Impact of Education, Medical Services, and Living Conditions on Health: Evidence from China Health and Nutrition Survey
Abstract
:1. Introduction
2. Data, Variables, and Summary Statistics
2.1. Data
2.1.1. Explained Variable
2.1.2. Explanatory and Control Variables
2.2. Summary Statistics
3. Basic Model
4. Empirical Results
4.1. Results from the OLS Method
4.2. Endogeneity Test
4.3. Robustness Test
4.4. Heterogeneity Analysis
4.4.1. Heterogeneity in Age
4.4.2. Heterogeneity in Age–Sex and Age–Residence
5. Conclusions
6. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Youth | Middle Age | Old Age | |
---|---|---|---|
Self-rated health | 3.92 (0.024) | 3.75 (0.024) | 3.60 (0.024) |
Symptoms | 0.07 (0.011) | 0.11 (0.013) | 0.18 (0.018) |
Disease history | 0.06 (0.007) | 0.22 (0.015) | 0.54 (0.021) |
Physical health | 3.97 (0.022) | 3.82 (0.022) | 3.72 (0.021) |
Psychological health | 3.534 (0.022) | 3.43 (0.022) | 3.50 (0.021) |
Years of education | 13.22 (0.153) | 10.41 (0.134) | 8.69 (0.150) |
Sex | 0.46 (0.016) | 0.50 (0.015) | 0.51 (0.014) |
Age | 36.00 (0.215) | 52.78 (0.119) | 67.91 (0.195) |
Family income | 11.32 (0.290) | 11.08 (0.029) | 10.87 (0.030) |
Medical insurance | 0.97 (0.005) | 0.98 (0.004) | 0.98 (0.004) |
Medical institution | 0.01 (0.003) | 0.04 (0.006) | 0.07 (0.007) |
Residence | 0.57 (0.015) | 0.54 (0.015) | 0.63 (0.014) |
Marital status | 0.86 (0.011) | 0.95 (0.006) | 0.86 (0.010) |
Job | 0.89 (0.010) | 0.64 (0.014) | 0.17 (0.011) |
Working hours | 0.04 (0.030) | −0.04 (0.030) | 0.01 (0.030) |
Individual income | 10.22 (0.043) | 9.87 (0.051) | 9.66 (0.047) |
Smoking | 0.23 (0.013) | 0.29 (0.014) | 0.28 (0.013) |
Drinking | 0.26 (0.014) | 0.32 (0.014) | 0.27 (0.013) |
Running water | 0.91 (0.009) | 0.88 (0.010) | 0.90 (0.009) |
Toilet | 0.67 (0.015) | 0.59 (0.015) | 0.64 (0.014) |
Sanitary conditions | 0.91 (0.009) | 0.89 (0.009) | 0.88 (0.009) |
Young Men | Young Women | Middle-Aged Men | Middle-Aged Women | Elderly Men | Elderly Women | |
---|---|---|---|---|---|---|
Self-rated health | 3.95 (0.034) | 3.89 (0.033) | 3.76 (0.035) | 3.75 (0.033) | 3.63 (0.034) | 3.57 (0.034) |
Symptoms | 0.07 (0.013) | 0.08 (0.017) | 0.11 (0.020) | 0.11 (0.018) | 0.13 (0.020) | 0.23 (0.030) |
Disease history | 0.07 (0.011) | 0.05 (0.010) | 0.24 (0.021) | 0.21 (0.020) | 0.55 (0.029) | 0.54 (0.029) |
Physical health | 3.99 (0.032) | 3.95 (0.029) | 3.83 (0.031) | 3.80 (0.031) | 3.75 (0.029) | 3.69 (0.029) |
Psychological health | 3.55 (0.031) | 3.52 (0.3307) | 3.42 (0.031) | 3.44 (0.032) | 3.47 (0.029) | 3.53 (0.030) |
Years of education | 13.41 (0.218) | 13.05 (0.213) | 11.27 (0.175) | 9.57 (0.196) | 9.71 (0.198) | 7.62 (0.220) |
Family income | 11.35 (0.042) | 11.29 (0.040) | 11.12 (0.041) | 11.04 (0.042) | 10.91 (0.040) | 10.84 (0.044) |
Medical insurance | 0.99 (0.006) | 0.97 (0.008) | 0.98 (0.006) | 0.98 (0.006) | 0.98 (0.006) | 0.98 (0.005) |
Medical institution | 0.01 (0.004) | 0.01 (0.004) | 0.04 (0.008) | 0.04 (0.009) | 0.06 (0009) | 0.09 (0.012) |
Residence | 0.57 (0.023) | 0.56 (0.021) | 0.55 (0.021) | 0.54 (0.021) | 0.62 (0.020) | 0.64 (0.020) |
Marital status | 0.86 (0.016) | 0.86 (0.015) | 0.96 (0.008) | 0.94 (0.010) | 0.91 (0.011) | 0.81 (0.016) |
Job | 0.92 (0.012) | 0.86 (0.015) | 0.80 (0.017) | 0.47 (0.021) | 0.23 (0.017) | 0.12 (0.013) |
Working hours | 0.03 (0.044) | 0.05 (0.040) | −0.03 (0.043) | −0.05 (0.041) | 0.03 (0.043) | −0.02 (0.042) |
Individual income | 10.38 (0.063) | 10.09 (0.057) | 10.10 (0.070) | 9.63 (0.074) | 9.84 (0.057) | 9.48 (0.074) |
Smoking | 0.50 (0.023) | 0.01 (0.004) | 0.56 (0.021) | 0.03 (0.007) | 0.50 (0.020) | 0.05 (0.009) |
Drinking | 0.52 (0.023) | 0.04 (0.009) | 0.60 (0.021) | 0.05 (0.009) | 0.48 (0.020) | 0.05 (0.009) |
Running water | 0.90 (0.014) | 0.91 (0.012) | 0.90 (0.013) | 0.86 (0.014) | 0.90 (0.012) | 0.91 (0.012) |
Toilet | 0.67 (0.022) | 0.66 (0.020) | 0.61 (0.021) | 0.56 (0.021) | 0.64 (0.019) | 0.64 (0.020) |
Sanitary conditions | 0.90 (0.014) | 0.91 (0.012) | 0.89 (0.013) | 0.88 (0.014) | 0.88 (0.013) | 0.88 (0.013) |
Urban Youth | Rural Youth | Urban Middle Age | Rural Middle Age | Urban Elderly | Rural Elderly | |
---|---|---|---|---|---|---|
Self-rated health | 3.97 (0.032) | 3.84 (0.036) | 3.85 (0.032) | 3.64 (0.035) | 3.66 (0.030) | 3.49 (0.039) |
Symptoms | 0.09 (0.016) | 0.05 (0.013) | 0.09 (0.017) | 0.13 (0.021) | 0.17 (0.023) | 0.20 (0.029) |
Disease history | 0.05 (0.010) | 0.06 (0.012) | 0.22 (0.020) | 0.22 (0.021) | 0.61 (0.027) | 0.42 (0.031) |
Physical health | 4.03 (0.028) | 3.89 (0.033) | 3.90 (0.029) | 3.71 (0.033) | 3.77 (0.026) | 3.64 (0.033) |
Psychological health | 3.57 (0.030) | 3.48 (0.032) | 3.48 (0.031) | 3.37 (0.032) | 3.59 (0.026) | 3.35 (0.034) |
Years of education | 15.65 (0.164) | 10.05 (0.195) | 12.55 (0.155) | 7.86 (0.168) | 10.72 (0.173) | 5.24 (0.190) |
Sex | 0.47 (0.021) | 0.46 (0.024) | 0.50 (0.020) | 0.49 (0.022) | 0.51 (0.018) | 0.52 (0.024) |
Family income | 11.58 (0.035) | 10.97 (0.044) | 11.31 (0.030) | 10.80 (0.051) | 11.22 (0.026) | 10.28 (0.056) |
Medical insurance | 0.98 (0.006) | 0.97 (0.009) | 0.98 (0.006) | 0.97 (0.007) | 0.98 (0.005) | 0.98 (0.007) |
Medical institution | 0.01 (0.005) | 0.00 (0.002) | 0.05 (0.009) | 0.03 (0.007) | 0.10 (0.011) | 0.03 (0.008) |
Marital status | 00.83 (0.015) | 0.89 (0.015) | 0.94 (0.010) | 0.96 (0.008) | 0.87 (0.012) | 0.84 (0.017) |
Job | 0.98 (0.006) | 0.78 (0.020) | 0.66 (0.019) | 0.61 (0.022) | 0.08 (0.010) | 0.33 (0.022) |
