Does New Rural Pension Scheme crowd out private ...

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Longitudinal Study (CHARLS) and combination of regression discontinuity design and difference in difference method (RD-DiD) are used to perform the analysis ...
Does New Rural Pension Scheme crowd out private transfers from children to parents? Empirical evidence from China Ning Manxiu1, Liu Weiping1, Gong Jinquan2*, Liu Xudong3 1

College of Economics, Fujian Agricultural and Forestry University, China 2

National School of Development, Peking University, China

3

School of Computer Science and Technology, Harbin Institute of Technology, China

Abstract: Purpose - The primary objective of this paper is to examine the effect of New Rural Pension Scheme (NRPS) on the private transfer behavior of the non-co resident adult children to their elderly parents in rural China, and hence address the income redistribution effectiveness of public program for the elderly in rural China. Design/methodology - Pooled data from two waves of the China Health and Retirement Longitudinal Study (CHARLS) and combination of regression discontinuity design and difference in difference method (RD-DiD) are used to perform the analysis. Findings - No evidence is found that pension payment from NRPS program does significantly crowd out the economic support from the adult children to their elder parents. The heterogeneous effects at different income percentile indicate that pension payment significantly increases the probability of receiving gross transfers and likelihood of the net transfer being positive for those elderly individuals with low income; In particular, the distinctive “family binding” arrangement may dramatically contribute to increasing the probability of receiving private transfers for the pension recipients. Originality/value - The empirical findings would have far-reaching implications for the efficacy of public transfer or re-distributive programs such as NRPS; for the rural elderly, in particular, the unique “family binding” mechanism under the NRPS program may have positive welfare effects on the intended beneficiaries. Furthermore, an understanding of the inter-linkage between informal arrangements of elderly support and social re-distributive program provides further insight into the design of social security systems targeted to the vulnerable group in developing countries.

Key words: private transfer, crowding out effect, New Rural Pension Scheme

*

1

corresponding author, Email address: [email protected]

1. Introduction China has been undergoing an unprecedented demographic transition and rapid aging with the percentage of the population aged 60+ increased from 10.33% in 2000 to 13.26% in 2010 (National Bureau of Statistics, 2011)1. In response to the rapidly aging population and in order to improve the welfare of the elderly, the Chinese government launched an innovative program called the New Rural Pension Scheme (NRPS), for rural residents in late 2009, and then expanded the NRPS program by the end of 2012 to cover the full geographical area of rural China. Within the framework of NRPS, numerous debates have arisen on related issues: Does the pension reform provide reasonable protections against the risks of poverty in old age by efficiently allocating resources to the elderly? Does the NRPS program meet its distributive concerns? Whether and to what extent could the elderly in rural China benefit from the NRPS program? With an eye toward those debates concerning the extent to which the NRPS program serves as a “springboard” for social protection of the elderly, one critical issue concerns the effectiveness of the public pension program focusing on the potential interaction between private and public transfers. If private and public transfers are close substitutes, an expansion of public income redistribution could prompt a reduction in private transfers, thereby diluting the program’s effectiveness (Cox, 1987; Cox and Jimenez, 1990, 1995; Reil-Held, 2006;Fan, 2010)2. For 1By

international standards, an aging society is defined as a population aged 60 and 65 or over which exceeds 10% and 7%, respectively. 2

The economic theory concerning the effect of public transfers on familial transfers is associated with the motives of familial transfers. Altruism and self-interested exchange motives are identified as the two predominant ones (Arrondel and Masson, 2006). In an altruistic model indicated by Becker (1974), altruistic private transfers will be cut back after the introduction of public transfer programs, thereby the distributional impact of public transfers is dampened or completely neutralized by private behavior in this setting (Cox,1987; Cox and Jimenez,1990). In the case of exchange-motivated private transfers, public transfers will not necessarily undermine private donors desire to provide transfers because the donors expect a quid pro quo (Cox, 1987; Cox and Jimenez, 1990;Cox and Fafchamps, 2007).The main problem, however, is that the assumption of each individual having a single well-defined motive is unrealistic. There is competition and overlap among motives, and a person may hold several motives simultaneously that seemingly contradict each other (Finch and Mason, 1993;Künemund and Motel, 2000).In this paper, we do not aim to directly examine the motives of the donors, but we are particularly interested in estimating how and to what extent the government transfers substitute familial transfers. 2

example, wider social security coverage might cause children to reduce private transfers to their elderly parents so that the wellbeing of elderly parents ends up with being minimally affected by the public transfers originally targeted toward them. In addition, the government transfer programs have administrative costs, which would create deadweight losses in the “crowding out” setting (Cox and Jakubson, 1995).On the contrary, the distributional impact of public transfers can actually be reinforced by private behavioral responses (Cox and Rank, 1992; Cox and Jakubson, 1995). As a result, the primary objective of this paper is to address the following questions: whether and to what extent does the introduction of the NRPS program with characteristics of public transfer displace or crowd out the familial transfers from adult children to their aged parents? The empirical findings would have far-reaching implications for the efficacy of NRPS with unique “family binding” arrangement different from pension programs in other countries. Furthermore, an understanding of the inter-linkage between informal arrangements of elderly support and social re-distributive program provides further insight into the design of social security systems targeted to the vulnerable group in developing countries. A few empirical studies attempt to directly examine the “crowding out” effect of public re-distributive programs including old age pension schemes, but the extent and magnitude of the effect are inconclusive. Some studies find that public transfers have little effect on private transfers (for example, Rosenzweig and Wolpin,1994; Cox and Jakubson,1995), whereas studies by Cox and Jimenez(1992,1995),Jensen(2004), Juraez (2009), Fan(2010) and Chuang(2012) indicate a high possibility of crowding out effect. On the other hand, existing evidence points to the opposite conclusion: Welfare state provisions, far from crowding out family support, enable a family in turn to provide new inter-generational support and transfers (Kohli, 1999; Künemund and Rein, 1999). 3

