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Social Indicators Research

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13-07-2017

Measurement of Poverty in Multiple Dimensions: The Case of Vietnam

Authors: Anh Thu Quang Pham, Pundarik Mukhopadhaya

Published in: Social Indicators Research | Issue 3/2018

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Abstract

The notion of multidimensional poverty is widely conceded and is increasingly engaging the interest of researchers and scholars, however, it is rarely used in explorations of the situation of Vietnam. Using national data from 9399 households in the 2012 Vietnam Household Living Standard Survey, we apply the integrated fuzzy relative approach for poverty, a completely novel methodology in this context, to explore the current patterns of multidimensional deprivation in Vietnam at different levels, including the incidence and intensity of the poverty of migrants’ households. Our findings reveal that these households are most deprived in the basic services dimension, while in the health dimension they are least deprived. A stark disparity exists between ethnic, and educational groups, and that female-headed households are less deprived, except in the durable asset dimension. The results further reveal that the situation of migrants’ households is better than that of their non-migrant counterparts in almost all non-monetary dimensions, but not in the monetary dimension. However, migration of family members leads to improvement in the income of the left behind families in the least developed areas in Vietnam. Our observation exposes differences in the complex nature of poverty in regional Vietnam from that of the current headcount measure of absolute income poverty.

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The country experienced an average annual real GDP growth rate of 6.4% between 2004 and 2013 (ADB 2014), with low unemployment rates.

Vietnam currently has a population of about 90 million, approximately 61% of whom reside in rural areas (GSO 2014).

Some studies on the role of migration in alleviating poverty at the family level are available on the Middle East and North Africa (Adams and Page 2003); in 71 developing countries (Adams and Page 2005); in rural Mexico (Taylor et al. 2005); in Vietnam (De Brauw and Harigaya 2007); and in China (Luo 2008).

The AF measure labels an individual as either poor or not-poor and does not consider the cases, say, if someone is 40% poor.

See for example Qizilbash and Clark (2005), Dewilde (2004), Yu (2013), Batana (2013), Battiston et al (2013) etc.

Tran et al. (2015) use AF measure to analyse multidimensional poverty in Vietnam and thus the study suffers from all these limitations.

As in Cheli and Lemmi (1995).

As in Betti and Verma (1999).

This measure is more sensitive concerning the actual disparities in a dimension of poverty (such as income) than the simple cumulative distribution function (which is just the proportion of households less poor than the household concerned).

j is the indicator and i denotes each household.

The underlying motivations are: (i) the weight is not affected by the inclusion of irrelevant dimensions; (ii) the weight is only marginally changed by small correlations; and (iii) the weight is reduced proportionately to the number of redundant variables. See Betti and Verma (2008).

See http://www.abs.gov.au/ausstats/abs@.nsf/0/A390E2529EC00DFECA25720A0076F6C6?opendocument.

Equivalence scale is used here to adjust the incomes of households of different sizes and composition. We are aware of the fact that there is no “correct” choice of equivalence scale for universal use because it is based on the normative assumptions about individuals’ needs, economies of scale of household size, and the availability of data, etc. (Buhmann et al. 1988). Our study utilizes the OECD modified scale because it takes into account not only the economies of scale for different sizes of households, but also the full complexity of household composition (Chanfreau and Burchardt 2008). The OECD modified equivalent scale is used widely even outside OECD countries such as for Senegal (Maertens et al. 2011), South Africa (Streak et al. 2009), Russia (Lukiyanova and Oshchepkov 2012), Ukraine (Betti 2014) and others. Furthermore, researchers using data of Sri Lanka, Taiwan, Malaysia and China found out that the correlation of results on poverty using various equivalence scales is very high (Visaria 1980).

See Yang and Mukhopadhaya (2016b) for a synopsis of the theoretical and empirical literature of dimensions and indicators.

Because \(\mu_{M \cap N} \left( x \right) = \hbox{max} \left[ {0,\mu_{M} \left( x \right) + \mu_{N} \left( x \right) - 1} \right]\), where M and N are fuzzy sets that M, N \(\in\) X, x is an element in the X universe.

Socio-economic disparities were found not only between urban and rural areas of the country, but also among geographic regions due to diversity in cultures as well as the natural conditions and resources. The analysis of multidimensional poverty at both the rural–urban and regional levels informs the regional requirements of the country and provides policy makers with specific information of the dimensions and consequently allows for more targeted poverty reduction policies. This level of detail in the information is useful to improve the efficiency of allocation of government budgets. Previous literature, e.g., Tran et al (2015), however, did not pay attention to that.

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.