Working hours | 0.089 (0.040) | −0.03 (0.045) | 0.03 (0.040) | −0.14 (0.045) | 0.08 (0.037) | −0.10 (0.050) |
Individual income | 10.63 (0.042) | 9.69 (0.075) | 10.45 (0.032) | 9.17 (0.097) | 10.34 (0.027) | 8.52 (0.095) |
Smoking | 0.22 (0.017) | 0.26 (0.021) | 0.28 (0.018) | 0.31 (0.021) | 0.24 (0.015) | 0.35 (0.023) |
Drinking | 0.25 (0.018) | 0.28 (0.021) | 0.33 (0.019) | 0.31 (0.021) | 0.24 (0.016) | 0.32 (0.022) |
Running water | 0.97 (0.007) | 0.82 (0.018) | 0.97 (0.007) | 0.78 (0.018) | 0.97 (0.006) | 0.79 (0.019) |
Toilet | 0.87 (0.014) | 0.40 (0.023) | 0.84 (0.015) | 0.28 (0.020) | 0.85 (0.013) | 0.28 (0.021) |
Sanitary conditions | 0.98 (0.006) | 0.082 (0.018) | 0.94 (0.009) | 0.82 (0.017) | 0.97 (0.006) | 0.74 (0.021) |
Education N = 3352 | Physical N = 3335 | 2SLS1 N = 3335 | Psychological N = 3336 | 2SLS2 N = 3336 | |
---|---|---|---|---|---|
Intercept Term | 6.140 *** (0.980) | 3.284 *** (0.183) | 3.020 *** (0.209) | 2.347 *** (0.188) | 2.202 *** (0.208) |
Years of education/RWD | 1.939 *** (0.160) | 0.011 *** (0.003) | 0.058 *** (0.016) | 0.011 *** (0.003) | 0.037 ** (0.016) |
Sex | 1.513 *** (0.170) | −0.010 (0.032) | −0.084 ** (0.041) | −0.083 ** (0.033) | −0.125 *** (0.041) |
Age | −0.119 *** (0.006) | −0.004 *** (0.001) | 0.001 (0.002) | 0.002 (0.001) | 0.005 ** (0.002) |
Family income | 0.323 *** (0.084) | 0.045 *** (0.016) | 0.028 (0.017) | 0.022 (0.016) | 0.012 (0.017) |
Medical insurance | −0.131 (0.443) | −0.178 ** (0.082) | −0.167 * (0.085) | 0.287 *** (0.085) | 0.290 *** (0.086) |
Medical institution | −0.131 (0.443) | −0.315 *** (0.061) | −0.325 *** (0.063) | 0.001 (0.063) | −0.006 (0.064) |
Residence | 3.325 *** (0.172) | 0.030 (0.034) | −0.146 ** (0.068) | −0.002 (0.034) | −0.102 (0.068) |
Marital status | 0.041 (0.211) | −0.007 (0.039) | −0.007 (0.041) | 0.048 (0.040) | 0.048 (0.041) |
Job | 0.743 *** (0.170) | 0.026 (0.032) | −0.009 (0.035) | 0.023 (0.032) | 0.004 (0.035) |
Working hours | −0.027 (0.065) | 0.025 ** (0.012) | 0.026 ** (0.013) | 0.007 (0.012) | 0.007 (0.013) |
Individual income | 0.290 *** (0.054) | 0.020 * (0.010) | 0.005 (0.012) | 0.025 ** (0.010) | 0.016 (0.012) |
Smoking | −0.847 *** (0.182) | 0.007 (0.034) | 0.051 (0.038) | 0.020 (0.035) | 0.045 (0.038) |
Drinking | −0.067 (0.177) | 0.039 (0.033) | 0.040 (0.034) | 0.031 (0.034) | 0.032 (0.034) |
Running water | −0.193 (0.228) | 0.121 *** (0.042) | 0.132 *** (0.044) | 0.020 (0.043) | 0.026 (0.044) |
Toilet | 1.130 *** (0.169) | −0.009 (0.032) | −0.070 * (0.038) | 0.044 (0.032) | 0.008 (0.039) |
Sanitary conditions | 0.646 *** (0.223) | 0.026 (0.042) | −0.006 (0.044) | 0.113 *** (0.042) | 0.095 ** (0.044) |
SER | 3.745 | 0.697 | 0.720 | 0.712 | 0.719 |
R2 | 0.487 | 0.065 | 0.003 | 0.033 | 0.013 |
F | 145.88 | 144.40 |
Model 1 N = 3335 | Model 2 N = 3335 | Model 3 N = 3335 | Model 4 N = 3335 | Model 5 N = 3335 | Model 6 N = 3335 | Model 7 N = 3335 | |
---|---|---|---|---|---|---|---|
Intercept Term | 3.567 *** (0.028) | 4.155 *** (0.060) | 2.621 *** (0.137) | 4.005 *** (0.083) | 3.222 *** (0.077) | 3.820 *** (0.015) | 3.571 *** (0.048) |
Years of education | 0.025 *** (0.002) | ||||||
Sex | 0.046 * (0.025) | ||||||
Age | −0.008 *** (0.001) | ||||||
Residence | 0.156 *** (0.025) | ||||||
Marital status | 0.010 (0.039) | ||||||
Family income | 0.109 *** (0.012) | ||||||
Medical insurance | −0.164 * (0.084) | ||||||
Medical institution | −0.349 *** (0.062) | ||||||
Job | 0.149 *** (0.025) | ||||||
Working hours | 0.031 ** (0.012) | ||||||
Individual income | 0.053 *** (0.008) | ||||||
Smoking | −0.028 (0.032) | ||||||
Drinking | 0.060 * (0.032) | ||||||
Running water | 0.148 *** (0.043) | ||||||
Toilet | 0.096 *** (0.028) | ||||||
Sanitary conditions | 0.073 * (0.042) | ||||||
SER | 0.708 | 0.706 | 0.711 | 0.715 | 0.707 | 0.719 | 0.715 |
R2 | 0.032 | 0.038 | 0.023 | 0.011 | 0.033 | 0.001 | 0.013 |
Model 1 N = 3336 | Model 2 N = 3336 | Model 3 N = 3336 | Model 4 N = 3336 | Model 5 N = 3336 | Model 6 N = 3336 | Model 7 N = 3336 | |
---|---|---|---|---|---|---|---|
Intercept Term | 3.302 *** (0.028) | 3.437 *** (0.061) | 2.586 *** (0.139) | 3.168 *** (0.084) | 2.933 *** (0.078) | 3.490 *** (0.015) | 3.245 *** (0.048) |
Years of education | 0.017 *** (0.002) | ||||||
Sex | −0.027 (0.025) | ||||||
Age | −0.002 * (0.001) | ||||||
Residence | 0.153 *** (0.025) | ||||||
Marital status | 0.068 * (0.040) | ||||||
Family income | 0.081 *** (0.012) | ||||||
Medical insurance | 0.325 *** (0.085) | ||||||
Medical institution | 0.030 (0.062) | ||||||
Job | 0.018 (0.026) | ||||||
Working hours | 0.011 (0.012) | ||||||
Individual income | 0.055 *** (0.008) | ||||||
Smoking | −0.035 (0.032) | ||||||
Drinking | 0.021 (0.032) | ||||||
Running water | 0.035 (0.043) | ||||||
Toilet | 0.128 *** (0.028) | ||||||
Sanitary conditions | 0.145 *** (0.042) | ||||||
SER | 0.717 | 0.718 | 0.718 | 0.721 | 0.717 | 0.722 | 0.717 |
R2 | 0.016 | 0.012 | 0.013 | 0.004 | 0.016 | 0.001 | 0.016 |
Full Sample N = 3335 | Young Men N = 473 | Young Women N = 549 | Middle-Aged Men N = 555 | Middle-Aged Women N = 564 | Elderly Men N = 613 | Elderly Women N = 581 | |
---|---|---|---|---|---|---|---|
Intercept Term | 3.095 *** (0.176) | 3.244 *** (0.732) | 4.052 *** (0.422) | 2.208 *** (0.580) | 3.281 *** (0.454) | 3.025 *** (0.433) | 3.019 *** (0.408) |
Years of education | 0.056 *** (0.015) | 0.139 (0.101) | 0.033 (0.031) | 0.011 (0.070) | 0.077 * (0.042) | 0.082 ** (0.035) | 0.067 ** (0.028) |
