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Targeting Administrative Regions for Multidimensional Poverty Alleviation: A Study on Vietnam

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Abstract

This study investigates seven dimensions of poverty in Vietnam (income, health, education, housing, assets, basic services and economic status) using the Household Living Standard Survey data of 2014. The Government of Vietnam disburses funds for poverty alleviation to regions on the basis of incidence of household income poverty. Our study shows that this method neither fully captures the complex regional diversity of poverty nor does it accurately identify regions with a higher severity of poverty. For the first time in poverty studies of Vietnam, we explore the role of multiple spatial levels on poverty in multiple dimensions. Unlike the practice in the existing literature which classifies the poor with an arbitrary poverty cut-off, we use a fuzzy method that allows the inclusion of people who are in partial poverty. Furthermore, by utilizing random intercept multilevel models to decompose the variation of poverty at the household, commune, district and province levels, poverty maps for Vietnam are developed to visualize the spatial evidence of the severity and incidence of poverty. We identify that the provinces that are relatively less (more) poor in the income dimension are more (less) destitute in several other dimensions, which clearly shows a need for special policy attention. Our method reveals that the poverty ranking of provinces in regional Vietnam departs widely from those obtained through traditional single-level analysis. This suggests that poverty in Vietnam can be explained not only by characteristics at the household level, but also by contextual factors at higher levels (commune/village, district, province). These empirical findings can help Vietnamese policy makers determine suitable strategies to effectively target the most deprived regions and to develop more appropriate poverty-alleviation programs.

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Notes

  1. Based on the national poverty line: for 2006–2010 the poverty lines are 360,000 and 450,000VND/person/month for the rural and urban areas respectively; for the period 2011–2015, the poverty lines are respectively 630,000 and 780,000 VND/person/month.

  2. Because of the use of dichotomous variables to specify the poverty status of a household (for instance “1” indicates poor and “0” not poor) in previous studies on Vietnam, the relative influence of household level could not be calculated directly from the empirical model but was derived from the latent variable methods proposed by Browne et al. (2005) and Goldstein et al. (2002). This drawback is corrected through our approach as we will show later. See also Pham and Mukhopadhaya (2018).

  3. Empirical evidence also reveals that the impact on poverty is most powerful when poverty alleviation efforts use spatial targeting for small administrative or geographic units; for instance, districts, villages or communes (Baker and Grosh 1994; Amarasinghe et al. 2005; Elbers et al. 2004).

  4. A limited number of studies is available in this respect. See, for example, Gräb and Grimm (2008) on Burkina Faso, Kim et al. (2016) on India, and Arpino and Aassve (2014) on rural Vietnam among others. However, all these studies utilise the uni-dimensional approach to measuring poverty, which cannot capture the multidimensional nature of deprivation.

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

  6. For studies on other developing countries, see Amarasinghe et al. (2005), Carletto et al. (2007), Christiaensen et al. (2012), Demombynes et al. (2002) among others.

  7. Therefore, households from the same community (say, the minorities) can have a poverty status more similar to those in another area than to households from different communities, and the poverty status of households in the same community in the same district are more like another than they are like those households of the same community in a different district.

  8. We use STATA 15 software with the fully maximum likelihood (FML) method to obtain all parameter estimations. To show the gross heterogeneity across provinces, districts, and communes, and to examine which level(s) has the greatest impacts on poverty after controlling for household compositional effects, this study employs multilevel random intercept models.

  9. It ranges from 0 (not at all poor) to 1 (totally poor).

  10. 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.

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

  12. One of the targets of the SDG on health is “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all”.

  13. In 2009 the Government of Vietnam passed the Health Insurance Law, which offers up to 100% subsidies on health insurance premiums for the very poor, ethnic minorities, the elderly, and all children under 6 years of age.

  14. The results in the previous studies show that poverty rates are lowest in the regions of Red River and Mekong deltas; the Midlands and Northern Mountains region has the highest poverty rates in the income dimension (see among others, Lanjouw et al. 2017; Cuong 2009; Baulch and Dat 2010).

  15. See Glewwe et al. (2004), and Rambo and Lê (1996).

  16. The results reported here are from the best models based on the Akaike Information criterion (AIC) (see Goldstein, 2011). The likelihood ratio test was used to test whether a multilevel model is preferred to a single-level model. We provide the results in Table 9 in the Appendix. Our tests show that the random effects and residual errors in the random part of the model are independent of the vector of predictor variables and approximately normally distributed. A sample of test results are provided in Appendix Table 8.

  17. See, for example, in Romania (Mihai et al. 2015), in South Sudan (Shimeles and Verdier-Chouchane 2016), and in Vietnam (Klasen et al. 2015; Gloede et al. 2015).

  18. \(\left( \frac{{\sigma_{\text{u}}^{2} }}{{\sigma_{\text{u}}^{2} + \sigma_{\text{v}}^{2} + \sigma_{\text{w}}^{2} + \sigma_{\text{e}}^{2} }} = \frac{0.025}{(0.025 + 0.007 + 0.006 + 0.034)} = 0.364 \right)\). Table 10 in the Appendix provides the crude values of all variations. These are the ingredients of the computation of VPCs presented in Fig. 2.

