Testing equalization in municipal capital transfers: Case of North Macedonia

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Type of the article: Research Article

Abstract
This paper analyzes the determinants of municipal capital transfer allocation in North Macedonia using data for all 80 municipalities over 2018–2022. Capital transfers are a key investment instrument in fiscally decentralized systems, yet their fragmented and project-based delivery may weaken equalization. To test whether transfers follow developmental or fiscal-capacity criteria, the study combines baseline OLS estimates with a two-part (hurdle) model that separates access from transfer intensity, quantile regressions to capture distributional heterogeneity, and inequality measures. The results show no statistically significant association between municipal development level or fiscal capacity and the probability of receiving any capital transfer, indicating that entry into the system is not needs-based. Conditional on receipt, the development coefficient is negative but not robustly significant, indicating no systematic targeting of less-developed units among recipients. Inequality diagnostics reinforce this conclusion: the Gini coefficient for per-capita capital transfers (0.75) vastly exceeds that for municipal revenues (0.18), and Lorenz curves reveal a sharply more unequal transfer distribution. Overall, the evidence implies that unobserved institutional or political factors dominate the access margin and that capital transfers currently introduce an additional layer of territorial divergence rather than mitigating existing disparities.

Acknowledgments
The author gratefully acknowledges the United Nations Development Programme (UNDP) for providing access to data used in this research. The views expressed in this article are solely those of the author and do not reflect the views of UNDP.

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    • Figure 1. Lorenz curves for per capita capital transfers and per capita municipal revenues over 2018–2022
    • Figure A1. Per-capita capital transfers across municipalities (2018–2022, MKD per resident)
    • Table 1. Variable definitions
    • Table 2. Descriptive statistics
    • Table 3. Baseline OLS
    • Table 4. Logit – Probability of receiving capital transfers
    • Table 5. Conditional OLS – Per capita transfers among recipients
    • Table 6. Quantile regressions (τ = 0.50 and τ = 0.75)
    • Table B1. Panel pooled OLS with year fixed effects (DV: CapTrans_pc)
    • Table B2. Panel pooled Logit with year fixed effects (DV: has_transfer; Odds Ratios)
    • Table B3. Robustness of pooled (all-municipality) results
    • Table B4. Robustness of access and intensity results (two-part framework)
    • Conceptualization
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