Modelling Factors Affecting the Middle Income Trap in Indonesia Using Generalized Additive Models (GAM)

Dita Amelia, Suliyanto Suliyanto, Azizah Atsariyyah Zhafira, Aulia Ramadhanti, Billy Christandy Suyono, Firqa Aqila Hizbullah

Abstract


Indonesia is currently facing the risk of the Middle Income Trap (MIT), a condition in which economic growth stagnates after reaching middle-income status. This study aims to identify and model socio--economic factors affecting MIT at the provincial level in Indonesia during 2020--2023. The Generalized Additive Model (GAM) is employed to capture nonlinear and heterogeneous relationships between predictors and GRDP per capita with complex patterns that conventional linear or parametric models often fail to detect. The use of GAM in this context represents a methodological contribution, as studies applying GAM for MIT analysis in Indonesia remain very limited. This research therefore introduces a novel analytical approach by demonstrating how GAM can reveal flexible functional relationships and uncover nonlinear effects that are overlooked by traditional panel regression. GRDP per capita is modeled using six predictors: life expectancy, poverty rate, informal employment share, upper secondary education completion, food insecurity prevalence, and population density. The best model is obtained using the Gaussian family with an identity link, with five predictors showing nonlinear effects and food insecurity exhibiting a negative linear influence. The selected model demonstrates strong performance, indicated by an AIC value of 2743.279 and a R^2 of 98.6%, suggesting a very high explanatory power. In addition, the model achieves good predictive accuracy, with a MAPE of 8.04%. The findings support evidence-based policies aligned with Sustainable Development Goal (SDG) 8, promoting inclusive and sustainable economic growth.

Keywords


Generalized Additive Model; Goodness of Fit; GRDP per capita; Middle Income Trap.

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References


[1] S.Maryanti, P. Widayat, and N. Lubis, “Economic transformation to get out of the middle income trap condition to reach indonesia gold 2045,” ADPEBI International Journal of Business and Social Science, vol. 3, no. 1, pp. 63–78, 2023.

[2] L. Glawe and H. Wagner, “The middle-income trap: Definitions, theories and countries con cerned—a literature survey,” Comparative Economic Studies, vol. 58, pp. 507–538, 2016.

[3] K. Metreau, E. Young, and S. G. Eapen, World bank country classifications by income level for 2024–2025, https://blogs.worldbank.org/en/opendata/world-bank-country-class ifications-by-income-level-for-2024-2025, [Online; accessed 23-March-2025], 2024.

[4] D. N. Prasetiani, H. F. Anindya, and A. S. Yoshe, “Strategi menghadapi middle income trap: Dampak hilirisasi mineral terhadap pendapatan negara indonesia era joko widodo,” Indonesia Foreign Policy Review, vol. 11, no. 1, 2024.

[5] V. Wanggai, M. Delanova, and Y. M. Yani, “Stabilitas ekonomi indonesia dalam pandemi covid 19 dan potensi indonesia untuk terjebak middle income trap,” Jurnal Academia Praja : Jurnal Magister Ilmu Pemerintahan, vol. 6, no. 1, pp. 146–165, 2023.

[6] V. Ratnasari, S. H. Audha, and A. T. R. Dani, “Statistical modeling to analyze factors affecting the middle-income trap in indonesia using panel data regression,” MethodsX, vol. 11, no. 102379, 2023.

[7] R.K.Dewi,D.E.Sari,andD.Wahyuningsih,“Analisismakroekonomisebagailangkahindonesia keluar dari middle income trap,” Inspire Journal: Economics and Development Analysis, vol. 1, no. 1, pp. 99–111, 2021.

[8] A.W.Malaleand M.A.Sutikno, “Analisis middle-income trap di indonesia,” Jurnal Bppk, vol. 7, no. 2, pp. 91–110, 2014. First Author 437 380 Middle Income Trap in Indonesia 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398

[9] K.Larsen, “Gam: The predictive modeling silver bullet,” Multithreaded Stitch Fix, vol. 30, pp. 1 27, 2015.

[10] S. K. Sapra, “Generalized additive models in business and economics,” International Journal of Advanced Statistics and Probability, vol. 1, no. 3, pp. 64–81, 2013.

[11] N. Beck and S. Jackman, “Beyond linearity by default: Generalized additive models,” American Journal of Political Science, vol. 42, no. 2, pp. 596–627, 1998.

[12] H. Yozza, Siswadi, and B. Suharjo, “Analisis data longitudinal dengan metode regresi berstruktur pohon (kasus penyakit kencing manis),” Indonesian Journal of Statistics and Its Applications, vol. 6, no. 1, pp. 14–21, 2001.

[13] T. Hastie and R. Tibshirani, Generalized Additive Models. London: Chapman and Hall, 1990.

[14] L. Fahrmeir, T. Kneib, S. Lang, and B. D. Marx, Regression: Models, Methods and Applications. Berlin: Springer-Verlag, 2013.

[15] S. Wood, Generalized Additive Models: An Introduction with R. Boca Raton: Chapman & Hall, 2006.

[16] D. Gujarati and D. Porter, Basic Econometrics, 5th ed. New York: McGraw Hill Inc., 2009.

[17] J.M.Wooldridge,IntroductoryEconometrics:AModernApproach,8thed.Mason:South-Western Cengage Learning, 2016.

[18] S. N. Wood, Generalized Additive Models: An Introduction with R, 2nd ed. Boca Raton: CRC Press, 2017.




DOI: https://doi.org/10.18860/cauchy.v11i1.35119

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