Demographic and labor force impacts of future immigration flows into Europe: does an immigrant's region of origin matter?

Europe shifted from a net-emigration to a major immigration destination in the 21st century, and immigration has become central to population change in low-fertility European countries. Prior projection studies show immigration can substantially affect population size, but realistic migration levels cannot sustainably reverse or halt population ageing when measured by conventional age-based dependency indicators. Traditional indicators like the age dependency ratio (ADR) are limited because they assume all 15–64-year-olds are economically active and all 65+ are dependent, overlooking rising participation of older adults and women and differences in productivity by education. More nuanced measures—the labor force dependency ratio (LFDR) and productivity-weighted LFDR (PWLFDR)—present a less daunting ageing outlook. Prior simulations have tended to treat immigrants as a homogenous group, often only distinguishing intra-EU from extra-EU migrants. Yet place of birth correlates with education, fertility, labor market integration, and emigration propensities. The research question is whether substantial shifts in immigrants’ regions of origin would materially alter aggregate demographic outcomes—population size, age structure, and economic dependency ratios—in Europe. Using a new microsimulation model that incorporates place of birth, the study explores scenario-based projections to 2060 to assess policy-relevant impacts of changes in immigrants’ origins.

The paper reviews prior demometric and projection studies assessing migration’s impact on population size, ageing, and labor force structure. Findings generally show that higher immigration can slow or prevent population decline but cannot sustainably alter age structures without implausibly large inflows (e.g., UN replacement migration debates; Coleman 2002, 2008; Craveiro et al., 2019). Traditional ADR-based assessments overstate dependency by ignoring labor force participation and productivity heterogeneity; more sophisticated indicators (LFDR, PWLFDR) rise much less over time (Marois et al., 2020; Loichinger, 2015; Spijker & MacInnes, 2013). Prior CEPAM simulations indicate that migrants’ education and integration into the labor force can improve or worsen PWLFDR even with the same migration volume (Marois et al., 2020). Despite growing diversification of migrant origins (Abel & Cohen, 2019), most projections treat extra-EU immigrants as homogeneous, overlooking origin-linked differences in labor outcomes (Adsera & Chiswick, 2007), education (Ichou et al., 2017), fertility (Wilson, 2020), and emigration. The study addresses this gap by integrating place of birth explicitly into projections.

Design: The study employs QuantMig-Mic, a continuous time-based population microsimulation for 31 European countries (EU-27 plus UK, Norway, Switzerland, Iceland), building on CEPAM-Mic. The model projects population by age, sex, educational attainment, labor force participation, immigration status, duration of stay, age at arrival, and—novelly—place of birth, with 39 categories (31 countries plus 8 broader origin regions: East Asia, Latin America, North Africa, North America & Oceania, Other Europe incl. Russia/Ukraine/Turkey, Sub-Saharan Africa, South & South-East Asia, West Asia). Base population and data: The 2020 base population by age, sex, education, and detailed place of birth was estimated using the 2011 census and pre-simulations reproducing 2011–2020 dynamics with vital statistics (births, deaths), EU-LFS, Eurostat estimates, and QuantMig migration flow estimates (Aristotelous et al., 2022), applying imputation as needed. Behavioral modules incorporating place of birth: - Destination allocation: Destination probabilities depend on origin-region-specific historical patterns (2011–2019 EU-LFS/QuantMig), reflecting cultural/geographic proximity, historical links, and networks; the destination matrix is held constant across scenarios due to uncertainty and the role of networks. - Fertility: Differentials by maternal place of birth, duration of stay, and age at arrival (child vs adult), net of education. Women from Africa and the Middle East have elevated fertility shortly after arrival, converging over time, with faster convergence among those arriving in childhood. - Education: Migrants’ origin correlates with education (e.g., higher post-secondary among East Asia, North America & Oceania; lower among North/Sub-Saharan Africa). Parental place of birth influences children’s educational attainment via an education module that also includes sociocultural predictors (language, religion, mother’s education). - Labor force participation: Integration trajectories vary by origin and duration of stay beyond education effects; slower integration and persistent gaps observed for North Africa/Middle East, especially women. Dynamics modeled using EU-LFS. - Emigration/Onward/Return migration: Emigration rates are 2–10 times higher for foreign-born vs native-born, with differentials by place of birth incorporated. Scenarios (2020–2060): - Reference/baseline: Flows from world regions into Europe continue at average 2011–2019 intensity by applying region-specific emigration-to-Europe rates (Aristotelous et al., 2022) to WIC 2023 medium population projections (KC et al., 2023). Thus flows evolve with origin-region population size/ageing (e.g., rising from Sub-Saharan Africa, declining from Other Europe/East Asia). 2020–2024 flows adjusted for COVID-19 and Russia–Ukraine war; assume hostilities cease by 2025 with 60% refugee return to Ukraine. Emigration rates by place of birth vary with scenario. - Alternative origin-shift scenarios (from 2025 onward): Increase one region’s share of immigrants to 50% in each projection period, proportionally reducing other regions while keeping total immigration volume identical to the reference. Four scenarios: Asia+ (East, South, SE Asia), Europe+ (Other Europe incl. Turkey), MENA+ (North Africa, Middle East, West Asia), SSA+ (Sub-Saharan Africa). Destination allocation respects established origin–destination ties; thus, country-level inflows differ across scenarios despite equal European totals. Origin shifts also imply small differences in the educational mix of arrivals (e.g., more low-educated in SSA+). Outcome indicators: Besides population size, three dependency ratios are computed. - Age Dependency Ratio (ADR) = [Pop(0–14) + Pop(65+)] / Pop(15–64). - Labor Force Dependency Ratio (LFDR) = Inactive / Active (accounts for participation heterogeneity by age, sex, education, origin, and duration of stay). - Productivity-Weighted LFDR (PWLFDR) = Inactive / [Σ w(e)*Active(e)], with productivity weights by education: low 0.62, secondary 1.0, post-secondary 1.66 (derived from wage differentials per Marois et al., 2020).

