DP2022-30

Does a Universal Pension Reduce
Elderly Poverty in China?

Anqi ZHANG
Katsushi S. IMAI

June 13, 2022

Does a Universal Pension Reduce Elderly Poverty in China?

This Draft: 13th June 2022
Anqi Zhang ⁋† and Katsushi S. Imai ⁋ €⁋ The Department of Economics, The University of Manchester, The UK, † Institute of Belt and Road
& Global Governance, Fudan University, China, € RIEB Kobe University Japan

Abstract
This paper studies the impact of the universal pension programme on elderly poverty in both rural andurban China. Using the three rounds of panel data based on the Health and Retirement Longitudinal
Study (CHARLS) in 2011-2015, we examine whether the universal pension programme reduced elderly
poverty, comprehensively defined to cover both unidimensional and multidimensional poverty indicesof the households and individuals. To utilise the longitudinal nature of the data, we apply the robust
Fixed-Effects (FE) Model with Propensity Score Matching (PSM) and the FE Quantile Model with
PSM taking into account the unobservable individual characteristics, such as entrepreneurship or risk
preference. Our results show that the universal pension programme reduced poverty in monetary andnon-monetary terms in both rural and urban areas. While rural people tend to continue to work in thelabour market after the receipt of the pension, urban people work less due to the negative income effectof the programme. The panel quantile regression results suggest that the programme decreased theinequality in both monetary and non-monetary dimensions. Our results provide strong evidence tounderscore the success of the Chinese universal pension programme in reducing poverty and inequality
in both rural and urban areas.

Keywords: Poverty, Inequality, Multidimensional Poverty Index (MPI), Pension, Impact
evaluation, China
JEL Classification: C23, I32, I38, H75
Keywords: Poverty, Inequality, Multidimensional Poverty Index (MPI), Pension, Impact
evaluation, China
Corresponding Author: Katsushi S. Imai (Dr), Department of Economics, School of Social
Sciences, The University of Manchester, Arthur Lewis Building, Oxford Road, Manchester M13 9PL,
UK, Email: Katsushi.Imai@manchester.ac.uk.

Acknowledgements: We thank the valuable suggestions from Ana Vaz, Ralitza Dimova, Raghav
Gaiha, Indranil Dutta, Xiaobing Wang and colleagues at the University of Manchester. We also
appreciate all feedback and comments on the earlier version from reviewers and participants of the
WEAI 94th Annual Conference, US, in 2019, and the International Symposium on Contemporary
Labor Economics, China, in 2019. All shortcomings remain ours..

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Does a Universal Pension Reduce Elderly Poverty in China?
1.

Introduction

The objective of the present study is to examine whether a universal pension programme
reduced elderly poverty in China. While evidence from cross-country studies (outside China)
suggests that pensions play a crucial role in poverty alleviation and old-age support systems indeveloping countries (Barrientos et al., 2003), little is known about whether this conclusion
would apply to China. Despite the spectacular economic growth of China during the last few
decades, 14.9% of the population was still below the international poverty line at US$1.90(2011 PPP) a day in 2008, while the county further reduced the poverty rate based on the same
poverty line dramatically to 0.1% in 2019.1 Another serious problem faced by the country is
the acceleration of population ageing. As a consequence of longer life expectancy and lower
fertility rate, more than 176 million people were over 65 by the end of 2019, comprising 12.6%of all population.2 In contrast to well-developed welfare systems in Europe, a pension had been
regarded as a “privilege” only for workers of governments and State-Owned Enterprises(SOEs) - due to the low and unequal coverage - before the universal pension reform was
implemented in 2009. The earlier pension system mostly neglected the large population in rural
areas (particularly those working in the agricultural sector) and treated pensioners in different
occupations differently.
Given the dual problems - the acceleration of ageing and the low welfare of the elderly -,the Chinese government started a series of pension reforms in 2009 to cover all population into
the universal pension protection system in a phased manner. As stated in the State Council’s
announcement, the eligible rural workers over 60 became entitled to an annual pension of atleast 660 yuan in 2009 (about US$97 in the 2009 exchange rate3). This basic flat-rate pension
amount was adjusted every year with different increment rates applied in different provinces.
Due to the large population size in China, the government was not able to implement the2policy universally at once. Starting with the 10% of all prefecture-level cities, the pension
reform was planned to roll out to cover all of the country and achieve universal coverage by
2020.
The main research questions the present study aims to address are: (i) Did participation in
the new pension scheme reduce elderly poverty - proxied by unidimensional or
multidimensional poverty of the household to which a program participant belongs, or theindividual income, the labour market participation status and working hours?; and (ii) Were
the poverty-reducing or welfare-improving effects of the universal pension programmedifferent across different distributional points in the outcome variables? While the previous
studies showed poverty-alleviating effects of pension programmes in other countries (e.g.,Vietnam- Long and Pfau, 2009; India - Unnikrishnan and Imai, 2020), to our knowledge, there
has not been any work to evaluate the poverty-reducing effect of the pension system in China
except a few focusing only on rural areas (e.g., Zhang et al., 2020; Huang and Zhang, 2021).
The current study aims to fill the gap by estimating the impact of the universal pension
programme on both single-dimensional and multi-dimensional poverty using the nationallyrepresentative large panel data covering both urban and rural areas.
When analysing poverty using household survey data, the results are sensitive to the choiceof poverty indicators (e.g., the choice of poverty thresholds or units (Imai and You, 2014)). In
the present study, we proxy elderly poverty by using several variables to reflect this. First, in
our analysis, poverty is estimated at both individual and household levels. To avoid the problem
of collinearity, pension income is subtracted from total individual income to capture poverty at
the individual level. At the household level, the poverty incidence (based on both national andinternational poverty lines), per capita expenditure and the Multidimensional Poverty Index(MPI) are adopted as poverty indicators. Besides poverty, the labour market is another area ofconcern, likely to be affected by the introduction of a universal pension. Here we use a binary

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variable on the working status and individual’s weekly working hours to investigate the effectof a universal pension on labour market decisions.
To address the unobservable characteristics for obtaining robust estimates of the effect of auniversal pension programme on elderly poverty, this study employs the panel data constructedby using the China Health and Retirement Longitudinal Survey (CHARLS) in 2011, 2013 and2015.4 The identification strategy is based on the phased rollouts of the reform and the eligible
ageing residents’ characteristics. We use the eligibility status to identify the treated group,
while we also use the actual treatment status as a robustness check. Propensity score matching(PSM) allows us to construct comparable pairs of individuals with similar characteristics fortreatment and control groups. However, PSM applied to cross-sectional data suffers from alimitation that matching is based only on observable individual characteristics. In order toovercome this limitation and take into account the time-invariant unobservable characteristics,
we apply the Fixed Effect (FE)-PSM to the panel of 2011, 2013 and 2015. As poverty-reducing
effects are likely to differ depending on the level of consumption or income or the
probability/degree of poverty - single- or multi-dimensional -, we also estimate the FE quantile
model to address the heterogeneous impact of the universal pension programme. This will
provide an important insight into the programme’s impact on inequality.
The contribution of our paper to the literature is summarised as follows. First, using thelarge nationally-representative panel data, poverty and inequality indicators are calculated todemonstrate recent poverty and urban-rural inequality trends from 2011 to 2015 in China. The
past empirical works have analysed poverty trends in China, but few examined the urban-ruraldisparity among the middle and older populations (Du et al., 2005; Brown and Park, 2002; Imaiand You, 2014). The second contribution is identifying the causal effect of the universal
pension programme on elderly poverty by using the quasi-experimental methods based on thepanel data, such as the FE-PSM model and the FE-PSM quantile model. Although several

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studies examined the relationship between poverty and social welfare in developing countries(Barrientos et al., 2003; de Carvalho Filho, 2008; Long and Pfau, 2009), there have been only
a few studies on China and they focused only on rural areas. Our contribution focuses onpoverty among the elderly – comprehensively defined to cover both monetary and nonmonetary aspects and covering both rural and urban areas. Also, this is the first study toestimate the differential impacts of the universal pension programme at different distributional
points in the outcome variables to provide an implication for the change in inequality causedby the universal pension programme.
The rest of this paper is organized as follows. In Section 2, we introduce some institutional
background and review the relevant literature. Section 3 discusses our empirical strategies.
Section 4 presents the main results and Section 5 concludes.

2.

Literature review

The relationship between pension and poverty reduction is widely researched worldwide. Most
studies confirm the role of pensions in alleviating elderly poverty (Case and Deaton, 1998;
Duflo, 2003; Barrientos, 2003). For instance, Barrientos (2003) finds that non-contributory
pensions have a measurable impact on poverty reduction in Brazil and South Africa.
Unnikrishnan and Imai (2020) show that India’s pension programme helps improve
beneficiaries’ consumption expenditure, food and non-food expenditure and assets. Viet
Nguyen (2021) finds that social pensions in Vietnam have a significant effect on householdincome, but effects on working, saving, and healthcare services are limited among the elderly.
The accelerating ageing in China has caused poverty problems among the older population(Lloyd-Sherlock, 2000). The low fertility rate as a consequence of the One-Child Policy
introduced in the 1970s - together with higher longevity due to the improvement in health andnutrition – increased the overall dependency of the elderly on younger generations over time.

