Association between Hometown Landholdings and Housing Quality of Rural Migrants in Urban Areas: Evidence from China
Abstract
:1. Introduction
- First, following a recent work [25], an index of rural migrants’ urban housing quality in host cities is constructed, showing their housing stratification in urban areas.
- Second, this study explores how retaining landholdings related to contracted farmland or a rural homestead may exert impact on rural migrants’ urban housing quality, that is, we examine how retaining hometown landholding may negatively “lock in” the level of housing quality for rural migrants. Such an analysis can enrich knowledge on the association between rural land arrangements and migrants’ residential livability in host cities, which is one of the issues of concern for urbanization in China.
- Third, this study examines how the “lock-in” impact of hometown landholdings varies across different subgroups, especially for migrants with cohorts, different employment statuses and flowing into various sizes of cities.
- Plus, the association between rural landholdings and migrants’ urban housing quality is further extended by investigating the possible “wealth effect” of revenue derived from hometown landholdings on the housing quality of rural migrants.
2. Research Background
2.1. Rural Migrants’ Housing Quality in China
2.2. Land Reform and Policies in China
3. Literature Review and Hypothesis Development
3.1. Dimensions of Housing Quality
3.1.1. Influencing Factors of Housing Quality
3.1.2. Measurement of Housing Quality
3.2. The Role of Rural Landholdings in Migration
3.3. Hypothesis Development
4. Data and Variables
4.1. Data Collection
4.2. Variables
4.2.1. Dependent Variable
4.2.2. Key Independent Variable
4.2.3. Control Variables
5. Econometric Methodology
6. Empirical Results
6.1. Benchmark Regression Results
6.2. Robust Checks
7. Discussion on the Influencing Mechanism
8. Heterogeneity in Subgroup
8.1. Cohort Difference
8.2. Heterogeneity across Employment Attributes
8.3. Regional Heterogeneity
9. Conclusions and Policy Implications
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Composition | Housing Quality Index (HQI) |
---|---|
Housing type (L1) | If living in a building, L1 = 5; in a cottage, L1 = 4; in a shed, L1 = 3; in a basement, L1 = 2; otherwise, L1 equals to 1. |
Accessibility to health service facilities (L2) | The time required to commute from migrants’ place of residence to the nearest health service (including community health centers, village clinics, hospitals, etc.) by the most accessible means of transport): if it is within 15 min, L2 = 4; if 15–30 min, L2 = 3; if 30 min–1 h, L2 = 2; if more than 1 h, L2 = 1. |
Sanitary installation (L3) | If equipped with an indoor toilet, L3 = 1; otherwise, L3 = 0. |
Dwelling environment (L4) | If living in a neighborhood with few pests, L4 = 1; otherwise, L4 = 0. |
Water source available (L5) | If tap water or bottled water is available on a daily basis, L5 = 1; otherwise, L5 = 0. |
Quality of drinking water (L6) | If the drinking water is purified, L6 = 1; otherwise, L6 = 0. |
Privacy of residence (L7) | If migrants do not share a house with someone other than their families, L7 = 1; otherwise, L7 = 0. |
