Abstract
For decades, international communities have developed poverty measures to inform needs assessment and aid allocation. Building on these efforts, this paper examines the discrepancies between global poverty measures and brings that analysis to bear on identifying the salient dimensions of poverty. First, a comparison is made between the monetary and capability approaches to poverty and identifies comparable indices: the poverty headcount ratio (P0) and the multidimensional poverty headcount ratio (H), respectively. The paper then describes the degree of discrepancy between P0 and H for 102 developing countries from 2010 to 2019, synthesizing data from the Multidimensional Poverty Index, the World Development Indicators, and OECD aid activity. Next, the position of countries are analyzed with respect to the fitted line of the two measures, classifying countries into either income-poor or capability-poor categories. Findings suggest that countries such as Pakistan and Ethiopia, for example, experience capability poverty while Malawi and Mozambique experience income poverty. Finally, I examine whether sector aid composition corresponds to a country’s relative income and poverty status, finding that capability-poor countries receive marginally higher social sectoral aid compared to economic sector aid. This study suggests that the discrepancies between measures of international poverty can be used to target, monitor, and evaluate global aid distribution.
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Data Availability and Material
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Code Availability
Not applicable, the custom algorithm or software is not central to the paper.
Notes
Under the focus axiom, the measure should not vary if the income of the non-poor varies. Under the monotonicity axiom, any income gain for the poor should reduce poverty; and under the transfer axiom, inequality-reducing transfers among the poor should reduce poverty.
The World Bank raised the international poverty line from $1.25 a day in 2008 to $1.90 a day in 2015 and introduced additional $3.20 and $5.50 a day poverty metrics in 2018.
Foster, Geer, and Thorbecke proposed a general class of poverty indicators, the Pα class, also known as FGT, defined as \({\mathrm{P}}_{\mathrm{\alpha }}=\frac{1}{\mathrm{n}}\sum_{i=1}^{q } {[\frac{\left(z-{y}_{i}\right)}{z}]}^{\alpha },\), where n denotes population size, z denotes a poverty line, and yi denotes achievement (e.g., income).
Bourguignon and Chakravarty (2003), Atkinson (2003), and Alkire and Foster (2007) also argue that poverty should be measured from a multidimensional perspective. A crucial issue in operationalizing the capabilities approach is deciding upon a set of capabilities, which is analogous to how the monetary approach makes budgetary determinations (Laderchi et al., 2003).
The dimension adjusted FGT measures, denoted Mα is defined by Mα = μ (gα (k)) for α ≥ 0, where gα denotes the 0–1, censored matrix of deprivations associated with y of the n (the number of people) by d (the number of dimensions) matrix.
In the 1990s and early 2000s, development was often taken to mean reducing poverty by raising income (Anand & Ravallion, 1993), and the effect of income on other indicators of wellbeing such as health was examined with longitudinal, cross-country data (Pritchett & Summers, 1996). More directly, Anand and Bärnighausen (2004) argued that absolute poverty (the proportion of a country’s population living below purchasing power parity $1 a day), in addition to income per person, explain national variations in mortality rates. In this line of thought, income poverty serves as a proxy for factors that affect social or human development outcomes. As for policy interventions, the aid required to reach the social and environmental goals was approximated by the amount of aid required to achieve the income poverty goal (Devarajan et al., 2002).
Sub-groups of the population vary across regions, income, gender, and age.
Although less ideal, globally comparative health survey has a proxy income, and thus wealth income and health can be used. For instance, the World Health Survey (WHS) has an abbreviated consumption module. The Demographic and Health Survey (DHS) does not contain an expenditure module, but it has the wealth index that represents assets and economic status.
In some countries, monetary poverty reductions have exceeded the pace of progress, while the reverse happened in other sets of countries. In two cases, even the directionality between the two measures was different.
Global Multidimensional Poverty Index country brief by Oxford Poverty and Human Development Imitative (OPHI) reports the $1.25 poverty rates that are the closest to the MPI survey years while the 2015 OPHI report the $1.25 poverty rates collected within three years of the MPI survey years. Alkire et al. (2017) extrapolated or interpolated the income headcount ratio if information on income poverty is less than four years apart from the survey years used for estimating multidimensional poverty.
The index can be decomposed to headcount, intensity, and inequality. The CSPI is the squared sum of weighted deprivations suffered by the multidimensionally poor individuals divided by the maximum possible number of weighted deprivations.
Specifically, Burchi et al. (2021) used access to sanitary drinking water as a proxy indicator for health.
This measure, asking employment status during the seven days preceding the survey, would be limited given that the labor market structure in emerging economies is more seasonable and informal than that of advanced economies. More applicable indicators for low-income countries’ employment characteristics are not available, nor are they sufficiently harmonized in the different surveys (Lugo, 2007).
The World Bank presents the intensity-sensitive and the deprivation sensitive measures but mostly use the multidimensional poverty headcount H.
Other techniques not mentioned in this paper include the dominance approach, fuzzy sets, and the axiomatic approach (Alkire et al., 2015). Marginal methods are implemented using aggregated data from different sources. Another group of methods reflects the joint distribution, and thus they are implemented using data in which information on each dimension is available for each unit of analysis. Not all of these methods are applicable to analyzing two different poverty indices.
Similar to MPI, the three dimensions are weighted equally, and within each dimension each indicator is also equally weighted in the MPM. If households fall short of the threshold in at least one dimension (if they are deprived in indicators whose weigh adds up to 1/3 or more), they are considered poor. The monetary dimension is measured using only one indicator with 1/3 weights.
Classified by 2021 World Bank income classification.
All other sub-sector codes in the social and economic sectors were excluded from this analysis, along with aid to the production section.
Pearson’s correlation coefficient evaluates the linear relationship between two continuous variables, while the Spearman rank correlation coefficient evaluates the monotonic relationship between two continuous or ordinal variables.
- $$\beta =7.91 \left(p<0.001\right), {R}^{2} = 0.44$$
Detailed analysis on the comparison between the poverty classifications of this study and Burchi et al. (2018) can be provided upon request.
It is imputed value and should be taken with caution. Negative imputed values in the process of interpolation and extrapolation are forced to be 0 and may not reflect actual income poverty rates.
The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions.
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Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant DGE-1747486. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. I am grateful to Dr. Neil Gilbert, Dr. Jill Duerr Berrick, Dr. Clair Brown, Dr. Neil Gilbert, and Dr. Alain de Janvry from U.C. Berkeley, for their insight and expertise. I also thank Dr. Fred Finan from U.C. Berkeley and Dr. Bill Easterly for constructive feedback. Lastly, I thank Ajay Shivhare and Natalie E. Pope, students from Rutgers University for research support.
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This material is based upon work supported by the National Science Foundation under Grant No. DGE-1633740.
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Jung, W. The Discrepancy Between Two Approaches to Global Poverty: What Does it Reveal?. Soc Indic Res 162, 1313–1344 (2022). https://doi.org/10.1007/s11205-021-02866-6
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DOI: https://doi.org/10.1007/s11205-021-02866-6