PLAN DE CONTINGENCIAS ESTACION DE SERVICIO “JAMBELÍ”

5.1 Selection on observable skills

Are the insights derived from the previous Section robust to the inclusion of more information about how redistribution has changed over time, and across countries? To reach deeper into the association between the level of redistribution and the self-selection of migrants, we run the following linear regression:

Figure 10: Returns to skills and self-selection of immigrants - Counterfactual earnings

Notes: All variables are averages over the complete observation period. Sample are Italian immigrants aged 35-55.

Sources: AIRE, SWIID, own calculations.

s^∗_{i jtc}= α + β Rtc+ δ⁰Z_tc+ γ⁰X_{i jtc}+ ζ⁰M_c+ λj+ τt+ ϕc+ εi jtc, (5) where s^∗_{i jtc}is the skill level of individual i from the Italian region of origin j who registered in year t to the registry of Italians living abroad in country c, either measured by the relative educational position or by the predicted counterfactual log labour earnings that i would have obtained had he stayed in Italy.

The regression equation relates this measure of skill to R_tc, the average level of relative redistribution in country c in all years from t to the last year of the data (2015). Since t is usually the year of arrival of the migrant in the destination country, this definition of redistribution captures the effect of the tax-transfer-system on the entire income stream received by the migrant in that country.⁸

The same procedure is applied to other macroeconomic variables that are included in Z_tc. This vector of controls includes the unemployment rate, the growth rate of GDP, and the level of GDP per capita. These variables are indicators that may shape the income expectations of individuals that are potentially willing to migrate. X_{i jtc} is a vector of controls for individual characteristics: year of birth (polynomial of second degree), sex, Italian region of origin, month of birth, year of arrival of the first immigrated member of the household, and dummies indicating whether the individual has a prior

8Regressions that employ the level of redistribution only in the year of arrival yield similar results and are included in the Supplemental Material.

internal migration experience, was the first household member to arrive in the country of destination, rural or urban location of origin (definition: < 150 inhabitants/km²), and if the individual lives in the capital of the host country.⁹ M_cis a vector of controls for non-varying country characteristics that act as proxies for the costs of migrating to a particular country. It includes the distance from the Italian border, a dummy that equals 1 if this country and Italy signed an immigration agreement, and the share of migrants from the same Italian province residing in that particular country. λ_j, τ_t and ϕ_c are fixed effects for the Italian region of origin, the year of arrival, and the country of destination.

Standard errors are clustered at the country-year level.

Table4and Table5show the results of estimating equation (5) for our two definitions of skills on the sample of Italian immigrants worldwide. The coefficient of the variable that indicates the relative level of redistribution in the country of destination is negative, and highly significant in all but one specification of the model. An increase of relative redistribution by 10 percentage points is associated with a decrease in the degree of self-selection of Italian migrants of between 4 and 9 percent. These findings corroborate the insight suggested by the descriptive evidence in the previous Section; less egalitarian countries attract migrants with higher skills than those attracted by egalitarian countries.

Furthermore, the results hold for both measures of skills, relative education and earnings, as well as when we perform the analysis separately for men and women, for each Italian macro-region of origin, and only for those Italian migrants from provinces with at least 10,000 emigrants. Further results of the regressions which include inequality, as well as measuring redistribution by the coefficient of residual progression or the marginal tax rate progression with alternative measurements and datasets, yield the same patterns, and can be found in the Supplemental Material.

Interestingly, two variables that capture so-called network effects show the expected negative relationship with the selectivity of migrants. Past research argued that networks lower the cost of migration with a gradient, making it more attractive for low-skilled individuals to migrate, and hence lowering, on average, the pattern of positive self-selection of particular immigrant groups (e.g. Mck-enzie and Rapoport,2007;McKenzie and Rapoport, 2010). Our results confirm those findings. The presence of people from the same province of origin is negatively and significantly associated with the skill level of migrants. Moreover, the presence of a family member in the country of residence is associated with a 4 and 20 percent lower degree of skill selection for education and earnings,

re-9When the skill level is measured using earnings as dependent variable, only control variables are included in the regression that have not been used for the prediction of these earnings.

