## The Macroeconomic Effects of Large Immigration Waves

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### Abstract and main findings
- Approach: combine analysis of episodes of large immigration waves with instrumental variables techniques to measure dynamic macroeconomic effects on destination countries.
- Main findings for advanced economies (OECD):
  - Large immigration waves raise domestic output and productivity in both the short and the medium term.
  - A 1 percentage point increase in the ratio of the immigrant flow relative to total employment raises output by almost 1 percent by the fifth year.
  - No evidence of negative effects on aggregate employment of the native-born population.
- Main findings for refugee flows into emerging and developing countries:
  - Analysis does not find clear evidence of macroeconomic effects on the host country.
  - Results are consistent with evidence that refugee immigrants are at a disadvantage compared to other types of immigrants.

### Motivation and conceptual framework
- Challenge: disentangling macroeconomic effects of migration from drivers of migration (reverse causality where economic prospects draw migrants).
- Identification strategy:
  - Focus on large immigration waves more likely driven by external forces in source countries.
  - Construct an instrumental variable (IV) based on total emigration from other countries and the share of immigrants already hosted in the destination country (leveraging migrant network effects).
- Definitions:
  - “Migrant”: non-refugee migrants.
  - “Refugee”: individuals forced to leave their country to escape war, persecution, or natural disaster (United Nations definition).

### Data and recent trends (selected statistics preserved exactly)
- Between 1990 and 2019, the migrant population increased by 120 million people, to reach 270 million.
- The number of migrants has remained stable at 3 percent of the world population.
- Immigrants make up about 12 percent of the population in advanced economies, up from 7 percent in 1990.
- Between 1990 and 2019, the share of migrants from emerging and developing economies to advanced economies rose from 4 to 9 percent of the advanced economy population.
- Migration sample and scope:
  - Estimation based on a sample of 34 OECD countries, the vast majority of which are advanced economies (29 countries based on the IMF World Economic Outlook income classification).

### Methodology and estimation procedure
- Three-step methodology:
  1. Identify “large” immigration episodes: an annual inflow (as a share of population) greater than the host country’s median inflow during 1980-2018 and greater than the median inflow experienced by all OECD countries during the previous five-year period and the following five-year period. Inflow values set to zero if they do not meet this “shock” criterion.
  2. Construct appropriate instrumental variables (IVs) to capture exogenous components of immigration flows:
     - Advanced economies: shift-share IV following Card (2009) constructed from past stocks of immigrants from different origin countries.
     - Refugee flows into EMDEs: IV combining historical bilateral migration flows and proximity variables from the migration gravity literature.
  3. Use a two-stage local projections regression (Jordà 2005) to trace the dynamic impact of immigration shocks on macroeconomic variables (output, employment, productivity).
- Rationale: difficult conditions in source countries are more likely to trigger sudden migration surges, helping to isolate migration driven by emigration rather than destination-country economic booms.

### Instrument construction for large migration inflows (non-refugee)
- Shift-share IV specification:
  - Constructed as the sum across origins j of the share of the stock of migrants from origin j in destination i over the past 5 to 10 years multiplied by the total outflow of migrants, ΔMjt, from origin j in year t.
  - Instrument winsorized at the top one percent (upper bound only).
  - Resulting winsorized instrument correlation with actual flows: 83 percent over the shock episodes.

### Instrument construction for refugee inflows
- Refugee shock definition:
  - An inflow (as a share of population) that is within the country’s top 10th percentile of inflows during the period 1980–2018 and is also greater than the top 10th percentile (relative to the recipient country’s population) experienced by all countries in the world during the previous five-year period and the following five-year period.
  - Shock must be sustained for at least two consecutive years.
  - Mean refugee shock episode: 0.96 percent of the host country population per year.
  - Standard deviation comparison: refugee shocks 1.5 versus migration shocks 0.43.
- Refugee IV design:
  - Captures proximity determinants: combines historical flows and gravity-style proximity variables (distance and contiguity).
  - Functional form multiplies a share proxy by total refugee outflows ΔRjt from origin j in year t.
  - Gravity coefficients used: 훼 = 0.4, 훾 = 0.8, 휁 = 0.6.
  - Instrument winsorized at the top one percent (upper bound only).
  - Resulting correlation with actual refugee flows: about 72 percent over the shock episodes.
- Limitation: winsorizing means extremely large refugee inflows (episodes > 4 percent of recipient population, e.g., Colombia, Jordan, Lebanon) are not fully captured.

