The Economic Effects of Providing Legal Status to DREAMers

Our data are based on the special extract of the ACS for the year 2012 provided by the (Center for Migration Studies 2014), which contains a sophisticated individual-level measure of imputed undocumented status (Warren 2014).

First developed by Passel and Clark (1998), the method has continued to evolve in Baker and Rytina (2013), Warren and Warren (2013), Passel and Cohn (2015).

Existing estimates of the characteristics of the imputed unauthorized population obtained from the Census, the ACS and the CPS tend to be largely consistent with each other (Warren 2014, Borjas 2016, Pastor and Scoggins 2016). Nevertheless, the broader validity of the imputation is still being analyzed. Assessments remain constrained by lack of large representative surveys that ask legal status.

The Survey of Income and Program Participation (SIPP), also a Census product, directly asks respondents about legal status but is roughly one-sixth the size of the ACS. See Van Hook et al. (2015) for a comparison of results based on the SIPP and the ACS.

We restrict to the population age 17–70 in the 2012 ACS. We distinguish between documented individuals, defined as those that were born in the United States or born abroad but deemed as likely authorized on the basis of the imputation, and likely unauthorized foreign-born individuals. Among the likely undocumented population, we will concentrate on DREAMers, defined as individuals that arrived in the country before the age of 18 and have obtained a high school diploma (or similar).

We classify individuals as employed, in college, or doing neither of those activities. More specifically, we consider individuals as enrolled in college if they have a high school degree and report that are currently enrolled in school. An individual is considered employed if he stated so in the ACS survey. Last, we define individuals as non-employed if they are not employed and not enrolled in school. Table 1 provides a summary of the data. Column 1 shows that our data accounts for 232.4 million individuals (age 17–70). Among these, 61% were employed, 11% in college, and 28% doing neither of these two activities. Column 2 reports on the documented population, which amounts to 222 million individuals. Column 3 reports on 10.4 million (likely) undocumented individuals. Their employment rate is 68%, 7 percentage points higher than for the documented population, and their college enrollment rate is 6%, 5 percentage points lower than for the documented population. Column 4 restricts the sample to (likely) unauthorized individuals that arrived in the United States at age 17 or younger, which amounts to 2.93 million individuals. About 1.76 million of these are currently employed and the employment and college attendance rates are essentially the same as for the documented population. In column 5, we further restrict to the 1.65 million undocumented individuals with at least a high school diploma (or similar), which corresponds to our main population of interest. The data show an employment rate of 60% (or about 0.99 million employed individuals) and a college attendance rate of 22%. The last column also imposes the condition of being 32 years old or younger in year 2012, which was required to qualify for DACA. In our data, there are 1.42 million potentially DACA-eligible individuals. The table also reports the mean hourly wages of full-time workers for each column. On average, documented workers earn close to $21, whereas undocumented workers earn roughly 5 dollars less. Naturally, the bulk of the gap is explained by the lower average education and experience of undocumented workers, but not entirely.

It is also interesting to examine the relative size of these groups. In year 2012, undocumented individuals made up for 4.5% of the population, but almost 5% of employment. Undocumented individuals that arrived in the United States prior to age 18 accounted for about 1.25% of both the population and employment. When we further restrict to undocumented individuals that arrived as children and have a high school diploma (or similar), we find that this group accounts for 0.7% of the population and of employment. Because DREAMers are such a small fraction of the population, the effects of gaining legal status on overall GDP will necessarily be relatively small.

Importantly, our analysis will distinguish between workers by education and age, besides legal status. Specifically, we define five age groups: (1) 17–26, (2) 27–36, (3) 37–46, (4) 47–56, and (5) 57–70. We also define four groups on the basis of completed education (in year 2012): (1) high school dropouts, (2) individuals with a high school diploma or General Education Diploma (GED), (3) individuals with some college (i.e., at least 1 year of college or an associate’s degree), and (4) individuals with a bachelor’s degree (and possibly higher degrees as well). On the basis of our definition, there are no high school dropout DREAMers.

