

30

This type of unusual exercise shows the importance that the choice of a placebo

plays in generating estimates of the impact of natural experiments. It might be prudent to

withhold drawing many substantive inferences from such experiments until we see how

the “preferred” estimate of the policy impact compares to the distribution of potential

impacts.30

It is instructive to conclude by extending the synthetic control approach to show the

distribution of wage effects implied by an intriguing counterfactual exercise. What would

the distribution of estimated wage effects look like if we “acted as if” a city had experienced

a shock in year t, and simply calculated the pre-post wage change attributable to this

imaginary supply shock?

To be more specific, suppose we define a pre-treatment period of 3 years and a post-

treatment period of 6 years. We can imagine that the city of Akron was hit by a phantom

supply shock in 1988. We can then calculate the wage change experienced by Akron

between 1985-1987 and 1989-2004, and contrast this wage effect with what happened to

wages in the synthetic placebo implied by the pre-existing conditions in Akron.31

Presumably, the wage effect resulting from this exercise should be near zero simply

because Fidel Castro did not suddenly decide to relocate over 100,000 Cubans to Akron in

1988. However, other (random) things may have happened in post-1988 Akron that we

know nothing about and that may have changed the relative wage of low-skill workers in

that city relative to the synthetic placebo.

We can obviously carry out this exercise for every single permutation of a city

receiving an imaginary supply shock and every single pre-post period allowed by the data

between 1977 and 2003. To generate the synthetic control, I used the city’s rate of total

employment growth in the 4-year period prior to the treatment, and the rates of

employment and wage growth for the specific education group being examined. The top

panel of Figure 10 illustrates the distribution of the estimated wage effects of these

30 There is, in fact, a strong negative correlation between the wage effect estimated in placebo p and

the mean rate of employment growth in the cities that form that placebo.

31 To be consistent with the analysis of the Mariel supply shock, the pre-treatment employment growth is measured in the four-year period prior to the hypothetical shock, or 1985-1988 in the Akron example discussed in the text.

31

hypothetical shocks using the log wage of high school dropouts as the dependent variable,

and Table 9 summarizes some of the characteristics of the resulting distributions. The data

reported in Table 9, of course, allow a permutation inference analysis of the wage effect of

the Mariel supply shock.

There is obviously a lot of dispersion in the estimated wage effects across all these

hypothetical shocks. As expected, the mean effect is zero. It is important to emphasize,

however, that the March CPS implies that the wage effect induced by the real Mariel supply

shock in Miami was -0.335 (0.090), which is in the 3rd percentile of the counterfactual

distribution where each imaginary shock is effectively being compared to the “best”

possible placebo for that city at that time. If we narrow down the comparison to the 1980

treatment year, the Mariel effect is by the most negative across all metropolitan areas.

It is also instructive to document the wage impact on the other education groups

resulting from this counterfactual exercise. As the last three columns of Table 9 show, the

mean effects across all city-year permutations are always near zero, and the effect

observed in Miami at the time of Mariel is typically not significantly different from zero.

Nevertheless, as the bottom panel of Figure 9 illustrates, the impact of Mariel on the wage

of high school graduates is positive, again rejecting the conjecture that high school

graduates and high school dropouts are perfect substitutes.

Finally, Figure 11 illustrates the dynamics of the estimated wage effect resulting

from the Mariel supply shock in Miami, and from the hypothetical supply shocks in all other

metropolitan areas in the 1980 treatment year. Specifically, I use the synthetic control

method and calculate for each metropolitan area in each year through 1992 the pre-post

difference between the wage of high school dropouts in a specific metropolitan area and in

its synthetic placebo. The calculated double difference, of course, implies that each point in

the figure measure the impact of the supply shock on the wage of high school dropouts as

of time t. For example, the trend in the Miami wage effect implied by the March CPS data

indicates that the wage of high school dropouts in Miami relative to the synthetic placebo is

around 30 percent lower in 1985 than it was in 1979. Note that the trend for the wage

effect in the Miami metropolitan area from a shock in treatment year 1980 forms a lower

“envelope” for the entire distribution of potential wage effects resulting from

contemporaneous supply shocks in all other metropolitan areas. This exercise again shows

32

that the wage effect of the Marielitos disappears after a decade in the March CPS data, but

persists in the ORG.

In sum, the Mariel supply shock had a very specific target and it hit that target with

impressive laser-like precision: The Marielitos had a substantial depressing effect on the

earnings of the least educated workers in Miami.

