Underlying push and pull factors in undocumented immigration in the United States

ABSTRACT

There is continued empirical and theoretical debate about the push and pull factors of immigration and the effects that legislation has. This study contributes towards this debate by identifying the drivers within the United States between 2005 and 2010. This study also puts forth a new more refined definition of restrictive immigration legislation and examines its impact on immigration. Hypotheses are developed to test for the effects of different policies, which are tested using a cross-sectional time series model with fixed effects and Driscoll-Kraay. The results show that undocumented populations were not broadly deterred by state immigration policies, but an E-Verify scheme, which limits access to employment and is punitive against employers was successful in deterring immigration. These findings have important consequences because to date, state immigration policies seem to function as symbolic overtures with very little or no actual policy output.

KEYWORDS:

• Undocumented immigration
• hostile immigration policies
• symbolic policies
• E-Verify English Only

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 In Chae Chan Ping v. United States and Fong Yue Ting v. United States the Supreme Court ruled that Congress had plenary power (i.e. complete power) over the issue of immigration, including the right to exclude and/or expel aliens (Chae Chan Ping v. United States 130 U.S. 581; Fong Yue Ting v. United States 149 U.S. 698). More recently, in 2017, U.S. President Donald Trump signed Executive Order 13769 banning foreign nationals from seven predominately Muslim countries from entering the U.S.—in 2018, the U.S. Supreme Court upheld the ban.

2 The terms English-Only and English as the official language are used interchangeably in this analysis. This interchangeability allows for one main definition: laws that formally affirm that English is the official language of a state and often requires the dissemination of government documents in English (Preuhs 2005).

3 Several states with EOL laws enacted between 2000 and 2010 required all official government documents be printed and distributed in English. See Wyoming’s State Code §8-6-101, the Iowa English Language Reaffirmation Act of 2001.

4 As of 2019, the most recent version of the E-Verify process compares information on Form I-9 to U.S. Department of Homeland Security (DHS) and SSA (Department of Homeland Security and U.S. Citizenship and Immigration Services 2019).

5 In spite of a state being labeled as a sanctuary state by immigrant support groups, immigrant communities and undocumented immigrants, sanctuary cities and states were not officially recognized by the U.S. Department of Homeland Security, U.S. Immigration and Customs Enforcement or U.S. Citizenship and Immigration Services between 2005 and 2010. On the condition of anonymity officials from all three Departments acknowledge that the external classifications do exist for cities and states that are considered to be sanctuaries, but at the time, the federal government did not retain such evidence to support the classifications. The rational as communicated by DHS was that the federal government has jurisdiction in any state or city. Outside the purview of this study is a discussion on how undocumented immigrants communicate with each other on places of sanctuary. As future research on immigration continues to evolve this is a area ripe for examination.

6 Under LAW, undocumented immigrants who had lived in the United States since January 1982 could become temporary legal residents if they met certain criteria. After 18 months of temporary legal status, undocumented immigrants could then apply for legal permanent residence on the premise that all necessary criteria had been met. SAW was specifically implemented for undocumented immigrants who worked in the United States agriculture sector for at least 90 days in the previous three years. Individuals who could verify such employment were considered for temporary resident status. After a one to two year wait, applicants could then apply for legal permanent status.

7 Driscoll Kraay standard errors offer a correction for spatial correlation minimizing the need to include yearly dummy variables in the model. However, a model including yearly dummy variables was estimated with no significant difference from the results reported in this analysis.

8 Although Monogan also parses out welcoming legislation in his analysis, at times the legislation overlapped with policies used to categorize sanctuary locations.

