On the effectiveness of SB1070 in Arizona.
Amuedo-Dorantes, Catalina ; Lozano, Fernando
I. INTRODUCTION
During the past years, as the national immigration debate stalled,
states started to take immigration enforcement into their own hands.
Recent proposals at the state and local government level included the
passage of laws where local authorities may ask a person suspected of
being in the United States illegally to show proof of documented legal
status in the country. Among these proposals perhaps the most
controversial one has been Arizona's Senate Bill 1070 (SB 1070),
which was passed into law in April 2010 and enforced in July 2010 (later
it was changed to AZ House Bill 2162). The Act forbids state and local
officials from avoiding or limiting the enforcement of federal
immigration laws. It also persecutes the transporting, sheltering, or
hiring of illegal aliens. But what has attracted the most attention in
the press and political debates, especially after being recently upheld
by the Supreme Court, has been the so-called "show me your
papers" clause. The clause calls for police to make an effort to
determine the immigration status of any person suspected of being an
illegal alien during a lawful stop. Lack of proper documentation in the
form of any valid federal, state, or local government-issued
identification by an alien is considered a misdemeanor and can carry a
fine of up to $100, court costs, and up to 20 days in jail for a first
offense. (1) Although the effectiveness of this bill may be limited with
its most controversial parts blocked by the Courts, (2) there are
reasons to believe that SB 1070 may have had a significant chilling
effect and successfully achieved its aim of reducing the incidence of
unauthorized immigration in the state.
In this article, we examine whether that has been the case. Recent
work by Lofstrom, Bohn, and Raphael (2014) has shown how the employment
verification mandate to all employers contained in the 2007 Legal
Arizona Workers Act (LAWA)--henceforth universal E-Verify (3)--reduced
the shares of non-citizen Hispanics, a group more likely to encompass
"likely unauthorized immigrants." Hence, a priori, one might
expect SB1070 to be particularly effective in further reducing the share
of likely unauthorized immigrants in the state. After all, omnibus
immigration laws (OILs) are significantly tougher in at least two
regards. First, unlike E-Verify mandates, OILs target all likely
unauthorized immigrants, not just those formally applying for a job.
Anybody can be stopped and asked for proper documentation. Secondly,
unlike E-Verify mandates, OILs are directly linked to police enforcement
and deportation, thus imposing a much greater risk to likely
unauthorized immigrants than the risk of being found ineligible for
employment through an employment verification system. Nevertheless, the
effectiveness of SB1070 depends, to some degree, on that of its
predecessor. If LAWA was particularly effective at reducing the share of
likely unauthorized immigrants in the state, it is possible that SB1070
might not have added much. Thus, our questions are the following: Has
SB1070 proven effective in further reducing the share of likely
unauthorized immigrants in the state? And, if so, how? Has it
complemented the E-Verify mandate in LAWA by targeting specific
subgroups within the likely unauthorized population less impacted by its
predecessor?
To answer these questions, we assess the impact of SB1070, as well
as how it may have differed from that of the E-Verify mandate in LAWA.
We do so by, first, estimating the joint impact of LAWA and SB1070 in
deterring potentially undocumented immigrants from settling in Arizona.
We then compare the estimated joint impact of LAWA and SB1070 to the
effect of LAWA up until the enactment of SB1070. For the analysis, we
extract monthly data from the Current Population Surveys (CPSs) for the
period running from January 1998 through December 2013--a period long
enough to gauge the effectiveness of both LAWA and SB1070. Using those
data, we implement the quasi-experimental approach proposed by Abadie,
Diamond, and Hainmueller (2010) to learn about the effectiveness of
SB1070 in reducing the shares of "likely unauthorized
immigrants" in Arizona relative to states in a synthetic control
group.
We believe the analysis is of interest for various reasons. First,
by gauging the impact of SB1070 and how it might have differed from that
of the E-Verify mandate in LAWA, the study sheds some light on the
long-run effectiveness of prior immigration enforcement measures. Had
some of the effect of LAWA died out by the time SB1070 was enacted? That
could have been the case if, over time, the likely unauthorized
population responded to the E-Verify mandate by moving from the formal
to the informal sector or by becoming self-employed. Alternatively, did
LAWA's impact remain intact?
Second, the analysis informs about the effectiveness of OILs, such
as SB1070, in deterring unauthorized immigrants from settling in the
state. Given its deleterious impact on personal freedom, the emerging
accusations of racial profiling by the police and the record level of
spending on interior immigration enforcement, the merit of SB1070
depends entirely on its ability to deter unauthorized immigration.
Hence, determining whether the law exhibits any of its hoped benefits,
it is an important policy question in itself. Has SB1070 achieved its
goal? And, if so, what can we learn about its impact dynamics and scope?
Has SB1070 proven effective in reducing the shares of unauthorized
immigrants less responsive to the E-Verify mandate in LAWA?
Finally, all the aforementioned questions are also of relevance in
the design of future immigration policy given the numerous states
following in the footsteps of Arizona as comprehensive immigration
reform stalled. (4) If comprehensive immigration reform fails, it is
likely that states will continue to enact their own immigration
enforcement legislation. In that case, understanding the effectiveness
of these measures will become particularly important. And even if
comprehensive immigration reform succeeds, there might be important
lessons to be learned from state-level experiments with these tougher
immigration enforcement measures before they are extended nationwide.
II. BRIEF LITERATURE REVIEW
A vast literature has explored the impact of immigration measures
adopted at the federal level--often times following the passage of more
comprehensive measures, as was the case with the 1986 Immigration Reform
and Control Act (IRCA)--on the flow of undocumented immigrants. Most of
that literature examines changes in apprehension data before and after
the implementation of the aforementioned measures. (5) Others use
individual level data from small samples collected in specific Mexican
localities at a particular point in time, (6) or rely on individual
level data collected from a large number of Mexican communities over an
extended period of time. (7) The overall consensus emanating from this
literature is that enforcement policies, traditionally centered along
the border, do not seem to have much of an impact on illegal
immigration.
Other studies explore, instead, the labor market implications of
more stringent immigration enforcement measures at the federal level.
For instance, Bansak and Raphael (2001) explore the impact that
graduated sanctions to employers knowingly hiring undocumented
immigrants included in the 1986 IRCA had on the employment and earnings
of Latinos. They document a decline in the earnings of Latinos after the
passage of IRCA--a decline potentially due to growing discrimination
against all Hispanics.
