Immigration Enforcement Policies, Labor and
Housing Markets in the US: Evidence from Secure
Communities
Wilfried Adohinzin∗
May 2020
Abstract
In this paper, I look at the effects of the Secure Communities- an immigration enforcement program on housing and labor market outcomes of undocumented immigrants and
natives in the US. I use a difference in differences approach to document the direct and
spillover effects of this program.
Key words:
JEL:
∗
wda3@illinois.edu (UIUC)
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1. Introduction
According to Passel and Cohn (2016), about 8 million undocumented immigrants belonged to
the US workforce in 2015. Per these estimates, they account for about 5% of the labor force.
Not only there has been a longstanding interest in the economic scientific literature on the social
and economic impacts of immigration, but these also remain some of the most debated topics
in public policy spaces (Menasce Horowitz (2014); Givens (2018) and Thompson (2018)). Over
the last two decades, many policies tried to address this issue, which led to a rapid increase
in deportations and immigrants detention in the US. In 2014, annual detentions and deportations increased respectively by 320% to 160,000 and by 400% to 200,000. In this paper, I
investigate the effects of a large immigration enforcement policy, the Secure Communities (SC)
on labor market and rental prices in US neighborhoods. The SC program was introduced in
2005 and rolled out over US counties until 2014. The main objective of this program was to
facilitate the sharing of information between state, federal and agencies such as the Federal
Bureau of Investigation (FBI) and the Department of Homeland Security (DHS) related to
undocumented immigrants. While some states enacted the policy before others, it was eventually adopted by all states and; according to statistics from the Transactional Records Access
Clearinghouse (TRAC), under the SC program, over 450,000 undocumented immigrants were
removed between 2008 and 2014. Therefore, the program was able to significantly reduce the
stock of undocumented immigrants. Moreover, it had the potential to create additional "chilling
effects", leading illegal immigrants to voluntarily out-migrate as they fear deportation. (Alsan
and Yang (2018)). This paper leverages the plausible exogenous rollout nature over counties
of the program to estimate its impact on employment outcomes and housing prices, using a
difference-in-differences empirical strategy.
How relevant are immigration policies for housing markets? Why do we care for such effects
and what distinguishes these effects from effects stemming from population growth? Studying
the impacts of immigration policies on labor and housing markets is relevant for many reasons.
First, while traditional research on the effects of immigration on cities tend to focus exclusively
on labor markets, there is a relatively small but growing strand of literature in urban economics
that looks at the housing market as a potential channel through which immigration impacts
cities. A few of these papers are Susin (2001), Saiz (2007), Greulich, Quigley, Raphael, Tracy,
and Jasso (2004) and Ottaviano and Peri (2012). Second, the theoretical impacts of such
policies on housing markets are mixed. On one hand, for a sudden decrease in the stock of
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undocumented immigrants through deportation will lead to
Second, there is abundant evidence suggesting that the characteristics of residents in a specific neighborhood are important factors that homeowners and renters value (Farley, Schuman,
Bianchi, Colasanto, and Hatchett (1978), Taeuber and Taeuber (1965), Black (1999)). Third,
w. Finally, while the literature on immigration relies more generally on shift-share instruments,
the availability of a natural experiment such as the SC program allows us to have a cleaner
identification of the causal effects of immigration on local outcomes.
Saiz (2007) uses a "shift-share" instrumental variable approach and finds that an immigration
inflow equal of 1% of a city’s population is associated with increases in average rents and housing
values of about 1%. His results suggest that the impacts found for housing are higher than those
found on labor outcomes. Ottaviano and Peri (2012) build a general equilibrium model and
show a strong positive association between immigration flows and changes in average wages
and average house rents for native US residents across states. Heterogeneity analyses reveal
that immigration inflows affect positively both wages and rents for highly-skilled natives while
reducing wages and increasing rents for low-skill natives. They calibrate the model using US
data and show that it matches most of the effects observed in the data in the period 1990-2005.
