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## Occupation-Skill Mismatch and its Determinants

In this paper, two methods of computing the occupation-skill mismatch are em-ployed. The first method due Verdugo and Verdugo (1989) requires computing the mean and standard deviation of the education for each occupation. Under this method, overeducated (respectively undereducated) workers are defined as work-ers whose education level is more (respectively less) than one standard deviation above (respectively below) the mean for that occupation. The second method first proposed by Kiker et al. (1997) computes mismatch by using the modal years of ed-ucation for each occupation. Workers are classified as overeducated (respectively undereducated) if their education attainment is higher (respectively lower) than the modal education attainment associated to their occupation.

Following these methods, we compute our occupation-skill mismatch variables using the three digit Classificação National de Profissões (CNP 94). The CNP is a stan-dard detailed classification of occupations which contains four digits. Limiting our-selves to the third digit leaves us with 276 occupations. However, in order to mini-mize measurement errors, we follow Tsai (2010) and assume sector heterogeneity in valuating education. Therefore, for each year, the modal and mean education attain-ment is computed by occupation and by sector using the total sample of workers whatever their migration status .

. The estimates show that depending on the method used (range method or modal procedure),the incidence of over-education for the full sample varies between 33 and 52 percent. They also show that the job-skill match is less likely among highly-skilled workers than among low-skilled ones. In order to compute the probability of being overeducated, correctly matched or undereducated by country and year, a multinomial logit model is employed. This model can be expressed as: eβ j Xi ∑3k=1 eβk Xi Yij |Xi = ; i = 1, 2…, n; j = 1, 2, 3. (1.7).

**The Effects of Mismatch on Selection of Immigrants**

From the theoretical section, we observed that the selection of immigrants is deter-mined by wages, occupation skill mismatch, and costs of migration. We can use equation 6 to analyze the effects of occupation-skill mismatch on the selection of immigrants by using the following econometric specification. lnSit = γlnSit−1 + α0OEdi f fit + α1Wagedi f fit + β� Xit + ηi + µt + �it (1.8).

where Sit is the ratio of highly-skilled to low-skilled flows of migrants from country i in time t, and OEdi f f is the difference between the probability of being overedu-cated for highly-skilled and low-skilled immigrants computed from the multinomial regression estimates. Wagedi f f is the hourly wage difference between highly-skilled and low-skilled immigrants in Portugal. It is worth mentioning that, unlike Chiquiar and Hanson (2005), due to data limitations, wages at origin are excluded from the analysis. X is a vector of time-variant country characteristics that affects migration flows such as GDP per capita and population, while ηi and µt are country fixed-effects and time fixed-effects respectively.

To estimate the above specification, we employ the system generalized method of moments (SGMM) proposed by Blundell and Bond (1998). Unlike the Arellano and Bond (1991) first-differenced generalized method of moments (DGMM) estima-tor, the SGMM estimation technique uses both the level equation of Equation 9 and its first difference. According to Blundell and Bond (1998), compared to the DGMM estimator, the SGMM estimator improves efficiency especially when the dependent variable is highly persistent and the variance of the unobserved individual hetero-geneity is high. Both the SGMM and DGMM estimators rely on the assumption that the first difference errors are autoregressive of order one.

Throughout the estimation, we assume that the lagged diaspora variable is pre-determined while the rest of the variables in Equation 9 are endogenous thus requir-ing instruments for these variables. These instruments are obtained by using the lags of the variables in the model. Fortunately, the validity of these instruments can be tested using the Sargan overidentifying restriction test.

To estimate the above specification, we employ the system generalized method of moments (SGMM) proposed by Blundell and Bond (1998). Unlike the Arellano and Bond (1991) first-differenced generalized method of moments (DGMM) estima-tor, the SGMM estimation technique uses both the level equation of equation 8 and its first difference Blundell and Bond (1998). According to Blundell and Bond (1998), compared to the DGMM estimator, the SGMM estimator improves efficiency espe-cially if the dependent variable is highly persistent and when the variance of the unobserved individual heterogeneity is high. Both the SGMM and DGMM estima-tor rely on the assumption that the first difference errors are autoregressive of order one.

Throughout the estimation, we assume that the variable lagged diaspora is pre-determined while the rest of the variables in equation 9 are endogeneous; thus re-quiring instruments for these variables. These instruments are obtained by using the lags of the variables in the model. Fortunately, the validity of these instruments can be tested using the Sargan overidentifying restriction test.

**Mismatch and Selection of Immigrants**

Table 1.4 shows how occupation-skill mismatch affects the selection of immigrants. As mentioned earlier, both definitions of occupation-skill mismatch (range and mode) are employed. However, irrespective of the definition, we find a significant negative effect of mismatch on selection. Specifically, column 1 of Table 4 shows how mismatch affects selection using the range definition. We observe that the coefficient of over-education differences as expected is negative and statistically significant at 5 percent. These results suggest that when the probability of overeducation is greater for the highly-skilled than for the lowskilled, the former are more likely to migrate compared to the latter (negative selection). Column 2 of Table 4 shows similar findings though with a coefficient of higher magnitude. These results are consistent with our theoretical predictions that indeed the selection of immigrants is also explained by skill mismatch.

The estimated coefficients of the control variables included in the estimation have the expected signs with the exception of the coefficient associated to the wage differ- ence of the highly-skilled and low-skilled. The potential reason for the negative sign of the coefficient is due to omitting the wages prevailing in the country of origin. As shown in Grogger and Hanson (2011), estimating the selection equation using origin country wages is crucial. However, in this paper, we do not have data on wages in the origin countries. As observed elsewhere in Beine et al. (2011), diaspora leads to negative selection of immigrants. The reason for this negative effects stems from the established fact that diaspora provides networks assistance that tend to reduce the cost of migration especially for the unskilled. Moreover, from the coefficient of GDP per capita, the results suggest that immigrants from richer countries are positively selected.