More recently, as states have started to take action on these
issues within their jurisdiction, researchers have started to also look
at the impact that state-level legislation has on the residential
choices and labor market outcomes of unauthorized immigrants. (8) For
instance, Good (2013) explores the impact of OILs by exploiting the
geographic and time variation in the enactment of those measures
countrywide. His focus is on whether immigrant outflows in states with
OILs have been accompanied by native inflows. He finds evidence of the
former, but no statistically significant evidence of the latter. While
interesting, the methodology employed fails to account for pre-existing
differences in immigrant outflows across the states being compared,
which can invalidate the control group and, in turn, the estimated
impact of the policy.
Zeroing-in on the differential impact of state-level legislation by
gender, Amuedo-Dorantes and Bansak (2012a, 2012b) look at how employment
verification (E-Verify) mandates have impacted the employment and wages
of likely unauthorized men and women, also nationwide. The authors find
that the mandates have significantly curtailed the employment of both
groups, but had mixed effects on their wages. Specifically, likely
unauthorized women experienced an increase in wages, whereas likely
unauthorized men did not. The authors discuss alternative explanations
for the observed gender differences, including: (a) the possibility that
women, if in charge of dependent children, are more risk averse than
men--fleeing to "non-E-Verify" states and reducing their labor
supply immediately following the enactment of these measures; and (b)
the fact that likely unauthorized men and women might work in sectors
impacted differently by E-Verify mandates. For instance, relative to
men, likely unauthorized female workers are more often employed by
private households to clean or to provide childcare services, as well as
by small retail trade and food-related businesses often exempt from such
regulations. In contrast, likely unauthorized male workers heavily
concentrate in the construction industry. Therefore, relative to those
of female workers, their employment and wages are more likely to be
shaped by significant reductions in the demand for their services by
construction companies and contractors following the passage of E-Verify
mandates.
Of particular interest to us is the study by Lofstrom, Bohn, and
Raphael (2014), who also explore the impact of E-Verify mandates, but
focusing on Arizona. The authors examine how the universal E-Verify
mandate contained in the 2007 LAWA altered the internal demographic
composition of the resident population of the state. They note that,
despite the controversial efficacy of the employment verification
mandates, (9) the universal E-Verify mandate in LAWA reduced the share
of non-citizen Hispanics residing in the state of Arizona. Like
Lofstrom, Bohn, and Raphael (2014), we look at Arizona. However, instead
of focusing on the impact of its universal E-Verify mandate, our
interest lies on the more recent and significantly tougher OIL SB1070.
Specifically, we seek to better understand its effectiveness in further
reducing the share of likely unauthorized immigrants.
The origins of SB1070 go back to 1996, when the legislature passed
a law requiring proof of legal status in order to get a driver's
license--a law proposed by Russell Pearce--director of the state Motor
Vehicle Division at the time. Years later, in January 2010, Pearce
introduced Senate Bill 1070. The bill passed the Senate 17-13 in
February 2010. An amended version passed the House and the Senate in
April 2010 and was signed into law on April 23, 2010 by Arizona's
Governor Jan Brewer. Supporters of the new law sought border security,
while opponents feared racial profiling. The U.S. Department of Justice
filed a lawsuit, and the U.S. Supreme Court heard arguments to uphold or
overrule an injunction on certain aspects of the law. A ruling was
released June 25, 2012. The U.S. Supreme Court upheld the provision
enabling the police to perform immigration status checks during a lawful
stop, but stroke down three other provisions considered in violation of
the federal government's primacy in immigration policy.
Additionally, the Supreme Justices warned that the courts would be
watching carefully the implementation of the law.
Owing to its aim--namely to identify and detain unauthorized
immigrants--SB1070 is expected to reduce unauthorized immigration.
Unlike E-Verify, SB1070 targets all individuals, not just those seeking
employment in the formal sector. Anybody can be stopped by the police
and asked for proper identification, regardless of their labor force
status. Yet, the effectiveness of SB1070 partially depends on the impact
of its predecessor. If the E-Verify mandate in LAWA already
significantly reduced the population of likely unauthorized immigrants
in the state, SB1070 might not have done much more. In contrast, if the
E-Verify mandate in LAWA primarily reached specific groups of likely
unauthorized immigrants, such as men more likely to be seeking
employment in the formal sector, or if its impact weakened over time as
likely unauthorized workers accommodated to the policy by moving from
the formal to the informal sector or by becoming self-employed, SB1070
might have had an added effect. In what follows, we examine which of
these two hypothesized outcomes is supported by the data.
III. METHODOLOGY
To learn about the impact of SB1070 on the population of likely
unauthorized immigrants in Arizona, one cannot simply look at changes in
that population following the enactment of SB1070 owing to the
confounding impact of factors like the recovery from the Great
Recession, which greatly impacted sectors employing a large share of
immigrants, such as construction. Instead, we need to find a control
group of states that we can use as a counterfactual to then compare
changes in the population of likely unauthorized immigrants in Arizona
pre-post SB1070 to changes in that same population in the control group.
There are several strategies one can follow to choose a group of
control states. We follow the data-driven methodology proposed by
Abadie, Diamond, and Hainmueller (2010)--henceforth, synthetic control
method--which relies on finding affinities between the treatment and
control units using observed characteristics. The advantages of
following this methodology are twofold. First, we take advantage of 15
years of data to generate a convex combination of states that serves as
a better comparison (or control group) to Arizona than any individual
state. The weights assigned to each state in the control group reveal
the contribution of each state to the counterfactual of interest. They
are based on similarities between the treatment and control units in
preintervention outcomes and other factors expected to influence the
post-intervention outcomes. Second, we assess the statistical
significance of our estimates by comparing the population estimates for
Arizona to estimates for all other states in the sample derived from
placebo tests, as in Abadie, Diamond, and Hainmueller (2010).
The first step in the implementation of Abadie, Diamond, and
Hainmueller's (2010) methodology is to identify the pool of states
potentially used as control units--namely the "donor pool."
One option is to choose states that: (a) looked like Arizona in the
sense that they already had a universal E-Verify mandate, but (b) had no
OIL in place. Because half of the states with universal E-Verify
mandates had also enacted OILs, we end up with a rather small and not
necessarily Arizona-like group of states. (10) Thus, while this is still
a strategy that we pursue later on as a robustness check, we first
consider other options.
