New Interactive Computer Model Graphs How Changes to Immigration Policy Would Impact the U.S.

As we delve deeper into election season, the subject of immigration reform is poised to remain a hot button issue. Candidates are asked to lay out their respective positions on questions ranging from ‘What should we do with the 11 million undocumented foreign nationals currently living in the United States?’ to ‘Should we increase or reduce the number of skilled workers U.S. companies are able to bring to this country each year?’ Meanwhile, voters are faced with uncertainty over the true effect any proposed immigration plans may have if actually put into action. Fortunately, a new interactive budget model has been unveiled that seeks to provide nonpartisan, data-driven projections about the impact changes to our immigration system would have on this nation.

The Penn Wharton Budget Model (PWBM) is a tool developed by the Ivy League Wharton Business School at the University of Pennsylvania. While the tool was designed to provide legislators with insight into how a variety of policy outcomes may impact the economy, it does not advocate for any specific policy, nor does it make policy recommendations to lawmakers. And, even though it is primarily intended for policymakers in government, Penn Wharton Budget Model Managing Director of Legislation and Special Projects, Kimberly Burham, noted in a recent interview with The Daily Pennsylvanian that anyone can access and use the model for free on its website. “You don’t have to pay for it, you don’t get access to just one part of it. It’s really trying to be transparent and open school.” [See Wharton Debuts Predictive, Interactive Budget Model, by Jenna Want, The Daily Pennsylvanian, 6.Jul.2016.]

PWBM currently has three modules available that allow users to input data and derive projections – demographics, social security, and immigration. The immigration module allows for up to 125 different simulations showing the impact of immigration on areas such as population, employment, and the overall economy. As detailed in The Wall Street Journal, the model predicts that under our current immigration policy, employment will grow by 10 percent over the next two decades. If we were to adopt a policy providing a path to citizenship for 10 percent of immigrants living without lawful status in the United States, overall employment would likely experience a very slight decline over the next two decades. This can be attributed to the fact that while illegal immigrants have a very high labor force participation rate, once granted legal status, they would be able to afford to be more selective about their employment opportunities. The model also predicts “the largest positive impact on employment and GDP comes from increasing the net flow of immigrants.”

In contrast to the relative stability afforded the job market by pro-immigration policies, the PWBM predicts dire consequences in the event that we begin mass deportations of illegal immigrants. An increase in deportations of just 10 percent would result in an employment decrease of over six million people by the year 2050. The gross domestic product is also predicted to decline dramatically under this scenario, with a decrease of almost 2 trillion dollars by 2050. [See The Ultimate Budget Do-It-Yourself: Simulate Immigration, Social Security Policies, by Nick Timiraos, The Wall Street Journal, 18.Jul.2016.]

The main takeaways from the PWBM immigration module? Rounding up undocumented immigrants and kicking them out of the country would not create jobs for U.S. workers and would have an overall negative impact on our economy. Boosting the number of legal immigrants, on the other hand, would serve to energize our economy and spur job growth. Hopefully, our legislators, as well as the presidential candidates currently vying for our votes, will rely on the impartial, data driven analysis offered by models such as the PWBM when developing immigration policy, instead of lingering fear and misinformation that inaccurately portray immigrants as a threat.

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