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Identifying Single or Multiple Poverty Trap: An Application to Indian Household Panel Data

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Abstract

The paper examines the household asset dynamics in India as well as Indian rural States. The paper contributes to the empirical analysis of poverty trap by investigating the presence of one potential poverty trap to simultaneous poverty trap. The paper uses the India Human Development Survey for the year 1993 and 2005. We use the local polynomial regression with Epanechnikov kernel weights to test the existence of multiple or single equilibrium in asset poverty dynamics. Moreover, we use the partial linear mixed model to test the impact of illiteracy trap and under-nutrition trap on asset dynamics process. Across all the States we find only single dynamic asset equilibrium for rural households. However the nature of the asset dynamics varies from one state to another. We find that, in most of the States, asset accumulation does not take place and welfare dynamics is very poor in rural areas. Further, we find under-nutrition trap uniformly affect the asset accumulation in most of the States. However an illiteracy trap affects the asset level heterogeneously over the income and regional distribution. We find the most deprived States (Bihar, Uttar Pradesh, Orissa and Madhya Pradesh) have the multiple poverty trap compared to richer States. Our result implies that asset dynamics of the household varies in the long term according to the types of traps. Government and policy makers should take pointed policy and programme based on whether the poor are trapped and in what ways.

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Notes

  1. When households are always poor we referred as “chronic poor” and when households are sometimes poor we referred as “transient poor”.

  2. They have considered Epanechnikov kernel with arbitrary bandwidth.

  3. IHDS is conducted by National Council for Applied Economic Research (NCAER), a well-known applied economics research institution in “India”. It is a nationally representative, multi-topic survey across India. Survey included on health, education, employment, economic status, marriage, fertility, gender relations, and social capital. IHDS was jointly organized by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER), New Delhi. Various authors have used the same data for various purposes (Zimmermann 2012; Singh 2011; Pou and Goli 2013, etc.).

  4. Barrett et al. (2006) mentioned three reasons why asset dynamics is better than income dynamics for poverty analyses. Firstly, Income components are stochastic in nature; hence household may be poor in one period and better off in the next period and vice versa because of stochastic factor such as good luck or receiving a lucky gift. Secondly, stochastic incomes are likely to exaggerate income inequality in cross sectional analysis and thirdly it generates spurious economic mobility in longitudinal analysis.

  5. It is a common cut-off to identify abnormal anthropometry (WHO 1995).

  6. By Chi square test we get, χ 21  = 20.01 and p = 0.00.

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Authors and Affiliations

  1. Institute for Financial Management and Research, Chennai, India

    Swati Dutta

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  1. Swati Dutta
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Correspondence to Swati Dutta.

Appendix

Appendix

See Tables 3 and 4.

Table 3 Livelihood regression to derive asset index (panel data model)
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Table 4 Multidimensional poverty trap analysis
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Dutta, S. Identifying Single or Multiple Poverty Trap: An Application to Indian Household Panel Data. Soc Indic Res 120, 157–179 (2015). https://doi.org/10.1007/s11205-014-0586-x

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