On the Intended and Unintended Consequences of Enhanced U.S. Border and Interior Immigration Enforcement: Evidence From Mexican Deportees

Abstract

Since about 2000, a number of federal and state policies have been implemented in the United States with the intention of stemming the flow of illegal immigration. In this article, we focus on two initiatives: (1) Operation Streamline, as an example of increased border enforcement by the federal government; and (2) state-level omnibus immigration laws, as an illustration of enhanced interior enforcement by state governments. We investigate whether these policies have reduced the intentions of deported Mexican immigrants to attempt a new unauthorized crossing. Although state-level omnibus immigration laws reduce the proportion of deportees intending to attempt a new crossing, increased border enforcement has proven to be far less effective. In addition, we ascertain the human costs associated with these policies. Our findings are mixed in this regard. Noteworthy is how the adoption of more stringent interior enforcement seems to result in a “herding” or “ganging-up” effect, whereby the incidence of verbal and physical abuse rises with the number of states enacting such measures. Additionally, our estimates suggest that deportees are more likely to respond that they have risked their lives to cross into the United States as a result of enhanced border enforcement.

Motivation, Objectives, and Contributions

With the onset of the past Great Recession in the United States, we observed a heighted sense of animosity toward undocumented immigrants. The charged climate was due, perhaps, to the belief that undocumented immigration was “out of control,” adding to the rising competition for scarce jobs. Fiscal and job market pressures led, in turn, to the adoption of various measures intended to reduce the presence of unauthorized immigrants. Broadly speaking, enforcement increased at both border and interior points by federal and state governments. For instance, at the federal level, programs such as Operation Streamline (OS) significantly increased the penalties for being apprehended while crossing the border. Before the implementation of OS, first-time unlawful border crossers with no criminal history typically were simply returned to Mexico. However, OS changed that approach, requiring that all unauthorized crossers be charged with a criminal act and imprisoned (Lydgate 2010). Simultaneously, state governments started to implement policies that dealt with unauthorized migration. This began with the widespread adoption of employment verification (E-Verify) systems, which are free web-based programs that employers can use to verify that job applicants are eligible to work in the United States. E-Verify was soon followed by the enactment of state-level omnibus immigration laws authorizing state and local police to check the immigration status of individuals they had probable cause to arrest.¹ In some instances, these laws went even further; for example, the law in Alabama required that public school officials check the immigration status of students.

At the same time, reports of abuses against immigrants were on the rise (Diaz and Kuhner 2007; Fernández 2011). Violations of migrant’s rights ranged from verbal and physical abuse to failure to return personal belongings or inform migrants of their rights. These practices were documented and denounced by the United Nations, the Organization of American States Special Rapporteurs, the Mexican Human Rights Commission, and numerous NGOs (Organization of American States 2003; United Nations 2002). For instance, the Arizona humanitarian aid organization No More Deaths (2008, 2011) issued two reports—“Crossing the Line” and “A Culture of Cruelty”—documenting more than 30,000 incidents of human rights abuses against undocumented immigrants in short-term detention.

These events make us wonder about the intended and unintended consequences of immigration policies as captured by their effectiveness in curbing deportees’ intents to attempt a new unauthorized crossing as well as the policies’ role, if any, in the reported mistreatment of apprehended migrants. We address these two inquiries by examining the effectiveness of increased border enforcement (through OS implementation) in curbing undocumented migrants’ intent to engage in repetitive unauthorized crossings. We then explore how OS might be affecting the treatment received by deportees during their detention and deportation. Data on approximately 36,000 migrants apprehended while crossing the border from 2005–2012 are used in that analysis. After examining the effect of increased border enforcement, we explore the impact of increased interior immigration enforcement through the enactment of state-level Omnibus Immigration Laws (OIL). How effective are they in curbing undocumented migrants’ intent to engage in repetitive unauthorized crossings? How do they affect the treatment received by deportees? We address these questions using data on approximately 24,000 illegal immigrants who successfully crossed the border but were later detained—usually at home, at work, or on the street in interior points—from 2005 and onward.

