# An Evaluation of the Security Ability of the Basic Endowment Insurance System for China’s Urban and Rural Residents

Authors: Bing Liu; Yuhong He; Zhi Liu
Journal: Journal of Mathematics
 (2022)

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## Abstract

In this paper, security ability refers to the standard of evaluation that is used to measure the implementation performance of the basic endowment insurance system for China’s urban and rural residents (EIUR). To evaluate security ability, a three-level evaluation index system with a total of 19 indicators is designed. The research results show that it is low and even does not coincide with the country’s level of economic development, for the security ability of EIUR, due to the top-level design of the insurance system under analysis, the operating environment, and residents’ willingness to participate and contribute. Moreover, there are regional differences in regard to security ability; the development of various targets is not balanced. These findings suggest that it is necessary to promote the balanced development of various targets between regions and improve the completion of each target, especially in regard to “flexibility” and “fairness” targets.


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## Body

## 1. Introduction

With the rapid development of China’s basic endowment insurance system for urban and rural residents (EIUR), the national fund revenue and expenditure scale, and per capita pension level was improved obviously. By the end of 2018, the number of residents who had enrolled in the system reached 524 million people, of whom 159 million had received treatment. Fund income was 383.8 billion renminbi (RMB), fund expenditure was 290.6 billion RMB, and the pension reached 152.33 RMB per month. (The data in the “2018 Human Resources and Social Security Development Statistical Bulletin” on the official website of the Ministry of Human Resources and Social Security of China are quoted here.)However, in regard to the development of the system, numerous problems persist, such as low treatment levels [1–3] and regional treatment differences [4]. Additionally, low individual contribution levels [5, 6] and excessive dependence on financial subsidies [7, 8] are persistent problems. Some scholars have evaluated the implementation effect of the EIUR from the economic, sustainability, fairness, and satisfaction perspectives [9–13]. On this basis, they have made suggestions for its improvement.The extant research of the EIUR lacks a macro-level evaluation of the system; rather, it focuses primarily on a certain aspect of the system, which it evaluates from the micro-level. For instance, some research focuses on qualitative descriptions, comments, and policy suggestions; others have evaluated the implementation effect of EIUR from system treatment and fund operation. Therefore, this paper will construct a comprehensive scientific evaluation index system based on multiple dimensions and perspectives, such as the basic principles of system design, security functions, and development vision. It will analyze the current status of the implementation of the EIUR in China’s provinces and put forward suggestions to deepen the reform of the system.