### Estimation strategy and variables
- Empirical framework: local projections methodology (Jordà, 2005) with country and time fixed effects.
- Dependent variables ((yit+h − yit−1)) include:
  - output, total employment, native-born employment, total labor force, native-born labor force, labor productivity, total factor productivity (TFP) (all in logs), capital-output ratio, aggregate unemployment rate, unemployment rate of the native-born population.
- Treatment variables:
  - Migration shocks measured as immigration inflows (ΔIMit) relative to previous period’s total employment level (Eit−1).
  - Refugee shocks analogous: ΔIRit / Eit−1 (annual increase in refugee stock derived from difference in stocks).
- Estimation method: two-stage least squares (2SLS) where changes in inflows are instrumented by the constructed IVs expressed relative to employment (Δm_hat_it / Eit−1 for migrants; Δr_hat_it / Eit−1 for refugees).
- Coefficient of interest: β1h (cumulative impact of the migration/refugee shock over h years).
- Data sources:
  - Migration: OECD, UN, World Bank (5-year migration stock data: World Bank pre-1990; UN post-1990s).
  - Refugee: UNHCR annual refugee stock data (refugees defined as sum of individuals classified by UNHCR as refugees, asylum seekers, and other persons of concern).
  - Macroeconomic variables: Penn World Tables, IMF WEO database, OECD.
- Sample sizes and coverage:
  - Migration to advanced countries: 34 countries with 229 shock episodes over the period 1981-2016.
  - Refugee analysis: 137 countries with 179 shock episodes over the period 1981-2017.

### Evidence on magnitudes and episode distribution
- Distributional facts:
  - Mean shock episode is just under 1 percent of the host country population per year.
  - For the full sample it is around 0.25 percent.
  - In certain countries, the average size of the annual shock is well over 1 percent of the host country’s population.
  - There were five episodes where the migration shock was over 2 percent of the host country’s population in a single year (Solvenia 2008; Spain 2007; Germany and Austria 2015; New Zealand 2016).

### Main results — large migration waves to advanced economies
- Response scale: reported effects are the response to a 1 percentage point increase in the ratio of the immigrant flow relative to (the lag of) total employment.
- Key findings:
  - Output increases by almost 1 percent by the fifth year following a migration shock.
  - About two-thirds of the output increase attributed to an increase in labor productivity (total output divided by total employment).
  - The remaining one-third attributed to employment growth (employment growth effect described as borderline insignificant).
  - Total factor productivity (TFP) increases and matches the rise in labor productivity.
  - Capital stock responds immediately to higher TFP; capital growth outstrips employment growth, raising the capital-labor (capital/employment) ratio.
  - No detectable effect on aggregate growth rate of native-born employment in the advanced-country sample.
- Interpretation:
  - Aggregate gains from immigration materialize quickly, with immediate labor productivity responses indicating significant dynamic gains even in the short term.
- Caveats:
  - Potential residual endogeneity if prolonged (over 10 years) upswing in destination economic conditions acts as a pull factor, making the shift-share IV imperfectly exogenous.
  - Additional specifications controlling for persistent macro dependent variables and other macro conditions are estimated as robustness checks; coefficient estimates should be interpreted as an upper bound.

### Main results — large refugee waves in emerging and developing economies
- Detectability and precision:
  - No clear macroeconomic effects of refugee immigration detected within a 5-year horizon for emerging and developing economies.
  - Compared with migration-to-advanced results, point estimates are either much smaller (e.g., output response) or standard deviations are much larger for most variables.
- Statistically significant result:
  - A statistically significant rise in employment—attributed to a rise in population following large refugee inflows—with no evidence of native-born workers being crowded out.
  - This employment increase is not matched by a rise in capital or output; therefore, no positive impact on labor productivity is observed following refugee shocks.
- Possible explanations:
  - Statistical: refugee sample has fewer observations (179 shock episodes across 137 countries) so impacts may be swamped by statistical noise.
  - Economic: refugee integration into labor markets appears relatively inefficient (skills mismatch), reducing potential productivity and output contributions.
- Data limitation: inability to estimate native-born employment impact in emerging market and low-income country sample due to data limitations.

### Robustness
- Additional controls included: lags of the dependent variable, lagged real GDP growth, lagged native-born employment growth.
- Results: impulse response functions from robustness specifications broadly consistent with baseline estimates in both magnitude and precision, suggesting main results are not driven by omitted variable bias.

