The Impact of Health Insurance Policy on the Fertility Intention of Rural Floating Population in China: Empirical Evidence from Cross-Sectional Data
Abstract
:1. Background
2. Materials and Methods
2.1. Data Source
2.2. Variables
2.2.1. Dependent Variable
2.2.2. Independent Variable
2.2.3. Controlled Variables
2.3. The Model
2.3.1. Basic Model
2.3.2. Propensity Score Matching (PSM)
3. Results
3.1. The Logit Regression Model on the Impact of Health Insurance Policy on Fertility Intention of China’s Rural Floating Population
3.2. Regression Results of the Impact of Health Insurance Policy on Fertility Intention of Rural Floating Population under Propensity Score Matching
3.3. Robustness Test
3.4. Impact of Health Insurance on Fertility Intention of Rural Floating Population: Age Difference
3.5. Impact of Health Insurance on Fertility Intention of Rural Floating Population: Sex and Regional Differences
4. Discussion
4.1. Health Insurance and Fertility Intention of China’s Rural Floating Population
4.2. Other Factors Affecting Fertility Intention of Rural Floating Population in China
4.3. The Impact of Health Insurance Policy on Fertility Intention of Rural Floating Population in China Is Different in Ages, Genders and Regions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Definition | Mean | SD | Min | Max | |
---|---|---|---|---|---|---|
Dependent Variable | Fertility Intention | Dummy variable: yes = 1, no = 0 | 0.226 | 0.418 | 0 | 1 |
Independent Variable | Health Insurance | Dummy variable: participated in the NRCMS or BMISUR or BMISURR = 1, otherwise = 0 | 0.727 | 0.445 | 0 | 1 |
Demographic Characteristics Variables | Age | Continuous variable: participant’s age (range 15 to 49) | 32.670 | 8.094 | 15 | 49 |
Age squared | Continuous variable: the squared age of the participant | 1133.000 | 542.5 | 225 | 2401 | |
Gender | Dummy variable: male = 1, female = 0 | 0.489 | 0.500 | 0 | 1 | |
Education | ||||||
Lower education level | Dummy variable: primary education level and below =1, otherwise = 0 | 0.118 | 0.323 | 0 | 1 | |
Secondary Education level | Dummy variable: secondary school education =1, otherwise = 0 | 0.477 | 0.499 | 0 | 1 | |
Higher education level | Dummy variable: higher high school education or above =1, otherwise = 0 | 0.405 | 0.491 | 0 | 1 | |
Economic Characteristic Variables | Personal income | Continuous variable: log of personal income | 8.126 | 0.609 | 1.609 | 11.513 |
Housing property | Dummy variable: purchase = 1, otherwise = 0 | 0.249 | 0.433 | 0 | 1 | |
Employment | Dummy variable: regular employment = 1, informal employment = 0 | 0.669 | 0.471 | 0 | 1 | |
Migration Characteristic Variables | Migration times | Continuous variable: participant’s migrated frequency and times | 1.346 | 1.007 | 1 | 36 |
co-residents scale | Continuous variable: participant’s living together, number in the local area | 3.105 | 1.132 | 1 | 10 | |
Range of Migration | ||||||
Interprovince | Dummy variable: participant migrated from one province to another = 1, otherwise = 0 | 0.492 | 0.500 | 0 | 1 | |
Intercity | Dummy variable: participant migrated from one city to another within the same province = 1, otherwise = 0 | 0.335 | 0.472 | 0 | 1 | |
Intercounty | Dummy variable: participant migrated from one county to another within the same city = 1, otherwise = 0 | 0.173 | 0.377 | 0 | 1 | |
Region of Migration | Categorical variable: Eastern = 1, Central = 2, Western = 3, Northeast = 4 | 2.054 | 1.015 | 1 | 4 |
Variables | Regression | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Health insurance | 0.237 *** | 0.274 *** | 0.169 *** | 0.190 *** | 0.201 *** | 0.186 *** |
(0.022) | (0.025) | (0.032) | (0.035) | (0.022) | (0.035) | |
Age | 0.323 *** | 0.332 *** | 0.329 *** | |||
(0.017) | (0.028) | (0.028) | ||||
Age squared | −0.007 *** | −0.007 *** | −0.007 *** | |||
(0.000) | (0.000) | (0.000) | ||||
Gender | −0.235 *** | −0.255 *** | −0.242 *** | |||
