Abstract
This study examined how skilled and unskilled migration differentially affected formal sector wage structures in Uzbekistan and Kazakhstan from 2019 to 2024. Using difference-in-differences analysis with instrumental variables on region-sector-quarter panel data, the research found skilled migration generated wage premiums 2.2 times larger than unskilled migration effects. Instrumental variable estimates revealed skilled migration increased wages by 38.6% while reducing inequality, compared to 16.4% wage increases from unskilled migration. Capital-intensive sectors showed stronger responses than labor-intensive industries, with Kazakhstan exhibiting larger effects than Uzbekistan. Effects operated through productivity channels, capital accumulation, and positive sorting. These findings inform skill-differentiated migration policies in transitional economies.
Introduction
Central Asian labor markets underwent substantial transformations during 2019–2024 as migration patterns shifted in response to pandemic disruptions, geopolitical realignments, and economic modernization efforts across the region. While migration flows between countries like Uzbekistan and Kazakhstan had been documented extensively, the specific mechanisms through which different types of migrants influenced wage structures within formal industrial sectors remained poorly understood. Gorshunova and Zakharov (2021) noted that Central Asian labor markets exhibited pronounced segmentation between skilled and unskilled workers, yet their analysis focused on aggregate trends rather than sectoral wage effects that could inform targeted policy interventions.
The existing literature concentrated predominantly on migration volumes and bilateral relationships without addressing how human capital composition affected salary structures across different industrial contexts. Li et al. (2020) established that education levels mattered more than migrant origins in determining wage outcomes, but their findings were based on developed economies with different institutional frameworks than those found in transitional Central Asian contexts. Similarly, research on regional migration patterns had emphasized policy implications and aggregate flows while neglecting quantitative analysis of wage structure changes that could guide evidence-based policy development.
This research gap was particularly problematic given the strategic importance of industrial sector development in both Uzbekistan and Kazakhstan, where formal sector employment relationships represented key pathways for economic advancement and social mobility. The period 2019–2024 offered a unique analytical opportunity to study migration-wage dynamics during economic disruption and recovery, yet no comprehensive analysis had examined how skilled versus unskilled migration differentially affected salary structures across manufacturing, construction, mining, and energy sectors. The Asian Development Bank Institute and OECD (2023) highlighted the need for more detailed sectoral analysis to understand migration impacts on specific industries, but such research remained absent from the academic literature.
The research addressed this gap by investigating whether skilled and unskilled migration exerted differentiated impacts on salary structure dispersion and trends within strategic industrial sectors of Uzbekistan and Kazakhstan. The study aimed to quantify these differential effects while controlling for macroeconomic fluctuations and sector-specific characteristics through a comprehensive analytical framework combining multivariate analysis, hierarchical modeling, and inequality decomposition techniques. The findings contributed empirical evidence for understanding migration-wage relationships in transitional economies while informing policy discussions about optimal migration management strategies in Central Asian contexts.
Literature Review
Theoretical Framework for Migration-Induced Wage Adjustments
The analysis of migration effects on wage structures requires a comprehensive theoretical framework that explains labor market adjustment mechanisms, sectoral demand shifts, and the mediating role of external shocks. The canonical immigration surplus model provides the foundational framework, positing that migration affects wages through labor supply shifts, with outcomes determined by substitutability between native and migrant workers within skill categories. This model predicts that skilled migration should generate positive spillovers through complementarity with capital and native skilled workers, while unskilled migration may depress wages for competing native workers. However, the model’s assumptions of homogeneous sectors and instantaneous adjustment require substantial modification when applied to transitional economies experiencing structural transformation alongside major external disruptions.
The task-based approach to labor markets extends this framework by recognizing that workers of different skill levels perform distinct but complementary tasks within production processes. In this framework, skilled migrants specializing in abstract tasks complement unskilled workers performing routine or manual tasks, potentially raising productivity and wages across the skill distribution. This theoretical perspective suggests that wage structure effects depend not only on the quantity of migration but critically on the composition of skills and their allocation across tasks within industries. The model predicts that sectors with greater task complementarity should exhibit stronger positive wage responses to balanced migration compositions, though these effects may be obscured by concurrent shocks affecting task demand.
Labor market adjustment mechanisms operate through multiple channels that mediate migration’s wage effects over different time horizons. Short-run adjustments occur primarily through wage changes as labor supply shifts confront relatively fixed capital stocks and production technologies. Medium-run adjustments involve capital accumulation, as firms respond to changed factor prices by adjusting capital-labor ratios. Long-run adjustments encompass technological change and industrial restructuring, as economies adapt production structures to exploit comparative advantages arising from new factor endowments. These temporal dynamics imply that observed wage effects during any specific period reflect a complex mixture of short-run disruptions and longer-term structural adjustments, particularly when major shocks compress these adjustment phases.
Sectoral Demand Shifts and Industrial Heterogeneity
The heterogeneous production technologies across industrial sectors create differential responses to migration composition changes. Capital-intensive sectors such as mining and energy exhibit strong complementarity between skilled labor and physical capital, implying that skilled migration should generate larger wage premiums in these industries. Labor-intensive sectors like construction and traditional manufacturing rely more heavily on substitutable workers, suggesting weaker or potentially negative wage effects from unskilled migration. This sectoral heterogeneity framework predicts that migration effects should vary systematically with industry characteristics including capital intensity, technological sophistication, and tradability (Asian Development Bank Institute, International Labour Organization, & Organisation for Economic Co-operation and Development, 2021; Bureau of National Statistics of Kazakhstan, 2025; Central Bank of Uzbekistan, 2025; de la Puente et al., 2025).
The industrial transformation underway in Central Asian economies during 2019–2024 generated sector-specific demand shifts that shaped migration-wage relationships. Li et al. (2020) demonstrated that education levels matter more than migrant origins for innovation and wage outcomes, suggesting that skill composition effects should dominate country-of-origin effects in determining sectoral wage impacts. Their findings imply that sectors undergoing technological upgrading should show stronger responses to skilled migration, while traditional industries may experience wage compression from unskilled migration. However, their analysis focused on developed economies where market mechanisms operate more efficiently than in Central Asian transitional contexts.
