Reducing Multidimensional Poverty of Elderly: The Role of the New Rural Pension Scheme in China

Table 10

Poverty-reduction effect of the NRPS (comparison of different models).

Variable	Participating effect (age: 45–59, participation = 0/1)				Receiving effect (age:60+, pension = 0/1)
	OLS	Probit	Eprobit	FE	OLS	Probit	Eprobit
Multipoverty	−0.0367 (0.0074)	−0.0367 (0.0072)	−0.0479 (0.0159)	−0.032 (0.012)	−0.0519 (0.0087)	−0.0508 (0.0084)	−0.051 (0.008)
Lifepoverty	−0.0098 (0.0079)	−0.0118 (0.0080)	−0.0322 (0.0184)	−0.002 (0.011)	−0.0199 (0.0086)	−0.0219 (0.0087)	−0.022 (0.009)
Healthpoverty	−0.0752 (0.0072)	−0.0780 (0.0069)	−0.0713 (0.0149)	−0.063 (0.012)	−0.0354 (0.0090)	−0.0378 (0.0089)	−0.038 (0.009)
Socialpoverty	−0.02031 (0.0071)	−0.020 (0.0068)	−0.0121 (0.0151)	−0.0221 (0.012)	−0.0187 (0.0080)	−0.0187 (0.0076)	−0.0191 (0.008)
Swell-beingpoverty	−0.0359 (0.0074)	−0.0342 (0.0070)	−0.0604 (0.0158)	−0.0375 (0.012)	−0.0164 (0.0085)	−0.0147 (0.0082)	−0.0152 (0.008)
Province fixed	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	15076	15076	15076	11320	11456	11456	11456

Note: robust standard errors in brackets. The symbols , , and indicate statistical significance at the 10%, 5%, and 1% levels, respectively. And the control variables are the same as in Table 4. The probit and eprobit models show the marginal effect of participation and pension variables. The model used by eprobit is the extended regression model (ERM), a new module in STATA 15 that allows endogenous explanatory variables and being interpreted. When the variables are all 0-1 variables. The regression coefficient of the instrumental variable in the first-stage of eprobit model is 4.0819, and the t statistic is 56.03. The FE model uses a sample with at least two-period observations, and the sample includes 4,721 individuals (PID).