At this juncture, it is worthwhile to make a few clarifications. The first clarification concerns our dependent variables: migrants’ intent to cross the border again, in the immediate or more distant future. Such intent may or may not translate into a future crossing. However, we have no reason to suspect that migrants will deliberately misstate their intent to cross. Furthermore, although migrants’ actual future crossing behavior might ultimately deviate from their stated intent, ideally (if effective) the policy should start by affecting migrants’ crossing intentions. As such, learning how increased enforcement affects migrants’ intent to cross is definitely of interest. A second important clarification is that our data refer to deportees. Although it would be ideal to ascertain the effects of these polices on the overall intent to migrate illegally by all Mexicans, no survey data currently allow for such an exercise. Nevertheless, examining deportees’ crossing intentions can shed some light on how effective increased enforcement is in deterring the repetitive unauthorized crossing of those already exposed to the system. After all, deported individuals have already demonstrated a propensity to migrate. Finally, we should emphasize that our aim is to gauge the impact of tougher immigration policies on undocumented migrants’ intents to engage in repetitive unauthorized crossings and on their mistreatment during the detention and deportation process—whether indirectly by altering the type of migrant that chooses to migrate, or directly by affecting the behavior of Border Patrol agents, interior police agents, or apprehended migrants.

Gaining a better understanding of the intended and unintended consequences of increased immigration enforcement is important for various reasons. Current discussion at the national level is pointing to the possibility of comprehensive immigration reform that will include a pathway to citizenship for undocumented immigrants. It has been argued that a path to legalization may increase new unauthorized border crossings (Morgan and Inserra 2014). Some legislators have, therefore, been insisting on stepped-up immigration enforcement as quid pro quo for a bipartisan compromise on comprehensive immigration reform as, for example, the Border Security, Economic Opportunity and Immigration Modernization Act (Senate Bill 744), which passed the U.S. Senate on June 27, 2013 (see https://www.govtracks.us/congress/bills/113/s744). Yet, the effectiveness of increased enforcement in curbing deportees’ recidivism intentions, potential unintended consequences, and the human and social costs of increased immigration enforcement remain unknown. Our analysis addresses these gaps.

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 and the unintended human costs of these measures will become particularly important. And even if comprehensive immigration reform succeeds, important lessons may be gleaned from the state-level experiments that may be extended nationwide. Are these measures achieving their goals? What are their unintended costs? Answers will allow policy-makers to stand on firmer ground if asked to provide cost-benefit analyses of alternative immigration policies by illuminating the intended and unintended consequences of record spending on border and interior immigration enforcement at the federal and state levels.

Background

Immigration enforcement has increased dramatically since the enactment of the 1986 Immigration Reform and Control Act (IRCA). A series of immigration enforcement operations along the U.S.-Mexico border (e.g., Operation Hold the Line in El Paso in 1993, and Operation Gatekeeper in San Diego in 1994) resulted in an increasingly fortified U.S.-Mexican border that shifted migration flows to more isolated and dangerous routes, increasing migrant fatalities (Massey et al. 2002). Yet, most apprehended migrants were released back to Mexico after signing a voluntary departure contract, a policy that became known as “catch and release,” leading to the notion that the border was a “revolving door” (Kossoudji 1992). Voluntary returns were relatively inexpensive for the U.S. government and for migrants, who avoided a long detention process and a formal removal order. A prolific literature examines the impact of these federal policies (e.g., increased border enforcement) on several outcomes, including unauthorized immigration flows, recidivism, and smuggling costs.²

Enforcement strategies changed substantially after 9/11 when the U.S. Department of Homeland Security (DHS) instituted a consequence delivery system (CDS) intended to increase the cost of immigrating illegally to the United States. The new system ended the “catch and release” practices and provided Border Patrol agents with some latitude for choosing among a number of consequences for offenders. Examples of consequence enforcement actions include expedited removals and the Alien Transfer Exit Program (ATEP).³ A consequence enforcement action of particular interest to us is Operation Streamline (OS), introduced in 2005 in the Del Rio sector. OS implemented a zero-tolerance policy subjecting all unauthorized immigrants to criminal prosecution. As shown in Table 8 in the appendix, OS progressively expanded to five more Border Patrol sectors, likely contributing to the 330 % increase in the number of prosecutions for first-time unauthorized entry (Lydgate 2010). In FY2011, a total of 164,639 people were referred to the U.S. Attorney’s office for prosecution (Rosenblum 2012), significantly raising police, jail, criminal justice, and legal costs. Hence, it is not surprising that federal spending on immigration enforcement reached $18 billion in 2012, exceeding spending by all the other major federal law enforcement agencies combined (Meissner et al. 2013).

Spending on immigration enforcement has also increased at the state level with the passage of state-level OILs. Following the example of Arizona SB1070 in 2010, five more states enacted omnibus immigration legislation in 2011: Alabama (HB56), Georgia (HB87), Indiana (SB590), South Carolina (S20), and Utah (H116, H466, H469, and H497). These laws address a variety of topics, including immigration enforcement by local and state police, verification for employment and public benefits, and, in a few cases imposing requirements on schools to verify students’ legal status.

