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The Topography and Sources of Multidimensional Poverty in Turkey

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Abstract

Economic inequality and poverty have been extensively analyzed in monetary terms. However, other aspects of poverty, such as education, health, environment and standards of living are important factors, essential for human well-being and capabilities. Using a host of non-monetary aspects of poverty, this paper sheds light on the geographical distribution of multidimensional poverty in Turkey. Results from survey data highlight that the regional distribution non-monetary dimensions of poverty is conspicuously different than that of relative monetary poverty in Turkey. Unlike the relative monetary poverty rate, multidimensional poverty measurement reflects the regional underdevelopment problem of Turkey. On the contrary, once monetary poverty is considered in absolute terms, multidimensional and absolute monetary poverty have a similar geographical pattern. Moreover, the decomposition analyses point out that females, old disabled and socially excluded individuals suffer the most from multidimensional poverty. Our combined results show that the isolated eastern regions realize the highest poverty and deprivation at each decomposition level.

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Fig. 1
Fig. 2

Source: SILC-Turkstat (2017), authors’ own calculations

Fig. 3

Source: SILC-Turkstat (2017), authors’ own calculations

Fig. 4

Source: SILC-Turkstat (2017), authors’ own calculations

Fig. 5

Source: SILC-Turkstat (2017), authors’ own calculations

Fig. 6

Source: SILC-Turkstat (2017)

Fig. 7

Source: SILC-Turkstat (2017), authors’ own calculations

Fig. 8

Source: SILC-Turkstat (2017), authors’ own calculations

Fig. 9

Source: SILC-Turkstat (2017), authors’ own calculations

Fig. 10

Source: SILC-Turkstat (2017), authors’ own calculations

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Notes

  1. See Alkire and Foster (2011a) for technical details and a brief comparison with other approaches. Also see Sect. 2 for a brief overview of alternative approaches to measure multidimensional poverty.

  2. Here we briefly sketch-out the central studies dealing with different methods to measure multidimensional poverty. See Deutsch and Silber (2005), Alkire and Foster (2011a), Betti et al. (2013) for a more detailed discussion on the pros and cons of different approaches to measure multidimensional poverty.

  3. Betti et al. (2013) use the fuzzy set approach and examine the regional dimension of multidimensional poverty at regional scale in Turkey. Results remark the existence of regional differences in multidimensional poverty at NUTS I level (12 Regions).

  4. We implemented further exercises by including additional dimensions and ended up with virtually similar spatial patterns and results. These results are available upon request.

  5. See Section A.1 of the “Appendix” for further details.

  6. See Sect. 5 for further discussions on the cutoff and weight assignments.

  7. In order to increase the time dimension, we are required to work with SILC that has representation level at a broader disaggregation level. This contradicts our central aim, which is the examination of the geographical dimension of multidimensional poverty.

  8. See Pacifico and Poege (2017) for details.

  9. Our spatial analyses throughout the sample period are plotted with a fixed quantile thresholds in order to provide both an interregional and an intertemporal comparison. It is also possible to have individual quantiles for each year and make a comparison just focusing on the interregional dimension. These figures yield comparably similar results and are available from the authors upon request.

  10. Additionally, we investigate the spatial distribution of dimension contribution to MPI in Sections A.3 and A.4 of the “Appendix”.

  11. We would like to thank an anonymous referee for pointing this out.

  12. Note that, we use the poverty cut-off line that has not been adjusted for PPP. Spatial patterns with PPP adjusted figures are virtually the same and available upon request.

  13. The Moran’s I spatial autocorrelation statistic is used to test for spatial randomness. \(I=(n/s)(\sum _{i}w_{ij}(x_{i}-{\bar{x}})(x_{j}-{\bar{x}})/{\sum \left( x_{i}-{\bar{x}}\right) ^{2}})\)where n is the number of cross-sections, s is the summation of all the elements \(w_{ij}\) of the weight matrix W of provinces i and j where W is defined as an inverse distance weight matrix.

  14. We would like to thank two of the anonymous referees for pointing this out.

  15. The spatial distribution of MPI for the remaining age groups are available from the authors upon request.

  16. The spatial distribution of MPI for the remaining employment status categories are available from the authors upon request.

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Acknowledgements

Earlier versions of the manuscript is presented at the ERF Workshop on Non-Monetary Dimensions of Inequality and Poverty Among Youth in the ERF Region, Cairo (2019), 20th National Economic Congress of Turkish Economic Association, Adana (2019). Authors would like to thank to participants and particularly to Valérie Berenger, Asaf Savas Akat, Seyfettin Gursel, Serdar Sayan and Fatma Dogruel for constructive comments and suggestions. This work is supported by the Economic Research Forum (ERF) under Grant No. 2019-09 of the theme “Non-Monetary Dimensions of Inequality and Poverty Among the Youth in the MENA Region”. The content and the recommendations of this research do not necessarily reflect the views of the ERF.

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  1. Department of Economics and Finance, Piri Reis University, Faculty of Economics and Administrative Sciences, 34940, Istanbul, Tuzla, Turkey

    Burhan Can Karahasan

  2. Department of Economics, MEF University, Istanbul, Turkey

    Fırat Bilgel

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Karahasan, B.C., Bilgel, F. The Topography and Sources of Multidimensional Poverty in Turkey. Soc Indic Res 154, 413–445 (2021). https://doi.org/10.1007/s11205-020-02557-8

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