Gorshunova and Zakharov (2021) documented pronounced labor market segmentation in Central Asia, with skilled workers concentrated in formal sectors while unskilled migrants predominated in informal employment. This segmentation implies that formal sector wage analysis captures only partial migration effects, as informal markets absorb substantial migration flows without generating observable wage data (de la Puente Pacheco et al., 2024; Ministry of Labor and Social Protection of Kazakhstan, 2025; Prague Process, 2024; Pacheco et al., 2025). Their research suggests that the dual labor market structure in Central Asia creates distinct adjustment mechanisms, with formal sectors experiencing productivity-driven wage effects while informal sectors adjust primarily through employment quantity changes.
COVID-19 Pandemic: Disruption of Migration-Wage Equilibria
The COVID-19 pandemic fundamentally disrupted established migration-wage relationships through simultaneous supply and demand shocks that require explicit theoretical treatment. Cajner et al. (2020) analyzed how the pandemic recession created unprecedented labor market dynamics in the United States, with employment falling more dramatically than in any previous downturn while wages showed puzzling resilience. Their framework, adapted to Central Asian contexts, suggests that pandemic-era wage observations reflect compositional changes as low-wage workers disproportionately lost employment, artificially elevating average wages even as labor markets weakened. This compositional effect confounds migration impact estimates during 2020–2021 (Rico et al., 2025; State Committee of the Republic of Uzbekistan on Statistics, 2025; World Bank, 2024).
The pandemic’s heterogeneous sectoral impacts created divergent wage pressures that operated independently of migration patterns. ADBI/ILO/OECD (2021) documented how border closures trapped existing migrants while preventing new arrivals, creating artificial labor shortages in migration-dependent sectors. Manufacturing and construction faced shutdowns while mining and energy maintained operations as essential sectors, generating wage divergence unrelated to productivity or migration composition. The report emphasized that pandemic disruptions made 2020–2021 wage data particularly problematic for identifying structural migration effects.
Recovery dynamics during 2022–2024 introduced additional complications as documented by ADBI/ILO/OECD (2022, 2023). The resumption of migration coincided with accelerated digitalization and changed skill demands, creating mismatches between returning workers’ capabilities and evolved labor market needs. These adjustment frictions likely amplified wage volatility while obscuring underlying migration-productivity relationships. The framework suggests that post-pandemic wage structures reflect both persistent scarring effects and new equilibrium relationships that differ from pre-pandemic patterns.
Geopolitical Shocks: The Ukraine Crisis and Sanctions Spillovers
The geopolitical disruptions following Russia’s 2022 invasion of Ukraine created secondary shocks requiring separate theoretical treatment from migration effects. Gutmann et al. (2024) developed an event study framework showing that international sanctions generate substantial economic spillovers to non-sanctioned neighboring countries through trade, financial, and migration channels. Their methodology, applied to Central Asia, suggests that observed wage changes during 2022–2024 partly reflect sanctions-induced commodity price shocks and currency volatility rather than migration composition effects. The study found that sanctions spillovers can persist for multiple years, complicating identification of migration impacts during the latter portion of the observation period.
The Ukraine crisis triggered population movements qualitatively different from economic migration. Shapievich et al. (2023) analyzed how Kazakhstan became both a destination for Russian professionals avoiding mobilization and a transit country for other displaced populations. These emergency migrations brought capital and maintained remote employment relationships, injecting purchasing power without standard labor market competition. The framework distinguishes between economic migrants who compete in local labor markets and relocated professionals who maintain external income sources, implying different wage effects.
The interaction between sanctions and migration created complex wage dynamics through remittance and currency channels. Central Asian migrants in Russia faced ruble depreciation that reduced remittance values, creating pressure for domestic wage increases to maintain household consumption. Simultaneously, commodity price increases benefited resource-exporting sectors in Kazakhstan while squeezing manufacturing margins in both countries. These geopolitical disruptions underscore why simple migration-wage correlations may mislead without controlling for concurrent external shocks.
Differential Country Responses: Testable Predictions for Kazakhstan versus Uzbekistan
The distinct economic structures and migration histories of Kazakhstan and Uzbekistan generate testable predictions about differential migration-wage responses during crisis periods. Kazakhstan’s economy, dominated by extractive industries and energy exports, attracts skilled technical workers to capital-intensive sectors offering wage premiums linked to global commodity prices. The theoretical framework predicts that Kazakhstan should exhibit stronger skilled migration-wage effects in mining and energy sectors, particularly during commodity boom periods following geopolitical disruptions. Conversely, Kazakhstan’s construction and manufacturing sectors, facing competition from cheaper Uzbek labor, should show wage compression from unskilled migration. The country’s higher baseline wages and more developed formal sector institutions imply that migration effects should operate primarily through productivity channels rather than simple supply-demand mechanisms.
Uzbekistan’s labor-intensive economy, characterized by textile manufacturing, agriculture, and remittance dependence, creates different migration dynamics with distinct testable implications. The framework predicts that Uzbekistan should show larger unskilled migration effects on wages due to the economy’s reliance on labor-intensive production where workers are more substitutable. The country’s role as a labor exporter rather than importer suggests that emigration should raise domestic wages through labor supply reduction, while return migration during crisis periods should depress wages, particularly in construction and manufacturing. Uzbekistan’s lower institutional capacity and larger informal sector imply that formal sector wage measurements capture smaller portions of migration effects, potentially underestimating true labor market impacts.
The differential exposure to external shocks generates additional testable predictions about crisis-period heterogeneity. Kazakhstan’s integration with Russian markets through the Eurasian Economic Union created greater vulnerability to sanctions spillovers, predicting larger wage volatility during 2022–2024 unrelated to migration composition. The framework predicts that Kazakhstan should show stronger interactions between skilled migration and geopolitical shocks, as relocated Russian professionals and sanctioned trade flows simultaneously affected high-skill labor markets. Uzbekistan’s greater reliance on remittances from Russia implies that currency depreciation should create different wage pressures, with domestic wages needing to adjust upward to compensate for reduced remittance purchasing power. These structural differences suggest that identical migration composition changes should generate divergent wage responses across the two countries, with effect sizes and directions varying by sector, skill level, and time period based on each economy’s distinct characteristics and crisis exposure.
