International Migration Projections across Skill Levels in the Shared Socioeconomic Pathways
Abstract
:1. Introduction
2. The SSP–RCP Scenario Framework
3. Econometric Model
4. Overlapping Generations Model
5. Projections of Migration and Demographic Outcomes
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. SSP Narratives
Appendix B. Econometric Model: Data and Results
Statistic | N | Mean | St. Dev. | Min | Max |
---|---|---|---|---|---|
population_origin | 38,416 | 35.929 | 127.241 | 0.115 | 1269.117 |
governance_origin | 24,800 | 0.531 | 0.195 | 0.128 | 0.954 |
gdppc_origin | 38,416 | 9862.675 | 15,351.970 | 176.507 | 105,723.400 |
gini_origin | 27,696 | 0.390 | 0.090 | 0.206 | 0.661 |
temperature_mean_origin | 38,416 | 19.165 | 6.845 | −1.313 | 28.878 |
adults_origin | 38,416 | 23.126 | 83.544 | 0.062 | 891.457 |
adults_s_origin | 38,416 | 11.515 | 43.661 | 0.005 | 545.809 |
adults_u_origin | 38,416 | 11.611 | 44.958 | 0.013 | 417.970 |
I_u_origin | 27,696 | 1619.118 | 2258.951 | 37.790 | 12,482.420 |
I_s_origin | 27,696 | 14,930.430 | 18,537.480 | 427.439 | 115,032.800 |
migration_stock | 50,338 | 6920.799 | 99,573.170 | 0.000 | 9,367,910.000 |
distance | 55,138 | 5525.726 | 4379.361 | 1.000 | 19,147.900 |
migration_flow | 24,144 | 4270.829 | 63,890.900 | 0.000 | 6,959,408.000 |
migration_probability | 19,883 | 0.001 | 0.006 | 0.000 | 0.257 |
migration_probability_u | 19,883 | 0.002 | 0.019 | 0.000 | 0.683 |
migration_probability_s | 19,883 | 0.002 | 0.018 | 0.000 | 0.756 |
temp_change_origin | 12,656 | 0.439 | 0.747 | −0.914 | 2.619 |
Pooled | High-Skilled | Low-Skilled | |
---|---|---|---|
R | 0.11 | 0.13 | 0.13 |
Adj. R | 0.10 | 0.11 | 0.11 |
Num. obs. | 25117 | 15629 | 15629 |
Appendix C. OLG Model Description
Appendix C.1. Preferences
Appendix C.2. Consumption
Appendix C.3. Production
Appendix C.4. Inequality
Appendix C.5. Equilibrium
Appendix C.6. Calibration
Appendix C.7. Solution Algorithm
- Run the model through time from 2000 to 2100 with 20-year intervals
- (a)
- Calculate the number of children and their education levels for all countries. For each country, solve the parents’ utility optimization problem given the current wage ratios of high-skilled to low-skilled labor and the projections of technology growth and climate impacts in different sectors (Equations (A26) and (A23))
- (b)
- Calculate the bilateral migration probabilities for every pair of countries using their current population, wages, and distance according to Equation (A1).
- (c)
- Redistribute the next generation’s population calculated in step (a) according to the migration probabilities obtained in step (b).
- (d)
- Update the next generation’s population and skill ratio and calculate the economic output and wages for the next time step as follows:These wages will be used to calculate the probability of migration in the next period as well as the utility optimization that the parents in next generation use to find the optimal skill ratio of their children (Equation (A23)). Therefore, the impacts of migration on local wages can influence the wage inequality and migration patterns in subsequent periods.