VII. Conclusion Card’s (1990) classic paper on the labor market impact of the Mariel supply shock

stands as a landmark study in labor economics. His finding that the supply shock seemed to

have little effect on the labor market opportunities of native workers has profoundly

influenced what we think we know about the economic consequences of immigration. The

elegance of the methodological approach—the exploitation of a fascinating natural

experiment to estimate a parameter of great economic interest—has also influenced the

way that many applied economists frame their questions, organize the data, and search for

an answer.

This paper brings a new perspective to the analysis of the Mariel supply shock. I

revisit the question and the data armed with the insights provided by three decades of

research on the economic impact of immigration. One key lesson from this voluminous

literature is that the effect of immigration on the wage structure depends crucially on the

differences between the skill distributions of immigrants and natives. The direct effect of

immigration is most likely to be felt by those workers who had similar capabilities as the

Marielitos.

It is well known that the Mariel supply shock was composed of disproportionately

low-skill workers, and at least 60 percent were high school dropouts. Remarkably, none of

the previous examinations of the Mariel experience documented what happened to the pre-

existing group of high school dropouts in Miami, a group that composed over a quarter of

the city’s workforce. Given the literature sparked by Borjas (2003), it seems obvious that a

crucial component of any analysis of the Mariel supply shock should focus on the labor

market outcomes of these low-skill workers.

33

The examination of wage trends among high school dropouts quickly overturns the

“stylized fact” that the supply shock did not affect Miami’s wage structure. In fact, the

absolute wage of high school dropouts dropped dramatically, as did their wage relative to

that of either high school graduates or college graduates. The drop in the average wage of

the least skilled Miamians between 1977-1979 and 1981-1986 was substantial, between

10 and 30 percent (depending on whether the analysis uses the CPS-ORG or the March CPS

data). In fact, the examination of wage trends in every single city identified by the CPS

throughout the period shows that the steep post-Mariel wage drop experienced by Miami’s

low-skill workforce was a very unusual event.

The reappraisal presented in this paper also strikingly illustrates that the

researcher’s choice of a placebo is an important component of any such empirical exercise,

and that picking the “wrong” placebo can easily lead to a weaker measured impact of

immigration. The analysis documented the importance of placebo choice by estimating the

impact of Mariel across all potential (four-city) placebos allowed by the data. The

distribution of estimated wage effects is very informative. The measured wage impact of

the supply shock is largest when the comparison group consists of cities that had a similar

rate of pre-Mariel employment growth as Miami. The methodological approach of

estimating the entire distribution of potential effects across all possible placebos can be a

useful component of studies that examine the consequences of natural experiments.

The empirical evidence also has many lessons for the vast literature that purports to

measure the wage impact of immigration. For instance, many studies measure the effect by

estimating spatial correlations between wages and the number of immigrants in a

particular locality. These spatial correlations, many of which cluster around zero, are

plagued both by endogeneity problems (i.e. immigrants settle in high-wage regions) and by

native adjustments (i.e., firms and workers may respond to the supply shock by relocating

to other cities). The fact that the spatial correlation implied by the Mariel supply shock is

strongly negative suggests that the existing non-experimental literature has not

successfully purged those statistical difficulties. There is still some way to go before non-

experimental spatial correlations can be presumed to estimate a parameter of economic

interest.

34

The evidence also has potentially important implications for estimates of the

economic benefits from immigration. The benefit that accrues to the native population, or

the “immigration surplus,” is the flip side of the wage impact of immigration. In fact, it is

well known that the greater the wage impact, the greater the immigration surplus. Borjas

(2014, p. 151) estimates the current surplus to be around 0.24 percent of GDP (or around

$43 billion annually). The fact that there was a much larger reduction in the earnings of the

workers most likely to be affected by the Marielitos than was previously believed suggests

that we may also need to reassess existing estimates of the immigration surplus. That

surplus could easily be twice or three times as large if the Mariel context correctly

measures the wage impact.

It has been a quarter-century since the publication of Card’s Mariel study. More

likely than not, that analysis has been replicated often as part of an empirical exercise in an

econometrics or labor economics class. The reappraisal of the evidence provided in this

paper teaches an important lesson. Although replication obviously serves an extremely

useful role in the advancement of applied science, there is much to be gained by revisiting

many of those persistent old questions with a new perspective, a perspective that uses the

insights accumulated over the years. If nothing else, the reappraisal of the Mariel evidence

shows that even the most cursory reexamination of some old data with some new ideas can

reveal trends that radically change what we think we know.

