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DOI :10.26650/B/SS10.2021.013.26   IUP :10.26650/B/SS10.2021.013.26    Full Text (PDF)

Investigation of Household Poverty With Random Effects Ordinal Logit Model: A Bayesian Approach

Çiler Sigeze

1. Introduction

This study aims to contribute to econometrics literature both socially and empirically. Socially, this study investigates the determinants of the dynamic structure of poverty in Turkey using Turkish Statistical Institute, Income and Living Conditions Survey (SILC) panel micro data during 2009-2012. Empirically, random effects ordered logit model estimated both Classical and Bayesian approaches and the estimation results were compared.

2. Method

In this study, ordered logit model with random effects is used to consider the ordered structure of the dependent variable by allowing the correlation between repeated observations of households. In this model, the dependent variable is classified according to its value on the interval of threshold value of the unobservable latent variable (Long, 1997, pp. 123-124).

The random effects ordered logit model can be expressed as

where is household’s properties and is unobservable individual effect and is cumulative possibility of dependent variable (Zheng, vd., 2014).

The estimation of the likelihood function of the random effects ordered logit model were estimated both utilizing Bayesian and Classical approaches. The maximum likelihood estimation by Gauss-Hermite numerical integration was used in the STATA programme. In addition, Bayesian approximation via Markov Chain Monte Carlo methods was used in the MLwiN programme which provides an advantage in estimation of multilevel models.

3. Results and Conclusion

The similar results were obtained from both Bayesian approach and also maximum likelihood method, according to the analysis, due to several causes such as the initial points of the iteration of Bayesian estimation were determined as the coefficients obtained from the LS method, the prior distribution of the parameters was a non-informational prior distribution and the number of samples was large enough.

In the study, the determinants of the household's entry-exit status were similar and these households were classified as temporary poor households. However, it can be more appropriate if the factors of entering and exiting poverty in the households were investigated in more details with using longer term panel data which is the worry of further studies. Thus, it should be possible to determine economic policies to fight against poverty.

According to the findings obtained in the study, possibility of being both transient and chronic poor of households increases as the age of the household head increases, but after a certain age, the possibility of being poor of households decreases as the age of the household head increases. In addition, being a female household head has a negative effect on both types of poverty. And, possibility of being both transient poor and chronic poor are decreases as the education level of the household head increases.

It is a fact that a retired household head has a negative effect on the possibility of being transient and chronic poor. This finding indicates that the household head receiving a regular salary reduces the risk of poverty of the household. Probability of being transient and chronic poor of elderly, disabled or inoperable head of households is also positive. According to the single households, the households with dependent children and those without dependent children are at higher risk of becoming transient poor and chronic poor. The coefficients of income variable are negative and statistically significant on the possibility of being transient and chronic poor, as expected.

According to the findings obtained in the study, it is necessary to provide income arrangements such as social and financial aid to increase the income of the temporary and chronic poor households. In addition, policies such as the pension system, unemployment insurance, child benefit, household income improvements, and social assistance should be developed to get rid out of poverty.

DOI :10.26650/B/SS10.2021.013.26   IUP :10.26650/B/SS10.2021.013.26    Full Text (PDF)

Hanehalkı Yoksulluğunun Rassal Etkiler Sıralı Logit Model İle İncelenmesi: Bayesyen Yaklaşım

Çiler Sigeze

Panel veri çalışmalarında, aynı birimlerin tekrarlı gözlemleri arasındaki korelasyonun dikkate alınması için birimlere özgü rassal etki modele dâhil edilmektedir. Hem rassal hem de sabit değişkenlerin bulunduğu modeller karma modeller olarak bilinmek üzere rassal etkinin yer aldığı doğrusal panel veri modelleri doğrusal karma modeller olarak tanımlanmaktadır. Bunun yanında rassal etkilerin bulunduğu bağımlı değişkeni nitel modellere genelleştirilmiş doğrusal karma modeller (GLMM) denilmektedir. Genelleştirilmiş doğrusal karma modellerin tahmini, örneklem olabilirliğini maksimize etmek için genellikle çok değişkenli normal dağılımlı olduğu varsayılan rassal etkinin dağılımı üzerinden integrallerin hesaplanabilmesini gerektirmektedir. Özellikle panel veri ile oluşturulan multinomial/sıralı probit/logit modellerde bağımlı değişkendeki alternatiflerin sayısının artması ile birlikte olabilirlik fonksiyonunun rassal birim etkilere göre çok boyutlu integrallerin çözümü gerektirmesi en çok olabilirlik tahminini oldukça karmaşık hale getirmektedir. Bu nedenle, panel veri ile oluşturulan multinomial/sıralı probit/logit modellerinin analizi için birçok çözüm tekniği geliştirilmekle birlikte bayesyen yaklaşım diğer yöntemlere göre daha çok tercih edilmektedir. Bu çalışmada Türkiye İstatistik Kurumu tarafından yayınlanan 2009- 2012 yılları Gelir ve Yaşam Koşulları Araştırması panel mikro veri anketinden yararlanılarak hanelerin yoksulluk durumlarının belirleyicileri rassal etkiler sıralı logit model oluşturularak hem klasik hem de Bayesyen yöntem ile tahmin edilmiştir. Tahmin sonuçlarına göre her iki yaklaşımla da benzer sonuçlar elde edilmiştir.

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