Special Economic Zones and the slow path to regional convergence

Across emerging Europe, regional income disparities remain stubborn, narrowing at just 1% annually. This final column in a three-part series examines the effectiveness of Special Economic Zones in addressing these gaps. The analysis shows that while they tend to boost economic activity at a very local level, only 40% of Special Economic Zones can be deemed successful. The performance of the zones correlates with port proximity, skilled workforce availability, and local law and order. However, even these factors explain little of their success or failure, highlighting significant uncertainty in using this industrial policy to tackle regional disparities.

Regional inequality remains deeply entrenched across emerging Europe (Storper et al. 2022), despite some modest convergence. While many countries show a gradual decline in overall income inequality, stark disparities persist both between and within nations. Between countries, a pronounced East–West gradient exists, with Central European and Baltic states enjoying higher GDP per capita than Central Asia and parts of Eastern Europe. Within countries, divisions are also clear: Poland’s West–East split, Türkiye’s coastal–interior gap, and the general concentration of wealth in capital cities and regions bordering developed economies.

Figure 1 illustrates within-country convergence patterns for various EBRD economies and comparator countries during 2010–2019. The left axis displays beta-convergence coefficients from country-specific regressions, where negative values indicate faster growth in poorer regions. For example, a coefficient of -0.02 indicates that the income gap between regions narrows by about 2% annually. Conversely, positive coefficients indicate that wealthier regions are growing faster, widening the income gap. Diamond markers represent sigma-convergence, measured by changes in the standard deviation of log GDP per capita over time, where negative values indicate declining cross-regional inequality.

On average, poorer regions in EBRD countries are converging with wealthier ones at a rate of approximately 1% per year. Although this indicates progress, the pace is slow: at the current rate, it would take about 70 years to halve the existing income gap between regions. This convergence rate is slower than the typical cross-country rate of 2%, often referred to as the ‘iron law of convergence’, where poorer economies tend to grow faster than wealthier ones (Barro 2015).

Figure 1 In most EBRD economies, regional income gaps are closing only slowly

Source: ARDECO database, Wenz et al. (2023), Kazakhstan’s Bureau of National Statistics, World Bank WDIs and authors’ calculations.
Note: Analysis is based on NUTS-3 regions for EBRD economies in the EU, Albania, North Macedonia, Serbia and Türkiye, and GADM-1 regions for all other EBRD economies. Data for Morocco relate to the period 2013-19; data for all other economies relate to the period 2010-19. Negative rates of beta convergence indicate that poorer regions have grown faster than richer ones.

Regional disparities and Special Economic Zones

Special Economic Zones (SEZs) have proliferated across EBRD countries, surging from fewer than 100 in 1990 to over 1,150 by 2020. Using a newly compiled dataset of SEZs that includes their locations, timing, sizes, and purposes, the EBRD Transition Report 2024-25 analyses their performance through two complementary methods: a difference-in-differences approach and a synthetic control method.

Both approaches show that SEZs, on average, tend to boost local economic activity, with their impact growing over time. Measuring nightlight intensity within a 2km radius around each zone’s centre reveals effects that extend beyond the SEZs themselves (SEZ size is modest, with a median radius of 0.16 square km and a radius at the 75th percentile at just 0.97 square km). This analysis captures how SEZs can stimulate surrounding neighbourhoods through multiple channels: increased demand for services, enhanced infrastructure development, and business spillovers from operations within the zones.

Figure 2 SEZs stimulate local economic activity, on average

Source: EBRD Database of Special Economic Zones, Li et al. (2020), and authors’ calculation.
Note: The chart compares the increase in nightlights within a 2 km radius of the zones over time, using both the difference-in-differences method and the synthetic control method. The x-axis represents the years since the zone’s start, while the y-axis indicates the increase in nightlights. The blue points represent the difference in differences estimates, and the red points represent the synthetic control estimates. There is about a three-year gap between the announcement and the actual start of a new SEZ. A circle with a radius of 2 kilometers encompasses an area of approximately 12.57 square kilometers. Whiskers indicate 95 per cent confidence intervals.