## 2. Research Area and Data

The research area comprises 31 provinces (autonomous regions and municipalities that are directly under the central government) in China, excluding Hong Kong, Macao, and Taiwan. (Due to the “one country, two systems” system, Hong Kong, Macao, and Taiwan have implemented a social system that differs from that of China’s mainland.) Based on the Chinese government’s regulations on the minimum standard subsidies for urban and rural residents’ basic insurance pensions (BIP) in 2014 (according to the basic pension standard, the central government provides full subsidies to the central and western regions and 50% subsidies to the eastern regions), the 31 provinces are divided into two major blocks as follows: (The eastern provinces are Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan, while the central and western provinces are Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.) the eastern region and midwestern region.The data used in this paper were sourced from yearbooks, such asChina Statistical Yearbook 2019, China Labor Statistics Yearbook 2019, and China Population and Employment Statistics Year 2019, as well as public data retrieved from the China Statistics Bureau. There are three main data categories as follows: macroeconomic data, (The macroeconomic data include China’s GDP and per capita GDP from 2011 to 2018, the per capita disposable income of urban and rural residents, the per capita living consumption expenditure of urban and rural residents, and the minimum living consumption level.) population data, (The population data include the number of permanent residents by province from 2011 to 2018, the number of older people by region, and the number of people by age across the country.) and operation data, (The operating data include the number of insured persons, the number of people receiving benefits, fund income, fund expenditures, accumulated fund balances, individual contributions, and individual contribution rates.) and these three types of data can be collectively referred to as original data. Owing to the very nature of indicators, some original data cannot be directly utilized to calculate the evaluation index. Therefore, we preprocessed some of the original data, and we call it processed data. The processed data includes the number of people aged 16–59 years who are eligible for insurance coverage, the number of people over 60 years old who are eligible for the insurance coverage, per capita pension, per capita contribution, per capita government subsidy, and per capita income fund (see Table 1 for the specific processing methods). Some of the above three types of data cannot be directly used in the calculation of the indicator system.Table 1
Evaluation index system and weights of security ability of EIUR.
GoalTargetIndicatorIndicator description (definition)Indicator interpretationSecurity ability of EIURFull coverage (0.161)Participation ratio of urban and rural residents aged 16–59(0.198)Number of participants/number of people who meet the conditions of participationThe larger the participation rate, the wider the coverageParticipation ratio of urban and rural residents over 60(0.039)Number of participants/number of people who meet the conditions of participationThe larger the participation rate, the wider the coverageGrowth rate of insured persons aged 16–59(0.280)Insured rate for the current year/insured rate for the previous year-1The larger the growth rate, the wider the coverageGrowth rate of insured persons over 60(0.484)Number of people receiving treatment in that year/number of people receiving treatment in the previous year-1The larger the growth rate, the wider the coverageBasic protection (0.111)Pension replacement rate(0.329)Pension per capita ×12/per capita disposable income of rural residentsThe greater the replacement rate, the higher the security levelPension growth rate(0.375)Pension per capita in that year/pension per capita in the previous year-1The greater the growth rate, the higher the security levelPension contribution rate to minimum living security(0.296)Per capita pension/minimum living standardThe greater the contribution rate, the higher the security levelFlexibility (0.363)Resident income flexibility of individual contributions(0.054)Growth rate of per capita contribution/growth rate of per capita disposable income of rural residentsThe greater the flexibility, the more sensitive the individual’s contribution to the residents’ incomeIndividual payment flexibility of payment subsidies(0.502)Government subsidy growth rate per capita/contribution growth rate per capitaThe greater the flexibility, the more sensitive the government subsidies of individual contributionsResident income flexibility of basic pension(0.108)Basic pension growth rate/per capita disposable income growth rate of rural residentsThe greater the flexibility, the more sensitive the basic pension to residents’ incomeEconomic development flexibility of pension(0.336)Pension growth rate per capita/gross domestic product (GDP) growth rate per capitaThe greater the flexibility, the more sensitive the pension to economic developmentSustainability (0.163)Aging population(0.002)1-Elderly population/total populationThe larger the numerical value, the stronger the sustainabilityFund cumulative balance growth rate(0.618)Cumulative fund balance of the current year/cumulative fund balance of the previous year-1The larger the numerical value, the stronger the sustainabilityInstitutional maintenance rate(0.279)1-Number of people receiving treatment/number of paying peopleThe larger the numerical value, the stronger the sustainabilityLocal government maximum subsidy ability(0.031)1-Maximum local government subsidies per capita/per capita fiscal revenueThe larger the numerical value, the stronger the sustainabilityPersonal ability to pay(0.070)1-Minimum living standard/per capita disposable incomeThe larger the numerical value, the stronger the sustainabilityFairness (0.202)Maximum rate of return on personal account pension(0.048)Expected income of each pension account/amount paid per file-1The higher the rate of return, the greater the fairnessContribution rate of economic development(0.713)Fund income per capita/GDP per capitaThe higher the contribution rate, the greater the fairnessMaximum ratio of government subsidy to individual contribution(0.239)Each level of government subsidy/each level of individual contributionThe larger the numerical value, the greater the fairnessNote: the number in parentheses represents the weight of each index. (1) Number of persons aged 16–59 eligible for participation = population aged 16–59-number of students aged 16 and above-number of employees in enterprises and institutions-number of retired enterprise employees aged 55–59-institutions and institutions’ number of retirees 50–59 years old; (2) number of people who meet the conditions for participation = the number of people 60 years old and above-number of retirees over 60 years old who have enrolled the basic pension insurance for urban employees; (3) per capita pension = fund expenditure ÷ number of recipients ÷ 12; (4) per capita contribution = individual contribution income ÷ (number of participants-number of receiving benefits); (5) government subsidies per capita = (fund income-individual contribution income) ÷ number of participants; and (6) fund income per capita = fund income ÷ number of participants.


## 3. Method

### 3.1. Index System Construction

To fully reflect our respect for objective facts and advanced thinking, based on the current research results, combined with the actual operation process of the EIUR and select targets and indicators related to individual contribution and treatment, we determined the evaluation indicators of the effect of citizens’ insurance implementation (see Table1 for the definitions and explanations of the specific indicators).