35

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38

Figure 1. Number of Cuban immigrants, by year of migration, 1955-2010

Notes: The specific year of migration (through 1999) is first reported in the 2000 census. The counts are adjusted for mortality and out-migration by using information on the number of arrivals provided by the 1970 through 1990 censuses; see the text for details. The 2000-2008 counts are drawn from the pooled 2009- 2011 American Community Surveys (ACS), while the 2009-2010 counts are drawn from the 2012 ACS.

0"

10,000"

20,000"

30,000"

40,000"

50,000"

60,000"

70,000"

80,000"

90,000"

100,000"

110,000"

120,000"

1950" 1960" 1970" 1980" 1990" 2000" 2010"

N um

be r' of 'im

m ig ra nt s'

Year'of'migra1on'

39

Figure 2. Log wage of high school dropouts, 1972-2003

Notes: The log weekly wage is a 3-year moving average of the unadjusted average log wage of high school dropouts in each geographic area. The data are drawn from the March CPS files.

5"

5.1"

5.2"

5.3"

5.4"

5.5"

5.6"

5.7"

5.8"

5.9"

1970" 1975" 1980" 1985" 1990" 1995" 2000" 2005"

Lo g$ w ee kl y$ w ag e$

Year$

Miami

Outside Miami

40

Figure 3. The trend in the wage of low-skill workers, 1976-1992

A. Log weekly wage of high school dropouts

B. Log wage of high school dropouts relative to college graduates

C. Log wage of high school dropouts relative to high school graduates

Notes: The figures use a 3-year moving average of the age-adjusted average log wage of high school dropouts, high school graduates, and college graduates in each specific geographic area. The data are drawn from the March CPS files.

5"

5.1"

5.2"

5.3"

5.4"

5.5"

5.6"

5.7"

5.8"

5.9"

1976" 1978" 1980" 1982" 1984" 1986" 1988" 1990" 1992"

Lo g$ w ee kl y$ w ag e$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

!1.2%

!1%

!0.8%

!0.6%

!0.4%

!0.2%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

!0.7%

!0.6%

!0.5%

!0.4%

!0.3%

!0.2%

!0.1%

0%

0.1%

0.2%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami Synthetic placebo

Card placebo

Employment placebo

41

Figure 4. Distribution of pre-post wage changes, 1976-2003

A. Log wages of high school dropouts

Across all city-year permutations 1980 treatment year

B. Log wage of high school graduates

Across all city-year permutations 1980 treatment year

Notes: The pre-treatment period lasts 4 years; the post-treatment period lasts 6 years; and the year of the treatment is excluded from the calculation. The data are drawn from the March CPS files.

Mariel Mariel

Mariel Mariel

42

Figure 5. The trend in the wage of low-skill workers in the ORG, 1977-1992

A. Log weekly wage of high school dropouts

B. Log wage of high school dropouts relative to college graduates

C. Log wage of high school dropouts relative to high school graduates

Notes: The figures use a 3-year moving average of the age-adjusted average log wage of high school dropouts, high school graduates, and college graduates in each specific geographic area.

!0.6%

!0.5%

!0.4%

!0.3%

!0.2%

!0.1%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ee kl y$ w ag e$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

!1#

!0.9#

!0.8#

!0.7#

!0.6#

!0.5#

!0.4#

!0.3#

1976# 1978# 1980# 1982# 1984# 1986# 1988# 1990# 1992#

Lo g$ w ag e$ ga p$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

!0.5%

!0.4%

!0.3%

!0.2%

!0.1%

0%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

43

Figure 6. Wage trends in pooled group of high school dropouts and high school graduates, March CPS, 1977-1992

A. Log wage of pooled high school dropouts and high school graduates

B. Log wage of pooled high school dropouts and graduates relative to college graduates

Notes: The figures use a 3-year moving average of the age-adjusted average log wage of the pooled group of high school dropouts and high school graduates, and of college graduates in each specific geographic area.

!0.4%

!0.3%

!0.2%

!0.1%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

!0.9%

!0.8%

!0.7%

!0.6%

!0.5%

!0.4%

!0.3%

!0.2%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

44

Figure 7. Trends in the spread of the log weekly wage distribution, March CPS, 1977-1992

A. Log wage of worker at the 20th percentile

B. Difference in the log wage of workers at the 20th and 80th percentiles

Notes: The figures use a 3-year moving average of the age-adjusted log weekly wage in each specific geographic area for each specific percentile.

!0.7%

!0.6%

!0.5%

!0.4%

!0.3%

!0.2%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

!1.3%

!1.2%

!1.1%

!1%

!0.9%

!0.8%

!0.7%

!0.6%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami

Synthetic placebo

Card placebo

Employment placebo

45

Figure 8. Trends in the black-white wage differential, 1976-1992 A. March CPS

B. CPS-ORG

Notes: The figures use a 3-year moving average of the difference in the age-adjusted average log wage between black and white workers in each specific geographic area.