We collapse the individual-level data (using the appropriate sample weights) by education, age, and documentation status. The results are summarized in Table 2. The table reports the shares of the column totals. Columns 1–3 refer to the documented population, which can be broken down into 135 million employed individuals, 24 million attending college, and 63 million doing neither of those two activities. Note that by definition, individuals currently enrolled in college cannot be high school dropouts. Turning to DREAMers (columns 4–6), we find that 0.99, 0.37, and 0.29 million individuals were, respectively, employed, enrolled in college, or doing neither of those two. We also note that under our definition there are no DREAMers in age groups 4 (age 47–56) and 5 (age 57–70).

Let us now describe in detail the labor aggregate L. We allow workers to differ in education (e = 1,…,E), age (a = 1,…,A), and documentation status (Doc, Undoc). In total, the number of labor types is given by 2 × E × A. In our preferred specification, we will focus on four education groups (E = 4) and five age groups (A = 5).

We aggregate all these types of workers by means of a multi-nested CES aggregator, as in Borjas (2003), Manacorda et al. (2012), and Ottaviano and Peri (2012). To construct the labor aggregate, we need data on the number of workers by education, age, and documentation status. We denote the vector of data by V. In addition, we need values for an array of worker productivity terms Θ = {θ}, one for each worker type, and elasticities of substitution across worker types Σ = {σ}. It is helpful to employ the following compact notation to make explicit the inputs needed to compute the labor aggregates L(V;Θ,Σ).

Specifically, the labor aggregate is given by three levels of CES aggregation, with potentially different elasticities of substitution. To maximize comparability with previous studies, we choose the following nesting structure:

In a general form, the CES aggregator across M inputs is defined by

Implicitly, the equation for L_e,a assumes that within an education–age group, documented and undocumented workers are perfect substitutes (as in Borjas (2003)), despite evidence to the contrary (Manacorda et al. 2012, Ottaviano and Peri 2012). This choice is made to keep the framework as simple as possible and has virtually no effect on the estimated GDP effects (as shown in Edwards and Ortega (2017). However, it will tend to exaggerate the effects of changes in the size and skill composition of the immigrant population on natives. Thus, our analysis of wage effects should be interpreted as providing upper bounds for the effects on native wages.

On the contrary, the effects on the wages of legal immigrants will tend to be underestimated. We also note that we allow for different productivity (and therefore wage) levels between documented and documented workers within education-age cells to accommodate this important feature of the data.

The documented–undocumented relative productivity parameters θe,aDoc$\left\{ \theta _{e,a}^{Doc} \right\}$will play a crucial role in our analysis. In essence, when we simulate the effects of legalization, we endow undocumented workers with the productivity of documented workers with the same age and level of education. Thus, if these relative productivity parameters are larger than one, legalization will entail an increase in the labor aggregate L_e,a, as well as in the overall amount of labor L. The increase in labor will then trigger an investment response in the same direction to bring the capital–labor ratio and the marginal product of capital back to their initial level.

There is plenty of evidence suggesting that the performance of undocumented workers in the labor market is diminished by their lack of legal status. Clear evidence of this is the over-qualification phenomenon (Gonzales 2011, Gleeson and Gonzales 2012, Cho 2017), which is probably more widespread among undocumented workers than for immigrants in general. The typical example of over qualification is when a highly educated immigrant, for example, with a college degree, ends up employed in a low-skill occupation. These occupations are characterized by low productivity and, hence, pay low wages. Individuals in this situation will display very low wages given their education levels, which will translate into large documented–undocumented productivity gaps. More specific to the DREAMer population, there is also evidence that the threat of deportation creates anxiety and depression, which are likely to negatively affect the productivity of these workers (Leisy 2011, Gonzales 2011, Hainmueller et al. 2017). Last, undocumented workers are probably subject to a substantial degree of mismatch in their workplaces, reflecting the fact that they cannot obtain a driver’s license and are barred from many jobs because of E-Verify or licensing requirements. As a result, they often end up in jobs that are a poor match for their skills, which results in a very low return to their levels of experience and education.

