Migration and Rural Sustainability: Relative Poverty Alleviation by Geographical Mobility in China
Abstract
:1. Introduction
2. Literature Review
3. Methodology and Data
3.1. Methodology
3.2. Data
3.2.1. Dependent Variables
- (1)
- Relative poverty
- (2)
- Income
3.2.2. Explanatory Variables
- (1)
- Key explanatory variables
- (2)
- Other explanatory variables
3.2.3. Control Variables
3.2.4. Descriptive Statistics of Variables
4. Empirical Results and Discussion
4.1. Estimated Results of the Impact of Migration on Relative Poverty
4.1.1. Impacts of Migration on Relative Poverty
4.1.2. Impacts of Migration Distance on Relative Poverty
4.2. Estimates of the Impact of Migration on Relative Poverty Based on Regional Heterogeneity
4.2.1. Impacts of Migration on Relative Poverty
4.2.2. Impacts of Migration Distance on Relative Poverty
4.3. The Impact of Cross-Regional Migration Directions on Relative Poverty
4.4. Robustness Tests
4.5. The Interaction Effects of Migration with Work and Human Capital
4.6. Estimated Results of the Impacts of Migration on Income
4.6.1. The Impacts of Migration Distances on Income
4.6.2. The Impacts of Migration on Income Based on Regional Heterogeneity
4.6.3. The Impacts of Migration Distances on Income Based on Regional Heterogeneity
5. Discussion
6. Conclusions and Suggestions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Mestrum, F. Poverty reduction and sustainable development. Environ. Dev. Sustain. 2003, 5, 41–61. [Google Scholar] [CrossRef]
- Wan, G.; Hu, X.; Liu, W. China’s poverty reduction miracle and relative poverty: Focusing on the roles of growth and inequality. China Econ. Rev. 2021, 68, 101643. [Google Scholar] [CrossRef]
- Bulatao, R.A. Does low inequality cause low poverty? Evidence from high-income and developing countries. Poverty Public Policy 2012, 2, 29–52. [Google Scholar]
- Kämpke, T.; Pestel, R.; Radermacher, F.J. A computational concept for normative equity. Eur. J. Law Econ. 2003, 15, 129–163. [Google Scholar] [CrossRef]
- Guo, J.; Qu, S.; Zhu, T. Estimating China’s relative and multidimensional poverty: Evidence from micro-level data of 6145 rural households. World Dev. Perspect. 2022, 26, 100402. [Google Scholar] [CrossRef]
- Sun, H.; Li, X.; Li, W.; Feng, J. Differences and influencing factors of relative poverty of urban and rural residents in China based on the survey of 31 provinces and cities. Int. J. Environ. Res. Public Health 2022, 19, 9015. [Google Scholar] [CrossRef] [PubMed]
- Jolliffe, D.; Prydz, E.B. Societal poverty: A relative and relevant measure. World Bank Econ. Rev. 2021, 35, 180–206. [Google Scholar] [CrossRef] [Green Version]
- Luo, C.L. Poverty dynamics in rural China. Econ. Res. J. 2010, 45, 123–138. [Google Scholar]
- Luo, L.Q.; Ping, W.Y. Decomposition of poverty dynamic changes in China: 1991–2015. Manag. World 2020, 36, 27–40. [Google Scholar]
- Lee, E.S. A theory of migration. Demography 1966, 3, 47–57. [Google Scholar] [CrossRef]
- Stark, O. Research on rural-to-urban migration in LDCs: The confusion frontier and why we should pause to rethink afresh. World Dev. 1982, 10, 63–70. [Google Scholar] [CrossRef]
- Borjas, G.J. Self-selection and the earnings of immigrants. Am. Econ. Rev. 1987, 77, 531–553. [Google Scholar]
- Townsend, P. Poverty in the United Kingdom: A Survey of Household Resources and Standards of Living; University of California Press: Berkeley, CA, USA, 1979; p. 915. [Google Scholar]
- Lewis, W.A. Economic development with unlimited supplies of labour. Manch. Sch. 1954, 22, 139–191. [Google Scholar] [CrossRef]
- Todaro, M.P. Model of labor migration and urban unemployment in less development countries. Am. Econ. Rev. 1969, 59, 138–148. [Google Scholar]
- Stark, O.; Bloom, D.E. The new economics of labor migration. Am. Econ. Rev. 1985, 75, 173–178. [Google Scholar]
