CSII Interactive Map: Number Eligible for DREAM Act of 2017 and Economic Gains by U.S. Congressional District

Updated on December 12, 2017

CSII Interactive Map: Number Eligible for DREAM Act of 2017 and Economic Gains by U.S. Congressional District

With Deferred Action for Childhood Arrivals (DACA) coming to a close on March 5th, 2018, pressure has been placed on Congress to legislate a pathway to lawful status for undocumented individuals who arrived in the United States when they were young. This population, known as the “DREAMers” since the first DREAM act was introduced in 2001, generally includes those who entered the U.S. unlawfully when they were minors, have been in the U.S. for several years, are either enrolled in school or have completed a high school diploma in the U.S., and have “good moral character” (i.e. no serious criminal history).

Several bills were introduced by Congress in 2017 to address the DREAMers (see this Migration Policy Institute fact sheet for a concise summary of them along with estimates of the number of people eligible). The interactive map below provides the latest CSII estimates of the number of people currently eligible for one of these bills – the DREAM Act of 2017 – by U.S. Congressional District, as well as estimates from the Center for American Progress (CAP) of the economic gains that would stem from legalizing potentially eligible individuals already in the workforce.

Under the DREAM Act, undocumented individuals would first need to meet some basic age at arrival and length of residence requirements to be considered: they must have been under age 18 upon arrival and have lived continuously in the U.S. for at least four years. To be eligible for Conditional Permanent Resident (CPR) status, they must also either be enrolled in school or have a high school diploma (or equivalent), have good moral character, and pass a medical examination. From that point, those with CPR status would be eligible to apply for Legal Permanent Resident (LPR) status and eventually U.S. citizenship after meeting additional requirements around educational attainment, military service, or employment (again, see this Migration Policy Institute factsheet for a summary of the requirements).

The interactive map below provides the latest CSII estimates of both the number of individuals meeting the basic age at arrival and length of residence requirements, and the number eligible for CPR status under the DREAM Act of 2017. It also includes estimated gains in GDP from enactment of the DREAM Act, based on a scenario in which all those who meet the basic age at arrival and length of residency requirements attain CPR status, and half of them then achieve LPR status through the educational pathway noted above. While we have not generated estimates of the number of DREAMers that would be eligible for lawful status and the economic benefits that would accrue under the other bills currently under consideration, it is likely that their distribution across Congressional Districts would be similar.

We hope that this data grounded in Congressional Districts will be helpful to inform the civic debates that are currently underway across the nation about the policies being considered by Congress.

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View full chart: Estimated GDP Gains From Enactment of the DREAM Act of 2017 by U.S. Congressional District (115th Congress)

Notes

The USC Center for the Study of Immigrant Integration (CSII) estimates are based on a pooled sample of the 2010-2014 American Community Survey (ACS) microdata from IPUMS-USA, University of Minnesota, www.ipums.org. The methodology involves first using the ACS data to estimate the total undocumented population in the U.S.; for detailed methodology on how we do that, including the use of conditional screens, country controls, and a stratified probability model, see here. To determine who was DACA-eligible from the pool of undocumented residents, we first applied the age, age at arrival, educational requirements, and continuous residency criteria given in the 2012 executive order, and developed an estimate of who was initially eligible in 2012. We then followed a strategy similar to the Migration Policy Institute and “aged in” an additional four years of people who met the initial screen, applying an estimated drop-out rate to exclude those who might now meet the age criteria but not the educational requirements.

Once those eligible for DACA were determined, the number of actual recipients was estimated using a combination of an additional conditional screen and an Iterative Proportional Fitting (IPF) process. The screen involved education: we assumed that all those DACA eligible who had completed or were in grad school have applied for DACA and we assumed that the vast majority of those in college and nearly all of those with a B.A. had applied for and received DACA. The resulting number is well below the implicit numbers estimated by UC San Diego scholar Tom Wong in his most recent DACA survey; because there is reason to believe that the sampling strategy in that approach may over-sample the educated and also reason to believe that our “aging in” approach misses current college experience, this is a reasonable outcome. We then used an IPF process on the rest of DACA eligibles to match published data from the U.S. Citizen and Immigration Services on the total number of DACA approvals by state and country of origin as of the end of the third quarter of fiscal year 2017 (through June 30th 2017), available here. We chose to make the final fit tighter on country than on state because recipients can change their state of residence but they cannot change their country of origin.

The last step of methodology involved assigning the individual-level estimates of the DACA eligible and DACA recipients to Congressional Districts (115th Congress). To do so, we used a 2010 population-based crosswalk between Public Use Microdata Areas (PUMAs) and Congressional Districts from Mable/GeoCorr12. A geographic crosswalk was necessary because the ACS microdata includes only very limited geographic information, with the PUMA being lowest level of geography attached to individuals. PUMAs contain a population of at least at least 100,000 while Congressional Districts have an average population of about 700,000. We randomly assigned individuals to Congressional Districts in proportion to the 2010 population distribution, and then aggregated the results by Congressional District to derive the final estimates presented in the interactive map. While this introduces some unknown degree of geographic error in the resulting estimates, it is likely to be relatively minimal given that most PUMAs are either entirely contained or largely contained in a single Congressional District. To caution users of the map against utilizing unreliable estimates, we include an asterisk and a note alongside the data for Congressional Districts with fewer than 50 unweighted survey respondents identified as DACA eligible. We also do not report any data for Congressional Districts in which our estimates showed fewer than 250 DACA eligible.

Estimated GDP loss from removing DACA recipients from the workforce by Congressional District is from Center for American Progress analysis of the underlying CSII estimates. State-level estimates of GDP loss per DACA worker were applied to the estimated number of DACA workers by congressional district (91 percent of the DACA recipients in each Congressional District were assumed to be workers). For more on the methodology, see Nicole Prchal Svajlenka, Tom Jawetz, and Angie M. Bautista-Chavez, “A New Threat to DACA Could Cost States Billions of Dollars” (Washington: Center for American Progress, 2017). GDP loss is annual in 2013 dollars and is only reported for Congressional Districts in states with at least 500 estimated DACA recipients. Estimated GDP losses should be viewed as conservative for three reasons. The first is simply that they are based on GDP in 2013, and are reported in 2013 dollars. The second is that they assume the skill distribution of DACA workers reflects the broader unauthorized workforce, yet we know that DACA recipients are likely, given the requirements, to be more educated than the norm for unauthorized workers. The third is that many working DACA recipients are also in school and are likely to increase their income and productivity in the future (as are those who are not working, but in school).

Acknowledgements

Manuel Pastor was responsible for generating the underlying estimates of DACA eligible and DACA recipients by state, Justin Scoggins prepared data for mapping and coordinated the mapping project, Meara Algama assisted with map development, and Gladys Malibiran was responsible for the website and communications.

We thank the California Wellness Foundation, the James Irvine Foundation, The California Endowment, and Bank of America for their generous support of the CSII data and analytic capacities that made this project possible. We also thank the Center for American Progress for providing the estimates of GDP loss from removing DACA workers by Congressional District.

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