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Transitions in poverty and its deprivations

An analysis of multidimensional poverty dynamics

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This paper explores a novel way to analyse poverty dynamics that is specific to certain measures of multidimensional poverty, such as the “adjusted headcount ratio” of the Alkire–Foster class of measures. Assuming there is panel data available, I show that a simultaneous and comprehensive account of transitions in deprivations and poverty allows complex interdependencies between dimensions in a dynamic context to be handled and, at the same time, allows for several advanced types of analyses. These analyses include (i) a decomposition of changes in multidimensional poverty, which reveals why poverty decreases or increases; (ii) a framework to examine and understand the relationship between the dashboard approach and dimensional contributions and multidimensional poverty in a dynamic setting; (iii) a presentation of methods that illuminate the process of the accumulation of deprivations. The suggested types of analyses are illustrated using German panel data. Implications for monitoring and policy evaluation are discussed.

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  1. See also: Rodgers and Rodgers (1993), Jalan and Ravallion (2000), Hulme and Shepherd (2003), Mckay and Lawson (2003).

  2. Other emergent literature, for which panel data is essential, aims to measure lifetime poverty (e.g. Bossert et al. 2012). This literature accounts for the timing of poverty experiences (i.e. duration and sequencing of poverty spells are emphasised). Hoy and Zheng (2011), for instance, argue that poverty experiences early in the life cycle should be considered more severe; whereas Dutta et al. (2013) show how to account for the mitigating impact of affluent spells independent from the detrimental impact of consecutive spells in poverty.

  3. Therefore, this kind of analysis has no direct counterpart in monetary poverty analysis. The corresponding analysis in an unidimensional case appears to be trivial and unrevealing.

  4. Ordinality, for instance, facilitates empirical applications, see Alkire and Foster (2011a) for more details.

  5. Further arguments around this debate can be found in Alkire et al. (2011), Alkire and Foster (2011b), Ravallion (2011, 2012), Alkire and Robles (2016). Major points of discussion also include the substitutability and complementarity between dimensions as well as sensitivity to inequality (e.g., Silber 2011; Rippin 2016).

  6. See, for instance, Datt (2013), Dotter and Klasen (2014), Rippin (2016).

  7. Note that the headcount ratio H does not allow for a dimensional breakdown, unless the intersection approach is applied, because \(A=1\), \(H=M_0\).

  8. Note that censored headcount ratios are independent of achievements in other dimensions, once identification is accomplished (Alkire and Foster 2016, pp 10–11). However, poverty status may change over time and censored headcounts are sensitive to these changes through identification.

  9. Alternatively, one could also study relative changes, which can be obtained by dividing both sides of Eq. (2) by \(\underline{h}_d^{t-1}\). However, for convenience, the subsequent argumentation uses absolute changes.

  10. Accordingly, \(M_0\) can be decomposed into the uncensored headcounts only when using union identification (Alkire and Foster 2011a, p. 482), which implies “factor decomposability” in the way Chakravarty em et al. (1998, p. 179) use the term.

  11. Note that the first aspect presumes a difference in the conditional probabilities while the second results from the respective proportions (i.e. the factors the conditional probabilities are multiplied with).

  12. However, censored headcount ratios of housing and health would, of course, register these changes.

  13. Note that these relative risks can also be obtained by appropriate logit regressions.

  14. I use SOEP data v30 (DOI: 10.5684/soep.v30), provided by the DIW; see Wagner et al. (2007) for more details. The data used in this paper was extracted using the add-on package PanelWhiz for Stata. PanelWhiz ( was written by John P. Haisken-DeNew ( See Haisken-DeNew and Hahn (2010) for details. The PanelWhiz-generated DO file to retrieve the data used here is available from me upon request. Any data or computational errors in this paper are my own.

  15. More specifically, most indicators are collected only every other year or less frequently, so a comprehensive multidimensional poverty index can only be compiled for a few selected years. Increasing the time period between these years, however, compounds the issue of panel attrition, which may be detrimental to the present analysis, as it requires a balanced panel. Moreover, analysing several year-to-year changes, would involve in fact several different (longitudinal) populations, which renders a careful analysis less comprehensible. As the objective of the empirical part of this paper is to illustrate the different forms of analyses, focusing on one specific year-to-year change, seems appropriate.

  16. See, e.g, Rippin (2016), Suppa (2017) for alternative specifications and complementary justifications.

  17. A more detailed interpretation of the evidence requires additional years with more data. It should be noted, however, that the years of investigation cover, among other things, a major labour market reform.

  18. Note that this proportion of outside-poverty transitions in deprivations tends to increase with k.


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The author is thankful for helpful comments and suggestions provided by Sabina Alkire, Gordon Anderson, Paola Ballon, Javier Bronfman, Stephan Klasen, Natalie Quinn, Suman Seth, Gaston Yalonetzky, two anonymous referees and the participants of an OPHI seminar in Oxford 2015, the HDCA conference in Washington D.C. in 2015, the IARIW conference in Dresden 2016, the WEAI conference in Santiago de Chile 2017, and the Catalan Economic Society Conference in Barcelona 2017. The author also gratefully acknowledges funding from the German Research Foundation (DFG) (RI 441/6-1).

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Suppa, N. Transitions in poverty and its deprivations. Soc Choice Welf 51, 235–258 (2018).

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