### 3.2. Determination of the Index Weight

The entropy weight method is a widely used objective weighting method. Based on the varying degrees of each index, the information entropy can be used to calculate its entropy weight. The weight of each index is then corrected by the entropy weight, thereby obtaining a more objective index weight. Because we constructed a three-level index system to evaluate the security ability of the EIUR, we propose a layered entropy weight method to measure the actual weight of each index.


#### 3.2.1. Normalized Calculation of Indicator Data

The data for each index need to be normalized to eliminate the difference between the statistical standards and dimensions of the data. To unify the description,Xi represents the target, and Xij represents the indicator. Let yijt represents the normalized calculation of the t statistical data in indicator Xij.(1)yijt=xijt−minj∈ƛxijtmaxt∈ƛxijt−mint∈ƛxijt.


#### 3.2.2. Calculation of the Index Weight

The index weights are measured using the layered entropy weight method. First, the indicator index weights are calculated according to the indicator index statistics; then, the indicator indexes are calculated; finally, the target weights are calculated according to the indicator indexes.(1) Indicator Index Weight Calculation. To determine the weight of index Xij, the information entropy of index Xij must be calculated first. Let HXij denote the information entropy of index.(2)HXij=−1lnn∑t=1nwijtlnwijt,wijt=yijt∑t=1nyijt.In (2), if wijt=0, then wijtlnwijt=0. According to the information entropy, the weight of the index can be obtained as pXij.(3)pXij=1−HXijk−∑j=1kHXij.(2) Calculation of Target Index. Based on the weight and data of the indicator index, the target can be calculated. Let SIit denote the target index. (4)SIit=∑j=1kpXijyijt.In (4), SIit is used to measure the role of targets in evaluating the EIUR.(3) Weight Calculation of Target. According to the calculation result of the target, the target index weight can be calculated. First, the information entropy of Xi is calculated. Let HXi denote the information entropy of Xi. (5)HXi=−1lnn∑t=1nwitlnwit,wit=SIit∑i=1nSIit.In (5), if wit=0, then witlnwit=0. According to the information entropy, the weight pXi of the index Xi can be obtained.(6)pXi=1−HXi5−∑j=15HXi.


#### 3.2.3. Index Weight Analysis

This article uses a layered entropy weight method: the more significant the changes in the indicators, the more substantial the weighting. As Table1 shows, among the five targets, “flexibility” is the most heavily weighted, followed by “fairness,” “full coverage,” “sustainability,” and “preservation of basics.” The results show that after years of stable development, the EIUR has made significant progress in regard to coverage, flexibility, and fairness.The accelerated development of the aging population will lead to an increase in the number of people receiving treatment of the EIUR, thereby resulting in serious challenges to the long-term stable development of the system. These challenges will be reflected in the stability and continuity of urban and rural residential maintenance pension treatment levels, as well as in the sustainability of urban and rural residential insurance fund revenues and expenditures.Owing to the influence of the design model of the existing EIUR, the development of the system is increasingly dependent on finance from the government. Therefore, the benign development of the EIUR continues to depend on the financial ability of the government—especially local governments—in the short term. This way, a strong financial ability can ensure that the fruits of economic development benefit more urban and rural residents.


### 3.3. Evaluation Index Calculation and Classification

Based on the index and weight of the five targets—coverage, security level, elasticity, sustainability, and fairness—we can quantitatively evaluate the security ability of the basic endowment insurance system for China’s urban and rural residents, and its model can be expressed as follows:(7)BPCIt=∑i=15pXiSIit.In (7), BPCIt represents the evaluation index of security ability. Its value is between 0 and 1; the larger the value, the stronger the security ability.Based on the calculated evaluation index of the security ability of each province, this paper uses the ArcGIS 10.5 software’s natural breakpoint method to divide security ability into five levels: “high level,” “higher level,” “medium level,” “lower level,” and “low level” (see Figures1 and 2).Figure 1
Grade division of the security ability of the EIUR in 2018.Figure 2
Grade division of the targets of the EIUR in 2018. (a) Grade division of full coverage. (b) Grade division of basic protection. (c) Grade division of flexibility. (d) Grade division of sustainability. (e) Grade division of fairness.
(a)(b)(c)(d)(e)The completion of the five targets is divided into three levels: “basic implementation,” “at risk,” and “serious challenge.”