5Chen and Fang (2021) identify the causal impact of the family planning policies in 1970 onelderly parents. They found that the affected generation has a lower fertility rate. If fewer
children live close by, they are more likely to report depression symptoms. Thus, it places morepressure on the poverty problem among the old population, especially on those living in rural
areas (Qiao et al., 2006; Cai et al., 2012). A number of policy measures taken by the Chinese
government reduced poverty over the past decades where social assistance programmes play a
crucial role in poverty alleviation (Chen and Ravallion, 2004).
China had not developed a universal pension as a key component of the modern welfare
system before the New Rural Social Pension Program (NRSP) was introduced in 2009. The
previous pension programme was only available for urban workers of governments or SOEs.
NRSP started to provide the numerous rural workers with a flat-rate basic pension and acontribution-based personal account pension. It was compulsory for the local governments toprovide this programme but individuals can choose to participate or not. The basic part isfinanced by central and local governments, while personal pensions depend on how much they
have contributed. Beginning with NRSP, China’s central government implemented a series ofpolicies to reform the Chinese pension system. In 2011, the Urban Resident Pension Pilot
programme (URPP), which shared the same mechanism as NRSP, was introduced for urban
workers who were out of government or SOEs. These two similar pension programmes were
unified in 2014 into the Urban and Rural Basic Pension System (URBPS).
The earlier studies on the Chinese pension system were mainly qualitative or descriptive.
Many of them focus on the former pension system (e.g., Feldstein, 1999; Wang, 2006).
Regarding the studies on the universal pension reform since 2009, earlier studies focus on
aspects other than poverty reduction. Feng et al. (2011), for instance, reveal a significant offset
effect of pension wealth on household savings. Cai et al. (2012) thoroughly reviewed thewelfare of the rural elderly and explored the evolution of pension systems in rural China. The

6authors argued that more attention should be paid to rural areas where the dual problems ofaccelerating ageing and poverty are more pronounced than in urban areas. However, in more
recent years, a few studies have examined the poverty or welfare impacts of NRSP in rural
areas. For instance, using the CHARLS data in 2015, Zhang et al. (2020) apply the fuzzy
regression discontinuity design to the local average treatment effect of NRSP by utilising thefact that people became eligible at the age of 60 and find that NRSP significantly increased the
expected food expenditure amongst the elderly or decreased the vulnerability defined as theprobability of experiencing food poverty in the future. On the other hand, using the CHARLS
and CFPS datasets in 2010-2013, Huang and Zhang (2021) make use of the phased introductionof NRSP at the county level and apply the difference-in-difference method to identify the effecton the eligible group after the introduction of the programme. The authors find that the pension
scheme raises household income and food expenditure, while improving health and lowering
mortality. Using the Chinese Household Income Project survey data for 1988, 1995, 2002 and2013, Li et al. (2020) investigate the role of public pensions in income inequality amonghouseholds with elderly members and conclude that pension income was the largest source ofincome inequality for elderly households since 2002. However, it should be noted that theauthors did not take into account the endogeneity associated with participation in the pension
scheme (e.g., different probabilities of a household or an individual accessing the pension
programme) and it is not clear whether it actually increased overall inequality. To estimate thehealth impacts of NRSP, Cheng et al. (2018) apply the fixed-effects instrumental variable (FEIV) model to the 2008/09 and 2011/12 waves of Chinese Longitudinal Healthy Longevity
Survey data where the instrument is the programme implementation duration at the county
level. The authors have found, consistent with the findings by Huang and Zhang (2021), thatpension income improved objective measures of physical health and cognitive function of therural elderly. To our knowledge, however, there has not been any study to evaluate the impact

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of the universal pension programmes in both urban and rural areas. Our study contributes tothis literature by utilising the longitudinal nature of the CHARLS data in 2011-2015 - the period
during which the nation experienced a dramatic poverty reduction - and estimating the impactof the pension programme on the elderly poverty in both rural and urban areas. So this is thefirst comprehensive study to examine the heterogeneous impact of the universal pension
programme in both rural and urban areas of China to fill the gap in the literature.

3.

Data and Methodology

Data
To analyse the effect of the new universal pension programme on poverty among the ageing
population, we construct the panel data based on the three waves of the China Health andRetirement Longitudinal Study (CHARLS) in 2011, 2013 and 2015. 5 The survey is nationally
representative of the Chinese residents aged 45 and above and collects rich data on household
and individual characteristics, including demographics, family structures, household assets,
individual health status, income and expenditures. Households and individuals in the survey
are followed up every two years to enable us to construct the panel data.
The 2011 baseline survey was conducted in 28 provinces, 150 counties/districts, 450
villages/urban communities, across the country. All samples were drawn in four stages: countylevel sampling, neighbourhood-level sampling, household-level sampling and respondent-level
sampling.6 At the first stage, all county-level units (except Tibet) were stratified by region,
rural or urban and GDP per capita. After sorting, the population of each county was listed,along with the cumulative population. The 150 counties/districts were selected by a definedinterval.7 In the second stage of neighbourhood-level sampling, CHARLS selected 3 Primary
Sampling Units (PSUs) within each county-level unit, using ‘Probabilities Proportional to Size
sampling’. Next, CHARLS conducted household-level sampling based on maps with the8

support of local informants. If each PSU sampling frame was accurate and enumerated, 80
households were randomly sampled in the geographical frame. CHARLS then interviewed all
age-eligible sample households in each PSU who were available and willing to participate in
the survey8. In the final stage of Respondent-level sampling, CHARLS used a short filter survey
to identify whether the household had a member meeting our age eligibility requirements (i.e.,
45 or older)9. The selected qualified person becomes a main respondent and CHARLS alsointerviewed his or her spouse. It should be noted that CHARLS did not collect the data for theindividuals younger than 45, but this will not cause any problem in our analysis of poverty
impacts because the eligible individuals are matched by PSM (where virtually no individuals
under 45 will be matched to the eligible individuals).
Figure 1 provides sample distribution among provinces. In this administrative map of China,
deeper colour indicates that more samples were collected in that province. Among all 34
provinces, there are six provinces (Shandong, Henan, Sichuan, Yunnan, Hebei and Anhui) thattake up more than 5% of the whole sample. This reflects the survey design reflecting both the
population density and the proportion of rural residents. In the eastern region, Shandong,Henan, Hebei and Anhui are the four main agricultural provinces with a high population
density. According to the 2010 census, both Shandong and Henan had over 90 million residents
registered, taking up over 7% of the national population. Hebei and Anhui had 71 million and
59 million residents respectively, each counting for around 5% of the national population(NBS, 2010). In the south-east region, Sichuan and Yunnan, mountainous lands prevent theirurbanization process. They were with 80 million (Sichuan) and 45 million (Yunnan) residents,
mostly in rural areas. The sample distributions in other provinces are also consistent with theentire population distribution.

[Figure 1 inserted around here]

9The sample in the 2011 wave covers 17,708 individuals. The sample size changed to 18,567 in2013 and further increased to 21,061 in 2015. For the main analysis, only observations for theindividuals aged between 45 and 75 are kept as the people in this age group were most likely
to be affected by the universal pension programme in which the individuals not comparable to
the recipients are dropped by applying PSM. The individuals below 45 were not surveyed, but
this would not influence the results as those people are unlikely to be matched with the treatedindividuals. To eliminate the effect of the very rich on the results, individuals with per capita
expenditure above 1,000,000 yuan were capped at 1,000,000. After all these adjustments,
21,858 individuals remain in the dataset. To maximize the coverage of the individuals, we haveconstructed the unbalanced panel data where 53% are observed three times, 23% twice, andthe rest (24%) only once.
In order to consider regional differences across different pension areas properly, we employ
the household registration (HuKou) to identify whether the individual belongs to the urban orrural area. For instance, there are 3,918 individuals from urban areas among all 17,251
individuals in the first wave of data, taking up 23% of the total population. This proportion
does not change much in the next two waves.