Generally, these inconsistent findings result from difference in definitions of private transfers, endowment constraints, household-specific heterogeneity of preference, various econometric issues, and so on. Among these differences, the most important thing, however, is the specific targeting scheme of the social safety net programs in the individual countries, which includes contribution structure, benefit level and eligibility rules. As a consequence, gauging the responsiveness of private transfers to an exogenous increase in the income of the elderly is an empirical matter owing to different institutional contexts and programmatic details. In recent years, a large number of studies have attempted to identify causal effects of NRPS introduction on various outcomes (Chen and Zeng, 2013; Cheng, et al., 2013; Zhang, et al., 2014; Zhang and Chen, 2014; Chen,2016; Cheng et al., 2016; Chen, 2017). For instance, Chen and Zeng (2013), Cheng et al. (2013) and Zhang and Chen (2014) all examine the effects of the pension receipt on private transfer. To avoid endogenous bias, Chen and Zeng (2013) and Cheng et al. (2013) use the DID method combined with propensity score matching, while Zhang and Chen (2014) uses the RD method. In another related study, using CHARLS data sets, Zhang et al. (2014) examine the effects of the NRPS on household income, expenditure, labor supply, health, and subjective well-being using both the DID method and RD method.Clearly, the existing studies provide valuable insights for us to extensively understand effectiveness of the NRPS program and scientifically recognize the behavior response of the stakeholders. However, most of these studies could not separate the effect of the unique "family binding" from NRPS, and also could not answer how the adult children response to this special arrangement, because the "family binding" design has been suffered from a lot of criticism and doubt in the initial stage. To the best of our knowledge, the private transfer responses of adult children to a public pension scheme in rural China are far from being well understood. This study contributes to the existing literature by evaluating the welfare effect of public pension program with unique Chinese feature 4

of “family binding” arrangement that have not been well explored. Also, understanding the behavior response of the private transfer of adult children due to pension expansion in rural China is of particularly great importance for some less developed countries in Southeast Asia which have some similar economic and cultural background with China and are experiencing rapid demographic aging, as they have already taken actions to establish state-sponsored pension programs for the elderly. The remainder of this paper is organized as follows. Section 2 describes the institutional background of NRPS in China. Section 3 illustrates the data set and descriptive evidence; the empirical model and estimation strategies are shown in section 4. The empirical results are presented in section 5 and section 6, and conclusions are exhibited in section 7.

2. Institutional Background

In September 2009, the Chinese State Council issued guideline on the pilot projects of New Rural Pension Scheme (NRPS), suggesting that the NRPS program was officially launched since 2009. Participation in NRPS is voluntary for all rural residents who satisfy the following conditions: (1) be at least 16 years old; (2) not be a student; and (3) not already be covered in a basic urban pension scheme.3 Following the broad guideline issued by the central government, the NRPS program has been operated at the county level, each county may establish the NRPS program at different time; and local governments are encouraged to supplement the basic pension benefits at their discretion from their own fiscal revenues. Therefore, the contribution rate and basic pension benefit of the NRPS program could be varied by regions.4 3 4

The detailed description of the design of the contributory pension part can be found in Cai et al. (2012).

Theoretically, the implementation time at each county may be a feasible instrumental variable (IV) to overcome the endogenous problem for the NRPS participation decision of the elderly in the following econometric strategy, however, the county name of the CHARLS data cannot be released for public use due to 5

Under the NRPS program, the pension benefits are composed of two parts: individual pension accounts with matching contributions and a basic flat pension. The initial value of the basic pension under the scheme is 55 RMB per month, which is subsidized by the central government in full for the central and western regions and in half for the eastern regions. Furthermore, the basic benefits can be increased by local governments at their discretion from their own revenues. Individual account benefits are calculated based on the participant’s accumulated contributions from the individual account and accrued investment returns, and are paid monthly by dividing the accumulation at age 60 by 139. It is important to keep in mind that those who are currently 60 years old or over can directly receive the basic pension benefit at the time of the scheme’s introduction, provided all their eligible children are contributing to the scheme, (in other words, through “family binding” arrangements). These “family binding” arrangements reflect one unique feature distinct from public pension systems in other countries. In light of the uniqueness of NRPS as discussed, China provides an interesting setting for addressing the crowding out effect of the social pension scheme on economic support from adult children to their elderly parents. In summary, the eligibility rules for receiving an old age pension are illustrated in Figure 1.Obviously, all NRPS participants are eligible to receive monthly pension payments when they turn 60 (i.e., region A).

