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Taking the Skill Bias out of Global Migration. (2018). Machado, Joël ; Elsner, Benjamin ; Biavaschi, Costanza ; Burzynski, Micha.
In: Working Papers.
RePEc:ucn:wpaper:201808.

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  1. International Migration Projections across Skill Levels in the Shared Socioeconomic Pathways. (2022). Tavoni, Massimo ; Emmerling, Johannes ; Shayegh, Soheil.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:8:p:4757-:d:795035.

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  2. Highly skilled, yet invisible. The potential of migrant women with a STEMM background in Italy between intersectional barriers and resources. (2021). Grimaldi, Rosa ; Crivellaro, Francesca ; Bolzani, Daniela.
    In: Gender, Work and Organization.
    RePEc:bla:gender:v:28:y:2021:i:6:p:2132-2157.

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  76. To calibrate this elasticity, we use σt = −0.04, as estimated by Parsons & Vézina (2018). Given that this externality is based on immigration, it directly affects the receiving countries. The sending countries — having no immigrants by assumption — can only be indirectly affected through general equilibrium effects. Figure E.5 displays the welfare effects of skill-biased migration without network effects in trade (baseline), with trade costs being reduced by medium and high skilled migrants (intermediate scenario) and with trade costs being reduced by high skilled migrants only (optimistic scenario). The overall welfare effect in the world is larger when we allow for network effects.
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  77. To incorporate a brain gain mechanism into the model, we endogenize the share of high-skilled stayers in the sending countries.48 Define shS = Hn Hn+Mn+Ln and shE = He He+Me+Le , respectively, as the observed share of high-skilled stayers and emigrants under the baseline scenario, and d shS and d shE as the equivalent shares under the counterfactual. We compute the new counterfactual share of high-skilled stayers as 48 This represents a reduced-form relationship. The underlying microfoundations have been described in Mountford (1997) and Stark et al. (1997). d shS = shS 1 + σb d shE − shE shE !
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  78. To verify the credibility of our model and calibration, we show that our model reproduces the effects found in studies on the labor market effects of immigration, for example Borjas (2003) or Dustmann et al. (2013). We simulate two counterfactual scenarios. First, we compare our baseline findings with the distributional impact of turning from a world without migration to the current world with migration. Next, we simulate a change from zero migration to today’s levels and skill composition of migration, while at the same time assuming that migrants and natives with the same skills are perfect substitutes and setting the market size effect to zero.
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  45. Evidence of Returns to Schooling in Africa from Household Surveys: Monitoring and Restructuring the Market for Education. (2003). Schultz, T..
    In: Working Papers.
    RePEc:egc:wpaper:875.

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  46. Cross-Country Inequality Trends. (2003). Acemoglu, Daron.
    In: Economic Journal.
    RePEc:ecj:econjl:v:113:y:2003:i:485:p:f121-f149.

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  47. Technological Change and the Education Premium in Canada: Sectoral Evidence. (2003). Fares, Jean ; Yuen, Terence .
    In: Staff Working Papers.
    RePEc:bca:bocawp:03-18.

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  48. Cross-Country Inequality Trends. (2002). Acemoglu, Daron.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:8832.

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  49. Wage and Productivity Dispersion in U.S. Manufacturing: The Role of Computer Investment. (2002). Troske, Kenneth ; Haltiwanger, John ; Foster, Lucia ; Dunne, Timothy.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp563.

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  50. So Many Rocket Scientists, so Few Marketing Clerks: Occupational Mobility in Times of Rapid Technological Change. (2002). Campos, Nauro ; Dabusinskas, Aurelijus .
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:3531.

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