House of Green Cards: New research from MIT Sloan and Brown University shows a pattern of government bias in the employment authorization of immigrants

CAMBRIDGE, Mass., Nov. 20, 2014 /PRNewswire-USNewswire/ -- At a time when U.S. immigration policy is a touchstone for political debate, a new study by researchers at the MIT Sloan School of Management and Brown University shows that the process by which the government authorizes immigrant labor certification requests—one key stage for the granting of most employment-based green cards—is biased.

"Every year, tens of thousands of foreign immigrants seek jobs in this country and government agents—who are tasked with evaluating the qualifications of those prospective workers—play a major role in the hiring process," says Ben A. Rissing, a professor at Brown, and a co-author of the study. "Our research shows that despite laws that have been on the books for decades forbidding discrimination on the basis of nationality, government agents' labor certification approvals depend a great deal on the job candidate's citizenship."

The study, which examines data from immigrant work applications evaluated by agents at the U.S. Department of Labor, finds that Asian applicants tend to have high approval rates, while the approval rates of Latin American applicants are low. These findings remain consistent even after controlling for variables such as salary, job title, work location, industry, and prior visa credentials.

However, when applications are audited—a quasi-random occurrence whereby applications are assessed by a separate team of government agents who have access to more detailed and relevant information—the biases all but disappear, according to the study, which is published in the American Sociological Review.

The discrepancy in approval rates may be due to either conscious or unconscious preferences of government agents and/or statistical discrimination, which is the tendency to draw inferences about a potential worker based on the characteristics or performance of an average worker belonging to the same demographic group. Past studies and public opinion polls indicate that, for instance, Americans tend to perceive Mexican and Latino immigrants as having low competence; Asians, on the other hand, are viewed as professionally successful and well educated.

"The fact that the employment application process becomes fair and equitable only when agents have access to detailed employment-relevant information has huge implications for the way companies should approach their employment practices to attract, recruit, and hire top foreign talent," says Emilio J. Castilla, professor of management at the MIT Sloan School of Management and co-author of the study.

"Human resource and other managers in charge of hiring are not necessarily free of these demographic biases, which may also be operating, for example, when screening and selecting engineers and other highly-skilled information technology workers to work in U.S. companies."

Several possible remedies exist, according to the authors. The government could, for example, avoid biases by auditing all applications—a time-consuming, not to mention costly, endeavor. The government could also streamline its employment application process by using a point system, similar to Canada's, which determines eligibility. Points would be allocated based on an applicant's attributes such job skills, education, and language proficiency.

"A final option, and perhaps the simplest, would be to mask demographic information and other information such as first and last names on all applications so that biases are not introduced in the first place," says Rissing. "After all, the goal is to devise a hiring process driven by the qualifications of the individual—no matter which country he or she is from."

* House of Green Cards: Statistical or Preference-Based Inequality in the Employment of Foreign Nationals by Ben A. Rissing and Emilio J. Castilla, (American Sociological Review: forthcoming)

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SOURCE MIT Sloan School of Management

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