Beyond Monetary Poverty Analysis: The Dynamics of Multidimensional Child Poverty in Developing Countries


This study investigates transitions in monetary and multidimensional poverty using the 2006 and 2009 Young Lives surveys in Ethiopia, India, Peru, and Vietnam. While the headcount ratio in both measures of poverty decreases over time, there is only a small overlap between the groups in monetary and multidimensional poverty in either or both waves. Children remaining in monetary poverty are more likely to stay in multidimensional poverty. However, children escaping from monetary poverty do not always exit from multidimensional poverty. The results suggest the need to go beyond traditional monetary poverty indicators to understand and monitor poverty dynamics among children.

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Fig. 1

Data source: the 2006 Young Lives data. (Color figure online)

Fig. 2

Data source: the 2009 Young Lives data. (Color figure online)


  1. 1.

    Chronically poor and least poor households are determined based on monthly household expenditure per capita.

  2. 2.

    Child-level questionnaire contains information on school, time use, health, social support, feelings and attitudes, parents and household issues, perception of future, environment, and household wealth, and the Peabody Picture Vocabulary Test (PPVT).

  3. 3.

    The participating child refers the child that the Young Lives interviews in a household.

  4. 4.

    Alkire and Foster (2011) use income, health, health insurance, and years of schooling to estimate multidimensional poverty.

  5. 5.

    Narayan et al. (2000) include material well-being, bodily well-being, social well-being, security, freedom of choice and action, and psychological well-being to measure poverty.

  6. 6.

    Nussbaum (2000) provides a list of basic functional capabilities a human being requires for existence. It includes life, health, integrity, senses imagination, and thought, emotions, practical reason, affiliation, other species, play, control over one’s environment, and material.

  7. 7.

    Stiglitz, Sen, and Fitoussi (2009) select eight dimensions to measure multidimensional subjective and objective well-being. These are material living standards, health, education, personal activities including work, political voice and governance, social connectedness and relationship, environment, and economic and physical insecurity.

  8. 8.

    Biggeri et al. (2006) choose 14 capabilities to measure child poverty. These dimensions are life and physical health, love and care, mental well-being, bodily integrity and safety, social relations, participation, education, freedom from economic and non-economic exploitation, shelter and environment, leisure activities, respect, religion and identity, time-autonomy, and mobility.

  9. 9.

    UNICEF uses seven dimensions including nutrition, health, water, sanitation, shelter, education and information to measure child poverty in developing countries (Gordon et al. 2003).

  10. 10.

    UNICEF-ECLAC measures child poverty based on six dimensions including nutrition, water, sanitation, shelter, education, and information (UNICEF-ECLAC 2010).

  11. 11.

    For the European Union, six dimensions including nutrition, clothing, education, child development, information, and housing are selected for children below minimum compulsory school age. For children under 16, leisure is included. Child development is replaced with social. For children aged 17 and 18, clothing, activity, leisure and social, healthcare access, information, and housing are selected (Chzhen and Neubourg 2014).

  12. 12.

    Note that WHO recommends height-for-age, weight-for-age, and weight-for-height z-scores to measure stunting, undernourishment, and wasting respectively. Weight-for-age z-score is a good indicator of malnourishment for children under the age of 24 months but not for children over the age of 10 because it can misidentify children experiencing the pubertal growth spurt as children with excess weight. Weight-for-height measures short-term effects of negative environments such as diseases or changes in calorie intake (Setboonsarng 2005). Young Lives, however, did not report weight-for-age z-scores for children in the older cohort and weight-for-height z-scores for both cohorts. Height-for-age z-scores was selected as a health indicator available for both the young and older cohorts. It reflects cumulative effects of nutrition and health condition.

  13. 13.

    Tetrachoric correlations assumes bivariate normality for latent continuous variables underlying dichotomous variables (Agresti 2010).

  14. 14.