[.........………….……Insert Figure 1……………………….]

3. Data and Descriptive evidence privacy agreement, and the community questionnaire in 2013 did not be released for academic use. As a result, we did not use instrumental variable to estimate the model. Besides, the premium rates of NRPS should play a significant role on participation decision, at the beginning stage, however, most of the elderly in rural China would choose the lowest premium level with 100 Yuan when participating in the NRPS program, so the premium rates did not vary among the elderly and would not be taken into consideration in the analysis. 6

3.1 Data The primary data set employed in this study is drawn from China Health and Retirement Longitudinal Survey (CHARLS) gathered biennially by Peking University, which includes nationally representative samples of Chinese residents aged 45 and older. The first and the second wave survey were completed in 2011 and 2013 respectively, and the samples covered about 10,000 households and 17,500 individuals in 150 counties/districts and 450 villages/resident committees in each wave. The CHARLS data set consists of comprehensive aging-related modules including socio-demographic characteristics, family transfer, income, assets, family composition, health, employment, pension and other related information about respondents. Its detailed information on family transfers and pensions makes it ideal for examining the behavioral response of economic transfers from adult children to their elderly parents under the setting of the NRPS program. For the purposes of this study, we restrict our attention to a sub sample of the elderly in rural China, and further limit our sample to respondents who were between 50 and 70 years old 5 .Furthermore, according to the eligibility rules for NRPS, the villages where NRPS was not enforced in 2011 are dropped from the analysis since the private transfer behavior would not be affected by NRPS6. Intuitively, it is not logical for those NRPS participants younger than 60 and individuals not 5

It is essential to restrict the sample to ages between 50 and 70 to specify the regression discontinuity design model (see Section 4for details). 6

As indicated by Chen and Zeng (2013), the village in rural China was not randomly chose to

launch the NRPS program, but the decision to initiate the social pension scheme in the village was dependent on its economic development, improvement of social security system and revenue capability of the local government. Obviously, there may be some systematic differences between the pilot villages and the non-pilot villages. In order to overcome the sample selection bias due to these systematic differences, we could not identify the elderly observations in the villages without covering NRPS in 2011 as a control group. 7

enrolled in NRPS to receive monthly pension; if this is the case, these samples will be deleted from the analysis. In the CHARLS data set, every child has a main respondent parent chosen randomly by the survey. In order to take full advantage of the rich information on each adult child of the parent respondent, in this study, the basic samples of interest are the children of the CHARLS respondent parents. As a result, it is essential to match individual child and parental characteristics to quantitatively examine the crowding out effect of the public pension program on the private transfer in the following analysis. Furthermore, we restrict the analysis to non co-resident children because transfers within the household are not clearly specified conceptually and CHARLS, like other aging surveys, does not attempt to measure them. After excluding those observations with missing information for our chosen set of variables described below, we obtain 5951 adult children (18 or older 7 ) of the 2719 main respondents in the study.

3.2 Variables definition and descriptive evidence In this study, characteristics of adult children are measured by child demographics and socio-economic status, which include age, gender, birth order, schooling years, the number of their own children under age 16 (grandchildren of the parents), and the sorting category of total income of child and child’s spouse. 8 In addition, the living proximity specified by whether the child is living in the same county as his parents is also controlled in the empirical analysis.9 The descriptive 7The

observations with age below 18 and full-time school students are excluded from the samples because they are not capable of supporting their parents. 8The

total income of a child and his spouse in the past year is measured by sorting for six different groups: 1 is assigned for the group with no income; 2 for the group with income under 2,000 Yuan; 3 for the group with income from 2,000 to 5,000 Yuan; 4 for the group with income from 5,000 to 10,000 Yuan; 5 for the group with income from 10,000 to 20,000 Yuan; and 6 for the group with income from 20,000 to 50,000 Yuan. 9

Just as the anonymous reviewer pointed out that, co-residence is a very important type of transfer to or from adult children for rural elderly. The results remains robust after controlling for the dummy variable, i.e. living arrangement of the elderly, the results are available on request from the authors. 8

statistics of child characteristics are summarized in table 1. In line with previous studies, several variables reflecting socio-economic and demographic characteristics of the respondent parent and regional heterogeneity are specified: the respondent’s age, gender, and marital status. Marital status is divided into two discrete groups. One group is married with his or her spouse; the other one’s status is single, including separated, windowed, divorced and never married. Socio-economic status includes the respondent’s education attainment, chronic condition, and pre-transfer income. Education attainment is classified into three categories: one category is classified as less than primary education including those who were illiterate and who did not finish primary school but were capable of reading or writing or those who reported to have been in “Sishu”10;the second category is primary school, and the third category is junior high school and above. A dummy variable is coded as 1 if the parent had any chronic disease, and otherwise coded as 0 if he or she never had any type of chronic disease. Pre-transfer income without public transfer and subsidy is specified as per capita income before transfer in the household where the respondent parent lives 11. As demonstrated in table 2, in the entire sample, 79.2 percent of 2719 respondents participated in the NRPS program, and 35.9 percent of the full sample reported that they received pension payment from the NRPS program. Likewise, 45.3 percent of the total samples of NRPS insurants received a pension payment. The key outcome variables of interest are identified as three categories: (1) one binary discrete variable indicates the incidence of the main respondent parent receiving gross economic transfers including cash and in-kind transfers from each of their non co-resident children, taking a value of 1 if the parent received transfers from the child, and 0 otherwise; (2) another continuous variable reflects the net amount of the 10