In particular, another possibility is to evaluate the joint impact
of LAWA and SB1070, to then compare that impact to the impact of LAWA
alone prior to SB1070. Doing so involves using LAWA and SB1070 as the
treatment and constructing a synthetic control group with pretreatment
data from a pool of states with none of the two policies in place. We
thus omit from the donor pool states that have passed a universal
E-Verify mandate and/or OIL during the period under consideration, that
is, Alabama, Georgia, Indiana, Kansas, Louisiana, Mississippi, Missouri,
North Carolina, Rhode Island, South Carolina. Tennessee, and Utah.
Subsequently, we identify a combination of states in the donor pool
that mirrors Arizona in terms of the shares of likely unauthorized
immigrants and their predictors before the passage of the two
immigration measures in question. We start by collapsing the data into
state-year-month cells. In that manner, we are able to compare shares of
Hispanic non-citizens in Arizona and in states in the donor pool within
each time period, thus addressing any seasonality concerns. In
identifying a combination of states in the donor pool that closely
resembles Arizona prior to the enactment of LAWA and SB1070, we take
into consideration the share of likely unauthorized immigrants in the
state (the outcome of interest) prior to the enactment of both laws, as
well as the values of a range of predictors. Among the latter, we
include macroeconomic, political, and demographic characteristics that,
if they were significantly different in Arizona, could be thought of
driving our results. Of particular interest in this case given the
timing of LAWA and SB1070 is the Great Recession, which severely
impacted the construction sector in Arizona--a sector prone to hiring a
large share of immigrants. To address that concern, we include among our
predictors the overall employment to population ratio in each state, the
share of public employees, the share of self-employed, and the
states' employment distribution across the five industries hiring
most immigrants in the state of Arizona, that is, agriculture,
construction, administrative support, retail trade, and food services.
(11) This set of controls is intended to account for any differential
employment impact that the business cycle might have had in Arizona due
to the prevalence of industries like construction. (12) Additionally, we
include the share voting Republican in the last federal election and per
capita state spending as controls of the political environment, (13) as
well as some descriptors of the state's population composition.
(14)
Once we identify the combination of states in the donor pool that
closely resembles Arizona prior to the enactment of the two immigration
policies in terms of the share of likely unauthorized immigrants and the
predictors specified above, we compute the pre- and post-intervention
values of the series of likely unauthorized immigrants for that
combination of states--henceforth, synthetic control group--and for
Arizona. We use those values to calculate a simple
difference-in-difference estimate of the joint impact that LAWA and
SB1070 have had on the population of likely unauthorized immigrants in
the state as follows:
(1) [[DELTA].sub.LAWA&SB1070] = ([Y.sup.AZ.sub.Post] -
[Y.sup.Control.sub.Post]) - ([Y.sup.AZ.sub.Pre] -
[Y.sup.Control.sub.Post])
where [Y.sup.i.sub.j], is the outcome for group i in time period j
(Pre = period before the implementation of LAWA, that is, January 1998
through December 2006; Post = period after the implementation of SB1070,
that is, August 2010 through December 2013). Since both LAWA and SB1070
are immigration enforcement measures aimed at reducing unauthorized
immigration by restricting their employment opportunities and by
granting the police the ability to stop anybody at any time and request
proper identification, we would expect: [[DELTA].sub.LAWA&SB1070]
< 0.
Yet, as noted earlier, the effectiveness of SB1070 depends on the
impact of its predecessor. If LAWA already significantly reduced the
population of likely unauthorized immigrants in the state, SB1070 might
not have been able to do much more. In contrast, if LAWA only reached
specific groups of likely unauthorized immigrants, such as those seeking
employment in the formal sector, or if its impact died over time, SB1070
might have proven effective. To discern between those two hypotheses, we
compare [[DELTA].sub.LAWA&SB1070] and [[DELTA].sub.Lawa]--the
difference-in-difference estimate of the impact of LAWA prior to the
enactment of SB1070. Under certain scenarios, such a comparison sheds
some light on the effectiveness of SB1070. Perhaps, the most informative
scenario is if [absolute value of [[DELTA].sub.LAWA&SB1070]] <
[absolute value of [[DELTA].sub.LAWA]], in which case we can conclude
that: (a) the impact of LAWA diminished over time, and (b) that the
effect of SB1070 has not been large enough to counteract the loss of
impact of its predecessor.
However, if, for example: [[DELTA].sub.LAWA&SB1070] =
[[DELTA].sub.LAWA] as we in the analysis that follows, it could be the
case that: (a) LAWA's impact diminished over time and SB1070 made
up for its weakening effectiveness, or (b) LAWA's impact remained
intact and SB1070 has been rather ineffective. Either way, it would
reveal that one of the two immigration enforcement measures has proven
ineffective--either in the long run (as would be the case for LAWA) or
from the very beginning (as would be the case with SB1070). To the
extent that the share of likely unauthorized immigrants would not have
significantly changed since the end of 2009 despite the various measures
in place, that finding would underscore the need to rethink this
piece-meal approach to immigration enforcement.
IV. DATA
The intent of SB1070 was to reduce the population of undocumented
immigrants. Unfortunately, information on the legal status of immigrants
is not available in most representative surveys. Nonetheless, one can
look at population groups that have traits predictive of
immigrants' undocumented status, such as lack of citizenship and
Hispanic ethnicity (Passel and Cohn 2009a, 2009b). Therefore, as
Lofstrom, Bohn, and Raphael (2014) and others in the literature, we rely
on information on the citizenship status and ethnicity of immigrants
being surveyed in the CPS to identify a group of "likely
unauthorized immigrants," such as Hispanic non-citizens. Since
Mexican origin is, yet, another predictive factor of undocumented status
(Passel and Cohn 2009a, 2009b, 2011), along with not having a college
degree and being younger due to the broader amnesty offered by the 1986
IRC A, we also look at Mexican non-citizens 16 to 45 years of age who
did not attend college as a robustness check.
The CPS has the advantage of offering higher frequency data needed
for the analysis of the measure at hand. We use the CPS monthly surveys
from January 1998 through December 2013. The pre-intervention period
spans from January 1998 through December 2006. Our post-intervention
period starts in August 2010 and spans to December 2013. (15) Despite
being shorter in duration, we expect the post-intervention period to be
sufficiently long to examine the impact of SB 1070 owing to the
just-in-time nature of immigration flows and the high mobility of
unauthorized immigrants. (16) Finally, we combine the data within each
state-year-month and, to compare our outcomes to those reported for LAWA
by Lofstrom, Bohn, and Raphael (2014), we analyze the ratio of
immigrants in the population.