    Coefficients between indicators across the dimensions are less than 0.3505 in the young cohort and 0.3524 in the older cohort. Within the same dimension, the highest coefficients are 0.7861 in the young cohort and 0.7718 in the older cohort.

  15. 15.

    When using panel data, education indicators may capture a cohort effect, not deprivation in education. If school enrollment measures educational deprivation, it may identify older children who start to leave school by reaching the age of 15 in 2009 as the ones who are deprived in the education dimension. Years of schooling show a limited variation within the cohort over time. Vocabulary test scores were recorded for both cohorts in 2006 and 2009, however, there is no official age-specific cut-off point for the vocabulary test to measure children’s educational deprivation. The same test sets have been used regardless of children’s age. It implies that a child who repeated the same grade multiple times or who started school one or more years later may get a lower score than other children in the same cohort and be continuously identified as the one deprived in the education dimension.

  16. 16.

    A child is considered deprived in the education dimension if the child is not enrolled in a preschool or in a formal school when his or her age is five years old or above.

  17. 17.

    According to United Nations Educational, Scientific and Cultural Organization (UNESCO), the compulsory and primary school ages are 6 years with a 9-year duration in India and Vietnam, 5 years with a 12-year duration in Peru, and 7 years with an 8-year duration in Ethiopia.

  18. 18.

    Based on the analysis of multidimensional poverty, conditional cash transfer programs, regional development plans, and public services have been implemented, not only to mitigate economic hardships, but also to improve income-generating abilities, living standards, and community infrastructure (OPHI, 2013b).


  1. Agresti, A. (2010). Analysis of ordinal categorical data (Vol. 656). Hoboken, NJ: Wiley.

    Book  Google Scholar 

  2. Alkire, S. (2002). Dimensions of human development. World Development, 30, 181–205.

    Article  Google Scholar 

  3. Alkire, S. (2008). Choosing dimensions: The capability approach and multidimensional poverty. In N. Kakwani & J. Silber (Eds.), The many dimensions of poverty (pp. 89–119). New York: Palgrave-Macmillan.

    Google Scholar 

  4. Alkire, S. (2011). Multidimensional poverty and its discontents. OPHI working paper No. 46, University of Oxford.

  5. Alkire, S., Apablaza, M., Chakravarty, S., & Yalonetzky, G. (2014). Measuring chronic multidimensional poverty: A counting approach. OPHI working paper no.75, University of Oxford.

  6. Alkire S., & Foster J. (2008). Counting and multidimensional poverty measurement. OPHI working paper No.7, University of Oxford.

  7. Alkire, S., & Foster, J. (2011). Understandings and misunderstandings of multidimensional poverty measurement. Journal of Economic Inequality, 9, 289–314.

    Article  Google Scholar 

  8. Alkire, S., & Roche, J. M. (2011). Beyond headcount: Measures that reflect the breadth and components of child poverty. OPHI working paper No.45, University of Oxford.

  9. Alkire, S., & Santos, M. (2010). Acute multidimensional poverty: A new index for developing countries. Human development research paper, 2010/2011. UNDP, USA.

  10. Atkinson, A. B. (2003). Multidimensional deprivation: Contrasting social welfare and counting approaches. Journal of Economic Inequality, 1, 51–65.

    Article  Google Scholar 

  11. Atkinson, A. B., Cantillon, B., Marlier, E., & Nolan, B. (2002). Social indicators: The EU and social inclusion. Oxford: Oxford University Press.

    Book  Google Scholar 

  12. Battiston, D., Cruces, G., Lopez-Calva, L. F., Lugo, M. A., & Santos, M. E. (2013). Income and beyond: Multidimensional poverty in six Latin American countries. Social Indicators Research, 112, 291–314.

    Article  Google Scholar 

  13. Baulch, B., & Masset, E. (2003). Do monetary and nonmonetary indicators tell the same story about chronic poverty? A study of Vietnam in the 1990s. World Development, 31, 441–453.

