Skip to main content
SpringerLink
Log in

Transitions in poverty and its deprivations

An analysis of multidimensional poverty dynamics

  • Original Paper
  • Published:
Social Choice and Welfare Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  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 (http://www.panelwhiz.eu) was written by John P. Haisken-DeNew (john@PanelWhiz.eu). 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.

References

  • Alkire S, Foster J (2011a) Counting and multidimensional poverty measurement. J Public Econ 95(7–8):476–487

    Article  Google Scholar 

  • Alkire S, Foster J (2011b) Understandings and misunderstandings of multidimensional poverty measurement. J Econ Inequal 9(2):289–314

    Article  Google Scholar 

  • Alkire S, Foster JE (2016) Dimensional and Distributional Contributions to Multidimensional Poverty. OPHI Working Paper Series 100. Oxford Poverty and Human Development Initiative (OPHI), University of Oxford, Oxford

  • Alkire S, Robles G (2016) Measuring multidimensional poverty: dashboards, union identification, and the Multidimensional Poverty Index (MPI). OPHI Research in Progress Series 46a. Oxford Poverty and Human Development Initiative, Oxford

  • Alkire S, Santos ME (2014) Measuring acute poverty in the developing world: robustness and scope of the multidimensional poverty index. World Dev 59:251–274

    Article  Google Scholar 

  • Alkire S, Seth S (2015) Multidimensional poverty reduction in India between 1999 and 2006: where and how? World Dev 72:93–108

    Article  Google Scholar 

  • Alkire S, Foster J, Santos EM (2011) Where did identification go? J Econ Inequal 9(3):501–505

    Article  Google Scholar 

  • Alkire S, Ballon P, Foster J, James R, Roche JM, Santos EM, Seth S (2015) Multidimensional poverty measurement and analysis: a counting approach. Oxford University Press, Oxford

    Book  Google Scholar 

  • Alkire S, Roche JM, Vaz A (2017a) Changes over time in multidimensional poverty: methodology and results for 34 Countries. World Dev 94:232–249

    Article  Google Scholar 

  • Alkire S, Apablaza M, Chakravarty Satya R, Yalonetzky G (2017b) Measuring chronic multidimensional poverty. J Policy Model, (forthcoming)

  • Apablaza M, Yalonetzky G (2013) Measuring the dynamics of multiple deprivations among children: the cases of Andhra Pradesh, Ethiopia, Peru and Vietnam. Working Paper 101. Young Lives

  • Atkinson AB (2003) Multidimensional deprivation: contrasting social welfare and counting approaches. J Econ Inequal 1(1):51–65

    Article  Google Scholar 

  • Atkinson T, Cantillon B, Marlier E, Nolan B (2002) Social indicators: the EU and social inclusion. Oxford University Press, Oxford

    Book  Google Scholar 

  • Bane MJ, Ellwood DT (1986) Slipping into and out of poverty: the dynamics of spells. J Hum Resour 21(1):1–23

    Article  Google Scholar 

  • Bigsten A, Shimeles A (2008) Poverty transition and persistence in Ethiopia: 1994–2004. World Dev 36(9):1559–1584

    Article  Google Scholar 

  • Bossert W, Chakravarty SR, D’Ambrosio C (2012) Poverty and time. J Econ Inequal 10:145–162

    Article  Google Scholar 

  • Bourguignon F, Chakravarty S (2003) The measurement of multidimensional poverty. J Econ Inequal 1(1):25–49

    Article  Google Scholar 

  • Bundesregierung (2013) Lebenslagen in Deutschland. 4. Armuts- und Reichtumsbericht. Bundesministerium für Arbeit und Soziales (BMAS), Bonn

  • Chakravarty SR, Mukherjee D, Ranade RR (1998) On the family of subgroup and factor decomposable measures of multidimensional poverty. In: Slottje DJ (ed) Research on economic. Emerald, Bingley, pp 175–194

    Google Scholar 

  • Datt G (2013) Making every dimension count: multidimensional poverty without the “dual cut off”. Monash Economics Working Papers 32-13. Monash University, Department of Economics