## 3.1. Index System Construction

To fully reflect our respect for objective facts and advanced thinking, based on the current research results, combined with the actual operation process of the EIUR and select targets and indicators related to individual contribution and treatment, we determined the evaluation indicators of the effect of citizens’ insurance implementation (see Table1 for the definitions and explanations of the specific indicators).


## 3.2. Determination of the Index Weight

The entropy weight method is a widely used objective weighting method. Based on the varying degrees of each index, the information entropy can be used to calculate its entropy weight. The weight of each index is then corrected by the entropy weight, thereby obtaining a more objective index weight. Because we constructed a three-level index system to evaluate the security ability of the EIUR, we propose a layered entropy weight method to measure the actual weight of each index.


### 3.2.1. Normalized Calculation of Indicator Data

The data for each index need to be normalized to eliminate the difference between the statistical standards and dimensions of the data. To unify the description,Xi represents the target, and Xij represents the indicator. Let yijt represents the normalized calculation of the t statistical data in indicator Xij.(1)yijt=xijt−minj∈ƛxijtmaxt∈ƛxijt−mint∈ƛxijt.


### 3.2.2. Calculation of the Index Weight

The index weights are measured using the layered entropy weight method. First, the indicator index weights are calculated according to the indicator index statistics; then, the indicator indexes are calculated; finally, the target weights are calculated according to the indicator indexes.(1) Indicator Index Weight Calculation. To determine the weight of index Xij, the information entropy of index Xij must be calculated first. Let HXij denote the information entropy of index.(2)HXij=−1lnn∑t=1nwijtlnwijt,wijt=yijt∑t=1nyijt.In (2), if wijt=0, then wijtlnwijt=0. According to the information entropy, the weight of the index can be obtained as pXij.(3)pXij=1−HXijk−∑j=1kHXij.(2) Calculation of Target Index. Based on the weight and data of the indicator index, the target can be calculated. Let SIit denote the target index. (4)SIit=∑j=1kpXijyijt.In (4), SIit is used to measure the role of targets in evaluating the EIUR.(3) Weight Calculation of Target. According to the calculation result of the target, the target index weight can be calculated. First, the information entropy of Xi is calculated. Let HXi denote the information entropy of Xi. (5)HXi=−1lnn∑t=1nwitlnwit,wit=SIit∑i=1nSIit.In (5), if wit=0, then witlnwit=0. According to the information entropy, the weight pXi of the index Xi can be obtained.(6)pXi=1−HXi5−∑j=15HXi.


### 3.2.3. Index Weight Analysis

This article uses a layered entropy weight method: the more significant the changes in the indicators, the more substantial the weighting. As Table1 shows, among the five targets, “flexibility” is the most heavily weighted, followed by “fairness,” “full coverage,” “sustainability,” and “preservation of basics.” The results show that after years of stable development, the EIUR has made significant progress in regard to coverage, flexibility, and fairness.The accelerated development of the aging population will lead to an increase in the number of people receiving treatment of the EIUR, thereby resulting in serious challenges to the long-term stable development of the system. These challenges will be reflected in the stability and continuity of urban and rural residential maintenance pension treatment levels, as well as in the sustainability of urban and rural residential insurance fund revenues and expenditures.Owing to the influence of the design model of the existing EIUR, the development of the system is increasingly dependent on finance from the government. Therefore, the benign development of the EIUR continues to depend on the financial ability of the government—especially local governments—in the short term. This way, a strong financial ability can ensure that the fruits of economic development benefit more urban and rural residents.


## 3.2.1. Normalized Calculation of Indicator Data

The data for each index need to be normalized to eliminate the difference between the statistical standards and dimensions of the data. To unify the description,Xi represents the target, and Xij represents the indicator. Let yijt represents the normalized calculation of the t statistical data in indicator Xij.(1)yijt=xijt−minj∈ƛxijtmaxt∈ƛxijt−mint∈ƛxijt.


