## wp18148 - References

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### Key findings and context
- Slowing population growth and rising life expectancy have exerted pressure on old-age dependency ratios in most advanced economies for decades and these trends are projected to accelerate (Figure 1).
- Population growth is projected to slow by 0.34 percentage points in the median AE, 0.32 in both European AEs as well as the United States, and 0.47 percentage points in other AEs by 2050.
- Life expectancy at birth is projected to rise by 4.8 years in the median AE, 4.8 years in European AEs, 5 years in the United States, and 4.6 years in other AEs.
- Dependency ratios will reach 55 percent in the median AE, 60 percent in European AEs, 42 percent in the United States, and 65 percent in other AEs.
- Net migration has accounted for about half of the population growth in advanced economies since the mid-1980s, while natural population growth (difference between fertility and mortality) has been falling (Figure 3).

### Data and methods
- Population projections: United Nations; Eurostat.
- Historical labor force participation rates: Organisation for Economic Co-operation and Development (OECD).
- Microdata for individual-level analysis: Eurostat’s European Union Labour Force Survey (EULFS).
- Micro-sample used: random sample of 10,000 respondents per country per year for 25 European economies over the period 2000-2016.
- Participation status coding: employed, unemployed, or out of the labor force (based on activity during the reference week).
- Occupation routinizability measured following Das and Hilgenstock (forthcoming) using Autor and Dorn (2013) scores mapped to ISCO–88 one-digit groups.
- Migrant integration policy data: Migrant Integration Policy Index database (MIPEX).

### Role of age composition and migration scenarios
- Migrants are disproportionately prime working age relative to natives; age composition differences can raise aggregate labor force participation because participation is highest among prime working ages (Figure 5).
- Projection method for age effects: hold participation rates by gender and age group constant at 2015 levels and weight by evolving population shares to construct country-level labor force participation rates.
- Eurostat migration scenarios analyzed:
  - Baseline: trend extrapolation of net migration as a share of each receiving country’s population until 2050.
  - High (low) migration scenarios: one-third increase (decrease) in net migration relative to the baseline.
- Country example: under Eurostat’s baseline scenario Germany’s migrant stock would increase from the current 14 percent to 29 percent of the population by 2050; the low and high scenarios would imply migrant stocks of 25 and 33 percent of the population, respectively, by 2050.
- United Nations baseline scenario: continuation of recent migration trends for nonrefugee flows until 2050 and consideration of country migration policy stance; on average broadly consistent with the European Union’s low-migration scenario, but not necessarily identical for individual countries.

### Migrant participation patterns and integration
- Prime-age migrants’ participation is typically below that of natives, especially for migrant women, though participation increases with years since migration.
- The compositional effect of migrants being more likely to be prime-age alleviates some of the pressure of aging on aggregate participation; in the absence of migration the decline in participation would be significantly deeper.
- Differences across migrants and natives:
  - Education levels differ between migrants and natives.
  - Education has smaller positive effects on the odds of participation for migrants, especially for women—likely related to difficulties in the recognition of educational qualifications.
- Policy implication: targeted support for recognition of educational qualifications and other integration policies could yield significant further gains in migrant labor force participation.

### Micro-level drivers of participation
- The paper examines determinants of migrants’ decisions to participate using EULFS microdata, including:
  - Age, gender, education, family composition.
  - Years since migration (important for convergence in participation to native levels).
  - Occupation routinizability.
  - Country-level migrant integration policies (MIPEX).
- Noted literatures and empirical findings informing the analysis:
  - Convergence in participation to natives’ levels after about 20 years in some contexts (Meyer 2016).
  - Important roles for language skills and host-country qualifications (Meyer 2016).
  - Gender-specific dynamics: motherhood gaps, interactions between spousal income and gender roles, and cultural influences on migrant women’s participation (Rubin and others 2008; Holland and de Valk 2016; Khoudja and Fleischmann 2015; Kok and others 2011).
- Sample limitations: for some countries the country of birth variable is only available after 2000; country-of-origin detail often aggregated (e.g., EU vs non-EU), preventing analysis by origin or refugee status; information on family composition missing for Denmark, Finland, Iceland, Norway, Sweden and Switzerland for certain regressions.

