III. Results

We begin by discussing the licensing rate results from both the CPS and SIPP in section III.A. We then proceed to discuss the relationships between licensing and both hourly wage and hours worked in section III.B, again for both the CPS and SIPP. The estimation models are as similar as possible between the two datasets, with notable differences including years since migration in the CPS and language ability in the SIPP.

A. Probability of Having an Occupational License

We start by exploring the relationship between being an immigrant and the probability of being licensed. The summary statistics for both the CPS and SIPP indicate that licensed workers tend to be more educated than unlicensed workers (Kleiner and Vorotnikov 2017), though the licensing rate appears to increase more strongly with education among natives than among immigrants.

To visualize the relationship between licensing rates and educational attainment, figure 1 shows the fraction of people at each education level who report having a license, separately for men and women as well as separately for natives and immigrants, from the CPS probability sample. The increasing trend of licensing with education for all groups is clear; however, licensing increases with educational attainment more rapidly for natives than immigrants. The native/immigrant licensing gap is substantially larger for the most educated group (GRAD) than any other, particularly for women. In fact, native and immigrant women exhibit little difference in their licensing rates across the other four education levels. Thus, it seems plausible that the impacts of occupational licensing on the labor market may be more relevant for highly educated immigrants.

To give an idea of the type of occupations licensed workers perform, table 5 shows the ten most common occupations reported among licensed workers, again from the CPS probability sample. This is done separately for men and women as well as natives and immigrants for the full sample of licensed workers. Also, given the apparent relevance of the most educated group of workers in understanding the native/immigrant licensing gap, we repeat this exercise but for only the workers with the most education.

For both natives and immigrants, licensed women are frequently in nursing or a closely related occupation; 19.6 percent of licensed immigrant women are registered nurses, a further 17.1 percent are nursing orderlies, 3.1 percent are licensed practical nurses, 4.8 percent are physicians, 1.9 percent are health aides, and 1.7 percent are physical therapists. Thus, licensed immigrant women are well represented in the medical industry. After nursing, “hairdressers and cosmetologists” is the most common occupation category for licensed immigrant women at 8.6 percent, which is much higher than the rate for native licensed women (2.3 percent). Native women with a license are much more likely than immigrants to be teachers.

For workers at the highest education level with a license, native workers are much more likely than immigrants to be teachers; in total, 37 percent of native women with a GRAD level of education are some form of school teacher, while only 20 percent of immigrant women are teachers. For men, 23 percent of natives but only 11 percent of immigrants are teachers. So while likely not the full story, it seems that this occupation alone may be a large contributor to the significant native/immigrant licensing gap at the highest level of education; the data suggest that the teaching profession may be disproportionately unattractive or difficult to enter for immigrant workers.

On the other hand, while nursing is a common occupation among licensed workers, the rates are much lower for men than for women, especially for native male workers. Truck driver is a common occupation for both native and immigrant male licensed workers. Licensed immigrant men are much more likely to be physicians than native men, at 8.6 percent and 3.1 percent, respectively.

Considering the most educated group, where the native/immigrant licensing gap is most severe, we see for both immigrant men and women, physician is by far the most common occupation. Lawyer is the most common occupation among highly educated licensed male natives, but only 2.5 percent of highly educated licensed immigrant men are lawyers. Thus, for both men and women, while there are some interesting differences in the type of occupations that licensed workers engage in, there is a lot of overlap.

The descriptive statistics suggest large differences between natives and immigrants in their licensing rate. In order to statistically test whether, and to what extent, otherwise similar natives and immigrants differ in their probability of being licensed, we turn to our econometric model. We estimate a series of linear probability regressions where the dependent variable equals one if the individual reports having an occupational license, and zero otherwise:

where ( equals one if worker has a license in month , and zero otherwise; ( is a vector of individual and other characteristics; : is the fixed effect for state of residence ; and equals one if the worker is an immigrant, and zero otherwise. Years since migration values ( ( and ) equal zero for natives. We control for state of residence, age (introduced as a third-order polynomial), educational attainment (five categories), survey year, month of sample (i.e., first or fifth month), month of the year, whether the individual appears once or twice in the sample, racial and ethnic dummy variables (black, Asian, other, and Hispanic), a married dummy variable, and number of children. This estimation is performed on the full sample, as well as separately for men and women.