- Shearmur, R.; Polese, M. Do local factors explain local employment growth? Reg. Stud. 2007, 41, 453–471. [Google Scholar] [CrossRef]
- Posada, D.G.; Morollón, F.R.; Viuela, A. The determinants of local employment growth in Spain. Appl. Spat. Anal. Policy 2018, 11, 511–533. [Google Scholar] [CrossRef]
- Felkner, J.S.; Townsend, R.M. The geographic concentration of enterprise in developing countries. Q. J. Econ. 2011, 126, 2005–2061. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rappaport, J.; Sachs, J.D. The United States as a coastal nation. J. Econ. Growth 2003, 8, 5–46. [Google Scholar] [CrossRef]
- Holl, A. Local employment growth patterns and the Great Recession: The case of Spain. J. Reg. Sci. 2018, 58, 838–864. [Google Scholar] [CrossRef]
- Amara, M.; Ayadi, M. Local employment growth in the coastal area of Tunisia: Spatial filtering approach. Middle East Dev. J. 2014, 6, 255–284. [Google Scholar] [CrossRef]
- Partridge, M.; Bollman, R.D.; Olfert, M.R.; Alasia, A. Riding the wave of urban growth in the countryside: Spread, backwash, or stagnation? Land Econ. 2007, 83, 128–152. [Google Scholar] [CrossRef]
- Anselin, L.; Florax, R.; Rey, S. Advances in Spatial Econometrics: Methodology, Tools and Applications; Springer: Berlin/Heidelberg, Germany, 2004; pp. 433–453. [Google Scholar]
- Dibeh, G.; Fakih, A.; Marrouch, W. Labor market and institutional drivers of irregular youth migration in the Middle East and North Africa region. J. Ind. Relat. 2019, 2, 225–251. [Google Scholar] [CrossRef]
- Atnafu, A.; Oucho, L.; Zeitlyn, B. Poverty, Youth and Rural-Urban Migration in Ethiopia; Migrating out of Poverty RPC Working Paper 17; Migrating Out of Poverty Consortium University of Sussex: Brighton, UK, 2014. [Google Scholar]
- Min-Harris, C. Youth migration and poverty in Sub-Saharan Africa: Empowering the rural youth. Top. Rev. Dig. Hum. Rights Sub-Sahar. Afr. 2010, 159–186. [Google Scholar]
- Lokshin, M.; Bontch-Osmolovski, M.; Glinskaya, E. Work-related migration and poverty reduction in Nepal. Rev. Dev. Econ. 2010, 2, 323–332. [Google Scholar] [CrossRef]
- Deshingkar, P. Internal migration, poverty and development in Asia: Including the excluded. IDS Bull. 2006, 37, 88–100. [Google Scholar] [CrossRef] [Green Version]
- Stark, O.; Micevska, M.; Mycielski, J. Relative poverty as a determinant of migration: Evidence from Poland. Econ. Lett. 2009, 3, 119–122. [Google Scholar] [CrossRef]
- Li, S. Labour mobility and income growth and distribution in rural China. Soc. Sci. China. 1999, 2, 16–33. [Google Scholar]
- Du, Y.; Piao, Z.S. Migration and poverty reduction: Empirical evidence from rural household survey. Chin. J. Popul. Sci. 2003, 4, 60–66. [Google Scholar]
- Liu, J.P.; Zhang, Y.L. Research on the relation between migration of rural labor force and the development of rural economy in poverty areas—Statistical analysis based on the data of ten poverty villages in Gansu province. China Rural Surv. 2009, 3, 63–74. [Google Scholar]
- Yue, X.M.; Luo, C.L. Rural labour force working outside the home and poverty alleviation. J. World Econ. 2010, 33, 84–98. [Google Scholar]
- Bertoli, S.; Marchetta, F. Migration, remittances and poverty in Ecuador. J. Dev. Stud. 2014, 50, 1067–1089. [Google Scholar] [CrossRef] [Green Version]
- Han, J.L.; Wang, Z.Z.; Wang, H.J. The poverty reduction effect of rural labor mobility for impoverished areas in China’s new era: A study of contiguous impoverished areas. Popul. J. 2018, 40, 100–113. [Google Scholar]
- Sun, Y.N. Agricultural labour migration, human capital investment and rural poverty reduction. Study Explor. 2020, 11, 149–156. [Google Scholar]
- Zou, W.; Fan, Z.Z. The net poverty reduction effect of labor outflow and capital return. Chin. J. Popul. Sci. 2020, 4, 15–30. [Google Scholar]