### Research scope and structure
- Focus: effects of migration on future labor force participation in a large number of receiving advanced European economies.
- Analysis components:
  - Quantification of age-profile differences between migrants and natives and effects on aggregate participation.
  - Measurement of effects of convergence in labor market attachment likelihood between migrants and natives.
  - Microlevel analysis of drivers of migrants’ participation decisions to inform policy debate on accelerating convergence.
- Scope note: the paper focuses solely on participation and does not examine other labor market outcomes such as employment or impacts of migration on natives’ employment and wages (outside the paper’s scope).

### Macroeconomic consequences of projected participation decline
- Projected decline in labor force participation: 7.4 percentage points by 2050.
- Within a simple aggregate production framework with a labor share of 56 percent (the average labor share of income in 2017 for a subset of advanced economies), this translates into a 4.1 percentage point reduction in potential output by 2050.
- Migration scenarios effects on the decline in potential output:
  - High migration assumption: drop in potential output would be 0.8 percentage point less.
  - Low migration assumption: drop would be 0.8 percentage point more.
  - No new migration allowed: decline in participation would be 2.7 percentage points larger (relative to baseline).
- Labor force participation in the median economy in Europe could decline by more than 10 percentage points by 2050 in the absence of migration flows.

### Heterogeneity across countries
- Average spread between high and low migration scenarios: 1.1 percentage points.
- Country-specific spreads:
  - More than 2.5 percentage points in Austria and Luxembourg.
  - Less than 0.5 percentage points in Estonia, Greece, and Spain.
- Exceptions due to projected outmigration:
  - Latvia: low migration scenario would result in a 2.9 percentage point smaller decrease in labor force participation.
  - Lithuania: low migration scenario would result in a 3.2 percentage point smaller decrease.
- Examples of within high-migration-country variation:
  - France: difference between low and high migration scenarios is 0.8 percentage points.
  - Germany: 1.9 percentage points.
  - Spain, Italy, United Kingdom: about 1.6–1.7 percentage points each.
- Countries with large declines under no-migration scenario (examples): Austria, Luxembourg, Slovak Republic, Slovenia, Spain.
- Countries with large baseline-to-no-migration differences and large interquartile ranges (examples): Austria, Belgium, Germany, Luxembourg, Portugal — linked to high migrant inflows relative to population or large age-profile differentials between natives and migrants.
- No-migration scenario declines in labor force participation by country examples:
  - Sweden: around 6 percentage points.
  - Finland: around 12 percentage points.
  - Luxembourg and Spain: more than 15 percentage points.

### Participation effects of migration (age and gender patterns)
- Prime-age men participation: very similar for natives and migrants (25–54 age group).
- Prime-age women participation: migrants 75 percent versus natives 81 percent (prime-age migrant women participation is significantly lower).
- In some Southern European "new" migrant-receiving countries (Greece, Portugal, Spain), participation of migrant women exceeds that of native women due to younger migrant age profiles.
- Migrant participation increases with years in host country, especially for prime-age women.
- Young migrants participation versus young natives: 42 percent versus 36 percent (young natives more likely in education).
- Participation among migrants 55 and older: 5 percent versus 6 percent for natives in same age group.
- Migrants are more likely to be prime-age than natives.

### Potential gains from migrants’ participation convergence
- If prime-age and 55-plus migrants’ participation rates converge to natives’ levels (with young migrants’ participation unchanged), this would yield an additional 1.4 percentage point increase in overall participation (relative to a no convergence scenario), beyond the age composition effect.