The results from these estimations are shown in table 6. As our focus is on immigration and licensing, our primary coefficients of interest are those on the immigrant dummy and years since migration, though we also show the coefficients for gender and educational attainment. The row “Mean” shows the mean of the dependent variable (i.e., the fraction of the sample that has an occupational license).

Before discussing the immigrant-specific results, first note the gender gap in licensing rates: controlling for other characteristics, women are 5.4 percentage points more likely than men to be licensed. While the focus of this paper is immigration and licensing, it is worth speculating briefly regarding this large gender licensing gap that has received little attention in the literature. In the presence of asymmetric information that might be more severe for women than men, women may be more likely to acquire an occupational license, since a license may serve as a signal of worker characteristics such as ability or labor market attachment. Alternatively, it may be that occupations that are more attractive to women than men (possibly due to non-pecuniary characteristics like time flexibility) are more likely to be licensed, so accessing these occupations requires holding a license. These crucial questions are left for future work to explore.

Consistent with figure 1, educational attainment has a strong, positive relationship with licensing status; workers with a BA are 12.5 percentage points more likely than high school dropouts to be licensed, while workers with more than a BA (GRAD) are 31.1 percentage points more likely.

The coefficient on the immigrant dummy variable is negative, statistically significant, and economically large an all estimations. Consistent with labor market assimilation, the probability an immigrant is licensed increases with years since migration—ten years after migration, the probability increases by 3.6 percentage points. In the full sample, we find that ten years after migration, immigrants are 6.5 (9.7-3.6+0.4) percentage points less likely to have an occupational license; given the overall rate of 18.9 percent, this corresponds to a 34.4 percent lower probability.

Considering the results by gender, the licensing gap ten years after migration is 7.5 percentage points for men and 4.7 percentage points for women, so the licensing gap between natives and immigrants is greater for men than women. Furthermore, the overall licensing rate for women is quite a bit higher than men (22.4 percent versus 15.6 percent). In percentage terms, immigrant men and women, ten years after migration, are 48.1 percent and 20.1 percent, respectively, less likely to be licensed than natives. Also, the effect of years since migration is weaker for men than women (i.e., male immigrants seem to assimilate more slowly in terms of licensing status than female immigrants). Ten years after migration, the licensing probability increases by only 1.4 percentage points for men but by 4.8 percentage points for women, and thus the gender licensing gap for immigrants grows with years since migration.

A prominent phenomenon among immigrant workers in the United States is occupational clustering— immigrants of the same nationality tend to work in similar occupations, especially when clustered in the same city (Patel and Vella 2013). A well-known example in the occupational licensing literature is Federman, Harrington, and Krynski (2006), which studies Vietnamese manicurists. In the presence of occupational clustering, where certain occupations may require a license, we might expect variation in occupational licensing rates by birthplace, as is the case for Vietnamese manicurists.

We explore this idea by dividing immigrants by region of birth. Figure 2 shows the license rates of each region relative to natives, for men and women separately. Two values per region are shown: (1) the raw difference in licensing rate and (2) the adjusted difference that accounts for worker characteristics. These adjusted differences are computed by adding region of birth to the regression specification in equation (1), and then using the coefficients on these region dummy variables, adjusted such that years since migration equals 15, which is approximately the mean of the sample.

Licensing rates are lowest for immigrants from Mexico and Central America for both men and women. Interestingly, and consistent with occupational clustering, we find high licensing rates for immigrants from the Caribbean, Southeast Asia, and Africa. Immigrants from these regions, especially women, tend to work in occupations such as nursing, for which occupational licensing is more prevalent. For example, 12.0 percent of immigrant women from Southeast Asia work as “Miscellaneous personal appearance workers,” a category that includes manicurists, while only 0.2 percent of native women work in that occupation.

Adjusting for worker characteristics expands the licensing gaps for many regions; the main driver for this difference appears to be educational attainment. For many regions of origin, educational attainment is relatively high, and since licensing rates increase with educational attainment, these immigrants have larger adjusted licensing rates and thus a large gap relative to otherwise similar natives. For example, female immigrants from English-speaking developed countries have a mean licensing rate that is higher than rates for natives; however, their adjusted licensing rate is lower, due largely to their high levels of education. Immigrants from Mexico, on the other hand, have a smaller adjusted licensing rate gap than their raw gap (though both are negative), due largely to their low average education levels.