It is also possible that documented–undocumented wage gaps reflect other factors besides productivity gaps. Some studies (Hotchkiss and Quispe-Agnoli 2009, David Brown et al. 2013, Hirsch and Jahn 2015) suggest that undocumented workers are often not paid their full marginal product. Clearly, their bargaining power is diminished by their lack of legal status, and employers can appropriate a larger part of the surplus generated by the employer–employee match. If exploitation of this type is present and we ignore it, observed wages will underestimate the productivity of undocumented workers relative to legal immigrants and natives with the same education and experience. This will result in upwardly biased productivity gaps between documented and undocumented immigrant workers, and will lead to upwardly biased estimates of the gains from legalization.

To allow documented–undocumented relative wages to reflect both productivity differences and exploitation, we assume that unauthorized workers are “taxed” at a rate τ_e,a by employers.

For consistency with the rest of the model, we assume that the proceeds of this tax are distributed in a lump-sum manner to all documented workers.

where MPL stands for the marginal product of labor of that education–age group.

Because of perfect substitution between documented and undocumented immigrants, their relative wage (within an education experience cell) will be given by

As we shall see below, the data show substantial wage gaps between documented and undocumented workers in the same education–age category. Because the degree of exploitation is not known, we will need to make an identifying assumption to back out the relative productivity terms from the data on relative wages. In our main specification, we will choose the more standard approach of ignoring exploitation and assume that relative productivity equals relative wages but we will also analyze the alternative scenario where there are no productivity differences between documented and undocumented workers with the same observable skills, θe,aDoc=1,$\left\{ \theta _{e,a}^{Doc}=1 \right\},$and all wage gaps are explained on the basis of exploitation taxes {τ_e,a}.

Ortega and Hsin (2018) analyze the sources of the wage gaps between documented and undocumented workers in the United States. Employing a novel identification strategy, they find that the bulk of the wage gap is explained by the reduction in labor productivity associated with lack of legal status, with a small role for employer exploitation. They conclude that the diminished productivity is largely due to occupational barriers that are specific to undocumented workers.

As of June 2017, slightly less than 800,000 individuals have been granted DACA permits. This amounts to a take-up rate slightly above 0.5. To take this into account, we denote by ϕ the DACA take-up rate.

Several reasons explain the low participation in the DACA program. First, the application is a lengthy process and undocumented youth may be concerned about disclosing personal information to immigration authorities, including home address, financial information, and bio-metric data. Moreover, DACA grants temporary protection from deportation for eligible youth but offers no protection for parents or siblings who are not eligible. Thus, applying to the program means not only incurring personal risk but also exposing family members.

Based on the existing empirical evidence, it appears that DACA had two effects. First, DACA recipients were given work permits presumably allowing them to access the labor market under the same conditions as documented workers. In the model, we will assume that DACA recipients become indistinguishable from documented workers with the same age and education in terms of productivity. Because DREAMers graduated from a US high school, this assumption seems highly plausible. Quantitatively, the key terms in determining the resulting productivity boost are the relative documented–undocumented productivity terms, θe,aDoc.$\theta _{e,a}^{Doc}.$Second, there is evidence that DACA triggered a participation effect that led to an increase in employment. According to Pope (2016), the additional workers transitioned from unemployment and according to Amuedo-Dorantes and Antman (2017) and (Hsin and Ortega (2017), they dropped out of college in order to work. This participation effect will magnify the effect of DACA on GDP beyond the productivity boost, at least in the short run.

The long-run effects will depend on whether these individuals eventually return to college and graduate. As of now, we have no empirical evidence regarding whether or not this is the case.

To introduce the participation effect into our model, let δ_c be the fraction of DACA recipients that were in college and decided to drop out of school in order to work.

Note that δ_c is not the fraction of DREAMers in college, but only the fraction of that group that decided to drop out of college upon receiving temporary legalization.