!0.8%

!0.7%

!0.6%

!0.5%

!0.4%

!0.3%

!0.2%

!0.1%

1976% 1978% 1980% 1982% 1984% 1986% 1988% 1990% 1992%

Lo g$ w ag e$ ga p$

Year$

Miami

Card placebo

Employment placebo

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

1976 1978 1980 1982 1984 1986 1988 1990 1992

Lo g w ag e ga p

Year

Miami

Card placebo

Employment placebo Synthetic placebo

46

Figure 9. Distribution of short-run impacts across all possible four-city placebos, 1977-1986

A. March CPS

B. CPS-ORG

Notes: The figure shows the distribution of the interaction term from the difference-in-differences regression model in equation (3) resulting from comparing Miami to all possible 123,410 placebos in the March CPS data. The regressions use annual observations for each city in the period 1977-1986 (1980 excluded), and the coefficients measure the impact in the “short run” (i.e., 1981-1986). All regressions were weighted by the number of observations used to calculate the mean wage of high school dropouts in city r at time t.

Employment placebo

Card placebo

Synthetic placebo

Employment placebo Card

placebo

Synthetic placebo

47

Figure 10. Distribution of hypothetical short-run impacts relative to synthetic placebo, assuming a supply shock hits each city-year permutation

A. Log wage of high school dropouts

March CPS CPS-ORG

B. Log wage of high school graduates

March CPS CPS-ORG

Notes: Each year between 1980 and 1995 is assumed to be a potential treatment year. The pre-treatment period lasts 3 years; the post-treatment period lasts 6 years. The wage effect is estimated from a difference- in-differences regression model that excludes the year of the treatment. The frequency distributions do not include any of the wage effects estimated in the Miami metropolitan area.

Mariel Mariel

Mariel Mariel

48

Figure 11. Effect of hypothetical supply shock in 1980 on log wage of high school dropouts in each metropolitan area, relative to synthetic placebo

A. March CPS

B. CPS-ORG

Notes: The exercise traces the wage effect of a supply shock in treatment year 1980 in each of the 44 metropolitan areas. The wage effect is defined as the difference-in-differences Δwrt - Δwr0, where Δwrt gives the log wage gap between city r and its synthetic placebo at time t; and Δwr0 gives the equivalent average log wage gap in pre-treatment years 1977-1979. Beginning in 1980, the illustrated wage effects represent a 3- year moving average.

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1976 1978 1980 1982 1984 1986 1988 1990 1992

Es # m at ed

lo g w ag e eff

ec t

Year

Miami

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1976 1978 1980 1982 1984 1986 1988 1990 1992

Es # m at ed

lo g w ag e eff

ec t

Year

Miami

49

Table 1. Education distribution of adult Marielitos

Years of education Sample: < 12 12 13 - 15 ≥ 16 Sample size Marielitos: April 1983 CPS 57.9 25.6 3.5 13.1 31 June 1986 CPS 55.2 28.0 6.4 9.6 31 June 1988 CPS 58.7 26.1 4.4 10.9 46 1990 Census 64.8 15.8 12.9 6.5 4,234 1994 CPS-ORG 61.4 20.5 9.8 8.3 143 2000 Census 59.9 20.0 12.7 7.4 3,301

Miami’s pre-existing labor force:

1980 Census 26.7 28.4 26.0 18.8 32,971 Notes: The statistics are calculated in the sample of persons born in Cuba who migrated to the United States at the time of Mariel and were 18 years old in 1980. In the April 1983 CPS and 2000 census, the Marielitos are identified as persons born in Cuba who migrated to the United States in 1980. In all other samples, the Marielitos are identified as Cubans who entered the country in 1980 or 1981. The pre-existing labor force of Miami includes both natives and immigrants.

50

Table 2. The size of the Mariel supply shock Education group:

Size of Miami’s labor force in 1980 (1000s)

Number of Marielitos in labor

force (1000s)

Percent increase in supply

High school dropouts 176.3 32.5 18.4 High school graduates 187.5 10.1 5.4 Some college 171.5 8.8 5.1 College graduates 124.1 4.2 3.4

All workers 659.4 55.7 8.4 Notes: The pre-existing number of native workers in Miami is calculated from the 1980 census; the number of Marielito workers (at least 18 years old at the time of Mariel) is calculated from the 1990 census, and a small adjustment is made because the 1990 census reports the number of Cuban immigrants who entered the country in 1980 or 1981.

