SavedAdd your journal

Journals / Ekoist: Journal of Econometrics and Statistics / 2026 / Sayı: 42

Gamma Regression for the Poverty Rate in Türkiye

Müge Borazan(Gendarmerie and Coast Guard Academy, Faculty of Security Sciences, Department of Security Sciences)0000-0002-5796-9192
Serpil Aktaş(Hacettepe University, Faculty of Science, Department of Statistics)0000-0003-3364-6388
Pages
22–35
Publication Date
2025-06-25
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Furthermore, there are notable variations in poverty rates among education levels, according to the post hoc anlayses we performed to compare poverty rates across educational levels.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The family of gamma regression models makes the assumption that a linear predictor with unknown coefficients and a link function, such as an identity, inverse, or logarithmic function, are related to a set of predictors by means of the mean of the dependent variable with a gamma distribution.
📊
Key Findings & Data:When the dependent variable has a real value between 0 and ∞, gamma regression makes sense.
💡
Conclusion & Impact:Furthermore, there are notable variations in poverty rates among education levels, according to the post hoc anlayses we performed to compare poverty rates across educational levels.
🔬Design:Modeling & Simulation
📍Scope:2006–2023 Verileri
⚙️Variables:Gamma regression · parameter estimation · poverty

Abstract

The family of gamma regression models makes the assumption that a linear predictor with unknown coefficients and a link function, such as an identity, inverse, or logarithmic function, are related to a set of predictors by means of the mean of the dependent variable with a gamma distribution. This model also has a shape parameter, which can either depend on a collection of regressors via a link function or be constant like a logarithm function. The analysis of positive random variables can make extensive use of the gamma distribution. When the dependent variable has a real value between 0 and ∞, gamma regression makes sense. In this study, a gamma regression model is used to examine the relationship between poverty rates and household education levels in Türkiye for the period 2006–2023. The Turkish Statistical Institute (TurkStat) provided the study data set. According to TurkStat’s definition of poverty, which is 50% of the comparable household disposable median income, the poverty rates in the data set indicate the percentage of people who are at danger of becoming impoverished. The Gamma Regression Model reveals that the poverty rate is significantly influenced by education level. Furthermore, there are notable variations in poverty rates among education levels, according to the post hoc anlayses we performed to compare poverty rates across educational levels.

🔖

Cite This Article

Borazan, M., Aktaş, S. (2025). Gamma Regression for the Poverty Rate in Türkiye. Ekoist: Journal of Econometrics and Statistics, Sayı: 42, 22–35. https://doi.org/10.26650/ekoist.2025.42.1523478

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (38)