### Survey and Sampling Framework

The survey data used in our work were collected using a representative sample of 584 households across 60 enumeration areas in the Upper River Region of the Gam-bia. The enumeration areas were randomly chosen using population size propor-tional sampling based on the Gambia 2013 census. In each enumeration area, a random sample of 10 eligible households was drawn. Eligibility was determined by asking whether there is young man with ages 16-25 belonging to the household. If the household have more than one youth within the eligibility age category, one would be randomly selected. In each of these households, after surveying the house-hold head, the sampled young males were also surveyed.

The households were sampled using a simple random walk within each EA. Enu-merators surveyed every nth household, where the nth household depended on the size of the EA. Once they sampled the nth household, the participation criterion of the household was ascertained by asking the household whether the household had at least one young man with ages between 16-25 years. Households that did not sat-isfy this criterion were replaced by the geographically closest household to the right. Following this sampling procedure, 595 households were finally surveyed. Out of these households, a sample of 584 male youths were also surveyed, of which 406 participated in the experiment. Initially, enumerators were instructed to pick every second household to participate in the experiment. However, this strategy was sub-sequently discarded to allow one sampled young to participate in each household. The fieldwork took place in May 2017.

#### Lab-in-the-field Experiment

The experiment was implemented as a simple lab-in-the field game in which partici-pants were hypothetically endowed with 100,000 Gambia Dalasis (GMD)5. We frame the participants’ decisions as migration decisions with a 10-year time horizon. The precise framing of the experiment to players is provided in Appendix 6.

The experimental subjects must play 16 different rounds of an incentivized game in where migration-related decisions must be made, depending on different combi-nations of four different scenarios for the probability of dying en route to the mi-gration destination and for the probability of obtaining legal residence status at the destination.

The four scenarios in the games were 0, 10, 20, and 50 percent probability of dy-ing in the migration route, and 0, 33, 50, and 100 percent probability of obtaining a legal residence permit or asylum status at destination. These numbers were deter-mined based on data from our pilot survey, and other official databases. According to the International Organization for Migration (2017), 181,436 migrants arrived in Italy through the sea while 4,581 migrants lost their life from January to December 2016. These figures provide a lower bound for the mortality rate at sea, estimated at 2.46% deaths of attempted migration journeys. In addition, we obtained the proba-bility of dying en route by adding the probability of dying en route before reaching the sea. The MHUB (2017) survey reports the incidences of cases where migrants report dead bodies along the way (including the Sahara Desert, Libya, and Mediter-ranean Sea). According to the data from the January 2017 survey, 44% of respondents reported witnessing one or more dead in Libya, 38% in the Sahara, 15% in the Sea, and 3% in transit countries such as Niger. Combining the probability of dying at sea of 2.5% and the incidences of witnessing migrant deaths en route of 15%, we estimated the overall probability of dying en route as 16.5%. In the experiment, we use 20% as a proxy for the actual death rate over the migration route given the likely undercount of fatalities. The 50% threshold for the probability of dying matches expectation data from our pilot survey. Our pilot survey elicited the expected prob-ability of dying from 20 young males of ages 16 to 25 years from the region of the study. On average, the respondents expect that 5 out of 10 Gambians die along the “Backway”, corresponding to a 50 percent probability of dying. In addition, this sur-vey also reported the expected probability of obtaining a legal residence or asylum status.

**Table of contents :**

**1 Occupation-Skill Mismatch and Selection of Immigrants: Evidence from the Portuguese Labor Market **

1.1 Introduction

1.2 Related Literature

1.3 Methodology

1.3.1 Theoretical Framework

1.3.2 Data and Descriptive Statistics

1.3.3 Occupation-Skill Mismatch and its Determinants

1.3.4 The Effects of Mismatch on Selection of Immigrants

1.4 Results

1.4.1 Occupation-Skill Mismatch and its Determinants

1.4.2 Mismatch and Selection of Immigrants

1.5 Conclusion

1.A Appendix

1.A.1 Definition of Variables

1.A.2 Number of Workers

**2 Understanding Willingness to Migrate Illegally: Evidence from a Lab-inthe- Field Experiment **

2.1 Introduction

2.2 Country Context

2.3 Methodology

2.3.1 Survey and Sampling Framework

2.3.2 Lab-in-the-field Experiment

2.3.3 Descriptive Statistics

2.4 Econometric approach and main empirical results

2.4.1 Estimation strategy

2.4.2 Empirical Results

Main Results: Willingness to Migrate Illegally

Heterogeneous effects: expectations

Are experimental subjects behaving rationally?

2.4.3 Willingness to pay to migrate and willingness to receive to forgo migration

2.4.4 Do lab migration decisions reflect actual migration decisions? .

2.5 Conclusion

2.A Appendix

2.A.1 Flows of Illegal Migrants into Europe

2.A.2 Lab-in-the-Field Experiment Framing

2.A.3 Show Cards

**3 Polygamy, Sibling Rivalry and Migration **

3.1 Introduction

3.2 Related Literature

3.3 Data and Descriptive Statistics

3.4 Empirical Framework

3.4.1 Empirical Methodology

3.4.2 Identification Strategy

3.5 Empirical Results

3.5.1 Polygyny and Migration

3.5.2 Mechanisms

3.6 Conclusion

3.A Appendix

3.A.1 Figures

**Bibliography**