Their estimation strategy relies as well on shift-share instruments to control for the endogenous
nature of immigration. A more recent paper by Sharpe (2019) using shift share instruments
builds upon the Saiz (2007) by explicitly controlling for city-level initial characteristics that led
to increases in immigration inflows and rents growth. The paper finds that the positive effect
of immigration on rents from Saiz (2007) is attenuated. While the shift share instrumental
variable approach seems to be the favorite empirical strategy, recent work by Jaeger, Ruist,
and Stuhler (2018) has questioned its identifying assumptions. Therefore, a new wave of papers
using cleaner natural experiments with more testable assumptions have started to emerge. One
example that I follow closely is East, Luck, Mansour, and Velasquez (2018) that uses the SC
policy as a natural experiment and looks at labor market outcomes.
2. Context and Policy Background
3. Data
My analysis starts with a replication of the findings of the East et al. (2018) paper. Therefore,
I first merge data on SC activation dates for each county with 2005-2014 American Community
Survey (ACS) Integrated Public Use Microdata Series (IPUMS) (Ruggles et al. (2018)) to
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reproduce their findings. In the second part of my analysis, I look at the housing market
effects of the SC. For information on local housing outcomes, I use housing data (rental prices,
house characteristics, etc.) from the ACS and the Fair Market Rent series (FMR) from the
Department of Housing and Urban Development (HUD) following Saiz (2007). The FMR in
a given area refers to the average rental value of a vacant two-bedroom apartment and the
dataset is available at the county level. I also collect county-level data on the activation on
the SC from Congressional Reports and I supplement this data with county-level demographic
characteristics from various data sources. The final dataset runs from 2005 to 2014.
4. Empirical strategy
The staggered rollout of the SC policy across US counties permits us to use its timing to
estimate the causal effects of the policy. However, a few concerns need to be addressed before
analyzing these impacts. First, the rollout of the policy must be uncorrelated with countylevel characteristics before the enactment of the policy. Second, the validity of these estimates
could also fail if immigrants were able to arbitrage over counties and self-migrate to places that
had not yet introduced the new policy. A stronger requirement will be that the composition
of treated and control counties must be stable across time. As East et al. (2018) mentions,
the implementation of the enforcement policy over US counties was relatively fast. Moreover,
Painter and Yu (2014) show that Immigrants are less mobile than the native-born population.
Plus, since all counties eventually adopted the policy, the concern of immigrants moving between
counties should be minimal. Third and most importantly, housing prices and rents in treated
and control counties must have trended similarly in the absence of the program. Following East
et al. (2018), we estimate the following basic difference in differences equation:
yjt = α + βSCjt + X 0 jt γ + νj + ηt + εjt
(1)
Where yijkt refers to labor market/housing outcomes, SCjt refers to a county-level measure of
exposure to the policy. For instance, if the SC program was implemented in January of year t
in county j, then the exposure variable takes the value 1. Based on the month that the policy
was implemented in a given year, I can provide a continuous measure of exposure to the SC
policy. I also include in the model year fixed effects and county-level fixed effects to control for
year-specific economic shocks and time-invariant unobserved heterogeneity. For more robust
specifications, I may also include county-linear trends and add more controls to account for the
housing crash.
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5. Results
5.1. Effects of the SC on likely undocumented immigrants
5.2. Indirect Effects of SC on Citizens
5.3. Robustness Checks
5.4. Discussion
6. Conclusion
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References
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vanilla suburbs:? will the trend toward racially separate communities continue? Social
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Greulich, E., Quigley, J. M., Raphael, S., Tracy, J., & Jasso, G. (2004). The anatomy of rent
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Taeuber, K. E., & Taeuber, A. F. (1965). Negroes in cities: Residential segregation and neighborhood change.[in series] population research and training center monographs. Chicago:
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7. Figures
8. Tables
9. Appendix
9.1. Data Construction
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