- Fan, S.D.; Jiang, K.Z. China’s labor migration effects on alleviating poverty for rural household: Micro evidence based on CFPS data. Chin. J. Popul. Sci. 2016, 5, 26–34. [Google Scholar]
- Fan, S.D.; Zhu, K.P. Rural labor migration, income of migrant workers and family poverty: Based on the empirical study of 878 households in east poor county. Nanjing J. Soc. Sci. 2019, 6, 26–33. [Google Scholar]
- Sabates-Wheeler, R.; Sabates, R. Tackling poverty-migration linkages: Evidence from Ghana and Egypt. Soc. Ind. Res. 2007, 87, 307–328. [Google Scholar] [CrossRef]
- Nguyen, C.V.; Berg, M.; Lensink, R. The impact of work and non-work migration on household welfare, poverty and inequality: New evidence from Vietnam. Econ. Transit. 2011, 4, 19. [Google Scholar] [CrossRef]
- Zhao, Y.H. Rural labour mobility in China and the role of education in it—A study based on Sichuan province. Econ. Res. J. 1997, 2, 37–42. [Google Scholar]
- Knight, J.; Song, L. Chinese peasant choices:migration, rural industry or farming. Oxf. Dev. Stud. 2003, 2, 123–148. [Google Scholar] [CrossRef]
- Marré, A.W. Rural Out-Migration, Income, and Poverty: Are Those Who Move Truly Better Off? Agricultural & Applied Economics Association: Milwaukee, WI, USA, 2009. [Google Scholar]
- Kothari, U. Staying put and staying poor? J. Int. Dev. 2003, 15, 645–657. [Google Scholar] [CrossRef]
- Li, C.J. Labor migration, rural household income and poverty alleviation in poverty regions: Based on rural household panel data in Xinjiang. Northwest Popul. J. 2014, 35, 34–38. [Google Scholar]
- Yang, J. The influences of migration on rural poverty. Chin. J. Popul. Sci. 2006, 4, 64–69. [Google Scholar]
- Song, S.L.; Qi, W.N. A study on the transfer of surplus rural labour based on multiple linear regression—Taking Heilongjiang Province as an example. J. Agric. Econ. 2014, 4, 104–110. [Google Scholar]
- Taylor, J.E.; Martin, P.L. Chapter 9 Human capital: Migration and rural population change. Handb. Agric. Econ. 2001, 1, 457–511. [Google Scholar]
- Song, Y.; Zhao, J. The status and characteristics of poverty in China: A reanalysis based on equivalence scale adjustment. J. Manag. World 2015, 10, 65–77. [Google Scholar]
- Heintz, J. Globalization, Economic Policy and Employment: Poverty and Gender Implications; International Labour Organization: Geneva, Switzerland, 2006; p. 10. [Google Scholar]
- Vandelannoote, D.; Verbist, G. The Design of In-Work Benefits: How to Boost Employment and Combat Poverty in Belgium; Improve Working Papers No.16/15; Herman Deleeck Centre for Social Policy, University of Antwerp: Antwerp, Belgium, 2016. [Google Scholar]
- Gaiha, R.; Irnai, K.S.; Thapa, G. Does non-farm sector employment reduce rural poverty and vulnerability? Evidence from Vietnam and India. J. Asian Econ. 2015, 36, 47–61. [Google Scholar]
- Rani, U.; Furrer, M.; Lavers, T. Employment patterns, poverty and income inequality. World Employ. Soc. Outlook 2016, 2, 39–71. [Google Scholar] [CrossRef] [Green Version]
- Gábos, A.; Branyiczki, R.; Lange, B.; Tóth, G. Employment and Poverty Dynamics in the EU Countries before, during and after the Crisis; Improve Working Papers No. 15/06; Herman Deleeck Centre for Social Policy, University of Antwerp: Antwerp, Belgium, 2015. [Google Scholar]
- Sarangi, N. Economic Growth, Employment and Poverty in Developing Economies: A Focus on Arab Region; Economic and Social Commission for Western Asia working paper; ESCWA; United Nations: New York, NY, USA, 2015. [Google Scholar]
- Lanjouw, P. Nonfarm employment and poverty in rural El Salvador. World Dev. 2004, 29, 529–547. [Google Scholar] [CrossRef]
- Ruben, R. Nonfarm employment and poverty alleviation of rural farm households in Honduras. World Dev. 2001, 29, 549–560. [Google Scholar] [CrossRef]