Residential density (L8) | According to the per capita living area for rural migrants in urban areas, the residential density is divided into four levels (quartile), which are assigned from one to four values in order. |
(1) | |||||
Dummy/Categorical Variables | CMDS 2017 | ||||
Variable | Description | Freq. | Percent | ||
H_land | =1 if possessing either contracted farmland or homestead land in their hometown; =0 if possessing no land in outflow area. | 8377 | 80.39% | ||
2043 | 19.61% | ||||
Homeowner | =1 if yes to owning a house in the current city; =0 if no. | 1982 | 19.02% | ||
8438 | 80.98% | ||||
Female | =1 if female; =0 if male. | 5058 | 48.54% | ||
5362 | 51.46% | ||||
Married | =1 if married; | 8518 | 81.75% | ||
=0 if not married. | 1902 | 18.25% | |||
Junior | =1 if graduated with junior high school degree; | 4690 | 45.01% | ||
=0 if not graduated with junior middle school degree. | 5730 | 54.99% | |||
Highedu | =1 if graduated with high school degree; | 2667 | 25.60% | ||
=0 if not graduated with high school degree. | 7753 | 74.40% | |||
Graduate | =1 if graduated with junior college degree or above; | 1461 | 14.02% | ||
=0 if not graduated with junior college degree or above. | 8959 | 85.98% | |||
Child | =1 if having child, no matter where; | 9890 | 94.91% | ||
=0 if not having child. | 530 | 5.09% | |||
Childlocal | =1 if having child and living together locally; | 5243 | 50.32% | ||
=0 if without children locally. | 5177 | 49.68% | |||
East_origin | =1 if outflowing from eastern China; | 2707 | 25.98% | ||
=0 if outflowing from mid-west regions of China. | 7713 | 74.02% | |||
Self-employed | =1 if being self-employed or employer; | 3928 | 37.70% | ||
=0 if being employed. | 6492 | 62.30% | |||
Seconary_indu | =1 if working in secondary industry; | 2970 | 28.50% | ||
=0 if not working in secondary industry. | 7450 | 71.50% | |||
Tertiary_indu | =1 if working in tertiary industry; | 5592 | 53.67% | ||
=0 if not working in tertiary industry. | 4828 | 46.33% | |||
Professional | =1 if working as professionals; | 778 | 7.47% | ||
=0 if otherwise. | 9642 | 92.53% | |||
Businessman | =1 if conducting business or trade; | 2452 | 23.53% | ||
=0 if otherwise. | 7968 | 76.47% | |||
MedicareU | =1 if paying medical insurance in host cities; | 2765 | 26.54% | ||
=0 if not paying medical insurance in host cities. | 7655 | 73.46% | |||
Hukou_Transfer | =1 if willing to transfer the rural household to urban household registration; | 3963 | 38.03% | ||
=0 if unwilling to transfer the rural household to urban household registration. | 6457 | 61.97% | |||
(2) | |||||
Continuous Variables | CMDS 2017 | ||||
Variable | Description | Mean | S.D. | Min. | Max. |
Age | The household head’s age in the year surveyed (unit: year) | 35.5 | 9.98 | 18 | 59 |
Moveyears | Years of migration by the end of the year surveyed (unit: year) | 5.90 | 5.60 | 1 | 45 |
LN_PerDI | Per capita disposable income of migrant households in the inflow areas [unit:1000 yuan (in log)] 1 | 0.73 | 0.67 | −3.44 | 4.09 |
Distance to hometown | Geo-distance from current city to the provincial capital of the residential province (unit: km, in log) 2 | 2.71 | 3.40 | 0 | 8.38 |
LN_POP | The population of current city in 2016 * [unit: 10,000 persons (in log)] | 6.57 | 0.56 | 5.58 | 8.13 |
