THE TRANSFORMATION OF THE RELATIONSHIP BETWEEN ECONOMIC WELFARE AND CRIME UNDER THE IMPACT OF WAR: A LEGAL AND ECONOMETRIC ANALYSIS OF PANEL DATA FROM UKRAINIAN REGIONS BEFORE AND DURING THE FULL-SCALE INVASION

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published: Nov 2, 2026

  Yuliia Bulhakova

  Oleksandr Kuzmenko

Abstract

The full-scale Russian invasion of Ukraine has fundamentally altered the region's socioeconomic conditions, demographic structures and crime patterns, creating new challenges in authors’ understanding of the relationship between economic welfare and crime. While traditional economic theory assumes that improved economic conditions reduce criminal activity by increasing the opportunity cost of offending, wartime conditions can substantially modify these relationships through forced displacement, regional heterogeneity, and institutional disruption. This study examines how the relationship between economic welfare and crime rates in Ukrainian regions changed during the full-scale war, and whether this relationship varies according to regional exposure to military conflict. The study uses a balanced panel dataset covering 25 Ukrainian regions (24 oblasts and the city of Kyiv) from 2018 to 2025. Economic welfare is operationalised using average monthly wages, while crime rates are measured as the number of registered criminal offences per 100,000 people in a given region. The analysis incorporates the proportion of internally displaced persons (IDPs) and distance from the active front line as indicators of the demographic and spatial effects of war. The empirical strategy is based on two-way fixed-effects panel regression models with Driscoll–Kraay standard errors. These were selected following diagnostic testing for cross-sectional dependence, autocorrelation, multicollinearity and model specification. Robustness analyses include alternative functional forms, subsample estimation, sensitivity tests and Granger causality analysis. The results show that the relationship between economic welfare and crime changed significantly following the full-scale invasion. There is no statistically significant direct association between average wages and regional crime rates. However, the interaction between wages and the wartime period is significant, suggesting that war has altered the underlying relationship. The strongest finding is the positive correlation between the proportion of internally displaced people and crime rates, which remains consistent across different models and robustness checks. Furthermore, the impact of economic welfare varies according to regional exposure to war, confirming pronounced spatial heterogeneity. Further analyses indicate that the observed relationships are nonlinear and that registered crime does not provide statistically significant predictive information for macroeconomic forecasting. The present study makes a significant contribution to the extant literature by providing one of the first panel-data assessments of the interaction between economic welfare, forced displacement, wartime spatial heterogeneity, and regional crime in Ukraine. From a methodological perspective, the study highlights the importance of diagnostic-based model selection, demographic controls and spatial measures of war exposure. The findings support the development of evidence-based regional public safety policies and post-war recovery plans.

How to Cite

Bulhakova, Y., & Kuzmenko, O. (2026). THE TRANSFORMATION OF THE RELATIONSHIP BETWEEN ECONOMIC WELFARE AND CRIME UNDER THE IMPACT OF WAR: A LEGAL AND ECONOMETRIC ANALYSIS OF PANEL DATA FROM UKRAINIAN REGIONS BEFORE AND DURING THE FULL-SCALE INVASION. Baltic Journal of Economic Studies, 12(5), 124-137. https://doi.org/10.30525/2256-0742/2026-12-5-124-137
Article views: 0 | PDF Downloads: 0

##plugins.themes.bootstrap3.article.details##

Keywords

economics of crime, regional crime, panel data analysis, economic well-being, welfare, internally displaced persons, armed conflict, Ukraine

References

Almazán-Gómez, M. Á., Llop, M., & Sánchez-Chóliz, J. (2024). Socioeconomic impacts of Russian invasion of Ukraine: A multiregional assessment for Europe. Journal of Regional Science, 64(3), 661–692. https://doi.org/10.1111/jors.12676

Becker, G. S. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76(2), 169–217. https://doi.org/10.1086/259394

Bulhakova, Y. M. (2023). Criminological characteristics and prevention of criminal offenses committed by internally displaced persons in Ukraine.Dissertation, Dnipropetrovsk State University of Internal Affairs. https://dduvs.edu.ua/naukova-diyalnist/spetsializovani-vcheni-radi/ogoloshennya-pro-zahist-disertatsij/bulgakova/