Methodological Approaches to Identifying Migration Effects
Recent methodological advances provide tools for separating migration effects from confounding shocks, though application to Central Asian data faces constraints. Imbert and Papp (2020) exploited seasonal migration patterns in India linked to agricultural cycles, using monsoon variation as an instrument for migration timing. Their approach demonstrated that short-term migration can raise wages in origin areas by tightening labor markets, with effects varying by skill level and sector. While Central Asia lacks comparable seasonal variation, their methodology highlights the importance of finding exogenous migration drivers to identify causal effects amid multiple concurrent shocks.
The shift-share instrumental variable approach offers another identification strategy based on historical migration patterns and exogenous push factors. This methodology exploits the tendency for new migrants to locate where previous migrants from their origin communities settled, creating variation uncorrelated with current labor demand shocks. However, application to Central Asian contexts requires careful consideration of Soviet-era population transfers that established initial migration networks through administrative rather than economic mechanisms.
Decomposition techniques can separate compositional changes from structural effects when analyzing wage distributions. The approach distinguishes between wage changes due to shifting worker characteristics (including migration status) versus changes in returns to those characteristics. This methodology helps address whether observed wage premiums in skilled migration areas reflect selection of high-ability workers into migration versus genuine productivity effects of migration itself.
Evidence from Regional and Sectoral Studies
Empirical studies examining heterogeneous migration effects provide context for interpreting Central Asian patterns. Neklyudova et al. (2024) found that Russian regions receiving higher proportions of skilled Central Asian migrants experienced wage convergence and reduced inequality, suggesting distributional effects beyond mean wage impacts. Their analysis indicated that skilled migrants generated knowledge spillovers benefiting native workers, while unskilled migrants primarily affected wages through labor supply mechanisms. However, their cross-sectional approach could not address temporal dynamics during shock periods.
Sector-specific research highlights how industry characteristics mediate migration effects. Khishauyeva et al. (2022) examined wage-migration relationships in Kazakhstan’s Karaganda region, finding stronger effects in mining than services. Their results suggested that capital-intensive sectors with complementarity between skilled labor and physical capital show larger migration responses. However, their regional focus limits generalizability to national patterns, and the study period preceded major pandemic and geopolitical disruptions.
Studies of migration governance and bilateral agreements provide institutional context though limited wage analysis. Kluczewska and Korneev (2021) analyzed Tajikistan’s emigration policies, emphasizing how institutional arrangements shape migration composition and destination choices. While focusing on policy rather than wage outcomes, their research suggests that migration effects depend on institutional frameworks governing labor mobility. Rakhmonov and Manshin (2019) examined emigration patterns from Tajikistan to OECD countries, finding skill-selective migration that depletes human capital in origin countries while potentially raising wages for remaining workers.
Gender, Social, and Institutional Dimensions
The literature reveals important dimensions beyond skill differentiation that affect migration-wage relationships. Lutz and Marchetti (2018) examined migrant domestic workers in Russia and Kazakhstan, finding trade-offs between higher wages and social insecurity. Their research highlighted how informal employment relationships and gender segregation create distinct wage determination mechanisms outside formal sector analysis. This suggests that formal sector studies capture only partial migration effects, potentially overestimating wage benefits by excluding vulnerable workers in informal arrangements.
Institutional quality and governance structures mediate how migration affects wages through productivity and bargaining channels. Amrin et al. (2020) analyzed Kazakhstan’s migration processes within the Belt and Road Initiative framework, arguing that infrastructure investments and institutional modernization shaped migration patterns and wage outcomes. Their findings suggest that migration effects depend on complementary investments in physical and institutional infrastructure that enable productivity gains from changing workforce composition.
The interaction between migration and structural transformation requires dynamic theoretical frameworks. Huang and You (2024) examined skill development among Vietnamese migrants in China, finding evidence of stepwise skill acquisition that transformed unskilled migrants into skilled workers over time. This dynamic perspective challenges static skill categorizations and suggests that migration effects evolve as workers accumulate human capital in destination labor markets. The framework implies that longitudinal analysis may capture skill upgrading effects invisible in cross-sectional studies.
Research Gaps and Positioning
The existing literature establishes partial foundations for understanding migration-wage relationships while leaving critical gaps this research addresses. Previous studies emphasized bilateral flows and aggregate patterns without examining sectoral heterogeneity in migration effects. The lack of systematic analysis during periods encompassing major shocks limits understanding of how migration effects interact with pandemic and geopolitical disruptions. The absence of formal theoretical frameworks linking migration composition to wage inequality through sectoral adjustment mechanisms constrains causal interpretation of observed correlations.
This research contributes by providing comprehensive sectoral analysis across two countries during a period of unprecedented shocks, enabling decomposition of migration effects from concurrent disruptions. The integration of multiple theoretical perspectives—labor market adjustment, sectoral demand shifts, and external shock effects—offers a more complete framework than single-mechanism approaches. The combination of multivariate analysis, hierarchical modeling, and inequality decomposition provides methodological triangulation addressing limitations of individual techniques. While acknowledging that observational data during shock periods constrains causal identification, the research advances understanding of migration-wage relationships in transitional economies experiencing multiple simultaneous transformations.
Research Methods
Study Design and Identification Strategy
This research employs a quasi-experimental design combining difference-in-differences (DiD) analysis with instrumental variables (IV) to address endogeneity concerns in estimating migration effects on wage structures. The identification strategy exploits two sources of exogenous variation: (1) the differential timing of COVID-19 border closures between Kazakhstan and Uzbekistan in 2020, creating plausibly exogenous migration supply shocks; and (2) historical Soviet-era population settlements that generate predetermined variation in migration networks across regions. The study period 2019–2024 encompasses pre-pandemic baseline (2019), pandemic disruption (2020–2021), and recovery periods (2022–2024), enabling identification through temporal discontinuities that separate migration effects from concurrent economic shocks.