Variables
As described above, NRSP was implemented first in 10 % of all prefecture countries andgradually rolled out nationwide later. In principle, all workers above 60 years old can receive
an unconditional pension if their HuKou is included as part of the NRSP-available areas in thetransition periods (2011-15). URPP in the urban area was implemented in the same way. We
define the eligibility status by using an indicator variable that takes 1 if (i) the individual isover 60 years old and (ii) the universal pension programme is available in his/her Hukou place10and 0 otherwise. This would measure the “Intention to Treat (IIT)” of the universal pension
programme. As a robustness check, we also define the treated group based on whether anindividual actually received the pension by utilising the questionnaire on the enrolment status.
We adopt several poverty indicators to estimate poverty at both individual and household
levels as the results are sensitive to the choice of the poverty threshold or proxies for poverty.
At the individual level, we adopt the individual income, the labour market participation status
or working hours as the main outcome variable. At the household level, the poverty incidence(under both national and international poverty lines), per capita expenditure and theMultidimensional Poverty Index (MPI) are adopted as poverty measures. In 2003, China raised
the national poverty line to 2,300 yuan per person per year to reflect the rise in the averageliving standard. This was yet much lower than the international poverty line of US$1.25 (at2011 PPP) per day (equivalent to 2,875 yuan per year in 2011). This study mainly uses thenational poverty line as a primary poverty threshold, while also adopting the international
poverty line.
However, the monetary indicators are not adequate to understand the complexity ofpoverty. This is because the access to pension programme would mitigate not only monetary
poverty - which is more or less expected -, but also non-monetary poverty, such as ill-health(e.g. chronic illness) or deteriorated living standards (e.g. poor sanitation). Our focus on bothmonetary poverty and the MPI is justified because (i) the MPI embodies human beings’
capabilities in multiple dimensions, which are unlikely to be captured by the monetary poverty(Alkire and Foster, 2011) and (ii) subjective well-being (e.g., happiness or life satisfaction) iscorrelated with not only income or consumption but ill health or education (Kahneman andKrueger, 2006). Based on Alkire and Santos’s (2014) method and the data available in ourstudy context, we develop the MPI by keeping the same three dimensions (education, health,living standards) as in Alkire and Santos but using different variables in each dimension. The

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composition of our MPI is detailed in Table 1.

(Table 1 to be inserted around here)

The MPI ranges between 0 and 1 where the higher index indicates the more impoverishedsituation of the household. Each of the three aspects contributes one-third to the index with adifferent number of sub-dimensions. In the category of education, if no one completed primaryschool in the household, it is considered ‘deprived’ (taking 0) or ‘not deprived’ (1) otherwise.
Even though the pension programme targets the elderly, the income effect at the household
level could relax the budget constraint and would help children complete primary school.
Within the health category, two binary variables on disability and chronic illness are used. If
there is any disabled household member, the binary variable on “Disabled” takes the value 1
and 0 otherwise. If any household member has more than three chronic illnesses, the binary
variable on “Chronic illness” takes 1 and 0 otherwise. Both binary variables are given the same
weight (1/6) and the two variables together compose the health dimension, taking up 1/3 of theentire MPI. The income effect of the pension income at the individual or household level would
prevent household members from suffering from illness or old-age disability. Apart from theshort-run income effect, the pension would provide the household with some security or
insurance that would buffer any future shortfall in non-pension income. This would have apositive effect on mental health, which would indirectly influence the probability of sufferingfrom illness, even if the amount of pension income is relatively small. For the category of theliving standards, we employ five binary variables on the basic facilities of the house, i.e.,electricity, running water, separate toilet, solid floor and necessary home appliances. The
household is regarded as ‘deprived’ in each of these five dimensions if they do not have it.
Each of the five sub-indicators is given the weight of 1/15, adding up to 1/3 for the dimension

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of living standards. Here we would expect the income effect or the insurance effect which
would positively influence the living standard, such as improvement in the household assets.
Apart from monetary or non-monetary poverty discussed above, the labour market outcomeof the individual could be affected by access to a universal pension program. There are manystudies discussing the relationship between pension and labour outside China (see the studies
on South Africa, e.g., Posel et al., 2006; Bertrand et al., 2003) but none for China. As the mostimportant source of individual income, the role of labour income is non-negligible in poverty
reduction. Here we employ a binary variable of working status as well as an individual’s weekly
working hours to investigate the effect of the universal pension on labour market decisions.
In the main econometric analyses, we choose covariates based on the empirical literature
reviewed in the last section as well as the data availability. We include gender, education level,
marriage status and self-rated health as covariates at the individual level, which are considered
to be important determinants of income or poverty. Meanwhile, household size and structure(proportion of aged members) are employed to capture household characteristics, which would
influence the intra-household resource allocations as well as the probability of having thehousehold members eligible for the pension program. Table 2 lists all variables described above
and their summary statistics in all three waves from 2011 to 2015.

(Table 2 to be inserted around here)

The first three rows list the critical identifiers in this quasi-experimental context. Individuals’
age in 2011 is used to determine the qualification to receive the NRSP pension. If they are over
60 years old and living in the policy area, they are eligible for the new programmes. As
participation in NRSP is on a voluntary basis, individuals may be eligible but choose not tojoin. Our definition of the NRSP participation based on the eligibility criteria thus capture the13

“intention to treat (ITT)” effect, although the case where eligible individuals did not participate
is relatively rare in practice. If the NRSP works well, it is supposed to reduce poverty andlabour market participation among the ageing population. Therefore, we expect individual
income and per capita expenditure to increase, but the probability of falling below the poverty
line (using domestic and international standards) and the probability and duration of working
in the labour market to decrease.

An Empirical Framework
Before estimating the causal effect of pension reforms on the ageing population, we first use aclass of the Foster-Greer-Thorbecke Indices (FGT) (Foster et al., 2010) and the Sen Poverty
index (Sen, 1976) to measure the overall poverty and inequality trends in China from 2011 to2015. We present the results based on the headcount ratio (the proportion of the population
living below the poverty line), the poverty gap (the poverty depth measure defined as theaverage distance to the poverty line as a proportion of the poverty line) and the squared gap(the poverty severity measure where poverty gap is squared to capture inequality as well as
poverty) (Bisogno, Chong, 2001). The Sen- Shorrocks-Thon poverty index places a greater
weight on poorer people and is derived from equity considerations without necessarily using
interpersonally comparable cardinal utility functions (Sen, 1976).
After we describe the overall change of poverty and inequality, we will estimate the maineconometric model. We first apply PSM to construct a counterfactual distribution of theindividuals without receiving NRSP. The Propensity Score (PS), the probability of an individual
eligible for NRSP, is estimated by the Probit model (Equation (1)) which will be applied toeach round of the panel data.

𝑃𝑖𝑡 (𝐷𝑖𝑡 = 1 | 𝑋𝑖𝑡 ) = ∅(𝛼𝑋𝑖𝑡 ),𝑡 ∈ {1, 2, 3}

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(1)

where 𝑃𝑖𝑡 denotes the probability of individual i being eligible to the NRSP in wave t, 𝐷𝑖𝑡 =1 if individual i is eligible to receive NRSP at wave t and 0 otherwise, ∅ represents the
cumulative distribution function of the standard normal distribution, α is the vector ofparameter estimates and 𝑋𝑖𝑡 denotes a vector of covariates for the individual i for the wave t.
Covariates for matching and scoring should affect both participation and outcome variables(Jalan and Ravallion, 2003). Here we include gender, an age dummy variable showing whether
the individual is equal to or above 60 years old, education, marital status and self-reported
health in all three waves. To ensure the matching validity, the balancing property should bechecked. If it is satisfied, each participant is matched to non-participants based on thepropensity score, using the kernel density matching algorithm.10 The incomparable samples
outside the region of common support are dropped during the matching process.
After PSM and dropping all the observations outside the common support region, we apply
the FE model to wipe out the effect of unobservable time-invariant characteristics of theindividual (e.g., entrepreneurship and risk preference) which would affect outcome variables,such as MPI. As not all individual characteristics can be included, the unobserved difference
would cause bias in the main equation for the outcome variable. The FE model is applied only
for the individuals matched after PSM to address the within-individual variation in the balanced
panel dataset as in Equation (2).

𝑦𝑖𝑡 = 𝛼0 + 𝛼1 𝐷𝑖𝑡 + 𝛼2 𝐶𝑖𝑡 + 𝑎̇ 𝑖 + 𝑢𝑖𝑡(2)

where 𝑦𝑖𝑡 is the outcome variables defined in the last subsection, 𝐷𝑖𝑡 is the individual i's
eligibility (or enrolment) at the time t, 𝐶𝑖𝑡 is a vector of control variables including education,
marital and health status, 𝑎̇ 𝑖 is the individual fixed effect and 𝑢𝑖𝑡 is the error term. It should be15

noted that, as all samples are collected among the middle-aged and above, the education leveland gender hardly or never change over time. Thus, gender and education variables are only
included in the matching process and dropped in the FE estimation.
As described above, we estimate the policy effect on both individual and household levels.
However, the CHARLS data is collected on a household basis in a way that a sampling
distribution of households reflects the distribution of the entire households in each geographicalunit (e.g., village, province, nation). That means error terms are not independent but correlated
within the household. Conventional error terms will lead to biased confidence intervals and tstatistics (Cameron and Miller, 2015). Meanwhile, possible outliers may exist in the data andaffect the distribution of observations. To relax the basic assumptions on the independence andthe normality of error term distributions, the variance-covariance matrix is adjusted by using arobust and clustered estimate where the standard errors are clustered at the household level inall the cases.
As an extension, we estimate the FE-PSM quantile model to (i) examine the heterogeneous
impacts of the universal pension programme at different percentile points in the distribution of
the outcome variable, 𝑦𝑖𝑡 and (ii) to carry out the robustness check for the (robust) FE-PSM
model addressing the heteroscedasticity in a different way by estimating the model at different
quantiles with location-specific fixed effects. While there is a growing body of literature on
quantile regression using panel data (e.g., Canay, 2011), it is not technically straightforward to
estimate the panel quantile model. We follow Machado and Silva (2019) who proposed “thequantiles-via-moments estimator” based on the location-scale model for the panel quantile
estimation. Suppose the vector 𝑋𝑖𝑡′ consists of 𝐷𝑖𝑡 , 𝐶𝑖𝑡 and constant terms in Equation (2). The
location-scale model can be written as:

𝑦𝑖𝑡 = 𝛼𝑖 + 𝑋𝑖𝑡′ 𝛽 + (𝛿𝑖 + 𝑋𝑖𝑡′ 𝛾)𝑢𝑖𝑡

(3)

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𝜂(𝜏)𝑖 = 𝛼𝑖 + 𝛿𝑖 𝑄𝑢 (𝜏)𝛽(𝜏) = 𝛽 + 𝛾𝑄𝑢 (𝜏)where 𝜏 stands for 𝜏-th quantile, 𝑋𝑖𝑡′ and 𝑢𝑖𝑡 are assumed to be independent, 𝑃𝑟((𝛿𝑖 + 𝑋𝑖𝑡′ 𝛾) >0) = 1 (Machado and Silva, 2019, p. 147).11

4.