### Determinants of participation — econometric findings
- Empirical approach:
  - Logit models on a random sample of 10,000 respondents per country per year.
  - Dependent variable: dummy for being in the labor force.
  - Regressions estimated separately for subgroups: prime-age migrant men, prime-age native men, prime-age migrant women, prime-age native women, older natives, older migrants.
  - Controls: age, urban/rural, education (lower secondary, upper secondary, tertiary), routinization score of (last) occupation, number of children, other employed adults in household, household composition categories, country, region and year fixed effects. Standard errors clustered at country-year level.
- Key quantitative regression insights (selected):
  - Tertiary education effect:
    - Roughly doubles the odds of participating for prime-age native men (relative to up to lower secondary).
    - Around 2.5 times larger effects for prime-age native women (relative to base).
    - For migrants, tertiary education increases odds of participating by about 20 percent for prime-age men (relative to base), much smaller than for natives.
  - Years since migration: an additional year in host country increases odds of participation by 4–7 percent (controlling for observables).
  - Household composition:
    - Being in a couple and having children increases participation odds for prime-age men, lowers it for prime-age women.
    - Effects of household composition are somewhat larger for migrants (e.g., being married and having children has larger negative effects on participation of migrant women than on native women).
  - Local labor market effect: presence of other employed adult(s) in household associated with higher odds of participation.
  - Routinizable occupations: working or having worked in more routinizable occupations is associated with lower odds of participation.
- Table 1 highlights (odds ratios shown in table; significance denoted by *, **, *** at 10 percent, 5 percent, 1 percent respectively):
  - Age and age squared coefficients vary by subgroup (examples in table).
  - Upper secondary and tertiary education odds ratios:
    - Men, 25–54, natives: Upper secondary 1.840***; Tertiary 3.051***.
    - Men, 25–54, migrants: Upper secondary 1.443***; Tertiary 2.213***.
    - Women, 25–54, natives: Upper secondary 1.918***; Tertiary 3.573***.
    - Women, 25–54, migrants: Upper secondary 1.377***; Tertiary 1.969***.
  - RTI (routinizability) score of occupation: consistently below 1 (e.g., 0.817*** for native men 25–54), indicating higher routinability lowers odds of participation.
  - Years since migration coefficients shown: 1.036***, 1.071***, 1.064* for migrant subgroups in table.
  - Number of observations varies by subgroup (examples): 369,411; 126,272; 34,048; 356,781; 120,957; 36,299; 69,597; 17,651; 1,442.

### Obstacles to integration and returns to education
- Migrants typically have lower educational attainment than natives; difference especially pronounced for women:
  - Prime-age migrant men with up to lower secondary: 26 percent (versus 20 percent for prime-age native men).
  - Prime-age migrant women with up to lower secondary: 27 percent (versus 18 percent for prime-age native women).
- Returns to education (in terms of increasing odds of participating) are smaller for migrants than for natives:
  - Example: tertiary education roughly doubles odds for prime-age native men, but increases odds by about 20 percent for migrant men relative to base.
  - For women, returns to education are higher for native prime-age women than native prime-age men; returns are even smaller for migrant women than migrant men.
- Possible reasons: difficulties in having qualifications recognized; migrant wage gaps documented in literature.
- Migrant integration policies matter:
  - Analysis uses MIPEX indicators: recognition of academic qualifications, recognition of professional qualifications, validation of skills.
  - Countries with favorable policies (BEL, DEU, ESP, GBR, NLD, PRT) versus non-favorable policies (AUT, FRA, GRC, IRL, ITA, LUX).
  - Findings:
    - Smaller differences between natives and migrants in countries with favorable policies for upper secondary education.
    - Favorable policies increase positive effects of tertiary education on odds of participating for both natives and migrants (but do not eliminate the difference between them).

### Policy implications and recommendations
- Migration plays a very significant role in alleviating aging pressures primarily via an age composition effect (migrants are more likely to be prime-age).
- Higher migration flows could:
  - Contribute to labor supply.
  - Increase output per capita by boosting demand and investment.
  - Contribute to technological progress.
  - Increase labor productivity through skill complementarity.
- To enhance migration’s positive effects on participation beyond age composition, policies that support migrant integration are recommended, including:
  - Recognition of educational qualifications earned abroad.
  - Language training.
  - Validation of skills and professional qualifications.
- Such integration policies could mitigate future negative effects of aging and help make social safety nets more sustainable.

### Conclusions
- Migration substantially offsets declines in labor force participation related to population aging; absence of migration deepens participation declines and reduces potential output.
- Migrants have lower participation rates than natives, but gaps narrow with years since migration.
- Lower average educational attainment among migrants and smaller returns to education for migrants reduce their participation; favorable integration policies can increase migrants’ participation responsiveness to education and improve aggregate outcomes.

