多维贫困：为什么不弥补缺失的联合分布数据？

Policy Research Working Paper Multidimensional Poverty Why Not Make Up the Missing Joint Distribution Data?Public Disclosure Authorized Benoit DecerfMery FerrandoBalint Menyhert Development EconomicsDevelopment Research Group A verified reproducibility package for this paper isavailable athttp://reproducibility.worldbank.org,clickherefor direct access. Policy Research Working Paper11348 Abstract Poverty is inherently multidimensional, encompassing bothmonetary and non-monetary dimensions. However, theseoutcomes are often collected in separate surveys, leaving thejoint distribution partially unobserved. To improve socialpoverty comparisons, this paper proposes a new, simplemethod to address this data constraint: assume a fixed valuefor the missing part of the joint distribution. This approach countries where both dimensions are observed, the papershows that the method systematically outperforms tradi-tional single-survey measures and “mash-up” measures.Monte Carlo simulations further confirm the robustness ofthe results across a wide range of data-generating scenarios.The findings highlight the value of the proposed method This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by theWorld Bank to provide open access to its research and make a contribution to development policy discussions aroundthe world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authorsmay be contacted at benoit.decerf@unamur.be. A verified reproducibility package for this paper is available athttp:// The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developmentissues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry thenames of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely thoseof the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and MultidimensionalPoverty:WhynotMakeUpthe BenoitDecerf,†MeryFerrando,‡ JEL:I32,I38,C81,O15.Keywords:MultidimensionalPovertyMeasurement;SocialComparisons;DataCon- 1Introduction Poverty, like well-being, is widely recognized as a multidimensional phenomenon encom-passing both monetary and non-monetary dimensions (Stiglitz et al., 2009). An individ-ual’s poverty status may thus depend on whether or not she cumulates deprivations across several dimensions. However, a common data limitation is that outcomes in different di-mensions are often collected through separate surveys.For example, at the global level,outcomes in key non-monetary dimensions such as health are typically not gathered inthe same surveys that measure monetary outcomes.1 As a result, while we can observepartial distributions for each dimension separately, the joint distribution of individual-level The literature has developed three main approaches to deal with the missing joint distribution data.2Currently, the mainstream solution is to restrict the set of dimensions to a subset whose outcomes are all collected in a single survey.Prominent examplesinclude monetary poverty measures as well as the main global multidimensional povertymeasures, such as the Global MPI of UNDP-OPHI. These “single-survey” measures enjoycredibility because all their necessary data is observed, but they ignore the dimensions notcaptured in their survey. The second solution allows combining data from several surveys,but adopts an index whose definition does not depend on the unobserved dependence. In this paper, we propose a new solution to the problem of missing joint distributiondata that (i) allows the use of partial distribution data from separate surveys, (ii) is simpleto implement, and (iii) does not require the use of conceptually weak poverty indices. Ina nutshell, our proposal is to assume (i.e., “make up”) a fixed value for the missing jointdistribution data.We call “made-up” the measures obtained in this way.In the special For pedagogical purposes, we select a particular multidimensional headcount ratio asour benchmark poverty measure.Individuals are identified as multidimensionally poorwhen they are monetary poor and/or non-monetary poor. These latter two statuses canbe determined from the partial distributions respectively observed in the monetary andnon-monetary surveys. The multidimensional headcount ratio is a function of three statis- We empirically study which of these solutions (single-survey, mash-up, and made-upmeasures) yields less biased social poverty comparisons. We draw on rich datasets from sixcountries (Bolivia, Brazil, Ecuador, Ethiopia, Ghana, and Uganda) where both monetaryand non-monetary outcomes are observed for the same households (Evans et al., 2024).These data allow us to construct the