(0.020) | (0.034) | (0.032) | ||||
Secondary education level | −0.023 | −0.033 | −0.046 | |||
(0.045) | (0.074) | (0.074) | ||||
Higher education level | 0.007 | 0.022 | −0.010 | |||
(0.046) | (0.077) | (0.077) | ||||
Personal income | 0.169 *** | 0.058 * | 0.086 ** | |||
(0.029) | (0.033) | (0.034) | ||||
Housing property | 0.077 ** | 0.064 * | 0.079 *** | |||
(0.033) | (0.035) | (0.036) | ||||
Employment | 0.115 *** | 0.071 ** | 0.078 ** | |||
(0.034) | (0.036) | (0.036) | ||||
Migration times | 0.019 ** | 0.058 *** | ||||
(0.010) | (0.014) | |||||
Co-residents scale | 0.272 *** | 0.055 ** | ||||
(0.017) | (0.027) | |||||
Intercity | 0.149 *** | 0.204 *** | ||||
(0.023) | (0.037) | |||||
Intercounty | 0.118 ** | 0.246 *** | ||||
(0.029) | (0.047) | |||||
Region of migration | Control | Control | Control | Control | Control | Control |
n | 29,437 | 29,437 | 29,437 | 29,437 | 29,437 | 29,437 |
Pseudo-R2 | 0.0118 | 0.1019 | 0.0135 | 0.0890 | 0.0271 | 0.0914 |
Matching Method | PsR2 | p > chi2 | Mean Bias | Med Bias | B | R | ATT | T |
---|---|---|---|---|---|---|---|---|
Unmatched | 0.224 | 0.000 | 31.9 | 18.0 | 123.1 * | 0.91 | 0.179 *** | 5.08 |
Nearest neighbor matching | 0.003 | 0.000 | 2.4 | 2.0 | 12.1 | 1.00 | 0.220 *** | 5.80 |
Radius matching | 0.003 | 0.000 | 2.7 | 2.2 | 11.9 | 0.98 | 0.220 *** | 5.80 |
Kernel matching | 0.003 | 0.000 | 2.8 | 2.9 | 12.4 | 1.07 | 0.220 *** | 5.66 |
Spline matching | 0.003 | 0.000 | 2.9 | 2.9 | 13.4 | 0.94 | 0.220 *** | 4.66 |
Variables | Mlogit Model | Probit Model | |
---|---|---|---|
Uncertain | Willing to Give Birth | ||
Health insurance | 0.095 *** | 0.108 *** | 0.237 *** |
(0.035) | (0.020) | (0.040) | |
Other variables | Control | Control | Control |
Region of migration | YES | YES | YES |
n | 29,437 | 29,437 | 29,437 |
Pseudo-R2 | 0.1274 | 0.0915 | 0.1274 |
Variables | Classification of Age (Year) | ||||||
---|---|---|---|---|---|---|---|
15–19 | 20–24 | 25–29 | 30–34 | 35–39 | 40–44 | 45–49 | |
Health insurance | 3.133 | 1.333 ** | 1.121 *** | 1.170 ** | 1.310 *** | 1.132 | 1.610 |
(3.910) | (0.171) | (0.069) | (0.073) | (0.120) | (0.182) | (0.606) | |
Other variables | YES | YES | YES | YES | YES | YES | YES |
Region of migration | YES | YES | YES | YES | YES | YES | YES |
n | 59 | 1662 | 8227 | 7927 | 5002 | 4057 | 2503 |
Pseudo-R2 | 0.1581 | 0.0154 | 0.0124 | 0.0194 | 0.0344 | 0.0414 | 0.0357 |
Variables | Classification of Sex | Classification of Region | ||||
---|---|---|---|---|---|---|
Male | Female | Eastern | Central | Western | Northeast | |
Health insurance | 1.325 *** | 1.079 | 1.241 *** | 1.120 | 1.231 *** | 1.275 |
(0.062) | (0.058) | (0.058) | (0.110) | (0.089) | (0.317) | |
Other variables | YES | YES | YES | YES | YES | YES |
Region of migration | YES | YES | YES | YES | YES | YES |
n | 16,417 | 13,020 | 15,418 | 4250 | 7542 | 2227 |
Pseudo-R2 | 0.0921 | 0.0942 | 0.0880 | 0.0730 | 0.0847 | 0.1269 |
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Xing, Y.; Tarimo, C.S.; Ren, W.; Zhang, L. The Impact of Health Insurance Policy on the Fertility Intention of Rural Floating Population in China: Empirical Evidence from Cross-Sectional Data. Int. J. Environ. Res. Public Health 2023, 20, 175. https://doi.org/10.3390/ijerph20010175
Xing Y, Tarimo CS, Ren W, Zhang L. The Impact of Health Insurance Policy on the Fertility Intention of Rural Floating Population in China: Empirical Evidence from Cross-Sectional Data. International Journal of Environmental Research and Public Health. 2023; 20(1):175. https://doi.org/10.3390/ijerph20010175
Chicago/Turabian StyleXing, Yiqing, Clifford Silver Tarimo, Weicun Ren, and Liang Zhang. 2023. "The Impact of Health Insurance Policy on the Fertility Intention of Rural Floating Population in China: Empirical Evidence from Cross-Sectional Data" International Journal of Environmental Research and Public Health 20, no. 1: 175. https://doi.org/10.3390/ijerph20010175