A growing number of researchers have turned to examining the impact of these state-level polices on unauthorized immigration levels and on undocumented immigrants themselves.⁴ For instance, Bohn et al. (2014) explored the effects of the 2007 Legal Arizona Workers Act (LAWA). The enactment of LAWA, which mandated the use of an employment verification system for all employers, reduced the share of Hispanic non-citizens—a group more likely to be unauthorized—residing and working in the state of Arizona.

Amuedo-Dorantes and Lozano (2014) examined the timing and effectiveness of Arizona SB1070 in reducing the share of likely unauthorized immigrants in the state, and Amuedo-Dorantes et al. (2013) assessed how the enactment of E-Verify mandates has affected the migration experience, trajectory, and future plans of unauthorized immigrants. They found no evidence of a statistically significant association between E-Verify mandates and the difficulties reported by unauthorized immigrants in accessing a variety of services, including social or government services, legal assistance, and health care services. However, the enactment of these mandates infuses deportation fear and reduces interstate mobility among voluntary returnees during their last migration spell, helping to curb deportees’ intentions of returning to the United States in the near future.

Consequence enforcement actions and state-level measures are costly in a variety of respects (Immigration Policy Center 2011). They increase police, jail, criminal justice, and legal costs. They impose costs on state agencies, which require additional personnel and time to carry out checks, as well as on schools (possibly losing federal and state funding as enrollments drop), not to mention broader economic costs resulting from loss of business, decreases in consumer spending, and reductions in tax revenues. In addition, other nonmonetary costs have received increased attention. For example, reports of mistreatment range from physical and verbal abuse to failure to return personal belongings as documented by the United Nations, the Organization of American States Special Rapporteurs, the Mexican Human Rights Commission, and numerous NGOs (Organization of American States 2003; United Nations 2002). More importantly, reports of mistreatment have been on the rise (Diaz and Kuhner 2007; Fernández 2011).

Despite the monetary costs of OILs and OS, we still know very little about their effectiveness and their impact, if any, on the mistreatment of detainees. For instance, although Cañas et al. (2012) explored the impact of OS on the aggregate number of apprehensions across the Mexico-U.S. border, we still do not know how and if the policy is reducing undocumented migrants’ intent to reoffend. Furthermore, to date, no studies have looked at one important potential (and presumably unintended) consequence of increased enforcement: the objectionable treatment that some undocumented immigrants may be receiving while apprehended and deported to Mexico.

We shed light on these questions by analyzing one of the intended objectives of the policies, as captured by deterred reentry plans of unauthorized Mexican immigrants. We also analyze unintended consequences—measured by reported mistreatments and life risks—of two immigration enforcement measures: (1) Operation Streamline, which is an example of increased federal spending on immigration enforcement along the border; and (2) Omnibus Immigration Laws, which are an illustration of state-level initiatives on interior immigration enforcement.

Data

We use the Encuesta Sobre Migración en la Frontera Norte de México (EMIF-Norte) (as translated, Survey on Migration to the Northern Mexican Border). The EMIF-Norte is a cross-sectional migration survey, conducted by El Colegio de la Frontera Norte (COLEF) for the Secretaría del Trabajo y Previsión Social and the Consejo Nacional de Población, along the U.S.-Mexico border. The EMIF-Norte surveys contain a module that is highly representative of the overall population of unauthorized Mexican immigrants apprehended in the United States and returned to Mexico. The survey has been conducted annually in Mexico at the centers where deportees from the United States are delivered by the U.S. immigration authorities.⁵

We use data from the 2005–2012 waves, which contain detailed information on the outcomes being examined for approximately 58,000 deportees apprehended at various points in time.⁶ More than one-half (about 34,000 migrants) were apprehended while crossing the border, and the remainder were detained in the interior of the United States. In many respects, deportees apprehended while crossing the border and those apprehended after having successfully crossed into the United States are similar. However, they differ in other regards. For instance, as displayed in Table 1, 26 % of those apprehended in the interior (“successful crossers”) speak English, compared with only 6 % of those apprehended while crossing. This might be partially due to the fact that unsuccessful crossers have had shorter or fewer migration spells compared with their successful counterparts, thus providing fewer opportunities to learn English. Unsuccessful crossers have also relied more often on a coyote to cross relative to migrants who were apprehended in the interior.

Table 1 Descriptive statistics

Of special interest to us is the information on the timing and place of apprehension, both of which are needed to identify whether apprehension took place at a particular Border Patrol sector or in a state with one of the immigration enforcement measures in place. The timing is given by the month and year of the interview, which occurs immediately after deportation. Additionally, the EMIF-Norte contains information on place of apprehension. Approximately 58 % of the sample (33,811 of 57,920) were apprehended while crossing the border. Because deportees also indicated the border city through which they were crossing into the United States, we are able to identify the Border Patrol sector in which they were likely captured and, therefore, whether it was a Border Patrol sector participating in OS at that time.⁷ About 58 % of detained border-crossers were captured in a sector with OS in place.