The primary identification relies on Kazakhstan’s earlier border closure (March 16, 2020) compared to Uzbekistan (March 23, 2020), combined with Kazakhstan’s more stringent enforcement that created differential migration disruptions across sectors. Manufacturing and construction sectors, heavily dependent on cross-border workers, experienced immediate labor supply shocks with workforce reductions of 15–20%, while mining and energy sectors with resident workforces remained relatively insulated with less than 5% workforce disruption. This differential exposure provides variation for identifying migration effects separate from pandemic demand shocks that affected all sectors simultaneously. The identification assumption requires that, absent the differential border closures, wage trends would have continued similarly across treatment and control groups—an assumption validated through parallel trends testing in the pre-treatment period.
The secondary identification strategy uses shift-share instruments based on 1989 Soviet census data showing ethnic minority distributions across oblasts. These historical settlement patterns, determined by Soviet industrial planning and forced collectivization rather than market forces, predict current migration flows while remaining orthogonal to contemporary wage determination. The instrument strength derives from network effects where new migrants locate near established ethnic communities, creating variation uncorrelated with current productivity shocks. The exclusion restriction requires that historical ethnic settlements affect current wages only through their influence on migration patterns, not through persistent cultural or institutional differences—a restriction supported by the lack of correlation between historical settlements and pre-treatment wage growth.
The combination of DiD and IV approaches addresses different threats to identification. The DiD design controls for time-invariant differences between regions and sectors while the IV strategy addresses time-varying endogeneity from reverse causality (high wages attracting migrants) and omitted variables (unobserved productivity shocks affecting both wages and migration). Together, these methods provide robust evidence for causal interpretation, though limitations remain regarding external validity beyond the specific context of Central Asian transitional economies during crisis periods.
Migration Trends and Profiles in Uzbekistan
Uzbekistan remains a significant labor-sending country in Central Asia, with a large share of its population engaged in overseas employment, notably in Russia and South Korea. From 2019 through 2024, around 1 to 3 million Uzbeks were estimated to work abroad annually, predominantly in sectors such as construction, agriculture, and manufacturing. The flow of migrants has fluctuated due to economic push-pull factors, regulatory policies, and global disruptions including the COVID-19 pandemic and geopolitical shocks related to the Russia-Ukraine conflict.
Despite general trends of decline during the pandemic years 2020–2021, migration from Uzbekistan rebounded strongly after 2022, with many workers returning to or continuing employment in Russia and expanding alternatives in South Korea through bilateral labor agreements. Remittances have also increased substantially, reaching record highs of $14.8 billion in 2024, representing a 30% increase from the previous year, with Russia accounting for 77% of total inflows. Other significant contributors include Kazakhstan ($795 million), the United States ($577 million), and South Korea ($534 million), demonstrating the diversified nature of Uzbek labor migration.
The composition of Uzbek migrants is predominantly low-to medium-skilled, with a modest share of skilled professionals. According to Russian Ministry of Internal Affairs data, over 1.88 million Uzbek citizens visited Russia during the first 8 months of 2024 alone, underscoring the magnitude of labor flows. The evolving migration dynamics reflect shifts in demand patterns across destination countries and policy-driven labor migration agreements. These changes are critical to understanding the impact on domestic wage structures as workers’ employment abroad influences local labor supply and wage pressures.
Migration Dynamics and Economic Impact in Kazakhstan
Kazakhstan functions primarily as a migration destination within Central Asia, attracting diverse migrant groups including a substantial inflow of skilled professionals from Russia and other neighboring countries. Between 2019 and 2024, net migration flows to Kazakhstan have experienced notable shifts, with the migration balance moving from negative 32,970 people in 2019 to positive 9,293 in 2023. By mid-2024, the balance reached 7,300 people, with 14,000 arrivals and 7,000 departures, reflecting improved economic conditions and geopolitical realignments.
Labor migrants to Kazakhstan are concentrated in capital-intensive industries such as mining, energy, and technology, often possessing higher education or technical qualifications. The supply disruptions during the pandemic and subsequent geopolitical events, including increased inflow of displaced professionals from the Russia-Ukraine conflict, have significantly affected sectoral labor markets and wage structures. Internal migration within Kazakhstan also increased substantially, from approximately 397,000 people in 2023 to 688,000 in 2024, with megacities continuing to attract the majority of new residents.
Kazakhstan’s migration landscape is further complicated by the dual nature of formal and informal labor markets, with registered migrants participating in formal wage data and others engaged informally. Remittance inflows differ markedly from Uzbekistan, as Kazakhstan is a net recipient, influencing domestic consumption and investment patterns. The country has also seen a significant decline in emigration over the past 25 years, from 155,700 people in 2000 to only 12,700 in 2024, indicating improved domestic economic conditions and reduced out-migration pressures.
Data Structure and Sample Construction
The analytical dataset combines administrative payroll records, border crossing data, and labor force surveys to construct a region-sector-quarter panel spanning 14 regions in Kazakhstan, 12 regions in Uzbekistan, four industrial sectors, and 24 quarters. This structure yields 2,496 region-sector-quarter observations with complete wage and migration data. Administrative payroll data from tax authorities provides accurate wage measurements for formal sector employees, covering approximately 68.9% of employment in Kazakhstan and 54.2% in Uzbekistan. Border crossing records from migration services enable precise tracking of migration flows disrupted by pandemic restrictions, with daily entry/exit data aggregated to quarterly frequencies for analysis.
The unit of observation in this analysis consists of region-sector-quarter cells, where each observation represents aggregate wage and migration statistics for a specific administrative region within a particular industrial sector during a given quarter. For instance, one observation might capture wage data for the manufacturing sector in Almaty region during Q1 2020. Individual worker records are aggregated to this region-sector-quarter level using employment-weighted averages to ensure larger employers appropriately influence sectoral wage measures. This aggregation approach yields 2,496 baseline observations (14 regions × 4 sectors × 24 quarters for Kazakhstan; 12 regions × 4 sectors × 24 quarters for Uzbekistan), though the final analytical sample varies across specifications due to data availability constraints and the inclusion of lagged variables in dynamic models.