Family income | 0.028 (0.018) | −0.095 (0.140) | 0.006 (0.044) | 0.093 ** (0.040) | 0.012 (0.046) | 0.046 (0.046) | −0.003 (0.036) |
Medical insurance | −0.165 * (0.085) | 0.202 (0.321) | 0.018 (0.167) | −0.345 * (0.202) | −0.513 ** (0.225) | −0.145 (0.218) | −0.060 (0.241) |
Medical institution | −0.318 *** (0.063) | −0.589 (0.459) | −0.507 (0.350) | −0.394 ** (0.167) | −0.196 (0.162) | −0.430 *** (0.131) | −0.282 *** (0.107) |
Residence | −0.139 ** (0.062) | −0.401 (0.343) | −0.077 (0.131) | −0.010 (0.195) | −0.150 (0.213) | −0.148 (0.136) | −0.318 (0.157) |
Marital status | −0.010 (0.040) | 0.019 (0.149) | −0.122 (0.104) | 0.340 ** (0.165) | 0.245 (0.150) | −0.143 (0.109) | 0.004 (0.097) |
Job | −0.028 (0.050) | −0.175 (0.244) | 0.161 (0.113) | 0.003 (0.128) | −0.099 (0.089) | 0.118 (0.079) | −0.014 (0.100) |
Working hours | 0.025 ** (0.013) | 0.028 (0.046) | 0.035 (0.031) | 0.029 (0.031) | 0.043 (0.034) | 0.032 (0.029) | −0.002 (0.029) |
Individual income | 0.003 (0.011) | 0.023 (0.044) | −0.067 * (0.035) | 0.061 ** (0.028) | 0.003 (0.024) | −0.062 * (0.035) | 0.025 (0.027) |
Smoking | 0.017 (0.033) | 0.232 (0.171) | 0.052 (0.348) | −0.025 (0.103) | 0.033 (0.192) | 0.088 (0.066) | −0.029 (0.147) |
Drinking | 0.010 (0.032) | −0.028 (0.096) | 0.070 (0.149) | −0.026 (0.064) | −0.218 (0.160) | 0.068 (0.064) | 0.412 *** (0.147) |
Running water | 0.131 *** (0.044) | −0.034 (0.145) | −0.001 (0.112) | −0.011 (0.106) | 0.182 * (0.105) | 0.316 *** (0.112) | 0.284 ** (0.118) |
Toilet | −0.068 * (0.038) | −0.223 (0.216) | 0.029 (0.099) | 0.015 (0.100) | −0.128 (0.093) | −0.160 * (0.086) | −0.072 (0.084) |
Sanitary conditions | −0.007 (0.044) | −0.034 (0.167) | 0.050 (0.112) | −0.105 (0.129) | −0.069 (0.105) | 0.150 (0.109) | −0.002 (0.105) |
SER | 0.719 | 0.827 | 0.686 | 0.705 | 0.770 | 0.744 | 0.711 |
R2 | 0.006 | −0.373 | 0.015 | 0.101 | −0.060 | −0.062 | 0.007 |
Full Sample N = 3336 | Young Men N = 474 | Young Women N = 550 | Middle-Aged Men N = 557 | Middle-Aged Women N = 563 | Elderly Men N = 612 | Elderly Women N = 580 | |
---|---|---|---|---|---|---|---|
Intercept Term | 2.526 *** (0.177) | 2.778 *** (0.691) | 3.520 *** (0.446) | 2.300 *** (0.587) | 2.257 *** (0.437) | 2.371 *** (0.408) | 1.816 *** (0.442) |
Years of education | 0.034 ** (0.015) | 0.041 (0.085) | 0.060 * (0.033) | 0.025 (0.069) | −0.010 (0.041) | 0.012 (0.033) | 0.084 *** (0.030) |
Family income | 0.008 (0.018) | −0.078 (0.117) | −0.098 ** (0.047) | 0.045 (0.043) | 0.011 (0.045) | 0.056 (0.044) | 0.092 ** (0.039) |
Medical insurance | 0.296 *** (0.086) | 0.015 (0.315) | 0.389 ** (0.177) | 0.141 (0.213) | 0.494 ** (0.218) | 0.071 (0.204) | 0.621 ** (0.261) |
Medical institution | 0.017 (0.063) | 0.846 ** (0.374) | −0.019 (0.370) | −0.067 (0.178) | −0.043 (0.157) | −0.083 (0.122) | −0.008 (0.117) |
Residence | −0.082 (0.062) | −0.209 (0.291) | −0.220 (0.139) | −0.056 (0.194) | 0.088 (0.207) | 0.085 (0.127) | −0.260 (0.173) |
Marital status | 0.051 (0.041) | 0.115 (0.115) | 0.030 (0.111) | −0.034 (0.174) | 0.002 (0.146) | 0.168 * (0.102) | −0.113 (0.106) |
Job | −0.070 (0.049) | 0.345 * (0.195) | 0.035 (0.119) | 0.081 (0.129) | −0.011 (0.087) | −0.012 (0.074) | 0.129 (0.107) |
Working hours | 0.006 (0.013) | −0.062 (0.039) | 0.018 (0.033) | 0.031 (0.032) | −0.002 (0.032) | 0.042 (0.027) | −0.004 (0.032) |
Individual income | 0.016 (0.011) | 0.049 (0.035) | −0.014 (0.038) | 0.013 (0.029) | 0.055 ** (0.023) | −0.003 (0.032) | −0.045 (0.029) |
Smoking | −0.001 (0.033) | −0.038 (0.144) | 0.061 (0.368) | 0.100 (0.108) | −0.216 (0.185) | 0.091 (0.061) | 0.157 (0.163) |
Drinking | −0.009 (0.032) | 0.061 (0.077) | 0.123 (0.157) | 0.024 (0.068) | −0.066 (0.155) | 0.002 (0.060) | 0.210 (0.162) |
Running water | 0.026 (0.044) | 0.118 (0.119) | −0.024 (0.119) | 0.069 (0.112) | −0.050 (0.101) | −0.039 (0.105) | 0.130 (0.127) |
Toilet | 0.014 (0.038) | −0.012 (0.177) | 0.003 (0.107) | −0.044 (0.103) | 0.039 (0.090) | 0.037 (0.081) | −0.033 (0.091) |
Sanitary conditions | 0.092 ** (0.044) | 0.191 (0.135) | 0.184 (0.119) | −0.036 (0.133) | 0.156 (0.102) | 0.104 (0.102) | 0.021 (0.114) |
SER | 0.719 | 0.672 | 0.726 | 0.746 | 0.745 | 0.699 | 0.770 |
R2 | 0.011 | 0.063 | 0.010 | 0.012 | 0.033 | 0.058 | −0.133 |
Full Sample N = 3335 | Urban Youth N = 577 | Rural Youth N = 445 | Urban Middle Age N = 610 | Rural Middle Age N = 509 | Urban Elderly N = 755 | Rural Elderly N = 439 | |
---|---|---|---|---|---|---|---|
Intercept Term | 3.167 *** (0.181) | 3.505 *** (0.542) | 3.653 *** (0.481) | 2.831 *** (0.566) | 2.161 *** (0.540) | 3.011 *** (0.579) | 3.340 *** (0.475) |
Years of education | 0.047 *** (0.011) | 0.035 (0.045) | 0.071 * (0.041) | 0.018 (0.048) | 0.176 * (0.091) | 0.058 ** (0.025) | 0.161 ** (0.073) |
Sex | −0.069 ** (0.035) | −0.002 (0.073) | −0.099 (0.108) | −0.040 (0.085) | −0.318 (0.277) | −0.190 ** (0.076) | −0.338 (0.217) |
Family income | 0.028 (0.018) | 0.006 (0.056) | −0.000 (0.060) | 0.051 (0.050) | 0.060 (0.047) | 0.038 (0.046) | −0.005 (0.041) |
Medical insurance | −0.171 ** (0.084) | 0.244 (0.224) | −0.117 (0.199) | −0.451 ** (0.203) | −0.552 * (0.295) | −0.221 (0.207) | 0.006 (0.290) |
Medical institution | −0.335 *** (0.062) | −0.368 (0.271) | −1.177 (0.771) | −0.225 * (0.133) | −0.410 (0.252) | −0.431 *** (0.088) | 0.170 (0.263) |
Marital status | −0.003 (0.040) | −0.091 (0.109) | −0.083 (0.123) | 0.253 ** (0.126) | 0.289 (0.242) | 0.041 (0.087) | −0.409 ** (0.186) |