- Kijima, Y.; Matsumoto, T.; Yamano, T. Nonfarm employment, agricultural shocks, and poverty dynamics: Evidence from rural Uganda. Agric. Econ. 2010, 35, 459–467. [Google Scholar] [CrossRef]
- Schultz, T.W. Capital formation by education. J. Polit. Econ. 1960, 12, 571–583. [Google Scholar] [CrossRef]
- Schultz, T.W. The value of the ability to deal with disequilibria. J. Econ. Lit. 1975, 13, 827–846. [Google Scholar]
- Brewer, D.J.; McEwan, P.J. Economics of Education; Academic Press: New York, NY, USA, 2010; pp. 80–87. [Google Scholar]
- Wang, J.Y.; Feng, Q.Y.; Zhang, J. Education and targeted poverty alleviation. Educ. Res. 2016, 7, 12–21. [Google Scholar]
- Becker, G.S. Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education; National Bureau of Economic Research: New York, NY, USA, 1975; pp. 194–198. [Google Scholar]
- Janjua, P.Z.; Kamal, U.A. The role of education and health in poverty alleviation a cross country analysis. Br. J. Econ. Manag. Trade 2014, 4, 896. [Google Scholar] [CrossRef]
- Behrman, J.R. The Action of Human Resources and Poverty on One Another: What We Have Yet to Learn; Living Standards Measurement Study Working Paper No. 74; The World Bank: Washington, DC, USA, 1990. [Google Scholar]
- West, P. Rethinking the health selection explanation for health inequalities. Soc. Sci. Med. 1991, 32, 373–384. [Google Scholar] [CrossRef]
- Bartley, M.; Plewis, I. Increasing social mobility: An effective policy to reduce health inequalities. J. R. Stat. Soc. 2007, 170, 469–481. [Google Scholar] [CrossRef]
- Mcdonough, P.; Amick, B.C. The social context of health selection: A longitudinal study of health and employment. Soc. Sci. Med. 2001, 53, 135–145. [Google Scholar] [CrossRef]
- Haan, P.; Myck, M. Dynamics of health and labor market risks. J. Health Econ. 2009, 28, 1116–1125. [Google Scholar] [CrossRef]
- Grossman, M. On the concept of health capital and the demand for health. J. Polit. Econ. 1972, 80, 223–255. [Google Scholar] [CrossRef] [Green Version]
- Ecob, R.; Smith, G.D. Income and health: What is the nature of the relationship? Soc. Sci. Med. 1999, 48, 693–705. [Google Scholar] [CrossRef] [PubMed]
- Smith, J.P. The impact of childhood health on adult labor market outcomes. Rev. Econ. Stat. 2008, 91, 478–489. [Google Scholar] [CrossRef] [Green Version]
- Cheng, M.W.; Shi, Q.H.; Yanhong, J.; Gai, Q.E. Farm household income disparity and its root causes: Models and empirical evidence. J. Manag. World 2015, 7, 17–28. [Google Scholar]
- Tan, X.W. A study on the approach of reducing relative poverty and achieving common prosperity. Chin. Rural Econ. 2020, 6, 21–36. [Google Scholar]
- Luo, B.L.; Hong, W.J.; Geng, P.P.; Zheng, W.L. Empowering people, strengthening capacity and ensuring inclusiveness: Enhancing farmers’ subjective well-being in reducing relative poverty. J. Manag. World 2021, 37, 166–181. [Google Scholar]
Year | Median | 60% | 50% |
---|---|---|---|
2012 | 20,000 | 12,000 | 10,000 |
2014 | 25,000 | 15,000 | 12,500 |
2016 | 30,000 | 18,000 | 15,000 |
2018 | 36,000 | 21,600 | 18,000 |
2020 | 40,000 | 24,000 | 20,000 |
Variable Type | Variable | Measurement | |
---|---|---|---|
Dependent variables | Relative poverty | Yes = 1, no = 0 | |
Income | The logarithm of their annual income | ||
Key explanatory variables | Migration | Yes = 1, no = 0 | |
Cross-regional migration | Yes = 1, no = 0 | ||
Cross-provincial migration | Yes = 1, no = 0 | ||
Cross-county migration | Yes = 1, no = 0 | ||
Cross-regional migration directions in the west | Migration in the west = 1, Migration from the west to the centre = 2, Migration from the west to the east = 3 | ||
