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5. Determinants of Poverty and Living Conditions

5.1. Poverty Indicators

Poverty is a multi-faceted phenomenon which affects not only the ability to purchase goods, but also vulnerability towards various pressures that may prohibit an individual from enjoying life. This vulnerability may be gauged from living conditions such as employment, health, education, and housing. It is important to monitor gender differences in poverty, vulnerability and living conditions, and also to understand the causes of these differences, in order to prepare strategies for more efficient intervention schemes aimed at poverty reduction.

Poverty typically is measured by purchasing power or per capita expenditures made by the household, in the form of poverty rates or expenditure quintiles. Purchasing power has a strong correlation to most other living condition indices and is therefore used as a main indicator of poverty and vulnerability. Productivity and incomes from occupations and livelihoods are important factors for reducing poverty. Social conditions such as health, nutrition, education and housing influence productivity, thus affecting poverty status. These in turn are influenced by poverty, affecting the ability of households to gain access to adequate social conditions to improve their productivity. Efforts towards poverty alleviation therefore, require a complete intervention scheme, not simply in economic aspects, but including social dimensions as well, so that poverty may be addressed as a socio-economic phenomenon.

5.2. Variables in Poverty and Living Conditions

Analysis of determinants of poverty is essential for preparing strategies towards efficient intervention.

This section of the report presents results of a multi-variate analysis of the relationship between various social and economic aspects of living conditions and poverty as measured by purchasing power. The data provides a picture of the living conditions in Viet Nam, and evaluates the various probable determinants of these living conditions. It provides valuable indicators of poverty in order to inform suggestions for poverty reduction policy reform in the light of gender analysis.

Multi-variate analysis of the likely determinants of poverty was conducted separately for female-headed households and all households (a majority of which are male-headed) and for male and female individuals. The results show the probable differences in factors such as education and place of residence that affect poverty amongst FHHs compared to all households, when other characteristics such as education, age, ethnicity, etc. are the same. Poverty is typically determined at a household level. Therefore, this section focuses primarily on the regression results of FHHs versus all households rather than individual level results.

Interactions of economic and social dimensions of poverty must be researched, as strong inter-linkages exist.

The analysis is presented in terms of location of households in order to identify geographic determinants of poverty when other factors such as educational level, ethnicity and employment are held constant. Such analysis will help determine whether geographic targeting (with lower administrative costs) or other forms of targeting form more appropriate poverty reduction strategies.

The link between poverty and rural residence is strong but appears to be more important for FHHs than for all households.

Rural residence is strongly correlated with poverty overall, but more so for FHHs compared to all households. For individuals, when the sex of the household head is held constant, rural residence has a higher impact on the probability of an individual living in poverty amongst males than amongst females. Clearly the relationship between gender, urban/rural residence and poverty is complex. Nevertheless, targeting of poverty reduction efforts for both men and women in rural areas is important, and special efforts aimed at FHHs may be required.

Regional and provincial differences in probability of being in poverty among FHHs suggest that geographic targeting may be important.

Region of residence also has a strong association with household poverty. For all households, when other factors were held constant, residence in provinces of the Southeast region led to a lower likelihood of poverty compared to other regions. Other regions had the same or higher probability of poverty compared to Bac Lieu province in the Mekong Delta which was the comparator. However, FHHs in most provinces exhibited higher probability of poverty than in Bac Lieu province, which is a relatively poor province. Further research may be required into these provincial level effects before geographic targeting is used for gender-specific poverty reduction programmes.

Targeting of gender-specific interventions among ethnic minorities will be important.

Female headed households from Kinh and Muong groups had lower likelihood of poverty compared to other ethnic groups. This finding supports earlier studies showing a need to focus gender based poverty interventions among ethnic groups other than among the Kinh or ethnic groups that closely resemble the Kinh.

Educational improvements pay off for FHHs.

The higher the educational attainment, the lower the likelihood of poverty for all households and for FHHs, even with occupation and geographic residence held constant. The greater the share of household members receiving apprenticeships or informal training, the lower the odds of poverty overall, but for FHHs, the impact is slightly stronger.

A clearer definition of what constitutes a FHH household is warranted both for research and targeting purposes.

The data analysis does not show an association between age of household head and poverty within FHHs. However, in the analysis of all households, older household heads still of working age tend to have a lower likelihood of poverty. As a household head grows older, experience, accumulated capital and greater labour supply (due to less childcare, older aged children), is typically associated with lower poverty. However, the nominated household heads in FHHs may not be the true decision-making household head, but rather someone, usually older, selected for administrative reasons. The relationship between age of household head and poverty may not be so clear, and inferences should be used with caution.

Single person households may require specific interventions.

Household size does not affect the probability of FHHs in poverty, except for the case of one-person households where the likelihood of poverty for one-person FHHs is substantially higher than for MHHs. Single-person households typically involve a lack of labour, which is more detrimental to women than to men when other factors are held constant. The group of single-person households is likely to be small, and overall not very likely to be in poverty, but single female households should nevertheless be considered as a special target group for interventions. More detailed analysis of labour supply within different types of households may be required to understand gender differences in poverty.

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