Орталық Африкадағы және Жерорта теңізіндегі бейресми экономика: машиналық оқытуға негізделген талдау

The informal economy remains a stable and multidimensional phenomenon, especially in developing regions, where its dynamics are determined by a combination of institutional constraints and external shocks. The aim of the study is to develop a predictive model for assessing informal economy factors in Central African and Mediterranean countries based on machine learning methods. The methodological basis of the research includes the use of modern machine learning algorithms such as Random Forest, XGBoost, Support Vector Regression (SVR) and Elastic Net, using nested cross-validation (5-fold) and Bayesian hyperparameter optimization. The empirical base consisted of panel data for 28 countries (12 Central African countries and 16 Mediterranean countries) for the period 2005-2023. The share of employment in the informal sector was used as a dependent variable, while institutional indicators (quality of regulation, social spending, education) and external determinants (foreign direct investment, remittances, trade openness, and geopolitical risk) were used as factors. An analysis of the importance of the attributes shows that the quality of institutions and the coverage of social protection are the dominant internal predictors, while trade volatility and the influx of remittances act as critical external variables. Random Forest (R2 = 0.983; MAPE = 2.57%) and SVR (R2 = 0.982; MAPE = 2.17%) also confirmed the high accuracy of forecasting. It was found that among the factors, the geopolitical risk index has the greatest influence (up to 0.86 in correlation), as well as institutional indicators the quality of regulation (up to -0.96) and social spending (up to -0.93). The results show that external shocks can have a comparable or stronger impact on the level of informality compared to internal institutional factors.

1. Acosta, P. A., Lartey, E. K. K., & Mandelman, F. S. (2009). Remittances and the Dutch disease (Working Paper No. 2007-8a). Federal Reserve Bank of Atlanta. Retrieved March 30, 2026 from https://www.econstor.eu/handle/10419/70606

2. Alises, G. F. P., Avignone, T., & Jiménez, M. T. (2025). Predicting migrant children’s social exclusion risk through an innovative digital tool: Application of machine learning methods to Spanish residential centres. Children and Youth Services Review, 175, 108345.f https://doi.org/10.1016/j.childyouth.2025.108345

3. Apaydın, M., Yumuş, M., Değirmenci, A., & Karal, Ö. (2022). Evaluation of air temperature with machine learning regression methods using Seoul City meteorological data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(5), 737-747.f https://doi.org/10.5505/pajes.2022.66915

4. Arık, D. T., Karal, Ö., & Şahin, A. B. (2020). A Comparative Study of Artificial Neural Networks and Naïve Bayes Techniques for the Classification of Radar Targets. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(4), 1779-1788. fhttps://doi.org/10.17798/bitlisfen.676973

5. Bedford, K. (2024). Gender and development in the World Bank: An evaluation of the business case for equality. In A. Vetterlein & T. Schmidtke (Eds.), The Elgar companion to the World Bank (pp. 227–238). Edward Elgar Publishing. https://doi.org/10.4337/9781802204780.00032

6. Ben Dalla, L. O. F., Medeni, T. D., Medeni, I. T., & Ulubay, M. (2025). Enhancing Healthcare Efficiency at Almasara Hospital: Distributed Data Analysis and Patient Risk Management. Economy: Strategy and Practice, 19(4), 54–72. https://doi.org/10.51176/1997-9967-2024-4-54-72

7. Ben Dalla, L., Medeni, T. M., Agila, A. A., & Medeni, İ. M. (2024a). Architectural Synergy: Investigating the Role of Artificial Neural Networks in Enabling Deep Learning. The International Journal of Engineering & Information Technology, 12(1), 96-103.f https://doi.org/10.36602/ijeit.v12i1.483

8. Ben Dalla, L., Medeni, T., Zbeida, S., & Medeni, İ. (2024b). Unveiling the evolutionary journey based on tracing the historical relationship between artificial neural networks and deep learning. The International Journal of Engineering & Information Technology (IJEIT), 12(1), 104–110. https://doi.org/10.36602/ijeit.v12i1.484