Main results

Poverty trends
Table 3 shows the results of various poverty measurements in 2011, 2013 and 2015. Overall,
all these aggregate figures indicate a reduction in poverty from 2011 to 2015 consistent with
the national estimates based on the international poverty line discussed earlier. The first two
rows compare the poverty headcount ratios by both domestic and international standards. In
the pooled sample, the national poverty headcount decreased from 17% in 2011 to 8% in 2013
and further down to 6% in 2015. These figures are higher than those based on the official data12(9.12%, 6.06% and 4.06% in 2011, 2013 and 2015, respectively). It is reasonable as CHARLS
data were collected among those over 45 with a higher representation in the rural area where
people earn less than those in urban areas. This can be further verified by the headcount ratios
for urban and rural subgroups. In 2011, the year when NRSP started, the domestic poverty
headcount ratio was 5.4% in the urban sample, while over 20% in the rural sample. After four
years of implementation, the poverty headcount ratio decreased to 2.3% in the urban subsample
and 7.3% in rural ones. Using the international poverty line based on US$1.25 per day (2005
PPP), the above trend still holds with the absolute levels higher. Although poverty andinequality situations were getting better from 2011 to 2015, the large urban-rural disparity
persisted for all poverty indicators.

17

(Table 3 to be inserted around here)

PSM
In the matching process, the inclusion of too many covariates often leads to violations of thecommon support assumption. We have thus chosen gender, education, marital status and healthas covariates. The final models used for estimating the PS for all waves, i.e., 2011, 2013 and2015, are presented in Table 4.

(Table 4 to be inserted around here)

After constructing the PS model, we use a Kernel density matching algorithm to pair treatedindividuals with their counterfactual ones. There are two key assumptions to make PSM valid.
The first is the Conditional Independent Assumption (CIA), which means that outcomes are
independent of programme participation and conditional on a set of observable characteristics(Becerril and Abdulai, 2010). The second assumption requires that the ATT is only definedwithin the region of common support (Heckman et al., 1997). After the Kernel density
matching, we build the counterfactual group to pair treatment with control units. We repeatedthe same procedure separately for 2011, 2013 and 2015 waves.
Figure 1 shows common support regions. In all three figures, the proportion of matched
controls is similar to that of the treated unit. To make PSM valid and meet the requirement of
the second assumption, individuals outside the region of common support are dropped. In 2011,
9,567 of the total of 15,671 individuals are dropped, taking up 61.05% of the first wave. Thisis expected as the sample includes individuals 45 years or older while those 60 years or older
are eligible for NRSP. In 2013, 9,284 of 16,312 samples are dropped, taking up 56.92%. In
2015, 9,815 of the total 17,926 samples are dropped, taking up 54.75%. A balanced panel18dataset is constructed for the individuals within the region of common support in all threewaves. Removing the individuals present only once or twice in the panel, there are 11,533
individuals in the reconstructed panel.

(Figure 1 to be inserted around here)

After matching and dropping all individuals out of common support areas, Figure 2 plots
the distribution of the propensity score in the treatment and matched control group. It can beobserved that the treatment and control units have a similar distribution in all three years,
indicating that the overlap assumption is well satisfied. Our results have confirmed that theabove two assumptions hold, which would validate the PSM procedure. The results of thebalance test are reported in Appendix 1.

(Figure 2 to be inserted around here)

FE Model with PS Weighting
To wipe out the effect of time-invariant unobservable characteristics that would affect outcome
variables, we utilise the panel data structure and estimate the treatment effect on the outcome
variables using a FE model. The results for rural areas are presented in Table 5 and those forurban areas in Table 6 based on the eligibility status. The results based on the actual treatment(enrolment) are reported in Tables A5 and A6 in Appendix 2. As all samples are collectedamong the middle-aged and above, their education level and gender hardly changed over time.
Thus, gender and education variables are omitted and dropped in the FE estimations.

(Tables 5 and 6 to be inserted around here)

19

The first row in Table 5 indicates the effect of NRSP eligibility on several outcome
indicators for rural areas. While the individual income without pension or the household income
is not affected by the access to NRSP, the individual income increased by 189% (the first threecolumns in Table 5). We find in the fourth column that per capita household expenditure
increased by 11.1%. This implies that, although the pension is received by the recipient, theaverage household expenditure per person significantly increased. These results imply that thewelfare - proxied by monetary measures - at the individual or household level improved
significantly as a result of participation in NRSP.
However, while NRSP increased the individual income and the per capita household
expenditure significantly, it had no significant effect on monetary poverty indices - defined aswhether per capita household income is below the national poverty line of 2,300 (or 2,875)
yuan per person per year. This would imply that, while the income (at the individual level)
increased significantly as a result of participation in NRSP, we do not observe significant
estimates on the probability of a household falling into consumption poverty (the fifth and sixth
columns of Table 5). However, the panel quantile model shows that consumption poverty
reduced significantly as a result of participation in NRSP (Table 7). Overall, while we do nothave the disaggregated income data on all the household members, the results imply that otheryounger household members were dependent on the pension income. On the contrary, MPI,
our proxy for non-monetary poverty, decreased by 6.2% if the household had a member eligible
for the universal pension programme (the seventh column). We have disaggregated this into
the three sub-components of MPI, education, health and living standards (the eighth to tenthcolumns). We find that pension income improved education poverty by increasing theschooling years of younger members of the household, suggesting that the pension income
relaxed the budget constraint of the households and helped them spend more on education.

20

Poverty is defined in terms of disability and chronic illness reduced significantly, which isconsistent with earlier findings (Cheng et al., 2018; Huang and Zhang, 2021). Poverty definedin terms of the living standard did not reduce significantly, as it is more about the asset
deprivation which cannot change in the short run. Finally, whether or not the individual iseligible for the pension hardly changed the labour market outcomes, in terms of whether theindividual worked in the labour market or the hours of working per week. So we do not observe
the evidence that those who accessed the pension scheme significantly reduced their labour
supply. However, the variable on self-evaluation of health status, one of the control variables,significantly increases the labour supply. Given the positive effect of pension on health status(Cheng et al., 2018), we cannot deny the possibility that the universal pension programme will
have a positive effect on the labour market outcomes over time.
Table 6 indicates the results on the effect of the universal pension programme on poverty
and well-being indicators in urban areas. Overall, we have confirmed the poverty-reducing
effects of the universal pension programme, though the relative effects are smaller in urban
areas than in rural areas, reflecting the initially high standards of monetary and non-monetarywell-being in urban areas. The first and second columns indicated that the individual income
with the pension income increased by 74%, but that without the pension income is not
significantly influenced. Household income or expenditure was not significantly influenced(the third and fourth columns). As in rural cases, we do not observe statistically significant
effects on income poverty indices (the fifth and sixth columns) - though significant effects areobserved in the results of the quantile regression (Table 8), while MPI is significantly reduced
in urban areas, but to a lesser degree than in rural areas. A similar pattern of the results is foundfor urban areas on sub-components of MPI. That is, poverty in education and poverty in healthreduced significantly, while poverty in living standards (in terms of assets) was not
significantly influenced by the household access to the universal pension. On the effects of the21

universal pension programme on the labour market outcomes, whilst the probability of theparticipant in the universal pension scheme working in the labour market is found to be notinfluenced by the pension income, hours of working per week are reduced significantly bynearly 3 hours at the 5 per cent significance level in urban areas. This result is in sharp contrast
with the corresponding result for rural areas. While people in rural areas continued to work forroughly the same number of hours after they started to receive a pension, people in urban areas
worked less, possibly due to the overall higher level of income in urban areas. Theoreticallyspeaking, the transfer could have a negative income effect to reduce the incentive to work in
the labour market, while the degree of reducing the labour supply is lower if the individual or
the household’s relative valuation of leisure to wage income, which tends to be lower among
poorer households who are close to the subsistence level and have a higher incentive to keep
or raise the income than to enjoy the leisure. It is surmised from our result on the negative andsignificant effect of the universal pension programme found for only urban areas, that thenegative income effect is stronger for urban residents than for rural residents given that theformer’s income level is higher.