Sishu is a kind of old style private Chinese education that mainly taught young children Chinese classics before the twentieth century. 11

The pre-transfer income of the elderly is related with labor supply, which is also affected by the NRPS pension status. Thus, there is a general concern about the endogeneity of the pre-transfer income, estimates only change slightly when the pre-transfer income is dropped out of the regressions, and the conclusions from the empirical analysis in this study remain robust. The results are available on request from the authors. 9

transfers defined by subtracting the amount given to a particular child from the amount received from the same child 12, and (3) the other category is also a dichotomous variable representing whether the respondent parent is the net recipient conditional on the net amount of the transfers being positive, taking a value of 1 in the case of positive net transfer, and 0 otherwise. In this paper, private transfer is defined as transfer behavior between elderly parents and non co-resident adult children, which includes financial transfers and in-kind transfers (in the form of goods) received from and given to each child. Financial transfers involve living expenses, foodstuff, vegetables, clothes, water and electricity, telephone rate and other daily consumption, and medical expenditure. As exhibited in table 3, splitting the sample according to the respondent’s NRPS status and age group is informative. On average, the highest incidence of transfers from children to parents is found for the respondents who are older than 60 and are NRPS participants (64.95%). In general, NRPS enrollees received a higher net amount in total transfers than non-insurants. Regarding the respondent parent aged 60 and older, for instance, the average net amount of total transfers for the insurants and non-insurants are 586.47 RMB and 240.58 RMB per child respectively. The average incidence of positive net transfer received from children differs by NRPS status and the respondents’ age. The NRPS insurants aged 60 and older have the highest average incidence (61.37%), while the non-insurants younger than 60 years old have the lowest (44.49%).

[…………………Insert Table 1……………..….]

[…………………Insert Table 2…………..…….] 12Transfers

are measured at the couple level when the main respondent is currently married, and transfers are measured at the individual level when the respondent is single, widowed or divorced, to be consistent with the two kinds of definitions; the net amount of transfers at the couple level are measured by per capita net amount. 10

[…………………Insert Table 3…………..…….]

4. Empirical Methodology Following Chang(2013) and Ning et al.(2016), we combine the advantages of the regression discontinuity design, difference-in-difference and inverse probability weighting approach to estimate the casual effect of the NRPS program on private transfer in this study, due to the potential endogeneity issue 13 . Our estimation strategies are documented in two stages. In the first stage, we estimate a binary choice model for NRPS participation and then calculate the propensity score (i.e., predicted probability) that each respondent would participate in the NRPS program. The combination of regression discontinuity design and difference-in-difference (RD-DiD) method will be adopted in the second stage to estimate the transfer equation, and to further correct for the potential endogeneity bias for NRPS participation on transfer behavior, the inverse of predicted probability of NRPS participation of each respondent is then employed as the appropriate sampling weight for estimating the transfer equation. 4.1 First-stage Analysis of NRPS Program Participation Let Di∗ denote the unobserved latent variable of the NRPS program participation for the ith elderly, Di is the observed counterpart, 13

The potential endogeneity may result from individual’s voluntary participation in the NRPS program. Obviously, a large number of unobservable characteristics may simultaneously determine an individual’s decision to participate in NRPS and private transfer behavior. For example, heterogeneity in preference for old age support and expected future income stability, as well as the trust level for the public program, may lead to selection bias (or omitted-variable bias). As a consequence, the endogeneity bias resulting from participation in the NRPS program should be appropriately overcome in the adult child’s transfer behavior equations. With our data, we could also try to discuss the endogeneity problem using a fixed-effects estimator combined with regression discontinuity and difference in difference method, since many individual characteristics are likely to remain constant over a short time horizon like our 3-year period. The results suggest that the conclusions are robust. And the results are available on request. 11

Xi is a vector of exogenous determinants, and τ is the vector of the associated unknown parameters. μi is the random error. The binary choice model of the NRPS participation decision of the elderly can be specified as: D∗i = Xi′ τ+μi and Di = 1[D∗i ≥ 0]

(1)

The probability of the program participation can be shown as: Pr(Di = 1|X) = Pr(Xi′ τ + μi ≥ 0) = 1 − F(−Xi′ τ)

(2)

Under the normality assumption of μi , F(∗) is a cumulative density function of the standard normal distribution. As a result, consistent estimates of τ in Equation (2) can be obtained through binary probit model by implementing the maximum likelihood estimation method (MLE) (Wooldridge, 2010). With consistent estimates for the NRPS participation equation, the ̂(Di = predicted probability of NRPS participation for each respondent (P 1)) is calculated. Then, the inverse probability weights are calculated as (see Robins et al., 1994; Funk et al., 2011; Chang, 2013): IPWi = √̂

Di

P(Di =1)

+

1−Di ̂ (Di =1) 1−P

(3)

The variable IPWi is applied as an appropriate sampling weight variable to correct for self-selection bias that is due to the NRPS participation decision when estimating the equation in the second stage analysis (see below).

