Configuración de Spring

Occupational entry restrictions and the acquisition of an occupational license may have important implications for the economic assimilation of immigrants. In order to assess the importance in this case, the median monthly earnings of immigrant physicians that acquired a license are compared, at different points in time since arrival, to the median monthly earnings that immigrants would have received as native physicians.²⁶

The median monthly earnings of licensed immigrant physicians over time are sim-ulated using the coefficients from the quantile treatment effects model. The median earnings of licensed immigrants as comparable natives are simulated using the coef-ficients from a native physician median regression and the means of the covariates among the licensed immigrants. The native physician data are drawn from the Is-rael Central Bureau of Statistics Income Surveys of 1988 through 1995. The sample contains 324 observations on male and female native physician earnings (including

26The immigration literature usually assesses conditional economic assimilation in terms of mean earnings convergence (Chiswick (1978)) and/or the coefficient on time since migration in a mean earnings regression (Lalonde and Topel (1992)). See also Borjas (1999) for further discussion.

zeros for the unemployed). The covariates in the median regression are age and age squared, an indicator for being male, an indicator for being married, an indicator for being older than 46 and a full set of interactions with this latter age dummy.

Native physicians that are older than 46 have much stronger wage growth as a high percentage of them are specialists. Immigrant physicians are more comparable with younger native physicians since only 3% of the immigrants are specialists and 15%

are in specialist residency at the time of the survey.²⁷

The differences in median earnings between immigrants and comparable natives are illustrated in Figure 6. The figures show a large and rather persistent earnings gap over the first 5 years since arrival. The median earnings of immigrants are 25% and 34% the median earnings of comparable natives in months 24 and 60, respectively.

Although acquisition of a license has a large percentage impact on the earnings of immigrant physicians, acquisition of a license does not appear to substantially close the earnings gap between licensed immigrants and comparable natives at the general practitioner level. The excess wages that immigrants capture in the regulated occupa-tion may be tempered by consumer and/or employer preferences for native services.

It is also possible that the large and persistent gap is due to positive selection of native physicians into the licensed occupation in comparison to the negative selection among immigrants.

8 Conclusion

This paper uses novel data on the early labor market outcomes of Soviet immigrant physicians in Israel, and an exogenous immigrant re-training assignment rule, to iden-tify the returns to an occupational license. Instrumental variables estimates of the

27The earnings of younger native physicians were not observed to be adversely affected by the mass immigration (see Sussman and Zakai (1999)). The coefficients of the native physician median regression are available upon request from the authors.

returns are large and considerably higher than those estimated by OLS. The large returns to an occupational license are highly suggestive of excess wages due to occu-pational entry restrictions.

The returns to an occupational license are also measured using a quantile treat-ment effects model. The quantile treattreat-ment effects model is less sensitive to the inclusion of zero earnings for the unemployed as well as high earnings outliers. The quantile treatment effects estimates also yield large returns to a license, especially in the upper quantiles of the complier earnings distribution. More highly skilled prac-titioners thus benefit more from license acquisition than do less skilled pracprac-titioners.

The quantile treatment effects estimates also reveal negative selection into licensing status, contrary to the usual hypothesis of positive selection.

In order to illustrate how occupational licensing can lead to both excess wages and negative selection, a model of the decision to acquire a license is developed. The model, together with the empirical findings, suggests that the wages that high-skilled immigrant physicians earn as nonphysicians outweigh the lower direct costs that these immigrants face in acquiring a license. Licensing thus leads to lower average quality of service. However, the positive earnings effect of entry restrictions far outweighs the lower practitioner quality earnings effect that licensing induces.

Lastly, the analysis of median earnings convergence between licensed immigrant physicians and comparable natives shows that the importance of license acquisition for the economic assimilation of immigrants is not substantial. There is a large and persistent median earnings gap between immigrants and comparable natives.

Employer and/or consumer preferences for native services may temper the excess wages that immigrants capture in the regulated occupation.

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Table 1: Descriptive Statistics

% Married Upon Arrival 84.3 79.36

No. of Children under 18 Upon Arrival

% from City > 1,000,000 52.17 53.33

% Advanced Medical Degree 26.81 25.4

% Former Specialist 40.34 85.1

% Former General Practitioner 22.95 18.73

% Former Pediatrician 16.18 12.7

% Former OBGY 7.49 5.71

% Arrived in 1990 77.3 67.62

% Arrived in 1991 20.05 26.03

N 414 315

Notes: The Table reports means and percentages by assigned re-training track. Standard deviations are in parentheses. Monthly earnings are in 1994 New Israeli Shekels (NIS) where 1 NIS equals 0.33 US dollars. There are 382 exam track earnings observations and 294 observation track earnings observations (including zeros for the unemployed).

Table 2: OLS Estimates of the Returns to a Medical License

% Impact 1.0948 0.9786 0.904 1.1366 1.1366 0.0772

Physician − − − 2,324

Other Regressors NO YES YES YES YES YES

Root MSE 1,876 1,672 1,648 1,390 1,631 1,447

R² 0.0711 0.2847 0.3073 0.5086 0.3227 0.4702

N 676 676 676 676 181 181

Notes: Robust standard errors are in parentheses. Other regressors include dummies for age upon arrival, year of arrival months in Israel, gender, marital status, profession of spouse, number of children, size of last city of residence, Republic of origin, advanced medical degrees, previous specialist status, previous type of medical practice and type of reported earnings (after-tax and/or after other deductions). The discontinuity sample in columns (5) and (6) uses the subsample of observations between 14 and 26 years of previous physician experience.

