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

Jing Yang; Pundarik Mukhopadhaya

doi:10.1515/bejeap-2021-0193

Published by De Gruyter November 12, 2021

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

Jing Yang and Pundarik Mukhopadhaya

From the journal The B.E. Journal of Economic Analysis & Policy

https://doi.org/10.1515/bejeap-2021-0193

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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.

Keywords: monetary poverty; multidimensional poverty; urban China; rural migrants; CHIP

Corresponding author: Pundarik Mukhopadhaya, Economics Department, Macquarie University, Sydney, Australia, E-mail: pundarik.mukhopadhaya@mq.edu.au

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 M 0 r 2 and M 0 u 2 the value of the indicator M 0 2 among rural migrants and urban locals, and label Δ M 0 2 the difference M 0 r 2 − M 0 u 2 .

Using the concept of Shapley decomposition we can then derive the respective contributions to Δ M 0 2 of differences between rural migrants and local urbans in the incidence, intensity and inequality of poverty. Define ΔH, ΔA and ΔC as

Δ H = H r − H u ; Δ A = A r − A u ; Δ C = C r − C u .

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

C Δ H = 2 6 Δ H ≠ 0 ; Δ A ≠ 0 ; Δ C ≠ 0 − Δ H = 0 ; Δ A ≠ 0 ; Δ C ≠ 0 + 1 6 Δ H ≠ 0 ; Δ A ≠ 0 ; Δ C = 0 − Δ H = 0 ; Δ A ≠ 0 ; Δ C = 0 + 1 6 Δ H ≠ 0 ; Δ A = 0 ; Δ C ≠ 0 − Δ H = 0 ; Δ A = 0 ; Δ C ≠ 0 + 2 6 Δ H ≠ 0 ; Δ A = 0 ; Δ C = 0 − Δ H = 0 ; Δ A = 0 ; Δ C = 0

We then derive that

C Δ H = 2 6 H r A r 2 1 + C r 2 − H u A u 2 1 + C u 2 − H u A r 2 1 + C r 2 − H u A u 2 1 + C u 2 + 1 6 H r A r 2 1 + C u 2 − H u A u 2 1 + C u 2 − H u A r 2 1 + C u 2 − H u A u 2 1 + C u 2 + 1 6 H r A u 2 1 + C r 2 − H u A u 2 1 + C u 2 − H u A u 2 1 + C r 2 − H u A u 2 1 + C u 2 + 2 6 H r A u 2 1 + C u 2 − H u A u 2 1 + C u 2 − H u A u 2 1 + C u 2 − H u A u 2 1 + C u 2

↔ C Δ H = 2 6 H r A r 2 1 + C r 2 − H u A r 2 1 + C r 2 + 1 6 H r A r 2 1 + C u 2 − H u A r 2 1 + C u 2 + 1 6 H r A u 2 1 + C r 2 − H u A u 2 1 + C r 2 + 2 6 H r A u 2 1 + C u 2 − H u A u 2 1 + C u 2

Similarly

C Δ A = 2 6 Δ H ≠ 0 ; Δ A ≠ 0 ; Δ C ≠ 0 − Δ H ≠ 0 ; Δ A = 0 ; Δ C ≠ 0 + 1 6 Δ H ≠ 0 ; Δ A ≠ 0 ; Δ C = 0 − Δ H ≠ 0 ; Δ A = 0 ; Δ C = 0 + 1 6 Δ H = 0 ; Δ A ≠ 0 ; Δ C ≠ 0 − Δ H = 0 ; Δ A = 0 ; Δ C ≠ 0 + 2 6 Δ H = 0 ; Δ A ≠ 0 ; Δ C = 0 − Δ H = 0 ; Δ A = 0 ; Δ C = 0

so that

C Δ A = 2 6 H r A r 2 1 + C r 2 − H u A u 2 1 + C u 2 − H r A r 2 1 + C r 2 − H u A r 2 1 + C u 2 + 1 6 H r A r 2 1 + C r 2 − H u A u 2 1 + C r 2 − H r A r 2 1 + C r 2 − H u A r 2 1 + C r 2 + 1 6 H r A r 2 1 + C r 2 − H r A u 2 1 + C u 2 − H r A r 2 1 + C r 2 − H r A r 2 1 + C u 2 + 2 6 H r A r 2 1 + C r 2 − H r A u 2 1 + C r 2 − H r A r 2 1 + C r 2 − H r A r 2 1 + C r 2