Appendix D. Alternative Setups
Appendix D.1. PPML Migration Probability Estimation
Pooled | High-Skilled | Low-Skilled | |
---|---|---|---|
Num. obs. | 25,117 | 15,629 | 15,629 |
Period | Destination | Constants | Sum | Probability | |||
---|---|---|---|---|---|---|---|
2000–2020 | Spain | 20.215 | −3.279 | −17.009 | 0.759 | 0.686 | 0.198% |
USA | 24.078 | −3.279 | −19.259 | 0.889 | 2.429 | 1.135% | |
2020–2040 | Spain | 20.215 | −3.387 | −17.208 | 1.971 | 1.591 | 0.491% |
USA | 24.078 | −3.387 | −19.491 | 2.057 | 3.257 | 2.597% | |
2040–2060 | Spain | 20.215 | −3.397 | −17.125 | 3.163 | 2.856 | 1.739% |
USA | 24.078 | −3.397 | −19.663 | 3.236 | 4.254 | 7.039% | |
2060–2080 | Spain | 20.215 | −3.342 | −16.917 | 4.353 | 4.309 | 7.437% |
USA | 24.078 | −3.342 | −19.761 | 4.422 | 5.397 | 22.074% | |
2080–2100 | Spain | 20.215 | −3.213 | −16.699 | 5.545 | 5.848 | 34.654% |
USA | 24.078 | −3.213 | −19.814 | 5.614 | 6.665 | 78.446% |
Period | Destination | Constants | Sum | Probability | |||
---|---|---|---|---|---|---|---|
2000–2020 | Spain | 8.547 | 2.819 | −6.438 | 0.204 | 5.132 | 0.513% |
USA | 18.287 | 2.819 | −7.289 | 0.239 | 14.056 | 1.406% | |
2020–2040 | Spain | 8.547 | 2.908 | −6.519 | 0.529 | 5.465 | 0.547% |
USA | 18.287 | 2.908 | −7.390 | 0.553 | 14.358 | 1.436% | |
2040–2060 | Spain | 8.547 | 2.910 | −6.500 | 0.849 | 5.806 | 0.581% |
USA | 18.287 | 2.910 | −7.470 | 0.869 | 14.596 | 1.460% | |
2060–2080 | Spain | 8.547 | 2.864 | −6.440 | 1.169 | 6.140 | 0.614% |
USA | 18.287 | 2.864 | −7.515 | 1.187 | 14.823 | 1.482% | |
2080–2100 | Spain | 8.547 | 2.786 | −6.358 | 1.489 | 6.464 | 0.646% |
USA | 18.287 | 2.786 | −7.532 | 1.507 | 15.048 | 1.505% |
Appendix D.2. Migration Policy
Appendix D.3. RCP2.6 Scenario
Appendix D.4. Climate Change Foresight
Percentage
Change in Total Population | SSP1 | SSP2 | SSP3 | SSP4 | SSP5 | |
---|---|---|---|---|---|---|
2000 | RCP2.6 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
RCP6.0 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | |
2020 | RCP2.6 | −0.21% | −0.22% | −0.23% | −0.28% | −0.21% |
RCP6.0 | −0.24% | −0.25% | −0.26% | −0.31% | −0.24% | |
2040 | RCP2.6 | −0.15% | −0.15% | −0.12% | −0.12% | −0.15% |
RCP6.0 | −0.20% | −0.21% | −0.20% | −0.22% | −0.20% | |
2060 | RCP2.6 | −0.17% | −0.16% | −0.11% | −0.11% | −0.17% |
RCP6.0 | −0.19% | −0.21% | −0.23% | −0.23% | −0.19% | |
2080 | RCP2.6 | −0.17% | −0.16% | −0.06% | −0.07% | −0.17% |
RCP6.0 | −0.19% | −0.22% | −0.33% | −0.35% | −0.19% | |
2100 | RCP2.6 | −0.17% | −0.16% | −0.01% | 0.02% | −0.17% |
RCP6.0 | −0.20% | −0.26% | −0.47% | −0.56% | −0.20% | |
Percentage
Change in Skill Ratio | SSP1 | SSP2 | SSP3 | SSP4 | SSP5 | |
2000 | RCP2.6 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
RCP6.0 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | |
2020 | RCP2.6 | 0.84% | 0.78% | 0.66% | 0.78% | 0.84% |
RCP6.0 | 0.95% | 0.89% | 0.76% | 0.87% | 0.95% | |
2040 | RCP2.6 | −0.67% | −0.59% | −0.50% | −0.62% | −0.67% |
RCP6.0 | −0.40% | −0.34% | −0.28% | −0.38% | −0.41% | |
2060 | RCP2.6 | −0.19% | −0.20% | −0.18% | −0.19% | −0.19% |
RCP6.0 | −0.13% | −0.01% | 0.15% | 0.06% | −0.15% | |
2080 | RCP2.6 | 0.04% | −0.28% | −0.54% | −0.41% | 0.07% |
RCP6.0 | 0.24% | 0.44% | 0.67% | 0.61% | 0.18% | |
2100 | RCP2.6 | −0.11% | −0.60% | −0.98% | −0.82% | 0.04% |
RCP6.0 | 1.37% | 1.34% | 1.19% | 1.13% | 1.15% | |
Percentage
Change in Total Migration | SSP1 | SSP2 | SSP3 | SSP4 | SSP5 | |
2000 | RCP2.6 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
RCP6.0 | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | |
2020 | RCP2.6 | −0.08% | −0.07% | −0.06% | −0.10% | −0.08% |
RCP6.0 | −0.12% | −0.12% | −0.10% | −0.14% | −0.12% | |
