## CHAPTER 3 ONLINE ANNEX: JOURNEYS AND JUNCTIONS — SPILLOVERS FROM MIGRATION AND REFUGEE POLICIES

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### Migration and refugee trends
- EMDEs host a larger share of refugees; refugees account for over half of total inflows in the average EMDE.
- Migrant inflows are larger into advanced economies than into EMDEs.
- For the largest corridors in the most recent data (Between 2015-2020), EMDE-to-EMDE corridors are the largest and are dominated by refugee flows.

### Labor market outcomes: data sources and scope
- Microdatasets and primary sources:
  - Luxembourg Income Survey (LIS) household surveys: Austria (2021), Belgium (2021), Colombia (2023), Canada (2020), Chile (2017), Denmark (2022), Spain (2022), France (2020), Germany (2020), Italy (2020), Netherlands (2021), Peru (2021), South Africa (2017), Sweden (2021), United Kingdom (2021), United States (2023), Uruguay (2022).
  - Türkiye: Labor Force Statistics for 2021–22 from Turkstat.
  - Mexico: ENOE database from INEGI for 2023-24.
  - EU countries: Eurostat data for 2021 and 2023.
  - Non-EU refugee statistics: UNHCR covering multiple periods in the late 2010s.
- Outcomes disaggregated where feasible by migrants versus refugees, by skill groups, arrival cohorts, and gender.

### Labor force participation: patterns and gaps
- Advanced economies:
  - Labor market participation rates are lower for foreigners than for natives for both low and high-skilled workers in most advanced economies.
  - Participation gaps are generally larger for foreigners who have arrived within the last year.
- EMDEs:
  - Heterogeneous patterns: low-skilled foreigners often have higher labor force participation rates, potentially absorbed into the informal sector.
  - For recent arrivals, gaps are either negligible or slightly positive for the low-skilled.
- EU-specific:
  - Participation gaps are negative for refugees and positive for migrants in EU countries—suggesting barriers for refugees and better integration/skills-matching for migrants.
- Non-EU comparisons:
  - Labor force participation gaps for refugees compared to aggregate foreign workforce; favorable outcomes for low-skilled foreign workers and refugees in EMDEs may indicate substitutability in informal and less-educated formal sectors.

### Wage differentials and skill composition
- General findings:
  - Negative wage differentials are common for new and/or low-skilled foreign workers.
  - Incumbent high-skilled foreigners earn more than natives in some economies: Mexico, South Africa, and the United Kingdom.
- Skill composition effects:
  - Positive wage differentials for high-skilled foreigners appear to reflect skill differences and higher high-skilled shares among foreigners in some countries (notably the United Kingdom).
  - In some EMDEs, low-skilled foreign workers experience positive wage differentials relative to natives due to within-group education differences.
- Country and pathway nuances:
  - Türkiye: negative gaps among lower-skilled, more severe for women and non-language-speakers.
  - United States (UNHCR refugee data): among refugees, labor force participation for the high-skilled is 20 percentage points higher than for the low-skilled; wage differentials are negligible with hourly wages very close to the minimum wage.

### Skills analysis rationale and evidence (Online Annex 3.2)
- Motivation:
  - Migration into advanced economies is predominantly migrants (not refugees), often pulled by economic opportunities and can alleviate labor shortages.
- Empirical evidence:
  - Advanced economies: upward trend in median and interquartile range of job vacancy ratios across countries and sectors; ratios peaked at onset of Covid-19 but remained elevated (Online Annex Figure 3.2.1).
  - Job-vacancy ratios are positively correlated with share of foreign-born workers across sectors over 2010–21 in select advanced economies (EU, UK, US).
- Mechanism:
  - Immigrant workers have higher elasticity to changing labor market conditions and can flow into sectors with shortages; barriers (language, regulation, enforcement) alter mobility.

### Regression framework for migrant mobility response
- Specification and interpretation:
  - Dependent variable: annualized change in migrant share ∆migr_share_{c,m,t}.
  - Key regressor: annualized change in job vacancy ratio ∆JVR_{c,m,t}.
  - Interaction with lagged migration policy indicator migrant_policy_{c,t-1}.
  - Lagged migrant share included; time, sector, and sector-time fixed effects included; country-fixed effects excluded.
  - Errors clustered at sector-time level; β2 < 0 implies immigrant mobility inhibited by tighter migration policies.
- Data inputs:
  - Migrant work share from LIS household surveys.
  - Job-vacancy ratios from national statistics offices.
  - Migration and refugee policy indicators from IMPIC (higher values = more restrictive).

### Labor migration policies — key regression results (Online Annex Table 3.2.1)
- Sample and fit:
  - Number of Observations: 1,466 (columns (1)–(4)).
  - R^2: 0.273, 0.298, 0.276, 0.286; Within R^2: 0.142, 0.172, 0.145, 0.158.
  - Sector-time, sector, and time fixed effects: YES.
- Statistically significant coefficients (selected and preserved):
  - Lagged. Labor-Skills Targeting: 6.053*** (1.919) in column (2).
  - Interaction (Lagged. Labor-Skills Targeting) x (Change in JVR): -10.84** (4.174) in column (2).
  - Interaction (Lagged. Labor-Language Skills Requirement) x (Change in JVR): 4.378** (2.053) in column (3).
  - Interaction (Lagged. Labor-Equal Work Conditions Natives/Migrants) x (Change in JVR): -9.831** (4.780).
  - Constant example: 4.749*** (0.611) in column (1).
- Interpretation:
  - Migrant mobility is supported by frameworks that do not specifically target high-skilled workers and do not impose eligibility conditions on hours and pay.
  - Regulations requiring knowledge of the native language support shifts towards a foreign workforce when labor shortages are large (positive interaction coefficient in column (3)).

### Asylum policies — key regression results (Online Annex Table 3.2.2)
- Sample and fit:
  - Number of Observations: 1,466 (columns (5)–(8)).
  - R^2: 0.278, 0.290, 0.286, 0.278; Within R^2: 0.148, 0.162, 0.158, 0.148.
  - Sector-time, sector, and time fixed effects: YES.
- Statistically significant coefficients (selected and preserved):
  - Lagged. Asylum-UNHCR Resettlement: 2.551*** (0.740) in column (6).
  - Interaction (Lagged. Asylum-UNHCR Resettlement) x (Change in JVR): -2.616* (1.356) in column (6).
  - Lagged. Asylum-Eligibility: 6.016*** (2.256) in column (7).
  - Interaction (Lagged. Asylum-Eligibility) x (Change in JVR): -7.095* (4.076) in column (7).
  - Lagged. Asylum-Security of Status: 1.865* (0.945) in column (8).
  - Interaction (Lagged. Asylum-Security of Status) x (Change in JVR): -3.735* (1.894) in column (8).
  - (Lagged. Asylum-Free Movement) x (Change in JVR): -3.949* (2.020) in column (5).
- Interpretation:
  - Policies that explicitly support refugee mobility and integration—national law or UNHCR resettlement—enhance foreign workers’ ability to move toward sectors with labor shortages.
  - Several results are robust to jointly including all policy dimensions and an enforcement indicator.

### Youth-related skills, migration, and youth-intensive sectors
- Measurement:
  - Youth-related skills: physical abilities, communication, memory, multi-tasking (including divided attention and speed of closure) using O*Net (2023) occupation-level data.
  - Principal components construct indices; industry-level youth-labor intensity derived from occupational structure (Occupational Employment Statistics).
  - Youth-intensive trade index: size-weighted intensity of net exports (revealed comparative advantage in youth-intensive sectors) combined with Comtrade at HS4 level.
- Empirical association:
  - Locations with higher demand for youth-intensive skills tend to have higher net migration flows (Online Annex Figure 3.2.2); bubbles represent age dependency ratios; axes show net migration inflows (percent of population) and youth-intensive trade index.