LN_PERGDP | GDP per capita of current city in 2016 * [unit: CNY 10,000 (in log)] | 2.30 | 0.32 | 1.76 | 2.68 |
LN_HPrice | House price per square meter of current city in 2016 * [(unit: CNY 1000/m2(in log)] | 2.13 | 0.35 | 1.64 | 2.64 |
Full Sample | Baseline Regression Model | Extended Investigation | ||
---|---|---|---|---|
M1 | M2 | M3 | M4 | |
Independent Variables | Coef. | Coef. | Coef. | Coef. |
H_land | −0.225 *** | −0.323 *** | −0.368 *** | −0.446 *** |
(0.077) | (0.077) | (0.079) | (0.127) | |
Land_revenue | 0.034 *** | |||
(0.011) | ||||
Land_quantity | 0.024 * (0.020) | |||
Micro-level attributes | ||||
Age | 0.037 ** | 0.039 * | 0.038 | 0.038 |
(0.023) | (0.023) | (0.023) | (0.023) | |
Age_sqr | −0.000 | −0.000 | −0.000 | −0.000 |
(0.000) | (0.000) | (0.000) | (0.000) | |
Female | 0.190 *** | 0.166 *** | 0.175 *** | 0.171 ** |
(0.063) | (0.063) | (0.063) | (0.063) | |
Junior | 1.291 *** | 1.190 *** | 1.176 *** | 1.191 *** |
(0.112) | (0.110) | (0.110) | (0.110) | |
Highedu | 1.940 *** | 1.777 *** | 1.761 *** | 1.777 *** |
(0.120) | (0.119) | (0.119) | (0.119) | |
Graduate | 2.173 *** | 1.993 *** | 1.977 *** | 1.992 *** |
(0.136) | (0.136) | (0.136) | (0.119) | |
Married | 0.068 *** | 0.018 | 0.016 | 0.021 |
(0.106) | (0.105) | (0.105) | (0.105) | |
Child | −0.024 | −0.083 | −0.086 | −0.085 |
(0.136) | (0.136) | (0.136) | (0.136) | |
Childlocal | 0.153 ** | 0.179 ** | 0.170 ** | 0.177 ** |
(0.076) | (0.076) | (0.076) | (0.076) | |
East_origin | 0.512 *** | 0.844 *** | 0.824 *** | 0.845 *** |
(0.067) | (0.078) | (0.079) | (0.078) | |
Self-employed | 0.179 * | 0.109 *** | 0.109 | 0.110 |
(0.103) | (0.102) | (0.102) | (0.102) | |
Professional | 0.171 (0.115) | 0.197 * (0.114) | 0.198 * (0.114) | 0.199 * (0.114) |
Businessman | 0.689 *** | 0.639 *** | 0.644 *** | 0.638 *** |
(0.095) | (0.095) | (0.094) | (0.094) | |
Seconary_indu | −0.054 | −0.037 | −0.044 | −0.039 |
(0.113) | (0.113) | (0.113) | (0.113) | |
Tertiary_indu | 0.739 *** | 0.644 *** | 0.640 *** | 0.645 *** |
(0.109) | (0.109) | (0.109) | (0.109) | |
Moveyears | −0.112 *** | −0.090 *** | −0.090 *** | −0.090 *** |
(0.015) | (0.015) | (0.015) | (0.015) | |
Moveyears_sqr | 0.002 *** | 0.002 *** | 0.002 *** | 0.002 *** |
(0.001) | (0.001) | (0.001) | (0.001) | |
MedicareU | 0.092 | 0.034 | 0.036 | 0.033 |
(0.078) | (0.078) | (0.078) | (0.078) | |
LN_PerDI | 0.652 *** (0.057) | 0.610 *** (0.058) | 0.605 *** (0.058) | 0.606 *** (0.058) |
Homeowner | 2.652 *** | 2.553 *** | 2.549 *** | 2.544 *** |
(0.068) | (0.070) | (0.069) | (0.070) | |
Geographic factors | ||||
Distance to hometown | 0.052 *** (0.002) | 0.053 *** (0.011) | 0.052 *** (0.011) | |
City-level attributes | ||||
LN_PERGDP | 1.357 *** | 1.398 *** | 1.377 *** | |
(0.167) | (0.167) | (0.168) | ||
LN_POP | 0.493 *** (0.052) | 0.510 *** (0.052) | 0.506 *** (0.053) | |
LN_HPrice | −1.887 *** (0.150) | −1.915 *** (0.150) | −1.891 *** (0.150) | |
Constant | −3.702 *** (0.430) | −5.829 *** (0.610) | −5.976 *** (0.615) | −5.939 *** (0.620) |
Pseudo R2 | 0.232 | 0.246 | 0.247 | 0.246 |
Observations | 10,420 | 10,420 | 10,416 | 10,416 |
Full Sample | Alternative Measures of H_land OLS Regression | Alternative Measures of HQI Quartile Regression | |||