Table 3: Reduced Form Estimates of the Effect of Track on Monthly Earnings,

Other Regressors NO YES YES YES NO YES YES YES NO YES YES YES

Root MSE 1,900 1,740 1,705 1,675 0.3961 0.3908 0.3854 0.3269 0.4891 0.4676 0.4639 0.4821

R² 0.0469 0.2257 0.2589 0.2857 0.038 0.0879 0.1153 0.226 0.0456 0.1506 0.1664 0.1755

N 676 676 676 181 729 729 729 203 729 729 729 203

Notes: Robust standard errors are in parentheses. Other regressors include dummies for age of arrival, year of arrival, months in Israel, gender, marital status, profession of spouse, number of children, size of last city of residence, Republic of origin, advanced medical degrees, previous specialist status, previous type of medical practice and type of reported earnings (after-tax and/or after other deductions). The discontinuity sample in Columns (4), (8), and (12) uses the subsample of observations between 12 and 26 years of previous physician experience. The licensed and physician regressions are linear probability models.

Table 4: 2SLS Estimates of the Returns to a Medical License

% Impact -0.827 0.0857 1.8185 2.6171 3.4221

Experience − − -1

Other Regressors NO YES YES YES YES

Root MSE 3,245 1,722 1,659 1,672 1,646

R² − 0.2417 0.2982 0.2883 0.3099

N 676 676 676 676 181

Notes: Robust standard errors are in parentheses. Licensed is instrumented with Track. Other regressors include dummies for age upon arrival, year of arrival, months in Israel, gender, marital status, profession of spouse, number of children, size of last city of residence, Republic of origin, advanced medical degrees, previous specialist status, previous type of medical practice and type of reported earnings (after-tax and/or after other deductions). The discontinuity sample in Column (5) uses the subsample of observations between 14 and 26 years of previous physician experience.

Table 5: Quantile Regression and Treatment Effects Estimates

Quantile Regression Estimates Treatment Effects Estimates

0.15 0.25 0.5 0.75 0.85 0.15 0.25 0.5 0.75 0.85

% Impact 0.9316 1.1748 0.8316 0.7825 0.588 0.4959 0.4992 1.4664 1.0764 1.6884 Pseudo R² 0.1626 0.2467 0.2397 0.2116 0.2055 0.119 0.1744 0.2315 0.1579 0.1748

N 676 676 676 676 676 548 548 548 548 548

Notes: Other regressors include a quadratic in previous physician experience, dummies for age upon arrival, year of arrival, months in Israel, Republic of origin, advanced medical degrees, previous specialist status, previous type of medical practice and type of reported earnings (after-tax and/or after other deductions). The discontinuity sample is the subsample of observations between 14 and 26 years of previous physician experience. Bootstrapped standard errors are in parentheses. The bootstrapped standard errors when licensed are treated as endogenous and adjusted for the first step estimation.

Table 6: Earnings Quantiles without a License Quantiles

0.15 0.25 0.5 0.75 0.85

A. Full Sample

Licensed Immigrants 239 515 760 1,476 1,643

Unlicensed Immigrants 385 548 1,199 1,859 2,563

% Selection Bias -0.379 -0.0597 -0.3656 -0.2059 -0.359 B. Discontinuity Sample

Licensed Immigrants − − 751 − −

Unlicensed Immigrants − − 1,016 − −

% Selection Bias − − -0.2606 − −

Notes: The table reports monthly earnings quantiles without a license for immigrants that acquired a license and for immigrants that did not acquire a license. Earnings without a license for immigrants that acquired a license are calculated using quantile treatment effect estimates. Earnings without a license for immigrants that did not acquire a license are calculated using quantile regression estimates.

The discontinuity sample is the subsample of observations between 14 and 26 years of previous physician experience.

Proportion

Figure 1: Track Assignment, License and Employment OutcomesPhysician Experience in former USSR Proportion on Observation Track Proportion Licensed Proportion Employed As Physicia

5 10 15 20 25 30

0 .2 .4 .6 .8 1

Mean Monthly Earnings (NIS)

Figure 2: License Acquisition and Monthly EarningsPhysician Experience in former USSR

Proportion

Mean Monthly Earnings Proportion Licensed

1 5 9 13 17 21 25 29

2000 2200 2400 2600 2800

.7 .8 .9 1

Proportion

Figure 3: License and Physician Employment Outcomes - ResidualsPhysician Experience in former USSR License Probability Residuals Physician Employment Residuals

1 5 9 13 17 21 25 29

-.05 0 .05 .1 .15

Mean Monthly Earnings Residuals

Figure 4: License Acquisition and Monthly Earnings - ResidualsPhysician Experience in former USSR

License Probability Residuals

Mean Monthly Earnings Residuals License Probability Residuals

1 5 9 13 17 21 25 29

-200 -100 0 100 200

-.05 0 .05 .1 .15

Mean Monthly Earnings Residuals

Figure 5: Physician Employment and Monthly Earnings - ResidualsPhysician Experience in former USSR

Physician Employment Residuals

Mean Monthly Earnings Residuals Physician Employment Residuals

1 5 9 13 17 21 25 29

-200 -100 0 100 200

-.05 0 .05 .1 .15

NIS

Figure 6: Median Earnings ConvergenceMonths Since Arrival Licensed Immigrants Licensed Natives

5 10 15 20 25 30 35 40 45 50 55 60

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

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