### Measuring effective changes in policy barriers (policy-to-flow mapping)
- Mapping formula:
  - Ω_{d,c} = exp{β IMPIC_{d,c−5} + X_{d,c}γ + δ_c + γ_d + ν_{d,c}}.
  - 100*(exp(β)−1) captures percent change in bilateral migration flows associated with a one-standard-deviation tightening in policy barriers.
- Key quantified estimates (Online Annex Table 3.3.1):
  - One standard deviation tightening in the IMPIC policy indicator in a destination → 19 percent decline in bilateral inflows to this economy over 5 years (column 4).
  - For the median destination, implied change in total inflows represents a 0.44 percent drop in the initial population.
  - Labor migration regulations: one standard deviation tightening → 18 percent decline in bilateral inflows within 5 years.
  - Refugee policies (IMPIC refugee subindex or DWRAP overall): one standard deviation tightening → 40 percent decline in bilateral refugee inflows over 5 years.

### Cross-border spillovers — destination substitution (HDFE local projections)
- Main HDFE estimate (Online Annex Table 3.3.2):
  - Predicted by overall tightening: coefficient 0.545*; Predicted Percentage Change: 9.21*; Predicted GDP Impact: 1.946*.
- By policy area (selected):
  - External Regulation: coefficient 0.549**; Predicted Percentage Change 9.40*; Predicted GDP Impact 2.232**.
  - Internal Regulation: coefficient 1.428***; Predicted Percentage Change 24.7***; Predicted GDP Impact 2.220**.
  - Control: coefficient 0.372**; Predicted Percentage Change 8.04**; Predicted GDP Impact 1.932*.
- By targeted group:
  - Migrants and Refugees: coefficient 0.547**; Predicted Percentage Change 10.18**; Predicted GDP Impact 2.328**.
  - Refugees: coefficient 0.642*; Predicted Percentage Change 10.38**; Predicted GDP Impact 1.980*.
- By country group:
  - AE: coefficient 0.540***; Predicted Percentage Change 9.12***; Predicted GDP Impact 4.599***.
  - EMDE: coefficient 0.388**; Predicted Percentage Change 6.55**; Predicted GDP Impact 0.214.
- Interpretation:
  - A one standard deviation tightening in policy barriers across other destinations increases inflows to a given destination by between 7 and 25 percent over five years depending on specification.
  - Inflows induced by tighter policies elsewhere generate output gains of about 2 percent after 5 years (aggregate HDFE estimate).
  - External regulations matter more for migrants; internal (integration) regulations matter more for refugees (one standard deviation increase in stringency of refugee internal regulations across other destinations associated with an 11 percent increase in refugee inflows after five years).

### DWRAP-based and refugee-specific quantified results
- DWRAP-based findings (Online Annex Figure 3.3.1 and text):
  - A one standard deviation policy tightening — a reduction in the DWRAP index of 0.15 across other destinations — is associated with a 14 percent increase in migration and refugee flows over 5 years.
  - A one standard deviation increase in redirected migration and refugee flows — about 0.88 percent of population — is associated with an output increase of 1.62 after 5 years on average; estimated impact larger for EMDEs.
- Refugee-specific DWRAP results (Online Annex Figure 3.3.2 and text):
  - A one standard deviation tightening in DWRAP by other destinations is linked to increases in refugee inflows of about 15 percent for both AE and EMDE.
  - A one standard deviation increase in deflected refugee inflows is followed by output gains of 0.6 percent after five years; gains higher for EMDEs.
- DWRAP subindices (Online Annex Table 3.3.5):
  - Stricter integration policies elsewhere (citizenship and participation; movement) increase migration and refugee inflows.
  - Evidence for spillovers from changes in legal access is weaker or dominated by integration policy spillovers.
  - Note: DWRAP higher values = looser policy barriers; negative coefficients are qualitatively consistent with IMPIC-based estimates.

### Robustness checks (data sources, frequency, and estimators)
- Data and coefficient examples (Online Annex Table 3.3.3):
  - Baseline: gross flows (Abel and Cohen 2019), 5-year: coefficient 0.545** (0.237).
  - Stocks (UN), 5-year: 0.640* (0.333).
  - Net Flows (UN), 5-year: 0.528* (0.305).
  - Gross Flows (OECD), annual: 0.701*** (0.141).
  - Stocks (UNHCR), annual, refugees only: 0.369** (0.180).
  - Gross Flows (UNHCR), annual, refugees only: 0.451*** (0.0773).
- Estimation method comparisons (Online Annex Table 3.3.4):
  - Baseline HDFE: 0.545** (0.237), Observations 175,592, R^2 0.873.
  - PPML: 0.665*** (0.201), Observations 50,587, Pseudo R^2 0.988.
  - CCFE: 0.211*** (0.0137), Observations 166,162.
  - Domestic migration policy in CCFE: -0.156*** (0.0130).
- Additional robustness:
  - Results robust using UN DESA stocks/net flows (1990–2020), OECD annual gross inflows (1998–2021), UNHCR annual refugee stocks/gross flows (1990–2021).
  - Online Annex Table 3.3.5 shows heterogeneous effects across DWRAP subindices (access, services, livelihoods, citizenship and political rights, movement).

### Cross-border spillovers — categorical substitution (dynamic gravity + projections)
- Policy exposure coefficients (Online Annex Table 3.3.6) — HDFE Policy Exposure coefficients (Years Ahead 1–5):
  - Year 1: 1.304*** (0.110)
  - Year 2: 1.096*** (0.0874)
  - Year 3: 0.745*** (0.0836)
  - Year 4: 0.301** (0.134)
  - Year 5: 0.411*** (0.117)
  - Observations decline from 64,747 (Year 1) to 44,268 (Year 5).
- PPML Policy Exposure coefficients (Years Ahead 1–5):
  - Year 1: 0.0216 (0.296)
  - Year 2: 0.362 (0.267)
  - Year 3: 0.553** (0.253)
  - Year 4: 1.034*** (0.221)
  - Year 5: 2.595*** (0.613)
  - Observations decline from 59,102 (Year 1) to 40,873 (Year 5).
- Annualized output effects of standardized policy-driven refugee flows (Years Ahead 1–5):
  - Year 1: 0.254*** (0.0597)
  - Year 2: 0.195*** (0.0355)
  - Year 3: 0.162*** (0.0260)
  - Year 4: 0.118*** (0.0207)
  - Year 5: 0.0623*** (0.0179)
  - Observations decline from 3,346 (Year 1) to 2,074 (Year 5).
- Interpretation:
  - Tighter migration policies increase refugee flows after 5 years.
  - Associated output effects are positive in the short-to-medium term, but gains wane over time, consistent with transitory movement dynamics.
  - Results qualitatively robust to PPML estimation.