We denote the baseline population in the 2012 data by:

The counterfactual undocumented population under DACA is therefore: for each (e, a),

Turning now to the documented population, for each (e, a),

Note that the overall population is the same in the counterfactual and baseline scenarios. However, there may be an increase in the overall amount of labor because of the differential productivity between documented and undocumented workers. A bit of algebra delivers the key expression summarizing the effects of DACA on the labor aggregates: for each (e, a), the increase in labor is given by

The first term is the productivity boost associated with legalization. The second term is the participation boost because of DREAMers that were initially in college or non-employed and decided to seek employment because of DACA. Aggregation over age and education groups will deliver the overall increase in L. Clearly, the documented–undocumented relative productivity terms,{,θe,aDoc,$,\left\{ \theta _{e,a}^{Doc} \right\},$will play a key role in determining the economic effects of DACA. To the extent that these coefficients are larger than one, temporary legalization through DACA will lead to a net increase in the overall amount of labor. Moreover, because of the linear relationship between labor and output, the percent change in labor will translate into an equal percent change in output. Thus

and we shall calculate dollar amounts for the effect of DACA on GDP using

Parameter ϕ stands for the DACA take-up rate. According to United States Citizenship and Immigration Services (USCIS), between its inception in 2012 and 2017 (September 30), 798,980 individuals received protection through DACA. We will set ϕ equal to the ratio between the actual number of DACA applications approved (not counting renewals) and the number of DACA eligible individuals according to our dataset (1.42 million as shown in the last column of Table 1). This results in a value of ϕ = 0.56.

Parameter δ^C is the probability that a DACA recipient who was in college decides to drop out and join the labor market. Hsin and Ortega (2017) estimate that the college dropout rates for DREAMers in college increased by 4 percentage points when DACA was implemented (reaching 7 percentage points in senior colleges). Their data do not identify DACA recipients and therefore they interpret their estimate as an intent-to-treat effect. Therefore, their estimates correspond more closely to ϕδC=0.04.$\phi {{\delta }^{C}}=0.04.$Given the value for ϕ, we therefore pick δ^C = 0.07. Using CPS data, Amuedo-Dorantes and Antman (2017) also found evidence that DACA reduced college attendance among DACA-eligible college students. Their estimates are somewhat larger than those of Hsin and Ortega (2017), but their identification of unauthorized individuals in the data is less accurate, so we base our calibration on the more conservative estimates.

Parameter δ_N is the probability that a DACA recipient who was non-employed, defined as not working and not enrolled in college, successfully seeks employment. According to Pope (2016), DACA increased the probability of employment for DACA-eligible individuals by 4 to 5 percentage points. His estimates suggest that the increase in employment was fueled by an increase in labor force participation and a decrease in unemployment. As before, a conservative interpretation of his estimates implies that ϕδN=0.04$\phi {{\delta }^{N}}=0.04$and therefore the probability that an actual DACA recipient who was previously non-employed obtains employment is around δ^N = 0.07. However, it is important to keep in mind that we need to avoid duplicating the increase in employment triggered by DACA (by maintaining δ^N + δ^C = 0.07). The studies above largely agree on the increase in employment generated by DACA, but disagree on whether the newly employed individuals originated from college or from non-employment. Thus, we will consider the two scenarios separately. The top panel of Table 4 summarizes the DACA-specific parameters.

As explained above, our calibrated model matches several relevant moments about the US economy in year 2012. Specifically, we match overall GDP and the structure of wages and employment in terms of education, age and documentation status. Now, we turn to the results of our simulation. In terms of outcomes, we first quantify the effects of DACA on GDP and later turn to the effects on the wage structure, emphasizing the effects on the wages of the individuals gaining temporary legalization through the DACA program.

According to the 2017 Senate version of the DREAM Act, obtaining permanent residence is a two-stage process.

The current version of the House bill has similar requirements, though a little more restrictive.

On the basis of the 2014 ACS, the Migration Policy Institute estimates that 1.8 million individuals are eligible for conditional status in year 2017, out of an overall 3.3 million individuals that arrived in the country illegally as children. In comparison, our estimates based on the 2012 ACS for these figures are 1.4 million—undocumented that arrived by the age of 17 and currently hold a high school diploma—and 2.9 million, respectively (Table 1). Our main estimates of the economic effects of the DREAM Act will be based on the 1.4 million individuals already eligible for conditional status.

