Analysing the impact of migration flows on regional per capita GDP in Türkiye: a spatial panel data approach

Ali Osman Öztop*

Ali Osman Öztop

Affiliation: Department of Economics and Finance, Muğla Sıtkı Koçman University, Fethiye, Muğla, Türkiye

0000-0002-7568-5927

Correspondence
aliosmanoztop@mu.edu.tr

   

Tuna Köse*

Tuna Köse

Affiliation: Independent Researcher, Muğla, Türkiye

0000-0002-8703-0215

Article   |   Year:  2026   |   Pages:  259 - 286   |   Volume:  50   |   Issue:  2
Received:  August 28, 2025   |   Accepted:  December 21, 2025   |   Published online:  June 5, 2026

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https://doi.org/10.3326/pse.50.2.4

Abstract

1 Introduction

Empirical studies directly testing this effect are rare (Chen, 2006; Orefice, 2010; Di Maria and Lazarova, 2012; Boubtane, Dumont and Rault, 2016; Șerban et al., 2020). We believe that this study is among the first to analyse the effect of settled migrants’ educational levels on per capita GDP using the spatial panel data approach.

To check the robustness of our baseline result, we added a lagged dependent variable (to account for trend population growth) and indicators of financial inclusion such as population per bank branch and per capita bank deposits, which have been shown in the literature to affect per capita GDP growth (e.g. Kim, Yu and Hassan, 2018; Sonkar and Sarkar, 2020; Kanga et al., 2022; Afonso and Blanco-Arana, 2024). The robustness checks confirm the stability of our main empirical results. The inclusion of a lagged dependent variable, time trend, and financial inclusion indicators does not alter the direction or significance of estimated relationships. Moreover, when spatial dependencies are accounted for through spatial autoregressive models with additional autoregressive error structure (SARAR), the positive link between migration and per capita GDP remains intact. This consistency across model specifications suggests that our baseline findings are robust to variations in model design.

2 Literature review

2014

2016

2023

In the broader study of immigration, research dating back to Ravenstein (1885) both explores and explains the complexity of international and internal migration. More recently, Greenwood (1975; 1997) examined internal migration in advanced economies, Lucas (1997) addressed internal migration from the perspective of developing economies, Borjas (1999) analysed the phenomenon of migration on an international scale, and Etzo (2008) surveyed the literature on internal migration.

In studies of migration and economic growth specifically, three dominant frameworks have emerged: early Solow-Swan growth models, human capital models, and convergence models. The early models treated migration as an extended version of the traditional Solow-Swan growth model with a production technology that features declining marginal productivity of labour. In such models, population growth is traditionally associated with negative effects on per capita productivity and per capita income growth (see e.g. Dolado, Goria and Ichino, 1994).

2.1 International migration

2009

2014

2014

2024

2013

2016

When account is taken of factors such as skill level, time, and age distribution, the picture becomes even more varied. Some models found that skilled migration flows had a less negative impact on per capita income growth than overall population growth (Barro and Sala-i-Martin, 2004; Dolado, Goria and Ichino, 1994). Boubtane, Dumont and Rault (2016), Orefice (2010) and Șerban et al. (2020) measured migrants’ human capital based on their educational attainment and found it to be positively correlated with per capita GDP growth in host countries. Kang and Kim (2018) highlighted the role of migration from developed economies in enhancing growth of developing countriesthrough the transfer of advanced knowledge and skills. But Chen (2006) and Di Maria and Lazarova (2012) found that skilled labour migration, regardless of origin, negatively affected economic growth of the host country

With regard to the time dimension, Brunow, Nijkamp and Poot (2015) concluded that net migration helped raise growth in the long run in both developed and developing economies, but they could not detect a statistically significant relationship between migration and growth in the short run. By contrast, Ortega and Peri (2009) showed that migration increased the overall GDP level of the host country in the short run and was neutral in the long run.

In a study that considered the age of migrants, Aleksynska and Tritah (2015) found that a high proportion of young immigrants had a negative impact on host country per capita income, whereas a high proportion of older immigrants had a positive effect.