↔ C Δ A = 2 6 H u A r 2 1 + C u 2 − H u A u 2 1 + C u 2 + 1 6 H u A r 2 1 + C r 2 − H u A u 2 1 + C r 2 + 1 6 H r A r 2 1 + C u 2 − H r A u 2 1 + C u 2 + 2 6 H r A r 2 1 + C r 2 − H r A u 2 1 + C r 2

And finally

C Δ C = 2 6 Δ H ≠ 0 ; Δ A ≠ 0 ; Δ C ≠ 0 − Δ H ≠ 0 ; Δ A ≠ 0 ; Δ C = 0 + 1 6 Δ H ≠ 0 ; Δ A = 0 ; Δ C ≠ 0 − Δ H ≠ 0 ; Δ A = 0 ; Δ C = 0 + 1 6 Δ H = 0 ; Δ A ≠ 0 ; Δ C ≠ 0 − Δ H = 0 ; Δ A ≠ 0 ; Δ C = 0 + 2 6 Δ H = 0 ; Δ A = 0 ; Δ C ≠ 0 − Δ H = 0 ; Δ A = 0 ; Δ C = 0

so that

C Δ C = 2 6 H r A r 2 1 + C r 2 − H u A u 2 1 + C u 2 − H r A r 2 1 + C r 2 − H u A u 2 1 + C r 2 + 1 6 H r A r 2 1 + C r 2 − H u A r 2 1 + C u 2 − H r A r 2 1 + C r 2 − H u A r 2 1 + C r 2 + 1 6 H r A r 2 1 + C r 2 − H r A u 2 1 + C u 2 − H r A r 2 1 + C r 2 − H r A u 2 1 + C r 2 + 2 6 H r A r 2 1 + C r 2 − H r A r 2 1 + C u 2 − H r A r 2 1 + C r 2 − H r A r 2 1 + C r 2

↔ C Δ C = 2 6 H u A u 2 1 + C r 2 − H u A u 2 1 + C u 2 + 1 6 H u A r 2 1 + C r 2 − H u A r 2 1 + C u 2 + 1 6 H r A u 2 1 + C r 2 − H r A u 2 1 + C u 2 + 2 6 H r A r 2 1 + C r 2 − H r A r 2 1 + C u 2

It is then easy to check that

C Δ H + C Δ A + C Δ C = 2 6 H r A r 2 1 + C r 2 − H u A r 2 1 + C r 2 + 1 6 H r A r 2 1 + C u 2 − H u A r 2 1 + C u 2 + 1 6 H r A u 2 1 + C r 2 − H u A u 2 1 + C r 2 + 2 6 H r A u 2 1 + C u 2 − H u A u 2 1 + C u 2 + 2 6 H u A r 2 1 + C u 2 − H u A u 2 1 + C u 2 + 1 6 H u A r 2 1 + C r 2 − H u A u 2 1 + C r 2 + 1 6 H r A r 2 1 + C u 2 − H r A u 2 1 + C u 2 + 2 6 H r A r 2 1 + C r 2 − H r A u 2 1 + C r 2 + 2 6 H u A u 2 1 + C r 2 − H u A u 2 1 + C u 2 + 1 6 H u A r 2 1 + C r 2 − H u A r 2 1 + C u 2 + 1 6 H r A u 2 1 + C r 2 − H r A u 2 1 + C u 2 + 2 6 H r A r 2 1 + C r 2 − H r A r 2 1 + C u 2

↔ C Δ H + C Δ A + C Δ C = H r A r 2 1 + C r 2 − H u A u 2 1 + C u 2 i . 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

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	–	–	–	–	–	–	–	–	–

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 M 0 2 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

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.

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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

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

Abstract

Appendix A ^[21]

References

Article note

Journal and Issue

Articles in the same Issue

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

Abstract

Appendix A [21]

References

Article note

Journal and Issue

Articles in the same Issue

Appendix A ^[21]