FE Quantile Model with PS Weighting
In order to examine the heterogeneous impacts of the universal pension programme, we haveestimated the panel quantile model with PS weighting as an extension. The results for rural
areas are presented in Table 7 and those for urban areas in Table 8. We mainly highlight thevariables which show a statistically significant estimate.13
The second column of Table 7 indicates that the log of individual income (with the pension
income) of rural residents increased significantly as a result of accessing the universal pension
programme, but the percentage increase is much larger for poorer individuals (at the 10th
percentile of the individual income) where the increase rate is 274% than for the ‘average’22

individual at the median (at the 50th percentile, with the increase rate 253%) or for the ‘rich’households (at the 90th percentile, with the increase rate 224%) given that the individual income
is defined in logarithm.14 That is, in a relative sense, inequality between poorer and richer
individuals is found to decrease as a result of the introduction of the universal pension
programme. A similar pattern is found for the household expenditure (the fourth column) where
the percentage increase as a result of the access to the universal pension programme changes
from 24.1% at the 10th percentile (i.e., for the poor households) to 21.1% at the median and17.9% at the 90th percentile (i.e., for the rich households). So the universal pension programme
reduced inequality of household expenditure. This is in sharp contact with Li et al. (2020) who
found that the public pension contributed to the widening of inequality between 2002 and 2013at the national level. The difference could be due to the fact that we focus on a different period(2011-2015) using a different dataset as poverty reduced dramatically between 2013 and 2015
as noted earlier. More importantly, while Li et al. used the repeated cross-sectional data without
controlling for the sample selection bias of the pension access, we use the panel data and control
for the sample selection bias through PSM.
A broadly consistent result is found for poverty indices. For the income poverty index basedon the upper poverty line in the sixth column (the lower poverty line in the fifth column), theabsolute value of the estimated (negative) coefficient of the access to the universal pension
programme decreases from a 7.7% (6.6%, though statistically non-significant) reduction forthe poorest households at the 90th percentile (i.e. with the highest probabilities of being poverty)
to 5.4% (4.2%) for the median households and 4.7% (3.4%) for the richest households at the10th percentile.15 Hence, poverty reduced in rural areas but at a larger degree for the poorest
households. That is, not only did poverty decrease on average but also the severity of poverty
decreased, which can be shown without using the inequality-sensitive poverty measures, suchas a poverty gap measure. Multi-dimensional poverty also reduced significantly for not only

23

the aggregate MPI but also sub-dimensions in MPI, including MPI in the living standards,
while the magnitude of reduction is larger for the poorer households (the seventh to tenthcolumns). That is, the universal pension reduced inequality in multi-dimensional welfare. As
found in Table 5, we do not observe any statistically significant estimates for the labour market
outcomes in Table 7. This is consistent with statistically non-significant results for theindividual income without the pension income across different percentiles (the first column ofTable 7). That is, rural residents did not reduce their labour supply after participating in theuniversal pension programme regardless of their income level.
For urban residents, individual income without pension income significantly increased inurban areas across different percentiles (the first column of Table 8). This is more clearlyobserved for richer individuals (at the 10th percentile) with a larger coefficient estimate thanfor poorer individuals (at the 90th percentile). No significant estimates are found for theindividual income without the pension income (the second column). This is consistent with theresult that working hours reduced significantly across different percentiles (the last columns).
Here, as expected, those who worked longer in the labour market reduced more working hours
after the introduction of the universal pension programme. The probability of working in thelabour market is not significantly influenced.
Household expenditure increased significantly to a different degree across different
percentiles (e.g., by 12% at the 10th percentile (the poorest), by 10% at the median and by 8.6%
at the 75th percentile). So as in rural cases, the universal pension programme reduced inequality
to some extent. Income poverty is found to reduce only at the median, not at different
percentiles. Consistent with the FE-PSM results in Table 6, the aggregate MPI decreased acrossdifferent percentiles, but it is significant from the 10th percentile to the median with the degreeof reduction higher at the upper percentiles, suggesting some reduction in inequality inmultidimensional welfare. Statistically significant estimates are found for all the sub-24

dimensions in MPI in education, health and living standards across different distributional
points. We find that multi-dimensional wellbeing in sub-dimensions improved as a result ofparticipation in the universal pension programme across different percentiles and its inequality
was also reduced.

5. Conclusion
Evidence from cross-country studies found that pensions play a key role in reducing poverty
among the ageing population in both rural and urban areas. Despite the rapid economic growthin the last several decades, China’s old-age support system was not well developed until theuniversal pension reform in 2009. This study has examined the effect of China's universal
pension programme on poverty in both monetary and non-monetary terms based on theMultidimensional Poverty Indices (MPI). More specifically, we have assessed the effects ofthe programme on per capita income, per capita consumption expenditure, and poverty
headcount using domestic and international standards and the Multi-Dimensional Poverty
Index (MPI). Besides, a pension provides the elderly an extra income, which could affect theirlabour market decision. Then we further investigate the effect of the new universal pension on
the work decision and weekly working hours.
In this study, we have used the three waves of the household survey data CHARLS,
namely, 2011, 2013 and 2015. The general trends of poverty and urban-rural inequality aredemonstrated by a class of FGT indices and the Sen poverty index. Overall, all thesemacroeconomic figures indicate a reduction in poverty from 2011 to 2015. Although the hugeurban-rural difference still exists severely in all three waves from 2011 to 2015, the inequality
level decreased during the period.
With the help of the Propensity Score Matching (PSM) method, we reconstructed thesample as a treatment and control group by matching them based on the household

25characteristics. In the reconstructed balanced sample, the robust FE method is applied toeliminate the effects of time-invariant heterogeneity where the standard errors are clustered at
the household level. As an extension, we have estimated the panel FE quantile model toexamine the heterogeneous impact of the universal pension program.
Our econometric results show that, when a rural worker is eligible to receive NRSP, theprogramme positively affects the individual income (without the pension income) and thehousehold consumption expenditure. Household consumption poverty was also significantly
reduced (only in the panel quantile regression). We have found that the aggregate MPI as wellas its sub-dimensions in education and health at the household level reduced significantly. It isnotable that the pension income had a statistically-significant indirect effect of increasing theschooling years of children in the households to assist them to help them prevent dropouts or
adopting a higher level of education. We do not observe any effect on the labour market
outcomes of the individual after he or she accesses the universal pension programme,suggesting that those who started to receive a pension continued to work in the labour market
to increase his or her income. The panel quantile regression results have suggested that NRSP
reduced the inequality in both monetary and non-monetary wellbeing measures. For instance,
we have found that NRSP reduced the inequality among the individual income with the pension
due to the progressive nature of the pension system where everyone is entitled to the sameamount of the pension regardless of the income level or the past contribution (during the periodof our study). We have also found that NRSP reduced the inequality in household consumption
expenditure. The panel quantile regression results suggest that MPI reduced to a greater degree
among poorer households, that is, the inequality in non-monetary well-being also declined asa result of NRSP. The results suggest that rural participants in NRSP did not change their labour
supply and kept their income. Hence, the pension income was used to raise their income andliving standards in rural areas.

26

We have also confirmed that the universal pension reduced monetary and non-monetary
poverty in urban areas to a lesser extent than in rural areas, reflecting the initial low levels ofpoverty and higher levels of income and consumption in urban areas. Individual income (withpension) increased, household consumption poverty reduced significantly (only in the panel
quantile regression results), and MPI reduced significantly. The urban participants in theuniversal pension programme reduced on average weekly working hours by 2.8 hours, that is,
the negative income effect of the pension due to the disincentive effect is observed only inurban areas. It is notable that the inequality in the individual income, household consumption
expenditure and multi-dimensional well-being proxied by MPI decreased in urban areas. The
overall smaller ‘poverty-reducing’ or ‘well-being improving’ effects of the universal pension
programmes in urban areas than in ‘rural areas’ suggested that the rural-urban disparity also
declined as a result of the introduction of the universal pension programme.
In sum, our empirical results based on the national panel data in 2011-2015 serve as strong
evidence to underscore the success of the universal pension reform in 2009 introduced by the
Chines government since the programme reduced both monetary and non-monetary measuresin rural and urban areas. Notably, the universal pension programme reduced inequality among
rural residents or urban residents to some extent. Given that the proportion of elderly in China
keeps rising, the universal pension programme continues to play an essential role in the welfareof the elderly. The programme served as a milestone in unifying the long-divided welfare
system for workers in the public and private sectors. What we have not examined, however,were the fiscal implications and the sustainability of the universal pension programme as the
proportion of the elderly is expected to rise in the future. The future study should examine thecost-effectiveness of the universal pension programme as well as their poverty and inequality
reducing potentials under different scenarios of growth performances and changes in the
demographic structure using more recent data.

27

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31

Table 1: Revised MPI Framework
Dimensions

Indicator

Poverty Threshold

WeightEducation

Years of
Schooling

Deprived if no household member
has completed primary school

1/3

Disabled

Deprived if any household
member is disabled.

1/6

Chronic Illness

Deprived if any household
member has more than 3 chronic
illnesses.

1/6Electricity

Deprived if the household has noelectricity.

1/15

Water

Deprived if the household has no
running water.

1/15

Sanitation

Deprived if the household has noseparate toilet.