What determines the success of SEZs?

Why do some SEZs succeed while others fail? To analyse this, we define success as above-average growth in nightlight density (compared to the country as a whole) within a 5km radius around the SEZ over the decade following establishment. By this measure, only 40% of SEZs prove successful, with effectiveness varying markedly even within countries. Further analysis reveals that among infrastructure factors, only port proximity significantly predicts success. Other variables – including railway and road access, telecommunications quality, and local amenities – show no consistent relationship with SEZ performance. In fact, infrastructure factors explain just 3.5% of the variation in SEZ success, suggesting limited predictive power.

Figure 3 Proximity to a port, larger numbers of workers with a tertiary education, and better law and order are associated with economically successful SEZs

Source: EBRD database of SEZs, Li et al. (2020), US National Geospatial-Intelligence Agency’s VMAP0 dataset and World Port Index (2010), ITOS’s gROADS dataset (version 1), Gallup World Polls 2005-08 and authors’ calculations.
Note: An SEZ is regarded as successful if cumulative growth in NTL density within a 5 km radius over a 10-year period is stronger than the average for the economy as a whole over the same period. The community satisfaction index measures satisfaction with public transport, roads and highways, the quality of schools, healthcare and the environment. The access to communications index assesses the availability of high-quality telephone and internet infrastructure, and the law-and-order index evaluates the level of security. The national institutions index gauges citizens’ confidence in national government, the judicial system and the fairness of elections. The tertiary education index measures the percentage of a subnational region’s population who have a tertiary education. All of these indices are derived from Gallup World Poll data at subnational level. The whiskers indicate 95 per cent confidence intervals.

Figure 4 Even with infrastructure, governance and fixed effects accounted for, much of the success – or failure – of SEZs remains unexplained

Source: EBRD database of SEZs, Li et al. (2020), US National Geospatial-Intelligence Agency’s VMAP0 dataset and World Port Index (2010), ITOS’s gROADS dataset (version 1), Gallup World Polls 2005-08 and authors’ calculations.
Note: The factors analysed include infrastructure (distance to a railway, distance to a port, distance to a main road, access to communications and community satisfaction), governance (tertiary education, law and order and national institutions), and country and region fixed effects.

The presence of tertiary-educated workers in a region also strongly correlates with SEZ success, while a stable security environment (measured by a local law-and-order index) shows a modest significant positive relationship. Adding these governance factors raises the model’s explanatory power to 7%, increasing to 11% with country fixed effects. That is, most variation in SEZ success remains unexplained.

Prior research has shown that SEZ performance is also influenced by many factors that are difficult to quantify. These include the quality of relevant policy frameworks and institutional structures at the national and local levels (Farole and Akinci 2011, Aggarwal 2012, Frick et al. 2019). Including region fixed effects – unobserved characteristics of various regions that do not change over time – further improves the R-square of models explaining the success of SEZs, with the percentage of variation explained rising to (just) 24%.

Lastly, SEZ performance is shaped by dynamic global factors, including shifting production networks and evolving comparative advantages across countries. Local factors – particularly effective zone management and implementation – also play a crucial role, though these are even harder to capture in statistical analysis. Indeed, SEZs typically seek to overcome deficiencies in governance at regional and national levels by creating a more favourable environment for business within the zone itself.

Conclusions

As Frick and Rodríguez-Pose (2023) emphasise, Special Economic Zones need careful tailoring to regional contexts and potential in order to maximise the likelihood that they turn out be successful. This requires investing in fundamentals: human capital development is crucial for enhancing SEZ performance and enabling transitions to higher-value activities (Rodrik and Stantcheva 2021), particularly through expanded education and skills programs. Success also depends on robust institutions – our analysis highlights how sound governance and security at the local level increases the chances that a SEZ turns into a success rather than a costly failure.

Source : VOXeu