Abstract
This paper presents an indicator for measuring multidimensional poverty in the Lao People’s Democratic Republic applying the Alkire–Foster methodology to the Lao Expenditure and Consumption Survey 2002/2003 and 2007/2008. We calculated a multidimensional poverty index (MPI) that includes three dimensions: education, health, and standard of living. Making use of the MPI’s decomposability, we analyse how much each of the different dimensions and its respective indicators contribute to the overall MPI. We find a marked reduction in the multidimensional poverty headcount ratio over the study period, regardless of how the indicators are weighted or how the deprivation and poverty cut-offs are set. This reduction is based on improvements regarding all indicators except cooking fuel and nutrition. We observe no significant reduction in the intensity of poverty, however; there are wide disparities between the country’s regions and between urban and rural areas. The proportion of poor people in rural areas is more than twice as high as that in urban areas. By complementing the traditional income-based poverty measure, we hope to provide useful information that can support knowledge-based decision-making for poverty alleviation.
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Notes
Although many countries base their official poverty classification on a variety of criteria such as health, education, and access to services, the poverty line is then calculated by estimating the costs of these criteria (Haughton et al. 2009). Thus poverty is in fact measured via a single dimension, i.e. income or consumption. The basic needs approach is one of the most widespread approaches to measuring absolute poverty in developing countries. It measures poverty via a single dimension, i.e. income (often proxied by measuring consumption), by comparing it to an agreed poverty line. This is calculated by estimating the costs of an adequate nutrition and other essentials such as clothing and shelter (Haughton et al. 2009).
The MPI makes it possible to look at joint distribution as opposed to marginal distribution. The relevance of joint distribution in multidimensional analysis was articulated by Atkinson and Bourguignon (1982), who observed that multidimensional analysis is intrinsically different because it is capable of revealing differing degrees of interdependence between dimensions even if the marginal distributions per dimension are identical. Marginal distribution shows how deprivation is distributed within one specific dimension without reference to any other dimension (as in the above-mentioned detailed thematic studies). However, by looking only at the marginal distribution, one does not know who is simultaneously deprived in other dimensions.
A poverty identification of k = 1 is in line with the union approach, which defines poverty as deprivation regarding only one indicator. At the other extreme, k = d corresponds to the intersection approach, which defines poverty as deprivation regarding all indicators at once, meaning that a person is considered poor only if he or she is deprived with regard to all indicators.
According to the classification of Foster (2006), the headcount ratio does not fulfil the dominance axioms of monotonicity and transfer. A poverty measure that does not fulfil these axioms is unsustainable because it encourages policymakers with a limited budget to assist the marginally poor rather than the severely poor.
We considered including the LECS 1997/1998 as well, but decided against it because its method of data collection differed from that used in the later surveys.
The LECS is conducted every five years.
In his blog, Duncan Green, who works as a strategic advisor for Oxfam GB and authored From Poverty to Power, features a discussion on the MPI between its co-creator, Sabina Alkire, and Martin Ravallion of the World Bank. Ravallion criticizes the MPI for being based on normative rather than price-based weights. Alkire responded by arguing that some aspects of poverty are impossible to price (e.g. morbidity, mortality, illiteracy), but giving them zero weight would not seem right either.
For example, market accessibility measured as travel time to towns has a strong positive association with poverty (Epprecht et al. 2008) but is not included in the MPI.
The same is true when other weighting schemes are applied.
Our aim was to test whether the food price increase in 2007/2008 mainly affected non-agricultural urban households. Accordingly, we selected the provinces of Vientiane capital, Vientiane, Xayabury, Borikhamxay, and Champasak, for which Schönenweger et al. (2012) had indicated that food security was at risk; in addition, we selected Phongsaly and Saravane because they have a high proportion of agricultural households (Lao PDR 2012).
We assigned 50 % to the education dimension, with each indicator in this dimension weighing 25 % and the health indicator also weighing 25 %, whereas the indicators of standard of living each received a relative weighting of 4.16 %.
When the health dimension is assigned 50 %, the average intensity (A) rises from 0.53 to 0.65; a 50 % weight for the standard of living dimension leads to a decrease in (A) from 0.53 to 0.45.
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Acknowledgments
We acknowledge support from R4D FATE, co-funded by the Swiss National Science Foundation (SNSF), the Swiss Agency for Development and Cooperation (SDC), and the Centre for Development and Environment, University of Bern, Switzerland, as well as from the SDC-funded Lao DECIDE Info project. The authors also wish to thank the Lao Statistics Bureau (LSB) for providing the LECS 2002/2003 and 2007/2008.
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Bader, C., Bieri, S., Wiesmann, U. et al. A Different Perspective on Poverty in Lao PDR: Multidimensional Poverty in Lao PDR for the Years 2002/2003 and 2007/2008. Soc Indic Res 126, 483–502 (2016). https://doi.org/10.1007/s11205-015-0900-2
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DOI: https://doi.org/10.1007/s11205-015-0900-2