DACA: Where Do Dreams Take Dreamers?
Labor Market Impacts of DACA Eligibility

A Difference-in-Differences Study

Method
Difference-in-Differences (WLS)
Data
IPUMS ACS microdata,
2008–2016
Sample
99,489 Hispanic surveyees,
ages 18–35
Tools
Python (pandas, statsmodels)

Project Overview

DACA eligibility pushed young Hispanic immigrants out of farming and manual labor and into higher-paying technical, sales, and management jobs.

This project tests whether DACA eligibility changed occupational mobility for young Hispanics from 2008 to 2016, using a difference-in-differences design. Occupational shifts are the right thing to measure because they show whether work authorization actually lets people pursue higher wages and skills-matched work, not just employment for its own sake. The results below confirm an employment surge stemming from DACA's implementation.

+3.42

Percentage Points
Technical, Sales & Admin Support

+1.58

Percentage Points
Management & Professional

-1.12

Percentage Points
Farming, Forestry & Fishing

Background & Literature

Implemented on June 15, 2012, the Deferred Action for Childhood Arrivals (DACA) program granted eligible undocumented youth (Dreamers) temporary deportation relief and renewable two-year work permits. Beyond direct employment and deportation relief, indirect benefits included access to state driver’s licenses, employer-provided health insurance, and financial aid opportunities.

DACA Eligibility Requirements
  • Arrived in the United States before turning 16 years old.
  • Continuously resided in the U.S. since June 15, 2007.
  • Under the age of 31 as of June 15, 2012.
  • In the U.S. on June 15, 2012, with no lawful status.
  • Obtained a high school diploma, GED, be enrolled in school, or be honorably discharged.
  • Passed a background check with no significant criminal history.
  • At least 15 years old to apply.

To isolate the policy’s causal impacts and prevent outside political factors — such as the program’s ongoing legal battles since 2017 — the scope of this research is strictly from 2008 to 2016.

Data & Operational Assumptions

The ACS doesn't ask "are you a Dreamer?", so DACA eligibility here is a proxy variable, built from the program's own age, education, and residency requirements.

Individual-level data comes from the American Community Survey (IPUMS ACS). Since the ACS doesn't track DACA participation directly, any surveyee in this randomized sample who meets DACA's strict eligibility criteria is treated as a Dreamer for this analysis.

Methodological Adjustments
  • Education: Educational attainment is measured by using most recent school enrollment status, followed by the most recent diploma acquired. If a person has a bachelor’s but is in a master’s program, they are recorded as a master’s student.
  • Residency: Continuous residency is mapped to the start of the 2007 calendar year rather than June 2007 due to ACS lacking month-to-month residency data.
  • Age: The ACS protects privacy by recording birth dates in quarterly intervals instead of exact days. If an individual turns 31 in the 2nd quarter of 2012, they are counted as disqualified for failing to be under 31 by June 15, 2012.
  • Criminal History: The ACS lacks criminal history variables. It is assumed that the sample population is randomized cleanly enough to minimize bias.

Sample Selection & Age Demographics

By the end of 2017, there were 689,800 active Dreamers. Of that population, these were the three largest countries of origin: 548,000 (79.4%) from Mexico, 25,900 (3.7%) from El Salvador, and 17,700 (2.6%) from Honduras (USCIS 2017). Because Hispanics made up over 80% of Dreamers, the total sample population was narrowed to Hispanic migrants (131,457 surveyees) who met the methodological adjustments.

Following Nolan G. Pope's approach, the control group is built to be nearly identical to the treatment group, with one difference: they were too old, either on arrival or by June 15, 2012, to qualify (Pope 2016). Specifically, the control group is made up of two populations:

  • individuals who are 16 or older at arrival in the U.S. BUT meet the age criteria by June 15, 2012
  • individuals who meet the arrival age criteria BUT are 31 or older by June 15, 2012

By keeping the control group nearly identical to the treatment group, the model minimizes bias and isolates the causal effect of DACA on occupational mobility, ceteris paribus. But the "too old by June 15, 2012" branch has no upper age bound, so left alone it would pull in people in their 50s and 60s who are already established in their careers. To keep the comparison local to DACA's age cutoffs, the analytical sample is restricted to ages 18 to 35, which is how the total sample lands at 99,489.

