Poverty and inequality in Vietnam

• 查看报告

作者：

NicholasMinot; BobBaulch; MichaelEpprecht

年份：

2006

总页数：

72

语种：

English

信息来源：

国际粮食政策研究所（IFPRI）

摘要：

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... 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... 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 anti-poverty programs may not be very effective.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. Related Publications How are farmers adapting to climate change in Vietnam? Endogeneity and sample selection in a rice yield model This paper examines how a changing climate may affect rice production and how Vietnamese farmers are likely to adapt to various climatic conditions using an innovative yield function approach, taking into account sample selection bias and endogeneity o…