Our sample consists of 16-65-year-old individuals in the CPS--an
age range that encompasses the prime age of migration (Waldinger and
Reichl 2006). As noted above, we pay close attention to two different
groups of immigrants more likely representative of unauthorized
immigrants: (a) a broader group comprised by non-citizen Hispanics, and
(b) a narrower group composed by non-citizen Mexicans between the ages
of 16-45 with no college education. Finally, given the potential
distinct impact that immigration enforcement policies may have on men
and women, especially E-Verify mandates owing to their different
propensities to work in the uncovered informal sector, we also carry the
analysis by gender.
Table 1 provides an overview of population changes in Arizona
before and after the passage of the most recent immigration laws. At
first sight, the populations of Hispanic non-citizens, both men and
women, appear to have significantly declined from before to after the
passage of LAWA (and still before the enactment of SB 1070) by
approximately 140,333 in the case of men and by 101,199 among women.
These declines correspond to a drop of about 5 and 4 percentage points
in the share of non-citizen Hispanic men and women in the state,
respectively. While the number of non-citizen Hispanic men continued to
drop until after the passage of SB 1070 by nearly 20,000 men, its share
of the Arizona's population stabilized at about 10%. In the case of
women, the population of Hispanic non-citizens stopped decreasing and
actually recovered between the first quarter of 2010 (post-LAWA and
pre-SB 1070) and the first quarter of 2012 (post-SB 1070). Table 1
displays similar trends taking place among Mexican non-citizens 16 to 45
years of age--possibly the most likely to be unauthorized among
non-citizen Hispanics (Passel and Cohn 2009a, 2009b).
Overall, the figures in Table 1 suggest a significant impact of
LAWA, but not of SB 1070, on the population of non-citizen Hispanic men
and women. Yet, while interesting, the population trends displayed in
Table 1 cannot shed much light on the impact that Arizona's 2007
and 2010 immigration measures may have had on the demographic
composition of the state. After all, they might be capturing other
confounding impacts, such as the one of the Great Recession or the slow
employment recovery that followed thereafter. The analysis in the next
section addresses that shortcoming.
V. FINDINGS
A. The Joint Impact of LAWA and SB 1070
As discussed earlier in Section III, the synthetic control group is
a combination of the states in the donor pool that most closely resemble
Arizona in terms of the shares of non-citizen Hispanic and Mexican men
and women, as well as their predictors, prior to the enactment of LAWA
in 2007. Table 2 displays the weight of each state in the synthetic
control group for each of the population outcomes being examined. Small
gender differences aside, the weights reveal that the Hispanic
non-citizen population trends observed in Arizona prior to LAWA and SB
1070 are best reproduced by the following combination of states:
California (0.376), Texas (0.359), Nevada (0.088), Florida (0.059), New
Mexico (0.097), and, to a much lesser extent, Kentucky (0.020). This
combination changes to California (0.661), Nevada (0.191), New Mexico
(0.11), and Texas (0.038) when we look at the share of Mexican
non-citizens 16 to 45 years of age with no college education.
To double check whether the aforementioned combinations of states
are sensible controls, Table 3 shows the average values of the
population series being examined, along with those of their predictors,
for Arizona, its synthetic control group and the entire donor pool
before the enactment of LAWA and SB 1070. The mean values of all the
series prior to the passage of both immigration measures are rather
similar in Arizona and its synthetic control group--differences are not
statistically different from zero. As a result, average pre-intervention
differences in the shares of Hispanic and Mexican non-citizens between
Arizona and its synthetic control groups are close to zero, as shown in
the first column of Table 4.
Thus, using Arizona and the synthetic control groups described in
Table 2, we proceed to compute the difference-in-difference estimates
for the shares of Hispanic non-citizens and Mexican non-citizens as
described in Equation (1). The first two columns of Table 4 show the
differences in those series between Arizona and its synthetic control
before and after the enactment of LAWA and SB 1070, whereas the third
column lists the estimated difference-in-difference estimates. To
evaluate the statistical significance of our findings, the fourth column
presents the ranking of Arizona's difference-in-difference
estimates in the distribution of all difference-in-difference estimates
derived from running the placebo tests on states in the donor pool. (17)
It can be seen how the estimated joint impact of LAWA and SB 1070 on the
share of Hispanic non-citizens 16 to 65 years of age is the largest for
Arizona out of the 39 states. The difference-in-difference estimate
suggests a 2 percentage point decline in the proportion of Arizona
residents in that category. The effect appears to be driven by an
average drop in the population of likely unauthorized men (of 1.2
percentage points) and women (of 0.9 percentage points). The joint
impact of LAWA and SB 1070 on younger non-citizen Mexicans (a 1
percentage point reduction) is also the largest in Arizona and greater
among men (0.6 percentage points) than women (0.3 percentage points).
(18)
How do these effects compare to those of just LAWA up until the end
of 2009? According to Lofstrom, Bohn, and Raphael (2014), LAWA lowered
the share of non-citizen Hispanic men and women ages 16 to 65 residing
in Arizona by approximately 2 percentage points. Hence, the estimated
joint impact of LAWA and SB 1070 does not appear to be different from
that of LAWA alone. Similar conclusions can be reached when comparing
the estimates for the other population subgroups being examined.
In summary, just as we concluded earlier, the estimated joint
impact of LAWA and SB 1070 does not appear to significantly differ from
that of LAWA alone. This finding implies that either: (a) SB 1070 has
had no significant impact while LAWA's effectiveness has persisted,
or (b) LAWA's effectiveness declined over time, but SB 1070 has
helped offset the diminishing impact of LAWA. In what follows, we
attempt to further distinguish between these two possibilities.
B. Isolating the Impact of SB 1070
To be able to say something more conclusive about the effectiveness
of SB 1070, we repeat the analysis using as a control group the states
that, like Arizona, had enacted a universal employment verification
mandate; however, unlike Arizona, they had no OIL. As noted earlier,
these states are Louisiana, Mississippi, North Carolina, and Tennessee.