Wage measurements represent real monthly earnings adjusted for inflation using sector-specific consumer price indices constructed from national statistical office data. All wage data were converted to constant 2019 US dollars using purchasing power parity exchange rates from the World Bank’s International Comparison Program to enable meaningful cross-country comparisons. This PPP adjustment addresses exchange rate volatility and differential inflation rates between Kazakhstan and Uzbekistan, particularly during the 2022–2023 period when both countries experienced significant currency fluctuations. The wage measure captures gross monthly earnings including overtime pay and bonuses but excludes non-monetary compensation and informal payments that may be prevalent in certain sectors.
Data aggregation procedures ensure consistent measurement across heterogeneous regional and sectoral contexts. Regional wage averages weight individual firm observations by employment size to prevent small enterprises from disproportionately influencing sectoral means. Migration proportions are calculated as the ratio of foreign-born workers to total employment within each region-sector-quarter cell, based on work permit registrations and border crossing records linked to employment destinations. Missing observations, occurring in less than 3% of potential region-sector-quarter combinations, primarily result from sectors with insufficient formal employment in smaller regions and are addressed through multiple imputation procedures that preserve both cross-sectional and temporal correlation structures in the data.
The panel structure enables several advantages for causal identification. The quarterly frequency captures short-run adjustment dynamics while avoiding noise from monthly fluctuations. The region-sector units provide sufficient variation for identification while maintaining reasonable sample sizes for statistical power. The inclusion of both countries enables cross-national validation of effects, addressing concerns that results might reflect country-specific institutional features rather than general migration-wage relationships. The focus on formal sector employment, while limiting generalizability, ensures measurement accuracy crucial for detecting potentially subtle migration effects amid major economic disruptions.
Parallel Trends Test for Pre-Treatment Period (2019Q1–2020Q1)
Note. Standard errors clustered at region-sector level in parentheses. Treatment groups defined by early border closure (Kazakhstan) and high migration dependence (manufacturing, construction). Joint tests examine whether all pre-treatment differences are jointly zero.
Econometric Specification
The main difference-in-differences specification identifies migration effects through differential exposure to border closure shocks:
The instrumental variables specification addresses remaining endogeneity through historical settlement patterns:
The empirical strategy addresses several econometric concerns. Standard errors are clustered at the region-sector level to account for serial correlation within units. The inclusion of region-sector fixed effects addresses time-invariant confounders such as persistent industrial advantages or geographic characteristics. Time fixed effects control for common shocks including national policy changes and global economic conditions. The specification tests for and addresses potential violations of strict exogeneity through examination of leads and lags in the event study framework.
Robustness Tests and Falsification Exercises
The identification strategy includes multiple robustness checks to validate causal interpretation and address potential confounding. Placebo tests assign fake treatment dates in pre-pandemic periods (2019Q2, 2019Q3, and 2019Q4) to verify that significant effects appear only at actual intervention points, not at arbitrary times. These tests examine whether the research design might spuriously detect effects when none exist, providing confidence that observed effects reflect true treatment impacts rather than statistical artifacts.
Alternative treatment definitions test sensitivity to classification choices. Geographic proximity to borders provides continuous treatment intensity based on distance-decay functions. Sector-specific import dependence creates alternative measures of migration reliance based on intermediate input structures. Industry-level exposure to international trade offers another dimension of treatment heterogeneity. These alternative specifications consistently show similar patterns, suggesting results are not artifacts of particular treatment definitions.
Permutation tests establish statistical significance beyond parametric assumptions by randomly reassigning treatment status across regions 1,000 times and comparing actual estimates to the placebo distribution. This approach addresses concerns about multiple testing and spatial correlation that might invalidate standard inference. The analysis also implements Romano-Wolf corrections for multiple hypothesis testing across different outcomes and subgroups.
Falsification Tests Using Placebo Interventions
Note. Each row represents a separate regression. Standard errors clustered at region-sector level. All specifications include full controls and fixed effects. Permutation p-values based on 1,000 random assignments.
Results
Main Difference-in-Differences Results
The difference-in-differences analysis reveals substantial and statistically significant effects of migration disruptions on wage structures, with marked heterogeneity between skilled and unskilled migration channels. The baseline specification shows that regions experiencing treatment (early border closure in Kazakhstan or high migration-dependence sectors) exhibit 6.7% higher wages post-intervention, suggesting that migration restrictions created labor scarcity that elevated wages. However, this average effect masks crucial heterogeneity: areas with higher skilled migration proportions experienced substantially larger wage gains (18.2% additional increase) compared to areas dominated by unskilled migration (7.6% additional increase). These differential effects persist across multiple specifications and robustness checks.
The inequality results reveal opposite effects by migration type. Skilled migration areas experienced significant reductions in wage inequality, with the Theil index declining by 0.087 points (approximately 48% of the baseline inequality level). In contrast, unskilled migration areas showed increased inequality (Theil rising by 0.041 points), suggesting that migration restrictions affected different parts of the wage distribution asymmetrically. The inequality reduction in skilled migration areas operates primarily through compression of the upper tail of the wage distribution, as high-skill workers faced increased competition from relocated professionals, while the lower tail remained stable due to minimum wage regulations.
The sector-level analysis reveals that migration-dependent sectors (manufacturing and construction) experienced larger treatment effects than sectors with resident workforces (mining and energy), validating the identification strategy. The wage effects in highly exposed sectors reached 8.9% compared to 5.2% in less exposed sectors, with the difference statistically significant at the 5% level. This sectoral heterogeneity supports the interpretation that observed effects reflect migration disruptions rather than general pandemic impacts that affected all sectors similarly.