Cross-regional migration directions in the central region | Migration in the centre = 1, Migration from the centre to the west = 2, Migration from the centre to the east = 3 | ||
Cross-regional migration directions in the east | Migration in the east = 1, Migration from the east to the west = 2, Migration from the east to the centre = 3 | ||
Other explanatory variables | In work | Yes = 1, no = 0 | |
Industries | The primary industry = 1, The tertiary industry = 2, The secondary industry = 3 | ||
Education | Primary school and below = 1, Junior high school = 2, Senior high school = 3, College and undergraduate = 4, Postgraduate = 5 | ||
Health | Very unhealthy = 1, Unhealthy = 2, Generally healthy = 3, Healthy = 4, Very healthy = 5 | ||
Control variables | Macro-finance | Year | 2012 = 1, 2014 = 2, 2016 = 3, 2018 = 4, 2020 = 5 |
Region | The western region = 1, The central region = 2, The eastern region = 3 | ||
Local general budget expenditure | Logarithm of local general budget expenditure | ||
Local general budget expenditure per capita | Logarithm of local general budget expenditure per capita | ||
Micro-security | Medical insurance | No medical insurance = 0, No financial support = 1, Partial financial support = 2, Full financial support = 3 | |
Insurances | Yes = 1, no = 0 | ||
Housing funds | Yes = 1, no = 0 | ||
Individual characteristics | Age | Number of their age | |
Gender | Male = 1, female = 0 | ||
Married | Married = 1, unmarried = 0 |
Variable Type | Variable | Observed Value | Mean | Standard Deviation | |
---|---|---|---|---|---|
Dependent variables | Relative poverty | 18,953 | 0.26 | 0.44 | |
Logarithm of income | 18,893 | 10.09 | 0.97 | ||
Key explanatory variables | Migration | 18,953 | 0.25 | 0.43 | |
Cross-regional migration | 18,404 | 0.07 | 0.26 | ||
Cross-provincial migration | 17,095 | 0.04 | 0.20 | ||
Cross-county migration | 16,350 | 0.15 | 0.35 | ||
Cross-regional migration directions in the west | 809 | 2.17 | 0.96 | ||
Cross-regional migration directions in the central region | 788 | 2.76 | 0.59 | ||
Cross-regional migration directions in the east | 457 | 1.32 | 0.68 | ||
Other explanatory variables | In work | 18,953 | 0.94 | 0.24 | |
Industries | 18,953 | 2.52 | 0.53 | ||
Education | 18,953 | 2.31 | 0.84 | ||
Health | 18,953 | 3.39 | 1.07 | ||
Control variables | Macro-finance | Year | 18,953 | 2.88 | 1.48 |
Region | 18,953 | 2.20 | 0.83 | ||
Log of local general budget expenditure | 18,953 | 8.58 | 0.52 | ||
Log of local general budget expenditure per capita | 18,953 | 9.15 | 0.40 | ||
Micro-security | Medical insurance | 18,953 | 1.64 | 0.70 | |
Insurances | 18,953 | 0.37 | 0.48 | ||
Housing funds | 18,953 | 0.14 | 0.35 | ||
Individual characteristics | Age | 18,953 | 32.00 | 8.76 | |
Gender | 18,953 | 0.59 | 0.49 | ||
Married | 18,953 | 0.71 | 0.46 |
(1) | (2) | (3) | |
---|---|---|---|
LPM | Probit | Marginal Effects | |
Migration | −0.0450 *** | −0.1393 *** | −0.0409 |
(0.0072) | (0.0253) | ||
In work | −0.1278 *** | −0.3636 *** | −0.1066 |
(0.0148) | (0.0412) | ||
Industries | −0.0284 *** | −0.0821 *** | −0.0241 |
(0.0061) | (0.0205) | ||
Education | −0.0200 *** | −0.0693 *** | −0.0203 |
(0.0042) | (0.0149) | ||
Health | −0.0096 *** | −0.0279 *** | −0.0082 |
(0.0030) | (0.0099) | ||
Control variables | Yes | Yes | |
Constant | 2.2231 *** | 6.3216 *** | |
(0.1284) | (0.4991) | ||
N | 18,953 | 18,953 | |
R2/Pseudo R2 | 0.11 | 0.10 |
(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|
LPM | Probit | LPM | Probit | LPM | Probit | |
Cross-regional migration | −0.0835 *** | −0.2662 *** | ||||
(0.0123) | (0.0439) [−0.0781] | |||||
Cross-provincial migration | −0.0367 ** | −0.1135 ** | ||||
(0.0155) | (0.0554) [−0.0333] | |||||
Cross-county migration | −0.0267 *** | −0.0767 ** | ||||
(0.0093) | (0.0328) [−0.0225] | |||||