Job | 0.015 (0.037) | −0.026 (0.225) | 0.057 (0.124) | −0.035 (0.093) | −0.196 (0.143) | 0.215 ** (0.099) | 0.020 (0.091) |
Working hours | 0.024 * (0.012) | 0.009 (0.031) | 0.041 (0.037) | 0.029 (0.030) | 0.013 (0.045) | 0.028 (0.025) | −0.004 (0.040) |
Individual income | 0.001 (0.012) | −0.040 (0.049) | −0.030 (0.029) | 0.047 (0.074) | 0.019 (0.024) | −0.021 (0.063) | −0.024 (0.030) |
Smoking | 0.045 (0.037) | −0.004 (0.113) | 0.187 (0.119) | 0.025 (0.108) | 0.011 (0.121) | 0.108 (0.077) | −0.057 (0.106) |
Drinking | 0.041 (0.034) | 0.108 (0.091) | −0.049 (0.097) | −0.024 (0.079) | −0.076 (0.123) | 0.184 *** (0.071) | 0.013 (0.111) |
Running water | 0.112 *** (0.043) | −0.031 (0.180) | 0.001 (0.094) | 0.086 (0.171) | 0.095 (0.105) | 0.265 * (0.159) | 0.382 *** (0.128) |
Toilet | −0.098 ** (0.042) | 0.044 (0.121) | −0.112 (0.113) | −0.115 (0.086) | −0.142 (0.149) | −0.117 (0.077) | −0.138 (0.116) |
Sanitary conditions | −0.013 (0.045) | 0.155 (0.196) | 0.019 (0.098) | 0.056 (0.164) | −0.145 (0.112) | 0.002 (0.156) | −0.042 (0.115) |
SER | 0.712 | 0.686 | 0.732 | 0.714 | 0.942 | 0.711 | 0.851 |
R2 | 0.025 | 0.007 | −0.085 | 0.043 | −0.574 | 0.040 | −0.484 |
Full Sample N = 3336 | Urban Youth N = 581 | Rural Youth N = 443 | Urban Middle Age N = 609 | Rural Middle Age N = 511 | Urban Elderly N = 749 | Rural Elderly N = 443 | |
---|---|---|---|---|---|---|---|
Intercept Term | 2.572 *** (0.183) | 3.981 *** (0.600) | 2.176 *** (0.432) | 2.671 *** (0.604) | 2.616 *** (0.409) | 2.933 *** (0.596) | 1.994 *** (0.397) |
Years of education | 0.029 ** (0.011) | 0.086 * (0.051) | −0.002 (0.038) | −0.000 (0.050) | 0.042 (0.069) | 0.061 ** (0.026) | −0.023 (0.059) |
Sex | −0.102 *** (0.036) | −0.007 (0.081) | 0.013 (0.096) | −0.111 (0.091) | −0.152 (0.211) | −0.229 *** (0.078) | −0.095 (0.175) |
Family income | 0.007 (0.018) | −0.139 ** (0.062) | 0.013 (0.054) | 0.016 (0.054) | 0.023 (0.036) | 0.037 (0.047) | 0.096 *** (0.035) |
Medical insurance | 0.294 *** (0.085) | 0.141 (0.247) | 0.324 * (0.180) | 0.712 *** (0.217) | −0.138 (0.225) | 0.489 ** (0.213) | 0.149 (0.241) |
Medical institution | 0.006 (0.063) | 0.440 (0.300) | −0.109 (0.685) | −0.046 (0.144) | −0.140 (0.192) | −0.096 (0.091) | 0.306 (0.213) |
Marital status | 0.058 (0.041) | 0.099 (0.122) | 0.114 (0.108) | 0.028 (0.136) | −0.069 (0.181) | 0.014 (0.090) | 0.216 (0.151) |
Job | −0.043 (0.037) | 0.102 (0.249) | 0.178 (0.111) | 0.040 (0.097) | −0.010 (0.108) | 0.055 (0.101) | 0.041 (0.076) |
Working hours | 0.006 (0.012) | −0.012 (0.034) | −0.043 (0.033) | 0.035 (0.032) | −0.010 (0.034) | 0.011 (0.026) | 0.021 (0.033) |
Individual income | 0.016 (0.012) | −0.023 (0.054) | 0.036 (0.025) | −0.007 (0.078) | 0.041 ** (0.018) | −0.076 (0.065) | 0.005 (0.025) |
Smoking | 0.042 (0.037) | 0.012 (0.125) | −0.094 (0.107) | 0.050 (0.116) | 0.024 (0.092) | 0.121 (0.079) | 0.150 * (0.089) |
Drinking | 0.033 (0.034) | 0.049 (0.100) | 0.056 (0.086) | −0.012 (0.085) | 0.028 (0.094) | 0.062 (0.073) | −0.011 (0.092) |
Running water | 0.014 (0.043) | −0.251 (0.200) | 0.108 (0.083) | 0.074 (0.182) | −0.008 (0.080) | 0.015 (0.164) | −0.045 (0.107) |
Toilet | −0.005 (0.043) | −0.145 (0.138) | 0.136 (0.102) | −0.029 (0.091) | −0.022 (0.114) | 0.034 (0.080) | 0.016 (0.097) |
Sanitary conditions | 0.089 ** (0.045) | 0.143 (0.217) | 0.177 ** (0.087) | −0.050 (0.175) | 0.101 (0.085) | −0.099 (0.160) | 0.167 (0.096) |
SER | 0.716 | 0.760 | 0.649 | 0.761 | 0.718 | 0.730 | 0.707 |
R2 | 0.020 | −0.071 | 0.094 | 0.024 | 0.032 | −0.062 | 0.036 |
References
- Abel, T. Cultural capital and social inequality in health. J. Epidemiol. Community Health 2008, 62, e13. [Google Scholar] [CrossRef]
- Cheng, L.; Zhang, Y.; Shen, K. Understanding the pathways of the education-health gradient: Evidence from the Chinese elderly. China Econ. Q. 2015, 14, 305–330. [Google Scholar]
- Zhao, H.; Hu, Y. Will education definitely improve health level?—An empirical analysis based on China Family Panel Studies (CFPS). World Econ. Pap. 2016, 6, 90–106. (In Chinese) [Google Scholar]
- Irannejad, E.; Ahmadi, S.; Heidari, A. Prediction of physical health (the risk of developing diabetes) based on social, cultural, and economic capitals in Tabriz. Strateg. Res. Soc. Probl. Iran 2019, 8, 19–34. [Google Scholar]
- Zhang, W.; Chen, X. The influence of education to individual health condition. Shandong Soc. Sci. 2020, 7, 84–93. [Google Scholar]
- Cao, Y.; Hu, H.; Chen, Y. An analysis of the influence of education on young rural-urban migrant laborers’ psychological stress and its mechanism. Popul. Dev. 2014, 20, 35–42. [Google Scholar]
- Cao, Q. Structural equation model analysis of mental health among floating population in urban cities. Stat. Inf. Forum 2016, 31, 70–75. [Google Scholar]
- Alegria, M.; Drake, R.E.; Kang, H.A.; Metcalfe, J.; Liu, J.; Dimarzio, K.; Ali, N. Simulations Test Impact of Education, Employment and Income Improvements on Minority Patients with Mental Illness. Health Aff. 2017, 36, 1024–1031. [Google Scholar] [CrossRef]
- Ma, J.; Shi, B. Analysis of the influence of ideological and political education in colleges and universities on the development of psychological health education in the past 30 years. Ideol. Theor. Educ. 2018, 1, 97–102. (In Chinese) [Google Scholar]
- Huang, X. Research on the influence of college ideological and political education on college students’ mental health. Theory Pract. Educ. 2018, 38, 29–31. [Google Scholar]