### Key quantified takeaways (preserved exactly)
- A one standard deviation tightening in destination IMPIC → 19 percent decline in bilateral inflows over 5 years; median destination total inflows decline equivalent to 0.44 percent of initial population.
- For refugees specifically, a one standard deviation tightening → 40 percent decline in bilateral refugee inflows over 5 years.
- Spillovers: a one standard deviation tightening in other destinations → increases inflows to a given destination by between 7 and 25 percent over five years depending on specification.
- Output effects: inflow increases induced by third-party tightening generate output gains of about 2 percent after 5 years (aggregate HDFE estimate).
- DWRAP-based redirected inflows of about 0.88 percent of population → output increase of 1.62 after 5 years on average.
- Country-group heterogeneity: Advanced economies receive larger output gains per redirected inflow (Predicted GDP Impact 4.599*** for AE vs 0.214 for EMDE in Online Annex Table 3.3.2).

### Structural model of migration and inflation (Online Annex 3.4)
- Model form and purpose:
  - New Keynesian model with capital accumulation and population growth extending Cheremukhin and others (2024); two-agent New Keynesian (TANK) structure to quantify migration surge effects.
  - Two simulated scenarios: (1) large influx of low-skilled workers; (2) large influx of high-skilled workers.
- Calibration ranges and preserved parameter values (Online Annex Table 3.4.1):
  - Wage skill premium: 0.4 - 1.2
  - Capital income share: 0.47 - 0.72
  - Steady state hours worked per person (per year): 1340 - 2226
  - Steady state inflation rate (in percent): 2.0 - 4.5
  - Steady state population growth rate (in percent): 0.2 - 13.0
- Mechanisms and main finding:
  - Investment adjustment costs slow capital response, especially for high-skilled influxes with capital complementarity.
  - Lagged capital adjustment creates wedge between supply and demand, driving inflationary responses.
  - High-skilled influx initially depresses high-skilled wages, which revert as capital adjusts.
  - Country characteristics do not alter qualitative or quantitative results meaningfully.

### Structural model of trade and migration (Online Annex 3.5) — overview and parameters
- Purpose:
  - Multi-country dynamic general equilibrium model extending Caliendo and others (2021, 2023) to quantify effects of increased trade and migration barriers and role of international coordination.
- Key parameter values (Online Annex Table 3.5.1 — preserved exactly):
  - Elasticity of substitution between low- and high-skilled workers: 4
  - Trade cost elasticity: 4.5
  - Five-year discount factor: 0.86
  - Migration cost elasticities (across pathways and destinations): 1.7
  - Agglomeration elasticity: 0.2
  - Depreciation rate of capital (percent, annualized): 0.05
- Data sources for calibration:
  - Gross migration and refugee flows: United Nations Global Migration Database; Abel and Cohen (2019).
  - Trade flows and total spending: Eora Global Supply Chain Database.
  - Labor income shares, capital returns, depreciation: Penn World Table 10.01.
  - Characteristics of immigrants and natives: LIS and national sources; Barro and Lee (2021 update).
  - Integration and naturalization data: Brell and others (2020); UNHCR datasets; national sources.
- Model features:
  - Households (natives and migrants) are forward-looking; migrants can enter via migrant or refugee pathways with differing integration speeds and mismatch probabilities.
  - Refugees face observed lower labor force participation and more severe skill mismatches.

### Production, agglomeration, congestion, and capital owners
- Production inputs: unskilled and skilled labor and capital.
- Capital owners (rentiers) are immobile, consume local goods, invest domestically, and rent capital to firms.
- Short-term forces:
  - Agglomeration: larger population increases total factor productivity.
  - Congestion: capital is effectively fixed short term; increased labor supply strains capital and affects prices and returns.
  - Net short-term outcome: increased output but decline in output per capita.
- Long term:
  - If capital can be built, higher output per capita due to agglomeration.

### Targeted unilateral restrictions — experiment design and shocks
- Calibration regions: large advanced destinations; origin economies with humanitarian outflows; economies bordering origin; rest of world.
- Policy shock implemented:
  - Advanced economy tightens labor migration policies targeting migrants from origin, reducing economic migration flows by 20 percent relative to baseline for both high and low skill workers.
  - This tightening ≡ 0.3 standard deviations increase in IMPIC labor migration regulations index.
- Welfare metric:
  - Aggregate real income effects via income-weighted compensating variation (individual compensating variation as percent of initial income, weighted by income share for country measure).
- Assumptions:
  - Involuntary labor force participation rates and transition probabilities follow stylized facts from Online Annex 3.1.
  - Other destinations accommodate more migrants/refugees so total net emigration from origin equals baseline between start and 2025 (except where specified).

### International cooperation — experiment design and scenarios (Online Annex Table 3.5.4)
- Scenarios (preserved exactly):
  - Large, non-bordering, advanced destination economy tightens: Reduce net inflows from the origin from 2010 through 2025 by 25 percent relative to baseline.
  - Bordering, emerging and developing destination economies tightens: Reduce net inflows from the origin from 2010 through 2025 by 25 percent relative to baseline.
  - Multilateral cooperation:
    - Large, advanced destination economy tightens to reduce net inflows by 12.5 percent relative to baseline.
    - Bordering EMDEs tighten to reduce net inflows by 12.5 percent relative to baseline.
- Policy shock magnitude preserved:
  - Unilateral tightening simulated as 20 percent reduction in economic migration flows ≡ 0.3 standard deviations on IMPIC labor migration regulations index; scenario shocks include 25 percent and 12.5 percent reductions in net inflows between 2010–2025.

### Calibration details, historical episodes, and robustness
- Historical episode calibrations:
  - Episode 1: capital stock set at steady state 2000–20; depreciation rate computed from Penn World Table mean for 2000–20: 0.045 (annualized).
  - Episode 2: real return on capital from Penn World Table internal rate of return; depreciation rate 0.04 (annualized).
- Robustness:
  - Results broadly similar across episodes and alternative inferences of rate of return via investment data and model’s optimal investment equation.

### Results and comparative scenarios (qualitative and temporal patterns)
- Unilateral tightening effects:
  - Short-to-medium term (2025): reduction in congestion in tightening country/region, boosting per capita consumption relative to baseline.
  - Long term (2075): per capita consumption declines as capital adjusts to lower labor supply, reducing agglomeration and TFP.
  - Aggregate consumption declines in short-to-medium term as per capita gains are offset by lower labor force and reduced investment.
  - Unwinding policies back to baseline after 2025 partly offsets long-run effects.
- Multilateral cooperation:
  - Destinations accommodate more migrants and refugees than under unilateral tightening.
  - Relative to unilateral actions, coordinated scenarios imply:
    - More congestion short-to-medium term; stronger agglomeration long term.
    - Aggregate consumption declines by less over time due to smaller declines in labor force.
  - Conclusion: coordinated destination policies can produce higher long-term benefits for destination economies.

*Source: IMF staff calculations as reported in Online Annexes 3.1–3.5 of CHAPTER 3, "JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND REFUGEE POLICIES", International Monetary Fund | April 2025.*

### CHAPTER 3 JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND

### ch3onlineannex - CHAPTER 3 JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND REFUGEE POLICIES

### Migration and Refugee Trends
- EMDEs host a larger share of refugees; refugees account for over half of total inflows in the average EMDE.
- Migrant inflows are larger into advanced economies than into EMDEs.
- For the largest corridors in the most recent data (Between 2015-2020), EMDE-to-EMDE corridors are the largest and are dominated by refugee flows.
- Online Annex Figure 3.1.1 (referenced) documents composition by refugees vs migrants across top destination economies.

