Abstract
This paper studies how a society with traditional gender norms and competitive marriage market conditions may favor unequal distribution of resources within households and a consequent increase in female poverty. We propose a method to estimate individual consumption from household expenditure data. After estimating individual consumption, we apply a fuzzy approach for poverty analysis. Compared to standard poverty measures, the approach is less sensitive to changes in the distribution of consumption around the poverty line, generated when accounting for unequal distribution of household resources. The approach, applied to the analysis of individual poverty in Albania, revealed considerable intrahousehold inequality that specifically affects women and is correlated with imbalances in the sex ratio induced by past migrations. This leads to an expected general increase in poverty rates, mainly driven by a previously unperceived issue, female poverty, which emerges as an aspect of concern to consider in future anti-poverty policies.
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Notes
This method has been successfully implemented in different economic fields; for instance to monitor the effects of marital disruption on well-being (Aassve et al. 2007), to measure the multidimensional education mismatch (Betti et al. 2011), and to study multidimensional measures of quality of life (Betti et al. 2016). The positive results achieved by the fuzzy set approach in areas other than poverty demonstrate its wide applicability and robustness.
The Albanian Institute of Statistics (INSTAT) reports that in 2002, 25.2% of Albanians were living in absolute poverty, which reduced to 17.9% in 2005 and 12.5% in 2008. The recent global financial crisis has interrupted the positive trend with the poverty rate increasing to 14.3% in 2012. For 2002 and 2005, Mastromarco et al. (2014) found that living in rural and mountain areas, being female, poorly educated and with a large family increased the probability of suffering from deprivation. Additionally, Mangiavacchi and Verme (2013) provided evidence that existing income support programs had a weak targeting capacity and a non-significant impact on different household outcomes in the same period.
To deal with households of size larger than 3 with different composition, we propose to “rescale” these households as if they had only three members. Accordingly, qki is expenditure of member category k divided by the number of members in category k, \(y_i = \mathop {\sum}\nolimits_k q_{ki} + Kc_{ki}\), and K = 2,3 is the number of household member categories.
The choice of a proper set of correction factors is crucial for identifying individual consumption and is discussed in Section 2.3.
The proposed method comes with one caveat. At present, the empirical estimation of intrahousehold inequality does not account for economies of scale in consumption. This, however, is not an issue for the present study because the official poverty measurement strategy of the Albanian Statistical Institute (INSTAT), supported by the World Bank, is based on per-capita consumption, which likewise does not account for economies of scale.
A similar procedure has been applied by Dunbar et al. (2013) within a collective consumption framework on a sample of couples with children in Malawi. Differences with our approach are substantial. First, we proposed a reduced form approach that is compatible with but not restricted to collective models. Second, the authors make a different use of assignable consumption: they propose a system of Engel curves for assignable goods only, one good for each member category k, which depends of preferences and resource share of that member only. Third, they achieve identification of the distribution of resources by imposing restrictions on preferences rather then using distribution factors (the equivalent of our correction factors in the collective literature).
The traditional approach is a special case of the fuzzy approach, where the membership function may be seen as μ(yl) = 1 if yl < z, μ(yl) = 0 if yl ≥ z where yl is consumption expenditure of individual l and z is the poverty line. For a systematic comparison of the theory of fuzzy sets with other approaches to poverty analysis, especially in a multidimensional framework, see Deutsch and Silber (2005).
The aggregate FM indicator has a straightforward economic interpretation as it can be expressed in terms of generalized Gini measures Gα, i.e., \({\mathrm{FM}} = \frac{{\alpha + G_\alpha }}{{\alpha \left( {\alpha + 1} \right)}} = {\mathrm{HCR}}\). So the proposed FM indicator is a generalized inequality measure where the weight given to the poorer tail of the distribution (α) is such that the FM value equals the HCR.
This choice is motivated by the definition of the assignable consumption variable as expenditure on children’s clothing and footwear, used to compute σki in Eq. (2), recorded for children under 15 years.
Had we been willing to avoid this household scaling procedure, we would have had to estimate a different Engel curve system for each possible combination of household composition, each on its own subsample, leading to estimation difficulties for all those household compositions with insufficient sample size (namely almost all of them).
The F-statistics of the exclusion restrictions of the first stage (asset index and household income) are 159.93 for model (i) and 152.62 for model (ii), well beyond the critical values for a maximum relative bias of 5% compared to OLS, i.e., 20.25 (Stock and Yogo 2005).
WiHo stands for Work in Household, namely the amount of work performed in the household by each member to the exclusive benefit of the other member.
The distribution of the predicted and estimated mk(z) functions were very similar, suggesting that excluding certain families from the estimation of the collective Engel curve system did not generate significant sample selection bias in the estimation of intrahousehold distribution of resources. Figures and statistics are available on request.
This value was computed by the Cost of Basic Needs method. It is therefore an absolute poverty line, and the official consumption variable used for poverty measurement in subsequent years was deflated regionally.
Indeed, this measure has exactly the same population mean as per-capita consumption, but of course, and this is the value added by accounting for intrahousehold inequality, the dispersion is not the same.
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Acknowledgements
We are very grateful to the editor Shoshana Grossbard and two anonymous referees for their helpful comments and suggestions. Lucia Mangiavacchi and Luca Piccoli received financial support from the Spanish Ministry for the Economy and Competitiveness (grant ECO2015-63727-R).
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Betti, G., Mangiavacchi, L. & Piccoli, L. Women and poverty: insights from individual consumption in Albania. Rev Econ Household 18, 69–91 (2020). https://doi.org/10.1007/s11150-019-09452-3
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DOI: https://doi.org/10.1007/s11150-019-09452-3