## 3.2.2. Calculation of the Index Weight

The index weights are measured using the layered entropy weight method. First, the indicator index weights are calculated according to the indicator index statistics; then, the indicator indexes are calculated; finally, the target weights are calculated according to the indicator indexes.(1) Indicator Index Weight Calculation. To determine the weight of index Xij, the information entropy of index Xij must be calculated first. Let HXij denote the information entropy of index.(2)HXij=−1lnn∑t=1nwijtlnwijt,wijt=yijt∑t=1nyijt.In (2), if wijt=0, then wijtlnwijt=0. According to the information entropy, the weight of the index can be obtained as pXij.(3)pXij=1−HXijk−∑j=1kHXij.(2) Calculation of Target Index. Based on the weight and data of the indicator index, the target can be calculated. Let SIit denote the target index. (4)SIit=∑j=1kpXijyijt.In (4), SIit is used to measure the role of targets in evaluating the EIUR.(3) Weight Calculation of Target. According to the calculation result of the target, the target index weight can be calculated. First, the information entropy of Xi is calculated. Let HXi denote the information entropy of Xi. (5)HXi=−1lnn∑t=1nwitlnwit,wit=SIit∑i=1nSIit.In (5), if wit=0, then witlnwit=0. According to the information entropy, the weight pXi of the index Xi can be obtained.(6)pXi=1−HXi5−∑j=15HXi.


## 3.2.3. Index Weight Analysis

This article uses a layered entropy weight method: the more significant the changes in the indicators, the more substantial the weighting. As Table1 shows, among the five targets, “flexibility” is the most heavily weighted, followed by “fairness,” “full coverage,” “sustainability,” and “preservation of basics.” The results show that after years of stable development, the EIUR has made significant progress in regard to coverage, flexibility, and fairness.The accelerated development of the aging population will lead to an increase in the number of people receiving treatment of the EIUR, thereby resulting in serious challenges to the long-term stable development of the system. These challenges will be reflected in the stability and continuity of urban and rural residential maintenance pension treatment levels, as well as in the sustainability of urban and rural residential insurance fund revenues and expenditures.Owing to the influence of the design model of the existing EIUR, the development of the system is increasingly dependent on finance from the government. Therefore, the benign development of the EIUR continues to depend on the financial ability of the government—especially local governments—in the short term. This way, a strong financial ability can ensure that the fruits of economic development benefit more urban and rural residents.


## 3.3. Evaluation Index Calculation and Classification

Based on the index and weight of the five targets—coverage, security level, elasticity, sustainability, and fairness—we can quantitatively evaluate the security ability of the basic endowment insurance system for China’s urban and rural residents, and its model can be expressed as follows:(7)BPCIt=∑i=15pXiSIit.In (7), BPCIt represents the evaluation index of security ability. Its value is between 0 and 1; the larger the value, the stronger the security ability.Based on the calculated evaluation index of the security ability of each province, this paper uses the ArcGIS 10.5 software’s natural breakpoint method to divide security ability into five levels: “high level,” “higher level,” “medium level,” “lower level,” and “low level” (see Figures1 and 2).Figure 1
Grade division of the security ability of the EIUR in 2018.Figure 2
Grade division of the targets of the EIUR in 2018. (a) Grade division of full coverage. (b) Grade division of basic protection. (c) Grade division of flexibility. (d) Grade division of sustainability. (e) Grade division of fairness.
(a)(b)(c)(d)(e)The completion of the five targets is divided into three levels: “basic implementation,” “at risk,” and “serious challenge.”