4.2 Second-stage Analysis for Transfer: RD-DiD Framework The crucial feature of regression discontinuity design is that a treatment assignment of program is established according to the assignment rule (or eligibility criterion). That is to say, individuals are assigned to a treatment group if and only if they exhibit a value of an observed pre-program characteristic not below a specified threshold for 12

the eligibility criterion. This implies that the probability of treatment receipt varies discontinuously at that threshold(Imbens and Lemieus, 2008; Lee and Lemieux, 2010; and Jacob et al., 2012). In the specific setting of the NRPS program in China, entitlement for pension benefits for insurants is not affected by a claimant’s past employment history, but is restricted to those aged 60 and above. As exhibited in Figure 1, all of the NRPS enrollees are eligible to receive pension payment when they reach 60 years old. Use of this eligibility rule is straightforward in an RD study because the probability of receiving pensions from the NRPS program changes discontinuously at the cut-point of age 60. Specifically, the graph in Figure 2 illustrates the relationship between the forcing variable (respondent’s age) and treatment status (pension receipt) for those NRPS participants, and provides straightforward information that the probability of receiving treatment jumps from zero to one as the forcing variable crosses the cut-point of 60.

[……………………Insert Figure 2……………………….]

A Standard Sharp RD Design for NRPS Participants only If we merely take the NRPS participants (regions A+B in figure 1) into account, a standard sharp RD design can be directly used because all of the NRPS participants aged 60 and older are eligible to receive pensions. Following Chang (2013), consider the regression: Yij = β0 + β1 OPi + f(αi ) + γCij′ + ϑPi′ + εij

(4)

where Yij are the outcome variables of transfer behavior, i.e. incidence of receiving gross transfers including financial and in-kind transfers, the net amount of total transfers and the incidence of net transfers being positive from the jth non-coresident child to the ith respondent parent in this study; for the ith individual respondent, the 13

effect of age on the outcome variables is captured by the function f(𝛼𝑖 ), indicates the respondent’s age normalized to be zero around 60 (i.e. ; f(𝛼𝑖 ) is a polynomial function for 𝛼𝑖 . Cij′ and Pi′ are vectors of the exogenous characteristics of the adult child and his or her parent respectively that are related to private transfers, 𝛾 and 𝜗 are vectors of the corresponding estimated parameters. OPi is the key explanatory variable of policy concern indicating whether the ith respondent parent receives old age pension payment from the NRPS program, in other words, OPi is a treatment dummy variable and is defined as 1 OPi = { 0

if respondent ′ s agei ≥ 60, if respondent ′ s agei < 60

Therefore, the coefficient β1 captures the causal effect of the NRPS payment on private transfers. Consistent estimates of Equation (4) can be obtained by implementing the Ordinary Least Squares (OLS) method. The RD-DiD Model for All Rural Elderly Although the sharp RD approach can be applied to investigate the causal effect of the pension payment on private transfers, the results are validated only when the sample of NRPS participants is used. Recall that, in figure 1, a complete picture of the NRPS program effect may be better quantified by comparing the pension recipients to pension non-recipients. To further illustrate the nature of combining the DiD method with the sharp RD analysis, Equation (4) can be modified to the case in which both the NRPS insurants and non-insurants are included to investigate the causal effect of pension payment from the NRPS program on private transfers. The specification of the RD-DiD model can be defined as: Yij = β0 + δ1 OPi + f(αi ) + δ2 Di +δ3 I(Agei ≥ 60) + γCij′ + ϑPi′ + εij 14

= β0 + δ1 Di ∗ I(Agei ≥ 60) + f(αi ) +δ2 Di + δ3 I(Agei ≥ 60) + γCij′ + ϑPi′ + εij

(5)

As revealed in Equation (5), the second equality holds because all of the NRPS participants will receive the pension payment (OP) when they reach age 60. Interestingly, some key effects can be captured from Equation (5).First, the term (δ1 +δ3 ) captures the effect of pension payment on receiving a transfer from an adult child between the parents aged 60 or older and the parents under 60 among the NRPS participants, all things being equal (that is, the difference between region A and region B in Figure 1). Second, the term δ3 identifies the effect on private transfers between the parents aged 60 or older and those under 60 among the NRPS non-participants, all things being equal (that is, the difference between region C and region D in Figure 1). The net effect of the DiD method can be identified by parameter δ1 . It is worth noting that the treatment group (i.e. NRPS participants) and control group (i.e. NRPS non-participants) are usually exogenously determined in the standard DiD analysis. Recall that, however, the individual’s decision to participate in the NRPS program is voluntary, and it is unlikely to be exogenously determined. Consequently, it is necessary to correct for the potential selection bias due to NRPS participation decision when both of the NRPS participants and NRPS non-participants are taken into account in the analysis. Following Chang (2013), Dai et al. (2015) and Ning et al. (2016), we adopt the inverse probability weighting method proposed by Robins, Rotnitzky and Zhao (1994) and Wooldridge (2007) to deal with this self-selection bias. In our study, the sampling weights () calculated by the probit model for NRPS participation equation (equation 3) are used when estimating the RD-DiD model (equation 5). Equation 5 is estimated by using the OLS method for the dummy outcome variable of prevalence of receiving transfers and for the continuous variable of the net amount of total transfers. Furthermore, the standard errors for the estimates are calculated by bootstrap method with a replication of 50 15

times in order to measure the accuracy of the estimates of the treatment effects.