Individuals that obtain conditional status will benefit from relief from deportation and a work permit, much like was the case for DACA recipients. In terms of the model, we will simply consider them as having the same productivity as documented workers with the same age and education. This is exactly the productivity boost considered earlier, potentially differing only in the take-up rate ψ. Unlike in the case of DACA, we believe that the take-up rate for the DREAM Act will be practically universal among eligible individuals since there is no fear from deportation (ψ = 1). We also believe that, unlike DACA, conditional status is unlikely to induce DREAMer college students to drop out. The reason is that their planning horizon remains unchanged and the returns to a college degree will increase thanks to the permanent legal status.

In fact, the requirements to obtain permanent residence in the DREAM Act will likely generate dynamic participation effects. As noted earlier, one of the routes to satisfy the permanent-residence requirement in the second stage is to obtain at least 2 years of college education. This will raise college attendance among individuals in conditional status, relative to what we would have observed otherwise. Thus, unlike for DACA, we may see a negative labor market participation effect in the short run when some employed DREAMers quit their jobs to enroll in college. In the long run, these workers will come back to the workforce with some college education and enhanced productivity, which will imply a positive participation effect. We believe that this educational boost will take place primarily among DREAMers with a high school degree, who are the most likely population to choose the college education route to fulfill the permanent residence requirement.

Evidence of educational responses by DREAMers to increased returns to education is provided by Kuka et al. (2018). Because these dynamics are driven by the educational incentive built into the DREAM Act, the likely length of the transition phase is 2–4 years. At that point, the long-run effects would materialize.

More specifically, we define γeLandγeN$\gamma _{e}^{L}\ \text{and}\ \gamma _{e}^{N}$to be, respectively, the increase in the probability of college enrollment for working and non-employed DREAMers, respectively. Lacking empirical estimates of the size of these effects, we shall assume that these probabilities are zero for individuals with less than a high school education or already having some college education: γeL=γeN=0$\gamma _{e}^{L}=\gamma _{e}^{N}=0$for e ≠ 2. The reason is that it is much more likely that these individuals will choose to fulfill the permanent residence requirement by joining the Army or being continuously employed for the required number of years. However, many DREAMers with a high school diploma are likely to choose to attend college to fulfill the additional requirement. Thus, we will set γ2L≥0$\gamma _{2}^{L}\ge 0$and γ2N≥0$\gamma _{2}^{N}\ge 0$in our calibration.

As a result, the short-run counterfactual undocumented population under the DREAM Act is as follows. For each (e, a), a fraction ψ of all DREAMers (undocumented that arrived in the country as children) receives conditional status:

Turning now to the documented population, a fraction γeL$\gamma _{e}^{L}$of the Dreamers in the workforce with education level e that received conditional status ψLe,aDream$\left( \psi L_{e,a}^{Dream} \right)$will quit their jobs in order to enroll in college. Likewise, a fraction γeN$\gamma _{e}^{N}$of the non-employment Dreamers with education level e that received conditional status ψNe,aDream$\left( \psi N_{e,a}^{Dream} \right)$will enroll in college. More specifically, for each (e, a),

In our calibration, we shall set γ2L=γ2N=γ$\gamma _{2}^{L}=\gamma _{2}^{N}=\gamma $for simplicity. We believe that a plausible value for this parameter is γ = 1/2, that is, one in two DREAMers with a high school degree will choose to obtain some college education in order to qualify for permanent residence. However, we will also produce estimates for higher and lower values of this parameter. Table 4 gathers the key parameter values in the simulation of the effects of the DREAM Act.

The DREAMers that were initially in the workforce or non-employed in the baseline data but decide to attend college because of the eligibility requirements, ψγL2,aDream+N2,aDream,$\psi \gamma \left( L_{2,a}^{Dream}+N_{2,a}^{Dream} \right),$are now graduating from college with their enhanced skills. We make two conservative assumptions. First, we assume that the DREAMers that went back to school obtain only the minimum college education required to satisfy the permanent residence requirement. Namely, those individuals transition from education group 2 (high school graduate) to education group 3 (some college or an associate’s degree). Second, we assume that individuals that were initially non-employed stay in that state despite their increased educational attainment.