  19. The Vietnam Government’s poverty lines for 2014 were updated by CPI as 605 and 750 thousand VNDs per capita per month for the rural and urban areas respectively (GSO 2016).

  20. Public social support programs include: health insurance support; exemption and reduction of 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).

  21. In this context, it is worth noting that the largest ethnic minority populations who are considered the poorest in the country reside in the poorest regions of the country (GSO 2014). Since 2005, the government has implemented policies to support poor households and ethnic minorities in sustainable poverty reduction. According to the VHLSS 2014, nearly 70 % of the beneficiaries of social support programs are in the three poorest regions. According to MOLISA (2016), on an annual average, the total state budget in the period 2012–2014 allocated for social support programs reached nearly VND 25,500 billion, including health care for the poor (VND12,500 billion); education, training and vocational training for the poor (nearly VND12,000 billion); accommodation support, clean water and electricity provision (VND,000 billion). Although these programs contribute to notable achievements in poverty reduction of the country, our findings indicate obvious drawbacks in monitoring and evaluating poverty.

  22. We also note that the government does provide ad hoc support on the basis of regional characteristics. For example, households in the Northern and Coastal Central regions are more likely to be affected by natural disasters than the households in other regions. The proportion of households affected by storms, typhoons, floods and flash floods is highest in these regions (Bui et al. 2014, and Chaudhry and Ruysschaert 2007). During the period 2012–2014, more than 500,000 poor households of this region were supported in housing and building houses to respond to floods and flash floods. The highest percentage of households benefiting from the housing support programs to this region might be one reason that the provinces of this region present significantly lower levels of deprivation in the housing dimension than the overall average. Although ethnic minorities have higher levels of housing deprivation than the ethnic “majorities”, the former living in the Northern and Coastal Central region may be better off in the housing dimension than households living in other regions.

  23. To date, there are only two studies in which multilevel models are applied to explore the role of location-specific contributions to households’ welfare in Vietnam. Using panel data from two waves of the Vietnam Living Standards Measurement Surveys (VLSMS) 1993 and 1997, Arpino and Aassve (2014) investigate the contribution of villages in households’ exit from poverty. Haughton and Nguyen (2010) focus on investigating geographical variations in the inequality gap in expenditure levels between urban and rural areas. While both studies apply the income approach to measure the welfare levels of households, we examine multiple dimensions of poverty for the whole of the country. Arpino and Aassve (2014) employed EB predictions of village-level random effects to find the good and bad villages and regions in rural Vietnam. Since the authors do not include the higher levels in their multilevel model, the random errors accumulate all the unobserved contextual factors at village and higher geographic levels. Therefore, the variance component at the village level is inflated because the specified model disregards higher hierarchical levels (Tranmer and Steel 2001). There are four administrative levels involved in our empirical models that allow us not only to distinguish the random effects of each higher level, but also to gain more efficient estimations. Haughton and Nguyen (2010) show that spatial effects play an important role in inequality gaps between urban and rural areas. Although the authors take into account all four administrative levels in their empirical model, they do not examine the relative importance of these levels on household welfare nor which members in each level report higher effects on household welfare.

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

    Anh Thu Quang Pham, Pundarik Mukhopadhaya & Ha Vu

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Appendix

Appendix

Since the major analyses of the study are based on province level, we provide here tests of normal distribution at province level. We utilize the Skewness-Kurtosis (Jarque–Bera) test in Stata to test against the null hypothesis that random effects and residual errors are normally distributed. Therefore, in Table 8, a value of adj chi2 greater than 0.05 (Prob > chi2) implies its significance at 5% level. Consequently, the null hypothesis cannot be rejected and random effects and residual errors show normal distribution. In Table 8, the normality assumption of deprivation is satisfied in all dimensions (Figs. 4, 5).

Table 8 Tests for normal distribution of random effects (province level)
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Fig. 4
figure 4

Map of Vietnam by regions and provinces

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Fig. 5
figure 5

EB predictions of province-level random effects for multiple dimensions of poverty with 95% confidence intervals (Since residuals are assumed to follow a normal distribution with zero mean, the zero line in Fig. 1 represents the estimated overall mean,\(\widehat{{\beta_{0}^{k} }}\). Black dots represent the estimated province-level residuals, \(\widehat{{u_{p}^{k} }}\), which are the differences between province j’s mean of the kth dimension of poverty, and the estimated overall mean, \(\widehat{{\beta_{0}^{k} }}\).)

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Based on the result of the likelihood ratio test in Table 9, the estimated multilevel models (Models in Table 4) is preferred to OLS models (Tables 10, 11, 12).

Table 9 Likelihood ratio tests for multilevel models versus single-level models
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Table 10 Variances from multilevel models
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Table 11 Measures of poverty in Vietnam by provinces
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Table 12 Ranking of provinces based on poverty measures (ascending from least to most deprived) – single and multilevel analyses
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Pham, A.T.Q., Mukhopadhaya, P. & Vu, H. Targeting Administrative Regions for Multidimensional Poverty Alleviation: A Study on Vietnam. Soc Indic Res 150, 143–189 (2020). https://doi.org/10.1007/s11205-020-02285-z

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  • DOI: https://doi.org/10.1007/s11205-020-02285-z

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