5. Empirical results14 The estimated effects of the determinants of incidence of receiving gross transfers, net amount of total transfers, and occurrence of net transfer being positive are presented in column (1), (2) and (3) of Table 4 respectively15. The key findings of particular interest in this study concern the impact of the pension payment from the NRPS program on private transfers. At the bottom of columns 1 and 2 in table 4, several different treatment effects, which are defined based on the comparison of OP recipients to different subgroups of OP non-recipients, are presented. Regarding the prevalence of receiving gross transfers, the differences between the elderly who are older than 60 and those at an age below 60 are -0.43% and -1.17% for NRPS participants and non-participants, respectively. Consequently, the net effect (in other words, DiD effect) of pension payment from the NRPS program on receiving transfers from children is 0.73%, showing a slight but statistically insignificant increase in receiving a financial transfer from an adult child as a result of pension receipt. Likewise, the positive net impact of pension receipt on the net amount of total transfers for the elderly is found, but nonetheless, it is statistically insignificant. Considering the fact that the net amount of transfers is highly skewed to the right and has many zeros, it makes perfect sense for us to investigate the effect of pension payment on the prevalence of the positive net amount of total transfers (including cash and in-kind transfers), which conveys straightforward information about private transfer flows between parents and adult children, and indicates whether 14

The results in the first stage for participation of the elderly in the NRPS are not presented in this study due to length limitations, and the results are available on request from the authors. 15

In order to check the robustness and sensitivity test of the main results to a different type of bandwidth in RD analysis, we also conduct an additional regression for the subsample of the 5-year age window around the cut-point of age 60(i.e., the group between 55 and 65 years), the results are very robust for the choice of a 10-year age window used in the estimation, and reinforce our considerable confidence in the findings. The robustness results are available on request from the authors. 16

the parents are net recipients. As indicated in column (3), the DiD effect of pension receipt on the probability of being a net recipient for the elderly parent is also positive (3.14%) but with statistical insignificance. In sum, the effect of pension payment on private transfer in all three models is positive but insignificant. These results reflect that, inconsistent with the theoretical expectation and existing empirical evidence from other developed countries, the crowding out of family financial support from adult children due to expansion of the NRPS program does not explicitly occur in rural China; on the contrary, a crowding in effect of private transfers due to the NRPS program is to some extent observed. Possible explanations for the findings discussed above could be derived from several reasons. First,the payment amount of the old age pension and thus the replacement ratio is not large enough to completely displace the family function with respect to support for the elderly and to bring about a dramatic crowding out effect of private transfers from children to parents. In other words, effective formal safety net programs are not sufficient during the initial phase of the NRPS program; therefore, private transfers possibly complement public transfers. Second, the adult children would not suffer benefit losses because the pension contributions of adult children would be entered into their individual accounts as a result of the unique institutional design of “family binding” under the framework of the NRPS program, suggesting that the state of “incentive compatibility” between parents and children has been reached. Third, these findings may result from a realistic state of mind whereby there is competition and overlap between different transfer motives, and a person may hold several mixed motives simultaneously, such as parental repayment, risk-sharing, and reciprocity motive. When looking at the effects of child characteristics, we note that age, education attainment, gender and adult child (donor) income have a positive and significant effect on the probability of receiving gross transfers and the likelihood of the net transfers being positive. Interestingly, the geographical proximity of living arrangements between non-co resident adult child and his or her parent has a significant and negative impact on private transfers in columns (1), (2) and (3). With regard to the incidence of receiving gross transfers and of positive net 17

transfers, on average, the children, compared to those who do not live in the same home county as their parents, are significantly less likely to provide economic transfers by 13.8% and 14.4%, other things being equal. In addition, when considering the net amount of total transfers, these children tend to significantly reduce economic transfer to their parents by 671.4 RMB. A possible explanation for the results mentioned above may rest with the following fact: the adult children, living far away from the same home county as their parents due to rural-urban migration, are frequently employed in the off-farm sectors in urban areas. Although they are incapable of providing sufficient daily life care for their elderly parents, they would like to provide more economic supports to make up for the lack of daily care. This suggests a substitution occurs between daily supports through living proximity and supports through financial transfers. Concerning the estimated effects of the respondent parental characteristics on the adult child’s behavior of private transfers, it is found that, in contrast to those elderly individuals without any chronic disease, the parents suffering chronic diseases are more likely to receive private transfers from an adult child at a higher probability of 2.91%, and are observed to have a higher likelihood of receiving net positive transfers as indicated by the coefficient of 3.89%, showing the role of private transfers from children as an informal form of old age support. In addition, a highly negative association between per capita pre-transfer income of the parent’s household and non-co resident economic transfers is found. The results show that the lower the pre-transfer income, the higher the probability of receiving a (positive net) financial support and of getting more, confirming that Chinese familial transfers function in a compensatory fashion.