The results from such an exercise are displayed in Table 5. The first
two columns of Table 5 show the differences between Arizona and its
synthetic control group before and after the enactment of SB 1070,
whereas the third column lists the estimated difference-in-difference
estimates. It is worth noting that, unlike our synthetic control group
in Table 4, this one already appears "less Arizona-like" prior
to the enactment of SB 1070, as can be seen from the first column of
Table 5. The differences between Arizona and this new control group of
states somewhat increase following the enactment of SB 1070 and,
overall, reductions in the shares of Hispanic and Mexican non-citizens
end up being the largest for Arizona, as can be seen in the last column
of Table 5. Nevertheless, the difference-in-difference estimates of the
impact of SB 1070 in the third column of Table 5 are close to zero.
In summary, the figures in Table 5 suggest that SB 1070 has had a
minimal to null effectiveness, explaining why the impact of LAWA did not
significantly differ from the joint impact of the two policies.
C. The Underlying Impact Dynamics of LAWA and SB1070
We have thus far answered the first question we posed ourselves,
namely: what has been the overall effectiveness of SB 1070 in further
reducing the share of likely unauthorized immigrants in Arizona? As
summarized above, the impact of SB 1070 appears to have been minimal to
null. Yet, by measuring the average impacts of the policies, the figures
in Tables 4 and 5 do not shed light about the dynamics characterizing
such impacts--namely, what the impacts were at various points in time
and how they changed throughout the years being examined.
[FIGURE 1 OMITTED]
Figures 1 through 6 address that gap by displaying with a thick
line the evolution of the difference in the Hispanic and Mexican
non-citizen population shares between Arizona and its synthetic control
group (from Tables 4 and 5) at various points in time. The gray lines
represent placebo tests capturing the difference in those population
shares between each state in the donor pool and its respective synthetic
version. In each case, we apply the synthetic control method used to
test the effect of LAWA and SB 1070 in Arizona to every one of the
remaining states included in the donor pool. Each time, we reproduce the
impact that LAWA and SB 1070 would have had in each one of the states
included in the donor pool, shifting Arizona to the donor pool itself.
This iterative process gives us a distribution of the estimated effects
for each of the states in the donor pool where no intervention or
treatment took place. The graphs also indicate with vertical lines the
enactment dates of LAWA and SB 1070.
A few findings are worth discussing. First, Figure 1 confirms the 2
percentage point decline in the share of Hispanic non-citizens 2 years
after the passage of LAWA documented by Lofstrom, Bohn, and Raphael
(2014). The graph also reveals how the share of Hispanic non-citizens
seems to stall thereafter and, to some degree, slightly recover just
before the enactment of SB 1070 in 2010. In that regard, one might be
tempted to interpret the enactment of SB 1070 as an effort to further
reduce the population of likely unauthorized immigrants using policies
that target a population not necessarily engaged in the formal labor
market. Yet, as suggested by the figures in Table 5, SB 1070 does not
appear to have helped much. To some extent, the share of Hispanic
non-citizens slightly dropped right after the enactment of SB 1070 but,
overall, hovered around the same level it had already reached in 2009.
This explains why the estimated joint impact of LAWA and SB 1070 is not
that different from the impact of LAWA alone and supports the conclusion
that SB 1070 did not add much. Figure 2 displays the evolution of the
difference-in-difference estimates for the share of Mexican
non-citizens. There is much more noise in this graph as the sample size
gets smaller. Nevertheless, it reveals how the trend exhibited in Figure
1 is largely driven by the experience of Mexican non-citizens.
[FIGURE 2 OMITTED]
Second, a closer look at the trajectory of the impacts of these
policies on non-citizen Hispanic men and women may reveal some
interesting gender differences in their impact dynamics. In particular,
according to Figure 3, the share of Hispanic non-citizen men experienced
a significant decline following the enactment of LAWA up until the time
when SB 1070 was enacted. However, SB 1070 does not appear to have had
much bite on that population. In fact, the share of Hispanic non-citizen
men seems to have progressively recovered after reaching its minimum in
2009. Something similar is observed for the share of non-citizen Mexican
men in Figure 4. In contrast, the shares of non-citizen Hispanic and
Mexican women in Figures 3 and 6 dropped less than those of non-citizen
Hispanic and Mexican men following the enactment of LAWA. However, they
were slightly more responsive to SB 1070 during the two years following
its enactment.
As discussed by Amuedo-Dorantes and Bansak (2012a, 2012b), these
gender differences in the impact of LAWA and SB 1070 could be related to
their traditional employment sectors--not all of them equally impacted
by the E-Verify mandate. In particular, relative to men, likely
unauthorized female workers are more often employed by private
households and small retail trade and food-related businesses exempt
from using E-Verify. Therefore, their employment is less likely to have
been negatively impacted by the E-Verify mandate relative to that of
their male counterparts. However, women might have been more responsive
to the "show me your papers" clause in SB 1070 to the extent
that it targets everyone in the household, regardless of their labor
force status and sector of employment.
In any case, the main message from the analysis of the impact
dynamics of LAWA and SB 1070 is that, as shown by Figure 1, SB 1070 had
a limited to null effect in reducing the overall share of likely
unauthorized immigrants in Arizona.
VI. SUMMARY AND CONCLUSIONS
The long-overdue immigration reform at the federal level has
spurred a series of legislative measures at the state level intended to
curb down illegal immigration. Arizona has been one of the states in the
forefront of this crusade with the passage of a universal E-Verify
mandate in its 2007 LAWA and the subsequent enactment of SB 1070 in
April 2010. Previous analyses have found that LAWA significantly lowered
the share of non-citizen Hispanics in the state. We thus ask ourselves
about the effectiveness of SB 1070 in further reducing the population of
likely unauthorized immigrants in the state or, at the minimum, in
targeting specific population groups less impacted by its predecessor.
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
[FIGURE 5 OMITTED]
A close examination of the dynamics of these two policies'
impacts reveals that the enactment of SB 1070 in April 2010 coincided
with the stalling, and sometimes slight recovery, of the shares of
non-citizen Hispanics, especially women, in the state. And, although the
share of Hispanic non-citizens slightly dropped right after the
enactment of SB 1070, it overall hovered around the same level it had
already reached in 2009. As such, a comparison of the average joint
impact of LAWA and SB 1070 to the average impact of LAWA prior to the
enactment of SB 1070 on the shares of non-citizen Hispanics reveals that
the two effects were similar in size. When we further explore the
isolated impact of SB 1070, we find that, as suggested by the impact
dynamics, its impact has been minimal to null, explaining why the impact
of LAWA did not significantly differ from the joint impact of the two
policies.