9. Blumenstock, J. (2019). Fighting Poverty with Data. Science.Retrieved March 30, 2026 from https://www.science.org/doi/10.1126/science.aah5217

10. Borisov, A. N., Borodin, A. I., Gubarev, R. V., Dzuyba, E. I., & Kulikova, O. M. (2024). Assessing the Level of Employment in the Informal Sector of the Economy of Russian Regions Using Modern Machine Learning Methods. Review of Business and Economics Studies, 12(4), 42-57.f https://doi.org/10.26794/2308-944X-2024-12-4-42-57

11. Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823

12. Chen, M. A. (2012). The informal economy: Definitions, theories and policies (WIEGO Working Paper No. 1). Women in Informal Employment: Globalizing and Organizing (WIEGO). https://www.wiego.org/wp-content/uploads/2019/09/Chen_WIEGO_WP1.pdf

13. Dabla-Norris, E, J Brumby, A Kyobe, Z Mills and C Papageorgiou (2012). Investing in public investment: An index of public investment efficiency. Journal of Economic Growth, 17, 235–266. https://doi.org/10.1007/s10887-012-9078-5

14. Dalla, L. O. B., Karal, Ö., & Degirmenciyi, A. (2025). Leveraging LSTM for Adaptive Intrusion Detection in IoT Networks: A Case Study on the RT-IoT2022 Dataset implemented On CPU Computer Device Machine.f 5th International Conference on Engineering, Natural and Social Sciences, April 15-16, 2025: Konya, Turkey, 2025.

15. De Soto, H. (1989). The other path: The invisible revolution in the third World. New York: Harper & Row. https://pdfs.semanticscholar.org/ed48/ad31ae1779a3dce330c1afdfe152e1f7fc55.pdf

16. Degirmenci, A., & Karal, O. (2021). Robust incremental outlier detection approach based on a new metric in data streams. IEEE Access, 9, 160347–160360. https://doi.org/10.1109/ACCESS.2021.3131402

17. Degirmenci, A., & Karal, O. (2022a). iMCOD: Incremental multi-class outlier detection model in data streams. Knowledge-Based Systems, 258, 109950.f https://doi.org/10.1016/j.knosys.2022.109950

18. Degirmenci, A., & Karal, O. (2022b). US accent recognition using machine learning methods. In 2022 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-6). IEEE. https://doi.org/10.1109/ASYU56188.2022.9925265

19. Dulkadir, S., Tecimer, H.U., Parlaktürk, F., Altındal Ş. & Karal Ö. (2020). The effect of radiation on the forward and reverse bias current–voltage (I–V) characteristics of Au/(Bi4Ti3O12/SiO2)/n-Si (MFIS) structures. Journal of Materials Science: Materials in Electronics, 31, 12514–12521. https://doi.org/10.1007/s10854-020-03801-0

20. Duplock, R., Casali, G. L., & McLennan, C. L. (2025). Predicting Entrepreneurial Intentions and Behaviour: A Machine Learning Approach Using Latent Constructs from Australian GEM Data. Sage Open, 15(4), 21582440251404789. fhttps://doi.org/10.1177/21582440251404789

21. Erkkilä, T., & Piironen, O. (2014). (De) politicizing good governance: The World Bank Institute, the OECD and the politics of governance indicators. Innovation: The European Journal of Social Science Research, 27(4), 344-360. https://doi.org/10.1080/13511610.2013.850020

22. Faraj, L. O. (2017). Observations on evolution of lean software development [Yalın yazılım geliştirme süreci üzerine gözlemler] [Master's thesis, Atilim University]. Council of Higher Education National Thesis Center. Retrieved March 30, 2026 from https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=R_EJxYiWWNffOuWM4F4eXQ&no=fiwArXgOvJPKmFC-nX3H-w

23. Farjallah, N. (2025). Machine Learning-Based Modeling of Economic Growth and Governance Quality: The MENA region. Journal of Cultural Analysis and Social Change, 1003-1011.f https://doi.org/10.64753/jcasc.v10i3.2536

24. Felix, J., Alexandre, M., & Lima, G. T. (2025). Applying machine learning algorithms to predict the size of the informal economy. Computational Economics, 65(3), 1169-1189.f https://doi.org/10.1007/s10614-024-10593-6