6. Additional results

6.1 treatment effects at different income percentiles The underlying relationship between the private transfers received from children and the public pension program at different income levels of the beneficiaries conveys strong policy implications for the 18

effectiveness of public transfers for the intended group. As far as the NRPS program is concerned, the intended beneficiary (i.e the poor elderly) would scarcely be any better off if the financial support from adult children were substantially displaced due to expansion of the NRPS program. And as such, the policymakers should design targeting schemes more carefully to prevent such a crowding-out effect within social safety net programs. To examine the heterogeneous treatment effects at different income percentiles, we divide the full sample into three groups according to per capita pre-transfer income: the low income group is defined as those elderly individuals whose incomes are located below 25th percentile in the full sample, the middle income group is assigned for those individuals with incomes located between the 25th percentile and 75th percentile, and the high income counterpart is specified as the individuals whose incomes are located in the upper 25th percentile. Therefore, we also conduct an analysis to estimate three separate RD-DiD models (i.e. Equation 5) by dividing the whole population into three groups according to income percentile to examine the heterogeneous effects of the NRPS program on private transfers16. As reported in table 5, the non-linear responsiveness of private transfers to introduction of a public pension program is gauged. For the low income group, the net effects of pension payment from the NRPS program on the likelihood of receiving gross transfers from adult children and on the probability of a net transfer greater than zero are 8.95% and 12.6%, respectively, indicating a substantial and statistically significant increase in receiving economic supports from adult children; Interestingly, when turning to the middle income group, the net effects 16

In order to check the robustness of the heterogeneous effects using different income thresholds, we define low income group as those whose pre-transfer income per capita is less than x percentile. High income group is defined as income higher than 100 – x percentile, and the remaining part is defined as middle income group. X percentile varies from 10 to 40 percentile, for each x percentile value, the whole sample could be divided into low, middle and high income group. Then, for each specified income group, we run RD-DID-IPW regression model (including bootstrap) and obtain the t value for the coefficient of the key variable (OP), which means the significance of effect of pension receipt on the private transfer behaviors from the adult children to their parents. The conclusions still remain unchanged. The results are available on request from the authors. 19

of pension receipt are negative and significant at the statistical level of 5% and 10% as indicated by the coefficients of -0.116 and -0.0969. These findings suggest that, apart from the theoretical prediction pointed out by Cox et al. (2004), the private transfers from adult children to the elderly with low income are not dramatically crowded out, but strengthened as a result of the public pension program, and furthermore they imply that the NRPS program could have a beneficial impact on the well-being of the poor elderly. The possible explanation for this different response between the elderly groups with different income level may lie in the facts that, there are interactions between preferences, resource constraints, institutions and transfer behavior. Just as indicated by Laferrere and Wolff (2006): “It may be that preferences are the same, but that endowment constraints yield different behaviors, either geographically between countries with different institutions, within a country between families with different wealth levels, or along the life-cycle for a given family.”(Page951). Besides, the results suggest that the effects of pension payment on the private economic transfers are non-linear, also perhaps because households enter into mutually beneficial, self interested co-insurance contracts, warm-glow or a mixture of several types of transfer motivation. 6.2 Potential impact of pension payment on private transfer under the “family binding” design As mentioned earlier, one unique feature of the NRPS program which is different from public pension schemes in other countries is the “family binding” mechanism, which aims at reducing the likelihood of adverse selection for the insurance participants and which plays an important role in improving the program’s coverage rate for the full geographic areas of rural China. In order to empirically investigate the potential effect of the “family binding” arrangement on private transfer from children to parents. In this section, we run an additional regression to estimate this effect by restricting our attention to the subsample of the respondents who participated in the NRPS program through the “family binding” rule and those elderly, aged 60 or above, who did not participate in the program. In order to correct for the self-selection bias of the NRPS 20

participants, inverse probability weights are also employed in the analysis17.The elderly insurants through “family binding” rule account for 33.35 percent in the whole sample of elder insurants, echoing the important role of this unusual policy arrangement in enhancing the universal coverage rate of the NRPS program. Since we are only interested in the impact of the “family binding” arrangement under the framework of the NRPS program on private transfers, we pay particular attention to the results presented in table 6 on the coefficient of the key variable (i.e., OP). It is evident that pension receipt from the NRPS program exerts a significant and positive effect on the probability of receiving gross transfers from adult children and on the likelihood of the net transfer being positive at the statistical level of 1%, although the effect of pension receipt on the net amount of total transfers is positive and statistically insignificant. On average, in contrast to those non-insurants, the probability of receiving private gross transfers from children and the likelihood of the net transfer being positive for the insurants through the “family binding” design under the NRPS program are increased by 4.96% and 5.34% (column (1) and (3) in table 6) , respectively. These encouraging results may reflect that the “family binding” rule of the NRPS program could actually crowd-in, rather than offset, the economic supports from adult children to their elderly parents, and provide us with more confidence that expansion of the NRPS program in rural China strengthens its income redistributive effect for recipients by increasing familial economic transfers.

[…………………Insert Table 4………………….]