  1. Algamal, Z. Y. (2018). Developing a ridge estimator for the gamma regression model, Journal of Chemometrics, vol. 32, no. 10, p. e 3054. google scholar
  2. Amin, M., Qasim, M. and Amanullah, M. (2019). Performance of Asar and Genç and Huang and Yang’s two-parameter estimation methods for the gamma regression model, Iranian Journal of Science and Technology, Transactions A: Science, vol. 43, no. 6, pp. 2951–2963. google scholar
  3. A. M. Al-Abood and D. H. Young, (1986). Improved deviance goodness of fit statistics for a gamma regression model, Communications in Statistics Theory and Methods, vol. 15, no. 6, pp. 1865–1874. google scholar
  4. Barry R. Chiswick and Michael D. McCarthy, (1977). A Note on Predicting the Poverty Rate. google scholar
  5. Basu, A. and Manning, W.G. (2006). A test for proportional hazards assumption within the class of exponential conditional mean models, Health Serv. Outcomes Res. Methodol. 6 (2006), pp. 81–100. google scholar
  6. Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073– 1076. https://doi.org/10.1126/science.aac4420. google scholar
  7. Bossio, M. and Cuervo, E.C., (2015). Gamma regression models with the Gammareg R package, Communicaciones en Estad´ıstica Diciembre, Vol. 8, No. 2, pp. 211–223. google scholar
  8. Chen, X., Aravkin, A.Y., Martin, R.D.(2018). Generalized Linear Model for Gamma Distributed Variables via Elastic Net Regularization, arXiv:1804.07780v1 [stat.ME], https://doi.org/10.48550/arXiv.1804.07780. google scholar
  9. Chiswick, B. R., & McCarthy, M. D. (1977). A note on predicting the poverty rate. The Journal of Human Resources, 12(3), 396-400. google scholar
  10. Elbers, C., J. O. Lanjouw, and P. Lanjouw. (2003). Micro Level Estimation of Poverty and Inequality, Econometrica, 71, 355–64. google scholar
  11. Economic Commission for Latin America and the Caribbean (ECLAC), (2019). Income poverty measurement: updated methodology and results, ECLAC Methodologies, No. 2 (LC/PUB.2018/22-P), Santiago. google scholar
  12. Dobson, A. and Barnett, A. (2008). An Introduction to Generalized Linear Models, 3rd edn. Boca Raton, FL: Chapman and Hall. google scholar
  13. Dunder E., Gumustekin, S. & Cengiz, M.A. (2016): Variable selection in gamma regression models via artificial bee colony algorithm, Journal of Applied Statistics, DOI: 10.1080/02664763.2016.1254730. google scholar
  14. Fox, J. (2008). Applied Regression Analysis and Generalized Linear Models, 2nd edn. Thousand Oaks, CA: Sage. google scholar
  15. Gallucci, M. (2019). GAMLj: General analyses for linear models. [jamovi module]. Retrieved from https://gamlj.github.io/. google scholar
  16. Gill, J. (2001). Generalized Linear Models: A Unified Approach. Thousand Oaks, CA: Sage. google scholar
  17. Hardin, J.W., Hilbe, J.M. (2007) Generalized linear models and extensions. 2nd ed. College Station, TX: Stata Press. google scholar
  18. Hoogstra, B., Velichety, S., Zhang, C., (2024): Developing a contextual model of poverty prediction using data science and analytics – The case of Shelby County, Decision Support Systems, Volume 177. google scholar
  19. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794. https://doi. org/10.1126/science. aaf7894. google scholar
  20. Liang, K. and Zeger, S. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73(1), 13–22. google scholar
  21. Lindsey, J. (1997). Applying Generalized Linear Models. New York: Springer. google scholar
  22. Mathiassen, A. (2013). Testing Prediction Performance of Poverty Models: Emprical Evidence from Uganda. Review of Income and Wealth Series 59, Number 1, March 2013, DOI: 10.1111/roiw.12007. google scholar
  23. Mathiassen, A., (2009). A Model Based Approach for Predicting Annual Poverty Rates Without Expenditure Data, Journal of Economic Inequality, 7, 117–35. google scholar
  24. M. Wasef Hattab, (2016). A derivation of prediction intervals for gamma regression, Journal of Statistical Computation and Simulation, vol. 86, no. 17, pp. 3512–3526. google scholar
  25. McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models. 2nd Edition, Chapman and Hall, London.http://dx.doi.org/10.1007/978-1-4899-3242-6. google scholar
  26. Manning, W.G. and Mullahy, J. (2001). Estimating log models: To transform or not to transform? J. Health Econ. 20 (2001), pp. 461–494. google scholar
  27. McLeod, A.I. and Xu, C. (2010). bestglm: Best subset GLM. Available at http://CRAN.Rproject.org/packag8e = bestglm. google scholar
  28. Myers, R.H., Montgomery, D.C., Vining, G.G., Robinson, T.J. (2010) Generalized linear models with applications in engineering and the sciences. 2nd ed. New York: Wiley. google scholar
  29. Nelder, J. and Wedderburn, R. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A 132, 370–384. google scholar
  30. Puttanapong N., Martinez Jr.,A.M., Addawe,M., Bulan,B., Durante,R.L. and Martillan, M. (2020): Predicting Poverty Using Geospatial Data in Thailand. ADB Economics Working Paper Series, Asian Development Bank. google scholar
  31. Pokhriyala, N. and Jacques, D.C. (2017) Combining disparate data sources for improved poverty prediction and mapping. www.pnas.org/ cgi/doi/10.1073/pnas.1700319114. google scholar
  32. R Core Team (2022). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. Retrieved from https:// cran.r-project.org. (R packages retrieved from CRAN snapshot 2023-04-07). google scholar
  33. Usmanova, A.; Aziz, A.; Rakhmonov, D.; Osamy, W. (2022): Utilities of Artificial Intelligence in Poverty Prediction: A Review. Sustainability, google scholar
  34. 14, 14238. https://doi.org/ 10.3390/su142114238. google scholar
  35. Wijaya, D.R., Satyaning Pradnya Paramita, N.L.P, Uluwiyah, A., Rheza, M., Zahara, A., Puspita, D.R. (2020). Estimating city‑level poverty rate based on e‑commerce data with machine learning. Electronic Commerce Research (2022) 22:195–221. google scholar
  36. Wedderburn, R. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss–Newton method. Biometrika 61(3), 439–447. google scholar
  37. The Journal of Human Resources, Summer, 1977, Vol. 12, No. 3 (Summer, 1977), pp. 396-400. google scholar
  38. The jamovi project (2023). jamovi. (Version 2.4) [Computer Software]. Retrieved from https://www.jamovi.org. google scholar