### Labor Market Outcomes: Data Sources and Scope
- Micro datasets used to compare labor force participation rates and wages of foreigners versus natives; where feasible, outcomes are disaggregated by migrants and refugees.
- Primary data sources and survey years:
  - Luxembourg Income Survey (LIS) household surveys: Austria (2021), Belgium (2021), Colombia (2023), Canada (2020), Chile (2017), Denmark (2022), Spain (2022), France (2020), Germany (2020), Italy (2020), Netherlands (2021), Peru (2021), South Africa (2017), Sweden (2021), United Kingdom (2021), United States (2023), Uruguay (2022).
  - Türkiye: Labor Force Statistics for 2021–22 from Turkstat.
  - Mexico: ENOE database from INEGI for 2023-24.
  - EU countries: Eurostat data for 2021 and 2023 for labor outcomes by category.
  - Non-EU refugee statistics: UNHCR covering multiple periods in the late 2010s.
- Note: Data showing labor outcomes by category uses Eurostat for EU countries; refugee statistics for non-EU countries are from UNHCR.

### Labor Force Participation: Patterns and Gaps
- Definition: Negative values imply labor force participation rates for foreigners are lower than for natives.
- Advanced economies:
  - In most advanced economies, labor market participation rates are lower for foreigners than for natives, for both low and high-skilled workers.
  - Labor force participation gaps are generally larger for foreigners who have arrived within the last year.
- Emerging market and developing economies (EMDEs):
  - Display heterogeneity; low-skilled foreigners often have higher labor force participation rates, which could be absorbed quickly into the informal sector.
  - For recent arrivals, gaps are either negligible or slightly positive for the low-skilled.
- EU-specific findings:
  - Labor force participation gaps are negative for refugees and positive for migrants in EU countries — suggesting important barriers for refugees and efficient integration/skills-matching for migrants.
- Non-EU comparisons:
  - Labor force participation gaps for refugees are compared to the aggregate foreign workforce.
  - Favorable outcomes for low-skilled foreign workers and refugees in EMDEs could indicate substitutability in informal and less-educated formal sectors.

### Wage Differentials and Skill Composition
- General patterns:
  - Negative wage differentials are common for new and/or low-skilled foreign workers.
  - Incumbent high-skilled foreigners earn more than natives in some economies: Mexico, South Africa, and the United Kingdom.
- Skill composition:
  - Positive wage differentials for high-skilled foreign workers appear to reflect differences in skills.
  - In the United Kingdom, the share of high-skilled workers among foreigners is higher than for natives; this contrasts with most other advanced economies in the sample.
  - This pattern (higher high-skilled share among foreigners than natives) also appears in some EMDEs.
- EMDE-specific nuance:
  - Low-skilled foreign workers in some EMDEs experience positive wage differentials relative to natives—possibly because within the non-college group, natives have lower years of education than foreigners.
  - For Latin America, evidence suggests wages of native workers with little education and engaged in informal work may drop slightly with large refugee inflows (referenced IMF, 2022 and country studies).
- Türkiye:
  - Negative gaps among lower-skilled capture integration challenges, more severe for women and for those who do not speak the language.
- Refugee skill heterogeneity and US UNHCR data:
  - Data limitations prevent detailed refugee outcomes by skill in many cases.
  - UNHCR data for the United States indicates: among refugees, labor force participation for the high-skilled is 20 percentage points higher than for the low-skilled, whereas wage differentials are negligible, with all refugees reporting hourly wage rates very close to the minimum wage.

### Online Annex 3.2 — Skills Analysis: Rationale and Evidence
- Motivation:
  - Migration into advanced economies is predominantly migrants (not refugees), who are often pulled by better economic opportunities.
  - Migrants can alleviate labor market imbalances arising from aging and labor shortages; implications include fiscal pressures and inflation (Boxes 3.2 and 3.4 referenced).
- Empirical evidence:
  - Advanced economies show broad-based and sustained labor shortages: upward trend in median and interquartile range of job vacancy ratios across countries and sectors (Online Annex Figure 3.2.1, panel 1). Ratios peaked at the onset of the Covid-19 pandemic but have remained elevated.
  - Job-vacancy ratios are positively correlated with the share of foreign-born workers across sectors over 2010–21 in select advanced economies (EU, UK, US), with within-country variation in correlation sizes (Online Annex Figure 3.2.1, panel 2).
- Mechanism:
  - Immigrant workers can flow into sectors with shortages and away from declining sectors due to higher elasticity to changing labor market conditions relative to natives (sunk mobility costs for migrants).
  - Additional barriers (language, regulation, enforcement) vary across countries and affect migrant mobility.

### Regression Framework for Migrant Mobility Response
- Empirical specification (as described):
  - Dependent variable: annualized change in the migrant share in country c, sector m, time t: ∆migr_share_cmc t.
  - Key regressor: annualized change in the job vacancy ratio at country-sector level: ∆JVR_cmc t.
  - Interaction with lagged migration policy indicator at country level: migrant_policy_c(t-1).
  - Lagged migrant share included to control for serial autocorrelation.
  - Time, sector, and sector-time fixed effects included.
  - Country-fixed effects not included due to little within-country variation in policy indicators.
  - Errors clustered at sector-time level (robust to country-time clustering).
  - Interpretation: β2 < 0 implies immigrant mobility is inhibited by tighter migration policies.
- Data used:
  - Migrant work share from LIS household surveys.
  - Job-vacancy ratios from national statistics offices.
  - Migration and refugee policy indicators from IMPIC (higher values = more restrictive policies).

### Labor Migration Policies — Key Regression Results (Online Annex Table 3.2.1)
- Sample and model fit:
  - Number of Observations: 1,466 (reported in columns (1)–(4)).
  - R^2 values reported: 0.273, 0.298, 0.276, 0.286 respectively.
  - Within R^2 values: 0.142, 0.172, 0.145, 0.158 respectively.
  - Sector-time Fixed Effects: YES; Sector Fixed Effects: YES; Time Fixed Effects: YES.
- Statistically significant findings (only significant dimensions reported in tables):
  - Lagged. Labor-Skills Targeting coefficient: 6.053*** (standard error (1.919)) in column (2).
  - Interaction (Lagged. Labor-Skills Targeting) x (Change in JVR): -10.84** (4.174) in column (2).
  - Lagged. Labor-Language Skills Requirement coefficient: -0.200 (1.025) in column (3) [not significant].
  - Interaction (Lagged. Labor-Language Skills Requirement) x (Change in JVR): 4.378** (2.053) in column (3).
  - Lagged. Labor-Equal Work Conditions Natives/Migrants coefficient: 0.334 (2.002) [not significant].
  - Interaction (Lagged. Labor-Equal Work Conditions Natives/Migrants) x (Change in JVR): -9.831** (4.780).
  - Constant terms reported as significant (e.g., 4.749*** (0.611) in column (1)).
- Interpretation summarized in text:
  - Migrant mobility is supported by frameworks that do not specifically target high-skilled workers and do not impose eligibility conditions on hours and pay.
  - Regulations requiring knowledge of the native language support shifts towards a foreign workforce when labor shortages are large (β2 > 0 in column (3)).