Another strand of the literature, often using the cointegrating framework, focused on the direction of causality between migration and growth, as the decision to emigrate was shown to be largely driven by differences in GDP levels(Draženović, Kunovac and Pripužić, 2018). Felbermayr, Hiller and Sala (2010) and Göv and Dürrü (2017) thus claimed that the causality ran from migration to growth; Boubtane, Coulibaly and Rault (2013a) and Morley (2006) that it ran from growth to migration; while Boubtane, Coulibaly and Rault (2013b) and Altunç, Uçan and Akyıldız (2017) emphasised a positive bidirectional relationship.

2.2 Internal migration

1999

The empirical work has mostly relied on convergence models to explain the effects of migration on growth. In early contributions, Barro and Sala-i-Martin (1992; 2004) found that net migration had a positive but insignificant effect on growth. This finding was echoed in several other studies (Bayraktar and Özyılmaz, 2019; Foldvari, van Leeuwen and van Zanden, 2013; Huber and Tondl, 2012; Özgen, Nijkamp and Poot, 2010; Peri, 2010). Studies of spatial interactions in migration, triggered for example by income differentials and labour market conditions in neighbouring regions, have been less common (see e.g. LeSage and Fischer, 2009).

When looking at Türkiye specifically, Özyılmaz and Bayraktar (2021) established that net internal migration increased growth in all of its NUTS-2 regions. Manavgat and Saygılı (2016) found that, as surplus labour moved from lower-productivity provinces, per capita income increased both in the provinces the migrants left and in those to which they relocated. Ulucan (2022) also noted that increases in per capita income in a given province contained emigration from that province and attracted a significant immigration from other provinces. This finding supports the view that income differences are the primary driver of internal migration, while demographic structure and education level of workers explain in addition which provinces receive or release domestic migrants. Yürük and Batmaz (2023) found that there was no statistically significant relationship between the extent of internal immigration and economic growth, but provinces that lost part of their workforce experienced a significant slowdown in growth. As in the case of international migration, it is important consider the direction of causality: there are studies suggesting that economic growth leads to internal migration (Bunea, 2012; Ghatak, Mulhern and Watson, 2008; Zhang and Song, 2003) rather than the opposite.

In contrast to these studies, Kırdar and Saraçoğlu (2012) found that internal migration had a negative effect on regional growth in Türkiye. Huber and Tondl (2012) found a similar negative long-term effect on per capita GDP of migrant-sending regions in the EU. Vakulenko (2014) looked at regional migration and per capita income differences in Russia and found a negative relationship between these two variables. Ortega (2008) reported that that regional migration in Spain slowed per capita income growth. Borozan (2017) found that the effect of net migration on long-term economic growth in Croatia was insignificant.

The level of human capital was found to be quite important in explaining the relationship between migration and growth. Several studies found a statistically significant and positive relationship between the share of highly educated settled migrants and per capita GDP (Akbari and Haider, 2018; Özyılmaz and Bayraktar, 2021). Pouliakas et al. (2009) found that migration of highly skilled workers led to productivity losses in certain regions in Greece, while migration of unskilled workers had a positive effect on regional GDP.

3 Data and empirical framework

We checked the stationarity of per capita GDP levels with unit root tests at both provincial and country levels. The augmented Dickey-Fuller (ADF) test, incorporating a deterministic trend and employing the Akaike Information Criterion (AIC) for lag selection, showed that within the 2009-22 panel, a trend was present in 8 of the 81 provinces at the 5% significance level; in the remaining 73 provinces there was no statistically significant trend. For the shorter 2016-22 subsample, only 5 provinces had a significant trend, with the other 76 exhibiting none. The cross-sectionally augmented Im, Pesaran, and Shin test (CIPS) test developed by Pesaran (2007) indicated that the panel dataset was stationary in first differences for both subsamples (with p < 0.01). Nevertheless, we checked for robustness of the baseline specification in levels by adding a lagged per capita GDP level and time trend to the regressions (see table 3).

H1: β₁ > 0 → Internal net migration positively affects the per capita GDP of a province. 

H2: β₂ > 0 → International net migration of the domestic population has a positive effect on the per capita GDP of a province. 

H3: β₃ > 0 → International net migration of foreign nationals has a positive effect on the per capita GDP of a province.