Note that in addition to male immigrants from English-speaking developed countries, both male and female immigrants from Northern and Western Europe have low adjusted licensing rates. This finding is somewhat curious since immigrants from developed (especially English-speaking) countries may be more familiar with the US labor market than immigrants from other regions and are the least likely to be illegal immigrants, which would make acquiring a license less difficult; nevertheless, they are substantially under-licensed relative to natives. One reason for this large adjusted gap is that a substantial fraction of immigrants from these regions are highly educated (around 52 percent for those from Northern and Western Europe), and so comparing them to otherwise-similar natives means comparing them to the most-educated (and as previously discussed, most-licensed) group of workers, leading to a large potential licensing gap that does indeed appear in the data. This suggests that a lack of familiarity with the US labor market may not be a large contributing factor in explaining the gap in licensing rates between natives and immigrants.

We now turn to discuss the licensing probability results using SIPP data. Since many of the results are quite similar between the datasets, our discussion focuses on the novel contributions of the SIPP and the instances where the CPS and SIPP results diverge either in direction or substantially in magnitude.

We first estimate a linear probability regression model, as described in equation (1), where the dependent variable equals one if the worker has a license, and zero otherwise. We include similar controls as the CPS probability estimations. Specifically, all estimations control for an immigrant dummy variable, state of residence, age (introduced as a third-order polynomial), educational attainment (five categories), racial and ethnic dummy variables (black, Asian, other, and Hispanic), a married dummy variable, number of children, a union dummy variable, government worker dummy variable, service worker dummy variable (derived from industry code), and a paid-by-the-hour dummy variable. Notably, unlike the CPS, we cannot control for years since migration. We then add a dummy variable for immigrants’ English proficiency.

Results are shown in table 7. Without language ability controls, immigrants are 4.8 percentage points less likely to have a license; given the overall licensing rate of 16.4 percent, this represents a 29.3 percent lower probability of having a license. Immigrants proficient in English are 23.2 percent less likely than natives to have a license, while immigrants who do not speak English well are 43.3 percent less likely to have a license.

Male immigrants are 5.6 percentage points (42.1 percent) less likely to have a license relative to natives, while female immigrants are only 3.6 percentage points (18.2 percent) less likely. Female immigrants who speak English very well are only 11.6 percent less likely to have a license than native women, while male immigrants who speak English very well are 35.3 percent less likely than male natives to have a license. The values for female and male immigrants who do not speak English well are 32.8 percent and 55.6 percent, respectively. The difference between the licensing rates of proficient and non-proficient English speakers differs at the 10-percent significance level for both men and women.

Comparing the SIPP and CPS results, we find that at the mean years since migration (about 15 in the CPS), the results are very similar at 29.3 percent lower probability in the SIPP and 27.5 percent lower probability of having a license in the CPS. Notably, the SIPP results provide suggestive evidence that language proficiency, or the lack thereof, is an important contributor to the native/immigrant licensing difference, especially for female immigrants.

We end our discussion of occupational licensing attainment and immigrants by investigating one potentially critical immigrant characteristic that we have thus far not discussed: citizenship. As documented in Calvo-Friedman (2014), there are numerous examples of states in which eligibility for particular occupational licensure is restricted to citizens or to citizens and permanent residents. For example, a funeral home director in Massachusetts is required to be a citizen, while a funeral home director in New York must be either a citizen or a permanent resident. Thus it seems quite plausible that a lack of citizenship may impede an immigrant’s ability to acquire an occupational license.

Indeed, as expected, immigrants who are citizens are more than twice as likely to have an occupational license as non-citizens. However, citizens are also on average more educated and have been in the country longer; these traits are positively related to the probability of having an occupational license, as the results above show. To see the relationship between citizenship status and occupational licensing while controlling for other characteristics, we repeat the linear probability model estimation described in equation (1), but we add a control for whether the immigrant is a citizen. Also, we include only immigrants in the estimation, since we are interested in whether citizen status affects licensing rates within the immigrant group, and for brevity we only include the results from the CPS.