  • Dotter C, Klasen S (2014) The multidimensional poverty index: achievements, conceptual and empirical issues. Occasional Paper. UNDP Human Development Report Office, New York

  • Dutta I, Roope L, Zank H (2013) On intertemporal poverty measures: the role of affluence and want. Soc Choice Welf 41:741–762

    Article  Google Scholar 

  • Ferreiera FHG, Lugo MA (2013) Multidimensional poverty analysis: looking for a middle ground. World Bank Res Observ 28(2):220–235

    Article  Google Scholar 

  • Foster J, Greer J, Thorbecke E (1984) A class of decomposable poverty measures. Econometrica 52(3):761–66

    Article  Google Scholar 

  • Haisken-DeNew JP, Hahn M (2010) PanelWhiz: efficient data extraction of complex panel data sets: an example using the German SOEP. Schmollers Jahrbuch 130(4):643–654

    Article  Google Scholar 

  • Hoy M, Zheng B (2011) Measuring lifetime poverty. J Econ Theory 146:2544–2562

    Article  Google Scholar 

  • Hulme D, Shepherd A (2003) Conceptualizing chronic poverty. World Dev 31(3):403–423

    Article  Google Scholar 

  • Jalan J, Ravallion M (1998) Transient poverty in postreform rural China. J Comp Econ 26(2):338–357

    Article  Google Scholar 

  • Jalan J, Ravallion M (2000) Is transient poverty different? Evidence for rural China. J Dev Stud 36(6):82–99

    Article  Google Scholar 

  • Lillard A, Willis RJ (1978) Dynamic aspects of earning mobility. Econometrica 46(5):985–1012

    Article  Google Scholar 

  • Mckay A, Lawson D (2003) Assessing the extent and nature of chronic poverty in low income countries: issues and evidence. World Dev 31(3):425–439

    Article  Google Scholar 

  • OECD (2011) How’s life? Measuring well-being. OECD Better life initiative. OECD Publishing, Paris

  • Ravallion M (2011) On multidimensional indices of poverty. J Econ Inequal 9(2):235–248

    Article  Google Scholar 

  • Ravallion M (2012) Mashup indices of development. World Bank Res Observ 27(1):1–32

    Article  Google Scholar 

  • Rippin N (2016) Multidimensional poverty in Germany: a capability approach. Forum Soc Econ 45(2–3):230–255

    Article  Google Scholar 

  • Rodgers JR, Rodgers JL (1993) Chronic poverty in the United States. J Hum Resour 28(1):25–54

    Article  Google Scholar 

  • Sen AK (1992) Inequality reexamined. Russell Sage Foundation book. Russell Sage Foundation, New York

    Google Scholar 

  • Silber J (2011) A comment on the MPI index. J Econ Inequal 9(2):479–481

    Article  Google Scholar 

  • Stevens AH (1999) Climbing out of poverty, falling back in: measuring the persistence of poverty over multiple spells. J Hum Resour 34(3):557–588

    Article  Google Scholar 

  • Stiglitz JE, Sen AK, Fitoussi J-P.(2009) Report by the Commission on the Measurement of Economic Performance and Social Progress. Tech. rept. Commission on the Measurement of Economic Performance and Social Progress

  • Suppa N (2017) Towards a multidimensional poverty index for germany. Empirica ((forthcoming))

  • Tsui KY (2002) Multidimensional poverty indices. Soc Choice Welf 19(1):69–93

    Article  Google Scholar 

  • Wagner GG, Frick JR, Schupp J (2007) The German Socio-Economic Panel Study (SOEP): scope evolution and enhancements. Schmollers Jahrbuch 127(1):139–169

    Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. Faculty of Business and Economics, TU Dortmund, Vogelpothsweg 87, 44221, Dortmund, Germany

    Nicolai Suppa

Authors
  1. Nicolai Suppa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolai Suppa.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suppa, N. Transitions in poverty and its deprivations. Soc Choice Welf 51, 235–258 (2018). https://doi.org/10.1007/s00355-018-1114-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00355-018-1114-8

Search

Navigation