| Literature DB >> 31470658 |
Zhaohua Zhang1, Yuxi Luo2, Derrick Robinson3.
Abstract
y applying a fuzzy regression discontinuity design, this study investigates whether sons, daughters, or parents are the beneficiaries of China's New Rural Pension Scheme. Using data drawn from the China Health and Retirement Longitudinal Survey, our results indicate that pension income crowds out approximately 27.9% of the monetary support from adult sons and decreases the likelihood that adult sons live with their parents by 6.5%. However, we do not find a significant effect of pension income on the likelihood that adult daughters live with their parents. In regards to the well-being of parents, which is measured by consumption and health outcomes, the results show that pension income increases food and non-food consumption by 16.3 and 15.1%, respectively, and improves the psychological health of the elderly. Accounting for the different effects of pension income for those with different income levels, our results show that the New Rural Pension Scheme only has a significant effect on the poor elderly.Entities:
Keywords: New Rural Pension Scheme; elderly health; family-based eldercare; intergenerational relationship; regression discontinuity
Mesh:
Year: 2019 PMID: 31470658 PMCID: PMC6747179 DOI: 10.3390/ijerph16173159
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Variable definitions and summary statistics.
| Variable | Definition | Mean | SD |
|---|---|---|---|
| NRPS | If the respondent received pension payment (=1) | 0.461 | 0.499 |
| Male | If the respondent is male (=1) | 0.470 | 0.499 |
| Age | Age of the respondent | 60.05 | 10.31 |
| Illiterate | If the respondent has no formal education (=1) | 0.280 | 0.449 |
| Elementary | If the respondent has primary education (=1) | 0.393 | 0.489 |
| Junior | If the respondent has junior high school education (=1) | 0.161 | 0.367 |
| Senior | If the respondent has senior high school education (=1) | 0.048 | 0.214 |
| Income | Household income (1000 CNY) | 12.94 | 106.3 |
| Food | Daily food consumption expenditure (yuan/day) | 14.95 | 53.8 |
| Non-Food | Daily non-food consumption expenditure (yuan/day) | 17.45 | 68.26 |
| IADL | If no IADL limitation (=1) | 0.741 | 0.438 |
| Depression | The sum of the mental well-being measurement score | 9.212 | 5.709 |
| Transfer_son | Transfer from adult son | 1261 | 4638 |
| Transfer_daughter | Transfer from adult daughter | 681.5 | 3162 |
| Coresident_son | If the respondent co-resides with son (=1) | 0.323 | 0.468 |
| Coresident_daughter | If the respondent co-resides with daughter (=1) | 0.065 | 0.247 |
|
| No. of observations | 9324 | |
Source: China Health and Retirement Longitudinal Survey (2015) [17]. Note: NRPS: New Rural Pension Scheme; IADL: instrumental activities of daily living; SD: Standard Deviation.
Figure 1Pension receipt according to normalized age. Source: China Health and Retirement Longitudinal Survey (2015) [17]. Notes: Figure 1 describes the relationship between rate of pension receipt and respondents’ normalized age, using the sample of respondents aged between 50 and 70. The dots show the sample mean of the indicator variable for receiving a pension (1 if receiving pension, 0 otherwise) in each half year. The line is a polynomial fit of order 2.
Figure 2Testing the continuity of age density. Source: China Health and Retirement Longitudinal Survey (2015) [17].
First-stage estimates for pension receipt.
| Depend Variable | Local Linear Regression | OLS with a 2nd-Order Polynomial | Local Linear Regression | OLS with a 2nd-Order Polynomial |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Indicator of pension receipt (Yes = 1; No = 0) | 0.529 *** | 0.511 *** | 0.567 *** | 0.554 *** |
| Bandwidth | 5.53 | 6.45 | ||
| Covariate | No | No | Yes | Yes |
|
| 7705 | |||
Source: China Health and Retirement Longitudinal Survey (2015) [17]. Note: Sample consists of respondents aged within 10 years of the cut-off (60). Columns (1) and (2) reports the local linear regression and the OLS estimation with the 2nd-order polynomial of the running variable, respectively. Columns (3) and (4) shows the estimation results when including covariates. Covariates include gender, educational attainment, household income, and region of residence. Robust standard errors are in parentheses. *** p < 0.01.