Before closing, it is worth noting some of the challenges faced in
an analysis of this kind. Some of them refer to data constraints, as is
the case with the possibility of survey non-response due to fear
inspired by the law. Additionally, even though the methodology used has
some important advantages--including the ability to properly weight the
states in our control group according to their similarities to
Arizona--we are unable to assess the statistical significance of our
results using traditional large sample inference.
Despite these challenges, given its deleterious impact on personal
freedom, the emerging accusations of racial profiling by the police and
the record level spending on interior immigration enforcement, learning
about the effectiveness of SB 1070 in reducing the share of likely
unauthorized immigrants is important. After all, its merit rests
entirely on its ability to deter illegal immigration. The fact that SB
1070 appears to have had a minimal to null impact on the share of likely
unauthorized immigrants in the state questions the merit of the law and,
more broadly, a piece-meal approach to immigration enforcement.
[FIGURE 6 OMITTED]
APPENDIX
TABLE A1
Robustness Checks
([Y.sup.AZ.sub.Pre]- ([Y.sup.AZ.sub.Post]-
[Y.sup.Control.sub. [Y.sup.Control.sub.
Series Post]) Post])
Panel A: Excluding
neighboring states
from control group
Share of Hispanic 0.0197 -0.0016
non-citizens
Male 0.0110 -0.0010
Female 0.0088 -0.0004
Panel B: Other
population groups
Share of naturalized -0.0002 0.0038
Hispanics
Share of U.S.-born 0.0007 0.0013
Hispanics
Share of non-Hispanic 0.0000 0.0000
non-citizens
[[DELTA].
Series sub.LAWA&B1070] Rank
Panel A: Excluding
neighboring states
from control group
Share of Hispanic -0.0213 1
non-citizens
Male -0.0120 1
Female -0.0092 1
Panel B: Other
population groups
Share of naturalized 0.0040 34
Hispanics
Share of U.S.-born 0.0006 20
Hispanics
Share of non-Hispanic 0.0000 32
non-citizens
Notes: The sample includes individuals 16-65 years of
age in the pooled January 199S to June 2007 and July
2010 to December 2013 monthly CPS surveys.
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ABBREVIATIONS
CPSs: Current Population Surveys
IRCA: Immigration Reform and Control Act
LAWA: Legal Arizona Workers Act
OILs: Omnibus Immigration Laws
doi: 10.1111/ecin.12138
Online Early publication August 25, 2014
Amuedo-Dorantes: Department of Economics, San Diego State
University, San Diego, CA 92182. Phone 619-594-1663, Fax 619-594-5062,
E-mail camuedod@ mail.sdsu.edu
Lozano: Department of Economics, Pomona College, Claremont, CA
91711. Phone 909-621-8985, Fax 909-621-8836, E-mail
[email protected]
(1.) Please refer to: http://www.azleg.gov/Format
Document.asp?inDoc=/legtext/491eg/2r/bills/hb2162c.htm for the full text
of the bill.
(2.) On July 28, 2010, one day before the law was intended to take
effect, Judge Bolton (a federal district court judge) struck down its
most controversial provisions. In June 2012, the Supreme Court ruled
that one key provision of the law--the one requiring an officer to make
a reasonable attempt to determine the immigration status of a person
stopped, detained or arrested if there's reasonable suspicion that
person is in the country illegally--is constitutional, thus paving the
way for it to go into effect. The decision is crucial given the recent
adoption by other states of alike measures (Liptak 2012).
(3.) E-Verify is an internet-based program run by the U.S.
government and offered freely to employers to compare the information
from employees' 1-9 form to data from U.S. government records. As
of June 2012, a total of nine states have enacted E-Verify mandates with
a broader scope--that is, impacting all employers as opposed to only
state agencies and/or contractors: Alabama (H56 in 2011), Arizona
(HB2779 in 2007. HB2745 in 2008), Georgia (HB87 in 2011), Louisiana
(Revised Statute Section 23:995 in 2011), Mississippi (SB2988 in 2008).
North Carolina (HB36 in 2011), South Carolina (SB20 in 2011), Tennessee
(HB1378 in 2011), and Utah (HB251 in 2010, HB116 in 2011).
(4.) Specifically, five states have enacted SB1070-like bills in
2011: Alabama HB56 in June 2011, Georgia HB87 in May 2011, Indiana SB590
in May 2011, South Carolina S20 in June 2011, and Utah's package
(H116, H466, H469, and H497) in March 2011. The trend has continued in
2012, with five additional states introducing omnibus enforcement bills:
Kansas (H2576), Mississippi (H488 and S2090), Missouri (S590), Rhode
Island (H7313), and West Virginia (S64). The bills in Mississippi and
West Virginia have, however, failed. For more information, visit:
http://www.ncsl.org/issuesresearch/immig/omnibus-immigration-legislation.aspx
(5.) Examples of these analyses are the works by Bean, Edmonston,
and Passel (1990a), Bean et al. (1990b), Espenshade (1990), White, Bean,
and Espenshade (1990), Singer and Massey (1988), Espenshade (1994),
Cornelius (1998). Hanson and Spilimbergo (1999), Davila, Pagan, and
Soydemir (2002), and Orrenius and Zavodny (2003).
(6.) See, for example, Cornelius (1989, 1990), Gonzalez de la Rocha
and Escobar LatapI (1990), Massey, Donato, and Liang (1990), Chavez,
Flores, and Lopez-Garza (1990), Bustamante (1990), and Kossoudji (1992).
(7.) See, for instance, Donato, Durand, and Massey (1992), Orrenius
(2001). and Angelucci (2005) for examples of studies using data from the
Mexican Migration Project (MMP) to examine the impact of the 1986
Immigration Reform and Control Act (IRCA) on border crossing behavior
and net illegal flows. Ritcher, Taylor, and Yunez-Naude (2007) use data
from the Encuesta Nacional a Hogares Rurales de Mexico (ENHRUM) also to
examine the impact of IRCA, as well as NAFTA and overall increased
border enforcement on migration. Amuedo-Dorantes and Bansak (2012a,
2012b) use data from the Encuesta sobre Migracion en la Frontera Norte
de Mexico (EMIF) to investigate if increased border enforcement has
reduced repetitive illegal crossings.
