Abstract
Migration has far-reaching implications for regional-economic growth and spatial disparities. This paper explores the role played by migration in spatial-economic development in Romania, a country that is facing large and persistent regional disparities. We develop a spatial-econometric model for studying the impact of migration movements in Romania (both domestic and international) on regional welfare patterns in this country. Employing a one-lag spatial dynamic panel model for the period 1995–2015 and accounting for the endogeneity of migration and human capital, our findings reveal that migration clearly adds to divergence. The composition effect outweighs the neoclassical quantitative effect and thus, migration undermines convergence. When migration inflows foster growth by skills-selectivity and strengthening agglomeration economies in richer destination regions, migration outflows appear to inhibit growth in source regions. Spatiality also matters, as regions turn out to grow faster when neighbouring other regions with a high development level, due to spillover effects. However, the clustering tendency of regions with a similar development level undermines convergence. In policy terms, our findings emphasize the need of economic incentives for raising human capital and investment stocks in regions that are lagging behind. This translates also into a strong argument in favour of EU Cohesion policy that can leverage the competitiveness–cohesion trade-off by increasing connectivity between regions and improving their business environment.
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Source: Own representation using data from the Romanian National Institute of Statistics. Made with Philcarto * http://philcarto.free.fr
Source: Own representation using data from Romanian National Institute for Statistics
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Notes
While in 1995 the median value was 94.6% of the mean, this dropped to just 70.7% in 2015.
Just 10 out of 42 regions are net migration receivers, whilst the average of the net migration rate (per 1000 population aged 15–64 years) ranges from − 5.3 to 4.7 for the 1995–2015 period.
Unlike other East-European countries, the economic transition in Romania occurred at a very slow pace. By the time of the political elections in 1996, the government had only privatized 12% of the assets owned by the state (Gallagher 2004).
Treating migration as endogenous accounts for reverse causality, as migrants are also attracted to the fastest growing regions (Ozgen et al. 2010).
GDP per capita is the dependent variable. See Eq. (2) for the full model description.
First, the spatial lag model reported better AIC and BIC scores than the spatial error model. Second, despite the fact that both Moran and LM tests indicate that spatial error autocorrelation might exist (Table 1), the spatial error coefficient does not turn statistically significant. Furthermore, LeSage and Fischer (2008) point out that using the spatial error model (SEM) would require that there are no omitted explanatory variables and that these are not correlated with included explanatory variables, which seems rather unrealistic.
A weighting scheme based on the travel time seems to be amongst the most reliable for capturing economic spillovers. Unlike contiguity matrices which use the same weights for all neighbours, this approach generates different weights based on connectivity. Also, unlike matrices computed based on geographic centroids, our matrix relies on travel time between regional capitals which are usually also the regional economic centres. For robustness reasons, we have used both a binary weights matrix with a cut-off point of 2 h of travel time, as well as a weighting scheme based on the inverse travel time. Whilst the former better captures the local spillovers from nearby regions, the latter assumes that all regions are inter-connected to some extent and thus captures the wider impact from all other regions.
Computing the inverse distance allows more distant regions to receive smaller weights than nearby regions due to a lower spillover probability. Furthermore, in order to better reflect inter-connectivity, the computation of travel time accounts for speed restrictions by type of roads.
Namely, spatial ML, spatial dynamic ML, spatial dynamic quasi-ML, least-square-dummy-variable and diff-GMM.
We preferred to use this Stata package, as it allows a better control on model specifications. Of course, this estimation strategy required the distinct computation of spatial lags before following the routine.
The usual practice in convergence studies is to rely on 5–10 year intervals (Islam 2003; Dobson et al. 2006; Ozgen et al. 2010). However, given that our data relies on a relatively short time span, namely 1995–2015 period, and that there is no clear criterion for deciding the minimum interval length, the decision to rely on 3 years averages was rather based on a numerical choice that helped us to better exploit the full data sample.
This evolution is also explained by the geographical expansion of the capital city Bucharest, with Ilfov as a peri-urban area of the capital.
We have also made simulations using other proxies measuring regional human capital stock, such as the share of students/graduates in total/active population. We have opted for the share of total enrolled population because previously described statistics are directly related to the tertiary education institutions and display null values for regions with no tertiary education centres. Results have not been included but may be provided upon request.
As a robustness test to our results, we have also run the estimation for the 2005–2015 period using the share of gross fixed capital formation in the GDP indicator. Results have not been included, but will be provided upon request.
This is different from other findings which find evidence for regional (NUTS3) divergence even when migration is held constant, but during a shorter period of time between 2004 and 2008 (Bunea 2011b).
One explanation for the higher convergence speed may be the fact that our study accounts for both internal and external flows.
Two separate weighting schemes were used in order to compute the row-standardized distance matrix, as described in the previous section.
Given the presence of spatial correlation, omitting the spatial effect in estimating the β-convergence would reduce the speed of convergence, as higher growth rates might be not only due to the gap between initial and steady-state level, but also due to spillover effects.
The Moran’s I score for log of GDP per capita is around 0.5 (regardless of weighting scheme used). The positive spatial autocorrelation suggests that regions with a similar development level tend to cluster. The correlation between the log of development level and its spatial lag is 0.75 (and 0.62 for the binary weighting matrix) also indicates the clusterization trend in terms of GDP per capita. This undermines convergence, as the higher spillover effects occur mainly between high developed regions.
Such findings are consistent with a cumulative causation network effect of emigration, although this becomes insignificant when the spatial lag was added.
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Acknowledgements
We would like to thank the two anonymous reviewers for their very valuable and constructive comments on earlier versions of this article. Cristian Incaltarau, Gabriela Carmen Pascariu and Peter Nijkamp would like to acknowledge the support of the Ministry of Research and Innovation, as this study was granted by CNCS–UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0166, within PNCDI III project ‘ReGrowEU – Advancing ground-breaking research in regional growth and development theories, through a resilience approach: towards a convergent, balanced and sustainable European Union’.
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Incaltarau, C., Pascariu, G.C., Duarte, A. et al. Migration, regional growth and convergence: a spatial econometric study on Romania. Ann Reg Sci 66, 497–532 (2021). https://doi.org/10.1007/s00168-020-01019-w
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DOI: https://doi.org/10.1007/s00168-020-01019-w