Individuals apprehended at interior points in the United States, after having successfully crossed the border (42 % of our sample of deportees), indicate whether they were apprehended at work, at home, on the street, or elsewhere. Sixty-three percent were detained on the street; 9 %, at home; 10 %, at work; and the remaining 18 %, elsewhere. In those instances, we use information on the U.S. state where they reported residing to identify whether the state had an OIL in place at the time of apprehension.⁸ About 9 % of deportees captured after having successfully crossed the border were apprehended in a state with an OIL in place.

In addition to the time and place of apprehension, we are interested in information regarding two outcomes: (1) deportees intentions to return to the United States;⁹ and (2) the treatment received during the apprehension and deportation process. In their responses, they can indicate whether they intend to try a new crossing within the next seven days or at a later date. These responses are used to construct dummy variables identifying the intention to cross illegally ever again, as well as more specifically within the next week. When examining the aggregated sample of individuals apprehended while crossing the border (first columns of Table 1), an overwhelming 88 % of deportees declare intending to cross again. Furthermore, 74 % intend to do so within the next seven days.

When we disaggregate by year, we observe that responses to these questions seem to have been changing over the period being examined. However, given the relatively short time span analyzed, we caution against making much of any patterns. Still, an examination of these figures might provide some insights not evident in the overall averages displayed in Table 1. For instance, it is interesting to see in Table 2 how despite yearly fluctuations, the percentages of deportees apprehended at the border intending to commit recidivism within the next seven days or ever appear to have fallen from 2005 to 2012.¹⁰ Table 3 shows a similar, perhaps more consistent picture for deportees apprehended at interior points. For instance, the share of deportees apprehended in states without OILs in place indicating an intent to return within a week decreased from 85 % in 2005 to 21 % in 2012, whereas the share expressing an intent to return ever dropped from 96 % in 2005 to 76 % in 2012.¹¹ Still, it is important to stress that these are simply descriptive statistics. They do not account for other factors that may be driving the intent to cross again; thus, any observed patterns cannot be attributed to specific enforcement policies.

Table 2 Share of deported immigrants apprehended while crossing reporting any of the following conditions

Table 3 Share of deported immigrants apprehended in the interior reporting any of the following

In addition to inquiring about future migration plans, the EMIF-Norte has been questioning deportees since 2005 about the treatment they received during the apprehension and detention process. Specifically, respondents are asked whether they were subject to physical aggression by the authorities, such as being pushed or hit; whether they were subject to verbal aggression (yelled at or insulted); and whether their belongings were confiscated. They are also asked whether they were informed of their right to speak with the Mexican Consulate. As can be seen in Tables 2 and 3, in some instances, the share of immigrants who reported being mistreated does not seem to have changed appreciably in a consistent direction over the years, as in the case of the share of deportees that reported being physically abused during the apprehension process. In contrast, the share of deportees indicating having personal property confiscated during their detention process and, for the most part, the share enduring separation from family members have risen appreciably over time. Last, although there appears to be some evidence of widespread failure to inform migrants of their right to contact their consulate, this violation seems to have been addressed, possibly through training. The share of deportees who reported being victims of such a practice has dropped from 86 % to 88 % in 2005 to 25 % to 41 % in 2012.¹² Still, the summary statistics in Tables 2 and 3 do not allow us to conclude anything about how these policies and laws have influenced the intent to cross again and how they have affected the treatment of deportees. To address these questions, we turn to more thorough regression-based modeling that controls for other concurrent determinants of deportees’ migratory intentions and treatment received during the apprehension process.

Methodology

Our first objective is to learn whether increased border enforcement—as captured by the implementation of OS—is deterring deported migrants from intending to return to the United States and how it may be affecting their treatment during the deportation process. To accomplish this aim, we pool individual-level data on deportees apprehended while crossing the border from the 2005–2012 waves of the EMIF-Norte. The data contain information that allow for the identification of the Border Patrol sector through which migrants crossed, allowing us to ascertain whether OS was in place. Specifically, we compare deportees’ reported intent to return to the United States, as well as changes in the circumstances surrounding the apprehension process, in treated versus control Border Patrol sectors pre- versus post-implementation of OS as follows:

$$ \begin{array}{c}\hfill {Y}_{ist}={\upalpha}_0+{\upalpha}_1O{S}_{st}+{\upalpha}_2\#Se{c}_t+{\upalpha}_3\left(\#Se{c}_t\kern0.5em \times \kern0.5em O{S}_{st}\right)+{\gamma}^{\prime }{\mathbf{X}}_{ist}+{\delta}_s+{\upvarphi}_t+{\delta}_st+{\varepsilon}_{ist},\hfill \\ {}\hfill {\varepsilon}_{ist}\sim N\left(0,1\right),\hfill \end{array} $$