### Interpretation and mechanisms
- Mechanisms emphasized:
  - Labor composition effects: immigrants induce native-born workers to move to new occupations, upgrading skills and contributing to economy-wide productivity gains.
  - Adjustment of physical capital over the medium term is a key element generating dynamic gains.
- Contrast:
  - “Economic” migrants (predominant in advanced-economy flows) display complementarities with native labor.
  - Refugees face selection disadvantages and weaker matching to recipient labor markets, limiting productivity gains.
- Scope limits:
  - Paper focuses on aggregate macroeconomic impacts and does not analyze detailed distributional effects within native populations or longer-term social dynamics.

### Conclusion (empirical implications)
- For OECD countries:
  - Large immigration waves lead to rapid and sizable positive effects on domestic TFP and on output (almost 1 percent by year 5 per 1 percentage point inflow-to-employment shock), with investment responding vigorously and no negative consequences on domestic employment detected.
- For emerging and developing countries receiving refugees:
  - No statistically or economically significant effects on the dynamic evolution of domestic aggregates within a 5-year horizon.
  - Findings consistent with literature indicating refugees are disadvantaged relative to other migrants; conditions of arrival and limited labor market opportunities reduce refugees’ potential contribution to host economies.

*Prepared by Philipp Engler, Margaux MacDonald, Roberto Piazza, Galen Sher; December 2023 — IMF Working Paper WP/23/259*

### Section 1

### The Macroeconomic Effects of Large Immigration Waves

### Abstract and main findings
- Approach: combine analysis of episodes of large immigration waves with instrumental variables techniques to measure dynamic macroeconomic effects on destination countries.
- Main findings for advanced economies (OECD):
  - Large immigration waves raise domestic output and productivity in both the short and the medium term.
  - A 1 percentage point increase in the ratio of the immigrant flow relative to total employment raises output by almost 1 percent by the fifth year.
  - No evidence of negative effects on aggregate employment of the native-born population.
- Main findings for refugee flows into emerging and developing countries:
  - Analysis does not find clear evidence of macroeconomic effects on the host country.
  - Results are consistent with evidence that refugee immigrants are at a disadvantage compared to other types of immigrants.

### Motivation and conceptual framework
- Challenge: disentangling the macroeconomic effects of migration from drivers of migration (reverse causality where economic prospects draw migrants).
- Identification strategy: focus on large immigration waves more likely driven by external forces in source countries; construct an instrumental variable (IV) based on total emigration from other countries and the share of immigrants already hosted in the destination country (leveraging the pattern that migrants choose destinations that already host many from their source country).
- Distinction made between:
  - “Migrant”: non-refugee migrants.
  - “Refugee”: individuals forced to leave their country to escape war, persecution, or natural disaster (United Nations definition).

### Data and recent trends (selected statistics preserved exactly as in source)
- Between 1990 and 2019, the migrant population increased by 120 million people, to reach 270 million.
- The number of migrants has remained stable at 3 percent of the world population.
- Immigrants make up about 12 percent of the population in advanced economies, up from 7 percent in 1990.
- Between 1990 and 2019, the share of migrants from emerging and developing economies to advanced economies rose from 4 to 9 percent of the advanced economy population.
- Migration sample and scope:
  - Estimation based on a sample of 34 OECD countries, the vast majority of which are advanced economies (29 countries based on the IMF World Economic Outlook income classification).

### Methodology and estimation procedure
- Three-step methodology:
  1. Identify “large” immigration episodes: an annual inflow (as a share of population) greater than the host country’s median inflow during 1980-2018 and greater than the median inflow experienced by all OECD countries during the previous five-year period and the following five-year period. Inflow values set to zero if they do not meet this “shock” criterion.
  2. Construct appropriate instrumental variables (IVs) to capture exogenous components of immigration flows:
     - For advanced economies: a shift-share IV following Card (2009) constructed from past stocks of immigrants from different origin countries.
     - For refugee flows into EMDEs: IV defined from a combination of historical bilateral migration flows and proximity variables from the migration gravity literature.
  3. Use a two-stage local projections regression (Jordà 2005) to trace the dynamic impact of immigration shocks on macroeconomic variables (output, employment, productivity).
- Rationale for focus on large episodes: difficult conditions in source countries are more likely to trigger sudden migration surges, helping to isolate migration driven by emigration rather than destination-country economic booms.