The result is a younger treatment group (DEH) and an older-skewing control group. As seen in Figure 1, the distribution is consistent with DACA's own age cutoffs, which favor people who arrived in the U.S. at a younger age.

99,489

Total Sample
(Hispanic Surveyees, Ages 18–35)

68,225

Treatment Group
(DEH)

31,264

Control Group

Note: The dataset initially covers working-age individuals ages 16 to 64, then is restricted to ages 18 to 35 for the causal analysis to keep the treatment and control groups locally comparable around DACA's age cutoffs.

Empirical Approach

Employment Impact

DEH employment grew +8.9 points after DACA — over four times the control group's +2.1 point gain.

This DiD comparison exists to confirm the sample shows a real employment effect before digging into occupational shifts. It also lines up with existing literature: a 2022 Center for American Progress survey found over 8 in 10 Dreamers employed and reporting benefits from the program (CAP 2022). While the survey results are not directly comparable to this data sample, they provide a strong macro-level indication that DACA's positive impact on employment has held years after implementation.

+8.9

Percentage Points
DEH Employment Growth
Pre– vs Post–DACA Average (58.8% → 67.7%)

+2.1

Percentage Points
Control Group Growth
Pre– vs Post–DACA Average (69.9% → 72.0%)

The control group starts from a higher baseline (Figure 2) because it's an older cohort already established in the workforce, while DEH skews younger — matching the demographic split in Figure 1. Averaging the four years before DACA (2008–2011) against the four years after (2013–2016) — and excluding the 2012 transition year, for the same reason it's dropped from the regression below — produces the +8.9 vs. +2.1 point comparison above.

Occupational Shift Regressions

DACA had a statistically significant effect on the types of occupations Dreamers pursued.

With the employment effect confirmed, the analysis turns to whether DACA also shifted which occupations Dreamers pursued. ACS data includes survey weights (perwt) to keep the sample representative of the U.S. population, so the regression uses weighted least squares (WLS) to account for them, with standard errors clustered at the state level. See the note below Figure 3 for the full set of controls. The regression:

yit = β₀ + β₁Eligiblei + β₂DACAt + β₃(DACAt × Eligiblei) + X'itβ₄ + εit

Where yit is a binary variable indicating whether individual i is employed in occupation category at time t (estimated for each category), β3 is the estimate of DACA's effect on DEH, and X'it represents a vector of control variables. To fit this regression, individuals are grouped into six occupation categories based on the IPUMS OCC1990 classification. Additionally, 2012 is omitted from the regression for Figure 3 since DACA began in June 2012, and the ACS does not provide month-to-month data. For a more extensive definition of the regression model, refer to the Occupation Regressions section below.

99,489

Total Sample (All Years)

88,021

Regression Sample (2012 Omitted)

Three occupation sectors show a pronounced, statistically significant effect (Figure 3): Technical, Sales, & Administrative Support; Management & Professional; and Farming, Forestry & Fishing.

Note: Military and Non-Occupational Response codes were excluded from Figure 3. Military occupations were dropped due to an extremely small sample size (n = 153) and limited relevance to DACA since Dreamers are not allowed to enlist. Non-occupational response codes were excluded as they overlap with the labor force participation trends already established in Figure 2. Sample sizes for the six included occupation categories: Technical, Sales, & Administrative Support (n = 19,798), Management & Professional (n = 7,040), Service (n = 17,688), Farming, Forestry, & Fishing (n = 4,558), Operators, Fabricators, & Laborers (n = 13,094), and Production, Craft, & Repair (n = 9,098). Sum of 71,276 surveyees, which is 81.0% of the total sample population (n = 88,021).

Note: The regression controls for most recent education attainment, sex, age, state, and year. Standard errors are clustered at the state level to account for potential correlation of errors within states. Occupation categories follow IPUMS's OCC1990 classification. The codebook lists the specific job titles included in each category.

+3.42

Percentage Points
Office & admin roles

+1.58

Percentage Points
Management & professional

Technical, Sales, & Administrative Support (95% CI [2.0, 4.9]) and Management & Professional (95% CI [0.8, 2.4]) both grew significantly among DEH — a clear move into higher-paying, skills-matched work. Earliest available active Dreamer data from USCIS post 2016 recorded 689,800 active Dreamers by the end of 2017.

Assuming the regression sample is representative of the broader Dreamer population, that translates to roughly 23,600 people moving into technical/admin roles and 10,900 into management.