Table 4: Redistribution and degree of selection: Education

(1) (2) (3) (4)

Relative Redistribution -0.476^∗∗∗ -0.463^∗∗∗ -0.503^∗∗∗ -0.870^∗∗∗

(0.0982) (0.0643) (0.0676) (0.319)

Network size (province of origin) -0.422^∗∗∗ -0.411^∗∗∗ -0.317^∗∗∗

(0.0143) (0.0142) (0.0115) Other family members migrated earlier (0/1) -0.0449^∗∗∗ -0.0431^∗∗∗ -0.0392^∗∗∗

(0.00444) (0.00431) (0.00428)

Female (0/1) 0.0103^∗∗∗ 0.00956^∗∗ 0.00963^∗∗

(0.00385) (0.00386) (0.00374)

Rural place of origin (0/1) -0.0576^∗∗∗ -0.0566^∗∗∗ -0.0539^∗∗∗

(0.00280) (0.00277) (0.00277) Internal migration experience before emigration (0/1) -0.0235^∗∗∗ -0.0228^∗∗∗ -0.0204^∗∗∗

(0.00354) (0.00351) (0.00342)

Resident in the capital (0/1) 0.106^∗∗∗ 0.103^∗∗∗ 0.0872^∗∗∗

(0.00624) (0.00636) (0.00622) Distance of country of residence from Italian border (in 1000 km) -0.00293^∗∗∗ -0.00540^∗∗∗

(0.000760) (0.00111) Migration agreement between Italy and country of residence (0/1) -0.0843^∗∗∗ -0.0733^∗∗∗

(0.00689) (0.00777) Migration policies oriented towards high skilled (0/1) -0.0200^∗∗ 0.00428

(0.00867) (0.00973)

Notes: Sample are Italian immigrants aged 30-75. Dependent variable in the regressions is the relative educational position as individual measure of relative skills. Demographic controls include year of birth (polynomial of second degree), sex, Italian region of origin, month of birth, year of arrival of the individual and of the first immigrated member of the same household, a dummy indicating whether the individual was the first household member to arrive in the country of destination, rural or urban location of origin (definition: < 150 inhabitants/km2). Standard errors clustered at the country-year level. Statistical significance level * 0.1 ** 0.05 *** 0.01. Source: AIRE, own estimations.

Table 5: Redistribution and degree of selection: Earnings

(1) (2) (3) (4)

Relative Redistribution -0.918^∗∗∗ -0.704^∗∗∗ -0.836^∗∗∗ -0.398

(0.111) (0.0654) (0.0705) (0.336)

Network size (province of origin) -0.347^∗∗∗ -0.333^∗∗∗ -0.259^∗∗∗

(0.0475) (0.0472) (0.0536) Other family members migrated earlier (0/1) -0.198^∗∗∗ -0.196^∗∗∗ -0.193^∗∗∗

(0.00926) (0.00906) (0.00902)

Rural place of origin (0/1) -0.120^∗∗∗ -0.119^∗∗∗ -0.115^∗∗∗

(0.00653) (0.00640) (0.00619)

Resident in the capital (0/1) 0.0814^∗∗∗ 0.0782^∗∗∗ 0.0652^∗∗∗

(0.00517) (0.00521) (0.00496) Distance of country of residence from Italian border (in 1000 km) 0.00130 -0.00117

(0.000865) (0.00108) Migration agreement between Italy and country of residence (0/1) 0.00728 0.0223^∗∗∗

(0.00687) (0.00752) Migration policies oriented towards high skilled (0/1) 0.0155^∗ 0.0217^∗∗

(0.00844) (0.0102)

Unemployment rate 0.0158^∗∗∗ -0.00136

(0.00254) (0.00311)

GDP growth 0.0194^∗∗∗ 0.0203^∗∗∗

(0.00701) (0.00525)