This assumption is probably overly conservative but we are unsure what fraction of these newly minted graduates would ultimately enter the workforce. If all the DREAMers that transition from education level 2 to education level 3 were to become employed, the number of documented individuals with education level 3 in the long-run DREAM Act scenario L˜˜3,aDoc$\left( \tilde{\tilde{L}}_{3,a}^{Doc} \right)$would have to be increased by ψγN2,aDream.$\psi \gamma N_{2,a}^{Dream}.$Accordingly, N˜˜3,aDoc$\widetilde{{\tilde{N}}}_{3,a}^{Doc}$would have to be decreased by the same amount. Clearly, this would have an additional positive effect on GDP growth.

The long-run undocumented population under the DREAM Act is the same as it was in the short-run scenario. For each (e, a),

Turning now to the documented population, for each (e, a), the workforce will be given by

where the group with some college (e = 3) includes the high school graduates that attended college to fulfill the permanent residence requirement. Importantly, these equations assume that initially non-employed DREAMers that decided to obtain some college education to qualify for permanent residence remain non-employed in the long-run counterfactual.

As for the non-employed and the college-enrolled population,

Our estimates for the long-run effects on GDP from passing the DREAM Act are reported in Table 7. We consider a variety of scenarios that differ in the value of the parameter governing the share of DREAMers with a high school degree that choose to attend college in order to obtain permanent residence γ2L=γ2N=γ.$\left( \gamma _{2}^{L}=\gamma _{2}^{N}=\gamma \right).$

The top panel in the table (scenarios 1–3) presents the results corresponding exactly to the long-run effects on GDP according to the set of equations in Section 7.3. It is helpful to begin by considering scenario 1, where college enrollment is unaffected by the DREAM Act (γ = 0). In this case, we find that GDP will increase by 0.05%, which amounts to an overall increase of $9 billion per year. To provide a more intuitive measure of the size of the effects, it helps to consider that 1.65 million individuals benefit from legalization in our calculations and, out of those, 0.99 million are working in the long-run counterfactual. On the basis of the latter figure, the average long-run increase in GDP per legalized worker results in $9,104. Because there are no participation effects in this scenario, the short-run change in GDP coincides exactly with the short-run value.

Let us now take into account the increased incentives to attend college (scenario 2). Specifically, we assume γ = 1/2, that is, we assume that one in two high school graduates with conditional status choose to obtain an associate’s degree (or 2 years of schooling toward a bachelor’s degree). In the short run, there will be two opposing effects on GDP. On the one hand, there is a productivity boost associated with obtaining conditional legal status, as was the case with DACA. However, this positive effect is practically neutralized by a sizable negative participation effect driven by the high school graduate DREAMers that leave the workforce to enroll in college. As a result, GDP is practically unaffected in the short run.

Even though not reported in Table 7, we can also calculate the short-run effects of legalization on GDP. These effects can be negative for high values of γ, reflecting the reduction in the workforce when DREAMers with a high-school degree choose to quit their jobs in order to enroll in college. However, this finding could easily be overturned if individuals can simultaneously work and attend college. In fact, this seems to be the case for a large share of immigrant students attending community colleges (Hsin and Ortega 2017).

In sum, our analysis implies that passing the DREAM Act will increase the economic contribution of DREAMers that obtain legal status. We estimate that GDP will increase annually by an average of 9 to 21 thousand dollars for each worker obtaining legal status. This amount would add to the economic contribution of DREAMers prior to legalization, which can be quantified by comparing GDP in the baseline scenario (prior to legalization) to the level of GDP in a counterfactual where DREAMers are removed from the economy. As reported in the bottom row of Table 7, removal of DREAMers from the workforce would entail a 0.42% reduction in GDP, amounting to $46,061 per worker. As a result, passing the DREAM Act would increase the overall annual contribution of DREAMers to GDP to be around $60,000 per worker.