    Article  Google Scholar 

  14. Betti, G., Cheli, B., Lemmi, A., & Verma, V. (2006). Multidimensional and longitudinal poverty: An integrated fuzzy approach. In A. Lemmi & G. Betti (Eds.), Fuzzy set approach to multidimensional poverty measurement (pp. 115–137). New York: Springer.

    Chapter  Google Scholar 

  15. Biggeri, M., Libanora, R., & Mariani, S. (2006). Children conceptualizing their capabilities: Results of a survey conducted during the first Children’s World Congress on child labour. Journal of Human Development, 7, 59–83.

    Article  Google Scholar 

  16. Bourguignon, F., & Chakravarty, S. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality, 1, 25–49.

    Article  Google Scholar 

  17. Boyden, J. (2016). Young lives: An international study of childhood poverty: Rounds 1-4 constructed files, 20022014. [data collection]. 2nd Edition. UK Data Service. SN: 7483,

  18. Bradshaw, J., & Finch, N. (2003). Overlaps in dimensions of poverty. Journal of Social Policy, 32, 513–525.

    Article  Google Scholar 

  19. Citro, C. F., & Michael, R. T. (Eds.). (1995). Measuring poverty: A new approach. Washington DC: National Academy Press.

    Google Scholar 

  20. Clark, D., & Hulme, D. (2005). Towards a unified framework for understanding the depth breadth and duration of poverty. Global Poverty Research Group (GPRG) working paper 020.

  21. Dhamija, N., & Bhide, S. (2013). Poverty in rural India: Variations in factors influencing dynamics of chronic poverty. Journal of International Development, 25, 674–695.

    Article  Google Scholar 

  22. Ferguson, K. M. (2006). Social capital and children’s wellbeing: A critical synthesis of the international social capital literature. International Journal of Social Welfare, 15, 2–18.

    Article  Google Scholar 

  23. Gordon, D., Nandy, S., Pantazis, C., Pemberton, S., & Townsend, P. (2003). Child poverty in the developing world. Bristol: Policy Press.

    Google Scholar 

  24. Greeley, M. (1994). Measurement of poverty and poverty of measurement. IDS Bulletin, 25, 50–58.

    Article  Google Scholar 

  25. Gunther, I., & Klasen, S. (2009). Measuring chronic non-income poverty. In A. B. Addison, D. Hulme, & R. Kanbur (Eds.), Poverty dynamics: Interdisciplinary perspectives (pp. 77–101). Oxford: Oxford University Press.

    Chapter  Google Scholar 

  26. Hulme, D., & McKay, A. (2005). Identifying and measuring chronic poverty: Beyond monetary measures? In N. Kakwani & J. Silber (Eds.), The many dimensions of poverty (pp. 187–214). New York: Palgrave Macmillan.

    Google Scholar 

  27. Hulme, D., & Shepherd, A. (2003). Conceptualizing chronic poverty. World Development, 31, 403–423.

    Article  Google Scholar 

  28. Klasen, S. (2000). Measuring poverty and deprivation in South Africa. Review of Income and Wealth, 46, 33–58.

    Article  Google Scholar 

  29. Laderchi, C. (1997). Poverty and its main dimensions: The role of income as an indicators. Oxford Development Studies, 25, 345–360.

    Article  Google Scholar 

  30. Laderchi, C. R., Saith, R., & Stewart, F. (2003). Does it matter that we do not agree on the definition of poverty? A comparison of four approaches. Oxford Development Studies, 31, 243–274.

    Article  Google Scholar 

  31. Martín, E. B., & Cowell, F. A. (2006). Static and dynamic poverty in Spain, 1993-2000. Hacienda Pública Española, Instituto de Estudios Fiscales, 179, 51–77.

    Google Scholar 

  32. Narayan, D., Patel, R., Schafft, K., Rademacher, A., & Koch-Schulte, S. (2000). Voices of the poor: Can anyone hear us. Washington, DC: World Bank.