### Evidence on magnitudes and episode distribution
- Distributional facts about identified shocks (preserved exactly):
  - Mean shock episode is just under 1 percent of the host country population per year.
  - For the full sample it is around 0.25 percent.
  - In certain countries, the average size of the annual shock is well over 1 percent of the host country’s population.
  - There were five episodes where the migration shock was over 2 percent of the host country’s population in a single year (Solvenia 2008; Spain 2007; Germany and Austria 2015; New Zealand 2016).

### Relation to existing literature and interpretation
- Findings align with literature documenting positive long-run impacts of immigration on productivity in advanced countries (Peri 2011b; Ortega and Peri 2014; Alesina, Harnoss, and Rapoport 2015; Jaumotte, Koloskova, and Saxena 2016).
- Mechanisms emphasized:
  - Labor composition effects: immigrants entering the labor market induce native-born workers to move to new occupations, upgrading skills and contributing to economy-wide productivity gains.
  - Adjustment of physical capital over the medium term is a key element generating dynamic gains.
- Contrast between “economic” migrants (predominant in advanced-economy flows) who display complementarities with native labor and refugees who face selection disadvantages and weaker matching to recipient labor markets.
- Acknowledged limits: paper focuses on aggregate macroeconomic impacts and does not analyze detailed distributional effects within native populations or longer-term social dynamics (those are covered in the microeconomic literature cited).

### Structure of the paper (procedural overview)
- Section 2: reviews trends in migration to motivate the analysis.
- Section 3: explains the estimation procedure, constructs and estimates exogenous IVs for migration and refugee shocks, and presents dynamic results and robustness tests.
- Section 4: concludes.

*Prepared by Philipp Engler, Margaux MacDonald, Roberto Piazza, Galen Sher; December 2023 — IMF Working Paper WP/23/259*

### Section 2

### Section 2

### Instrument construction for large migration inflows (non-refugee)
- Shock identification: large immigration inflow episodes defined; address reverse-causality via an instrumental variable (IV) approach exploiting migrant network effects.
- Shift-share IV (following Card (2001)) mechanically constructed as the sum across origins j of:
  - the share of the stock of migrants from origin j in destination i over the past 5 to 10 years (migration stock data available at five-year intervals) multiplied by the total outflow of migrants, ΔMjt, from origin j in year t (equation (1)).
- Winsorization and correlation:
  - Instrument winsorized at the top one percent (upper bound only).
  - Resulting winsorized instrument correlation with actual flows: 83 percent over the shock episodes.

### Instrument construction for refugee inflows
- Refugee shock definition:
  - An inflow (as a share of population) that is within the country’s top 10th percentile of inflows during the period 1980–2018 and is also greater than the top 10th percentile (relative to the recipient country’s population) experienced by all countries in the world during the previous five-year period and the following five-year period.
  - Shock must be sustained for at least two consecutive years.
  - Mean refugee shock episode: 0.96 percent of the host country population per year.
  - Standard deviation comparison: refugee shocks 1.5 versus migration shocks 0.43.
- Refugee IV design:
  - Captures proximity determinants: combines historical flows and gravity-style proximity variables (distance and contiguity).
  - Functional form multiplies a share proxy by total refugee outflows ΔRjt from origin j in year t (equation (2)).
  - Gravity coefficients used: 훼 = 0.4, 훾 = 0.8, 휁 = 0.6 (drawn from the gravity migration literature).
  - Instrument winsorized at the top one percent (upper bound only).
  - Resulting correlation with actual refugee flows: about 72 percent over the shock episodes.
- Limitation noted: winsorizing means extremely large refugee inflows (episodes > 4 percent of recipient population, e.g., Colombia, Jordan, Lebanon) are not fully captured.

### Estimation strategy
- Empirical framework: local projections methodology (Jordà, 2005) to trace dynamic response over h-year horizons.
- Estimated equation (3) (local projections specification) with country and time fixed effects.
- Dependent variables (yit+h − yit−1) include:
  - output, total employment, native-born employment, total labor force, native-born labor force, labor productivity, total factor productivity (TFP) (all in logs), capital-output ratio, aggregate unemployment rate, unemployment rate of the native-born population.
- Treatment variable:
  - Migration shocks measured as immigration inflows (ΔIMit) relative to previous period’s total employment level (Eit−1).
  - Refugee shocks analogous: ΔIRit / Eit−1 (annual increase in refugee stock derived from difference in stocks).
- Estimation method: two-stage least squares (2SLS) where changes in inflows are instrumented by the constructed IVs expressed relative to employment (Δm_hat_it / Eit−1 for migrants; Δr_hat_it / Eit−1 for refugees).
- Coefficient of interest: β1h (cumulative impact of the migration/refugee shock over h years).

