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Monetary and Non-Monetary Poverty within Urban Locals and Rural Migrants in China using Inequality-Sensitive Poverty Measures

Jing Yang ORCID logo and Pundarik Mukhopadhaya ORCID logo

Abstract

This paper measures monetary and non-monetary poverty among urban local and rural migrant groups in the urban labour market in China, capturing incidence, intensity and inequality of poverty. To measure non-monetary poverty in multiple dimensions the chosen indicators are education, health status, health insurance and pension insurance. Using data from the China Household Income Project for the years 2002, 2007, and 2013, it appears that although monetary poverty in both groups is low, migrants have higher levels of non-monetary deprivation for various levels of poverty thresholds. Compared to the urban locals, the rural migrants experienced relatively less severe poverty than mild or moderate poverty during 2002 and 2007. Our Shapley decomposition exercise on non-monetary poverty measure reveals that the incidence contributes most to the urban-migrant gap, and the contribution of intensity is higher than that of inequality. The most important factors in multidimensional poverty for both groups are health insurance and pension insurance in all years. Our logit analysis shows that the effects of demographic characteristics, level of contract, occupation, and the industry have different impacts on these two groups.


Corresponding author: Pundarik Mukhopadhaya, Economics Department, Macquarie University, Sydney, Australia, E-mail:

Funding source: Jiangxi Normal University

Award Identifier / Grant number: Jiangxi Humanities and Social Sciences Funds of Universities No. JJ18119

Award Identifier / Grant number: Jiangxi Social Science Funds No. 20YJ12

Appendix A [21]

Let’s denote respectively H r , H u , A r , A u , C r , C u the incidence, intensity and inequality of multidimensional poverty among rural migrants and urban locals. Also say M0r2 and M0u2 the value of the indicator M02 among rural migrants and urban locals, and label ΔM02 the differenceM0r2M0u2.

Using the concept of Shapley decomposition we can then derive the respective contributions to ΔM02 of differences between rural migrants and local urbans in the incidence, intensity and inequality of poverty. Define ΔH, ΔA and ΔC as

ΔH=HrHu;ΔA=ArAu;ΔC=CrCu.

The contribution CΔH of differences in the incidence of poverty may be expressed as

CΔH=26ΔH0;ΔA0;ΔC0ΔH=0;ΔA0;ΔC0+16ΔH0;ΔA0;ΔC=0ΔH=0;ΔA0;ΔC=0+16ΔH0;ΔA=0;ΔC0ΔH=0;ΔA=0;ΔC0+26ΔH0;ΔA=0;ΔC=0ΔH=0;ΔA=0;ΔC=0

We then derive that

CΔH=26HrAr21+Cr2HuAu21+Cu2HuAr21+Cr2HuAu21+Cu2+16HrAr21+Cu2HuAu21+Cu2HuAr21+Cu2HuAu21+Cu2+16HrAu21+Cr2HuAu21+Cu2HuAu21+Cr2HuAu21+Cu2+26HrAu21+Cu2HuAu21+Cu2HuAu21+Cu2HuAu21+Cu2
CΔH=26HrAr21+Cr2HuAr21+Cr2+16HrAr21+Cu2HuAr21+Cu2+16HrAu21+Cr2HuAu21+Cr2+26HrAu21+Cu2HuAu21+Cu2

Similarly

CΔA=26ΔH0;ΔA0;ΔC0ΔH0;ΔA=0;ΔC0+16ΔH0;ΔA0;ΔC=0ΔH0;ΔA=0;ΔC=0+16ΔH=0;ΔA0;ΔC0ΔH=0;ΔA=0;ΔC0+26ΔH=0;ΔA0;ΔC=0ΔH=0;ΔA=0;ΔC=0

so that

CΔA=26HrAr21+Cr2HuAu21+Cu2HrAr21+Cr2HuAr21+Cu2+16HrAr21+Cr2HuAu21+Cr2HrAr21+Cr2HuAr21+Cr2+16HrAr21+Cr2HrAu21+Cu2HrAr21+Cr2HrAr21+Cu2+26HrAr21+Cr2HrAu21+Cr2HrAr21+Cr2HrAr21+Cr2
CΔA=26HuAr21+Cu2HuAu21+Cu2+16HuAr21+Cr2HuAu21+Cr2+16HrAr21+Cu2HrAu21+Cu2+26HrAr21+Cr2HrAu21+Cr2

And finally

CΔC=26ΔH0;ΔA0;ΔC0ΔH0;ΔA0;ΔC=0+16ΔH0;ΔA=0;ΔC0ΔH0;ΔA=0;ΔC=0+16ΔH=0;ΔA0;ΔC0ΔH=0;ΔA0;ΔC=0+26ΔH=0;ΔA=0;ΔC0ΔH=0;ΔA=0;ΔC=0

so that

CΔC=26HrAr21+Cr2HuAu21+Cu2HrAr21+Cr2HuAu21+Cr2+16HrAr21+Cr2HuAr21+Cu2HrAr21+Cr2HuAr21+Cr2+16HrAr21+Cr2HrAu21+Cu2HrAr21+Cr2HrAu21+Cr2+26HrAr21+Cr2HrAr21+Cu2HrAr21+Cr2HrAr21+Cr2
CΔC=26HuAu21+Cr2HuAu21+Cu2+16HuAr21+Cr2HuAr21+Cu2+16HrAu21+Cr2HrAu21+Cu2+26HrAr21+Cr2HrAr21+Cu2

