Poverty and inequality in Vietnam: Spatial patterns and geographic determinants

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Abstract

This study uses a relatively new method called "small area estimation" to estimate various measures of poverty and inequality for provinces, districts, and communes of Vietnam. The method was applied by combining information from the 1997-98 Vietnam Living Standards Survey and the 1999 Population and Housing Census. The results indicate that the poverty rate (PO) is greatest in the remote areas of the Northeast and Northwest, the upland areas of the North Central Coast, and the northern part of the Central Highlands. Poverty rates are intermediate in the Red River Delta and the Mekong River Delta. The lowest poverty rates are found in the main cities, Hanoi and Ho Chi Minh City, and in the Southeast region. The accuracy of these estimates is reasonable for the provincial and district estimates, but the commune estimates must be used with caution because some are not very precise. Mapping the density of poverty reveals that, although the poverty rates are highest in the remote upland areas, these areas are sparsely populated, so most of the poor live in the Red River Delta and the Mekong River Delta. Comparing these results with the district-level estimates of poverty from MOLISA, we find very little correlation. Several possible explanations for these differences are explored, but the most likely reason is variation in the methods used by MOLISA from one district to another. This analysis confirms other studies indicating that the inequality in per capita expenditure is relatively low in Vietnam by international standards. Inequality is greatest in the large cities and (surprisingly) in parts of the upland areas. Inequality is lowest in the Red River Delta, followed by the Mekong Delta. Just one-third of the inequality is found between districts, and two-thirds within them, suggesting that district-level targeting of antipoverty programs may not be very effective. District-level poverty is very closely associated with district-level average per capita expenditure. In other words, inequality does not explain much of the variation in poverty across districts. We explored the geographic determinants of poverty using a global model (all rural areas) and a local model. In the global model, geographic determinants, including agro-climatic variables and market access, are able to explain about three-quarters of the variation in district-level rural poverty. Poverty is higher in districts with sloped land, bare and rocky land cover, soils that are poor (sandy, saline, or acid sulfate), and far from towns. By contrast, these agro-climatic and market access variables do not explain urban poverty very well. The local regression model, in which coefficients vary from one area to another, reveals that flat land and high road density are associated with lower poverty throughout Vietnam. But other variables, such as rainfall and forest cover, are positively associated with poverty in some areas and negatively associated in others. Overall, the relationship between agro-climatic variables and poverty varies significantly from one area of Vietnam to another. Many antipoverty programs are geographically targeted in Vietnam. The results from this study indicate that it may be possible to improve the targeting of these programs by adopting more precise estimates of poverty at the district and commune level, though further research is needed to better understand the discrepancies between estimates produced by different methods. The ability of market access and agro-climatic variables to explain a large portion of differences in rural poverty rates indicate that poverty in the remote areas is linked to low agricultural potential and lack of market access. This illustrates the importance of improving market access. The fact that poverty is closely related to low agricultural potential suggests that efforts to restrict migration out of disadvantaged regions may not be a good strategy for reducing rural poverty. Finally, the study notes that the small-area estimation method is not very useful for annual poverty mapping because it relies on census data, but it could be used to show detailed spatial patterns in other variables of interest to policymakers, such as income diversification, agricultural market surplus, and vulnerability. Furthermore, it can be used to estimate poverty rates among vulnerable populations too small to be studied with household survey data, such as the disabled, small ethnic minorities, or fishermen.

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Minot, N., Baulch, B., & Epprecht, M. (2006). Poverty and inequality in Vietnam: Spatial patterns and geographic determinants. Research Report of the International Food Policy Research Institute, (148), 1–72. https://doi.org/10.2499/0896291510

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