[…………………Insert Table 5………………….] 17

Similar to the estimation approach in the main results, the estimation method is also conducted in two stages: in the first stage, a binary probit model is estimated to calculate the inverse probability weights for the equation of NRPS participation, and the second-stage analysis estimates the private transfer outcome equations with the inverse probability weights.

21

[…………………Insert Table 6………………….]

7. Conclusions

In this study, we assess the impact of the NRPS program on adult children’s economic transfer behaviors to support their elderly parents by pooling CHARLS data within the analytical framework of combining the strengths of regression discontinuity design, difference in difference approach and the inverse probability weight method. Results indicate some interesting findings: First, pension payments from the NRPS program do not significantly mitigate the likelihood or amount of supporting private transfers from adult children to their elderly parents, which is in accordance with the conclusion drawn from the study of Huang and Zhang (2016). Conversely, the findings confirm that the NRPS program, in particular, the distinctive “family binding” arrangement, may dramatically contribute to increasing the probability and amount of private transfers for the OP recipients. These findings demonstrate that, unlike public pension programs in other countries, the NRPS program in general amplifies the possibility frontier for the utilities between parents and children, facilitates access to inter-generational Pareto improvement and further solidifies the traditional filial piety concept for the young generation in rural China. Second, in contrast with existing empirical evidence from the Philippines, the private transfers from adult children to the elderly parents with low income are not dramatically crowded out but strengthened as a result of a public pension program, which highlights implications for the effectiveness of the NRPS program as a tool of general poverty reduction and social protection for the targeted elderly group. Furthermore, in terms of a mild crowding in effect on the whole, private transfers possibly complement public transfers during the initial 22

phase of the NRPS program under the “old while not affluent” circumstance in rural China. This reflects that, both formal social security nets and informal familial support systems are not conflicting but effectively supplementary to each other in the short term. It is worth stressing, however, that the question of how to reduce the elderly parents’ economic dependence on private transfers from their adult children while keeping old age economic welfare improvements, and forwarding them into a new era of social pension systems which accompany with a departure from the traditional family support net, still need further research in the future. Obviously, the higher pension benefits are claims against increased future economic output and higher factor productivity for the whole country; any innovative design and reform of the social pension system should be recognized as supporting economic growth and development and diminishing possible distortions in labor markets. Overall, the findings provide some preliminary insights into human nature and the underlying relationship between formal public pension programs and private transfers in the informal family sector. However, the results are far from conclusive and need more careful research because of dynamic changes in the external economic and cultural environments, as well as the complexity of potential motives behind the inter-generational transfer behavior.

Acknowledgements The authors gratefully acknowledge the financial support by National Science Foundation of China (Grants: 71303050, 71770317), National Key R&D Program of China (Grant: 2017YFC1601903) and Fujian Agricultural & Forestry University (Grant: KFA17509A).

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Figures

NRPS insurants

Age>=60 region A

NRPS non-insurants

Age=60

region B

Age60 Age60 Age60 Age60 Age60 Age60 Age60 Pre_income/103 Spouse Male Chronic Less_primary Primary Age-60 (Age-60)^2 Age_c Gender_c Edu_c Birth_order Income_c Proximity Child_c Dum_west Dum_middle Year_2013 Constant

Coefficient 0.0154 0.0073 -0.0117 -0.0021** 0.0329** -0.0427*** 0.0291* -0.0147 0.0236 0.0076** -0.0005** 0.0079*** 0.2290*** 0.0068*** 0.0109 0.0453*** -0.1380*** 0.0064 -0.0112 0.0636*** 0.1850*** -0.206***

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Standard error 0.0201 0.0302 0.0336 0.0010 0.0160 0.0145 0.0139 0.0189 0.0206 0.0029 0.0003 0.0019 0.0134 0.0024 0.0068 0.0048 0.0135 0.0100 0.0155 0.0167 0.0164 0 0.0860

Column (2) Net amount of total transfers Coefficient -472.9 663.7 -565.2 -34.4** -653.3 -305.3 -382.6 355.8 971.2** -12.86 -3.483 19.72 51.30 31.81 9.580 417.8*** -671.4** 346.2 -224.4 13.63 180.0 -1,736

Standard error 589.7 604.9 631.8 17.79 457.2 262.3 275.4 287.7 483.3 28.10 3.165 20.00 200.1 30.01 82.19 168.9 344.6 288.5 370.2 376.2 147.2 1,703

Column (3) Probability of net transfers being positive Coefficient

0.0058 0.0314 -0.0447 -0.0037*** 0.0221 -0.0386** 0.0389** 0.0005 0.0445* 0.0082*** -0.0005** 0.0076*** 0.2240*** 0.0053*** 0.0153** 0.0463*** -0.1440*** 0.0096 -0.0328* 0.0528*** 0.1470*** -0.1870**

Standard error 0.0205 0.0309 0.0331 0.0010 0.0157 0.0150 0.0143 0.0196 0.0210 0.0029 0.0003 0.0018 0.0141 0.0024 0.0067 0.0050 0.0141 0.0104 0.0152 0.0166 0.0164 0.0847

Adjusted R2 Treatment effects (Age>=60-Age=60-Age=60-Age=60-Age=60-Age=60-Age=60-Age=60-Age