### Asylum Policies — Key Regression Results (Online Annex Table 3.2.2)
- Sample and model fit:
  - Number of Observations: 1,466 (columns (5)–(8)).
  - R^2 values: 0.278, 0.290, 0.286, 0.278.
  - Within R^2 values: 0.148, 0.162, 0.158, 0.148.
  - Sector-time Fixed Effects: YES; Sector Fixed Effects: YES; Time Fixed Effects: YES.
- Statistically significant findings (selected):
  - Lagged. Asylum-UNHCR Resettlement coefficient: 2.551*** (0.740) in column (6).
  - Interaction (Lagged. Asylum-UNHCR Resettlement) x (Change in JVR): -2.616* (1.356) in column (6).
  - Lagged. Asylum-Eligibility coefficient: 6.016*** (2.256) in column (7).
  - Interaction (Lagged. Asylum-Eligibility) x (Change in JVR): -7.095* (4.076) in column (7).
  - Lagged. Asylum-Security of Status coefficient: 1.865* (0.945) in column (8).
  - Interaction (Lagged. Asylum-Security of Status) x (Change in JVR): -3.735* (1.894) in column (8).
  - Lagged. Asylum-Free Movement interaction: (Lagged. Asylum-Free Movement) x (Change in JVR): -3.949* (2.020) in column (5).
- Interpretation summarized in text:
  - Policies that explicitly support refugee mobility and integration—either through national law or via participation in UNHCR resettlement programs—enhance foreign workers’ ability to move towards sectors experiencing labor shortages.
  - Several results are robust to jointly including all policy dimensions and an indicator of enforcement.

### Youth-Related Skills, Migration, and Youth-Intensive Sectors
- Concept and measurement:
  - Youth-related skills identified: physical abilities, communication, memory, multi-tasking (including divided attention and speed of closure) using O*Net (2023) occupation-level data.
  - Principal components used to construct indices for each ability group.
  - Industry-level youth-labor intensity derived by combining youth-skills principal components with occupational structure (Occupational Employment Statistics).
  - Youth-intensive trade index: combines sector-level indexes with Comtrade data at the HS4 level; measured as size-weighted intensity of net exports (revealed comparative advantage in youth-intensive sectors).
- Empirical association:
  - Online Annex Figure 3.2.2 shows where demand for youth-intensive skills is higher, net migration flows also tend to be higher.
  - Bubbles in the chart represent age dependency ratios; axes show net migration inflows (percent of population) and the youth-intensive trade index.

*International Monetary Fund | April 2025*

### CHAPTER 3 JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND

### CHAPTER 3 JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND REFUGEE POLICIES

### Data
- Bilateral gross migration and refugee flows: Abel and Cohen (2019), 194 countries, five-year intervals, 1995–2020; constructed from UN DESA migration stocks plus birth and death rates to derive consistent bilateral gross migration flows.
- Robustness sources: migration stocks and net flows from UN DESA; refugee stocks and gross flows from UNHCR; gross migration inflows from OECD for select destinations.
- Migration and refugee policy measures:
  - IMPIC (Immigration Policies in Comparison) project: detailed regulations for 33 OECD countries, 1980–2018; includes external (eligibility and entry) and internal (integration) regulations and enforcement; overall IMPIC indicator composed of sub-indices. (Note: for IMPIC variables higher values indicate stricter policies.)
  - DWRAP (Dataset of World Refugee and Asylum Policies): de jure refugee-specific policies for 193 countries, 1952–2022; indicators measure refugees’ access to protection, services, livelihoods, movement, and citizenship and participation. DWRAP index is linear, ranges from 0 to 1, with values closer to 1 representing less restrictive policies.
- Additional covariates: GDP, population, imports, free trade agreements, and other time-invariant country characteristics from CEPII.

### Measuring effective changes in policy barriers
- Destination-time fixed effects Ω_{d,c} capture overall inward resistance to receiving migration and refugee flows and are mapped to policy barriers via:
  - Ω_{d,c} = exp{β IMPIC_{d,c−5} + X_{d,c}γ + δ_c + γ_d + ν_{d,c}}
  - 100*(exp(β)−1) captures percent change in bilateral migration flows associated with a one-standard-deviation tightening in policy barriers.
- Empirical results (Online Annex Table 3.3.1, overall IMPIC indicator):
  - A one standard deviation tightening in the IMPIC policy indicator in a destination economy is associated with, on average, a 19 percent decline in bilateral inflows to this economy over 5 years (column 4).
  - For the median destination, the implied change in total inflows represents a 0.44 percent drop in the initial population.
  - Labor migration regulations: one standard deviation tightening in IMPIC subindex → 18 percent decline in bilateral inflows within 5 years.
  - Refugee policies (IMPIC refugee subindex or DWRAP overall): one standard deviation tightening → 40 percent decline in bilateral refugee inflows over 5 years.

### Methodology overview
- Core gravity specification with high-dimensional interactive fixed effects (HDFE):
  - Bilateral flow F_{o,d,c} (share of origin population) regressed on exposure measure Z_{o,d,c−1}, controls X_{o,d,c−1}, and origin-time, destination-time, and origin-destination fixed effects.
- Exposure (Bartik/shift-share) measure Z_{o,d,c−1}:
  - Captures a country’s exposure to migration and refugee policies elsewhere (IMPIC_{j,c−1}) weighted by the lagged share of migrants from origin o to other destinations j, further interacted with lagged flows from origin o to destination d.
  - By construction, destinations with larger pre-established diasporas from an origin are more predisposed to receiving deflected flows from that origin.
- Predicted migration flows from the gravity model are aggregated at destination level (normalized by population) and used in local projection regressions to estimate GDP spillovers:
  - Δ log GDP_{d,c} over 5 years regressed on predicted flows and controls.

### Cross-border spillovers — Destination substitution (findings)
- Main HDFE estimate (Online Annex Table 3.3.2):
  - Predicted by overall tightening: coefficient 0.545*; Predicted Percentage Change: 9.21*; Predicted GDP Impact: 1.946*.
- By policy area:
  - External Regulation: coefficient 0.549**; Predicted Percentage Change 9.40*; Predicted GDP Impact 2.232**.
  - Internal Regulation: coefficient 1.428***; Predicted Percentage Change 24.7***; Predicted GDP Impact 2.220**.
  - Control: coefficient 0.372**; Predicted Percentage Change 8.04**; Predicted GDP Impact 1.932*.
- By targeted group:
  - Migrants and Refugees: coefficient 0.547**; Predicted Percentage Change 10.18**; Predicted GDP Impact 2.328**.
  - Refugees: coefficient 0.642*; Predicted Percentage Change 10.38**; Predicted GDP Impact 1.980*.
- By country group:
  - AE (advanced economy): coefficient 0.540***; Predicted Percentage Change 9.12***; Predicted GDP Impact 4.599***.
  - EMDE: coefficient 0.388**; Predicted Percentage Change 6.55**; Predicted GDP Impact 0.214.
- Interpretation:
  - A one standard deviation tightening in policy barriers across other destinations increases migration and refugee inflows to a given destination by between 7 and 25 percent over a five-year period depending on policy dimension, targeted group, or destination (column 2).
  - Inflows induced by tighter policies elsewhere generate output gains of about 2 percent after 5 years (column 3).
  - External regulations matter more for migrants; internal (integration) regulations matter more for refugees (one standard deviation increase in stringency of refugee internal regulations across other destinations associated with an 11 percent increase in refugee inflows after five years).

### Visual and additional quantified results (destination substitution)
- DWRAP-based estimates (Online Annex Figure 3.3.1 and text):
  - A one standard deviation policy tightening — a reduction in the DWRAP index of 0.15 across other destinations — is associated with a 14 percent increase in migration and refugee flows over 5 years.
  - A one standard deviation increase in redirected migration and refugee flows — about 0.88 percent of population — is associated with an output increase of 1.62 after 5 years on average; estimated impact larger for EMDEs.
- Refugee-specific DWRAP results (Online Annex Figure 3.3.2 and text):
  - A one standard deviation tightening in DWRAP by other destinations is linked to increases in refugee inflows of about 15 percent for both AE and EMDE.
  - A one standard deviation increase in deflected refugee inflows is followed by output gains of 0.6 percent after five years; gains higher for EMDEs.
- DWRAP subindices (Online Annex Table 3.3.5) findings:
  - Stricter integration policies elsewhere (citizenship and participation; movement) increase migration and refugee inflows.
  - Evidence for spillovers from changes in legal access is weaker or dominated by integration policy spillovers.
  - Note: DWRAP higher values = looser policy barriers; negative coefficients are qualitatively consistent with IMPIC-based estimates.