51

Table 3. Rates of employment and wage growth before Mariel

Rank Metropolitan area Employment

growth: all workers Employment growth: high school dropouts

Wage growth: high school dropouts

1 San Diego, CA 0.194 0.067 -0.093 2 Greensboro-Winston Salem, NC 0.182 -0.063 -0.307 3 Kansas City, MO/KS 0.179 0.052 -0.191 4 Anaheim-Santa Ana- Garden Grove, CA 0.162 0.257 0.067 5 Rochester, NY 0.153 -0.172 0.065 6 Miami-Hialeah, FL 0.153 0.086 0.014 7 Nassau-Suffolk, NY 0.151 0.056 -0.057 8 San Jose, CA 0.137 0.130 0.124 9 Albany-Schenectady-Troy, NY 0.130 0.065 0.058 10 Boston, MA 0.121 -0.100 -0.008 11 Milwaukee, WI 0.121 -0.006 0.040 12 Indianapolis, IN 0.115 0.071 -0.032 13 Seattle-Everett, WA 0.110 -0.079 -0.051 14 Norfolk-Virginia Beach-Newport News, VA 0.103 0.052 0.111 15 Philadelphia, PA/NJ 0.102 -0.033 0.002 16 Newark, NJ 0.092 -0.116 -0.089 17 Tampa-St. Petersburg-Clearwater, FL 0.083 0.068 0.129 18 Denver-Boulder-Longmont, CO 0.082 -0.139 -0.012 19 Houston-Brazoria, TX 0.078 0.090 0.004 20 Sacramento, CA 0.078 0.152 -0.004 21 Dallas-Fort Worth, TX 0.076 0.062 -0.037 22 Portland-Vancouver, OR/WA 0.071 -0.074 -0.015 23 Riverside-San Bernardino, CA 0.071 -0.017 0.298 24 Atlanta, GA 0.069 -0.087 -0.062 25 Cincinnati-Hamilton, OH/KY/IN 0.063 0.038 -0.068 26 Washington, DC/MD/VA 0.061 0.028 0.082 27 Detroit, MI 0.060 -0.099 -0.010 28 Fort Worth-Arlington, TX 0.058 -0.006 -0.033 29 Los Angeles-Long Beach, CA 0.056 0.075 -0.112 30 Columbus, OH 0.048 -0.324 -0.016 31 Buffalo-Niagara Falls, NY 0.039 0.040 -0.143 32 Chicago-Gary-Lake IL 0.025 -0.082 -0.017 33 St. Louis, MO/IL 0.019 -0.060 -0.023 34 Bergen-Passaic, NJ 0.015 -0.051 0.011 35 Baltimore, MD 0.012 -0.108 -0.016 36 Minneapolis-St. Paul, MN 0.007 -0.050 -0.010 37 Cleveland, OH 0.001 -0.071 -0.017 38 New York, NY 0.000 -0.146 0.069 39 Pittsburg, PA -0.013 -0.111 0.127 40 Birmingham, AL -0.020 -0.172 -0.090 41 San Francisco-Oakland-Vallejo, CA -0.027 -0.200 -0.102 42 Gary-Hammond-East Chicago, IN -0.029 0.119 0.042 43 New Orleans, LA -0.046 -0.313 -0.038 44 Akron, OH -0.110 -0.351 -0.004 Notes: The rate of employment growth is the log ratio of average employment in 1979-1980 to average employment in 1977-1978, calculated using the March CPS from the 1977-1980 survey years. The rate of wage growth is the difference in the (age-adjusted) log weekly wage between 1978-1979 and 1976-1977.

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Table 4. Distribution of wage changes within metro areas across all potential city-year permutations, 1976-2003

Dependent variable: Log wage of education group Characteristics of distribution: < 12 years 12 years 13-15 years ≥ 16 years Value for Mariel -0.439 -0.025 -0.116 -0.062 Distribution across all city-year permutations

Mean of distribution outside Miami -0.100 -0.061 -0.024 0.006 Percentile of Mariel effect 1.8 65.6 19.0 20.7

Distribution in treatment year 1980:

Mean of distribution outside Miami -0.170 -0.152 -0.092 -0.043 Ranking of Mariel effect 1/44 42/44 14/44 17/44 Notes: The summary statistics are calculated from the distribution of wage changes between the pre- and post-period for all metropolitan areas (excluding Miami) for all possible permutations in the 1976-2003 March CPS data. The pre-treatment period lasts 4 years; the post-treatment period lasts 6 years; and the year of the treatment is excluded from the calculation. The distributions have 774 observations.

