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Estimating poverty and vulnerability to monetary and non-monetary poverty: the case of Vietnam

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Abstract

Drawing on three-wave panel data from the Vietnam Housing Living Standard Surveys (VHLSS) 2010, 2012, and 2014 and employing a fuzzy method, this paper estimates chronic and transient poverty across multiple dimensions (income, education, health, housing, basic services, durable assets, economic status) in Vietnam. Using standard deviation as a measure of risk, this study further defines vulnerability as a probability for becoming poor and estimates vulnerability to poverty from the stochastic variation of expected deprivation within a defined interval. We further apply the method of multilevel analysis to assess the deprivation of households and distinguish vulnerability as influenced by idiosyncratic (household-specific-level) and covariate (province-level) shocks. It is observed that while the number of chronic poor in all dimensions is quite low, the proportion of chronic poor in the housing dimension is the highest (around 5% over the applicable years nationwide). Regional variation in non-monetary dimensions of poverty is substantial and clearly distinct from monetary poverty. We show that there are more multidimensionally poor households that are vulnerable to idiosyncratic shocks than to covariate shocks, and the proportion of vulnerable households (to covariate shocks) in the housing dimension is significantly greater than that in other dimensions. Almost all covariates of household and province are significantly different between vulnerable and non-vulnerable groups across the multiple dimensions of poverty other than health. Our findings suggest an urgent need for policy attention on the explicit nature of vulnerability and on the many dimensions of poverty in specific regions, and to look beyond the current official monetary-based approach.

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Notes

  1. See Makdissi and Wodon (2004) for the shortcomings of this method.

  2. The method has been much discussed and widely applied to analyse poverty in various countries. See, for example, Martinetti (1994), Cheli and Betti (1999), Betti et al. (2002), Qizilbash (2003), Qizilbash and Clark (2005), Deutsch and Silber (2005, 2006), Betti et al. (2006a, 2006b), Chakravarty (2006), Abdullah (2011), Kim (2015, and Pham and Mukhopadhaya (2018). This approach is also utilized in Eurostat official publications (Giorgi and Verma 2002).

  3. In the case of binary indicators, dj,h = 1 (maximally deprived) or dj,h = 0 (not deprived).

  4. To construct the equivalent scale, the first adult in the household is given a point 1, while each extra member who is 15 years or above is assigned 0.5, and each member under the age of 15 is given 0.3.

  5. Comprising wages, salary, and incomes from services, agricultural, fishery, and forestry sectors.

  6. Gallardo (2018) argues that even though the approach of Chiwaula et al. (2011) addresses the drawback of sensitivity to variability in Eq. (14), this approach is still not able to reasonably order the deprivation of the two households if they are averse to downside risk. While this is recognized as a limitation of the study, our application of the panel dataset in measuring vulnerability to poverty in both monetary and non-monetary dimensions would provide helpful policy implication since most previous studies on vulnerability based on cross-sectional data and monetary dimension. This is also to acknowledge that, like all other subjective measures, fuzzy approach to measure poverty has its limitations (see Alkire et al. 2015). By using a number of robustness checks, we tried to make our results acceptable within these limitations..

  7. The cut-off 0.9 for \({\mathbb{Z}}\) is arbitrary here. In the empirical application, we have checked the sensitivity of this choice of \({\mathbb{Z}}\) by considering various other values (see Table 10). We also provide the robustness checks of other thresholds (0.85 and 0.95) in Table 13.

  8. The VHLSS data are large cross-sectional data sets, but it is possible to construct a panel data due to the overlap of samples. Although concerns about the sample attrition might raise while using the household panel data, there are previous studies using the same VHLSS dataset have reported that the evidences of attrition is random (see, Dang et al. (2019), Le et al. (2019), Le and Nguyen (2019), Nguyen (2019), Coxhead et al. (2019), and Liu et al. (2020).

  9. See Table 1 for the names of the variables included in these indices. Details are presented in Appendix Tables 8 and 9.

  10. Poverty rates based on the government's poverty lines for the period 2011–2015 (GSO, 2017).

  11. Nearly 70% of the population of Vietnam lives in rural areas and more than 40% of total employment in the country is in agriculture.

  12. The table below presents the participation in social support programs of households, by years and regions in per cent (GSO 2016).