(8.) In addition, some studies have started to look at the impact
of local-level agreements between local police and immigration
authorities. For instance, Parrado (2012) studies the effect of the
287(g) program on the geographic dispersion of the Mexican immigrants
and finds no direct impact of the program on the number of undocumented
Mexican migrants in the locality. More recently, Watson (2013) examines
how 287(g) agreements might have impacted the location of immigrants.
She finds no significant impact of these laws on the outflows of
non-citizen Hispanics, except in Maricopa County.
(9.) Rosenblum (2011) discusses the strengths and weaknesses of the
E-Verify system and discusses how E-Verify is highly vulnerable to
identify fraud and employer noncompliance as documented by Westat
Corporation (2009) and numerous audits by the Social Security
Administration (SSA) Office of the Inspector General.
(10.) Along with Arizona, four states with universal E-Verify
mandates have adopted SB1070-like measures: Alabama HB56 in June 2011,
Georgia HB87 in May 2011, South Carolina S20 in June 2011, and
Utah's package (HI 16, H466, H469, and H497) in March 2011. As a
result, only Louisiana, Mississippi, North Carolina, and Tennessee had
universal E-Verify mandates, but lacked omnibus immigration laws. We,
nevertheless, use these states in our robustness checks to help us sort
further the impact of SB1070.
(11.) These controls were created by the authors using data from
the monthly CPS for the time period in question.
(12.) Lofstrom, Bohn, and Raphael (2014) show how that, while
Arizona's labor market was seriously impacted by the recession, so
were the labor markets of other states, including those of their
neighbors.
(13.) These can be downloaded from: http://clerk.
house.gov/member_info/electionInfo/index.aspx and from
http://www.usgovemmentspending.com, respectively. Because elections are
every 2 years, we assign the results in year t to every month in year t
and t + 1. Additionally, due to the lack of election data, Washington,
DC is excluded from the sample.
(14.) These include the share of women, the share of immigrants not
born in Mexico or Central America, and the average age of the
population. These controls were created by the authors using data from
the monthly Current Population Survey for the time period in question.
(15.) These data are collected each month during the week that
includes the 19th day of the month. Labor market questions refer to the
week that includes the 12th day of the month. Note that the bill was
passed by the State House on April 13, 2010 and signed by Arizona
Governor on April 23, 2010. The bill, however, did not go into effect
until July 29. 2010. Hence, the bill was signed and implemented after
the monthly CPS surveys corresponding to the months of April and July
had been collected.
(16.) Notice also that this post-treatment period is as long as the
one used by Lofstrom, Bohn, and Raphael (2014).
(17.) As highlighted by Abadie, Diamond, and Hainmueller (2010),
large sample inference is not suited to comparative case studies with
aggregate data. Therefore, following both Abadie, Diamond, and
Hainmueller (2010) and Lofstrom, Bohn, and Raphael (2014), we run 38
placebo tests (one per state in the donor pool). Each time, we reproduce
the impact that LAWA and SB 1070 would have had in each one of the
states included in the donor pool, shifting Arizona to the donor pool
itself. The p values are based on the ranking of the AZ estimates
relative to the placebo estimates for the other states.
(18.) The estimated joint impact of LAWA and SB1070 is similar and
still the largest for Arizona when we exclude bordering states to which
likely unauthorized migrants might have moved, as shown in Panel A of
Table A1 in the Appendix. This is not surprising, as the vast majority
(i.e., 80% according to authors' tabulations using data from the
American Community Survey) of Hispanic non-citizens leaving Arizona
during the period in question did not move to other states. Rather, they
were removed from the United States (2013 ICE FOIA Request #33554).
Furthermore, a falsification test examining the joint impact of LAWA and
SB 1070 on groups not specifically targeted by the two legislative
pieces reveals how the latter is close to zero and among the smallest in
Arizona. In other words, the estimated impact of LAWA and SB 1070 is
unique to the likely unauthorized, appeasing concerns about the role of
the recession in our findings.
TABLE 1
Summary Arizona Population Statistics Pre- and Post-LAWA and SB 1070
Series Male
Hispanic Mexican
Non-Citizens Non-Citizens Natives
1st Quarter 2006 (Pre-LAWA)
Total 572.890.14 262,070.92 1.556.586.16
SD (270.27) (130.62) (611.21)
Share of population 0.15 0.07 0.40
1st Quarter 2010 (Post-LAWA and Pre-SB1070)
Total 432,556.81 212,563.49 1.757.124.50
SD (197.31) (100.48) (651.96)
Share of population 0.10 0.05 0.41
1st Quarter 2012 (Post-SB1070)
Total 412,853.83 181,260.17 1,607,473.35
SD (192.76) (83.43) (618.40)
Share of population 0.10 0.04 0.39
Series Female
Hispanic Mexican
Non-Citizens Non-Citizens Natives
1st Quarter 2006 (Pre-LAWA)
Total 498.098.14 199,014.63 1.584,459.55
SD (237.35) (97.23) (619.10)
Share of population 0.13 0.05 0.41
1st Quarter 2010 (Post-LAWA and Pre-SB1070)
Total 396,898.61 176,003.90 1,779.508.32
SD (180.29) (82.21) (658.40)
Share of population 0.09 0.04 0.42
1st Quarter 2012 (Post-SB1070)
Total 417,169.09 192,013.57 1,706,758.37
SD (193.77) (95.50) (644.80)
Share of population 0.10 0.05 0.42
Notes: The sample includes individuals 16-65 years of age in
the respective periods. Standard deviations are given in
parentheses.
TABLE 2
Weights Given to States in Constructing a Synthetic Arizona
Weight Given Weight Given Weight Given
for Estimates for Estimates for Estimates
on Share of on Share of on Share of
Hispanic Male Hispanic Female Hispanic
State Non-Citizens Non-Citizens Non-Citizens
FL 0.059 0.023 0.122
KY 0.020 0.030 0.000
TX 0.359 0.314 0.437
NM 0.097 0.105 0.086
NV 0.088 0.121 0.034
CA 0.376 0.407 0.321
N 453,800 240,586 213,214
Weight Given Weight Given Weight Given
for Estimates for Estimates for Estimates
on Share of on Share of on Share of
Mexican Male Mexican Female Mexican
State Non-Citizens Non-Citizens Non-Citizens
FL 0.000 0.000 0.000
KY 0.000 0.000 0.000
TX 0.038 0.065 0.017
NM 0.11 0.112 0.109
NV 0.191 0.189 0.194
CA 0.661 0.634 0.681
N 224,644 123.879 100,765
Notes: The sample includes individuals 16-65 years of age in
the pooled January 1998 to June 2007 and July 2010 to
December 2013 monthly CPS surveys.
