An analysis of the probability of escaping poverty between poor households in rural and urban
Keywords
Abstract
This study aims to shed light on the probability of escaping poverty between poor households in rural and urban Vietnam. As an alternative to the experimental method, this study takes advantage of the statistical methods, econometric models, and the census dataset (VHLSS) of 2020. We select 2,918 households classified as poor in 2019 to elucidate the impact of demographic and socio-economic characteristics of households and regions (rural and urban) on the household's poor status in 2020. The study combines binary logit regression models and the Blinder-Oaxaca decomposition method to rigorously evaluate the impact of factors and the difference in the probability of escaping poverty between poor households in rural and urban. The results show that although the poverty rate in rural is higher than that in urban areas, the probability of escaping poverty in rural is significantly higher than that in urban. The impact of demographic and socio-economic characteristics factors on the probability of escaping poverty in rural areas is more evident than in urban areas. Besides providing information for policymakers seeking solutions to save poor households from poverty, this study provides a scientific method for further studies on disparity analysis between the two groups.
Downloads
References
Albert, A., & Anderson. J. A. (1984). On the existence of maximum likelihood estimates in logistic regression models. Biometrika, 71(1), 1–10.
https://doi.org/10.1093/biomet/71.1.1
Allison, P. D. (2008). Convergence failures in logistic regression. SAS Global Forum, 360(1), 11.
Atkinson, A. C, & Woods, D. C. (2015). Designs for generalized linear models. Handbook of Design and Analysis of Experiments, (7), 471–514.
Gelman, A., Jakulin, A., Pittau, M. G., & Su, Y. (2008). A weakly informative default prior distribution for logistic and other regression models. The Annals of Applied Statistics, 2(4), 1360–1383.
https://doi.org/10.1214/08-AOAS191
Heinze, G. (2006). A comparative investigation of methods for logistic regression with separated or nearly separated data. Statistics in medicine, 25(24), 4216–4226. https://doi.org/10.1002/sim.2687
Huong, P. T. T., & Hoa, P. (2021). On the existence of posterior mean for bayesian logistic regression. Monte Carlo Methods and Applications, 7(3277-288), 277–288. https://doi.org/10.1515/mcma-2021-2089
Ghosh, Y., Li, J., & Mitra, R. (2018). On the use of cauchy prior distributions for bayesian logistic regression. Bayesian Analysis, 13(2), 359–383. https://doi.org/10.1214/17-BA1051
Polson, N. G., Scott, J. G., & Windle, J. (2013). Bayesian inference for logistic models using polya-gamma latent variables. Journal of the American statistical Association, 108(504), 1339–1349. https://doi.org/10.1080/01621459.2013.829001
Speckman, P. L., Lee, J., & Sun, D. (2009). Existence of the mle and propriety of posteriors for a general multinomial choice model. Statistica Sinica, 731–748.
Wakefield, J. (2013).Bayesian and Frequentist Regression Methods. Springer Science & Business Media, New York. https://doi.org/10.1007/978-1-4419-0925-1