Equation (1a) describes the overall effect of net migration on per capita GDP. Other things equal, net migration inflows increase labour supply and raise potential output and hence per capita GDP (Mariya and Tritah, 2009; Docquier, Peri and Ruyssen, 2014; Özyılmaz and Bayraktar, 2021). However, as internal migration in Türkiye comprises mainly low-skilled rural-urban movements, it might also affect per capita GDP of a province negatively (Kırdar and Saraçoğlu, 2012). The sign of the β₁ coefficient is thus a priori indeterminate. 

Similarly, the signs of β₂ and β₃ coefficients are a priori indeterminate as they depend, among other factors, on the skills composition of domestic workers leaving and foreign workers entering Türkiye. For different samples, Kang and Kim (2018), for example, estimated these coefficients to be positive, while Chen (2006) and Di Maria and Lazarova (2012) estimated them to be negative. 

In the second model we analyse internal migration. We consider separately internal migrants with at least a bachelor’s degree (IM^B) and those without a bachelor’s degree (IM^NB):

H4: Ø₁ < 0→Low-skillednet internal migration negatively affects the per capita GDP of a host province. 

H4: Ø₂ > 0→High-skilled net internal migration positively affects the per capita GDP of a host province.

In the empirical literature there is a somewhat greater consensus on the expected signs of coefficients Ø₁ and Ø₂: net inflow of low-skilled workers is generally estimated to dampen per capita GDP of the host province (e.g. Özyılmaz and Bayraktar, 2021), while net inflow of high-skilled workers is generally estimated to raise he per capita GDP of the host province through the positive effect on its potential output (e.g. Akbari and Haider, 2018).

3.1 Spatial panel model

2008

The use of the spatial panel model also improves the accuracy of estimates for larger sample sizes. As spatial dependence increases in large sample sizes, test power decreases and rejection rates in right-tailed tests approach zero. The spatial panel model prevents this loss of power by accounting for effects of neighbouring units (Kang, 2018). Spatial panel models also allow a better modelling of dynamic relationships across multiple time periods. For example, Pu et al. (2019) demonstrate that interprovincial migration flows in China exhibit strong temporal dependence and spatial spillovers. They use a spatial dynamic panel model to decompose migration effects over contemporaneous, short-run, and long-run periods, showing how spatial panel models can be efficient in capturing dynamic relationships over time.

4 Estimation results

ln(GDPpc)it = β10 + β20IMit + β31INMitD + β32INMitF + αi + εit

(2a)

ln(GDPpc)it = α0 + α1IMitNB + α2IMitB + αi + εit

(2b)

The random effects model treats the unobserved heterogeneity as random effects (ε_it ∼ N(0,σ²) i.i.d.) drawn from a larger sample, enabling efficient estimates that leverage both cross-sectional and time-series variation in data:

ln(GDPpc)it = β10 + β20IMit + β31INMitD + β32INMitF + α + uit

(3a)

uit = αi + εit

(3b)

ln(GDPpc)it = α0 + α1IMitNB + α2IMitB + α + uit

(3c)

uit =  αi + εit

(3d)

Panel A estimates suggest that internal migration (IM) has a statistically significant and quantitatively large positive effect on per capita GDP in both pooled and random effects models. This result aligns with findings of Barro and Sala-i-Martin (1992, 2004) and the Türkiye-specific findings of Özyılmaz and Bayraktar (2021). However, when country-specific unobservable heterogeneity is controlled for in the fixed effects model, the significance of internal migration disappears, suggesting that unobserved, time-invariant characteristics of provinces such as infrastructure or industrial structure rather than migration may account for the effect on per capita GDP. 

Table 1

Estimates without consideration for spatial autocorrelation

DISPLAY Table

International migration of domestic population (INM^D) does not seem to have any statistically significant effect on per capita GDP of provinces. By contrast, international migration of foreign residents (INM^F) is estimated to have a strong and statistically highly significant positive effect in both pooled data and random effects models. But as with internal migration, this effect disappears in the fixed effects specification, indicating that unobserved time-invariant characteristics of provinces are probably more important determinants of per capita GDP than foreign resident immigration.