(1)

where i =1, . . ., n individuals, s =sector, and t = (month, year). Y _ist equals 1 if the ith deported migrant, who last migrated in time t and was apprehended in Border Patrol sector s, indicates (1) planning on returning to the United States,¹³ or (2) experienced questionable treatment during the apprehension and detention process.¹⁴ OS _st is a dummy variable equal to 1 if the migrant was apprehended in a Border Patrol sector with OS in place. For example, OS _st equals 1 if the migrant was apprehended in a treated Border Patrol sector at that time, such as Tucson, Arizona, after January 2008. It is equal to 0 if the migrant was apprehended in the Tucson Border Patrol sector before January 2008, or if s/he was apprehended in a Border Patrol sector that never implemented OS, such as San Diego, California. In essence, the coefficient on OS _st gauges how OS might have affected deportees’ intent to return to the United States and their treatment during the apprehension process by comparing those two outcomes for deportees crossing through a Border Patrol sector with OS in place and for their remaining counterparts.

To account for the possibility that the impact of OS strengthens (or weakens) as more sectors adopt it, we control for the number of Border Patrol sectors implementing OS (#Sec _t ) at the time of apprehension and interact it with the policy dummy variable (i.e., #Sec _i ×O _st ). It is conceivable, for example, that as the number of Border Patrol sectors implementing OS rises, the deterrence effect strengthens because of a tighter “squeeze” on undocumented crossings. Similarly, as the number of sectors under OS increases, it is possible that Border Patrol agents’ behavior concerning the treatment of immigrants changes. The interaction term can help us capture these spillover effects.

The vector X includes individual-level characteristics that are traditionally incorporated when modeling migration experiences and decisions: gender, race, age, indigenous language, English proficiency, educational attainment, marital status, household head status, and family size. Migration-related characteristics—such as the number of times they have crossed in the past, the number of times they have been deported, the duration of their last migration spell, or whether they last crossed with a coyote—are also incorporated.

We include a series of Border Patrol sector dummy variables, time dummy variables, and Border Patrol sector-time trends in Eq. (1) in order to capture fixed and time-varying regional and macroeconomic factors affecting our outcomes. Border Patrol sector dummy variables can help capture time-invariant geographic characteristics, such as a political environment hostile to immigration, which is a characteristic that could potentially be related to the likelihood of reporting mistreatment. Border Patrol sector dummy variables can also capture the presence of networks in specific crossing points—a trait that can lower migration costs and increase the intent to return to the United States. Time dummy variables are also incorporated in the model because they help account for economy-wide shocks that could affect the likelihood of mistreatment by changing investments in police training or the probability of indicating a desire to return to the United States in the future given the economic climate.

The inclusion of Border Patrol sector–specific time trends addresses a key identification assumption in the analysis described earlier: namely, the existence of similar pretreatment trends in treated and control Border Patrol sectors. This assumption is violated if differences between the latter are driving the enactment of the policy, as would be the case if Border Patrol sectors with a growing number of unauthorized entries were more likely to implement OS. Because of that possibility, it is important to include Border Patrol sector–specific time trends capturing the overtime variation in apprehensions as a proxy for unauthorized entries. Alternatively, Border Patrol sector–specific time trends might capture changing economic conditions at the Border Patrol sector level, such as the development of migrant businesses, which is a phenomenon that might lure deportees to the United States.

Although our first objective is to explore the effectiveness and impacts of border policy, our second objective is to learn about (1) the effectiveness of state-level initiatives in immigration enforcement (as captured by the enactment of OILs) in curbing undocumented migrants’ intents to engage in repetitive unauthorized crossings, and (2) their impact on the treatment received by deportees when apprehended and detained. To that end, we use individual-level data from 2005 through 2012 on deportees apprehended at work, at home, on the street, or elsewhere after having successfully crossed the border. The data contain information on the timing of the apprehension and the state where the migrant resided, allowing us to identify whether a state-level omnibus immigration law was in place. Our benchmark model is thus given by

$$ {Y}_{ist}={\upbeta}_0+{\upbeta}_1OI{L}_{st}+{\upbeta}_2\# State{s}_t+{\upbeta}_3\left(\# State{s}_t\times OI{L}_{st}\right)+{\gamma}^{\prime }{\mathbf{X}}_{ist}+{\delta}_s+{\varphi}_t+{\delta}_st+{\upsilon}_{ist},{\upsilon}_{ist}\sim N\left(0,1\right), $$