Other explanatory variables | Yes | Yes | Yes | Yes | Yes | Yes |
Control variables | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | 2.2674 *** | 6.4880 *** | 2.3523 *** | 6.8056 *** | 2.3956 *** | 6.9725 *** |
(0.1304) | (0.5075) | (0.1325) | (0.5181) | (0.1353) | (0.5288) | |
N | 18,404 | 18,404 | 17,095 | 17,095 | 16,350 | 16,350 |
R2/Pseudo R2 | 0.11 | 0.10 | 0.11 | 0.11 | 0.12 | 0.11 |
Western China | Central China | Eastern China | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
LPM | Probit | LPM | Probit | LPM | Probit | |
Migration | −0.0257 * | −0.0232 | −0.0812 *** | −0.0804 *** | −0.0287 *** | −0.0243 ** |
(0.0140) | (0.0428) | (0.0132) | (0.0458) | (0.0110) | (0.0434) | |
In work | −0.1070 *** | −0.0975 *** | −0.1117 *** | −0.1006 *** | −0.1608 *** | −0.1233 *** |
(0.0285) | (0.0769) | (0.0240) | (0.0679) | (0.0253) | (0.0703) | |
Industries | −0.0424 *** | −0.0391 *** | −0.0249 ** | −0.0222 * | −0.0225 *** | −0.0194 ** |
(0.0132) | (0.0392) | (0.0118) | (0.0378) | (0.0084) | (0.0316) | |
Education | −0.0160 * | −0.0152 * | −0.0314 *** | −0.0320 *** | −0.0170 *** | −0.0180 *** |
(0.0087) | (0.0269) | (0.0085) | (0.0288) | (0.0058) | (0.0232) | |
Health | −0.0151 ** | −0.0143 ** | −0.0112 * | −0.0106 * | −0.0041 | −0.0027 |
(0.0062) | (0.0184) | (0.0058) | (0.0184) | (0.0042) | (0.0154) | |
Other explanatory variables | Yes | Yes | Yes | Yes | Yes | Yes |
Control Variables | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | 2.8835 *** | 7.3265 *** | 4.1759 *** | 11.5145 *** | 2.1256 *** | 6.3476 *** |
(0.5659) | (1.8458) | (0.8342) | (2.6961) | (0.1572) | (0.6221) | |
N | 4958 | 4958 | 5231 | 5231 | 8764 | 8764 |
R2/Pseudo R2 | 0.09 | 0.07 | 0.11 | 0.10 | 0.10 | 0.11 |
Western China | Central China | Eastern China | |||||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
Cross-regional migration | −0.161 ** | −0.377 *** | 0.117 | ||||||
(0.069) [−0.053] | (0.063) [−0.117] | (0.158) [0.030] | |||||||
Cross-provincial migration | −0.133 | 0.138 | −0.159 * | ||||||
(0.081) [−0.044] | (0.180) [0.044] | (0.085) [−0.041] | |||||||
Cross-county migration | 0.009 | −0.155 ** | −0.093 * | ||||||
(0.059) [0.003] | (0.063) [−0.049] | (0.052) [−0.024] | |||||||
Other explanatory variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Control variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | 8.34 *** | 9.06 *** | 8.57 *** | 11.97 *** | 14.27 *** | 14.37 *** | 6.27 *** | 6.45 *** | 6.84 *** |
(1.93) | (2.05) | (2.11) | (2.76) | (2.89) | (2.91) | (0.63) | (0.63) | (0.65) | |
N | 4771 | 4275 | 3962 | 5038 | 4314 | 4250 | 8595 | 8506 | 8138 |
Pseudo R2 | 0.08 | 0.08 | 0.08 | 0.10 | 0.10 | 0.10 | 0.11 | 0.11 | 0.11 |
Western China | Central China | Eastern China | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
LPM | Probit | LPM | Probit | LPM | Probit | |
Directions of cross-regional migration | −0.0145 | −0.0139 | −0.0487 * | −0.0467 ** | 0.0508 * | 0.0461 * |
(0.0166) | (0.0524) | (0.0269) | (0.0893) | (0.0297) | (0.0997) | |
In work | −0.1162 ** | −0.1122 ** | −0.1229 *** | −0.1172 *** | −0.0928 | −0.0749 |
(0.0514) | (0.1503) | (0.0402) | (0.1478) | (0.0771) | (0.2516) | |
Industries | −0.0010 | 0.0025 | 0.0165 | 0.0178 | 0.0418 | 0.0443 |
(0.0324) | (0.0976) | (0.0297) | (0.1160) | (0.0386) | (0.1482) | |
Education | 0.0028 | 0.0042 | −0.0189 | −0.0182 | 0.0090 | 0.0046 |
(0.0235) | (0.0725) | (0.0226) | (0.0863) | (0.0299) | (0.1043) | |
Health | −0.0249 * | −0.0240 * | −0.0194 | −0.0218 * | 0.0097 | 0.0108 |
(0.0146) | (0.0463) | (0.0131) | (0.0523) | (0.0175) | (0.0676) | |
Control variables | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | 1.9873 | 5.0258 | −0.2814 | −4.2355 | 0.5251 | 0.4251 |
(1.5016) | (5.1667) | (2.4928) | (10.3344) | (0.7048) | (2.7489) | |
N | 809 | 809 | 788 | 788 | 457 | 457 |