Regression discontinuity (RD) estimation on outcomes of Interest.
| Dependent Variable | Local Linear Regression | 2SLS with a 2nd-Order | ||
|---|---|---|---|---|
| Reduced-Form RD | Fuzzy RD | Reduced-Form RD | Fuzzy RD | |
| Transfer_son | −0.146 * | −0.291 * | −0.204 ** | −0.279 ** |
| Bandwidth | 9.412 | 7.573 | ||
| Transfer_daughter | −0.098 | −0.152 | −0.148 * | −0.203 ** |
| Bandwidth | 8.899 | 8.047 | ||
| Coresident_son | −0.101 *** | −0.135 *** | −0.047 ** | −0.065 ** |
| Bandwidth | 5.826 | 7.466 | ||
| Coresident_daughter | 0.018 | 0.033 | 0.021 | 0.029 |
| Bandwidth | 6.318 | 5.613 | ||
| Food | 0.112 * | 0.170 * | 0.118 ** | 0.163 ** |
| Bandwidth | 7.769 | 8.585 | ||
| Non-food | 0.098 * | 0.148 * | 0.109 ** | 0.151 ** |
| Bandwidth | 7.800 | 8.233 | ||
| IADL | −0.018 | −0.025 | −0.020 | −0.027 |
| Bandwidth | 6.470 | 7.101 | ||
| Depression | −0.437 | −0.719 | −0.758 ** | −1.037 *** |
| Bandwidth | 7.397 | 7.132 | ||
| First stage F-statistics for IV | 3742.46 | |||
|
| 7705 | |||
Source: China Health and Retirement Longitudinal Survey (2015) [17]. Note: Table 3 presents the reduced-form RD estimates and the fuzzy RD estimates using local linear regression and a Two-Stage Least Squares (2SLS) method with a 2nd-order polynomial, respectively. In the local linear regression, the optimal bandwidths are selected by applying Calonico et al.’s (2014) [19] method. All the regressions control for gender, educational attainment, household income, and living region. * p < 0.1, ** p < 0.05, and *** p < 0.01.
RD estimation on outcomes of interest by poverty status.
| Dependent Variable | Poor Elderly | Non-Poor Elderly | ||||||
|---|---|---|---|---|---|---|---|---|
| Local Linear Regression | 2SLS with a 2nd-Order | Local Linear Regression | 2SLS with a 2nd-Order | |||||
| Reduced-Form RD | Fuzzy RD | Reduced-Form RD | Fuzzy RD | Reduced-Form RD | Fuzzy RD | Reduced-Form RD | Fuzzy RD | |
| Transfer_son | −0.238 * | −0.426 * | −0.213 ** | −0.281 ** | 0.041 | 0.025 | −0.182 | −0.278 |
| Bandwidth | 7.212 | 6.558 | 6.574 | 7.615 | ||||
| Transfer_daughter | −0.105 | −0.167 | −0.197 ** | −0.260 | −0.046 | −0.082 | −0.005 | −0.008 |
| Bandwidth | 7.877 | 7.078 | 6.886 | 8.864 | ||||
| Coresident_son | −0.124 *** | −0.190 *** | −0.054 ** | −0.071 ** | 0.015 | 0.021 | −0.008 | −0.013 |
| Bandwidth | 6.661 | 7.262 | 7.524 | 8.237 | ||||
| Coresident_daughter | 0.029 | 0.059 | 0.026 | 0.035 | −0.012 | −0.011 | 0.001 | 0.001 |
| Bandwidth | 5.566 | 4.970 | 6.265 | 7.394 | ||||
| Food | 0.138 * | 0.254 * | 0.165 * | 0.221 * | 0.087 | 0.150 | −0.036 | −0.054 |
| Bandwidth | 6.258 | 5.949 | 6.275 | 6.799 | ||||
| Non-food | 0.173 *** | 0.308 *** | 0.153 ** | 0.204 ** | −0.143 | −0.041 | −0.052 | −0.081 |
| Bandwidth | 8.034 | 7.337 | 6.416 | 8.061 | ||||
| IADL | 0.004 | 0.003 | 0.026 | 0.034 | −0.063 | −0.084 | −0.006 | −0.009 |
| Bandwidth | 7.615 | 6.795 | 6.552 | 7.636 | ||||
| Depression | −0.245 | −0.493 | −0.732 ** | −0.966 ** | −0.726 | −1.106 | −0.657 | −1.008 |
| Bandwidth | 6.381 | 6.673 | 6.440 | 7.590 | ||||
| First stage F-statistics for the instrumental variable (IV) | 2974.42 | 783.89 | ||||||
|
| 5292 | 2413 | ||||||
Source: China Health and Retirement Longitudinal Survey (2015) [17]. Note: * p < 0.1, ** p < 0.05, and *** p < 0.01.