The results are shown in table 8 and include the full sample as well as the sample of men and women separately. We also repeat the estimation with and without the citizenship dummy variable, so each of our three samples has a pair of estimations. First, before discussing the citizenship results, it is worth noting that when focusing only on immigrants, the educational gradient in occupational licensing is much flatter than when considering all workers; immigrants with the highest level of education (GRAD) are only 15.9 percentage points more likely to be licensed than high school dropouts, whereas in the full sample that includes natives (table 6), this value was 31.1 percentage points. This result mirrors what is shown in figure 1.

Citizenship status, even in the presence of a rich set of other controls, is strongly related to occupational licensing attainment; in the full sample, immigrants who are citizens are 5.8 percentage points (45.7 percent) more likely to have a license than non-citizens. When looking at men and women separately, these values are 54.3 percent and 37.1 percent, respectively, suggesting that citizenship may be more relevant for male than female immigrants. Also, while years since migration continues to be informative even when controlling for citizenship status, the magnitude of the coefficients declines, suggesting that part of the “assimilation” process that years since migration captures is the naturalization process. Lastly, though not shown here, repeating these estimations for the SIPP we find that controlling for English proficiency only somewhat lowers the coefficient on the citizenship dummy variable. Thus it is unlikely that citizenship in the CPS is merely proxying for English language ability.

An obvious concern with these results is the strong potential for selection bias—immigrants who become citizens are probably not a random sample of the immigrant population. Since acquiring an occupational license is a costly investment, immigrants who acquire a license may also be the type who are likely to stay in the United States for a long period of time, perhaps permanently, and thus are also more likely to become citizens. It is beyond the scope of this paper to address this important issue. However, two things are clear: (1) many states restrict occupational licensing to only citizens and permanent residents for certain occupations, and (2) non-citizen immigrants are much less likely to be licensed than otherwise similar citizens. Using data from the 1990s, Bratsberg, Ragan, and Nasir (2002) find that naturalization of US immigrants accelerates their wage growth and reduces employment barriers. The increasing prevalence of occupational licensing in the economy since that time suggests that the returns to naturalization may have risen, a topic that deserves closer attention.

B. Licensing, Wages, and Hours Worked

We turn now to the relationship between occupational licensing and labor market outcomes, specifically wages and hours worked. We estimate the following regression model:

where ( is the labor market outcome of interest (i.e., either log wage or hours worked) for individual in month , ( is a dummy variable that equals one if the worker has a license in month , is a dummy variable that equals one if the worker is an immigrant, ( is a vector of control variables, and : is the fixed effect for state of residence . The interaction term tests if the effect of having a license differs between immigrants and natives.

The baseline estimation includes the same set of controls used in the license probability regressions. In addition, we include a number of other job-related controls that are not introduced in the probability estimations. These include controls for whether a worker is full-time or part-time, paid by the hour, a union member, or a government worker. We again perform these estimates on the full sample as well as separately for men and women. For brevity, only the immigrant dummy variable and the occupational licensing variables are shown. Table 9 shows the CPS results for both log wage (panel A) and hours worked (panel B), while table 10 shows the SIPP results, again for both log wage and hours worked.

We begin by discussing the log wage results from the CPS (table 9, panel A). The licensing coefficient is positive and statistically significant in all specifications. In the full sample without interacting licensing status with the immigrant dummy, we find a wage premium of 8.5 percent, which is somewhat low relative to the existing results in the literature, though it is higher than the results from Gittleman, Klee, and Kleiner (2018) and our own results from the SIPP (Kleiner and Krueger 2013; Pizzola and Tabarrok 2017). The licensing premium is much higher for women (12.3 percent) than men (4.5 percent), a finding that has received little attention in the literature.

The immigrant and license interaction terms are positive, statistically significant, and meaningfully large in all three samples; the wage premium for immigrants is 14.8 percent (7.8+7.0) for the full sample, while for natives it is only 7.8 percent, with the difference between these results significantly different at the 0.1 percent level. Considering the male and female samples separately, we find that immigrant men earn a much larger licensing premium than native men (3.7 percent versus 12.0 percent), while immigrant women also earn a higher premium than native women (11.8 percent versus 17.7 percent).