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Table 5. Difference-in-differences impact of the Marielitos on the wage of high school dropouts, March CPS

Dependent variable and treatment period

Card placebo

Employment placebo

Synthetic placebo

All cities

A. Log wage of high school dropouts 1981-1983 -0.137 -0.289 -0.210 -0.135 (0.093) (0.090) (0.086) (0.080) 1984-1986 -0.364 -0.495 -0.461 -0.378 (0.080) (0.071) (0.077) (0.033) 1987-1989 -0.216 -0.251 -0.210 -0.192 (0.085) (0.071) (0.068) (0.058) 1990-1992 0.188 0.096 0.021 0.188

(0.158) (0.136) (0.096) (0.111) B. Log wage relative to college graduates

1981-1983 -0.168 -0.390 -0.269 -0.180 (0.187) (0.164) (0.193) (0.170) 1984-1986 -0.387 -0.593 -0.552 -0.453 (0.159) (0.154) (0.193) (0.135) 1987-1989 -0.340 -0.482 -0.387 -0.357 (0.154) (0.159) (0.195) (0.132) 1990-1992 0.180 0.084 0.191 0.192 (0.223) (0.200) (0.141) (0.180)

C. Log wage relative to high school graduates

1981-1983 -0.276 -0.420 -0.383 -0.285 (0.131) (0.146) (0.104) (0.136) 1984-1986 -0.490 -0.627 -0.620 -0.470 (0.114) (0.093) (0.097) (0.094) 1987-1989 -0.344 -0.325 -0.298 -0.254 (0.098) (0.071) (0.041) (0.082) 1990-1992 0.067 0.016 -0.102 0.122 (0.195) (0.144) (0.101) (0.143)

Notes: Robust standard errors are reported in parentheses. The data consist of annual observations for each city between 1977 and 1992 (1980 excluded). All regressions include vectors of city and year fixed effects. The table reports the interaction coefficients between a dummy variable indicating if the metropolitan area is Miami and the timing of the post-Mariel period. The regressions that use the Card or employment placebos have 75 observations; the regressions that use the synthetic placebo have 30 observations; and the regressions in the last column have 658 observations. The regressions in Panel A are weighted by the number of observations size used to calculate the dependent variable. The regressions in Panels B and C are weighted by (n1ns)/(n1 + ns), where n1 is the number of observations used to calculate the mean wage of high school dropouts in city r at time t, and ns is the respective number of observations used to calculate the mean wage of the more highly educated group. The regressions that use the synthetic placebo are not weighted.

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Table 6. Difference-in-differences impact of the Marielitos on the wage of high school dropouts, CPS-ORG

Dependent variable and treatment period

Card placebo

Employment placebo

Synthetic placebo

All cities

A. Log wage of high school dropouts 1981-1983 -0.068 -0.153 -0.240 -0.092 (0.027) (0.060) (0.082) (0.027) 1984-1986 -0.032 -0.097 -0.203 -0.075 (0.039) (0.066) (0.078) (0.028) 1987-1989 -0.061 -0.206 -0.241 -0.137 (0.031) (0.055) (0.075) (0.018) 1990-1992 0.005 -0.105 -0.182 -0.051

(0.058) (0.078) (0.075) (0.041) B. Log wage relative to college graduates

1981-1983 -0.020 -0.171 -0.302 -0.066 (0.059) (0.114) (0.117) (0.069) 1984-1986 -0.018 -0.130 -0.275 -0.067 (0.056) (0.096) (0.109) (0.057) 1987-1989 -0.048 -0.291 -0.377 -0.152 (0.055) (0.111) (0.112) (0.061) 1990-1992 -0.017 -0.173 -0.283 -0.074 (0.086) (0.127) (0.115) (0.077)

C. Log wage relative to high school graduates

1981-1983 -0.141 -0.188 -0.320 -0.130 (0.033) (0.072) (0.088) (0.041) 1984-1986 -0.084 -0.122 -0.242 -0.092 (0.070) (0.094) (0.096) (0.060) 1987-1989 -0.081 -0.173 -0.222 -0.108 (0.041) (0.061) (0.077) (0.039) 1990-1992 0.023 -0.082 -0.184 -0.033 (0.069) (0.062) (0.076) (0.061)

Notes: Robust standard errors are reported in parentheses. The data consist of annual observations for each city between 1977 and 1992 (1980 excluded). All regressions include vectors of city and year fixed effects. The table reports the interaction coefficients between a dummy variable indicating if the metropolitan area is Miami and the timing of the post-Mariel period. The regressions that use the Card or employment placebos have 75 observations; the regressions that use the synthetic placebo have 30 observations; and the regressions in the last column have 660 observations. The regressions in Panel A are weighted by the sample size used to calculate the dependent variable. The regressions in Panels B and C are weighted by (n1ns)/(n1 + ns), where n1 is the number of observations used to calculate the mean wage of high school dropouts in city r at time t, and ns is the respective number of observations used to calculate the mean wage of the more highly educated group. The regressions that use the synthetic placebo are not weighted.

