Income maximization and the selection and sorting of international
Oct 25, 2016 - c) Positive sorting: countries with large absolute skill-related wage .... final sample of 15 (OECD) destination countries and 102 source countries.
Income maximization and the selection and sorting of international migrants
by Jeffrey Grogger and Gordon H. Hanson (2011) Presented by Pablo Jaramillo and Arnaud Wolff
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I. Introduction a) Context
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Most international migrants leave home bound for rich nations – In 2005, 40.9% of the global emigrant population resided in just eight rich economies, with 20.2% living in the US alone Another striking feature of international labor flows is that the more educated are those most likely to move abroad
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I. Introduction b) Positive selection: the more educated are more likely to move abroad
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I. Introduction c) Positive sorting: countries with large absolute skill-related wage differences attract a disproportionate share of more educated emigrants
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II. What is the aim of the paper? What do they want to prove and how are they going to proceed?
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They want to evaluate the contribution of wage differences to the destination choice of skilled migrants To do that, they construct a theoretical model and then estimate the: – scale of migration – selectivity of migrants in terms of schooling – sorting of migrants in terms of schooling
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III. Contribution of the paper to the literature
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The authors address conflicting results on migrants selectivity – Borjas (1987) predicts negative selection in most South-to-North migration The authors want to reconcile the Roy (1951) model with the strong positive selection seen in the data – Absolute wage differences (linear utility) are more important than relative wage differences (log utility) for migrant selection The authors take the role of taxes into account in the destination decision of the migrants
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IV. Theoretical comparison of the linear and log frameworks a) The wage and the cost function
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Notations: – i = migrant – s = source-country – h = destination-country – j = skill level (primary (1), secondary (2) or tertiary (3) education) Wage function j 2 2 3 3 – W ish = exp[µh + δh Dis + δh Dis ] Cost function j 1 1 2 2 3 3 – C ish = fsh + gsh Di + gsh Di + gsh Di
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IV. Theoretical comparison of the linear and log frameworks b) Utility functions
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Linear utility function j j j j – U ish = α[Wih − Cish ] + ish where α > 0 Log utility function j j j j λ – U ish = [Wih − Cish ] exp[vish ] where λ > 0 The workers choose whether and where to emigrate so as to maximize their utility
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IV. Theoretical comparison of the linear and log frameworks c) Scale equation: fraction of population that migrates
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The scale equation under linear specification –
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ln
j Esh Esj
j = α[Whj − Wsj ] − αfsh − αgsh
The scale equation under log specification –
ln
j Esh Esj
= λ[lnWhj − lnWsj ] − λmjsh with mjsh =
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j fsh −gsh j Wh
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IV. Theoretical comparison of the linear and log frameworks d) Selection equation: relative emigration of high-skill versus low-skill individuals
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The selection equation under linear specification –
IV. Theoretical comparison of the linear and log frameworks f) Predictions
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Their model predicts that an increase in the absolute difference in earnings between high and low-skilled workers in destination countries (Wh3 − Wh1 ) causes: n migration from source countries to rise for the more educated ones n the mix of migrants to become more skilled
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V. Data and illustrations a) Data description
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Data from Docquier et al. (2009) n stock of migrants of 25 years and older, by level of education (primary, secondary and tertiary) n final sample of 15 (OECD) destination countries and 102 source countries Wage data from Luxembourg Income Study (LIS) and World Development Indicators (WDI) th n 20 percentile of income distribution as low-skill wages and 80th percentile of income distribution as high-skill wages Data on taxes only for destination countries
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V. Data and illustrations b) This graph shows positive selection in the data
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V. Data and illustrations c) This graph shows relative differences in wages
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V. Data and illustrations d) This graph shows absolute differences in wages
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VI. Estimation procedure a) Scale equation: fraction of population that migrates
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The scale equation under linear specification – –
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ˆj E
j j j 3 ln Eˆsh j = α[Wh − Ws ] + xsh β + I(j = 3)xsh β + ηsh s with xsh a vector of characteristics of the source-destination pair (geographic and linguistic distance, etc.) I(A) is a dummy variable equal to 1 if A is true, 0 otherwise
The scale equation under log specification –
ln
ˆj E sh ˆsj E
j = λ[lnWhj − lnWsj ] + xsh θ + ηsh
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VI. Estimation procedure b) Selection equation: relative emigration of high-skill versus low-skill individuals
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The selection equation under linear specification –
VI. Estimation procedure c) Sorting equation: skill composition of migrants by destination
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The sorting equation under linear specification –
ˆ3 E
ln Eˆsh = α[Wh3 − Wh1 ] + xsh γ + τs + ηsh 1 sh
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The sorting equation under log specification –
ˆ3 E
ln Eˆsh = λδh3 + ρs + ηsh 1 sh
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VII. Main results a) Regression results from linear-utility model
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VII. Main results b) Regression results from log-utility model
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VII. Main results c) Summary
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The linear specification predicts positive selection, which is consistent with the data illustrations
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The log specification predicts negative selection
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Both specifications predict positive sorting. Why?
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Post-tax wage differences are more powerful to explain positive sorting compared to pre-tax wage differences – this may explain why Canada and US (which have less progressive tax systems) receive such a large share of skilled migrants
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VIII. Robustness checks a) They first check on alternative wage measures and PPP-adjusted wages
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VIII. Robustness checks b) They also check for the country where education has been done, quality of university, and networks
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IX. Decomposition of the immigrant skills gap
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X. Conclusion
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The authors showed that migrants for a source-destination pair are more educated relative to non-migrants, the larger is the skill-related difference in earnings between the destination country and the source – Positive selectivity is stronger where the reward to skill (in levels) in the destination is relatively large The relative stock of more educated migrants in a destination is increasing in the level earnings difference between high and low-skilled workers – The income maximization hypothesis has been confirmed
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XI. Critical assessment
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The generalization of the fact that they obtained meaningful estimates of wage differences from a commonly available data source Due to lack of data, the empirical strategy doesn’t account for individual migratory policies of destination countries Their specification of the log-utility model doesn’t take into account liquidity constraints, which could restrict emigration of low-skilled migrants
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