    Book  Google Scholar 

  33. Narayan, D., & Petesch, P. (2002). Voices of the poor: From many lands. New York, NY: A co-publication of the World Bank and Oxford University Press.

    Google Scholar 

  34. Nussbaum, M. C. (1988). Nature, functioning and capability: Aristotle on political distribution. Oxford Studies in Ancient Philosophy, 6, 145–184.

    Google Scholar 

  35. Nussbaum, M. C. (1992). Human functioning and social justice. In defense of Aristotelian essentialism. Political Theory, 20, 202–246.

    Article  Google Scholar 

  36. Nussbaum, M. C. (2000). Women and human development: The capabilities approach (Vol. 3). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  37. OECD. (2011). How’s life?: Measuring well-being. Paris: OECD Publishing.

    Book  Google Scholar 

  38. Oxford Poverty & Human Development Initiative (OPHI). (2013a). Global Multidimensional Poverty Index (MPI) 2013. Oxford Poverty and Human Development Initiative.

  39. Oxford Poverty & Human Development Initiative (OPHI). (2013b). Measuring multidimensional poverty: Insights from around the world. Oxford Poverty and Human Development Initiative.

  40. Robeyns, I. (2003). Sen’s capability approach and gender inequality: Selecting relevant capabilities. Feminist Economics, 9, 61–92.

    Article  Google Scholar 

  41. Roelen, K. (2015). Reducing all forms of child poverty: The need for comprehensive measurement. IDS Policy Briefing, 98.

  42. Roelen, K. (2017a). Monetary and multidimensional child poverty: A contradiction in terms? Development and Change, 48, 502–533.

    Article  Google Scholar 

  43. Roelen, K. (2017b). Poor children in rich households and vice versa: A blurred picture or hidden realities? European Journal of Development Research.

    Google Scholar 

  44. Roelen, K., Gassmann, F., & Neubourg, C. (2010). Child poverty in vietnam: Providing insights using a country-specific and multidimensional model. Social Indicators Research, 98, 129–145.

    Article  Google Scholar 

  45. Roelen, K., Gassmann, F., & Neubourg, C. (2012). False positives or hidden dimensions: What can monetary and multidimensional measurement tell us about child poverty in Vietnam? International Journal of Social Welfare, 21, 393–407.

    Article  Google Scholar 

  46. Samman, E., & Santos, M. E. (2010). Transitions in income poverty and multidimensional wellbeing: An empirical exploration of Chile, 2006-2009. Oxford Poverty and Human Development Initiative (OPHI), Department of International Development, University of Oxford.

  47. Santos, M. E., Villatoro, P., Mancero, X., & Gerstenfeld, P. (2015). A multidimensional poverty index for Latin America. OPHI working papers 79, Oxford University.

  48. Sen, A. K. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44, 219–231.

    Article  Google Scholar 

  49. Sen, A. K. (1979). Issues in the Measurement of Poverty. The Scandinavian Journal of Economics, 81, 285–307.

  50. Sen, A. K. (1984). Resources, values and development. Oxford: Basil Blackwell.

    Google Scholar 

  51. Sen, A. K. (1992). Inequality re-examined. Oxford: Clarendon Press.

    Google Scholar 

  52. Sen, A. K. (1999). Development as freedom. Oxford: Oxford University Press.

    Google Scholar 

  53. Sen, A. K. (2004). Capabilities, lists, and public reason: continuing the conversation. Feminist Economics, 10, 77–80.

    Article  Google Scholar 

  54. Setboonsarng S. (2005). Child malnutrition as a poverty indicator: an evaluation in the context of different development interventions in Indonesia. Retrieved from:

  55. Singh, R., & Sarkar, S. (2015). Children’s experience of multidimensional deprivation: Relationship with household monetary poverty. Quarterly Review of Economics and Finance, 56, 43–56.