- Hu, A. Can education make us healthier?—A comparative analysis of urban and rural areas based on the Chinese General Social Survey for 2010. Soc. Sci. China 2014, 5, 206. [Google Scholar]
- Li, C.; Wang, J. The casual effects of education on health—Evidence from China Family Panel Studies. Soc. Sci. Beijing 2017, 11, 56–69. [Google Scholar]
- Zheng, L.; Zeng, X. The Cohort Variations of Education Related Health Gradients in China: Analysis Based on Growth Curve. Model. Popul. Econ. 2018, 2, 69–79. [Google Scholar]
- Jun, L.; Liu, S. The Causal Effect of Education on Adult Health in China: Evidence from the Experiment of the 1986 Compulsory Education law in China. J. Quant. Tech. Econ. 2019, 36, 117–134. [Google Scholar]
- Braakmann, N. The causal relationship between education, health and health related behaviour: Evidence from natural experiment in England. J. Health Econ. 2011, 5, 753–763. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ross, C.E.; Mirowsky, J. Why education is the key to socioeconomic differentials in health. In Handbook of Medical Sociology; Bird, C.E., Conrad, P., Fremont, A.M., Timmermans, S., Eds.; Vanderbilt University Press: Nashville, TN, USA, 2010; pp. 33–51. [Google Scholar]
- Ye, X.; Liang, W. The Influencing Mechanism of Education on the Health of the Aged in China: Evidence from CLHLS in 2011. Educ. Econ. 2017, 3, 68–76, 96. [Google Scholar]
- Wang, Y.; He, X. Does education generate health benefits?—Heterogeneity based on propensity score matching. Educ. Econ. 2015, 5, 55–61, 72. [Google Scholar]
- Mangyo, E.; Park, A. Relative Deprivation and Health: Which Reference Groups Matter? J. Hum. Resour. 2011, 46, 459–481. [Google Scholar] [CrossRef]
- Mazzonna, F. The long lasting effects of education on old age health: Evidence of sex differences. Soc. Sci. Med. 2014, 101, 129–138. [Google Scholar] [CrossRef]
- Wang, Y.; Yang, L. Effects of family-centered educational intervention on adolescents with active epilepsy. Chin. J. Nurs. 2015, 50, 1157–1162. [Google Scholar]
- Song, Q.C.; Zhang, Q. Research on health status of the elderly migrant population in China and its determinants. Chin. J. Popul. Sci. 2018, 4, 81–92. [Google Scholar]
- Zhao, Z. Health status and influencing factors of rural population in China. Manag. World 2006, 3, 78–85. [Google Scholar]
- Mao, Y.; Feng, G.-F. The influence effect and the transmission mechanism of education to the health. Popul. Econ. 2011, 3, 87–93. [Google Scholar]
- Sun, J.; Son, Y. An empirical study of sex stratification in health status in Chinese urban area. Med. Philos. 2008, 10, 46–48. [Google Scholar]
- Yao, H.; Shi, Q.; Li, Y. The current status of health literacy in China. Popul. Res. 2016, 40, 88–97. [Google Scholar]
- Mu, Z.; Yang, Z. The effect of marital status on the death probability of the elderly: An empirical analysis of CLHLS queue data. South China Popul. 2016, 31, 38–39. [Google Scholar]
- Yang, X.; Cheng, L.; Feldman, M. The impact of marriage squeeze on the quality of life of rural men in China. Popul. J. 2017, 39, 28–37. [Google Scholar]
- Zhu, L. Working hours and the occupational health of rural migrant workers. Soc. Sci. China 2009, 1, 133–149. [Google Scholar]
- Zhang, K.; Liu, C.; Ding, S. How does Working Hours Affect Urban Workers’ health? An empirical analysis of the China Labor-force Dynamic Survey. Stud. Labor Econ. 2018, 6, 107–127. [Google Scholar]
- Liu, W.; Fan, Y. Research on the influence mechanism of mental health level of transfer students—Empirical evidence from China Education Panel Survey (CEPS). Shanghai Res. Educ. 2020, 2, 25–30. [Google Scholar]
- Chen, J. An analysis of main lifestyle cognition factors which affect the health status of Guangzhou residents. J. Phys. Educ. 2013, 20, 71–74. [Google Scholar]
- Xia, D.; Zhu, B. Can socioeconomic status affect health through lifestyle? A comparative analysis based on urban and rural labors. Hubei Soc. Sci. 2021, 2, 50–58. [Google Scholar]
- Xiong, Y. Study on the influence of family socioeconomic characteristics on the personal health of urban residents. World Surv. Res. 2018, 3, 62–65. (In Chinese) [Google Scholar]
- Dan, Y.; Sun, Q.; Zhou, B. The impact of family wealth on health of the residents: An empirical research into the CFPS survey data. Humanit. Soc. Sci. J. Hainan Univ. 2021, 39, 71–81. [Google Scholar]
- Jian, Z.; Deng, Q.; Liu, H. How does the basic medical insurance system affect health equity among elderly person. Financ. Econ. Res. 2020, 35, 147–160. [Google Scholar]
- Zhang, P. Impact of social medical insurance on the health of the elderly and its mechanism. J. Yunnan Minzu Univ. 2020, 37, 96–103. [Google Scholar]
- Harris, M.; Alzua, M.L.; Oabert, N.; Pickering, A. Community-level sanitation coverage more strongly associated with child growth and household drinking water quality than access to a private toilet in rural Mail. Environ. Sci. Technol. 2017, 51, 7219–7227. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hu, B.; Sun, W. Analysis on the health status of drinking water in Fushun City from 2014 to 2018. Chin. J. Health Stat. 2020, 37, 959–960. (In Chinese) [Google Scholar]
- Andualem, Z.; Dagne, H.; Azene, Z.N.; Taddese, A.A.; Dagnew, B.; Fisseha, R.; Muluneg, A.G.; Yeshaw, Y. Households access to improved drinking water sources and toilet facilities in Ethiopia: A multilevel analysis based on 2016 Ethiopian Demographic and Health Survey. BMJ Open 2021, 11, e042071. [Google Scholar] [CrossRef] [PubMed]