### Robustness checks
- Data sources and frequency (Online Annex Table 3.3.3):
  - Baseline: gross flows (Abel and Cohen 2019), 5-year; coefficient 0.545** (0.237).
  - Stocks (UN), 5-year: coefficient 0.640* (0.333).
  - Net Flows (UN), 5-year: coefficient 0.528* (0.305).
  - Gross Flows (OECD), annual: coefficient 0.701*** (0.141).
  - Stocks (UNHCR), annual, refugees only: coefficient 0.369** (0.180).
  - Gross Flows (UNHCR), annual, refugees only: coefficient 0.451*** (0.0773).
  - Full set of interactive fixed effects used in all specifications listed.
- Estimation methods (Online Annex Table 3.3.4):
  - Baseline HDFE: migration policy in other destinations 0.545** (0.237), Number of Observations 175,592, (R^2 0.873).
  - PPML: coefficient 0.665*** (0.201), Number of Observations 50,587, (Pseudo R^2 0.988).
  - CCFE: coefficient 0.211*** (0.0137), Number of Observations 166,162.
  - Domestic migration policy in CCFE: -0.156*** (0.0130).
  - Findings robust to alternative data sources and PPML/CCFE estimation approaches. PPML mitigates censoring due to zero flows; CCFE accounts for cross-sectional dependence and heterogeneous responses.
- Additional robustness (text and tables):
  - Results robust when using migration and refugee combined stocks and net flows from UN DESA (1990–2020); annual gross migration inflows for OECD (1998–2021); annual refugee stocks and gross flows from UNHCR (1990–2021).
  - Online Annex Table 3.3.5 provides policy-area Bartik indexes (DWRAP subindices) and shows heterogeneous effects across access, services, livelihoods, citizenship and political rights, and movement.

### Cross-border spillovers — Categorical substitution (findings)
- Dynamic gravity specification estimated for policy impacts on refugee inflows; predicted refugee flows used in local projections for GDP effects.
- Categorical substitution results (Online Annex Table 3.3.6):
  - HDFE, Policy Exposure coefficients (Years Ahead 1–5):
    - Year 1: 1.304*** (0.110)
    - Year 2: 1.096*** (0.0874)
    - Year 3: 0.745*** (0.0836)
    - Year 4: 0.301** (0.134)
    - Year 5: 0.411*** (0.117)
    - Number of observations declines from 64,747 (Year 1) to 44,268 (Year 5).
  - PPML, Policy Exposure coefficients (Years Ahead 1–5):
    - Year 1: 0.0216 (0.296)
    - Year 2: 0.362 (0.267)
    - Year 3: 0.553** (0.253)
    - Year 4: 1.034*** (0.221)
    - Year 5: 2.595*** (0.613)
    - Number of observations declines from 59,102 (Year 1) to 40,873 (Year 5).
  - Annualized output effects of standardized policy-driven refugee flows (Years Ahead 1–5):
    - Year 1: 0.254*** (0.0597)
    - Year 2: 0.195*** (0.0355)
    - Year 3: 0.162*** (0.0260)
    - Year 4: 0.118*** (0.0207)
    - Year 5: 0.0623*** (0.0179)
    - Number of observations declines from 3,346 (Year 1) to 2,074 (Year 5).
- Interpretation:
  - Tighter migration policies increase refugee flows after 5 years.
  - Associated output effects are positive in the short-to-medium term, though gains appear to wane over time, potentially reflecting the transitory nature of some refugee movements.
  - Results qualitatively robust to PPML estimation.

### Key quantified takeaways
- A one standard deviation tightening in destination IMPIC → 19 percent decline in bilateral inflows over 5 years; median destination total inflows decline equivalent to 0.44 percent of initial population.
- For refugees specifically, a one standard deviation tightening → 40 percent decline in bilateral refugee inflows over 5 years.
- Spillovers: a one standard deviation tightening in other destinations → increases inflows to a given destination by between 7 and 25 percent over five years depending on specification.
- Output effects: inflow increases induced by third-party tightening generate output gains of about 2 percent after 5 years (aggregate HDFE estimate); DWRAP-based redirected inflows of about 0.88 percent of population → output increase of 1.62 after 5 years on average.
- Country-group heterogeneity: Advanced economies receive larger output gains per redirected inflow (Predicted GDP Impact 4.599*** for AE vs 0.214 for EMDE in Online Annex Table 3.3.2).

*Source: IMF staff calculations as reported in Online Annex 3.3 of CHAPTER 3 JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND REFUGEE POLICIES, International Monetary Fund | April 2025.*

### CHAPTER 3 JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND

### CHAPTER 3 JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND REFUGEE POLICIES — Online Annexes 3.4–3.5

### Online Annex 3.4. Structural Model of Migration and Inflation
- Model type and purpose:
  - A New Keynesian model with capital accumulation and population growth, extending Cheremukhin and others (2024), used to quantify effects of migration surges of the magnitude observed in the US after the COVID-19 pandemic.
  - Two-agent New Keynesian (TANK) structure: share of population as hand-to-mouth, relatively low skilled; remainder as relatively high-skilled savers who are relatively more complementary to capital than low-skilled labor.
  - Two scenarios simulated: (1) large influx of low-skilled workers; (2) large influx of high-skilled workers.
- Calibration:
  - Baseline calibration follows Cheremukhin and others (2024) for the US.
  - Calibrated to Australia, Canada, Germany, Mexico and South Africa by adjusting: wage skill premium, capital income share, steady state hours worked per person, saver population share, steady state inflation rate, steady state population growth rate.
  - Online Annex Table 3.4.1 parameter ranges:
    - Wage skill premium: 0.4 - 1.2 (LIS)
    - Capital income share: 0.47 - 0.72 (National income statistics)
    - Steady state hours worked per person (per year): 1340 - 2226 (OECD (2022))
    - Steady state inflation rate (in percent): 2.0 - 4.5 (National central banks)
    - Steady state population growth rate (in percent): 0.2 - 13.0 (UN (2015 - 2020))
- Main finding:
  - Country characteristics do not alter results in either a qualitatively or quantitatively meaningful way (Figures 3.4.1 and 3.4.2).

### Investment Dynamics and Capital-Skill Complementarity (from Annex 3.4)
- Mechanisms emphasized:
  - Investment adjustment costs slow capital stock response to labor force changes, particularly important for large influxes of high-skilled workers with strong capital complementarity.
  - Lagged capital adjustment creates a wedge between aggregate supply and demand, driving inflationary responses (Figure 3.4.1).
  - Dynamics of wages:
    - An influx of high-skilled workers initially depresses high-skilled wages.
    - Wages revert as capital stock adjusts, increasing marginal product of high-skilled labor (Figure 3.4.2).
  - Counterfactual:
    - If capital stock is completely fixed, high-skilled wages do not recover as the marginal product of high-skilled labor remains muted.