### Data
- Migration data sources: OECD, UN, World Bank.
  - 5-year migration stock data: World Bank for pre-1990 values of the shift-share instrument; UN data for post-1990s shares.
  - Annual OECD migration flow data used for migration outflows from origin (IV numerator) and migration inflows to destination (main independent variable).
- Refugee data: annual refugee stock data from UNHCR; refugees defined as sum of individuals classified by UNHCR as refugees, asylum seekers, and other persons of concern.
- Macroeconomic variables:
  - Output, consumption, capital, total productivity: Penn World Tables.
  - Aggregate employment variables: IMF WEO database.
  - Native-born employment variables: OECD.
- Sample sizes and coverage:
  - Migration to advanced countries: 34 countries with 229 shock episodes over the period 1981-2016.
  - Refugee analysis: 137 countries with 179 shock episodes over the period 1981-2017.

### Main results — large migration waves to advanced economies
- Response scale: reported effects are the response to a 1 percentage point increase in the ratio of the immigrant flow relative to (the lag of) total employment.
- Key findings:
  - Output increases by almost 1 percent by the fifth year following a migration shock.
  - About two-thirds of the output increase attributed to an increase in labor productivity (total output divided by total employment).
  - The remaining one-third attributed to employment growth (employment growth effect described as borderline insignificant).
  - Total factor productivity (TFP) increases and matches the rise in labor productivity.
  - Capital stock responds immediately to higher TFP; capital growth outstrips employment growth, raising the capital-labor (capital/employment) ratio.
  - No detectable effect on aggregate growth rate of native-born employment in the advanced-country sample.
- Interpretation:
  - Aggregate gains from immigration materialize quickly, with immediate labor productivity responses indicating significant dynamic gains even in the short term.
- Caveats:
  - Potential residual endogeneity if prolonged (over 10 years) upswing in destination economic conditions acts as a pull factor, making the shift-share IV imperfectly exogenous.
  - Additional specifications controlling for persistent macro dependent variables and other macro conditions are estimated in Section 4 as robustness checks; coefficient estimates should be interpreted as an upper bound.

### Main results — large refugee waves in emerging and developing economies
- Detectability and precision:
  - No clear macroeconomic effects of refugee immigration detected within a 5-year horizon for emerging and developing economies.
  - Compared with migration-to-advanced results, point estimates are either much smaller (e.g., output response) or standard deviations are much larger for most variables.
- Notable statistically significant result:
  - A statistically significant rise in employment—attributed to a rise in population following large refugee inflows—with no evidence of native-born workers being crowded out.
  - This employment increase is not matched by a rise in capital or output; therefore, no positive impact on labor productivity is observed following refugee shocks.
- Possible explanations offered:
  - Statistical: the refugee sample has fewer observations (179 shock episodes across 137 countries) than the migration-to-advanced sample, so impacts may be swamped by statistical noise.
  - Economic: refugee integration into labor markets appears relatively inefficient (skills mismatch), reducing potential productivity and output contributions.
- Data limitation noted: inability to estimate native-born employment impact in emerging market and low-income country sample due to data limitations.

### Robustness
- Additional controls included in robustness exercises:
  - Lags of the dependent variable, lagged real GDP growth, lagged native-born employment growth.
- Results:
  - Impulse response functions from robustness specifications broadly consistent with baseline estimates (Figure 8 and Figure 9 referenced), in both magnitude and precision.
  - Suggests main results are not driven by omitted variable bias.

### Conclusion (empirical implications)
- For OECD countries:
  - Large immigration waves lead to rapid and sizable positive effects on domestic TFP and on output (almost 1 percent by year 5 per 1 percentage point inflow-to-employment shock), with investment responding vigorously and no negative consequences on domestic employment detected.
  - Findings consistent with and more optimistic than Ortega and Peri (2009), supporting complementarity between immigrant skills and native-born workers.
- For emerging and developing countries receiving refugees:
  - No statistically or economically significant effects on the dynamic evolution of domestic aggregates within a 5-year horizon.
  - Consistent with literature indicating refugees are disadvantaged relative to other migrants; conditions of arrival and limited labor market opportunities reduce refugees’ potential contribution to host economies.