*Source: wp18148 - References (IMF working paper content provided).*

### References .............................................................................................................

### wp18148 - References

### Key findings and context
- Slowing population growth and rising life expectancy have exerted pressure on old-age dependency ratios in most advanced economies for decades and these trends are projected to accelerate (Figure 1).
- Population growth is projected to slow by 0.34 percentage points in the median AE, 0.32 in both European AEs as well as the United States, and 0.47 percentage points in other AEs by 2050.
- Life expectancy at birth is projected to rise by 4.8 years in the median AE, 4.8 years in European AEs, 5 years in the United States, and 4.6 years in other AEs.
- Dependency ratios will reach 55 percent in the median AE, 60 percent in European AEs, 42 percent in the United States, and 65 percent in other AEs.
- Net migration has accounted for about half of the population growth in advanced economies since the mid-1980s, while natural population growth (difference between fertility and mortality) has been falling (Figure 3).

### Data and methods
- Population projections: United Nations; Eurostat.
- Historical labor force participation rates: Organisation for Economic Co-operation and Development (OECD).
- Microdata for individual-level analysis: Eurostat’s European Union Labour Force Survey (EULFS).
- Micro-sample used: random sample of 10,000 respondents per country per year for 25 European economies over the period 2000-2016.
- Participation status coding: employed, unemployed, or out of the labor force (based on activity during the reference week).
- Occupation routinizability measured following Das and Hilgenstock (forthcoming) using Autor and Dorn (2013) scores mapped to ISCO–88 one-digit groups.
- Migrant integration policy data: Migrant Integration Policy Index database (MIPEX).

### Role of age composition and migration scenarios
- Migrants are disproportionately prime working age relative to natives; age composition differences can raise aggregate labor force participation because participation is highest among prime working ages (Figure 5).
- Projection method for age effects: hold participation rates by gender and age group constant at 2015 levels and weight by evolving population shares to construct country-level labor force participation rates.
- Eurostat migration scenarios analyzed:
  - Baseline: trend extrapolation of net migration as a share of each receiving country’s population until 2050.
  - High (low) migration scenarios: one-third increase (decrease) in net migration relative to the baseline.
- Country example: under Eurostat’s baseline scenario Germany’s migrant stock would increase from the current 14 percent to 29 percent of the population by 2050; the low and high scenarios would imply migrant stocks of 25 and 33 percent of the population, respectively, by 2050.
- United Nations baseline scenario: continuation of recent migration trends for nonrefugee flows until 2050 and consideration of country migration policy stance; on average broadly consistent with the European Union’s low-migration scenario, but not necessarily identical for individual countries.

### Migrant participation patterns and integration
- Prime-age migrants’ participation is typically below that of natives, especially for migrant women, though participation increases with years since migration.
- The compositional effect of migrants being more likely to be prime-age alleviates some of the pressure of aging on aggregate participation; in the absence of migration the decline in participation would be significantly deeper.
- Differences across migrants and natives:
  - Education levels differ between migrants and natives.
  - Education has smaller positive effects on the odds of participation for migrants, especially for women—likely related to difficulties in the recognition of educational qualifications.
- Policy implication: targeted support for recognition of educational qualifications and other integration policies could yield significant further gains in migrant labor force participation.

### Micro-level drivers of participation
- The paper examines determinants of migrants’ decisions to participate using EULFS microdata, including:
  - Age, gender, education, family composition.
  - Years since migration (important for convergence in participation to native levels).
  - Occupation routinizability.
  - Country-level migrant integration policies (MIPEX).
- Noted literatures and empirical findings informing the analysis:
  - Convergence in participation to natives’ levels after about 20 years in some contexts (Meyer 2016).
  - Important roles for language skills and host-country qualifications (Meyer 2016).
  - Gender-specific dynamics: motherhood gaps, interactions between spousal income and gender roles, and cultural influences on migrant women’s participation (Rubin and others 2008; Holland and de Valk 2016; Khoudja and Fleischmann 2015; Kok and others 2011).
- Sample limitations: for some countries the country of birth variable is only available after 2000; country-of-origin detail often aggregated (e.g., EU vs non-EU), preventing analysis by origin or refugee status; information on family composition missing for Denmark, Finland, Iceland, Norway, Sweden and Switzerland for certain regressions.