## 4. Evaluation Results and Analysis

### 4.1. Low Level of Security Ability of the EIUR

As shown in Table2, the security ability of EIUR in China is currently low. First, Hubei, which ranks first in the national security ability index, has only 0.752. The security ability indexes for Jilin, Tianjin, and Anhui are all below 0.6–0.597, 0.586, and 0.561, respectively—and much lower than that of Hubei. There are only nine provinces with a security ability index of between 0.5 and 0.6; however, there are 21 provinces with an index below 0.5, accounting for 67.74%. In addition, four provinces (autonomous regions)—Inner Mongolia, Tibet, Guangdong, and Zhejiang—have a security ability index below 0.4. Among them, Zhejiang has an index of only 0.321, which is the lowest in the country.Table 2
EIUR: security ability index and rank in 2018.
RegionTargetGoalRankFull coverageBasic protectionFlexibilitySustainabilityFairnessHubei0.5170.450.6680.8600.3000.7521Jilin0.4160.3550.5260.6270.290.5972Tianjin0.3140.4320.5580.3910.3880.5863Anhui0.4660.5230.3000.8930.3100.5614Xinjiang0.6900.3940.2170.8130.3880.5455Hainan0.5350.4790.2160.9600.3560.5446Ningxia0.4240.4010.3430.7830.3370.5437Heilongjiang0.7360.3720.2630.6850.3170.5398Guizhou0.7700.3740.2310.6090.2750.5079Fujian0.5370.3060.3160.6810.2550.50210Shanghai0.2620.7040.1670.2340.9460.49811Yunnan0.5420.5090.2340.6470.3000.49412Shanxi0.4910.4190.2570.6950.3030.49113Beijing0.4610.5960.280.4030.3490.48514Liaoning0.4290.3440.3680.4370.2990.48215Qinghai0.6820.3370.1240.7850.3300.46716Shandong0.4920.3110.2110.7870.3070.46617Gansu0.4750.3720.1880.8040.2780.45618Jiangxi0.2470.4350.2350.9310.2280.45519Hebei0.4910.3010.2120.7170.2970.4520Henan0.4880.3460.2150.7590.2040.44621Chongqing0.4320.3410.2420.6360.2850.44422Sichuan0.4470.3690.2110.5560.3910.44423Shaanxi0.5300.3540.1860.6810.2850.44424Guangxi0.3030.4500.2100.6570.3150.42725Hunan0.3970.3650.2000.6900.2780.42626Jiangsu0.3880.2410.2600.5550.3110.42127Inner Mongolia0.4860.3430.1510.4690.2960.38028Tibet0.2340.3250.1530.6650.350.37129Guangdong0.3990.3420.2060.3990.2470.37030Zhejiang0.3280.2600.1500.3520.3300.32131Second, from a security level perspective (Figure2), a few provinces have a higher level of security ability while others have medium and low levels. Specifically, Hubei is the only high-level province, and there are only seven higher-level provinces, nine medium-level provinces, and then other provinces that are at a lower level and below, accounting for 45.16%.There are several reasons for the low level of security ability. First, target completion is poor. As shown in Table2, regarding the five targets, the provinces facing “certain risks” and “serious challenges” account for a high proportion: 87.10%, 93.55%, 90.32%, 67.74%, and 96.77%. Second, none of the provinces have been able to implement the targets. For example, Hubei, which ranks first in the country in regard to the security ability index, has only two targets—“flexibility” and “sustainability”—that have been “basically achieved,” while the “fairness” target is still facing “serious challenges.” Inner Mongolia, Guangdong, and Zhejiang, which have lower security ability index rankings, have the lowest target completion. Four targets in each province face “serious challenges,” and none can achieve “basic implementation.”


### 4.2. Uneven Development of the Targets of the EIUR

Table2 and Figure 2 show that among the five targets, “sustainability” and “full coverage” are developing well, followed by “basic protection.” In contrast, the “fairness” and “flexibility” targets are not being achieved. First, the “sustainable” target has the highest degree of completion among the five targets, and 10 provinces have reached the “basic implementation” level. The “full coverage” target is second only to the “sustainable” one, and in regard to the former, more than half of the provinces are above the national average. Second, the “basic protection” target’s performance was average, with 12 provinces above the national average and the others below. The only provinces in which “basic protection” can be “basically realized” are Shanghai and Beijing, signifying that the treatment level of the EIUR is low. Third, “fairness” and “flexibility” have the worst performance. Insurance flexibility has become the biggest shortcoming in the development of the EIUR. Regarding the “flexibility” index, the national average is only 0.261, and only nine provinces are above this threshold. “Fairness” is another target with poor performance, as 21 provinces are below the national average and are facing “serious challenges.” Moreover, there is an apparent gap in the degree of realization regarding the “fairness” target. In regard to this target, only Shanghai has achieved “basic realization,” and its index of 0.946 is 0.555 higher than that of Sichuan, which ranks second in the country.Based on the indicator calculation, we identified some reasons for the poor performance of “fairness” and “flexibility” targets: On the one hand, the low “fairness” index is due primarily to the failure of the incentives for government subsidies for individual contributions, which are reflected in the indicator such as individual account pension return rate and the financial subsidy ratio. However, we found that the lowest individual contribution level had the highest return rate, with the improvement of the individual contribution level, the pension return rate, and the financial subsidy ratio showing a downward trend. Obviously, residents will choose the lowest contribution level to pay, thereby enabling them to maximize their interests. This also explains why the individual contribution level has continued to decline in the past 5 years. (In recent years, the income funds of the basic endowment insurance system for China’s urban and rural residents have gradually increased. However, the proportion of individual contributions has gradually decreased from 38.79% in 2014 to 24.52% in 2018, indicating that the funds’ dependence on finance is higher than the income of the individual contributions.)On the other hand, the lowest “flexibility” index was due primarily to the mismatch between the individual’s contribution level and income level. Generally, with the improvement of residents’ income level and the increase in old-age awareness, the degree of residents’ participation should increase accordingly. However, the data analysis showed that a few provinces, including Jiangxi, Hainan, and Hunan, have greater flexibility in regard to personal payments. In most provinces, such as Tianjin, the individual contribution flexibility of the EIUR is deficient. It must be noted that residents’ per capita disposable income in China is increasing year by year, while individual contributions are decreasing. This means that the degree of urban and rural residents’ participation is decreasing. The question of how to reverse this trend and ensure that personal contributions are compatible with economic development and residents’ income is a significant issue in the development of the system. Moreover, the existing system design causes “flexibility” and “fairness” to influence each other. However, the system design’s contribution model is not conducive to the improvement of “flexibility” and “fairness” targets. In the future, the improvement of the system’s security ability needs to focus on “flexibility” and “fairness” targets.