*Source: IMF Working Paper — Section 2 of the provided PDF content.*

### Section 3

### Section 3

### Figure 9: Macro impact of large immigration waves — additional controls
- Scope and identification
  - Depicts the effect of a 1 percent increase in the migration inflow to the employment ratio in the destination country on the macroeconomic variables indicated.
  - Impulse response functions are estimated using a version of equation (4) using the local projections method of Jordà (2005), with additional controls for lagged dependent variable, real GDP, and native-born employment growth.
  - Sample: OECD countries from 1980–2018.
  - Timing: Year 0 is the year before the shock, and year 1 shows the effect of the shock on impact.
  - Presentation: The solid line represents the impulse response estimate and the dashed lines the 90 percent confidence interval. All dependent variables are in logs.

- Variables shown (panel titles and axis tick values preserved)
  - Employment
    - Axis ticks (percent): ‑0.4, ‑0.2, 0, 0.2, 0.4, 0.6, 0.8
    - Years since shock: 0 1 2 3 4 5 (displayed as 012345)
  - Output
    - Axis ticks (percent): ‑0.5, 0, 0.5, 1, 1.5, 2
    - Years since shock: 0 1 2 3 4 5 (displayed as 012345)
  - Labor productivity
    - Axis ticks (percent): ‑0.2, 0, 0.2, 0.4, 0.6, 0.8, 1
    - Years since shock: 0 1 2 3 4 5 (displayed as 012345)
  - TFP
    - Axis ticks (percent): ‑0.2, 0, 0.2, 0.4, 0.6, 0.8, 1
    - Years since shock: 0 1 2 3 4 5 (displayed as 012345)
  - Capital/Employment
    - Axis ticks (percent): 0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4
    - Years since shock: 0 1 2 3 4 5 (displayed as 012345)
  - Native Employment
    - Axis ticks (percent): ‑1, ‑0.5, 0, 0.5, 1, 1.5
    - Years since shock: 0 1 2 3 4 5 (displayed as 012345)

- Estimation details reiterated
  - Confidence bands: 90 percent confidence interval.
  - Dependent variables: all in logs.

### References (selected from section)
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- Alesina, Alberto, Reza Baqir, and William Easterly. 1999. “Public Goods and Ethnic Divisions.” Quarterly Journal of Economics 114(4): 1243–284.
- Alesina, Alberto, Johann Harnoss, and Hillel Rapoport. 2015. “Birthplace Diversity and Economic Prosperity.” NBER Working Paper 18699, National Bureau of Economic Research, Cambridge, MA.
- Alvarez, Jorge, Marco Arena, Alain Brousseau, Hamid Faruqee, Emilio Fernandez-Corugedo, Jaime Guajardo, Gerardo Peraza, and Juan Yépez Albornoz. 2022. “Regional Spillovers from the Venezuelan Crisis. Migration flows and their impact on Latin America and the Caribbean.” IMF Departmental Paper DP/2022/09.
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- Brell, Courtney, Christian Dustmann, and Ian Preston. 2020. “The Labor Market Integration of Refugee Migrants in High-Income Countries.” Journal of Economic Perspectives 34(1): 94–121.
- Beine, Michel, Simone Bertoli, and Jesús Fernández-Huertas Moraga. 2016. “A Practitioners’ Guide to Gravity Models of International Migration.” World Economy 39(4): 496–512.
- Borjas, George J. 2016. “The Wage Impact of the Marielitos: Additional Evidence.” NBER Working Paper 21850, National Bureau of Economic Research, Cambridge, MA.
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- Card, David. 2001. “Immigrant Inflows, Native Outflows, and the Local Labor Market Impacts of Higher Immigration.” Journal of Labor Economics 19(1): 22–64.
- Clemens, Michael A. “Economics and emigration: Trillion-dollar bills on the sidewalk?.” Journal of Economic Perspectives 25, no. 3 (2011): 83-106.
- Grogger, Jeffrey, and Gordon H. Hanson. 2011. “Income maximization and the selection and sorting of international migrants.” Journal of Development Economics 95(1): 42-57.
- International Monetary Fund. 2016. “Spillovers from China’s Transition and from Migration.” World Economic Outlook, Chapter 4, October. Washington, DC.
- International Monetary Fund. 2017a. “The Macroeconomic Impact of the Syrian Refugee Crisis and Regional Conflicts in Jordan.” IMF Country Report 17/232, Washington, DC.
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- United Nations. 2016. “New York Declaration for Refugees and Migrants.” Document A/71/L.1, New York.

*Source: Authors calculations.*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023259-print-pdf.pdf_