Frizell J., Muliavka V., Obinger H., & Schmitt C. (2025). War, solidarity and welfare attitudes: Survey evidence from the war in Ukraine. Journal of European Public Policy, 32(4), 1257–1285. https://doi.org/10.1080/13501763.2025.2463640

Cornwell, C., & Trumbull, W. N. (1994). Estimating the economic model of crime with panel data. Review of Economics and Statistics, 76(2), 360–366. https://doi.org/10.2307/2109893

DeepState. (30.06.2026). DeepStateMap. https://deepstatemap.live/en/#6/49.4383200/32.0526800

Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560. https://doi.org/10.1162/003465398557825

Ehrlich, I. (1973). Participation in illegitimate activities: A theoretical and empirical investigation. Journal of Political Economy, 81(3), 521–565. https://doi.org/10.1086/260058

Fajnzylber, P., Lederman, D., & Loayza, N. (2002). Inequality and violent crime. Journal of Law and Economics, 45(1), 1–39. https://doi.org/10.1086/338347

Glaeser, E. L., & Sacerdote, B. (1999). Why is there more crime in cities? Journal of Political Economy, 107(S6), S225–S258. https://doi.org/10.1086/250109

Hsiao, C. (2014). Analysis of panel data (3rd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139839327

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7

International Organization for Migration. (2023). Ukraine internal displacement report: General population survey, Round 15 (December 2023). https://dtm.iom.int/reports/ukraine-internal-displacement-report-general-population-survey-round-15-november-december

International Organization for Migration. (2025). Ukraine return report: General population survey, Round 19 (January 2025). https://dtm.iom.int/reports/ukraina-zvit-pro-poverennya-naselennya-v-ukraini-opituvannya-zagalnogo-naselennya-raund-19

International Organization for Migration. (2026). Ukraine internal displacement report: General population survey, Round 22 (January 2026). https://dtm.iom.int/reports/ukraine-internal-displacement-report-general-population-survey-round-22-january-2026

Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24. https://doi.org/10.1016/S0304-4076(01)00098-7

Machin, S., & Marie, O. (2011). Crime and economic incentives. Annual Review of Economics, 3, 1–26. https://www.researchgate.net/publication/227637136_Crime_and_Economic_Incentives

Minfin Media Limited Liability Company. Average monthly wage in Ukraine. https://index.minfin.com.ua/ua/labour/salary/average/

Minfin Media Limited Liability Company. Gross domestic product of Ukraine. https://index.minfin.com.ua/ua/economy/gdp/

Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46(1), 69–85. https://doi.org/10.2307/1913646

Neumayer, E. (2005). Inequality and violent crime: Evidence from data on robbery and violent theft. Journal of Peace Research, 42(1), 101–112. https://doi.org/10.1177/0022343305049670

Office of the Prosecutor General of Ukraine. (30.06.2026). Statistics. https://gp.gov.ua/ua/posts/statistika

Office of the Prosecutor General of Ukraine. Official response to the request. 17 March 2026, No. 25/4-39vykh-26.

Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics No. 0435. University of Cambridge. https://doi.org/10.17863/CAM.5113

State Statistics Service of Ukraine. (30.06.2026). Population publications. https://ukrstat.gov.ua/druk/publicat/kat_u/publnasel_u.htm

Suess, P. (2019). Regional market income inequality and its impact on crime in Germany: A spatial panel data approach with local spillovers. Jahrbücher für Nationalökonomie und Statistik, 240(4), 387–415. https://www.researchgate.net/publication/333780841_Regional_Market_Income_Inequality_and_its_Impact_on_Crime_in_Germany_A_Spatial_Panel_Data_Approach_with_Local_Spillovers

Temidayo J. A., Marcelo J., Vania C. (2024). Economic growth, income inequality and lethal violence in developed countries. EconomiA, 26(1), 127–145. https://www.eco.unicamp.br/artigos-academicos/economic-growth-income-inequality-and-lethal-violence-in-developed-countries

Ukrainian Parliament Commissioner for Human Rights. (2022). Special Report. https://ombudsman.gov.ua/storage/app/media/Доповіді/9626c072-dfba-45d2-b0ab-76c883c18340.pdf

United Nations Office on Drugs and Crime. (2025). Ukraine: Organized Crime Dynamics in the Context of War. https://www.unodc.org/documents/data-and analysis/Ukraine/Ukraine_OC_Study.pdf

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). MIT Press. https://ipcid.org/evaluation/apoio/Wooldridge