25. Friedman, E., Johnson, S., Kaufmann, D. and Zoido-Lobaton, P. (2000). Dodging the grabbing hand: the determinants of unofficial activity in 69 countries. Journal of Public Economics, 76(3), pp. 459-93. https://doi.org/10.1016/S0047-2727(99)00093-6

26. Gammeltoft, P. (2002). Remittances and other financial flows to developing countries. International migration, 40(5), 181-211.f https://doi.org/10.1111/1468-2435.00216

27. Heyneman, S. P. (1999). The sad story of UNESCO’s education statistics. International Journal of Educational Development, 19(1), 65–74. https://comparative-education.com/wp-content/uploads/2018/03/the-sad-story-of-unescos-education-statistics.pdf

28. Yalman, Y., Uyanık, T., Atli, I., Tan, A., Bayındır, K.Ç., Karal, Ö., Golestan, S., & Guerrero, J.M. (2022). Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid. Energies, 15(18), 6641. https://doi.org/10.3390/en15186641

29. Iftikhar, M. N., Justice, J. B., & Audretsch, D. B. (Eds.). (2020). Urban studies and entrepreneurship. Springer. https://doi.org/10.1007/978-3-030-15164-5

30. ILO. (2021). World Employment and Social Outlook: Trends 2021. Retrieved March 30, 2026 from https://www.ilo.org/wcmsp5/groups/public/@dgreports/@dcomm/@publ/documents/publication/wcms_795453.pdf

31. Karal, Ö. (2018). Destek vektör regresyon ile EKG verilerinin sıkıştırılması [Compression of ECG data by support vector regression method]. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 33(2), 743– 756. https://doi.org/10.17341/gazimmfd.416527

32. Karal, Ö. (2020). Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1–5). IEEE. https://doi.org/10.1109/ASYU50717.2020.9259880

33. Kotzinos, A., Canellidis, V., & Psychoyios, D. (2023). Informal sector, ICT dynamics, and the sovereign cost of debt: A machine learning approach. Computation, 11(5), 90. https://doi.org/10.3390/computation11050090

34. Loayza, N. V. (1996). The economics of the informal sector: a simple model and some empirical evidence from Latin America. In Carnegie-Rochester conference series on public policy, 45, 129-162. North-Holland.fhttps://doi.org/10.1016/S0167-2231(96)00021-8

35. Muttaqi, M., Degirmenci, A., & Karal, O. (2022). US accent recognition using machine learning methods. In 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), 1-6. IEEE. https://doi.org/10.1109/ASYU56188.2022.9925265

36. Osunnaiye, A. V., & Kucukaltan, B. (2025). Examining the influence of sustainable development indicators on economic development: a machine learning approach with evidence from Africa. International Journal of Productivity and Performance Management, 74(9), 3131-3152.f https://doi.org/10.1108/IJPPM-12-2024-0839

37. Salman, M., & Wang, G. (2025). Analysis of natural resource efficiency convergence in global North and South: The role of artificial intelligence and geopolitical risks in club formation. Environment, Development and Sustainability, 28, 7937–7969.f https://doi.org/10.1007/s10668-025-06863-4

38. Seyid A., E. A., Bounabi, M., Azmi, R., Diop, E. B., Hlal, M., Almouctar, M. A. S., Chenal, J., & Adraoui, M. (2025). Forecasting urban water demand in Ben Guerir Morocco using statistical and machine learning methods. Discover Sustainability, 6(1), 1-200. https://doi.org/10.1007/s43621-025-02086-9

39. Soliman, A., Shlibak, A., & Zencirci, N. (2026). Wheat Fungal Diseases: A Review. Wadi Alshatti University Journal of Pure and Applied Sciences, 4(1), 191-198. https://doi.org/10.63318/waujpasv4i1_20

40. UNCTAD. (2020). Impact of the coronavirus outbreak on global FDI (Investment Trends Monitor, Special Issue). Retrieved March 30, 2026 from https://unctad.org/press-material/impact-coronavirus-outbreak-global-fdi