- Kumwenda, S.; Msefula, C.; Kadewa, W.; Ngwira, B.; Morse, T. Estimating the Health Risk Associated with the Use of Ecological Sanitation Toilets in Malawi. J. Environ. Public Health 2017, 2017, 3931802. [Google Scholar] [CrossRef] [Green Version]
- Chuanming, L.; Liu, Y. Research on the impact of rural toilet reform on Farmers’ health expenditure. Issues Agric. Econ. 2020, 10, 89–102. [Google Scholar]
- Huang, Q.; Zhen, L.; Zhang, L.; Chen, D.; Liu, Y.; Jin, W.; Min, Y.; Zhao, L. Health in all policies: Taking the implementation path of “toilet revolution” in country as an example. Chin. J. Health Educ. 2020, 36, 92–95. [Google Scholar]
- Zhang, P.; Gao, J. Influence of rural toilet renovation on Rural Revitalization and its mechanism. Qinghai J. Ethnol. 2021, 32, 99–107. [Google Scholar]
- Novotný, J.; Ficek, F.; Hill, J.K.W.; Kumar, A. Social determinants of environmental health: A case of sanitation in rural Jharkhand. Sci. Total Environ. 2018, 643, 762–764. [Google Scholar] [CrossRef] [PubMed]
- Zhao, L.; Deng, X.; Wang, X. Socioeconomic status, environmental sanitation facilities and health of rural residents. Issues Agric. Econ. 2018, 7, 96–107. [Google Scholar]
- Du, P.; Li, T.T.; Shi, X.M. Establishing of surveillance, investigation and health risk assessment system with Chinese characteristics in the field of environment health. Chin. J. Dis. Control Prev. 2019, 23, 758–762. [Google Scholar]
- Wei, L.; Tang, Y.; Ge, M.; Sun, F.; Liu, C.; Zhang, J.; Xiong, L. Investigation on the risk factors of home environmental health in rural areas of Nanjing between 2016 and 2019. Mod. Prev. Med. 2020, 47, 2943–2945. [Google Scholar]
- Jakositz, S.; Pillsbury, L.; Greenwood, S.; Fahnestock, M.; McGreavy, B.; Bryce, J.; Mo, W. Protection through participation: Crowdsourced running water quality monitoring for enhanced public health. Water Res. 2019, 169, 115209. [Google Scholar] [CrossRef]
- Jalan, J.; Ravallion, M. Does Piped Water Reduce Diarrhea for Children in Rural India. J. Econom. 2003, 112, 153–173. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J. The Impact of Water Quality on Health: Evidence from the Drinking Water Infrastructure Program in Rural China. J. Health Econ. 2012, 31, 122–134. [Google Scholar] [CrossRef]
- Manczak, E.M.; Miller, J.G.; Gotlib, I.H. Water contaminant levels interact with parenting environment to predict development of depressive symptoms in adolescents. Dev. Sci. 2019, 23, e12838. [Google Scholar] [CrossRef]
- Xu, C. Study of physical and mental health of women left in rural areas away from their husband: Reports from the rural areas in Sichuan. South China Popul. 2009, 24, 49–56. [Google Scholar]
- Andres, L.; Briceño, B.; Chase, C.; Echenique, J.A. Sanitation and Externalities: Evidence from Early Childhood Health in Rural India. J. Water Sanit. Hyg. Dev. 2017, 7, 272–289. [Google Scholar] [CrossRef]
- He, W.; Shen, S. Does the integration of urban and rural medical insurance policy alleviate health inequality? Evidence from a quasi-natural experiment in prefecture-level of China. China Rural. Surv. 2021, 3, 67–85. [Google Scholar]
- Yang, J.; Deng, D. Influencing factors of health status among rural migrant workers. J. South China Agric. Univ. 2021, 20, 63–72. [Google Scholar]
- Stewart, R.W.; Hardcastle, V.G.; Zelinsky, A. Health Disparities, Social Determinants of Health, and Health Insurance. World Med. Health Policy 2015, 6, 483–492. [Google Scholar] [CrossRef]
- Liu, W.; Liu, C. Social insurance and rural ole-age health: Will participation in social insurance improve the health of the elderly?—Empirical study based on Multiple Order Logistic Model. Soc. Secur. Stud. 2018, 2, 47–53. [Google Scholar]
- Ma, Y.; Nolan, A.; Smith, J.P. Free GP Care and Psychological Health: Quasi-experimental Evidence from Ireland. J. Health Econom. 2020, 72, 102351. [Google Scholar] [CrossRef] [PubMed]
- Yang, H.; Mu, H. An Empirical Study on the impact of new rural cooperative medical system on the health level of rural residents. Subnational Fisc. Res. 2021, 4, 85–94. (In Chinese) [Google Scholar]
- Yu, X.; Hu, H.; Wu, Z.; Jing, S. The health status of citizens in China and its influencing factors. China Popul. Resour. Environ. 2010, 20, 151–156. [Google Scholar]
- Wu, Z. Study on the health status of rural residents in China and its risk influencing factors. Soc. Secur. Stud. 2012, 3, 79–85. [Google Scholar]
- Paolucci, F.; Mentzakis, E.; Defechereux, T.; Niessen, L.W. Equity and Efficency Preferences of Health Policy Makers in China—A Stated Preference Analysis. Health Policy Plan. 2014, 30, 1059–1066. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, J. Study on the Relationship between Obesity and Dietary Pattern among Chinese Adults (1991–2009); China Center for Disease Control and Prevention: Beijing, China, 2013. [Google Scholar]
No. | Min (M) | Max (X) | Average (E) | Standard Error | Standard Deviation | Variance | |
---|---|---|---|---|---|---|---|
Self-rated health | 3352 | 1 | 5 | 3.75 | (0.014) | 0.807 | 0.652 |
Symptoms | 3352 | 0 | 6 | 0.12 | (0.009) | 0.493 | 0.243 |
Disease history | 3336 | 0 | 4 | 0.29 | (0.010) | 0.566 | 0.321 |
Physical health | 3335 | 1 | 5 | 3.83 | (0.012) | 0.719 | 0.517 |
Psychological health | 3336 | 0.33 | 5.00 | 3.49 | (0.012) | 0.722 | 0.522 |
Years of education | 3352 | 0 | 21 | 10.66 | (0.090) | 5.216 | 27.206 |
Sex | 3352 | 0 | 1 | 0.49 | (0.009) | 0.500 | 0.250 |