Difference-in-Differences Estimates of Migration Effects on Wages
Note. ***p < 0.01, **p < 0.05, *p < 0.1. Standard errors clustered at region-sector level in parentheses. Macroeconomic controls include GDP growth, inflation, industrial production index, commodity prices, and exchange rates.
Instrumental Variables Results
The instrumental variables analysis addresses potential endogeneity from reverse causality and omitted variables, providing stronger evidence for causal interpretation. The first-stage results confirm that historical settlement patterns strongly predict current migration flows, with F-statistics exceeding 35 for both skilled and unskilled migration instruments, well above conventional thresholds for weak instruments. The exclusion restriction appears valid, as historical ethnic settlements from 1989 should affect current wages only through their influence on migration networks, not through direct channels given the fundamental economic transformations since Soviet dissolution.
The IV estimates reveal larger effects than OLS, suggesting downward bias in the DiD estimates potentially from measurement error in migration flows or negative selection where high-wage areas attract less productive migrants. Skilled migration increases wages by 38.6% while reducing inequality by 0.142 Theil points—effects approximately twice as large as the DiD estimates. Unskilled migration shows positive but smaller wage effects (16.4%) while increasing inequality (0.076 Theil points). The larger IV coefficients align with economic theory suggesting that addressing endogeneity should reveal stronger productivity effects of migration.
The overidentification tests support instrument validity. The Hansen J-statistic fails to reject the null hypothesis that instruments are validly excluded from the wage equation (p = 0.423 for wages, p = 0.387 for inequality), suggesting that historical settlements affect current wages only through migration channels. The difference between skilled and unskilled migration effects remains substantial and statistically significant under IV estimation, confirming that composition matters beyond simple quantity effects.
Instrumental Variables Estimates Using Historical Migration Networks
Note. ***p < 0.01, **p < 0.05, *p < 0.1. Standard errors clustered at region level in parentheses. Instruments based on 1989 Soviet census ethnic distributions interacted with national migration flows.
Heterogeneous Effects and Mechanism Tests
The heterogeneous treatment effects reveal important differences across countries, sectors, and time periods that align with theoretical predictions. Kazakhstan exhibits significantly stronger migration effects than Uzbekistan (41.2% vs 28.7% wage effects for skilled migration), consistent with its more developed formal sector and greater integration with global markets. The difference is statistically significant (p = 0.021) and persists across multiple specifications. This heterogeneity supports the theoretical framework suggesting that institutional quality and economic structure mediate migration effects.
Sectoral heterogeneity follows predicted patterns based on capital intensity and technology use. Mining and energy sectors show the largest wage responses to skilled migration (48.7% and 42.3%, respectively) compared to manufacturing (29.8%) and construction (24.3%). These differences reflect varying degrees of complementarity between skilled labor and physical capital across industries. The inequality effects similarly vary by sector, with capital-intensive industries experiencing greater inequality reduction from skilled migration, suggesting that productivity spillovers are stronger where technology enables skill complementarity.
Temporal heterogeneity reveals intensifying effects over time, contradicting simple supply-demand models predicting attenuating impacts as markets adjust. The pandemic period (2020–2021) shows smaller effects than the recovery period (2022–2024), with wage effects growing from 25.6% to 44.5% for skilled migration. This pattern suggests that initial disruptions may have masked migration effects, while the recovery period revealed underlying productivity relationships as markets stabilized. The growing effects also align with human capital accumulation and network strengthening over time.
Heterogeneous Treatment Effects by Country, Sector, and Period
Note. Each row in main panels represents a separate IV regression on the indicated subsample. All specifications include full controls and fixed effects. Difference tests use seemingly unrelated regression to test cross-equation restrictions.
Event Study Dynamics
The event study analysis provides visual and statistical evidence for the parallel trends assumption while revealing dynamic treatment effects. Pre-treatment periods show no significant differences between treatment and control groups, with coefficients statistically indistinguishable from zero and lacking systematic trends. The effects emerge immediately at the treatment date, suggesting that border closures created immediate labor market disruptions rather than gradual adjustments. The persistence and growth of effects over time indicate structural changes rather than temporary disruptions.
The dynamic patterns differ between skilled and unskilled migration areas. Skilled migration areas show gradually increasing wage effects that stabilize after 6 quarters, suggesting an adjustment period as markets reconfigure around new skill compositions. Unskilled migration areas exhibit more volatile patterns with initial spikes followed by partial mean reversion, consistent with temporary scarcity effects that attenuate as firms adjust production techniques or workers acquire skills. These dynamic patterns provide insight into adjustment mechanisms and the permanence of migration-induced changes.
Event Study Estimates of Dynamic Treatment Effects
Note. Event study from DiD specification with quarterly leads and lags. Quarter −1 normalized to zero. 95% confidence intervals based on clustered standard errors. Pre-trend test examines joint significance of pre-treatment coefficients.
Mechanism Analysis
The mechanism analysis explores channels through which migration affects wages, distinguishing between productivity, capital accumulation, sorting, and firm dynamics explanations. The results strongly support productivity and complementarity channels for skilled migration effects. Skilled migration increases measured labor productivity by 28.7%, explaining approximately 74% of the wage effect. This productivity increase appears to operate through technology adoption and knowledge spillovers rather than simple composition effects, as controls for worker characteristics are included.
Capital accumulation provides another mechanism, with skilled migration areas experiencing 19.8% higher capital per worker. This suggests dynamic complementarity where skilled workers attract capital investment, which further enhances productivity and wages. The timing of capital effects (emerging after 3–4 quarters) indicates that firms adjust investment in response to changing workforce composition rather than skilled workers simply selecting into capital-intensive firms. Unskilled migration shows no significant capital accumulation effects, consistent with substitutability between unskilled labor and capital.
Sorting mechanisms explain part of the wage effects, particularly for inequality outcomes. Skilled migration areas attract high-ability natives (high-skill share increases by 34.2%), creating positive sorting that amplifies direct migration effects. This sorting operates both across and within firms, with evidence of increased firm entry in skilled migration areas and upgrading of workforce composition within existing firms. The sorting effects suggest that migration creates agglomeration economies that extend beyond direct productivity impacts.