−1.12

Percentage Points
Farming, forestry & fishing

This category (95% CI [-2.0, -0.3]) is lower-paying and physically demanding, so its decline reads as a positive: DEH moving toward safer, more stable careers.

Under the same representativeness assumption, that's about 7,700 Dreamers leaving farm labor as a result of DACA.

These percentage-point shifts are small, but statistically significant, and represent a meaningful shift in occupational mobility. Scaled up, it suggests DACA supported economic integration for tens of thousands of Dreamers by shifting them into better-paying, safer work.

Key Findings

  • Occupational Sorting Following DACA's 2012 implementation, there is statistically significant evidence of an occupational shift from blue-collar work into office and professional roles among DACA-eligible individuals. In other words, DACA positively influenced migrants' economic mobility by enabling them to pursue higher-paying occupations that better match their skills and education.
  • Future Considerations DACA's applications have been closed to new applicants since 2021, limiting it to renewals only. Future work should look at how that uncertainty shapes Dreamers' incentive to invest in schooling or occupational mobility, and whether DACA's labor-market effects spill over into other areas of economic integration.
  • Policy Suggestions Renewal-processing delays are a recurring theme in Dreamers' own accounts (anecdotally, on forums like r/DACA): applicants report losing jobs when USCIS can't process a renewal before their work authorization expires. Staggering renewal deadlines would spread out the processing load and reduce that gap.

Dataset: Integrated Public Use Microdata Series (IPUMS ACS)

Occupation Regressions

Regression outcomes demonstrating DACA's impact on occupation mobility are shown below.

Technical, Sales, and Administrative Supportn = 19,798
Variable coef std err z P>|z| [0.025 0.975]
Eligible 0.0719*** 0.0040 18.95 0.000 0.0640 0.0790
DACA -0.0467*** 0.0060 -7.49 0.000 -0.0590 -0.0340
DACA*Eligible 0.0342*** 0.0070 4.58 0.000 0.0200 0.0490
Management and Professional Occupationsn = 7,040
Variable coef std err z P>|z| [0.025 0.975]
Eligible 0.0242*** 0.0020 9.78 0.000 0.0190 0.0290
DACA -0.0152*** 0.0040 -4.26 0.000 -0.0220 -0.0080
DACA*Eligible 0.0158*** 0.0040 4.03 0.000 0.0080 0.0240
Farming, Forestry, and Fishingn = 4,558
Variable coef std err z P>|z| [0.025 0.975]
Eligible -0.0138*** 0.0030 -5.30 0.000 -0.0190 -0.0090
DACA 0.0109* 0.0060 1.77 0.077 -0.0010 0.0230
DACA*Eligible -0.0112*** 0.0040 -2.64 0.008 -0.0200 -0.0030
Service Occupationsn = 17,688
Variable coef std err z P>|z| [0.025 0.975]
Eligible -0.0334*** 0.0060 -5.23 0.000 -0.0460 -0.0210
DACA 0.0290*** 0.0050 5.39 0.000 0.0180 0.0400
DACA*Eligible -0.0030 0.0070 -0.45 0.656 -0.0160 0.0100
Operators, Fabricators, and Laborersn = 13,094
Variable coef std err z P>|z| [0.025 0.975]
Eligible -0.0173*** 0.0050 -3.77 0.000 -0.0260 -0.0080
DACA -0.0052 0.0040 -1.23 0.220 -0.0130 0.0030
DACA*Eligible 0.0067 0.0060 1.12 0.261 -0.0050 0.0180
Production, Craft, and Repairn = 9,098
Variable coef std err z P>|z| [0.025 0.975]
Eligible -0.0222*** 0.0040 -5.64 0.000 -0.0300 -0.0140
DACA -0.0066 0.0040 -1.52 0.130 -0.0150 0.0020
DACA*Eligible -0.0070 0.0050 -1.45 0.147 -0.0170 0.0020

yit = β₀ + β₁Eligiblei + β₂DACAt + β₃(DACAt × Eligiblei) + X'itβ₄ + εit

  • DACA: 1 if DACA is active (2013-2016)
  • Eligible: 1 if surveyee meets DACA eligibility
  • DACA × Eligible: 1 if surveyee is DACA eligible while DACA is active
  • ε: error term

Bibliography

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