It is then easy to check that

CΔH+CΔA+CΔC=26HrAr21+Cr2HuAr21+Cr2+16HrAr21+Cu2HuAr21+Cu2+16HrAu21+Cr2HuAu21+Cr2+26HrAu21+Cu2HuAu21+Cu2+26HuAr21+Cu2HuAu21+Cu2+16HuAr21+Cr2HuAu21+Cr2+16HrAr21+Cu2HrAu21+Cu2+26HrAr21+Cr2HrAu21+Cr2+26HuAu21+Cr2HuAu21+Cu2+16HuAr21+Cr2HuAr21+Cu2+16HrAu21+Cr2HrAu21+Cu2+26HrAr21+Cr2HrAr21+Cu2
CΔH+CΔA+CΔC=HrAr21+Cr2HuAu21+Cu2i.e.d
Appendix B

Table A1:

Migrant/urban ratio of the multidimensional poverty index.

k Year M 0 0 M 0 1 M 0 2
0.25 2002 1.7808 2.6198 3.5010
2007 3.2613 3.4314 3.6025
2013 2.1020 2.3070 2.5774
0.5 2002 4.3846 4.5354 4.7562
2007 3.7593 3.7886 3.8333
2013 3.1643 3.2058 3.2677
0.75 2002 11.3324 11.4311 11.5626
2007 6.4972 6.5608 6.6462
2013 24.4761 24.4763 24.4909

  1. The migrant/urban ratio was not listed in the table when k = 1 because the multidimensional poverty indices of the urban group are zero.

Table A2:

Migrant/urban ratio of the multidimensional poverty index in each region.

k Year East Central West
M 0 0 M 0 1 M 0 2 M 0 0 M 0 1 M 0 2 M 0 0 M 0 1 M 0 2
0.25 2002 1.91 2.81 3.77 1.67 2.41 3.16 1.72 2.62 3.63
2007 4.28 4.68 5.09 2.87 2.87 2.90 2.39 2.15 1.96
2013 2.69 2.95 3.26 1.52 1.46 1.39 2.09 2.15 2.24
0.5 2002 4.85 5.00 5.22 3.96 4.06 4.21 4.29 4.54 4.91
2007 5.52 5.58 5.67 2.83 2.85 2.89 1.58 1.61 1.64
2013 3.59 3.66 3.75 1.15 1.15 1.14 2.81 2.79 2.76
0.75 2002 13.78 13.81 13.86 7.98 8.07 8.20 13.70 13.92 14.22
2007 15.76 15.87 16.03 4.21 4.32 4.46 3.94 3.94 3.94
2013

  1. The migrant/urban ratio was not listed in the table when k = 0.75 in the East in 2013 and k = 1 because some of the multidimensional poverty indices are zero.

Table A3:

Shapley decomposition of M02 among rural migrants and urban locals at regional level.

Wave k Value Percentage (%)
C ΔH C ΔA C ΔC C ΔH C ΔA C ΔC
(a) East
2002 0.25 0.0938 0.1104 −0.0148 49.52 58.30 −7.82
0.5 0.1953 0.0089 0.0018 94.81 4.32 0.87
0.75 0.0449 0.0001 0.0000 99.64 0.31 0.05
2007 0.25 0.0968 0.0141 −0.0006 87.78 12.81 −0.58
0.5 0.0924 0.0014 0.0003 98.13 1.50 0.37
0.75 0.0074 0.0001 0.00001 99.04 0.82 0.13
2013 0.25 0.0297 0.0061 0.0001 82.74 16.89 0.38
0.5 0.0286 0.0009 0.0002 96.24 3.04 0.71
(b) Central
2002 0.25 0.0834 0.1175 −0.0172 45.42 63.93 −9.35
0.5 0.1933 0.0081 0.0023 94.89 3.96 1.15
0.75 0.0453 0.0007 0.0001 98.24 1.52 0.24
2007 0.25 0.0900 0.0000 0.0007 99.19 0.04 0.78
0.5 0.0668 0.0013 0.0004 97.59 1.85 0.57
0.75 0.0079 0.0003 0.00005 95.49 3.93 0.58
2013 0.25 0.0099 −0.0018 −0.0004 128.30 −23.50 −4.80
0.5 0.0014 −0.0001 −0.00002 106.71 −5.38 −1.33
(c) West
2002 0.25 0.0938 0.1413 −0.0177 43.16 64.99 −8.15
0.5 0.2145 0.0198 0.0045 89.83 8.28 1.89
0.75 0.0857 0.0016 0.0002 97.90 1.82 0.28
2007 0.25 0.0737 −0.0192 0.0014 131.69 −34.27 2.58
0.5 0.0269 0.0017 0.0004 92.67 6.01 1.32
0.75 0.0059 0.0000 0.0000 100.00 0.00 0.00
2013 0.25 0.0225 0.0018 0.0004 91.23 7.26 1.51
0.5 0.0107 −0.0002 −0.00004 102.02 −1.62 −0.39

  1. The results when k = 0.75 at the regional level in 2013 are not reported because there is no poor urban local samples in the East regions as well as no poor rural migrant sample in the Central and West region.

Figure A1: 
FGT (α = 0, 1 and 2) curves: 2002, 2007, 2013.

Figure A1:

FGT (α = 0, 1 and 2) curves: 2002, 2007, 2013.

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Article note

The paper is a part of the research results supported by the Jiangxi Social Science Funds No. 20YJ12 and the Jiangxi Humanities and Social Sciences Funds of Universities No. JJ18119.

Received: 2021-05-26
Revised: 2021-10-10
Accepted: 2021-10-31
Published Online: 2021-11-12

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