### Research scope and structure
- Focus: effects of migration on future labor force participation in a large number of receiving advanced European economies.
- Analysis components:
  - Quantification of age-profile differences between migrants and natives and effects on aggregate participation.
  - Measurement of effects of convergence in labor market attachment likelihood between migrants and natives.
  - Microlevel analysis of drivers of migrants’ participation decisions to inform policy debate on accelerating convergence.
- Scope note: the paper focuses solely on participation and does not examine other labor market outcomes such as employment or impacts of migration on natives’ employment and wages (outside the paper’s scope).

*Source: wp18148 - References (IMF working paper content provided).*

### 7.4 percentage points by 2050. This would have significant macroeconomic consequences:

### wp18148 - 7.4 percentage points by 2050. This would have significant macroeconomic consequences:

### Macroeconomic consequences of projected participation decline
- Projected decline in labor force participation: 7.4 percentage points by 2050.
- Within a simple aggregate production framework with a labor share of 56 percent (the average labor share of income in 2017 for a subset of advanced economies), this translates into a 4.1 percentage point reduction in potential output by 2050.
- Migration scenarios effects on the decline in potential output:
  - High migration assumption: drop in potential output would be 0.8 percentage point less.
  - Low migration assumption: drop would be 0.8 percentage point more.
  - No new migration allowed: decline in participation would be 2.7 percentage points larger (relative to baseline).
- Labor force participation in the median economy in Europe could decline by more than 10 percentage points by 2050 in the absence of migration flows.

### Heterogeneity across countries
- Average spread between high and low migration scenarios: 1.1 percentage points.
- Country-specific spreads:
  - More than 2.5 percentage points in Austria and Luxembourg.
  - Less than 0.5 percentage points in Estonia, Greece, and Spain.
- Exceptions due to projected outmigration:
  - Latvia: low migration scenario would result in a 2.9 percentage point smaller decrease in labor force participation.
  - Lithuania: low migration scenario would result in a 3.2 percentage point smaller decrease.
- Examples of within high-migration-country variation:
  - France: difference between low and high migration scenarios is 0.8 percentage points.
  - Germany: 1.9 percentage points.
  - Spain, Italy, United Kingdom: about 1.6–1.7 percentage points each.
- Countries with large declines under no-migration scenario (examples): Austria, Luxembourg, Slovak Republic, Slovenia, Spain.
- Countries with large baseline-to-no-migration differences and large interquartile ranges (examples): Austria, Belgium, Germany, Luxembourg, Portugal — linked to high migrant inflows relative to population or large age-profile differentials between natives and migrants.
- No-migration scenario declines in labor force participation by country examples:
  - Sweden: around 6 percentage points.
  - Finland: around 12 percentage points.
  - Luxembourg and Spain: more than 15 percentage points.

### Participation effects of migration (age and gender patterns)
- Prime-age men participation: very similar for natives and migrants (25–54 age group).
- Prime-age women participation: migrants 75 percent versus natives 81 percent (prime-age migrant women participation is significantly lower).
- In some Southern European "new" migrant-receiving countries (Greece, Portugal, Spain), participation of migrant women exceeds that of native women due to younger migrant age profiles.
- Migrant participation increases with years in host country, especially for prime-age women.
- Young migrants participation versus young natives: 42 percent versus 36 percent (young natives more likely in education).
- Participation among migrants 55 and older: 5 percent versus 6 percent for natives in same age group.
- Migrants are more likely to be prime-age than natives.

### Potential gains from migrants’ participation convergence
- If prime-age and 55-plus migrants’ participation rates converge to natives’ levels (with young migrants’ participation unchanged), this would yield an additional 1.4 percentage point increase in overall participation (relative to a no convergence scenario), beyond the age composition effect.