2040 | RCP2.6 | 0.03% | 0.05% | 0.09% | 0.11% | 0.03% |
RCP6.0 | −0.05% | −0.06% | −0.08% | −0.07% | −0.05% | |
2060 | RCP2.6 | −0.15% | −0.24% | −0.46% | −0.51% | −0.14% |
RCP6.0 | −0.24% | −0.39% | −0.71% | −0.75% | −0.23% | |
2080 | RCP2.6 | −0.02% | −0.01% | 0.03% | 0.01% | −0.01% |
RCP6.0 | −0.11% | −0.19% | −0.49% | −0.61% | −0.11% | |
2100 | RCP2.6 | −0.03% | −0.03% | −0.08% | −0.05% | −0.03% |
RCP6.0 | −0.19% | −0.36% | −1.03% | −1.18% | −0.17% |
Appendix E. Comparison with SSP Migration
Country | SSP1 | SSP2 | SSP3 | SSP4 | SSP5 | |
---|---|---|---|---|---|---|
Brazil | SSP | −0.33 | 0.34 | −0.15 | −0.25 | −0.66 |
OLG | −7.94 | −7.59 | −6.91 | −7.41 | −7.94 | |
China | SSP | −0.14 | −0.15 | −0.07 | −0.11 | −0.29 |
OLG | 0.07 | 0.12 | 0.52 | 0.57 | 0.07 | |
Egypt | SSP | −0.60 | −0.60 | −0.25 | −0.46 | −1.20 |
OLG | −9.16 | −8.65 | −7.32 | −7.52 | −9.16 | |
Germany | SSP | 3.36 | 3.75 | 2.53 | 4.26 | 4.60 |
OLG | 7.25 | 8.01 | 12.20 | 15.30 | 7.21 | |
India | SSP | −0.30 | −0.30 | −0.13 | −0.24 | −0.59 |
OLG | −5.42 | −5.61 | −6.75 | −7.63 | −5.42 | |
Indonesia | SSP | −0.70 | −0.69 | −0.29 | −0.52 | −1.39 |
OLG | −7.78 | −7.38 | −6.38 | −6.52 | −7.78 | |
Mexico | SSP | −2.31 | −2.32 | −0.96 | −1.80 | −4.60 |
OLG | −9.41 | −8.92 | −5.32 | −4.90 | −9.42 | |
Nigeria | SSP | −0.45 | −0.44 | −0.18 | −0.40 | −0.90 |
OLG | −6.09 | −5.64 | −5.58 | −5.91 | −6.09 | |
Pakistan | SSP | −1.79 | −1.80 | −0.75 | −1.69 | −3.58 |
OLG | −6.95 | −7.40 | −10.31 | −11.69 | −6.95 | |
Korea | SSP | 0.31 | 0.35 | 0.24 | 0.40 | 0.43 |
OLG | −0.50 | 0.07 | 3.16 | 4.77 | −0.51 | |
Russia | SSP | 2.19 | 2.84 | 2.41 | 3.13 | 2.80 |
OLG | 0.28 | 0.67 | 2.30 | 2.29 | 0.28 | |
South Africa | SSP | 2.06 | 2.75 | 2.35 | 3.03 | 2.62 |
OLG | −0.90 | −0.27 | 3.15 | 3.72 | −0.90 | |
USA | SSP | 3.15 | 3.54 | 2.42 | 4.09 | 4.27 |
OLG | 3.90 | 4.42 | 6.98 | 8.83 | 3.91 |
Appendix F. Additional Results
- In the SSP1, SSP2, and SSP5 scenarios, although low-skilled and high-skilled migration probabilities are in the same range, high-skilled migrants come from a faster growing population base;
- In the SSP3 and SSP4 scenarios, low-skilled migration probabilities are lower than high-skilled ones, while low-skilled migrants come from countries with larger populations.
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Region | Abbreviation | Color |
---|---|---|
Canada | CAN | |
Japan-Korea | JPN | |
Oceania | OCE | |
Indonesia | IDN | |
South Africa | ZAF | |
Brazil | BRA | |
Mexico | MEX | |
China | CHN | |
India | IND | |
Non-EU Eastern European | TEC | |
Sub Saharan Africa | SSA | |
Latin America-Caribbean | LAC | |
South Asia | SAS | |
South East Asia | SEA | |
Middle East-North Africa | MEA | |
Europe | EUR | |
USA | USA |
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Shayegh, S.; Emmerling, J.; Tavoni, M. International Migration Projections across Skill Levels in the Shared Socioeconomic Pathways. Sustainability 2022, 14, 4757. https://doi.org/10.3390/su14084757
Shayegh S, Emmerling J, Tavoni M. International Migration Projections across Skill Levels in the Shared Socioeconomic Pathways. Sustainability. 2022; 14(8):4757. https://doi.org/10.3390/su14084757
Chicago/Turabian StyleShayegh, Soheil, Johannes Emmerling, and Massimo Tavoni. 2022. "International Migration Projections across Skill Levels in the Shared Socioeconomic Pathways" Sustainability 14, no. 8: 4757. https://doi.org/10.3390/su14084757
APA StyleShayegh, S., Emmerling, J., & Tavoni, M. (2022). International Migration Projections across Skill Levels in the Shared Socioeconomic Pathways. Sustainability, 14(8), 4757. https://doi.org/10.3390/su14084757