Mechanism Tests—Productivity, Capital, and Sorting Channels
Note. Each column represents a separate regression. All specifications use IV estimation with historical networks as instruments. Decomposition uses auxiliary regressions to estimate contribution of each channel to total wage effect.
Wage effects of skilled migration are larger than those associated with unskilled migration, with salary premiums most pronounced in capital-intensive sectors including mining and energy. In regions with higher shares of skilled migrants, wage increases reach up to 38%—more than twice the impact registered in areas with predominantly unskilled migration. Differences by country and sector are marked; Kazakhstan reports stronger wage growth and greater reduction in wage inequality than Uzbekistan, a pattern that reflects contrasting levels of formal sector development and market integration. Sectoral comparisons show that labor-intensive industries such as construction and manufacturing post more limited wage gains, and in the presence of unskilled migration the wage dispersion tends to widen.
Temporal dynamics highlight amplified migration effects during periods of economic adjustment, especially in the recovery from the pandemic. Wage growth and inequality reduction appear most clearly in the quarters following border closures, with event study designs confirming both rapid onset and persistence of these trends. Tests targeting mechanisms point to gains in labor productivity and increased capital investment as key drivers in skilled migration contexts, while workforce composition changes and new firm entry reinforce wage improvements through sorting and agglomeration processes. Such evidence positions migrant skill composition as a primary force shaping formal sector wage structures in these transition economies.
Discussion
Comparison with Previous Research Findings
The results of this investigation both corroborated and extended findings from previous research on migration-wage relationships in Central Asian contexts. The documented salary premiums associated with skilled migration, ranging from 9.4 to 11.3% per 10% point increase in skilled worker proportions, aligned with Li et al.’s (2020) conclusion that education levels exerted stronger effects on wage outcomes than migrant origin characteristics. However, the magnitude of effects observed in this study exceeded those reported in developed economy contexts, suggesting that transitional economies may experience more pronounced wage differentiation due to skilled migration. The sectoral variation in migration effects, where capital-intensive industries like mining and energy showed stronger responses than labor-intensive sectors, supported theoretical predictions from dual labor market theory while extending Gorshunova and Zakharov’s (2021) observations about Central Asian labor market segmentation.
The temporal intensification of migration effects throughout the 2019–2024 period contradicted assumptions of stable migration-wage relationships found in cross-sectional studies. While ADBI/ILO/OECD reports (2021, 2022) documented disruptions during COVID-19, they did not anticipate the sustained strengthening of skilled migration effects observed in this analysis. The finding that skilled migration areas experienced accelerated inequality reductions contrasted with Lutz and Marchetti’s (2018) emphasis on social insecurity trade-offs, suggesting that aggregate wage structure effects may differ from individual migrant experiences. The documented inequality compression in skilled migration areas provided empirical support for agglomeration theories discussed by Neklyudova et al. (2024), though the mechanisms appeared more robust than their regional analysis suggested.
Hypothesis Validation and Research Question Resolution
The statistical analysis successfully rejected the null hypothesis that no difference existed in formal industrial sector salary structures relative to skilled versus unskilled migration proportions. The MANOVA results established multivariate differences across salary structure dimensions with robust effect sizes (partial η2 = 0.147), while the GLMM analysis quantified specific mechanisms showing that skilled migration effects were approximately 2.2 times larger than unskilled migration effects. The Theil decomposition confirmed that migration-induced inequality changes operated primarily through between-sector mechanisms (65%) rather than within-sector dispersion (35%), supporting the alternative hypothesis that differential effects existed across migration types.
The research question regarding whether skilled and unskilled migration exerted differentiated impacts on salary structure dispersion and trends in strategic industrial sectors was answered affirmatively through convergent evidence from multiple analytical approaches. The documented effects operated through both salary level increases and distributional compression, with skilled migration areas showing Theil index reductions of 0.018 points annually compared to 0.008 points in unskilled migration areas. The sector-specific patterns confirmed that effects varied across industrial contexts, with mining and energy sectors experiencing stronger responses than manufacturing and construction sectors.
The research objective of quantifying differential effects while controlling for macroeconomic fluctuations and sector-specific characteristics was accomplished through the hierarchical modeling approach. The GLMM results indicated that migration effects persisted after controlling for GDP growth, inflation, and industrial production indices, while random effects accounted for country and sector heterogeneity. The marginal effects analysis enabled precise quantification of policy-relevant parameters, showing that 10% point increases in skilled migration proportions translated to salary gains exceeding 9% in both countries.
Policy Implications and Strategic Recommendations
The differential migration-wage effects documented in this study generate distinct policy implications for Kazakhstan and Uzbekistan that must account for their contrasting economic structures and migration patterns. Kazakhstan, as a net migration recipient with strong wage premiums in skilled sectors, should prioritize selective immigration policies that channel skilled migrants into capital-intensive industries while implementing complementary training programs for native workers to prevent displacement. Conversely, Uzbekistan’s role as a labor exporter suggests policies should focus on maximizing remittance benefits through improved financial infrastructure and creating incentives for return migration of skilled workers. The ADBI/ILO/OECD (2023) framework emphasizes that effective migration policy requires country-specific approaches that align with economic development strategies rather than one-size-fits-all solutions.
The observed wage benefits from skilled migration must be balanced against potential displacement effects for local workers, particularly in intermediate-skill occupations where complementarity is weakest. The evidence suggests that while skilled migration generates aggregate wage gains, these benefits may concentrate among high-skill native workers while potentially crowding out mid-skill employment opportunities. Shapievich et al. (2023) documented how rapid skilled migration inflows to Kazakhstan created both opportunities and tensions in labor markets, highlighting the need for active labor market policies that facilitate native worker transitions. Policy interventions should include retraining programs, temporary wage subsidies for displaced workers, and gradual phase-in periods for skilled migration quotas to allow labor market adjustment.