### Online Annex 3.5. Structural Model of Trade and Migration — Overview
- Purpose and experiments:
  - Multi-country dynamic general equilibrium model extending Caliendo and others (2021, 2023) to quantify effects of increases in trade and migration barriers.
  - Two policy experiments:
    1. Targeted unilateral tightening of policies related to economic migrants — highlights categorical, destination and origin substitution, and origin suppression spillover channels and impacts on natives and migrants.
    2. Role of international coordination — explores whether and how regional efforts can yield better economic outcomes.
- Model features:
  - Saturated with exogenous migration and trade cost parameters to match observed gross trade and migration flows (including by nationality and skill).
  - Trade costs: tariffs and non-tariff barriers modeled as iceberg costs.
  - Migration barriers: policy and non-policy components.
  - Forward-looking households (natives and migrants) choose consumption and relocation, differ by skills and nationalities, and can enter via two legal pathways: migrant or refugee (pathways differ in speed of integration and skills mismatch).
  - Refugees: observed lower labor force participation rates and more severe skill mismatches for high-skilled refugees (see Online Annex 3.1).
  - Idiosyncratic shocks determine household decisions to move.
- Integration and mismatch modeling:
  - "Naturalization shock" grants citizenship and unrestricted labor market access; absent naturalization, migrants may remain or return home.
  - High-skilled migrants may be initially mismatched and earn local low-skilled wages; transition back to high-skilled work occurs with positive probability each period.
  - Probabilities of mismatch and transition depend on country and migration pathway; refugees face higher mismatches and lower transition likelihoods.

### Online Annex 3.5. Model Parameters and Data Sources
- Key parameter values (Online Annex Table 3.5.1):
  - Elasticity of substitution between low- and high-skilled workers: 4 (Caliendo and others (2021))
  - Trade cost elasticity: 4.5 (Caliendo and others (2021))
  - Five-year discount factor: 0.86 (Caliendo and others (2021), adjusted for 5-year periods)
  - Migration cost elasticities (across all pathways and destinations): 1.7 (Caliendo and others (2021), adjusted for 5-year periods)
  - Agglomeration elasticity governing changes in TFP relative to population size: 0.2 (Caliendo and others (2021))
  - Depreciation rate of capital (percent, annualized): 0.05 (Caliendo and others (2021))
- Data sources used for calibration (Online Annex Table 3.5.2):
  - Gross migration and refugee flows: United Nations Global Migration Database; Abel and Cohen (2019)
  - Trade flows and total spending: Eora Global Supply Chain Database
  - Labor income shares, capital returns, depreciation rate: Penn World Table 10.01
  - Characteristics of immigrants and natives in destination economies: Luxembourg Income Survey (LIS) Database and selected national sources; Barro and Lee Educational Attainment dataset (2021 update)
  - Migrants and refugees’ integration: Brell and others (2020); UNHCR datasets and reports
  - Naturalization rates: National immigration and statistics offices including Eurostat and USCIS

### Production, Capital Owners, Agglomeration and Congestion (from Annex 3.5)
- Production inputs: unskilled and skilled labor and capital structures.
- Capital owners (“rentiers”):
  - Immobile, consume local goods, invest domestically to build capital structures, rent capital to local firms.
  - Invest to maximize present discounted value of consumption; capital stock can change through investment over the long term.
- Short-term forces:
  - Agglomeration: positive externality — larger population increases total factor productivity (captures knowledge spillovers, entrepreneurship, innovation).
  - Congestion: capital takes time to build (effectively fixed short term); increased labor supply strains capital structures, affecting prices and returns.
  - Net short-term outcome: increase in output and decline in output per capita (agglomeration insufficient to offset congestion initially).
  - Long term: if capital structures can be built, higher output per capita due to higher agglomeration effects.

### Targeted Unilateral Restrictions — Experimental Design
- Calibration and regions:
  - Calibrated on historical episodes for: (i) large advanced destination economies; (ii) origin economies where humanitarian flows dominate outflows; (iii) economies bordering the origin economies; (iv) rest of world.
- Policy shock:
  - Advanced economy tightens labor migration policies targeting new and incumbent migrants from origin economies, keeping other policies constant.
  - Raising barriers reduces economic migration flows by 20 percent relative to baseline, for both high and low skill workers.
  - Such tightening is equivalent to an increase of 0.3 standard deviations of the IMPIC labor migration regulations index (Table 3.6.3).
- Welfare measurement:
  - Within-country effects assessed via aggregate real income effects using income-weighted compensating variation (individual compensating variation expressed as percent of initial income; weighted by share of total income to obtain country compensation measure).
- Assumptions:
  - Involuntary labor force participation rates and transition probabilities rely on stylized facts from Online Annex 3.1.
  - When a country/region tightens policy, it targets immigration from the relevant origin country.
  - Countries/regions other than the Rest-of-World region that are not explicitly noted as tightening are assumed to accommodate more migrants and refugees so that total net emigration from the origin equals baseline between start of episode and 2025.

### International Cooperation — Experimental Design
- Simplifications for analysis of historical episodes:
  - Assume skill homogeneity and a single pathway as in baseline Caliendo and others (2021) model.
  - Capital stock in each location remains endogenous and responds to domestic real return on capital, accumulating through profit-maximizing consumption-savings decisions by domestic capital owners (as in Caliendo and others (2023)).
  - Countries aggregated into regions for simulations presented in the chapter (aggregating bordering and nearby emerging market and developing economies).
- Scenarios (Online Annex Table 3.5.4):
  - Large, non-bordering, advanced destination economy tightens:
    - Reduce net inflows from the origin from 2010 through 2025 by 25 percent relative to baseline.
  - Bordering, emerging and developing destination economies tightens:
    - Reduce net inflows from the origin from 2010 through 2025 by 25 percent relative to baseline.
  - Multilateral cooperation:
    - Large, advanced destination economy tightens migration policy to reduce net inflows from the origin between 2010 through 2025 by 12.5 percent relative to baseline.
    - Bordering emerging and developing destination economies tighten migration policy to reduce net inflows from the origin from 2010 through 2025 by 12.5 percent relative to baseline.

### Calibration details and historical episode treatment
- Historical episode 1:
  - Capital stock in each location assumed at steady state during 2000–20 by setting real return on capital equal to value that keeps capital constant given capitalists’ optimal investment condition, discount factor, and depreciation rate.
  - Depreciation rate computed from Penn World Table mean for 2000–20: 0.045 (annualized).
- Historical episode 2:
  - Real return on capital taken from Penn World Table internal rate of return.
  - Depreciation rate equal to 0.04 (annualized) from Penn World Table.
- Robustness:
  - Results broadly similar across episodes; robustness checked by alternative inference of rate of return from investment data and model’s optimal investment equation.

### Results and Comparative Scenarios
- Effects of unilateral tightening (first two scenarios):
  - Short-to-medium term (2025): reduction in congestion in tightening country/region, boosting per capita consumption relative to baseline (Figure 3.6.1).
  - Long term (2075): per capita consumption declines as capital stock adjusts to lower labor supply, reducing agglomeration and lowering total factor productivity.
  - Aggregate consumption:
    - Declines in the short-to-medium term as increased per capita consumption is more than offset by decline in labor force.
    - Lower investment amplifies negative short-to-medium term impact on aggregate consumption.
    - Unwinding of migration and refugee policies back to baseline after 2025 partly offsets long-run effects.
- Effects of multilateral cooperation (third scenario):
  - Destinations accommodate more migrants and refugees than under unilateral actions.
  - Relative to unilateral actions, both sets of destinations experience:
    - More congestion in the short-to-medium term.
    - Stronger agglomeration effects in the long term.
  - Aggregate consumption declines by less over time due to smaller decline in labor force in both sets of destinations.
  - Conclusion: Destination economies coordinating can choose policies that produce higher long-term benefits (Figure 3.6.1; gains/losses shown for per capita consumption and aggregate consumption, short-to-medium term and long term).