For the computation of average education we used the ordinal values of schooling achievements of the household members excluding children below 6 years. The obtained values were rounded up and divided into six groups according to Table 1. The estimated values of poverty does not change much if only adult members are considered for this grouping.

For example: in Vietnam (Badiani and Baulch 2012; Mont and Cuong 2011); in Tanzania and Zambia (Jung and Thorbecke 2003), in Kenya (Geda 2001), among others.

Unlike some previous studies where the female-headed households show as worse off in terms of income level. For example, see Buvinić and Gupta (1997) (review of 65 developing countries); Kossoudji and Mueller, (1983) (of rural Botswana); Merrick and Schmink (1983) (of Brazil).

Deere et al. (2012) in their study on eleven Latin American countries also observed the gender-differentiated asset ownership in the rural areas, where men traditionally own land, large cattle animals and vehicles while women own small cattle and traditionally care for household chores.

The social support programs include: health insurance support; exemption and reduction of healthcare expenses for the poor; exemption and reduction of tuition for the poor; scholarship; vocational training; accommodation support; provision of clean and clear water; and food support.

Although these social welfare programs are established and mandated at central government level, they are implemented by local authorities following local poverty standards, local norms, and are heavily dependent on local financing (Van de Walle 2002). Therefore, the poor, based on MOLISA’s poverty line, may not get support. This results in the disparity between the poverty rates and participation in the social support programs in the regions of the country. The findings of Van de Walle (2002) and Minot and Baulch (2004) indicate that Vietnam’s social support and antipoverty programs have had very little influence on poverty.

The VHLSS 2012 asked about the migrants: “Is there any member of the family who has left their family during the last ten years?” However, the survey does not ask any question of the migrants themselves. Thus, the migrants’ households referred to in this study are households from which any members have moved away to live or work during the last ten years. More than 85% of migrant households have from 1 to 3 migrated former members.

The VHLSS 2012 does not include information on where the former members of household move to. The Vietnam Population and Housing Census 2009 reports that the number of urban-to-urban migrants is the third-largest of the total number of internal migrants, while the number of rural-to-rural migrants is the largest. The censuses and surveys in Vietnam related to migration focus only on the broad definitions of destination of migration such as: rural and urban, interprovincial, etc. There is no systematic study available related to the destination of migration of urban people. However, the experimental evidence about the economic reasons for migration decisions indicates that people tend to move to larger cities that offer them more employment opportunities, higher income, education, etc. (Dang et al. 1997, 2003; Goldstein et al. 2001; Le and Nguyen 2011).

Many studies have found that migration helps households by improving the quality of consumption expenditure and making health care and education more accessible. For example in Indonesia (Hugo 1982); in Asia (Deshingkar 2006); in rural Philippines (Quisumbing and McNiven 2005); in Vietnam (Cuong 2009), which is especially important for the health and education of the children left behind; in El Salvador (Edwards and Ureta 2003). Moreover, in the Philippines (Yang 2008); in Vietnam (Marx and Fleischer 2010 and Ratha et al. 2011). McKay and Deshingkar (2014) in the study of Africa and Asia report that migration helps families left behind to improve their housing conditions.

This is to note that the VHLSS is considered as the most representative large-scale survey since it includes information on household background characteristics for the whole population of the country. However, it lacks important and instrumental information on migration such as, the destinations of migrants, the demographics of migrants, etc. Thus we were unable to explore further details of internal migration in Vietnam. The other large-scale surveys, such as Vietnam Migration 2004, and the Vietnam Migration and Health Survey 1997 which focus on migration, are limited to a small number of provinces and thus were not suitable for our multidimensional poverty analyses.

It is possible that if the migrant’s income is not included in the calculation of monetary poverty, then these households will appear to be poorer on the measure of monetary poverty than they would have been had this earned income (with remittance income) been included in calculation. This cannot be tested due to lack of information.

The remittances are included in the total income of the households left behind. While the statistics of international remittances are available for policy makers and scholars, there is no national figure of the remittances of internal migrants of Vietnam. The study of Le and Nguyen (2011), based on a small scale survey (2 major sending provinces and 2 most important receiving provinces), indicates that though the migrated former members work mostly in low-paid labour markets in the city, most of them try to send some remittances to their original home.

In the study, a migrant’s household left behind could also be a household that migrated from another province. The VHLSS 2012 data set does not distinguish between these two different aspects. Information on remittances is not available either. For further in-depth investigation related to poverty and migration micro-level study is necessary.

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Title: Measurement of Poverty in Multiple Dimensions: The Case of Vietnam
Authors: Anh Thu Quang Pham
Pundarik Mukhopadhaya
Publication date: 13-07-2017
Publisher: Springer Netherlands
Published in: Social Indicators Research / Issue 3/2018
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-017-1691-4

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