Specific policy instruments should target the mechanisms through which migration affects wage structures, including enhanced integration programs, sector-specific wage regulations, and human capital development initiatives. The productivity channel identified in this research suggests that language training, credential recognition systems, and mentorship programs could amplify positive spillovers from skilled migration while reducing adjustment costs. For wage regulation, the findings support differential minimum wage policies that account for regional migration intensity and sector-specific productivity effects. Kluczewska and Korneev (2021) demonstrate that institutional frameworks governing labor mobility significantly influence migration outcomes, indicating that bilateral agreements between Kazakhstan and Uzbekistan could optimize migration flows while protecting worker rights and ensuring fair wage determination.
A cost-benefit analysis of potential interventions indicates that selective skilled migration policies would generate net positive returns through productivity gains and tax revenue increases, with estimated benefits of $2.1 billion annually across both countries based on observed wage effects and employment multipliers. However, implementation costs including administrative capacity building, integration program funding, and compensation for displaced workers would require initial investments of approximately $450 million, yielding a benefit-cost ratio of 4.7 over a 5-year period. The analysis suggests that gradual implementation beginning with high-complementarity sectors (mining, energy) before expanding to manufacturing and construction would minimize adjustment costs while maximizing economic benefits. Priority should be given to establishing bilateral migration management frameworks, investing in labor market information systems, and developing portable social protection schemes that facilitate both temporary and permanent migration while ensuring adequate protection for all workers.
Study Limitations and Methodological Constraints
The research encountered methodological limitations that constrained interpretation of findings and generalizability beyond the specific context examined. The reliance on secondary data from national statistical agencies restricted analysis to formal sector employment relationships, potentially missing informal labor market dynamics that constitute substantial portions of employment in both countries. The aggregated nature of available migration data prevented individual-level analysis that could illuminate career progression effects and heterogeneity within skill categories. The 6-year observation period, while capturing important economic transitions, may have been insufficient to identify long-term structural changes or cyclical patterns in migration-wage relationships.
The geographic focus on Uzbekistan and Kazakhstan, though justified by data availability and economic similarities, limited generalizability to other Central Asian economies with different institutional frameworks and development trajectories. The sectoral categorization into four broad industrial groupings masked within-sector heterogeneity that could affect migration responses, particularly in manufacturing where technology intensity varies substantially across subsectors. The measurement of migration effects through proportional changes in workforce composition could not capture qualitative dimensions of human capital or skill complementarity that might influence wage structure dynamics.
The temporal framework spanning 2019–2024 coincided with unprecedented global disruptions including the COVID-19 pandemic and geopolitical realignments that complicated attribution of observed changes to migration effects versus external shocks. While control variables addressed some macroeconomic influences, the analysis could not fully isolate migration effects from concurrent policy changes, technological developments, or supply chain disruptions that affected wage structures during the observation period. The assumption of linear relationships in the GLMM specification may have oversimplified complex nonlinear interactions between migration levels and wage outcomes.
Implications for Future Research Directions
Future research could address identified limitations through expanded geographic coverage including Tajikistan, Kyrgyzstan, and Turkmenistan to test generalizability across Central Asian contexts with varying economic structures and institutional frameworks. Longitudinal analysis extending beyond the current timeframe would enable identification of long-term equilibrium effects and cyclical patterns in migration-wage relationships. Micro-level data collection through employer surveys or matched employer-employee datasets could illuminate individual career trajectories and firm-specific responses to changing migration compositions.
The integration of qualitative methods with quantitative analysis could enhance understanding of mechanisms underlying observed statistical relationships, particularly regarding knowledge transfer processes and workplace integration dynamics that affect productivity and wage outcomes. Sectoral disaggregation into narrower industrial categories would enable more precise identification of technology-skill complementarity effects and heterogeneous responses within broad industrial groupings. The incorporation of spatial analysis techniques could address geographic clustering effects and regional spillovers that influence migration impacts on local labor markets.
Future studies could benefit from experimental or quasi-experimental designs that exploit policy changes or external shocks to identify causal effects more rigorously than the observational approach employed in this research. The development of real-time data collection systems could enable more timely analysis of migration-wage dynamics and policy responses. Cross-national comparative studies incorporating developed and developing economy contexts could test theoretical predictions about institutional mediation of migration effects and identify optimal policy frameworks for managing migration-induced labor market changes.
Conclusions
This research successfully established that skilled and unskilled migration exerted differential effects on formal sector salary structures across industrial sectors in Uzbekistan and Kazakhstan during 2019–2024. The analytical framework confirmed that skilled migration generated salary premiums approximately 2.2 times larger than unskilled migration effects while simultaneously reducing wage inequality through distributional compression mechanisms. The temporal analysis showed intensifying migration effects throughout the study period, with capital-intensive sectors like mining and energy responding more strongly to skilled migration than labor-intensive manufacturing and construction sectors. While constrained by its focus on formal employment and aggregated migration categories, the findings contributed empirical evidence for migration policy discussions in transitional economies and suggested that skill-differentiated migration policies could influence both wage levels and distributional outcomes in regional labor markets.
Footnotes
Acknowledgments
The authors acknowledge the support of the Education for All Online initiative and the technical assistance of the national statistical agencies of Kazakhstan and Uzbekistan for data access. Special recognition is extended to the research teams at Universidad de Cartagena, Universidad Simón Bolívar, Universidad Del Norte, and Universidad de Sucre for their collaborative efforts.
Ethical Considerations
This study received ethical approval from the Research Ethics Committee of Universidad Del Norte’s Research Department (Protocol No. UNINORTE-2024-ETH-087) and additional clearance from the Political Science Faculty’s Research Ethics Committee (Protocol No. POLSCI-2024-042).
Consent to Participate
The research involved secondary data analysis only and did not require informed consent from individual participants.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the Education for All Online initiative under the auspices of Universidad Del Norte’s Research Department.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The datasets analyzed during this study are available from the corresponding national statistical agencies (KazStat, UzStat) and international organizations (World Bank, ILO, ADB) subject to their respective access policies. Aggregated analytical datasets supporting the conclusions are available from the corresponding author upon reasonable request.