Age | 3352 | 18 | 94 | 53.06 | (0.247) | 14.303 | 204.566 |
Family income | 3352 | 4.45 | 14.74 | 11.08 | (0.173) | 0.999 | 0.998 |
Medical insurance | 3352 | 0 | 1 | 0.98 | (0.003) | 0.147 | 0.022 |
Medical institution | 3352 | 0 | 1 | 0.04 | (0.003) | 0.201 | 0.040 |
Residence | 3352 | 0 | 1 | 0.58 | (0.009) | 0.493 | 0.243 |
Marital status | 3352 | 0 | 1 | 0.89 | (0.005) | 0.312 | 0.098 |
Job | 3352 | 0 | 1 | 0.55 | (0.009) | 0.498 | 0.248 |
Working hours | 3352 | −2.04 | 4.81 | 0.01 | (0.017) | 1.000 | 1.000 |
Individual income | 3352 | 0.00 | 14.69 | 9.90 | (0.028) | 1.594 | 2.539 |
Smoking | 3352 | 0 | 1 | 0.27 | (0.008) | 0.444 | 0.197 |
Drinking | 3352 | 0 | 1 | 0.29 | (0.008) | 0.452 | 0.204 |
Running water | 3352 | 0 | 1 | 0.90 | (0.005) | 0.304 | 0.092 |
Toilet | 3352 | 0 | 1 | 0.63 | (0.008) | 0.483 | 0.234 |
Sanitary conditions | 3352 | 0 | 1 | 0.89 | (0.005) | 0.310 | 0.096 |
Model 1 N = 3335 | Model 2 N = 3335 | Model 3 N = 3335 | Model 4 N = 3335 | Model 5 N = 3335 | Model 6 N = 3335 | Model 7 N = 3335 | |
---|---|---|---|---|---|---|---|
Intercept Term | 3.910 *** (0.065) | 3.221 *** (0.155) | 3.308 *** (0.172) | 3.405 *** (0.178) | 3.378 *** (0.79) | 3.374 *** (0.179) | 3.284 *** (0.183) |
Years of education | 0.019 *** (0.003) | 0.014 *** (0.003) | 0.015 *** (0.003) | 0.012 *** (0.003) | 0.011 *** (0.003) | 0.011 *** (0.003) | 0.011 *** (0.003) |
Sex | 0.002 (0.025) | 0.021 (0.025) | 0.017 (0.025) | 0.022 (0.025) | 0.012 (0.025) | −0.009 (0.032) | −0.010 (0.032) |
Age | −0.005 *** (0.001) | −0.005 *** (0.001) | −0.004 *** (0.001) | −0.005 *** (0.001) | −0.004 *** (0.001) | −0.004 *** (0.001) | −0.004 *** (0.001) |
Family income | 0.065 *** (0.013) | 0.070 *** (0.013) | 0.064 *** (0.014) | 0.047 *** (0.016) | 0.047 *** (0.016) | 0.045 *** (0.016) | |
Medical insurance | −0.164 ** (0.083) | −0.177 ** (0.082) | −0.183 ** (0.082) | −0.184 ** (0.082) | −0.178 ** (0.082) | ||
Medical institution | −0.300 *** (0.061) | −0.325 *** (0.061) | −0.319 *** (0.061) | −0.319 *** (0.061) | −0.315 *** (0.061) | ||
Residence | 0.081 ** (0.030) | 0.047 (0.031) | 0.048 (0.031) | 0.030 (0.034) | |||
Marital status | 0.007 (0.039) | −0.007 (0.039) | −0.008 (0.039) | −0.007 (0.039) | |||
Job | 0.027 (0.032) | 0.024 (0.032) | 0.026 (0.032) | ||||
Working hours | 0.026 ** (0.012) | 0.025 ** (0.012) | 0.025 ** (0.012) | ||||
Individual income | 0.021 ** (0.010) | 0.021 ** (0.010) | 0.020 * (0.010) | ||||
Smoking | 0.007 (0.034) | 0.007 (0.034) | |||||
Drinking | 0.037 (0.033) | 0.039 (0.033) | |||||
Running water | 0.121 *** (0.042) | ||||||
Toilet | −0.009 (0.032) | ||||||
Sanitary conditions | 0.026 (0.042) | ||||||
SER | 0.704 | 0.702 | 0.699 | 0.698 | 0.698 | 0.698 | 0.697 |
R2 | 0.042 | 0.049 | 0.058 | 0.059 | 0.062 | 0.062 | 0.065 |
Model 1 N = 3336 | Model 2 N = 3336 | Model 3 N = 3336 | Model 4 N = 3336 | Model 5 N = 3336 | Model 6 N = 3336 | Model 7 N = 3336 | |
---|---|---|---|---|---|---|---|
Intercept Term | 3.230 *** (0.067) | 2.641 *** (0.157) | 2.361 *** (0.176) | 3.416 *** (0.182) | 2.390 *** (0.183) | 2.384 *** (0.184) | 2.347 *** (0.188) |
Years of education | 0.019 *** (0.003) | 0.015 *** (0.003) | 0.015 *** (0.003) | 0.013 *** (0.003) | 0.012 *** (0.003) | 0.012 *** (0.003) | 0.011 *** (0.003) |
Sex | −0.051 ** (0.025) | −0.050 ** (0.025) | −0.051 ** (0.025) | −0.049 * (0.025) | −0.060 ** (0.026) | −0.084 ** (0.033) | −0.083 ** (0.033) |
Age | 0.001 (0.001) | 0.02 * (0.001) | 0.002 * (0.001) | 0.001 (0.001) | 0.002 (0.001) | 0.002 (0.001) | 0.002 (0.001) |
Family income | 0.056 *** (0.013) | 0.054 *** (0.013) | 0.049 *** (0.014) | 0.027 * (0.016) | 0.027 * (0.016) | 0.022 (0.016) | |
Medical insurance | 0.308 *** (0.085) | 0.302 *** (0.085) | 0.291 *** (0.085) | 0.290 *** (0.085) | 0.287 *** (0.085) | ||
Medical institution | −0.000 (0.063) | −0.004 (0.063) | 0.002 (0.063) | 0.001 (0.063) | 0.001 (0.063) | ||
Residence | 0.046 (0.031) | 0.030 (0.032) | 0.031 (0.032) | −0.002 (0.034) | |||
Marital status | 0.040 (0.040) | 0.042 (0.040) | 0.041 (0.040) | 0.048 (0.040) | |||
Job | 0.024 (0.032) | 0.022 (0.032) | 0.023 (0.032) | ||||
Working hours | 0.009 (0.012) | 0.009 (0.012) | 0.007 (0.012) | ||||
Individual income | 0.027 ** (0.010) | 0.027 ** (0.010) | 0.025 ** (0.010) | ||||
Smoking | 0.019 (0.035) | 0.020 (0.035) | |||||
Drinking | 0.030 (0.034) | 0.031 (0.034) | |||||
Running water | 0.020 (0.043) | ||||||
Toilet | 0.044 (0.032) | ||||||
Sanitary conditions | 0.113 *** (0.042) | ||||||
SER | 0.716 | 0.715 | 0.713 | 0.713 | 0.713 | 0.713 | 0.712 |
R2 | 0.017 | 0.022 | 0.026 | 0.027 | 0.030 | 0.030 | 0.033 |
Physical Health | Psychological Health | |||||
---|---|---|---|---|---|---|
2SLS1 N = 3335 | Robust1 N = 3335 | Robust3 N = 1985 | 2SLS2 N = 3336 | Robust2 N = 3336 | Robust4 N = 1985 | |
Intercept Term | 3.020 *** (0.209) | 3.118 *** (0.198) | 3.078 *** (0.280) | 2.202 *** (0.208) | 2.264 *** (0.198) | 2.151 *** (0.276) |
Years of education/highest education | 0.058 *** (0.016) | 0.179 *** (0.049) | 0.072 *** (0.020) | 0.037 ** (0.016) | 0.116 ** (0.049) | 0.043 ** (0.020) |
Sex | −0.084 ** (0.041) | −0.073 * (0.039) | −0.112 ** (0.055) | −0.125 *** (0.041) | −0.118 *** (0.040) | −0.149 *** (0.054) |
Age | 0.001 (0.002) | 0.001 (0.002) | 0.003 (0.003) | 0.005 ** (0.002) | 0.004 ** (0.002) | 0.005 * (0.003) |
Family income | 0.028 (0.017) | 0.031 * (0.017) | 0.025 (0.022) | 0.012 (0.017) | 0.014 (0.017) | 0.008 (0.021) |