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Table 7. Difference-in-differences short-run impacts of the Marielitos Sample

Card placebo

Employment placebo

Synthetic placebo

All cities

A. Coefficients from March CPS

1. Log wage of high school dropouts -0.237 -0.374 -0.335 -0.237 (0.088) (0.078) (0.090) (0.076) 2. Log wage of “pooled” high school -0.030 -0.064 -0.035 -0.039

dropouts and graduates (0.049) (0.063) (0.082) (0.059) 3. Interquantile range (20th – 80th percentile) -0.062 -0.029 -0.024 -0.044 (0.108) (0.096) (0.098) (0.100) 4. Black/white relative wage -0.107 -0.221 --- -0.092

(0.072) (0.098) (0.061) B. Coefficients from CPS-ORG

1. Log wage of high school dropouts -0.054 -0.130 -0.227 -0.087 (0.026) (0.049) (0.071) (0.022)

2. Log wage of pooled high school -0.003 -0.052 -0.050 -0.036

dropouts and graduates (0.018) (0.019) (0.022) (0.014) 3. Interquantile range (20th – 80th percentile) 0.017 -0.031 -0.063 -0.037 (0.041) (0.051) (0.059) (0.040) 4. Black/white relative wage -0.134 -0.135 -0.110 -0.127

(0.037) (0.051) (0.045) (0.037) Notes: Robust standard errors are reported in parentheses. The data consist of annual observations for each city between 1977 and 1986 (1980 excluded). All regressions include vectors of city and year fixed effects. The table reports the interaction coefficients between a dummy variable indicating if the metropolitan area is Miami and if the observation is drawn from the post-Mariel period. The regressions that use the Card or employment placebos have 45 observations; the regressions that use the synthetic placebo have 18 observations; and the regressions in the last column have 396 observations. See the notes to Table 6 for a description of the weighting used in the regressions.

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Table 8. The distribution of estimated short-run wage effects on the log weekly wage of high school dropouts across all four-city placebos, 1977-1986

Characteristics of distribution: March CPS CPS-ORG Mean -0.243 -0.089 Standard deviation 0.047 0.023 Statistical significance

Fraction of t-statistics above |1.6| 0.984 0.947 Fraction of t-statistics above |2.0| 0.939 0.845

Average employment growth of placebo cities within 0.5 standard deviations of Miami (N = 5,740)

Mean -0.282 -0.105 Fraction of t-statistics above |1.6| 0.998 0.933 Fraction of t-statistics above |2.0| 0.980 0.812

Employment growth for each placebo city within 0.5 standard deviations of Miami (N = 126)

Mean -0.333 -0.098 Fraction of t-statistics above |1.6| 1.000 0.833 Fraction of t-statistics above |2.0| 1.000 0.619

Actual impact using the Card placebo:

Coefficient -0.237 -0.054 Robust standard error (0.088) (0.026)

Actual impact using the employment placebo: Coefficient -0.374 -0.130 Robust standard error (0.078) (0.049)

Actual impact using the synthetic placebo: Coefficient -0.335 -0.227 Robust standard error (0.090) (0.071)

Notes: The table reports the distribution of the interaction coefficient between a dummy variable indicating if the metropolitan area is Miami and if the observation is drawn from the post-Mariel period. The regressions were estimated separately in all possible 123,410 four-city placebos. The regressions use annual observations for each city from 1977 through 1986 (excluding 1980). All regressions have 45 observations and are weighted by the sample size used to calculate the mean log age-adjusted wage of high school dropouts in city r at time t.