It is unclear why immigrants appear to benefit more than natives from holding an occupational license. One possible explanation is that, because an occupational license is a costly investment, it serves as a signal, and the value of that signal is greater for immigrants than natives. US employers likely have less information about immigrants than they do about natives (e.g., the quality of their educational credentials), and thus a license is a stronger signal for immigrants than natives. Alternatively, an occupational license may signal a lower probability of return migration and thus a stronger attachment to the US labor force, which may make employers more likely to train immigrant workers with a license relative to immigrants without a license.

Due to the positive interaction term between immigrant status and licensing status, the wage gap between licensed natives and immigrants is lower than the wage gap between unlicensed natives and immigrants. For unlicensed workers, the native/immigrant wage gap is 18.6 percent; however, comparing native and immigrant licensed workers, this wage gap narrows significantly to only 11.6 percent. Thus, immigrants able to acquire an occupational license do relatively well in the labor market compared to native workers. This result mirrors Blair and Chung (2017), which finds that occupational licensing can help to narrow the black/white wage gap.

To estimate the effect of occupational licensing on wages in the SIPP (table 10, panel A), we repeat a similar log wage regression as described in equation (2) with the same control variables on the SIPP linear probability estimations described in the previous section. In the full sample, we find a return to holding a license of 6.5 percent. Interacting the license and immigrant controls, we find a positive coefficient, which is consistent with the CPS results: immigrants with a license earn 8.8 percent higher wages, while natives with a license earn 6.2 percent higher wages. The interaction term, however, is not statistically significant at conventional levels, so we cannot rule out that natives and immigrants receive the same wage premium from having a license. Nonetheless, adding immigrant language proficiency controls results in the immigrant-license interaction term to be negative. This result suggests that holding a license may partly serve to proxy for English proficiency, which is consistent with the large, positive returns to English language proficiency for immigrants found in the literature.

Considering men and women separately, as in the CPS, we find a much larger licensing wage premium for women (11.0 percent) than for men (0.1 percent). In fact, we find no evidence of a licensing premium for men. Contrary to the wage results from the CPS, neither of the license and immigrant interaction terms are statistically significant at conventional levels, though both are positive. Adding English proficiency again results in the coefficient of the immigrant and licensing interaction term to become negative for both men and women, consistent with holding a license proxying for English ability.

Our wage results, using both datasets, identify a positive and statistically significant license premium. However, these estimates may not fully capture the relationship between licensing and worker earnings if hours worked is related to licensing status. In tables 8 and 9, panel B repeats the log wage analysis from panel A, with usual number of hours worked per week replacing log wages as the dependent variable.

In the CPS, we find that workers with a license work 1.1 more hours per week than those without a license. Interacting licensing status with immigrant status, we find a positive though statistically insignificant interaction term. Repeating these estimates separately for men and women, we find a much stronger (more positive) relationship between having a license and hours worked for men than women, though for both men and women the coefficient is positive and statistically significant. This larger effect of licensing on hours worked for men than women should, to some extent at least, offset the much larger wage premium for women than men found in panel A. One possible explanation for the overall positive licensing status and hours worked relationship is that workers who would tend to work more hours (for some exogenous reason, such as a relatively low preference for leisure) have additional incentive to acquire a license to boost their hourly wage.

The immigrant-license interaction term is large, negative, and statistically significant for men, which again would serve to reduce the overall earnings effect of having a license on immigrants versus natives. For women, the immigrant and license interaction term is positive and statistically significant at conventional levels, thus the larger wage premium for licensed immigrant women found in panel A understates the difference in the effect of holding a license on earnings between immigrant and native women.

Turning to the SIPP hours worked results (table 10, panel B), we find that as with the CPS result, there is a positive relationship between hours worked and having a license, and this positive relationship is also larger for men than women. Of note, the magnitude of the coefficient on licensing status is lower in the SIPP results than in the CPS results. The interaction terms between licensing status and immigrant status are negative, though they are statistically insignificant for men in all specifications and only marginally significant for women. Nonetheless, the SIPP and CPS differ notably in the relationship between immigrant women who hold a license versus those who do not.

Overall, while there are some differences between the SIPP and the CPS results in terms of hours worked, we find evidence that licensed individuals work more hours per week than unlicensed workers. This seems to suggest that the wage premium from being licensed likely understates the earnings premium from holding a license.