Medical insurance | −0.167 * (0.085) | −0.157 * (0.085) | −0.200 * (0.113) | 0.290 *** (0.086) | 0.297 *** (0.086) | 0.433 *** (0.111) |
Medical institution | −0.325 *** (0.063) | −0.323 *** (0.063) | −0.367 *** (0.081) | −0.006 (0.064) | −0.005 (0.064) | 0.045 (0.080) |
Residence | −0.146 ** (0.068) | −0.148 ** (0.068) | −0.177 ** (0.086) | −0.102 (0.068) | −0.103 (0.069) | −0.088 (0.085) |
Marital status | −0.007 (0.041) | −0.006 (0.040) | −0.054 (0.054) | 0.048 (0.041) | 0.049 (0.041) | 0.001 (0.053) |
Job | −0.009 (0.035) | −0.020 (0.036) | −0.030 (0.047) | 0.004 (0.035) | −0.003 (0.036) | −0.008 (0.046) |
Working hours | 0.026 ** (0.013) | 0.024 * (0.012) | 0.018 (0.017) | 0.007 (0.013) | 0.006 (0.013) | 0.008 (0.016) |
Individual income | 0.005 (0.012) | 0.006 (0.011) | −0.007 (0.016) | 0.016 (0.012) | 0.016 (0.011) | 0.012 (0.015) |
Smoking | 0.051 (0.038) | 0.047 (0.038) | 0.037 (0.051) | 0.045 (0.038) | 0.042 (0.038) | 0.030 (0.050) |
Drinking | 0.040 (0.034) | 0.042 (0.034) | 0.055 (0.045) | 0.032 (0.034) | 0.033 (0.034) | 0.057 (0.044) |
Running water | 0.132 *** (0.044) | 0.128 *** (0.044) | 0.135 ** (0.061) | 0.026 (0.044) | 0.024 (0.044) | 0.051 (0.059) |
Toilet | −0.070 * (0.038) | −0.065 * (0.038) | −0.070 (0.051) | 0.008 (0.039) | 0.011 (0.038) | −0.020 (0.050) |
Sanitary conditions | −0.006 (0.044) | −0.003 (0.044) | −0.037 (0.062) | 0.095 ** (0.044) | 0.097 ** (0.044) | 0.060 (0.061) |
SER | 0.720 | 0.717 | 0.732 | 0.719 | 0.719 | 0.719 |
R2 | 0.003 | 0.011 | −0.046 | 0.013 | 0.012 | 0.012 |
F | 145.88 | 155.86 | 89.62 | 144.40 | 154.65 | 88.44 |
Full Sample N = 3335 | Youth N = 1022 | Middle-Aged N = 1119 | Elderly N = 1194 | |
---|---|---|---|---|
Intercept Term | 3.105 *** (0.177) | 3.629 *** (0.329) | 2.582 *** (0.302) | 3.054 *** (0.292) |
Years of education/RWD | 0.057 *** (0.015) | 0.055 * (0.031) | 0.053 (0.036) | 0.070 *** (0.022) |
Sex | −0.080 ** (0.037) | −0.035 (0.061) | −0.038 (0.085) | −0.174 ** (0.068) |
Family income | 0.026 (0.018) | 0.002 (0.041) | 0.066 ** (0.029) | 0.020 (0.027) |
Medical insurance | −0.165 * (0.085) | 0.024 (0.142) | −0.421 *** (0.148) | −0.124 (0.158) |
Medical institution | −0.320 *** (0.063) | −0.472 * (0.256) | −0.263 ** (0.111) | −0.342 *** (0.082) |
Residence | −0.142 ** (0.062) | −0.142 (0.120) | −0.089 (0.140) | −0.216 ** (0.103) |
Marital status | −0.005 (0.041) | −0.072 (0.079) | 0.253 ** (0.107) | −0.060 (0.070) |
Job | −0.028 (0.050) | 0.065 (0.101) | −0.067 (0.070) | 0.071 (0.060) |
Working hours | 0.025 ** (0.013) | 0.021 (0.024) | 0.034 (0.022) | 0.017 (0.020) |
Individual income | 0.005 (0.011) | −0.036 (0.024) | 0.030 * (0.018) | −0.005 (0.021) |
Smoking | 0.051 (0.038) | 0.093 (0.081) | 0.018 (0.073) | 0.061 (0.057) |
Drinking | 0.040 (0.034) | 0.028 (0.064) | −0.049 (0.061) | 0.125 ** (0.056) |
Running water | 0.132 *** (0.044) | −0.009 (0.080) | 0.097 (0.073) | 0.304 *** (0.080) |
Toilet | −0.069 * (0.038) | −0.036 (0.082) | −0.052 (0.065) | −0.111 * (0.060) |
Sanitary conditions | −0.007 (0.045) | 0.048 (0.084) | −0.103 (0.077) | 0.061 (0.074) |
SER | 0.719 | 0.704 | 0.733 | 0.721 |
R2 | 0.004 | −0.031 | 0.023 | −0.018 |
F | 145.337 | 40.663 | 33.531 | 63.905 |
Full Sample N = 3336 | Youth N = 1024 | Middle Age N = 1120 | Old Age N = 1192 | |
---|---|---|---|---|
Intercept Term | 2.537 *** (0.178) | 3.098 *** (0.331) | 2.422 *** (0.305) | 2.240 *** (0.290) |
Years of education/RWD | 0.035 ** (0.015) | 0.048 (0.032) | 0.002 (0.036) | 0.047 ** (0.022) |
Sex | −0.108 *** (0.037) | −0.013 (0.061) | −0.084 (0.086) | −0.228 *** (0.067) |
Family income | 0.005 (0.018) | −0.088 ** (0.041) | 0.022 (0.029) | 0.063 ** (0.027) |
Medical insurance | 0.296 *** (0.086) | 0.229 (0.145) | 0.304 ** (0.150) | 0.342 ** (0.156) |
Medical institution | 0.014 (0.063) | 0.434 * (0.255) | −0.049 (0.113) | −0.050 (0.081) |
Residence | −0.086 (0.063) | −0.212 * (0.122) | 0.019 (0.139) | −0.048 (0.102) |
Marital status | 0.057 (0.041) | 0.086 (0.078) | −0.003 (0.108) | 0.038 (0.069) |
Job | −0.069 (0.049) | 0.155 (0.101) | 0.023 (0.069) | 0.049 (0.059) |
Working hours | 0.007 (0.013) | −0.022 (0.024) | 0.017 (0.023) | 0.020 (0.020) |
Individual income | 0.018 (0.011) | 0.026 (0.024) | 0.039 ** (0.018) | −0.020 (0.021) |
Smoking | 0.046 (0.038) | −0.037 (0.081) | 0.038 (0.073) | 0.104* (0.057) |
Drinking | 0.032 (0.034) | 0.067 (0.064) | 0.010 (0.061) | 0.021 (0.056) |
Running water | 0.026 (0.044) | 0.047 (0.080) | 0.009 (0.073) | 0.042 (0.079) |
Toilet | 0.012 (0.038) | −0.001 (0.084) | 0.007 (0.065) | 0.001 (0.059) |
Sanitary conditions | 0.092 ** (0.044) | 0.186 ** (0.083) | 0.069 (0.077) | 0.064 (0.073) |
SER | 0.719 | 0.701 | 0.740 | 0.713 |
R2 | 0.012 | 0.022 | 0.024 | 0.013 |
F | 144.220 | 38.592 | 34.884 | 63.242 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Dai, X.; Li, W. Impact of Education, Medical Services, and Living Conditions on Health: Evidence from China Health and Nutrition Survey. Healthcare 2021, 9, 1122. https://doi.org/10.3390/healthcare9091122
Dai X, Li W. Impact of Education, Medical Services, and Living Conditions on Health: Evidence from China Health and Nutrition Survey. Healthcare. 2021; 9(9):1122. https://doi.org/10.3390/healthcare9091122
Chicago/Turabian StyleDai, Xianhua, and Wenchao Li. 2021. "Impact of Education, Medical Services, and Living Conditions on Health: Evidence from China Health and Nutrition Survey" Healthcare 9, no. 9: 1122. https://doi.org/10.3390/healthcare9091122