GDP per capita 0.000565^∗∗ 0.00462^∗∗∗

(0.000223) (0.000692)

Notes: Sample are Italian immigrants aged 35-55. Dependent variable in the regressions is the predicted counterfactual log labour earnings in Italy as individual measure of relative skills. Demographic controls include month of birth, year of arrival of the individual and of the first immigrated member of the same household, a dummy indicating whether the individual was the first household member to arrive in the country of destination, rural or urban location of origin (definition: < 150 inhabitants/km2). Standard errors clustered at the country-year level. Statistical significance level * 0.1

** 0.05 *** 0.01. Source: AIRE, own estimations.

spectively. Furthermore, we observe that Italian immigrants originating from rural areas have a lower degree of selection, while those residing in the capital of their country of destination are more likely to be positively selected.

Among country characteristics, the distance of the country of destination from Italy is very weakly associated with selectivity. The same applies to the existence of a bilateral migration agreement between Italy and the country of destination. Unemployment, GDP growth, and GDP per capita in the country of destination tend to be positively associated with the degree of self-selection of Italian migrants.

5.2 Stochastic dominance

As discussed byBorjas et al.(2018), the Roy-Model of self-selection implies a first-order stochastic-dominance relationship between the skill distributions of movers and stayers. Investigating such a relationship can yield further insights into the role played by income redistribution in shaping migra-tion patterns.

We now test for stochastic dominance with respect to the behavior of Italian migrants. As argued by Chiquiar and Hanson (2005), the complete group of stayers might not be a proper comparison group for movers because of the selection that occurs on unobserved characteristics that also affect the distribution of earnings. We thus subdivide the group of stayers into two separate groups: i) internal migrants, i.e. individuals that migrated within Italy between regions; ii) non-migrants, i.e. individuals that reside in their region of birth. Movers, i.e. individuals in the AIRE dataset, are divided into two groups according to the amount of redistribution they experienced in the country of destination in comparison to the extent of redistribution in Italy (again, measured as the average redistribution from the year of arrival to the last available year in the dataset): i) Italian migrants in countries with higher redistribution than in Italy; ii) Italian migrants in countries with less redistribution than in Italy. Figure 11 plots the cumulative distributions of the corresponding skills, i.e. predicted earnings in Italy, of these four groups.

The results are consistent with the Roy-Model of self-selection. The skill distribution of migrants in countries with a low level of redistribution dominates the distributions of stayers and of migrants in countries with a high level of redistribution. At the same time, the skill distribution of migrants in countries with higher redistribution stochastically dominates the distribution of non-migrants, but

Figure 11: Cumulative distributions of counterfactual earnings

Notes: Counterfactual earnings of individuals have they stayed in Italy are log labor earnings predicted for all individuals in AIRE after running a Mincer-regression on SHIW data. Distributions for migrants are own estimations using AIRE.

Distribution of non-migrant and internal migrants are own estimations using SHIW. Sample are individuals aged 35-55.

is dominated by the distribution of internal migrants. A Kolmogorov-Smirnov test of equality of distributions shows that all of the differences between the distributions of the four groups visualized in Figure11are statistically significant.

5.3 Selection on unobservable skills

The variables we have hitherto employed to proxy for the individuals’ human capital may fail to capture idiosyncratic abilities and motivations that affect the individuals’earnings potential, but cannot be directly observed by the researcher. To assess the relationship between redistribution and the selection on those unobservable skills, we now conduct two empirical investigations. The aim of each is to ascertain whether the likelihood to attain certain occupational positions changes with the level of redistribution, holding observable skills constant. We apply a Probit model on a binary variable indicating the occupation status h_{i jtc} of individual i who registered in year t to the registry of Italians living abroad in country c. We adopt two different specifications for h: in one specification, this variable equals 1 if the individual is unemployed or inactive, and 0 if the individual is employed;

in the second specification, h_{i jtc} = 1 if the individual is an executive or manager, and h_{i jtc} = 0 if in another type of occupation or unemployed. The educational attainment of the individual, s^e_{i jtc}, is included in the equation as a binary variable that is 1 if the individual attained beyond compulsory education and 0 otherwise. Figure12highlights how the composition of Italians abroad varies across countries with respect to both educational attainment and occupation, and how this compares to those compositions in Italy.