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Table 9. Distribution of wage effects relative to the synthetic placebo for hypothetical supply shocks

Dependent variable: Log wage of education group Characteristics of distribution: < 12 years 12 years 13-15 years ≥ 16 years A. March CPS Mean effect -0.003 -0.000 -0.006 -0.000 Standard deviation 0.163 0.071 0.099 0.072 Fraction of t-statistics above |1.6| 0.250 0.301 0.262 0.278 Fraction of t-statistics above |2.0| 0.167 0.205 0.185 0.182 Mariel wage impact with synthetic control:

Coefficient -0.335 0.114 -0.042 0.104 Robust standard error (0.090) (0.076) (0.116) (0.086)

Placement of Mariel effect:

Percentile in distribution across all years 3.0 94.8 33.2 93.2 Rank in 1980 treatment year 1/44 44/44 14/43 43/44

B. CPS-ORG Mean effect 0.001 0.000 0.002 -0.002 Standard deviation 0.104 0.049 0.065 0.053 Fraction of t-statistics above |1.6| 0.253 0.306 0.292 0.328 Fraction of t-statistics above |2.0| 0.154 0.218 0.194 0.214 Mariel wage impact with synthetic control:

Coefficient -0.227 0.042 0.071 0.052 Robust standard error (0.072) (0.021) (0.057) (0.061)

Placement of Mariel effect:

Percentile in distribution across all years 1.8 81.6 89.4 84.8 Rank in 1980 treatment year 1/44 37/44 38/44 36/44

Notes: The pre-treatment period lasts 3 years; the post-treatment period lasts 6 years. Each regression excludes the year of the treatment and has 18 observations. There are 774 hypothetical shocks distributed across 43 metropolitan areas (outside Miami) for treatment years between 1980 and 1997. The predictors used to create the synthetic placebo are the city’s rate of total employment growth, the rates of employment and wage growth for the particular education group in the 4-year period preceding the treatment year.

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Appendix Table A-1. Weights defining the synthetic control

Measure of wage of high school dropouts Rank Metropolitan area Actual log weekly wage Age-adjusted log weekly wage March CPS March CPS CPS-ORG 1 San Diego, CA 0.015 0.239 0.234 2 Greensboro-Winston Salem, NC 0.000 0.006 0.000 3 Kansas City, MO/KS 0.560 0.010 0.016 4 Anaheim-Santa Ana- Garden Grove, CA 0.203 0.372 0.396 5 Rochester, NY 0.004 0.159 0.164 6 Miami-Hialeah, FL --- --- --- 7 Nassau-Suffolk, NY 0.013 0.011 0.013 8 San Jose, CA 0.009 0.043 0.007 9 Albany-Schenectady-Troy, NY 0.006 0.012 0.010 10 Boston, MA 0.005 0.008 0.010 11 Milwaukee, WI 0.005 0.009 0.010 12 Indianapolis, IN 0.008 0.007 0.007 13 Seattle-Everett, WA 0.005 0.006 0.008 14 Norfolk-Virginia Beach-Newport News, VA 0.005 0.008 0.007 15 Philadelphia, PA/NJ 0.005 0.006 0.008 16 Newark, NJ 0.004 0.005 0.006 17 Tampa-St. Petersburg-Clearwater, FL 0.005 0.006 0.006 18 Denver-Boulder-Longmont, CO 0.004 0.005 0.006 19 Houston-Brazoria, TX 0.007 0.005 0.005 20 Sacramento, CA 0.041 0.005 0.002 21 Dallas-Fort Worth, TX 0.007 0.005 0.005 22 Portland-Vancouver, OR/WA 0.004 0.005 0.005 23 Riverside-San Bernardino, CA 0.002 0.000 0.007 24 Atlanta, GA 0.004 0.004 0.005 25 Cincinnati-Hamilton, OH/KY/IN 0.007 0.004 0.005 26 Washington, DC/MD/VA 0.005 0.004 0.005 27 Detroit, MI 0.004 0.004 0.005 28 Fort Worth-Arlington, TX 0.005 0.004 0.005 29 Los Angeles-Long Beach, CA 0.007 0.004 0.004 30 Columbus, OH 0.002 0.004 0.003 31 Buffalo-Niagara Falls, NY 0.006 0.003 0.004 32 Chicago-Gary-Lake IL 0.004 0.003 0.004 33 St. Louis, MO/IL 0.004 0.003 0.003 34 Bergen-Passaic, NJ 0.003 0.003 0.003 35 Baltimore, MD 0.004 0.003 0.003 36 Minneapolis-St. Paul, MN 0.004 0.003 0.003 37 Cleveland, OH 0.003 0.003 0.003 38 New York, NY 0.003 0.003 0.003 39 Pittsburg, PA 0.002 0.003 0.003 40 Birmingham, AL 0.003 0.002 0.002 41 San Francisco-Oakland-Vallejo, CA 0.003 0.002 0.002 42 Gary-Hammond-East Chicago, IN 0.004 0.002 0.002 43 New Orleans, LA 0.002 0.002 0.001 44 Akron, OH 0.001 0.001 0.001 Notes: The metropolitan areas are ranked by the 1977-1980 rate of employment growth.