R2/Pseudo R2 | 0.09 | 0.08 | 0.11 | 0.11 | 0.09 | 0.09 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
A: The relative poverty line is replaced by 50% of the median income of rural residents. | ||||
Migration | −0.1305 *** | |||
(0.0267) | ||||
Cross-regional migration | −0.2770 *** | |||
(0.0472) | ||||
Cross-provincial migration | −0.1491 ** | |||
(0.0602) | ||||
Cross-county migration | −0.0492 | |||
(0.0343) | ||||
Other explanatory variables | Yes | Yes | Yes | Yes |
Control variables | Yes | Yes | Yes | Yes |
Constant | 4.8484 *** | 5.0328 *** | 5.3931 *** | 5.5320 *** |
(0.5249) | (0.5343) | (0.5452) | (0.5570) | |
N | 18,953 | 18,404 | 17,095 | 16,350 |
Pseudo R2 | 0.09 | 0.09 | 0.10 | 0.10 |
B: The method is replaced by logit model | ||||
Migration | −0.2489 *** | |||
(0.0435) | ||||
Cross-regional migration | −0.4703 *** | |||
(0.0764) | ||||
Cross-provincial migration | −0.2067 ** | |||
(0.0948) | ||||
Cross-county migration | −0.1371 ** | |||
(0.0564) | ||||
Other explanatory variables | Yes | Yes | Yes | Yes |
Control variables | Yes | Yes | Yes | Yes |
Constant | 11.0889 *** | 11.4042 *** | 11.9751 *** | 12.2130 *** |
(0.8710) | (0.8862) | (0.9051) | (0.9224) | |
N | 18,953 | 18,404 | 17,095 | 16,350 |
Pseudo R2 | 0.10 | 0.10 | 0.11 | 0.11 |
C: Select a province from each region to form a new sample | ||||
Migration | −0.1065 *** | |||
(0.0391) | ||||
Cross-regional migration | −0.2492 *** | |||
(0.0627) | ||||
Cross-provincial migration | −0.0990 | |||
(0.0835) | ||||
Cross-county migration | −0.0202 | |||
(0.0502) | ||||
Other explanatory variables | Yes | Yes | Yes | Yes |
Control variables | Yes | Yes | Yes | Yes |
Constant | 9.0398 *** | 9.5191 *** | 8.7057 *** | 8.9792 *** |
(2.5251) | (2.5929) | (2.6371) | (2.6874) | |
N | 6704 | 6490 | 5850 | 5528 |
Pseudo R2 | 0.10 | 0.10 | 0.11 | 0.11 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Migration | −0.1160 *** (0.0244) | −0.0331 (0.0378) | −0.1506 *** (0.0229) | −0.0807 *** (0.0252) |
In work | −0.1446 *** (0.0163) | |||
In work × Migration | 0.0806 *** (0.0256) | |||
Industries | −0.0245 *** (0.0071) | |||
Industries × Migration | −0.0040 (0.0145) | |||
Education | −0.0328 *** (0.0051) | |||
Education × Migration | 0.0484 *** (0.0097) | |||
Health | −0.0114 *** (0.0035) | |||
Health × Migration | 0.0110 (0.0070) | |||
Other explanatory variables | Yes | Yes | Yes | Yes |
Control variables | Yes | Yes | Yes | Yes |
N | 18,953 | 18,953 | 18,953 | 18,953 |
Pseudo R2 | 0.10 | 0.10 | 0.10 | 0.10 |
In Work | Industries | Education | Health | |
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
No cross-regional migration | −0.1282 *** (0.0135) | −0.0235 *** (0.0064) | −0.0249 *** (0.0046) | −0.0090 *** (0.0031) |
Cross-regional migration | −0.0883 *** (0.0095) | −0.0174 *** (0.0051) | −0.0144 *** (0.0030) | −0.0068 *** (0.0023) |
Difference | 0.0399 | 0.0061 | 0.0105 | 0.0022 |
No cross-provincial migration | −0.1243 *** (0.0139) | −0.0258 *** (0.0065) | −0.0223 *** (0.0047) | −0.0080 ** (0.0031) |
Cross-provincial migration | −0.1082 *** (0.0147) | −0.0226 *** (0.0065) | −0.0176 *** (0.0041) | −0.0075 *** (0.0029) |
Difference | 0.0161 | 0.0032 | 0.0047 | 0.0005 |
No cross-county migration | −0.1351 *** (0.0162) | −0.0259 *** (0.0070) | −0.0271 *** (0.0052) | −0.0088 *** (0.0034) |
Cross-county migration | −0.1192 *** (0.0126) | −0.0251 *** (0.0063) | −0.0218 *** (0.0039) | −0.0080 *** (0.0029) |
Difference | 0.0159 | 0.0008 | 0.0053 | 0.0008 |
Variables | Eastern China | Central China | Western China | |
---|---|---|---|---|
(1) | (2) | (3) | ||
(A) | Migration | −0.0841 ** (0.0381) | −0.1348 *** (0.0418) | −0.1275 ** (0.0508) |
In work | −0.1471 *** (0.0228) | −0.1294 *** (0.0284) | −0.1583 *** (0.0370) | |