We estimate the following model:

Prob(h_{i jtc}= 1) = Φ(ιRtc· s^e_{i jtc}+ β Rtc+ θ s^e_{i jtc}+ γ⁰X_{i jtc}+ δ⁰Z_tc). (6)

Individual-level covariates are included in X_{i jtc}, while country-level covariates appear in Z_tc. The marginal effect of the interaction term, determined through ι and computed at different values of R_tc, shows how the likelihood of being unemployed or of having a high occupational status (executive or manager) varies with redistribution for immigrants with high and low education, respectively. Be-cause of the lower and less stable attachment of women to the labor market, as evidenced in Figure 12, we restrict the sample for this analysis to men.

Figure 12: Occupation and Education of Italian immigrants aged 30-65 by country

Table 6: Selection on unobservable skills

Prob(Unemployed) Prob(Executive or Manager)

(1) (2) (3) (4) (5) (6) (7) (8)

Probit estimates

Beyond compulsory education (0/1) -0.299^∗∗∗ -0.380^∗∗∗ -0.288^∗∗∗ -0.415^∗∗∗ 1.184^∗∗∗ 0.507^∗∗∗ 1.163^∗∗∗ 0.657^∗∗∗

(0.0193) (0.0867) (0.0192) (0.0870) (0.0237) (0.0805) (0.0233) (0.0847)

Relative Redistribution 1.522^∗∗∗ 1.416^∗∗∗ 4.651^∗∗∗ 4.513^∗∗∗ -1.284^∗∗∗ -3.015^∗∗∗ 1.944 0.857

(0.183) (0.221) (1.548) (1.553) (0.196) (0.239) (1.324) (1.365)

Beyond compulsory · Relative Redistribution 0.223 0.352 2.041^∗∗∗ 1.545^∗∗∗

(0.225) (0.229) (0.246) (0.270)

Share of people from the same province in country of residence 0.233^∗∗∗ 0.242^∗∗∗ 0.134^∗∗ 0.139^∗∗ -0.771^∗∗∗ -0.740^∗∗∗ -0.469^∗∗∗ -0.462^∗∗∗

(0.0573) (0.0586) (0.0676) (0.0676) (0.0620) (0.0612) (0.0635) (0.0638) Other family members migrated earlier (0/1) 0.0589^∗ 0.0585^∗ 0.0590^∗ 0.0588^∗ -0.0661 -0.0689 -0.101^∗∗ -0.104^∗∗

(0.0318) (0.0318) (0.0318) (0.0319) (0.0456) (0.0456) (0.0452) (0.0453) Rural place of origin (0/1) -0.0752^∗∗∗ -0.0754^∗∗∗ -0.0815^∗∗∗ -0.0817^∗∗∗ -0.192^∗∗∗ -0.194^∗∗∗ -0.185^∗∗∗ -0.186^∗∗∗

(0.0165) (0.0165) (0.0166) (0.0166) (0.0179) (0.0180) (0.0181) (0.0181) Internal migration experience before emigration (0/1) 0.0178 0.0180 0.0151 0.0152 0.0727^∗∗∗ 0.0739^∗∗∗ 0.0761^∗∗∗ 0.0769^∗∗∗

(0.0232) (0.0232) (0.0235) (0.0235) (0.0158) (0.0158) (0.0159) (0.0160)

Resident in the capital (0/1) -0.0450 -0.0483 -0.0166 -0.0192 0.0389 0.0253 0.118^∗∗∗ 0.111^∗∗∗

(0.0363) (0.0367) (0.0384) (0.0385) (0.0247) (0.0247) (0.0241) (0.0241)