### Determinants of participation — econometric findings
- Empirical approach:
  - Logit models on a random sample of 10,000 respondents per country per year.
  - Dependent variable: dummy for being in the labor force.
  - Regressions estimated separately for subgroups: prime-age migrant men, prime-age native men, prime-age migrant women, prime-age native women, older natives, older migrants.
  - Controls: age, urban/rural, education (lower secondary, upper secondary, tertiary), routinization score of (last) occupation, number of children, other employed adults in household, household composition categories, country, region and year fixed effects. Standard errors clustered at country-year level.
- Key quantitative regression insights (selected):
  - Tertiary education effect:
    - Roughly doubles the odds of participating for prime-age native men (relative to up to lower secondary).
    - Around 2.5 times larger effects for prime-age native women (relative to base).
    - For migrants, tertiary education increases odds of participating by about 20 percent for prime-age men (relative to base), much smaller than for natives.
  - Years since migration: an additional year in host country increases odds of participation by 4–7 percent (controlling for observables).
  - Household composition:
    - Being in a couple and having children increases participation odds for prime-age men, lowers it for prime-age women.
    - Effects of household composition are somewhat larger for migrants (e.g., being married and having children has larger negative effects on participation of migrant women than on native women).
  - Local labor market effect: presence of other employed adult(s) in household associated with higher odds of participation.
  - Routinizable occupations: working or having worked in more routinizable occupations is associated with lower odds of participation.
- Table 1 highlights (odds ratios shown in table; significance denoted by *, **, *** at 10 percent, 5 percent, 1 percent respectively):
  - Age and age squared coefficients vary by subgroup (examples in table).
  - Upper secondary and tertiary education odds ratios:
    - Men, 25–54, natives: Upper secondary 1.840***; Tertiary 3.051***.
    - Men, 25–54, migrants: Upper secondary 1.443***; Tertiary 2.213***.
    - Women, 25–54, natives: Upper secondary 1.918***; Tertiary 3.573***.
    - Women, 25–54, migrants: Upper secondary 1.377***; Tertiary 1.969***.
  - RTI (routinizability) score of occupation: consistently below 1 (e.g., 0.817*** for native men 25–54), indicating higher routinability lowers odds of participation.
  - Years since migration coefficients shown: 1.036***, 1.071***, 1.064* for migrant subgroups in table.
  - Number of observations varies by subgroup (examples): 369,411; 126,272; 34,048; 356,781; 120,957; 36,299; 69,597; 17,651; 1,442.

### Obstacles to integration and returns to education
- Migrants typically have lower educational attainment than natives; difference especially pronounced for women:
  - Prime-age migrant men with up to lower secondary: 26 percent (versus 20 percent for prime-age native men).
  - Prime-age migrant women with up to lower secondary: 27 percent (versus 18 percent for prime-age native women).
- Returns to education (in terms of increasing odds of participating) are smaller for migrants than for natives:
  - Example: tertiary education roughly doubles odds for prime-age native men, but increases odds by about 20 percent for migrant men relative to base.
  - For women, returns to education are higher for native prime-age women than native prime-age men; returns are even smaller for migrant women than migrant men.
- Possible reasons: difficulties in having qualifications recognized; migrant wage gaps documented in literature.
- Migrant integration policies matter:
  - Analysis uses MIPEX indicators: recognition of academic qualifications, recognition of professional qualifications, validation of skills.
  - Countries with favorable policies (BEL, DEU, ESP, GBR, NLD, PRT) versus non-favorable policies (AUT, FRA, GRC, IRL, ITA, LUX).
  - Findings:
    - Smaller differences between natives and migrants in countries with favorable policies for upper secondary education.
    - Favorable policies increase positive effects of tertiary education on odds of participating for both natives and migrants (but do not eliminate the difference between them).

### Policy implications and recommendations
- Migration plays a very significant role in alleviating aging pressures primarily via an age composition effect (migrants are more likely to be prime-age).
- Higher migration flows could:
  - Contribute to labor supply.
  - Increase output per capita by boosting demand and investment.
  - Contribute to technological progress.
  - Increase labor productivity through skill complementarity.
- To enhance migration’s positive effects on participation beyond age composition, policies that support migrant integration are recommended, including:
  - Recognition of educational qualifications earned abroad.
  - Language training.
  - Validation of skills and professional qualifications.
- Such integration policies could mitigate future negative effects of aging and help make social safety nets more sustainable.