(2)

where i =1, . . ., n individuals, s =state, and t = (month, year). The dependent variable, constructed as a dummy variable, captures the migrant’s self-reported intent to return to the United States in the foreseeable future, or whether s/he reported mistreatment during the apprehension and detention process. OIL _st equals 1 if the migrant was apprehended in a state with an omnibus immigration law at that point in time, such as SB1070 in Arizona, after April 2010; otherwise, it is set equal to 0. As in the case of OS, we account for spillover effects that could result from the increase in the number of states with OILs by incorporating the variable #States _t and the interaction term (#States _i ×OIL _st ). Similarly, in addition to the demographic and migratory characteristics in vector X in Eq. (1), we include state and time dummy variables as well as state-specific time trends controlling for different trends in state-level characteristics potentially responsible for the adoption of OILs, such as differences in undocumented migration across states. Finally, we include information on whether the deportees, apprehended in the interior, were detained at work, at home, or on the street.

Equations (1) and (2) are both estimated as linear probability models (LPMs). LPMs have some potential limitations: (1) their linearity assumption, just as with ordinary least squares (OLS); (2) their violation of the homoskedasticity assumption, which can bias standard errors; and (3) the fact that they can yield predicted probabilities that fall outside the unit circle. Despite these limitations, LPMs perform quite well. They impose fewer restrictions on the distribution of the error term by avoiding the normality assumption and, of particular importance for our estimation, they easily converge when working with larger samples (Wooldridge 2008).

Findings

In what follows, we first discuss the estimated deterrence of OS on deportees’ intent to return, along with the impact of exposure to increased border enforcement on deportees’ mistreatment (physical abuse, verbal abuse, confiscation of personal belongings, separation from family members during the deportation process, and being uninformed about the right to contact the Mexican Consulate). We then present results on the effects of interior enforcement. All regressions include time (year, month) dummy variables as well as Border Patrol sector or state of apprehension dummy variables, depending on whether we are examining the effect of OS or OIL, respectively. Time trends specific to Border Patrol sector or state of apprehension are also included. The latter are an important check to our model specification, allowing us to control for preexisting differential trends in treated versus control Border Patrol sectors and states. In what follows, we interpret our findings in terms of marginal effects, thus assuming that all other determinants are held constant. For example, does apprehension in a Border Patrol sector with OS in place deter deportees from intending to attempt a new illegal crossing (i.e., ∂re − migrate/∂OS) as presumably intended by the policy?

Intended and Unintended Consequences of Operation Streamline

The estimates in Table 4 address the aforementioned question. Deportees’ intent to reoffend does not significantly differ according to whether they were detained while crossing through an OS sector.¹⁵ Similarly, expanding the policy to one more Border Patrol sector has no statistically significant impact on deportees’ intent to remigrate.¹⁶ In sum, OS does not seem to significantly shape deportees’ intents to return to the United States (i.e., commit recidivism) in the short run or long run.

Table 4 Linear probability model estimates of the intent of recidivism of deportees apprehended while crossing

In addition, apprehension in a Border Patrol sector with OS in place does not significantly increase deportees’ likelihood of enduring physical or verbal abuse, having personal property confiscated, being separated from family members, or being misinformed about rights to contact the Mexican Consulate (see Table 5). However, we do find that OS substantially affects life risks. In 2012, being apprehended in a sector with OS in place (∂life risks/∂OS) increased deportees’ life risks by a statistically significant 12.2 percentage points relative to those apprehended in non-OS sectors. Furthermore, we find that expanding OS to one more Border Patrol sector (∂life risks/∂ # Sec) increases life risks by a statistically significant 2.4 percentage points among deportees apprehended in sectors with OS in place. These findings are consistent with the idea that crossings are taking place in more remote and dangerous areas because of the “squeeze” by OS and, overall, corroborate news reports about the human cost of increased border enforcement.¹⁷ Last, the expansion of OS to one more sector along the border is also linked to a statistically significant increase of 9.1 percentage points in the likelihood of enduring family separation (∂family separation/∂ # Sec) for crossers using non-OS sectors and to a statistically significant increase of 8.8 percentage points in that likelihood among deportees apprehended in OS sectors. However, the expansion of OS to more Border Patrol sectors is associated with a lower likelihood of being subject to verbal abuse or being misinformed about one’s rights.