Robustness check for alternative bandwidths.
| Dependent Variable | Aged between 55 and 65 | Aged between 45 and 75 | ||||||
|---|---|---|---|---|---|---|---|---|
| Poor Elderly | Non-Poor Elderly | Poor Elderly | Non-Poor Elderly | |||||
| Reduced | Fuzzy RD | Reduced | Fuzzy RD | Reduced | Fuzzy RD | Reduced | Fuzzy RD | |
| Transfer_son | −0.246 * | −0.316 * | 0.133 | 0.193 | −0.184 * | −0.242 * | −0.048 | −0.078 |
| Transfer_daughter | 0.008 | 0.010 | −0.041 | −0.059 | −0.019 | −0.025 | −0.017 | −0.027 |
| Coresident_son | −0.223 *** | −0.287 *** | −0.010 | −0.014 | −0.057 * | 0.075 * | −0.023 | −0.039 |
| Coresident_daughter | 0.035 | 0.049 | 0.022 | 0.035 | 0.020 | 0.026 | −0.001 | −0.001 |
| Food | 0.239 * | 0.309 * | 0.024 | 0.157 | 0.170 ** | 0.228 ** | 0.025 | 0.040 |
| Non-food | 0.286 *** | 0.372 *** | 0.094 | 0.132 | 0.175 *** | 0.234 *** | −0.101 | −0.083 |
| IADL | 0.038 | 0.049 | 0.085 | 0.123 | 0.004 | 0.005 | −0.050 | −0.081 |
| Depression | −0.331* | −0.426 * | −1.600 | −1.157 | −0.503 * | −0.663 * | −0.827 | −1.340 |
| First stage F-statistics for IV | 127.43 | 44.50 | 2346.93 | 470.07 | ||||
|
| 1952 | 772 | 6287 | 2958 | ||||
Source: China Health and Retirement Longitudinal Survey (2015) [17]. Note: Samples aged 55–65 and 45–75 are applied in the OLS regression with a 2nd-order polynomial, respectively. * p < 0.1, ** p < 0.05, and *** p < 0.01.
Robustness check for alternative cut-offs.
| Dependent Variable | Cut-off Age of 54 | Cut-off Age of 64 | ||||||
|---|---|---|---|---|---|---|---|---|
| Poor Elderly | Non-Poor Elderly | Poor Elderly | Non-Poor Elderly | |||||
| Reduced | Fuzzy RD | Reduced | Fuzzy RD | Reduced | Fuzzy RD | Reduced | Fuzzy RD | |
| Transfer_son | 0.267 | 0.093 | 0.097 | 0.029 | −0.141 | −0.120 | 0.045 | 0.057 |
| Transfer_daughter | 0.145 | 0.050 | 0.046 | −0.014 | −0.025 | −0.021 | −0.014 | −0.015 |
| Coresident_son | 0.043 | 0.015 | −0.066 | 0.020 | 0.026 | 0.022 | 0.012 | 0.015 |
| Coresident_daughter | 0.015 | 0.052 | 0.090 | 0.027 | −0.027 | −0.023 | −0.082 | −0.104 |
| Food | 0.102 | 0.196 | 0.089 | −0.267 | 0.035 | 0.269 | 0.026 | −0.093 |
| Non-food | 0.086 | 0.109 | 0.038 | −0.065 | 0.008 | 0.064 | 0.017 | 0.016 |
| IADL | 0.045 | 0.041 | −0.021 | 0.062 | −0.035 | −0.030 | −0.046 | −0.058 |
| Depression | −0.341 | −1.192 | −0.221 | −0.666 | 1.024 | −0.867 | −0.915 | −1.161 |
| First stage F-statistics for IV | 0.126 | 1.513 | 1.729 | 0.275 | ||||
|
| 2968 | 1314 | 2324 | 1099 | ||||
Source: China Health and Retirement Longitudinal Survey (2015) [17]. Note: Age 54 and 64 were used as two alternative cut-offs for the OLS regression with a 2nd-order polynomial.