1/15

Flooring

Deprived if the household has adirt, sand or dung floor.

1/15

Asset Ownership

Deprived if the household does not
own more than one of: radio, TV,
telephone, motorcycle, washingmachine, refrigerator or Internet.

1/15

HealthLiving Standard

Source: Author's adaption from Alkire, Santos (2014) and CHARLS data.

32

Table 2: Variable Definition and Summary statistics
Variable

Definition

Obs

Mean

S.D.

Min

Max

age (i)Integer: age
dummy: =1 ifage>=60
An indicator variable:=1 if eligible toreceive the universal
pensionAn indicator variable:=1 if actually enrolled
in the universal
pension programme

34599

59.074

7.142

45

75

34599

0.456

0.498

0

1

34599

0.366

0.482

0

1

34465

0.474

0.499

0

1

34599

2.740

4.189

0

15.16178

34599

4.643

4.395

0

15.16178

24957

7.937

2.275

0

14.07706

25718

8.789

0.911

0

13.18693

25718

0.113

0.316

0

1

25718

0.169

0.375

0

1

34372

0.146

0.165

0

0.933333

34467

0.133

0.340

0

1

34447

0.117

0.232

0

1

34391

0.187

0.182

0

1

34,289

0.736

0.441

0

1

34,59930.08328.844

0

98

34599

0.477

0.499

0

1

34599

0.117

0.322

0

1

34599

0.845

0.362

0

1

34599

0.733

0.442

0

1

d (i)

Eligibility (i)Enrolment (i)Poverty Indicators

Log Individual
Income without
pensionLog Individual
lindi_inc (i)Income (withpension)Log Householdlinc (hh)income (withpension)Log per capita
lexp (hh)HouseholdExpenditure
An indicator variable=1 if per capita
p01e (hh)Expenditure <National Poverty Line
2,300
An indicatorvariable=1 if per
p02e (hh)capita Expenditure <International Poverty
Line 2,875
Multidimensional
mpi (hh)Poverty Index
Educational
mpi_edu (hh)Component of MPI
Health Component of
mpi_health (hh)MPI
Living-Standard
mpi_living (hh)Component of MPILabor Supply Indicators
dummy: =1 ifworking (i)working
Integer: Weekly
wwhour (i)working hours
Control Variables
male (i)dummy: =1 if maledummy: =1 if
edu (i)
receiving NRSP
married (i)dummy: =1 if married
dummy:=1 ifhealth_rate (i)Selfrated above
averagelincp (i)33hhsize (hh)aged_prop (hh)Integer: Household
size
Aged member
proportion

34599

3.518

1.692

1

16

34599

0.177

0.235

0

1

Note: (i) indicates the variable is on individual level, (hh) indicates it is on household level;
Source: Author's elaboration from CHARLS data

34

Table 3: Poverty Measurements in 2011, 2013 and 2015
Full Sample
2011
2013
2015
Headcount Ratio(2,300)
0.171
0.08
0.06
Headcount Ratio(2,875)
0.248 0.122
0.1
Poverty Gap Ratio
0.061 0.027
0.02
Squared Gap Ratio
0.031 0.013
0.01
Sen Poverty Index
0.115 0.052
0.04
Source: Authors' elaboration based on CHARLS.

2011
0.054
0.087
0.017
0.008
0.033

Urban
2013
0.025
0.044
0.007
0.003
0.013

2015
0.023
0.034
0.008
0.004
0.016

2011
0.203
0.292
0.073
0.037
0.136

Table 4: PS estimation in 2011, 2013 and 2015
2011
2013
2015
-0.01420.05830.0479(0.0329) (0.0430) (0.0351)
edu
0.326*** -0.152** 0.00908(0.0645) (0.0761) (0.0647)
married 0.0865** 0.0729 -0.00859(0.0402) (0.0526) (0.0440)
health
0.125*** -0.00920 0.0491(0.0345) (0.0461) (0.0380)_cons
0.0397 1.279*** 1.020***(0.0436) (0.0566) (0.0477)
N
6104
7028
8111
male

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Source: Authors' elaboration based on CHARLS data

35

Rural
2013
0.098
0.147
0.026
0.016
0.064

2015
0.07
0.12
0.02
0.01
0.05

Table 5: Full FE Estimation with PS weighting in Rural Areas - Eligibility
Eligibility
marriedhealthhhsizeagedpop
_cons

lincp

lindi_inc

linc

lexp

p01e

p02e

mpi

mpi_edumpi_healthmpi_living

Working

wwhour

-0.0203

1.891***

0.111

0.111***-0.0144-0.0184-0.0621***-0.113***-0.0231***0.00776-0.00599-0.115(0.101)(0.122)(0.116)(0.0409)(0.0177)(0.0202)(0.00569)(0.0132)(0.00550)(0.0178)(0.0115)(0.770)-0.710***-0.503***-0.351*0.250***-0.0668***-0.0964***0.009620.00457

0.00350-0.0315

0.0266*-0.578(0.153)(0.154)(0.187)(0.0621)(0.0241)(0.0262)(0.00715)(0.0165)(0.00703)(0.0256)(0.0148)(1.083)0.525***

0.430***

0.108-0.00622-0.00487

0.000399-0.00499-0.0113-0.00186-1.357***0.0450***3.016***(0.0894)(0.0954)(0.0904)(0.0283)(0.0117)(0.0132)(0.00391)(0.00905)(0.00407)(0.0137)(0.00928)(0.681)

0.0510*0.0748**0.211***-0.196***

0.0367***0.0542***-0.00596***-0.0287***

0.0101***0.00155

0.00569*

0.386*(0.0306)(0.0317)(0.0421)(0.0127)(0.00513)(0.00594)(0.00126)(0.00276)(0.00165)(0.00514)(0.00308)(0.229)

0.286

0.198

-0.0105

0.486***-0.173***-0.187***-0.122***-0.285***-0.0525***-0.0153-0.0599*-8.013***(0.297)(0.348)(0.365)(0.120)(0.0456)(0.0529)(0.0151)(0.0352)(0.0152)(0.0508)(0.0348)(2.213)2.530***

3.198***

7.013***

9.120***

0.0785**0.100***

0.195***

0.316***

0.166***

4.028***

0.720***

30.07***(0.203)(0.208)(0.253)(0.0826)(0.0311)(0.0342)(0.00875)(0.0200)(0.00972)(0.0336)(0.0201)(1.438)

27549

27549

19984

20307

20307

20307

27380

27456

27398

26917

27349

27549

N
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Source: Authors' elaboration based on CHARLS data

36

Table 6: Full FE Estimation with PS weighting in Urban Areas - Eligibility
Eligibility
marriedhealthhhsizeagedpop
_cons

lincp

lindi_inc

linc

lexp

p01e

p02e

mpi

mpi_edumpi_healthmpi_living

working

wwhour

-0.3790.741**

0.443

0.0587-0.00827-0.0432-0.0360***-0.0532**-0.0285***-0.00163-0.0355-2.872**(0.295)(0.375)(0.283)(0.0813)(0.0254)(0.0305)(0.00891)(0.0207)(0.00989)(0.0463)(0.0277)(1.406)-0.256-0.449

0.381

0.147

-0.0416-0.0599

0.01910.0132-0.001200.110-0.0443-0.616(0.451)(0.548)(0.423)(0.141)(0.0439)(0.0551)(0.0139)(0.0266)(0.0182)(0.0824)(0.0383)(2.225)0.904***

1.061***

0.414*-0.0382

0.02170.0227-0.0167**-0.0188-0.00579-1.291***0.01702.462*(0.257)(0.318)(0.251)(0.0619)(0.0163)(0.0206)(0.00738)(0.0145)(0.00832)(0.0356)(0.0240)(1.343)-0.0284

0.344***

0.0248-0.168***0.0150*0.0395***0.00200-0.003200.0108***-0.0149

0.005620.773(0.106)(0.119)(0.0853)(0.0294)(0.00857)(0.0128)(0.00288)(0.00647)(0.00353)(0.0161)(0.00921)(0.540)-0.709-1.599-0.8060.520**-0.03310.0420-0.0150-0.0463-0.00230-0.0284-0.144*-1.970(0.830)(1.012)(0.950)(0.227)(0.0623)(0.0867)(0.0233)(0.0464)(0.0269)(0.130)(0.0767)(3.806)

3.067***

5.529***

8.239***

9.522***

0.0189-0.0192

0.104***

0.0880**0.109***

3.886***

0.511***

16.19***(0.584)(0.692)(0.552)(0.183)(0.0574)(0.0753)(0.0174)(0.0365)(0.0209)(0.107)(0.0540)(3.225)

4902

4902

6281

6297

6282

6161

6248

6333

6333
6333
4559
4902
N
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Source: Authors' elaboration based on CHARLS data.

