The Economic Effects of Providing Legal Status to DREAMers

[i] Notes: The data are based on the 2012 American Community Survey (CMS version). We restrict to individuals aged 17–70. Non-employed means not working and not in college. Total count refers to the estimated number of individuals in each column (in millions). Hourly wages computed on the basis of full-time workers (35 hours minimum worked usually) at year 2012 prices. Column 1 reports on the total population, including documented (US-born or foreign-born) and likely undocumented. Column 2 refers to (likely) documented individuals only. Column 3 refers to the (likely) undocumented individuals. Column 4 reports on unauthorized individuals who arrived in the country at age 17 or younger. Column 5 adds the additional restriction of having a high school diploma or equivalent. Column 6 adds the additional restriction of being less than 32 years old in year 2012.

[i] Notes: The population is restricted to ages 17–70 and is based on the 2012 ACS. Columns 1–3 refer to the documented population (born in the United States or abroad). Columns 4–6 refer to the likely unauthorized individuals that arrived in the country in the age 17 (DREAMers), and columns 7–9 restrict to the subset of DREAMers with a high school diploma (or similar) in 2012. Education levels are defined as (HSD) high school dropouts, (HSG) high school graduates, (SoCo) some college education, and (CoGrad) college graduates. The age groups are defined as (1) 17–26, (2) 27–36, (3) 37–46, (4) 47–56, and (5) 57–70.

[i] Notes: Productivity terms based on the hourly wages of the corresponding full-time workers, assuming no exploitation. Column 1 reports the productivity (wage) of documented workers relative to undocumented workers who arrived as children in the same education and age groups. The average value reported in the last row uses the distribution of undocumented workers arrived as children over education–age groups as weights—the mode is 15% for HSD in age groups 2 and 3. Column 2 reports the productivity of each education– age type of labor, relative to the first age category in each of the education groups. For groups (CoGrad, 1) and (CoGrad, 2), the average wages for likely undocumented workers are slightly higher than for likely documented workers. We cap these ratios at 1 because they likely reflect the sampling variability due to small cell sizes. Column 3 reports the productivity of each education group relative to the HSD category. The age groups are (1) 17–26, (2) 27–36, (3) 37–46, (4) 47–56, and (5) 57–70.

[i] Notes: Key parameter values. The scenario more consistent with the estimates by (Pope 2016) is δ ^C = 0 and δ ^N = 0.07. And in any case we need to have δ ^C + δ ^N = 0.07.

[i] Notes: The eligible group consists of likely unauthorized individuals that entered the country younger than 17 with a high school diploma (or equivalent), and were younger than 32 in year 2012, as required by DACA. Columns 3 and 4 report the number of legalized individuals in our simulation, considering only employed (column 4) or also individuals in college or non-employed (column 3). In scenario 1, (ϕ, δ_C, δ_N) = (0.56, 0, 0). In scenarios 2 and 5, (ϕ, δ_C, δ_N) = (0.56, 0.07, 0). In scenario 3, (ϕ, δ_C, δ_N) = (0.56, 0, 0.07). In scenario 4, (ϕ, δ_C, δ_N) = (1, 0.07, 0). The dollar amounts in column 2 are computed multiplying the pct. change in GDP in column 1 by the latest GDP estimate available—third quarter of 2017. The last column is the ratio of column 2 to column 5.

[i] Notes: We report percent changes in DACA counterfactual relative to baseline. Doc-Doc (Undoc-Undoc) refers to individuals that were documented (undocumented) both in the baseline and in the counterfactual. Legalized individuals are those that had undocumented status in the baseline but were documented in the DACA counterfactual. Because documented and undocumented that do not change legal status with the same education and age are perfect substitutes in production, they experience identical wage growth rates. We use the baseline elasticities (scenario 2 in Table 5). Columns 1 and 4 report wage growth (in percent) by education–age. Columns 2 and 5 report the labor shares in the baseline among documented workers (column 2) and among DREAMers eligible for DACA (column 5). Columns 3 and 6 report age-weighted average wages by education on the basis of the respective previous two columns. A “.” denotes a missing value due to the fact that there are no individuals in that education–age-documentation status category. The age groups are (1) 17–26, (2) 27–36, (3) 37–46, (4) 47–56, and (5) 57–70.

[i] Notes: Scenarios 1–3 report the long-run gains in GDP associated with passing the DREAM Act when a fraction γ of DREAMers with a high school degree chooses to enroll in college to obtain an associate degree (education level 3). In all scenarios, the new graduates are assumed to work only if they were working in the baseline scenario. GDP amounts in columns 2 and 5 are in 2017 prices. Columns 3 and 4 report the number of individuals that obtain legalization according to our simulation (in millions), with the latter restricting to legalized individuals that are working in the long-run DREAM Act scenario. Column 5 is computed by dividing column 2 by column 4. The last row (Removal scenario) reports the change in GDP associated with removing all DREAMers (undocumented individuals that arrived as children).

[i] Notes: The table reports long-run percent changes in wages in the DREAM Act scenario. Eligible individuals are required to have a high school diploma in the baseline data. Columns 1–3 refer to documented individuals (Doc), either foreign-born or US-born. Columns 4–6 refer to individuals that obtained legal status through the DREAM Act. Columns 1 and 4 report wage growth (in percent) by education–age. Columns 2 and 5 report the labor shares in the baseline among documented workers (column 2) and among DREAMers (column 5). Columns 3 and 6 report age-weighted average wages by education. A “.” denotes a missing value due to the fact that there are no individuals in that education–age-documentation status category. The simulation assumes that a fraction γ = 0.50 of high school graduate DREAMers will obtain some college education (education level 3) to obtain legal permanent residence. Because documented and undocumented workers with the same education and age are perfect substitutes in production, they experience identical education–age-specific wage growth rates. Thus, column 2 can also be applied to undocumented workers that did not obtain legal status.