    Article  Google Scholar 

  56. Stiglitz, J., Sen, A., & Fitoussi, J-P. (2009). Report of the commission on the measurement of economic performance and social progress. Paris: OECD. Retrieved from

  57. Sumarto, S., & De Silva, I. (2014). Beyond the headcount: Examining the dynamics and patterns of multidimensional poverty in Indonesia. TNP2 K Working paper 21-2014. Jakarta, Indonesia: Tim Nasional Percepatan Penanggulangan Kemiskinan (TNP2 K).

  58. Suppa, N. (2017). Transitions in poverty and deprivations: An analysis of multidimensional poverty dynamics. OPHI working paper No.109, University of Oxford.

  59. Thorbecke, E. (2008). Multidimensional poverty: Conceptual and measurement issues. The many dimensions of poverty. In N. Kakwani & J. Silber (Eds.), The many dimensions of poverty (pp. 3–19). New York: Palgrave-Macmillan.

    Google Scholar 

  60. Tran, V. Q., Alkire, S., & Klasen S. (2015). Static and dynamic disparities between monetary and multidimensional poverty measurement: Evidence from Vietnam. OPHI working paper No. 97, University of Oxford.

  61. Trani, J., & Cannings, T. (2013). Child poverty in an emergency and conflict context: A multidimensional profile and an identification of the poorest children in Western Darfur. World Development, 48, 48–70.

    Article  Google Scholar 

  62. United Nations. (2002). A world fit for children, A/S-27/19/Rev.1, UNICEF. Retrieved from

  63. Chzhen, Y., & Neubourg, C. (2014). Multiple overlapping deprivation analysis for the European Union (EU-MODA) technical note. Office of research working paper. Office of Research, UNICEF.

  64. UN-HABITAT. (2004). Urban indicators: Guidelines. monitoring the habitat agenda and the millennium development goals. Retrieved from:

  65. UN-HABITAT. (2007). Slums: Overcrowding or “the hidden homeless”, State of the World’s Cities 2006/7. Retrieved from:

  66. UNICEF. (2011). CC-MODA technical note analysis. UNICEF. Retrieved from:

  67. UNICEF-Economic Commission for Latin America and the Caribbean (ECLAC). (2010). Child poverty in Latin America and The Caribbean. Santiago: ECLAC-UN/UNICEF.

    Google Scholar 

  68. WHO. (2006). WHO child growth standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. Geneva, World Health Organization, Department of Nutrition for Health and Development.

  69. WHO & UNICEF (2006). Meeting the MDG drinking water and sanitation the urban and rural and rural challenges of the decade. Geneva, World Health Organizations. Retrieved from:

  70. Woodridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge: MIT Press.

    Google Scholar 

  71. Yu, J. (2013). Multidimensional poverty in China: Findings based on the CHNS. Social Indicators Research, 112, 315–336.

    Article  Google Scholar 

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I would like to acknowledge Dr. Sophie Mitra at Fordham University for her invaluable contribution to the manuscript. I also thank Dr. Subha Mani and Dr. Andrew Simons at Fordham University, Dr. Ana Vaz at Oxford Poverty and Human Development Initiative, and two anonymous reviewers who made insightful comments on the earlier drafts of this study.

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Correspondence to Hoolda Kim.

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See Tables 8, 9, 10 and 11; Fig. 2.

Table 8 Tetrachronic correlation coefficients of indicators in Vietnam
Table 9 Multidimensional poverty by cohort and country (education is included).
Table 10 Monetary poverty and multidimensional poverty by cohort and country with alternative thresholds.
Table 11 Multidimensional poverty with different weighting schemes.

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Kim, H. Beyond Monetary Poverty Analysis: The Dynamics of Multidimensional Child Poverty in Developing Countries. Soc Indic Res 141, 1107–1136 (2019).

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  • Child
  • Poverty
  • Poverty dynamics
  • Multidimensional poverty
  • Monetary poverty
  • Developing countries
  • Ethiopia
  • India
  • Peru
  • Vietnam