### Conclusions
- Migration substantially offsets declines in labor force participation related to population aging; absence of migration deepens participation declines and reduces potential output.
- Migrants have lower participation rates than natives, but gaps narrow with years since migration.
- Lower average educational attainment among migrants and smaller returns to education for migrants reduce their participation; favorable integration policies can increase migrants’ participation responsiveness to education and improve aggregate outcomes.

*Italic: Source — wp18148 (IMF Working Paper content provided).*

### REFERENCES

### REFERENCES

### Major sources and themes
- Compilation of working papers, discussion papers, journal articles, IMF staff notes, and institutional reports addressing migration, labor force participation, integration, demographic change, and related macroeconomic impacts.
- Recurring research topics in the citations:
  - Migration and labor market outcomes for host countries and native workers.
  - Gender-specific labor force participation effects, including motherhood and family responsibilities.
  - Labor market integration of immigrants and refugees.
  - Demographic alternatives to aging (fertility, participation, immigration).
  - Remittances, left-behind household members, and time use.
  - Immigration’s role in innovation, STEM labor markets, and productivity.
  - Methodologies for population projections (European Commission; United Nations).

### Representative cited works (selection of entries preserved verbatim)
- Abdulloev, Ilhom, Ira N. Gang, and Myeong-Su Yun. 2014. “Migration, Education and the Gender Gap in Labour Force Participation.” Discussion Paper No. 8226, Forschungsinstitut zur Zukunft der Arbeit.
- Aiyar, Shekhar, Bergljot Barkbu, Nicoletta Batini, Helge Berger, Enrica Detragiache, Allan Dizioli, Christian Ebeke, Huidan Lin, Linda Kaltani, Sebastian Sosa, Antonio Spilimbergo, and Petia Topalova. 2016. “The Refugee Surge in Europe: Economic Challenges.” IMF Staff Discussion Note 16/02, International Monetary Fund, Washington.
- Akgunduz, Yusuf, Marcel van den Berg, and Wolter Hassink. 2015. “The Impact of Refugee Crises on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey.” IZA Discussion Paper 8841, Institute for the Study of Labor (IZA), Bonn.
- Beyer, Robert C. M. 2016. “The Labour Market Performance of Immigrants in Germany.” IMF Working Paper No. 16/6, International Monetary Fund.
- Borjas, George J. 2017. “The labor supply of undocumented immigrants.” Labour Economics 46: 1–13.
- Peri, Giovanni, Kevin Shih, and Chad Sparber. 2015. “STEM Workers, H-1B Visas, and Productivity in US Cities.” Journal of Labor Economics 33 (3): S225–55.
- United Nations, 2017. World Population Prospects: Methodology of the United Nations Population Estimates and Projections, 2017 Revision.
- International Monetary Fund. 2015. “International Migration: Recent Trends, Economic Impacts, and Policy Implications.” Staff Background Paper for G20 Surveillance Note. Washington.
- International Monetary Fund. 2018. “United Kingdom: Staff Report for the 2017 Article IV Consultation” Washington.

### Appendix Figure 1 — Change in Labor Force Participation Rates Relative to 2015 (Percentage points)
- Figure title: Change in Labor Force Participation Rates Relative to 2015 (Percentage points)
- Sources: Eurostat; United Nations; and authors' calculations.
- Vertical scale markers shown explicitly in the figure: -12, -10, -8, -6, -4, -2, 0 (for countries 1–4, 6–8); additionally for country 5 (Spain) the vertical scale markers include -20, -15, -10, -5, 0.
- Horizontal time axis labels shown explicitly in the figure: 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050.
- Legend entries preserved verbatim:
  - High to low scenarios
  - Baseline scenario
  - No migration scenario
  - UN projections
- Countries plotted (numbered in the figure):
  1. Germany
  2. United Kingdom
  3. France
  4. Italy
  5. Spain
  6. Netherlands
  7. Sweden
  8. Belgium

*Italic: Source document — wp18148 - REFERENCES*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2018/wp18148.pdf_