European-level outcomes (2020–2060): - All scenarios project increases in dependency ratios but with differing magnitudes: ADR rises by about 50%, LFDR by ~20%, and PWLFDR by ~5%, reflecting higher participation and rising educational attainment among younger cohorts entering the labor force. - Changing immigrants’ regions of origin has minimal effect on continental outcomes. SSA+ and MENA+ yield only slightly larger populations by 2060 (+~3 million, <0.7%) due to higher initial fertility among these origins, with negligible improvements (and in some cases slight deteriorations) in LFDR and PWLFDR because of lower participation (especially among women) and lower average education among these groups. Country-level population size effects: - Germany, UK, France: Reference 2060 populations ≈ Germany 83M, UK 74M, France 72M. Under Europe+ (more Other Europe/Turkey origins), Germany rises to ~87M, widening its lead (~20M more than UK/France). Under SSA+, Germany ~82M, UK ~76M, France ~74M, narrowing inter-country gaps because SSA migrants are less likely to settle in Germany than in UK/France. - Smaller/low-immigration countries: Europe+ substantially mitigates decline—e.g., reduces projected population loss by up to ~50% in Latvia, Czechia, and Croatia, and stabilizes Estonia—since migrants from “Other Europe” disproportionately choose these destinations. Dependency ratios at country level: - Despite sometimes sizable population size differences by scenario, ADR, LFDR, and PWLFDR in 2060 are very similar across scenarios in Germany, UK, France, and in traditionally low-immigration countries (e.g., Latvia, Czechia, Estonia, Croatia). PWLFDR—most sensitive to structural change—shows only moderate increases or near-stagnation across scenarios. Overall conclusion from results: Even large, systematic shifts in immigrants’ origin composition have minor impacts on ageing and labor-force-based dependency ratios; effects on population size are more about spatial redistribution across countries rather than changes in age or economic dependency structures.

The limited impact of origin shifts on dependency ratios arises from several mechanisms. First, immigrants’ age advantage and higher initial fertility (for some origins) diminish over time due to ageing and convergence toward native fertility; arrivals in childhood show rapid assimilation in fertility behavior. Second, origin-specific labor market integration differences narrow with duration of stay, especially for men; groups with lower participation never reach a share large enough to substantially depress national participation rates. Third, educational attainment differences by origin affect LFDR and PWLFDR in opposite directions: higher inflows can modestly improve age structure but may slightly reduce average productivity-weighted labor supply if arriving groups are less educated, largely offsetting gains. The findings align with broader evidence that migration has limited impact on aggregate age structures and macroeconomic indicators such as wages and GDP per capita. Policy implications include that the education and labor market integration of immigrants matter more for productivity-weighted dependency than origin composition per se. Removing administrative barriers to labor market access and improving integration policies, especially for women and groups facing structural obstacles, can enhance outcomes. While origin shifts have modest demographic-economic effects, they can meaningfully alter the spatial distribution of population across European countries, with implications for planning and regional policy.

This study is the first to explicitly integrate place of birth as a source of heterogeneity in European population and labor force microsimulations to test sensitivity of outcomes to major shifts in immigrants’ regions of origin. Across scenarios to 2060, origin shifts modestly affect population size at the European level and can reallocate population across countries, but they only marginally influence age structure and leave LFDR and PWLFDR largely unchanged. The main contribution is demonstrating that composition by origin, holding total inflows constant, does not materially alter Europe’s projected ageing or labor-force-based dependency. Future research should: (i) explore scenarios varying immigrants’ educational and skill composition and integration trajectories, (ii) assess policy changes that affect labor market access of newcomer groups, (iii) incorporate uncertainty in destination matrices and network evolution, and (iv) extend analysis to persistent characteristics (e.g., language, religion, ethnicity) and subnational spatial dynamics.

Key limitations include: (1) Strong uncertainty around future migration volumes and origin–destination patterns; destination matrix held constant to reflect network persistence, potentially understating future shifts. (2) Assumption of stable immigrant characteristics and integration dynamics over time; real-world policies and economic conditions may alter participation and educational profiles. (3) Data constraints limited origin disaggregation to eight broad regions for non-modeled countries/regions. (4) Focus on selected demographic-economic outcomes (population size, ADR, LFDR, PWLFDR); other persistent dimensions (e.g., language, religion, ethnicity) and subnational heterogeneity are not modeled here. (5) Small between-scenario differences could be eclipsed by plausible changes in other components (fertility, mortality, total immigration levels).