### Selected quantitative values and mechanics preserved exactly
- Parameter and calibration values reproduced exactly where provided:
  - Wage skill premium range: 0.4 - 1.2
  - Capital income share range: 0.47 - 0.72
  - Steady state hours worked per person (per year) range: 1340 - 2226
  - Steady state inflation rate (in percent) range: 2.0 - 4.5
  - Steady state population growth rate (in percent) range: 0.2 - 13.0
  - Elasticity of substitution between low- and high-skilled workers: 4
  - Trade cost elasticity: 4.5
  - Five-year discount factor: 0.86
  - Migration cost elasticities: 1.7
  - Agglomeration elasticity: 0.2
  - Depreciation rate of capital (percent, annualized): 0.05
  - Penn World Table-derived depreciation rates used in historical episodes: 0.045 (annualized) and 0.04 (annualized)
  - Policy shock equivalence: 20 percent reduction in economic migration flows ≡ 0.3 standard deviations increase in IMPIC labor migration regulations index; simulated 20 percent decline implemented in unilateral tightening exercises.
  - Scenario shocks: 25 percent reductions (unilateral scenarios) and 12.5 percent reductions (multilateral cooperation scenario) in net inflows from origin between 2010 through 2025, relative to baseline.

*Source: CHAPTER 3 ONLINE ANNEXES 3.4–3.5, "JOURNEYS AND JUNCTIONS: SPILLOVERS FROM MIGRATION AND REFUGEE POLICIES", International Monetary Fund | April 2025.*

### References

### References

### General migration research and labor market effects
- Abel, G. J., and J. E. Cohen. 2019. “Bilateral International Migration Flow Estimates for 200 Countries.” Scientific Data, 82. 
- Amior, M., and A. Manning. 2019. "Commuting, migration and local joblessness," CEP Discussion Papers dp1623, Centre for Economic Performance, LSE. 
- Amuedo-Dorantes, C., and S. De la Rica. 2010. "Immigrants’ responsiveness to labor market conditions and their impact on regional employment disparities: evidence from Spain," SERIEs: Journal of the Spanish Economic Association, Springer; Spanish Economic Association, vol. 1(4), pages 387-407, September. 
- Basso, G., and G. Peri. 2020. "Internal Mobility: The Greater Responsiveness of Foreign-Born to Economic Conditions." Journal of Economic Perspectives 34 (3): 77–98. 
- Basso, G., F. D’Amuri, and G. Peri. 2019. "Immigrants, Labor Market Dynamics and Adjustment to Shocks in the Euro Area," IMF Economic Review, Palgrave Macmillan; International Monetary Fund, vol. 67(3), 528-572, September. 
- Borjas, G. J. ( 2001). "Does Immigration Grease the Wheels of the Labor Market?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 69-134. 
- Cadena, B. C., and B.K. Kovak. 2016. "Immigrants Equilibrate Local Labor Markets: Evidence from the Great Recession." American Economic Journal: Applied Economics 8 (1): 257–90. 
- Dustmann, C., T. Frattini., and I.P. Preston. 2013. “The Effect of Immigration along the Distribution of Wages”, The Review of Economic Studies, 80(1), 145–173, January. 
- Røed, M., and P. Schøne. 2012. "Does immigration increase labour market flexibility?," Labour Economics, Elsevier, vol. 19(4), 527-540.  
- Shamsuddin, M., P.A. Acosta., R. Battaglin Schwengber., J. Fix., and N. Pirani. 2022. “The Labor Market Impacts of Venezuelan Refugees and Migrants in Brazil.” IZA Discussion Paper No. 15384.  

### Methodology, databases, and measurement
- Helbling, M., L. Bjerre., F.Römer., and M. Zobel. 2017. “Measuring Immigration Policies: The IMPIC Database.” European Political Science, 16(1), 79–98. 
- Cheremukhin, A., S. Hur., R. Mau., K. Mertens., A.W. Richter., and X. Zhou. 2024. “The Postpandemic U.S. Immigration Surge: New Facts and Inflationary Implications.” Federal Reserve Bank of Dallas Working Paper 2407, October.  
- Caliendo, L., L.D. Opromolla., F. Parro., and A. Sforza. 2021. “Goods and Factor Market Integration: A Quantitative Assessment of the EU Enlargement.” Journal of Political Economy, 129(12), 3491–3545.  
- Caliendo, L., L.D. Opromolla., F. Parro., and A. Sforza. 2023.  “Labor Supply Shocks and Capital Accumulation: The Short- and Long-Run Effects of the Refugee Crisis in Europe.” AEA Papers and Proceedings, 113, 577–584. 

### Regional and country case studies (Venezuelan migration focus)
- Bonilla-Mejía, L., L.F. Morales, D. Hermida., and L.A. Flórez. 2023. The Labor Market Effect of South-to -South Migration: Evidence From the Venezuelan Crisis. International Migration Review, 58(2), 764-799. 
- Caruso, G., C. Gomez Canon., and V. Mueller. 2021. “Spillover Effects of the Venezuelan Crisis: Migration Impacts in Colombia.” Oxford Economic Papers, 73(2), 771-795.  
- Lombardo, C., and L. Peñaloza-Pacheco. 2021. Exports “brother-boost”: the trade-creation and skill-upgrading effect of Venezuelan forced migration on Colombian manufacturing firms. CEDLAS Working Papers Nº 283, July, 2021 
- Morales, F., and M.D. Pierola. 2020. "Venezuelan Migration in Peru: Short-term Adjustments in the Labor Market," IDB Publications (Working Papers) 10541, Inter-American Development Bank. 
- Olivieri S., F. Ortega., A. Rivadeneira., and E. Carranza, 2021. “Shoring up economic refugees: Venezuelan migrants in the Ecuadoran labor market,” Migration Studies, Volume 9, Issue 4, December 2021, Pages 1590–1625 
- Peñaloza-Pacheco. L., 2022. “Living With the Neighbors: The Effect of The Venezuelan Forced Migration on the Labor Market in Colombia.” Journal for Labor Market Research, 56, 14 (2022). 
- Santamaria, J., 2020. “When a Stranger Shall Sojourn with Thee: The Impact of the Venezuelan Exodus on Colombian Labor Markets.” 
- United Nations High Commission for Refugees. 2024. “Venezuelans in Chile, Colombia, Ecuador, and Peru: A Development Opportunity.”  
- Zago, R. A., 2022. “Impacts of Forced Immigration: The Venezuelan Diaspora and the Brazilian Labor Market.” 
- Bonilla-Mejía, L., L.F. Morales, D. Hermida., and L.A. Flórez. 2023. The Labor Market Effect of South-to -South Migration: Evidence From the Venezuelan Crisis. International Migration Review, 58(2), 764-799. 

### Country-specific labor integration studies
- Demirci, M.., and M. Güray Kırdar. 2023. “The Labor Market Integration of Syrian Refugees in Turkey.” World Development, Elsevier, 162(C).

*IMF — Chapter 3 online annex: References*

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_Source: https://www.imf.org/-/media/files/publications/weo/2025/april/english/ch3onlineannex.pdf_