Skip to main content

Advertisement

SpringerLink
Log in

Dynamics of Multidimensional Poverty and Uni-dimensional Income Poverty: An Evidence of Stability Analysis from China

  • Published:
Social Indicators Research Aims and scope Submit manuscript

Abstract

In this paper, we construct an illustrative multidimensional poverty index for China and compare it with income poverty using the panel data from multiple waves of the China Health and Nutrition Survey. We use first order stochastic dominance method and regression analysis to test the stability of multidimensional poverty measures and probe the often-observed mis-match between multidimensional measures and income measures. We find as expected that China’s multidimensional poverty is significantly higher in rural areas and in the less developed western provinces. But relative to the income poverty, the multidimensional poverty is less volatile. Also, the ranking of provinces by income and multidimensional poverty varies. The multidimensional poverty measures are somewhat sensitive to the large change of weight, but if we control the indicators’ weight, then the multidimensional poverty measures are stable to a change of indicators.

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

Sources: The figure is from the CHNS website: http://www.cpc.unc.edu/projects/china/about/proj_desc/chinamap

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. The PPP data is from: http://siteresources.worldbank.org/ICPEXT/Resources/ICP_2011.html.

  2. For examples of governments having both official poverty statistics see, for example, Ecuador, Colombia, El Salvador, Costa Rica, Chile, and Bhutan. See Alkire et al. (2015) chapter 1.

  3. The sources and introduction of data are from CHNS website: http://www.cpc.unc.edu/projects/china.

  4. The introduction of sampling procedure is from CHNS website: http://www.cpc.unc.edu/projects/china/about/proj_desc/survey.

  5. In China, although the compulsory education law is issued in 1986, but there is no uniform time to change the years of primary education from 5 to 6 years for different regions. Hence, the cutoff for the years of education follows some important Chinese multidimensional poverty researches, such as Wang and Alkire (2009), Zou and Fang (2011) and so on.

  6. The reference and Stata computation procedures are from WHO: http://www.who.int/growthref/tools/en/.

  7. For the original household-level data, the proportion of missing data is below 1% for each dimension.

  8. We also processed the data into panel data and made a simple comparison analysis in the following part. After processing, there were 1537 household samples left each year for panel data.

  9. The Spearman’s rank correlation coefficients arrive at the same conclusions.

  10. The correlation coefficients among each measures of each year are shown in the Table 13 in this paper’s Appendix.

  11. The regression outcomes with household income controlled are not shown in this paper.

References

  • Alkire, S. (2007). The missing dimensions of poverty data: Introduction to the special issue. Oxford Development Studies, 35(4), 347–359.

    Article  Google Scholar 

  • Alkire, S. (2008). Choosing dimensions: The capability approach and multidimensional poverty. MPRA working paper No. 8862.

  • Alkire, S., & Foster, J. (2007). Counting and multidimensional poverty measurement. OPHI working paper.

  • Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487.

    Article  Google Scholar 

  • Alkire, S., Foster, J., Seth, S., Roche, J. M., & Ballon, P. (2015). Multidimensional poverty measurement and analysis. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Alkire, S., Jindra, C., Robles, G., & Vaz, A. (2016). “Multidimensional Poverty Index-2016: Brief methodological note and results.” OPHI briefing 42. Oxford: Oxford University Press.

    Google Scholar 

  • Alkire, S., & Santos, M. E. (2010). Acute multidimensional poverty: A new index for developing countries. United Nations Development Programme Human Development Reports Research Paper.

  • Alkire, S., & Santos, M. E. (2014). Measuring acute poverty in the developing world: Robustness and scope of the Multidimensional Poverty Index. World Development, 59, 251–274.

    Article  Google Scholar 

  • Alkire, S., & Seth, S. (2015). Multidimensional poverty reduction in India between 1999 and 2006: Where and How? World Development, 72, 93–108.

    Article  Google Scholar 

  • Anand, S. & Sen, A. (1997). Concepts of human development and poverty: A multidimensional perspective (pp. 1–20). United Nations Development Programme, Poverty and human development: Human development papers 1997. New York: United Nations.

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

    Article  Google Scholar 

  • Banerjee, A. V., & Duflo, E. (2011). Poor economics: A radical rethinking of the way to fight global poverty. U.S.: Public Affairs.

    Google Scholar 

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

    Article  Google Scholar 

  • Davidson, R., & Duclos, J.-Y. (2000). Statistical inference for stochastic dominance and for the measurement of poverty and inequality. Econometrica, 68(6), 1435–1464.

    Article  Google Scholar 

  • Decancq, K., & Lugo, M. A. (2013). Weights in multidimensional indices of wellbeing: An overview. Econometric Reviews, 32(1), 7–34.

    Article  Google Scholar 

  • Deutsch, J., & Silber, J. (2005). Measuring multidimensional poverty: An empirical comparison of various approaches. Review of Income and Wealth, 51(1), 145–174.

    Article  Google Scholar 

  • Duclos, J.-Y., & Makdissi, P. (2005). Squential stochastic dominance and the robustness of poverty orderings. Review of Income and Wealth, 51(1), 63–87.

    Article  Google Scholar 

  • Duclos, J.-Y., Sahn, D., & Younger, S. D. (2006). Robust multidimensional poverty comparisons. The Economic Journal, 116(514), 943–968.

    Article  Google Scholar 

  • Duflo, E., Greenstone, M., Guiteras, R. & Clasen, T. (2015). Toilets can work: Short and medium run health impacts of addressing complementaries and externalities in water and sanitation. Working paper.

  • Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–766.

    Article  Google Scholar 

  • Foster, J. E., McGillivray, M., & Seth, S. (2013). Rank robustness of composite indices. Econometric Reviews, 32(1), 33–56.

    Article  Google Scholar 

  • Khan, A. U., Saboor, A., Hussain, A., Sadiq, S., & Mohsin, A. Q. (2013). Investigating multidimensional poverty across regions in Sindh province of Pakistan. Social Indicators Research, 119(1), 515–532.

    Google Scholar 

  • Labar, K., & Bresson, F. (2011). A multidimensional analysis of poverty in China from 1991 to 2006. China Economic Review, 22(3), 646–668.

    Article  Google Scholar 

  • Montoya, A., & Teixeria, K. (2017). Multidimensional poverty in Nicargua: Are female headed households better off? Social Indicators Research, 132, 1037–1063.

    Article  Google Scholar 

  • Njong, M. A. & Ningaye, P. (2008). Characterizing weights in the measurement of multidimensional poverty: An application of data-driven approaches to Cameroonian data. OPHI working paper No. 21.

  • Oginni, A., Ahonsi, B., & Ukwuije, F. (2013). Are female-headed households typically poorer than male-headed households in Nigeria. The Journal of Socio-Economics, 45, 132–137.

    Article  Google Scholar 

  • Putnam, H. (2004). The collapse of the fact/value dichotomy and other essays. Cambridge: Harvard University Press.

    Google Scholar 

  • Ravallion, M. (2011). On multidimensional indices of poverty. Journal of Economic Inequality, 9(2), 235–248.

    Article  Google Scholar 

  • Sen, A. K. (1967). The nature and classes of prescriptive judgements. The Philosophical Quarterly, 17(66), 46–62.

    Article  Google Scholar 

  • Sen, A. K. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44(2), 219–231.

    Article  Google Scholar 

  • Sen, A. K. (1985). Commodities and Capabilities. Amsterdam: North Holland.

    Google Scholar 

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

    Google Scholar 

  • Tsui, K. Y. (2002). Multidimensional poverty index. Social Choice and Welfare, 19(1), 69–93.

    Article  Google Scholar 

  • UN. (2003). Indicators for monitoring the Millennium Development Goals. New York: United Nations.

    Google Scholar 

  • UNESCO. (2010). Reaching the marginalized. Oxford: Oxford University Press.

    Google Scholar 

  • Wang, X. L., & Alkire, S. (2009). Measurement of multidimensional poverty in China: Estimation and policy implications. Chinese Rural Economy, 12, 4–23. (in Chinese).

    Google Scholar 

  • Wang, X., Feng, H., Xia, Q. & Alkire, S. (2009). On the relationship between income poverty and multidimensional poverty in China. OPHI Working paper, University of Oxford.

  • Yu, J. (2013). Multidimensional poverty in China: Findings based on the CHNS. Social Indicators Research, 112(2), 315–336.

    Article  Google Scholar 

  • Zou, W., & Fang, Y. F. (2011). A multidimensional study of China’s poverty dynamics. Chinese Journal of Population Science, 6, 49–59. (in Chinese).

    Google Scholar 

Download references

Acknowledgements

Funding was provided by the Research project in Humanities and Social Sciences by the Ministry of Education of China (Grant No. 14YJC790026).

Author information

Authors and Affiliations

  1. Oxford Poverty and Human Development Initiative, University of Oxford, Mansfield Road, Oxford, OX1 3TB, UK

    Sabina Alkire

  2. Economics and Management School, Wuhan University, No. 299, Bayi Road, Wuchang District, Wuhan, 430072, China

    Yingfeng Fang

Authors
  1. Sabina Alkire
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingfeng Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingfeng Fang.

Appendix

Appendix

See Tables 12 and 13, Figs. 10, 11, 12, 13, 14, 15, 16 and 17.

Table 12 Decomposition of MPI under equal weight with 8 indictors by province and urban
Full size table
Fig. 10
figure 10

The contribution of each province’s MPI to total MPI

Full size image
Fig. 11
figure 11

The contribution of rural–urban MPI to total MPI

Full size image
Table 13 Kendall’s τ-b rank correlation coefficients for MPI with different weights
Full size table
Fig. 12
figure 12

The dominance analysis on MD poverty with the self-reported health indicators. Note: weight1s to weight4s refers to Multidimensional poverty under weight1 to weight4 with self reported health indicators.weight1s, weight2s, weight3s and weight4s refer to equal weights, 50, 25, 25, 25, 50, 25, 25, 25, 50% for education, health and standard of living, respectively

Full size image
Fig. 13
figure 13

The dominance analysis on MD poverty under different health indicators

Full size image
Fig. 14
figure 14

The dominance analysis on MD poverty under different health indicators

Full size image
Fig. 15
figure 15

The dominance analysis on MD poverty under different health indicators

Full size image
Fig. 16
figure 16

The dominance analysis on MD poverty under different health indicators

Full size image
Fig. 17
figure 17

The dominance analysis of different multidimensional measures

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alkire, S., Fang, Y. Dynamics of Multidimensional Poverty and Uni-dimensional Income Poverty: An Evidence of Stability Analysis from China. Soc Indic Res 142, 25–64 (2019). https://doi.org/10.1007/s11205-018-1895-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-018-1895-2

Keywords

JEL Classification

Search

Navigation