    Social Support Programs

    Region 1

    Region 2

    Region 3

    Region 4

    Region 5

    Region 6

    Country

    2010

    23.3

    49.7

    32.8

    32.0

    10.3

    20.6

    26.7

    2012

    20.5

    55.8

    36.2

    28.7

    10.1

    23.3

    27.7

    2014

    16.6

    45.3

    31.3

    26.4

    6.6

    20.2

    23.2

  13. Public social support programs include health insurance support; exemption and reduction in healthcare and tuition fees for the poor; scholarships; vocational training; housing support for the poor; provision of clean and clear water; and food support (GSO 2014).

  14. It should be noted that the highest residential density of ethnic minorities is in Regions 2, 3, and 4.

  15. There are contradictory results in previous studies that investigated only the monetary dimension of poverty and vulnerability about the relative importance of idiosyncratic and covariate shocks on households. While Paxson (1992) for Thailand, Udry (1994) for Nigeria, Carter (1997) for West Africa, and Dercon and Krishnan (2000) for Ethiopia report that the impact of covariate shocks is more crucial on households’ income than idiosyncratic shocks, Günther and Harttgen (2009) for Madagascar, Azam and Imai (2012) for Bangladesh, Mina and Imai (2017) for Philippines observe a relatively higher influence of idiosyncratic shocks on households. These studies, however, did not employ a multilevel analysis.

  16. The interval of lower \(\left( {\hat{d}_{thp}^{k} - \sigma_{{\hat{d}_{thp}^{k} }} } \right)\) and upper \(\left( {\hat{d}_{thp}^{k} + \sigma_{{\hat{d}_{thp}^{k} }} } \right)\) bounds of expected deprivation \(\hat{d}_{thp}\) equals (\(2\sigma_{{\hat{d}_{thp}^{k} }}\)).

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Authors and Affiliations

  1. Department of Economics and Public Management, Ho Chi Minh City Open University, Ho Chi Minh, Vietnam

    Anh Thu Quang Pham

  2. Department of Economics, Macquarie University, Sydney, Australia

    Pundarik Mukhopadhaya & Ha Vu

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  1. Anh Thu Quang Pham
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Appendix

Appendix

See Tables 8, 9, 10, 11, 12, 13 and Fig. 1.

Table 8 Description of dimensions used for the computation of poverty
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Table 9 Results of principal component analysis
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Table 10 Sensitive tests of vulnerability with different values of Z
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Table 11 Likelihood ratio tests for multilevel models versus single-level models
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Table 12 Spearman correlation coefficient and Kendall correlation coefficient, by equal weights
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Table 13 Spearman correlation coefficient and Kendall correlation coefficient
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Fig. 1
figure 1

Explanation of Eq. (15)

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The first principal component is therefore given by:

$$Index_{i} = \sum a_{i} X_{i}$$

where ai is principal component coefficients and Xi is the set of variables in the index i.

The propensity to poverty of a household or the fuzzy measurement of deprivation d varies between 0 and 1. We define a household whose values of deprivation are equal to or above 0.9 as definitely poor and equal to or below 0.1 as definitely not poor. In the context of shocks and risks existence, the deprivation value of a household is expected to fluctuate between B and D in Fig. 1. When a household is facing positive shocks or negative shocks, the standard deviation of expected deprivation \(\sigma_{{\hat{d}}}\) will be subtracted from or added to the expected deprivation of a household \(\hat{d}\) which is presented by the distance BC and CD, respectively.

The vulnerable index, V, in Eq. 15, equals one, if the highest potential deprivation, \(\hat{d} + \sigma_{{\hat{d}}}\), is above point E, and households are definitely vulnerable. Households are non-vulnerable (V = 0) if the lowest potential deprivation, \(\hat{d} - \sigma_{{\hat{d}}}\), is below point A. In Fig. 1, the distance DE represents the prospects of falling into the definitely poor category when the household is facing negative shocks, while BE depicts potential to become definitely poor when the household experiences positive shocks. The closer the household’s expected deprivation to E, the higher the probability that the household will be classified as definitely poor in the near future. Vulnerability index V measures vulnerability values of households, hence, is measured by one (1) minus a ratio of DE to BE.

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Pham, A.T.Q., Mukhopadhaya, P. & Vu, H. Estimating poverty and vulnerability to monetary and non-monetary poverty: the case of Vietnam. Empir Econ 61, 3125–3177 (2021). https://doi.org/10.1007/s00181-020-01991-4

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