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Determinants of Poverty Status in Taiwan: A Multilevel Approach

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Abstract

The aim of this study was to analyze the determinants of poverty in Taiwan, including family-level and regional-level factors. In contrast to previous studies, which have overlooked the interrelationships between individuals, families, and social structures because of methodological limitations, we applied hierarchical generalized linear models to a hierarchical structure. We used multiple data sources collected by the Directorate General of Budget, Accounting, and Statistics in the Taiwanese Executive Yuan, including the 2006 Survey of Family Income and Expenditure, the 2006 National Statistics, and the 2006 Manpower Utilization Survey. We examined 13,640 households from 23 cities and counties (regions). Our results indicated that poverty risks vary by region. Among the family-level factors studied, education, socioeconomic status, age, family type, dependency ratio, marital status, and number of earners are connected to poverty status. Significant relationships were also observed between poverty and structural characteristics, such as economic inequality, economic growth, structural transition, and labor market characteristics. We also attempted to detect cross-level interactions between family-level and regional-level factors. Surprisingly, none of the cross-level interactions were statistically significant. This article presents the unexpected results and research limitations.

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Notes

  1. When a traditional linear model is applied to analyze a hierarchical data structure, it causes the dependence of the observations at the first level, which can be measured according to an intraclass correlation. The presence of an intraclass correlation causes the standard errors of the regression coefficients to be underestimated and the type I error rates to increase (the alpha level, α). The null hypothesis is easily rejected (Heck and Thomas 2009; Kreft and De Leeuw 1998). Moreover, the statistical power indicating the probability that a null hypothesis is correctly rejected increases when α is set at a higher level (Hox, 2010). If we were to use OLS regression to analyze the multilevel data structure, the OLS estimators would be incorrect because the small standard errors increase the power (Kreft and De Leeuw 1998). See Barcikowski (1981) and Kreft and De Leeuw (1998) for more detailed discussions.

  2. The range of log odds for poverty was \(- 1.679 \pm 1.96 \times \sqrt {0.489} = ( - 3.050, - 0.308)\) and the corresponding odds ratios were (0.047, 0.735).

  3. The variance of the Level 2 residuals (τ 00) was 0.489 and the variance of the standard logistic distribution (σ 2) was equal to \(\pi^{2} /3 = 3.29\).

  4. This is called proportion variance explained, and is calculated using \(\tau_{00} (\text{Model 2}) - \tau_{00} (\text{Model 3})/\tau_{00} (\text{Model 2})\). See Raudenbush and Bryk (2002) and Hox (2010) for more detailed discussions.

  5. See Raudenbush and Bryk (2002) and Snijders and Bosker (1999) for more detailed discussions on explanatory variable selection and the choice between a fixed and random slope.

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Acknowledgments

This research was supported by a grant from the National Science Council of the Executive Yuan, Taiwan (NSC 101-2410-H-194-067). The authors thank the anonymous reviewers, Prof. Chao-Hsien Leu (Department of Social Work, Tunghai University, Taiwan), and Mr. Jun-Rong Chen (a lecturer at Department of Social Welfare, National Chung Cheng University, Taiwan) for the constructive and insightful comments.

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  1. Department of Social Welfare, National Chung Cheng University, 168 University Road, Minhsiung Township, Chiayi County, 62102, Taiwan, ROC

    Ke-Mei Chen & Te-Mu Wang

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Chen, KM., Wang, TM. Determinants of Poverty Status in Taiwan: A Multilevel Approach. Soc Indic Res 123, 371–389 (2015). https://doi.org/10.1007/s11205-014-0741-4

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