Panel B introduces information on the educational level of internal migrants, enabling an assessment of the impact of migrants’ human capital attainment on regional per capita GDP. The estimates substantiate the role of education: the coefficient on internal migrants with at least a bachelor’s degree (IM^B) is positive and statistically highly significant across all three specifications, corroborating the view that better educated internal migrants boost the per capita GDP of host provinces, as documented by Akbari and Haider (2018) and Özyılmaz and Bayraktar (2021).

Internal migrants with only secondary or primary level education (IM^NB) seem to contribute to the per capita GDP of host provinces in the pooled data model, lower it in the fixed effects model and are statistically insignificant in the random effects model. This sign instability suggests that the contribution of migrants with lower educational attainments is highly sensitive to, and often outweighed by, fixed province-specific factors. Negative values of adjusted R² in the fixed effects model arise from inclusion of many province-specific intercepts (α_i).

To ascertain whether the fixed or random effects specification offers a superior fit for the data, we performed a Hausman specification test (appendix table A3). The test results consistently reject the random effects specification at a 1% significance level for both panels (Panel A: χ² = 14.89, p < 0.01; Panel B: χ² = 17.82, p < 0.01). In what follows, our spatial panel models therefore use the fixed effects specification.

The Lagrange multiplier (LM) test statistics for spatial autoregressive (SAR) and spatial error (SEM) models have significant values for all specifications (appendix table A3). The more precise robust LM statistics for spatial lag and spatial error further confirm the presence of spatial dependence in the data across specifications, indicating that the omission of spatial effects would lead to biased and inefficient estimates.

We also conducted Moran’s I spatial autocorrelation analysis for per capita GDP to assess directly the degree of spatial dependence (appendix table A4). The test shows significant positive spatial autocorrelation with extremely low p-values, indicating a strong tendency for similar per capita GDP values to cluster spatially.

The SEM approach is often preferred over the SAR approach in method selection (Salima, Julie and Lionel, 2018). However, given that the null hypothesis of no spatial dependence is rejected by robust LM spatial lag and spatial error tests (table A3), as well as Moran’s I spatial autocorrelation test (table A4), we proceeded to estimate the spatial autoregressive lag and error (SARAR) model that incorporates both types of spatial interaction.

We estimate the pooled data and fixed effects models with spatial autocorrelation of errors and spatial autoregressive coefficients using several spatial panel econometrics techniques: SEM models that account for spatial correlation in the error term; generalised method of moments (GMM) models that control for fixed effects; and the SARAR models, which provide the most comprehensive framework for interpretation of estimates, as they simultaneously address interactions among provinces and unobserved spatial linkages. Specifically, the model based on Lee and Yu (2010a) incorporates both spatial lag and ensures consistent estimators and centred distributions.²

Table 2 presents estimates of the pooled data and fixed effects models with spatial parameters. A key result across both panels is the highly significant and large values of the spatial error ρ and the spatial autoregressive coefficient λ. In SARAR models, estimated coefficients on these variables are statistically significant at the 1% level, suggesting that per capita GDP and unobserved shocks in neighbouring regions strongly and significantly affect the per capita GDP of a given province.

In Panel A, the estimated coefficient on internal migration (IM) is statistically significant in the pooled data model, but not in the fixed effects models. The coefficient on international migration of domestic population (INM^D) is statistically insignificant in all models, suggesting there is no substantial effect on provinces’ per capita GDP from residents leaving the country – those who migrate abroad from Türkiye are mostly looking for better economic opportunities and their departure does not affect the per capita income of those who remain. But immigration of foreign residents is positively and statistically significantly correlated with per capita GDP in all specifications. For example, a 1% increase in net foreign immigration is associated with about 1.7% increase in per capita GDP in the fixed effects, and 1.6% in SARAR models. The reason for this positive link is most likely the increase in personal consumption of additional immigrant population (Gür, 2017). The receiving province also experiences this increase through spillovers from neighbouring provinces.

Table 2

Estimates of the pooled data and fixed effects models with spatial autocorrelation of errors and spatial autoregressive coefficients

DISPLAY Table

Estimates in Panel B suggest a positive effect of skilled internal migration on per capita GDP: coefficient values of 0.0227 on IM^B in fixed effects, and 0.0264 in SARAR models suggest, e.g. that a 1% (net) increase in skilled domestic migrant population in a province increase sits per capita GDP by 2.3-2.6%. This supports the view that better allocation of highly skilled labour improves per capita GDP of all provinces, even though for some it represents a “brain drain” (Pouliakas et al., 2009). Better-educated migrants bring valuable skills that contribute to overall growth, not least because the sectors that employ them tend to generate higher value added.