---|---|---|---|---|---|
Empirical Test | M5 | M6 | Quartile (25%) | Quartile (50%) | Quartile (75%) |
Explanatory variables | Coef. | Coef. | Coef. | Coef. | Coef. |
Land_index | −0.206 *** | ||||
(0.041) | |||||
Without any land(ref.) | |||||
Contracted_land | −0.604 *** (0.151) | ||||
Homestead_land | −0.105 (0.089) | ||||
Both_Lands | −0.409 *** (0.084) | ||||
H_land | −0.353 *** | −0.247 ** | −0.160 ** | ||
(0.115) | (0.014) | (0.077) | |||
Micro-level attributes | Y | Y | Y | Y | Y |
City-level attributes | Y | Y | Y | Y | Y |
Observations | 10,420 | 10,420 | 10,420 | 10,420 | 10,420 |
Pseudo R2 | 0.247 | 0.248 | 0.156 | 0.119 | 0.094 |
Mechanism Test | Step 1 | Step 2 | Step 3 | |
---|---|---|---|---|
Dependent variable | HQI | Hukou transfer intention | HQI | |
Empirical method | OLS | Probit | OLS | |
Model | Equation (1) | Equation (4) | Equation (5) | |
Variables | Coef. | Coef. | Marginal effect | Coef. |
H_land | −0.323 *** | −0.284 *** | −0.110 *** | −0.309 *** |
(0.077) | (0.033) | (0.013) | (0.078) | |
Hukou_Transfer | 0.129 ** | |||
(0.064) | ||||
Micro-level attributes | Y | Y | Y | Y |
City-level attributes | Y | Y | Y | Y |
Constant | −5.829 *** | 0.959 *** | 3.064 *** | |
(0.610) | (0.257) | (0.128) | ||
Pseudo R2 | 0.246 | 0.067 | 0.067 | 0.247 |
Observations | 10,420 | 10,420 | 10,420 | 10,420 |
(1) | ||||
Heterogeneity | Cohort | Employment Attributes | ||
(1) | (2) | (3) | (4) | |
Housing quality | New Generation | Older Generation | Low-skill jobs | High-skill jobs |
Explanatory variables | Coef. | Coef. | Coef. | Coef. |
H_land | −0.278 *** | −0.405 *** | −0.418 *** | −0.076 |
(0.091) | (0.145) | (0.095) | (0.128) | |
Micro-level attributes | Y | Y | Y | Y |
City-level controls | Y | Y | Y | Y |
Observations | 6444 | 3976 | 7190 | 3230 |
Pseudo R2 | 0.218 | 0.272 | 0.252 | 0.174 |
(2) | ||||
Heterogeneity | Inter-Provincial Migration | Intra-Provincial Migration | ||
(1) | (2) | (3) | ||
Housing quality | Inter-province | Inter-cities | Inter-counties | |
Explanatory variables | Coef. | Coef. | Coef. | |
H_land | −0.455 *** | −0.295 *** | 0.056 | |
(0.116) | (0.109) | (0.282) | ||
Micro-level attributes | Y | Y | Y | |
City-level controls | Y | Y | Y | |
Observations | 4124 | 5694 | 602 | |
Pseudo R2 | 0.227 | 0.281 | 0.178 |
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Share and Cite
Wang, W.; Luo, G.; Gong, X. Association between Hometown Landholdings and Housing Quality of Rural Migrants in Urban Areas: Evidence from China. Buildings 2024, 14, 1027. https://doi.org/10.3390/buildings14041027
Wang W, Luo G, Gong X. Association between Hometown Landholdings and Housing Quality of Rural Migrants in Urban Areas: Evidence from China. Buildings. 2024; 14(4):1027. https://doi.org/10.3390/buildings14041027
Chicago/Turabian StyleWang, Wei, Gai Luo, and Xinzhi Gong. 2024. "Association between Hometown Landholdings and Housing Quality of Rural Migrants in Urban Areas: Evidence from China" Buildings 14, no. 4: 1027. https://doi.org/10.3390/buildings14041027
APA StyleWang, W., Luo, G., & Gong, X. (2024). Association between Hometown Landholdings and Housing Quality of Rural Migrants in Urban Areas: Evidence from China. Buildings, 14(4), 1027. https://doi.org/10.3390/buildings14041027