Unemployment rate -0.0340^∗∗∗ -0.0332^∗∗∗ 0.00630 0.00720 0.0669^∗∗∗ 0.0682^∗∗∗ -0.00495 -0.00185

(0.0115) (0.0115) (0.0160) (0.0161) (0.0122) (0.0122) (0.0130) (0.0131)

GDP growth -0.0137 -0.0133 -0.0183 -0.0185 -0.109^∗∗∗ -0.106^∗∗∗ 0.00377 0.00236

(0.0210) (0.0212) (0.0238) (0.0241) (0.0294) (0.0296) (0.0250) (0.0251) GDP per capita -0.0113^∗∗∗ -0.0114^∗∗∗ -0.0125^∗∗∗ -0.0117^∗∗∗ 0.00573^∗∗∗ 0.00571^∗∗∗ -0.00490 -0.00325 (0.00112) (0.00118) (0.00328) (0.00334) (0.00154) (0.00154) (0.00336) (0.00340)

Constant 33.09^∗∗∗ 33.17^∗∗∗ 32.93^∗∗∗ 33.06^∗∗∗ 60.01^∗∗∗ 61.01^∗∗∗ 58.67^∗∗∗ 59.45^∗∗∗

(2.768) (2.783) (2.762) (2.777) (2.717) (2.797) (2.783) (2.848)

Demographic controls Yes Yes Yes Yes Yes Yes Yes Yes

Country F.E. No No Yes Yes No No Yes Yes

Observations 106214 106214 106214 106214 106137 106137 106137 106137

Pseudo R² 0.056 0.057 0.060 0.060 0.234 0.235 0.250 0.251

Notes: Dependent variable in columns (1) to (4) is one if the individual is unemployed or inactive, and zero if in an employment situation. Dependent variable in columns (5) to (8) is one if the individual is an executive or manager, and zero if unemployed or in another type of occupation. Demographic controls include year of birth, year of arrival, italian region of origin, a dummy indicating whether the individual was the first household member to arrive in the country of destination, rural or urban location of origin (definition: < 150 inhabitants/km2), and dummy variables indicating if the individual has an internal migration experience prior to emigration and if he or she lives in the capital of the host country.

Standard errors clustered at the country-year level. Sample are Italian immigrants aged 30-65. Statistical significance level * 0.1 ** 0.05 *** 0.01. Source: AIRE, own estimations.

Table 6 shows the estimated coefficients of the Probit models. In the first and third columns of both specifications of the dependent variable, the coefficient on the interaction term is restricted to be zero. The marginal effects of the interaction terms for different levels of relative redistribution are plotted in Figure 13. Again, our findings are in line with the hypothesis of the Roy-Model: there is a positive relationship between returns to skills and the self-selection of immigrants on unobservable characteristics. Controlling for education, redistribution is associated with an increased likelihood of being unemployed or inactive, and a lower likelihood of being an executive or manager. However, in the case of the latter dependent variable, once country fixed effects are included, the coefficient ceases to be significantly different from zero in both applications. The reason for this could be that the largest variation in the level of redistribution takes place between countries.

Figure 13: Selection on unobservable skills

Notes: Dots show the predicted probabilities for different levels of relative redistribution. Left figure shows the marginal effects of the interaction term in model (2) on Table6, right figure shows the marginal effects of the interaction term in model (6).

The evidence in favor of the Roy-Model is reinforced by looking at the likelihood of being in each of those states for individuals with different educational attainments. As is evident from the marginal effects displayed in in Figure13, the likelihood of being unemployed is significantly lower for individuals with higher educational attainments. Interestingly, this likelihood increases, regardless of an individual’s level of education, with increasing degrees of redistribution. The greater is the difference in the likelihood between levels of education, the higher is the level of redistribution.

The same pattern is observed for the likelihood of being an executive or a manager. Particularly among individuals with higher educational levels, the likelihood of being an executive or a manager is substantially higher when the level of redistribution is low.

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