37

Table 7: Quantile Estimation in Rural Areas - Eligibility
P10
P25
P50
P75
P90

lincp

lindi_inc

linc

lexp

p01e

p02e

mpi

mpi_edumpi_healthmpi_living

working

wwhour

0.07772.738***

0.09890.241***-0.0340***-0.0465***-0.0766-0.148***-0.0575***-0.0272***-0.0198-0.310(0.0947)(0.401)(0.0902)(0.0338)(0.0121)(0.0133)(0.0489)(0.0200)(0.00533)(0.00558)(0.0976)(0.986)0.09092.647***

0.09240.232***-0.0362**-0.0487***-0.0889**-0.164***-0.0633***-0.0364***-0.0206-0.725(0.0710)(0.312)(0.0706)(0.0272)(0.0141)(0.0122)(0.0399)(0.0172)(0.00500)(0.00413)(0.0604)(0.706)0.102

2.529***

0.07610.211***-0.0417*-0.0543***-0.104***-0.190***-0.0741***-0.0471***-0.0220-1.475(0.0775)(0.209)(0.0859)(0.0203)(0.0248)(0.0101)(0.0295)(0.0135)(0.00498)(0.00334)(0.0867)(0.918)0.132

2.345***

0.06510.189***-0.0596-0.0730***-0.147***-0.270***-0.132***-0.0646***-0.0229-2.313(0.171)(0.159)(0.132)(0.0298)(0.0704)(0.0150)(0.0145)(0.0177)(0.0113)(0.00499)(0.142)(1.750)0.144

2.244***

0.06070.179***-0.0659-0.0771***-0.157***-0.310***-0.172***-0.0716***-0.0234-2.693(0.215)(0.220)(0.152)(0.0371)(0.0864)(0.0176)(0.0191)(0.0253)(0.0179)(0.00623)(0.172)(2.168)

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Source: Authors' elaboration based on CHARLS data

38

Table 8: Quantile Estimation in Urban Areas - Eligibility

P10
P25
P50
P75
P90

lincp

lindi_inc

linc

lexp

p01e

p02e

mpi

mpi_edumpi_healthmpi_living

working

wwhour

-0.600*

0.474

0.324

0.120*-0.0180-0.0354-0.0369***-0.0480***-0.0320**-0.0305***-0.0526-2.504**(0.320)(1.008)(1.325)(0.0668)(0.0145)(0.0262)(0.0115)(0.0152)(0.0154)(0.0113)(0.117)(1.256)-0.585**

0.465

0.321

0.114**-0.0190-0.0369-0.0417***-0.0538***-0.0355**-0.0337***-0.0473-2.741***(0.244)(0.823)(1.052)(0.0534)(0.0130)(0.0226)(0.0115)(0.0147)(0.0144)(0.00839)(0.0838)(0.968)-0.560***

0.437

0.315

0.1000***-0.0218*-0.0397**-0.0520**-0.0701***-0.0475***-0.0368***-0.0387-3.092***(0.166)(0.340)(0.435)(0.0334)(0.0114)(0.0167)(0.0250)(0.0206)(0.0165)(0.00745)(0.0342)(0.717)-0.537***

0.426

0.310

0.0860**-0.0268-0.0469**-0.0655-0.0944**-0.0667**-0.0433***-0.0345-3.581***(0.205)(0.332)(0.256)(0.0414)(0.0189)(0.0192)(0.0475)(0.0375)(0.0304)(0.0122)(0.0244)(0.986)-0.522*

0.418

0.308

0.0796-0.0293-0.0496*-0.0717-0.113**-0.0796*-0.0461***-0.0293-3.803***(0.269)(0.426)(0.353)(0.0528)(0.0249)(0.0255)(0.0581)(0.0522)(0.0418)(0.0156)(0.045)(1.258)

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Source: Authors' elaboration based on CHARLS data.

39

Figure 1: Province Distribution in Constructed Dataset

Source: Author’s elaboration from CHARLS, 2011
Figure 2: Regions in common support in 2011, 2013 and 2015(a) 2011

40

(b) 2013(c) 2015

Source: Authors' elaboration based on CHARLS data

Figure 3: Full Distribution of PS across Treatment and Comparison Groups in 2011, 2013
and 2015

41

(a) 2011(b) 2013(c)
2015
Source: Authors' elaboration based on CHARLS data

42

Appendix 1: Balancedness Test for PSM
Various standard tests for overall balancing of the treated and untreated samples for 2011,2013 and 2015 are in Panels A, B and C of Table A1. After matching there should not be any
difference between the treatment and control group, which implies a low pseudo-R-square(Caliendo and Kopeinig, 2008). This is approved by the fairly low pseudo-R-square in thefirst column. The likelihood ratio test on the joint insignificance of the covariates is still
rejected in both 2013 and 2015. The reduced mean and median bias in the 4th and 5th columns
indicates a reduction in the systematic differences in the distribution of covariates between
treatment and control groups. Although there is an only exception in 2013, where mean bias
slightly increases from 5 to 5.4, the overall balancedness has improved. The Rubin's B is theabsolute standardised difference of the means of the PS. Rubin (2001) suggests that Rubin's
B-value after matching should be less than 25. Rubin's R-value should be higher than 0.5 but
less than 2 for the treatment and control group to be balanced. In all three waves, the Rubin's
B value after matching falls considerably below the recommended value of 25. Meanwhile,
the Rubin's R, which is the ratio of the treated to non-treated variances of the PS, wasrelatively well balanced before the matching and remained so after. These statistics all
suggest the overall balancing improvement is taken as a positive.
Table A1: Balance test for each covariate after Matching 2011, 2013 and 2015
Ps R2 LR chi2p>chi2MeanBias

MedBiasRubin’s B

Rubin’sR

%Var

0.006
0.001

47.890
7.240

0.000
0.299

5.600
2.200

4.000
1.900

18.000
6.400

1.350
1.070

0.000
50.000

0.002
0.005

9.120
85.560

0.167
0.000

5.000
5.400

5.600
5.500

12.40016.400

0.830
1.190

0.000
100.000

0.002
0.002

10.33041.120

0.112
0.000

3.500
3.100

4.000
2.000

10.30010.900

1.220
1.330

50.00050.0002011
Unmatched
Matched2013
Unmatched
Matched2015
Unmatched
MatchedSource: Authors' elaboration based on CHARLS data

43

Table A2: Balance test for each covariate after Matching for 2011
Unmatched
Variable
male

d

edu

marriedhealthHousehold size

Proportion of people

MatchedMean
Treated%reduct

Control%bias

U

0.51281 0.50372

1.8M

0.51281 0.51645

-0.7

U

1

1

.

M

1

1

.

U

0.09063

0.053714.3

M0.09063 0.08941

0.5

U

0.80411 0.77891

6.2

M

0.80411 0.79808

1.5

U

0.689

0.64014

10.4

M

0.689

0.69076

-0.4

U

3.414

3.4159

-0.1

M

3.414

3.4187-0.2

U0.39387 0.39513

-0.6

M

0.39387 0.39278

0.5|bias|

59.9

t-test

V(T)/

t

p>t

V(C)

0.7

0.484

.

-0.31 0.759

.

.

.

.

.

.

.

5.41

0

.

96.7

0.18

0.857

.

2.4

0.016

.

76.1

0.64

0.524

.

4

0

.

.

96.4

-145.713.5-0.16 0.872.

-0.04 0.969

1.01

-0.1

0.918

1.02

-0.22 0.826

0.97

0.21

1.01

0.834

* if variance ratio outside [0.98; 1.02] for U and [0.98; 1.02] for M
Source: Authors’ elaboration based on CHARLS data

Table A3: Balance test for each covariate after Matching for 2013
Unmatched
Variable
male

d

edu

marriedMean%reduct

MatchedTreatedControl%bias

U

0.50273

0.47525.5

M

0.50227 0.47703

5

U

1

1

.

M

1

1

.

U

0.07372

0.0944-7.5

M

0.07383 0.07776

-1.4

U

0.80796.3

0.782444|bias|

8.3.

81

t-test

V(T)/

t

p>t

V(C)

1.31

0.189

.

2.85

0.004

.

.

.

.

.

.

.

-1.87 0.062.

-0.84 0.401

.

1.54

.

0.124

healthHousehold size

Proportion of people

M

0.8076

0.79289

3.6

U

0.69327

0.696

-0.6

M

0.69341 0.69539

-0.4

U

3.53463.4256

5.8

M

3.5198

3.412

5.7

U

0.37502 0.38417

-4.4

M

0.37548 0.38374

-4

42.3

27.5

1.1

9.8

2.08

0.037

.

-0.14 0.888.

-0.24 0.808

.

1.37

0.172

1.04

3.28

0.001

1.02

-1.06

0.29

1.01

-2.28 0.022

1.04

1.01

* if variance ratio outside [0.98; 1.02] for U and [0.98; 1.02] for M
Source: Authors’ elaboration based on CHARLS data
Table A4: Balance test for each covariate after Matching for 2015
Unmatched
Variable
male

d

edu

marriedhealthHousehold size

Proportion of people

MatchedMean
Treated%reduct

Control%bias

U

0.49598 0.47263

4.7

M

0.49590.47689

3.8

U

1

1

.

M

1

1

.

U

0.08060.07732

1.2

M

0.08073 0.07833

0.9

U

0.80675 0.80626

0.1M

0.80673 0.80607

0.2

U

0.71509 0.69505

4.4

M

0.71478 0.70312

2.6

U

2.902

2.81157.2

M

2.88882.77728.9

U0.41311 0.41966

-3.5

M

0.41362 0.42217-4.6|bias|

18.6

.