In work × Migration | 0.0646 (0.0399) | 0.0552 (0.0442) | 0.1126 ** (0.0529) | |
(B) | Migration | 0.0158 (0.0576) | −0.1362 * (0.0740) | −0.0174 (0.0714) |
Industries | −0.0174 * (0.0091) | −0.0286 ** (0.0141) | −0.0406 ** (0.0162) | |
Industries × Migration | −0.0161 (0.0224) | 0.0195 (0.0280) | −0.0029 (0.0277) | |
(C) | Migration | −0.1241 *** (0.0343) | −0.1997 *** (0.0454) | −0.1461 *** (0.0433) |
Education | −0.0261 *** (0.0067) | −0.0480 *** (0.0106) | −0.0320 *** (0.0108) | |
Education × Migration | 0.0438 *** (0.0142) | 0.0512 *** (0.0194) | 0.0555 *** (0.0186) | |
(D) | Migration | −0.1179 *** (0.0384) | −0.0762 (0.0491) | −0.0730 (0.0480) |
Health | −0.0077 * (0.0045) | −0.0105 (0.0069) | −0.0199 ** (0.0078) | |
Health × Migration | 0.0270 ** (0.0106) | −0.0029 (0.0137) | 0.0144 (0.0136) | |
In each model | Other variables | Yes | Yes | Yes |
N | 8764 | 5231 | 4958 | |
Pseudo R2 | 0.11 | 0.10 | 0.07 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Migration | 0.1417 *** | |||
(0.0152) | ||||
Cross-regional migration | 0.2575 *** | |||
(0.0252) | ||||
Cross-provincial migration within the same region | 0.1295 *** | |||
(0.0337) | ||||
Cross-county migration within the same province | 0.0782 *** | |||
(0.0199) | ||||
Other explanatory variables | Yes | Yes | Yes | Yes |
Control variables | Yes | Yes | Yes | Yes |
Constant | 4.4799 *** | 4.3781 *** | 4.1387 *** | 4.0761 *** |
(0.2780) | (0.2775) | (0.2816) | (0.2867) | |
N | 18,893 | 18,345 | 17,039 | 16,297 |
R2 | 0.24 | 0.24 | 0.25 | 0.25 |
Estimated | Western China | Central China | Eastern China |
---|---|---|---|
Migration | 0.0999 *** | 0.2295 *** | 0.0942 *** |
(0.0283) | (0.0265) | (0.0251) | |
In work | 0.2543 *** | 0.2115 *** | 0.3577 *** |
(0.0637) | (0.0485) | (0.0594) | |
Industries | 0.0650 ** | 0.0633 *** | 0.0077 |
(0.0275) | (0.0244) | (0.0181) | |
Education | 0.0575 *** | 0.0743 *** | 0.0999 *** |
(0.0169) | (0.0166) | (0.0120) | |
Health | 0.0539 *** | 0.0270 ** | 0.0084 |
(0.0126) | (0.0117) | (0.0090) | |
Control variables | Yes | Yes | Yes |
Constant | 4.0410 ** | 2.2222 | 4.5563 *** |
(1.9561) | (1.6211) | (0.3377) | |
N | 4943 | 5214 | 8736 |
R2 | 0.19 | 0.23 | 0.25 |
Western China | Central China | Eastern China | |||||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
Cross-regional migration | 0.141 *** | 0.307 *** | 0.167 * | ||||||
(0.046) | (0.033) | (0.088) | |||||||
Cross-provincial migration | 0.118 ** | 0.052 | 0.170 *** | ||||||
(0.056) | (0.101) | (0.047) | |||||||
Cross-county migration | 0.051 | 0.141 *** | 0.061 ** | ||||||
(0.037) | (0.037) | (0.031) | |||||||
Control variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | 2.30 ** | 2.04 * | 2.19 * | 2.04 | 0.25 | 0.15 | 4.62 *** | 4.50 *** | 4.39 *** |
(1.12) | (1.15) | (1.19) | (1.65) | (1.72) | (1.77) | (0.34) | (0.34) | (0.35) | |
N | 4756 | 4261 | 3949 | 5021 | 4299 | 4236 | 8568 | 8479 | 8112 |
R2 | 0.20 | 0.22 | 0.23 | 0.23 | 0.23 | 0.23 | 0.25 | 0.25 | 0.26 |
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Xu, N.; Li, C. Migration and Rural Sustainability: Relative Poverty Alleviation by Geographical Mobility in China. Sustainability 2023, 15, 6248. https://doi.org/10.3390/su15076248
Xu N, Li C. Migration and Rural Sustainability: Relative Poverty Alleviation by Geographical Mobility in China. Sustainability. 2023; 15(7):6248. https://doi.org/10.3390/su15076248
Chicago/Turabian StyleXu, Ning, and Chang’an Li. 2023. "Migration and Rural Sustainability: Relative Poverty Alleviation by Geographical Mobility in China" Sustainability 15, no. 7: 6248. https://doi.org/10.3390/su15076248
APA StyleXu, N., & Li, C. (2023). Migration and Rural Sustainability: Relative Poverty Alleviation by Geographical Mobility in China. Sustainability, 15(7), 6248. https://doi.org/10.3390/su15076248