9 The sanctuary index created for this analysis is comprised of two components: report card grades on members of Congress based on how they voted on immigration legislation and the number of welcoming immigrant laws adopted by state. NumbersUSA Education and Research Foundation, a pro-legal immigration centre that monitors how members of Congress vote on immigration legislation (i.e. votes are pro or con within six legislation categories), and then assigns a letter grade ranging from A+ to F-. Current and former members of Congress from all 50 states were assigned a report card grade based on how they voted on immigration legislation. An overall grade for each senator and congressional representative was calculated by averaging six categories. NumbersUSA scores 10 items within six categories totalling 12 possible points: reduce chain migration, reduce visa lottery, reduce unnecessary worker vises, reduce refugee and asylum fraud, reduce amnesty enticements, reduce anchor-baby citizenship, reduce illegal immigration rewards, reduce immigration at borders, reduce illegal jobs and presence and challenge status quo. The second component to the sanctuary index is the number of welcoming laws adopted by a state. Data derives from Monogan’s (2013). Report card grades were subtracted from welcoming laws yielding an index of positive and negative scores ranging from 1 to -1. Positive index scores were considered sanctuary states.

10 The following Congressional reports and testimony have all used aggregate data to make inferences about immigrant group behavior. 108th Congress, 1^st Session, 149 Congressional Record H2463-The Costs of Immigration, Illegal and Legal; 108th Congress, 1st Session, 149 Congressional Record H4868-Immigration Policy and Immigration Reform; 113^th Congress, 1st Session, Hearing Before the Committee on the Judiciary House of Representatives, Erica’s Immigration System: Opportunities for Legal Immigration and Enforcement of Laws Against Illegal Immigration; House of Representative; 115^th Congress, Report 115–419 “Biometric Identification Transnational Migration Alert Program Authorization Act of 2018”; 116^th Congress, 1st Session, 165 Congressional Record H5915-Immigration Crisis Requires Immigration Reform;

Congressional Testimony before the U.S. Senate 2019, by Randy Howe Executive Director for Operations, Office of Field Operations, U.S. Customs and Border Protection and Rodolfo Karisch Chief Patrol Agent Rio Grande Valley Sector, U.S. Border Patrol, U.S. Customs and Border Protection, Unprecedented Migration at the U.S. Southern Border: Perspectives from the Frontline; House of Representative, 116th Congress, Report 116–197 “Homeownership for Dreamers”; Tanis J. Salant, John R. Weeks, Efrat Feferman, Jenna Berman and David Eisnberg “Undocumented Immigrants in U.S.-Mexico Border Counties: the Costs of Law Enforcement and Criminal Justice Services”; Congressional Budget Office “How Changes in Immigration Policy Might Affect the Federal Budget.” In addition, aggregate data has been used in legal court cases to estimate racial/ethnic voting blocs.

The Mexican Migration Project does conduct binational research on documented and undocumented Mexican migration to the U.S. The Ethnosurvey used by MMP only captures an individual’s first and last trip to the U.S. Although MMP’s data is extremely beneficial to multiple academic spaces, it is isolated to Mexican migration and does not include information of other racial and ethnic groups that migrate to the U.S.

11 Although the results indicate that E-Verify was effective in deterring undocumented immigration to a state we should also consider a “catch-22” scenario. In this analysis, states that enacted E-Verify deterred undocumented immigrants. However, historically states with strong agricultural economies have experienced large numbers of undocumented immigrant populations. Complicating the issue even further are states such as Alabama which enacted HB-56 in 2011—considered one of the most stringent state immigration policies enacted. HB-56 was considered a success in that it deterred existing and new undocumented immigration away from the state. HB-56 also negatively impacted the Alabama agricultural sector. Farmers were short on labor and lost thousands of dollars due to unharvested crops, and others were forced to reduce their planting. In the case of Alabama, elected officials with strong ideologically stances were willing to negatively impact their economies to deter undocumented immigration raising the issue of E-Very as a “catch-22.”

Additional information

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Sylvia Gonzalez-Gorman

Dr. Gonzalez-Gorman is an Assistant Professor of Political Science at the University of Texas Rio Grande Valley. Her research interests are in U.S. immigration, Latino politics, and transborder environmental sustainability issues. Dr. Gonzalez-Gorman has published on the indefinite detention of unaccompanied children and environmental sustainability issues in the transborder region. Her latest book Political Speech as a Weapon examines changing demographics and how political rhetoric is used to create an atmosphere of “us versus them.”