Table 5 Linear probability model estimates of self-reported mistreatment by deportees apprehended while crossing

In addition to our prior findings, the output in Table 5 uncovers several other interesting results. Specifically, deportees with higher levels of education appear more likely to indicate being physically or verbally abused by the border agents than their less-educated counterparts. Additionally, deportees who speak English seem to endure a higher likelihood of being separated from family relative to deportees who do not speak the language, possibly signaling that these were migrants with more established roots. It is sobering that migrants who used a coyote are 7.1 percentage points more likely to have risked their lives to cross than migrants who did not use such services. Coyotes might be using less-transited and more dangerous routes to avoid detection, and they may not hesitate to abandon their clientele when fearing apprehension by the Border Patrol.

Intended and Unintended Consequences of State-Level Omnibus Immigration Bills

Tables 5 and 6 also inform on the intended and unintended consequences of increased immigration enforcement at the state level, as captured by the enactment of state-level OILs. One difference with regard to the previous model specifications is that these equations include information on the circumstances surrounding apprehension. In the OS estimations, all apprehensions took place at the border, making it unnecessary to account for place of apprehension. For the OIL estimations, however, apprehension could have taken place in a variety of places: at home, at work, on the street, or elsewhere (the reference category). Accordingly, we control for the place of apprehension because it may reveal valuable information regarding the circumstances surrounding that event.

Table 6 Linear probability model estimates of the intent of recidivism of deportees apprehended in the interior

We saw that being detained in a Border Patrol sector with OS in place had no statistically significant effect on deportees’ intent to commit recidivism. Are OILs similarly ineffective? Or do OILs deter apprehended undocumented immigrants from planning repetitive unauthorized crossings? According to the results in Table 6, they do have a deterrence effect. In 2012, apprehension in a state with an OIL in place reduced deportees’ intents to ever return by about 7.6 percentage points¹⁸ (from 86 % to approximately 78 %), compared with apprehension in non-OIL states. The state-level measures are, in particular, effective at curbing deportees’ immediate reentry intentions. In 2012, apprehension in an OIL state reduced the intent to return next week by a statistically significant 24 percentage points relative to apprehension in states without OILs.

How does apprehension in a state with an OIL in place affect the incidence of mistreatment with respect to deportees? According to the estimates in Table 7, in 2012 (when six states had OILs in place), physical abuse was 0.2 percentage points higher for deportees apprehended in states with an OIL (∂physical abuse/∂OIL = –0.046 +0.008 ×6 =0.002) relative to their counterparts detained in states without such a law. The statistically significant interaction term reveals that as more states adopt immigrant legislation, migrants apprehended in states with OILs endure more physical abuse relative to when fewer states had adopted OILs—a pattern suggestive of herding behavior on the part of the authorities.

Table 7 Linear probability model estimates of self-reported mistreatment by deportees apprehended in the interior

In contrast, in that same year of 2012, verbal abuse was 8 percentage points less likely among deportees apprehended in a state with an OIL in place than among other deportees. However, the progressive expansion of OILs to other states has the opposite effect, increasing the likelihood of experiencing verbal abuse with each additional state adopting an OIL by 1.6 percentage points among migrants apprehended in states with an OIL in place, and by 1.1 percentage points among their counterparts apprehended elsewhere.¹⁹ Perhaps the growing number of states adopting OILs is a reflection of a growing anti-immigrant sentiment that generally manifests itself in a higher incidence of mistreatment. Finally, with six states having OILs in place, apprehension in a state with an OIL in place is also linked to a 2.1 percentage point higher likelihood of experiencing family separation in 2012.

Although OILs seem to increase the incidence of some forms of mistreatment, detention in a state with an OIL is associated with a lower incidence of other types of mistreatment. For instance, with six OILs in place by 2012, apprehension in an OIL state seemed to lower the incidence of being misinformed about the right to contact the Mexican Consulate by 27 percentage points. Similarly, the expansion of OILs to other states is associated with a reduced incidence of this type of violation, especially among those apprehended in a state with an OIL in place. Perhaps as OILs become more prevalent and receive growing public attention, certain groups publicize migrants’ rights to counsel. Alternatively, the Mexican government might be responding to public outcries about mishandling of detainees, increasing migrants’ awareness of their rights through information campaigns in border cities and through the Mexican government’s direct involvement in Mexican deportation cases.