TABLE 3
Means of Hispanic and Mexican Non-Citizens' Predictors
All
Synthetic Donor
Controls AZ AZ Pool
Hispanic Non-Citizens
Proportion living in the state 0.10 0.11 0.03
Proportion working in the 0.63 0.66 0.7
state
Proportion working in 0.14 0.13 0.11
construction
Proportion working in retail 0.04 0.05 0.04
Proportion working in food 0.05 0.05 0.07
services
Proportion working in 0.07 0.06 0.06
administrative support
Proportion working in 0.05 0.05 0.06
agriculture
% GOP votes 0.56 0.50 0.49
Per capita state and local 6,457.85 7,780.74 8,105.8
expenditures
Mexican Non-Citizens
Proportion living in the state 0.07 0.07 0.01
Proportion working in the 0.63 0.67 0.66
state
Proportion working in 0.17 0.15 0.14
construction
Proportion working in retail 0.03 0.05 0.03
Proportion working in food 0.06 0.06 0.08
services
Proportion working in 0.07 0.07 0.06
administrative support
Proportion working in 0.05 0.07 0.08
agriculture
% GOP votes 0.56 0.46 0.47
Per capita state and local 6,457.85 8,524.2 7,689.67
Men
Synthetic Donor
Controls AZ AZ Pool
Hispanic Non-Citizens
Proportion living in the state 0.06 0.06 0.02
Proportion working in the 0.83 0.83 0.83
state
Proportion working in 0.26 0.24 0.18
construction
Proportion working in retail 0.04 0.05 0.04
Proportion working in food 0.06 0.05 0.07
services
Proportion working in 0.09 0.07 0.07
administrative support
Proportion working in 0.08 0.07 0.1
agriculture
% GOP votes 0.56 0.50 0.48
Per capita state and local 6,457.85 7,794.48 7,991.31
expenditures
Mexican Non-Citizens
Proportion living in the state 0.04 0.04 0.01
Proportion working in the 0.86 0.85 0.8
state
Proportion working in 0.31 0.27 0.22
construction
Proportion working in retail 0.03 0.05 0.03
Proportion working in food 0.06 0.07 0.09
services
Proportion working in 0.10 0.09 0.07
administrative support
Proportion working in 0.08 0.10 0.11
agriculture
% GOP votes 0.56 0.46 0.46
Per capita state and local 6,457.85 8,531.96 7,545.98
Women
Synthetic Donor
Controls AZ AZ Pool
Hispanic Non-Citizens
Proportion living in the state 0.05 0.05 0.01
Proportion working in the 0.40 0.47 0.51
state
Proportion working in 0.01 0.01 0.01
construction
Proportion working in retail 0.04 0.05 0.03
Proportion working in food 0.05 0.05 0.06
services
Proportion working in 0.04 0.05 0.05
administrative support
Proportion working in 0.01 0.02 0.02
agriculture
% GOP votes 0.56 0.50 0.49
Per capita state and local 6.457.85 7,741.67 8,073.44
expenditures
Mexican Non-Citizens
Proportion living in the state 0.03 0.03 0.01
Proportion working in the 0.36 0.44 0.4
state
Proportion working in 0.00 0.01 0.01
construction
Proportion working in retail 0.04 0.04 0.03
Proportion working in food 0.06 0.05 0.07
services
Proportion working in 0.04 0.05 0.04
administrative support
Proportion working in 0.02 0.04 0.03
agriculture
% GOP votes 0.56 0.46 0.45
Per capita state and local 6,457.85 8,523.66 7,448.33
expenditures
Notes: The proportion living in the state is measured as a
share of the state's population. The proportion working in
the state and in each specific industry is measured as a
share of the total number of individuals in the demographic
group in question in the state. The sample includes
individuals 16-65 years of age in the pooled January 1998 to
June 2007 and July 2010 to December 2013 monthly CPS
surveys.
TABLE 4
Joint Impact of LAWA and SB 1070 on the Share of
Hispanic and Mexican Non-Citizens in Arizona
([Y.sup.AZ.sub.Pre]- ([Y.sup.AZ.sub.Post]-
[Y.sup.Control.sub. [Y.sup.Control.sub.
Series Post]) Post])
Share of Hispanic 0.0003 -0.0210
non-citizens
Male 0.0001 -0.0120
Female 0.0003 -0.0090
Share of Mexican 0.0004 -0.0092
non-citizens
Male 0.0003 -0.0058
Female 0.0003 -0.0033
[[DELTA].
Series sub.LAWA&B1070] Rank
Share of Hispanic -0.0213 1
non-citizens
Male -0.0121 1
Female -0.0093 1
Share of Mexican -0.0096 1
non-citizens
Male -0.0061 1
Female -0.0035 1
Notes: The sample includes individuals 16-65 years of age
in the pooled January 1998 to June 2007 and July 2010 to
December 2013 monthly CPS surveys.
TABLE 5
Impact of SB 1070 on the Share of Hispanic and
Mexican Non-Citizens in Arizona
([Y.sup.AZ.sub.Pre]- ([Y.sup.AZ.sub.Post]-
[Y.sup.Control.sub. [Y.sup.Control.sub.
Series Post]) Post])
Share of Hispanic -0.0318 -0.0334
non-citizens
Male -0.0187 -0.0210
Female -0.0148 -0.0155
Share of Mexican -0.0266 -0.0309
non-citizens
Male -0.0157 -0.0180
Female -0.0150 -0.0173
[[DELTA].
Series sub.SB1070] Rank
Share of Hispanic -0.0016 1
non-citizens
Male -0.0023 1
Female -0.0008 1
Share of Mexican -0.0043 1
non-citizens
Male -0.0023 1
Female -0.0023 1
Notes: The sample includes individuals 16-65 years of age in
the pooled January 1998 to June 2007 and July 2010 to
December 2013 monthly CPS surveys. The donor pool includes
those states with universal E-Verify mandates and no omnibus
immigration law--namely Louisiana, Mississippi, North
Carolina, and Tennessee.