By contrast, the effect of unskilled internal migration is estimated to be positive and statistically significant only in the pooled data model that does not consider spatial effects. When these effects are taken into account, coefficients on IM^NB are not statistically significant, i.e. integration of unskilled domestic migrants into local labour market neither increases nor decreases per capita GDP of the province.

Parameter estimates on spatial autoregressive coefficients r and λ confirm spillovers from per capita GDP of neighbouring provinces and unmodelled spatial shocks on per capita GDP in a given province. For instance, the spatial autoregressive coefficient λ = -0.58 in Panel A indicates that a 1% increase in per capita GDP in neighbouring provinces leads, other things being equal, to a 0.58% decrease in the per capita GDP of a given province. By contrast, the spatial autoregressive coefficient λ = 0.94 in Panel B suggests that a 1% increase in the per capita GDP of neighbouring provinces leads to an almost equivalent increase in the host province’s per capita GDP, holding other factors constant. This striking difference suggests that spatial interaction effects depend significantly on the time period and type of migration.

Positive estimates of the spatial error parameter ρ indicate that unobserved shocks such as regional economic distress or regional policy changes tend to propagate across provinces. In the SARAR specification for internal migration, for example, the negative value of the spatial error parameter (ρ = -0.78) alongside a positive spatial autoregressive coefficient (λ = 0.95) suggests that unobserved shocks can partly offset spatial spillovers. Technically, this negative coefficient serves as a correction mechanism, balancing the dominant positive effect of spatial dependence.

5 Robustness checks

It is instructive to view the dynamics of the lagged dependent variable through the lens of econometric theory. While the positive coefficients in pooled OLS models likely stem from upward bias due to unobserved heterogeneity, the negative estimates in standard fixed effects models reflect the so-called Nickell (1981) bias. Consequently, the persistence of negative coefficients in our bias-corrected LeeYu and GMM specifications should be interpreted not as an artifact, but as robust evidence of conditional convergence.

Regarding additional controls, the size of bank deposits per capita is positively and significantly associated with provincial per capita GDP. This result aligns with expectations: for instance, in SARAR models, 1% higher bank deposits are associated with a 0.15 – 0.20% higher per capita income. This supports findings by Kim et al. (2018) and Afonso and Blanco-Arana (2024) that retail deposit growth stimulated economic activity. Surprisingly, population per bank branch is negatively correlated with regional per capita GDP. The time trend coefficient varies by period: it is positive in 2016-22 (Panel A), reflecting a steady upward trend, but negative in the longer 2009-22 period (Panel B), likely capturing the lingering effects of the Global Financial Crisis.

6 Conclusion

The significant positive effect of educated native migrants on per capita GDP is in line with the literature suggesting that reallocation of a highly educated workforce benefits domestic economic growth (Akbari and Haider, 2018; Özyılmaz and Bayraktar, 2021). The finding that less-educated domestic migrants do not depress per capita GDP in the provinces they move into is also interesting. We believe that this interpretation is more important than the view that low-skilled labour merely has a “limited impact” on per capita GDP, as argued e.g. by Marto, Lourenço Marques and Madaleno (2022).

Appendix

Notes

2010a

2014

* The authors would like to thank two anonymous referees for their comments and suggestions.

¹ According to the Presidency of Migration Management of Türkiye (2024), around 2.5 million Syrians were living in the country in 2023 with temporary protection status. In addition, over 75,000 Syrians had residence permits, while about 239,000 people of Syrian origin had acquired Turkish citizenship. In addition, around 1.1 million foreigners had short-term residence permits. These groups add up to some 3% of the total population of Türkiye but are not included in the statistics of the address-based population registration system. 

² Lee and Yu (2010a) show that the transformation using the orthonormal eigenvector matrix of increases the accuracy of variance estimation by compensating for the degrees of freedom. This correction is particularly important when T is small. The variance parameter representing the correction is simply where is the residuals variance (Piras, 2014).

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