26.6

-34

41.8

-23.2

-30.4

* if variance ratio outside [0.98; 1.02] for U and [0.98; 1.02] for M
Source: Authors’ elaboration based on CHARLS data.

45

t-test

V(T)/

t

p>t

V(C)

1.47

0.142

.

2.24

0.025

.

.

.

.

.

.

.

0.38

0.704

.

0.52

0.6

.

0.04

0.968

.

0.1

0.921

.

1.39

0.164

.

1.51

0.13

.

2.19

0.028 1.24*

5.47

0

1.28*

-1.11 0.269

1.03-2.73 0.006

1.03

Appendix 2: FE Estimation based on the actual enrolment status
Table A5: Full FE Estimation with PS weighting in Rural Areas - Enrolment
Enrolment
marriedhealthhhsizeagedpop
_cons

lincp

lindi_inc

linc

lexp

p01e

p02e

mpi

mpi_edumpi_healthmpi_living

working

wwhour

0.0475

1.239***

0.201**

0.103***-0.0325***-0.0421***-0.0398***-0.0743***-0.0324***-0.0125***0.00532

0.0783(0.0748)(0.0820)(0.0872)(0.0268)(0.0108)(0.0126)(0.00364)(0.00836)(0.00354)(0.00384)(0.00723)(0.542)-0.715***-0.434***-0.350*0.255***-0.0671***-0.0971***

0.007360.001400.0178**0.00249

0.0265*-0.643(0.154)(0.154)(0.187)(0.0624)(0.0241)(0.0263)(0.00724)(0.0165)(0.00719)(0.00705)(0.0148)(1.083)0.500***

0.399***

0.103-0.00639-0.00502

0.000143-0.00521-0.0107-0.00252-0.00162

0.0449***2.597***(0.0901)(0.0952)(0.0902)(0.0283)(0.0118)(0.0132)(0.00397)(0.00913)(0.00372)(0.00410)(0.00928)(0.679)0.04900.167***

0.217***-0.190***0.0356***

0.0529***-0.00901***-0.0342***-0.00210

0.00898***

0.00541*

0.383*(0.0306)(0.0316)(0.0415)(0.0127)(0.00507)(0.00589)(0.00129)(0.00284)(0.00139)(0.00163)(0.00300)(0.224)0.253

2.342***

0.09120.615***-0.185***-0.203***-0.192***-0.411***-0.0848***-0.0794***-0.0683**-8.366***(0.272)(0.310)(0.303)(0.103)(0.0383)(0.0440)(0.0133)(0.0312)(0.0134)(0.0131)(0.0293)(1.930)2.535***

2.452***

6.896***

9.050***

0.0998***0.128***

0.220***

0.360***

0.126***

0.174***

0.717***

30.51***(0.208)(0.213)(0.257)(0.0846)(0.0320)(0.0351)(0.00934)(0.0211)(0.00932)(0.0101)(0.0204)(1.464)

20292

20292

27363

27431

27417

27377

27349

27458

N
27458
27458
19984
20292
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Source: Authors' elaboration based on CHARLS data.

46

Table A6: Full FE Estimation with PS weighting in Urban Areas - Enrolment
Enrolment
marriedhealthhhsizeagedpop
_cons

lincp

lindi_inc

linc

lexp

p01e

p02e

mpi

mpi_edumpi_healthmpi_living

working

wwhour

-0.2460.131-0.294

0.0968-0.0147-0.0410-0.0208*-0.0442*0.00827-0.0262**-0.0415-3.440*(0.361)(0.381)(0.275)(0.0854)(0.0298)(0.0321)(0.0108)(0.0228)(0.0128)(0.0121)(0.0319)(1.799)-0.336-0.566

0.351

0.152

-0.0424-0.06210.01940.01190.0478**-0.00152-0.0454-0.955(0.452)(0.544)(0.417)(0.141)(0.0439)(0.0551)(0.0140)(0.0268)(0.0244)(0.0183)(0.0383)(2.256)0.853***

0.948***

0.394-0.0403

0.02130.0220-0.0165**-0.0195-0.0238**-0.00599

0.01612.048(0.261)(0.321)(0.253)(0.0614)(0.0163)(0.0209)(0.00732)(0.0144)(0.0114)(0.00829)(0.0239)(1.368)-0.0692

0.377***

0.0460-0.163***0.0139*0.0359***-0.000464-0.00702-0.00321

0.00877**

0.00295

0.516(0.106)(0.119)(0.0866)(0.0287)(0.00830)(0.0126)(0.00288)(0.00636)(0.00355)(0.00352)(0.00918)(0.537)-1.326*-0.772-0.1730.586***-0.0472-0.0217-0.0626***-0.116***-0.0335-0.0383-0.189***-5.956(0.732)(0.832)(0.790)(0.187)(0.0475)(0.0714)(0.0217)(0.0424)(0.0315)(0.0242)(0.0711)(3.710)3.310***

5.804***

8.320***

9.503***

0.0250-0.00598

0.107***

0.0977***0.109***

0.113***

0.520***

17.69***(0.595)(0.691)(0.556)(0.183)(0.0567)(0.0752)(0.0176)(0.0372)(0.0256)(0.0209)(0.0547)(3.346)

4898

4898

6278

6293

6292

6279

6248

6307

N
6307
6307
4559
4898
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Source: Authors' elaboration based on CHARLS data.

47

Endnotes
1

Based on the World Bank estimates, the poverty rate (US$1.90) changed from 11.2% in

2010, 7.9% in 2011, 6.5% in 2012, 1.9% in 2013, 0.7% in 2015, 0.5% in 2016, 0.4% in 2017,0.3% in 2018 and further down to 0.1% in 2019(https://data.worldbank.org/indicator/SI.POV.DDAY?locations=CN Retrieved 7 June 2022).
The reason for such a dramatic poverty reduction in recent years, in particular, from 2009 to2015 is an important area of research the present study sheds light on.
2

National Bureau of Statistics of China:

https://data.stats.gov.cn/english/easyquery.htm?cn=C01 Retrieved 7 June 2022.
3

1 US Dollar = 6.8279 Chinese Yuan Renminbi on 12/31/2011:https://www.exchangerates.org.uk/historical/USD/31_12_2009; Retrieved on 7 June 2022.
4

Zhang et al. (2020) used only the 2015 data of CHARLS, while Huang and Zhang (2021)

used the 2011 and 2013 waves of the CHARLS to be combined with the 2010 and 2012
waves of the CFPS (the China Family Panel Studies). These studies focused only on rural
areas. Our study is unique as we use the panel for the entire nation covering both rural andurban areas based on the 2011, 2012, and 2015 waves of CHARLS, the period when poverty
reduced dramatically.
5

The latest wave of 2018 survey data was recently released but we have not included it

because the identification of our econometric analysis relies on the phased introduction of the
programme in 2011-15 where the program coverage increased stage by stage and our method
cannot be credibly applied to the panel of 2015-2018 as nearly all the eligible people hadalready accessed the programme in 2015. That is, the 2015 wave is regarded as ‘the fullydeveloped case’ because, in 2014, the NRSP in rural areas and URPP in urban areas were
unified as UPBRP and the universal pension system was fully developed and more consistentby 2014. However, the analysis of the 2018 wave or any future wave using a different
48

estimation method could be an important topic for future research.
6

CHARLS website: http://charls.pku.edu.cn/pages/about/sample/en.html. Retrieved 8 June

2022.
7

If N is the total population of all the county-level units and 150 is the number of counties tobe sampled, then an interval is defined as n=N/150
8Some dwellings had multiple households living in them. In these cases, CHARLS randomly

chose one household that had an age-eligible member. Thus, some variation exists in thenumber of completed household surveys in each PSU. This is corrected by using the samplingweights.
9

If a household had more than one person older than 40 and met our residence criterion, theyrandomly selected one of them.
10

Among different matching algorithms, Kernel matching is applied here and it compares theoutcome of each participant to a weighted average of the outcomes of all non-participants
where the weight depends on the distance between the two sample points (Heinrich et al.,2010). The advantage of the Kernel matching estimator is to minimize information loss
(Caliendo and Kopeinig, 2008). The weights are then smoothed using a standard normal
distribution. The use of other matching algorithms has not changed the final results
significantly.
11

See Machado and Silva (2019) for details. We use a Stata command, xtqreg written by

Silva (https://www.stata.com/meeting/uk19/slides/uk19_santos.pdf Retrieved 8 June 2022).
12

NBS: Statistical Bulletin of National Economic and Social Development, 2011, 2013,

2015.
13

A full set of the results including the estimated coefficients of control variables will be

provided on request.
14

It should be noted that the FE result (at the mean) and the quantile FE result at the median49

do not necessarily match. This is because (i) the distribution of most of the variables, such as
income, is skewed towards the left and (ii) estimation methods are different where the panel
quantile regression reflects the location-scale model (Machado, and Silva, 2019).
15

It should be noted that for income or consumption P10 (the 10th percentile) represents thepoorest households, while for poverty measures P10 represents the richest households.

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