In addition to the effects of OIL enactments, our results provide other insights into the determinants of deportees return intentions and the treatment that they receive. Turning first to return intentions (Table 6), we find that English-speaking migrants have higher intentions to cross again than migrants who do not speak the language. English speakers are likely more assimilated and, therefore, might have more to gain from crossing over because they have U.S.-specific human capital. Those who crossed with a coyote are 4 percentage points less likely to expect to return within a week and 2 percentage points less likely to ever attempt another crossing than those who did not use such services. Given the steepness of coyote fees, cost considerations may explain these findings, particularly for intentions of an immediate return. Finally, it is interesting that migrants with primary and tertiary education are less likely to report planning on ever crossing again relative to those with a secondary education—a finding consistent with the intermediate selection of immigrants from Mexico.²⁰

Also worth noting is the variation in the incidence of mistreatment according to where apprehensions take place (see Table 7). For instance, with the exception of being uninformed about the right to counsel, all other migrant rights violations are more likely to take place when apprehensions occurred at work (relative to “elsewhere,” the reference category). Similarly, various types of mistreatment—including physical abuse, separation from family members, and misinformation about the right to contact the Mexican Consulate—appear more prevalent during home apprehensions than during apprehensions “elsewhere.” Apprehensions at work and at home are less public, allowing for the escalation of a confrontation or for negligence during the detention procedure. In contrast, relative to those in the reference category, deportees apprehended on the street, albeit more likely to be uninformed about the right to counsel, do not experience a higher exposure to other types of mistreatment.

Summary and Conclusions

In this study, we examine some of the intended and unintended consequences of increased border and interior immigration enforcement as captured by Operation Streamline and by Omnibus Immigration Laws. We first assess the effectiveness of federal- and state-level initiatives in curtailing deportees’ intent to return to the United States in the immediate and more distant future—an aim or intended consequence of these policies. Subsequently, we explore some unintended consequences of these policies: namely, how they have affected the treatment received by deportees during their detention. In the case of OS, we also look at how the policy may have induced migrants to risk their lives in order to cross over into the United States.

We find that apprehension in a Border Patrol sector with OS in place has no statistically significant impact on deportees’ intents to return to the United States. In contrast, apprehension in a state with an OIL in place does lower migrants’ immediate (within a week of deportation) return intentions by 7.6 percentage points, while stemming the intent to attempt a new crossing in the more distant future by 24 percentage points.

The scorecard of the federal and state policies with regard to migrants’ rights violations and well-being is mixed. Apprehension in a Border Patrol sector with OS in place does not seem to increase the likelihood of mistreatment along many dimensions. In fact, evidence points to declines in deportees’ likelihood of being verbally abused and in their likelihood of being misinformed about the right to contact the Mexican Consulate as OS is progressively adopted by other Border Patrol sectors. However, the likelihood of risking one’s life when crossing is significantly higher among deportees apprehended in an OS sector than among their counterparts detained in non-OS sectors. Furthermore, the progressive expansion of OS also seems to increase the likelihood of enduring life risks among those apprehended in sectors with OS in place, as well as the incidence of family separations among all deportees. These concerns have been reported in the press (Cave 2011; Santos and Resekah 2013), and our results seem to corroborate them.

Significant costs are associated with OILs as well. As in the case of OS, by 2012, apprehension in a state with an OIL in place was linked to a greater likelihood of being separated from family. Additionally, the incidence of physical and verbal abuse rises as the number of states implementing OILs increases. Perhaps, the growing number of state-level OILs is an expression of an evolving anti-immigrant sentiment propelled by a “herding” or “ganging-up” effect. This is an area worthy of further research. However, it is also interesting to note some positives stemming from the implementation of state-level OILs with regard to migrant mistreatment. For instance, we observe a reduced incidence of misinformation about the right to contact the Mexican Consulate among those apprehended in a state with an OIL. Maybe increased awareness by law enforcement personnel of the importance of informing detainees of their right to counsel has contributed to this outcome.

As noted, these findings are specific to the data available, which provides migrants’ intentions to migrate as opposed to their actual behavior. Furthermore, we do not have information on all Mexicans but, rather, only on deportees who, because of their past behavior, have shown a predisposition for migrating to the United States illegally. Finally, we emphasize that our focus is on the overall impact of the policies, regardless of whether these impacts originate from their effects on the behavior of Border Patrol officers, interior enforcement personnel, deportees, or the type of prospective migrant.

Still, the results reveal that although the policies in some instances are somewhat effective at reducing the intention to commit recidivism by deportees, recent immigration enforcement policies at the federal and state level have substantially increased some human costs endured by migrants. Examples of such costs include a significant increase in the proportion of deportees who claim risking their lives to cross as well as an increased likelihood of being separated from family members during the deportation process. A full accounting of these human costs, alongside the limited effectiveness of these policies, should be taken into consideration when evaluating alternative programs. Doing so is imperative, especially considering the rise in mistreatment that we observe with the prevalence of the policies, raising important questions about their ultimate impacts if implemented throughout the country.

Appendix

Table 8 Operation streamline and omnibus immigration laws dates