### 4.3. Significant Regional Differences in Security Ability

There are regional differences in the security ability of the EIUR in China. In the midwestern region, performance in regard to security ability is better than in the eastern region. The completion of the targets between regions varies. As Figure3 shows, there are 8 provinces in which the insurance security ability index has reached a higher level and above. Among the above 8 provinces, only Tianjin and Hainan in the eastern region have reached a higher level. Based on the rankings (Table 2), there are seven midwestern provinces in the top 10 in the country, and Hubei ranks first, with an absolute advantage of 0.752. Most provinces in the eastern region, such as Jiangsu, Guangdong, and Zhejiang, are ranked lower than those in the midwestern region, even appeared at the bottom of the rankings.Figure 3
The differences between the targets in the (a) eastern region and (b) the central and western regions.
(a)(b)There are also regional differences in the completion of targets. On the one hand, the “full coverage” target of the provinces in the eastern region is underdeveloped. As shown in Figure1, none of the eastern provinces “basically achieved” “full coverage,” and most have “certain risks.” For example, Shanghai, Tianjin, and Zhejiang still face “serious challenges” in achieving the “full coverage” target. Based on the participation rate of urban and rural residents in the provinces of the eastern region, as shown in Figure 4, in all except Beijing, the participation rate of residents aged 16–59 is lower than that of residents over 60 years old. This shows that the residents in the eastern region, especially the residents between youth and middle age, are less enthusiastic about participating than residents in the midwestern region in the EIUR. The system’s severe lack of attractiveness lowers the eastern provinces’ scores and rankings on the “full coverage” index.Figure 4
Participation of the EIUR.On the other hand, the provinces in the midwestern region are poorly developed in terms of the “basic protection” target. No provinces in the midwestern region have reached the “basic realization” level. In addition, all provinces in the midwestern region face “certain risks” and “serious challenges.” This is due primarily to the lower level of the basic endowment insurance treatment in these regions. Although most provinces in the central and western regions raised the basic pensions of the EIUR in 2018, (In May 2018, the Chinese government issued the “Notice on Raising the Minimum Standards of Basic Pension Insurance for Urban and Rural Residents in China in 2018.” Most provinces in the central and western regions—such as Shanxi, Anhui, Jiangxi, Guangxi, Chongqing, Sichuan, Guizhou, Shaanxi, Gansu, and Xinjiang—have raised the basic pension standard only slightly since 2014, and some did not adjust basic pension standards between 2015 and 2017.) they cannot change the current low level of treatment. According to the perspective of system design, the low level of treatment is directly related to individual contributions and financial subsidies. The central and western regions have no advantage in terms of individual contribution or financial subsidy ability; consequently, the government can provide only a relatively low subsidy amount due to downward economic pressure and financial affordability.


### 4.4. Mismatched Security Ability and Economic Development Level

At present, there is an apparent mismatch between the security ability of the EIUR and the level of economic development. As shown in Figure5, the economically underdeveloped areas have high security abilities. For instance, Hubei, Jilin, Heilongjiang, Anhui, Ningxia, Guizhou, and Xinjiang, which have a relatively low per capita GDP, have relatively high system security abilities. In contrast, the economically developed areas, such as Shanghai, Beijing, Jiangsu, Guangdong, and Zhejiang, show low security abilities in regard to both ranking and index. The purpose of the EIUR is to protect the basic living needs of citizens in old age. However, if the security ability is too low, it cannot provide sufficient protection for citizens who have not participated in other endowment insurance, especially in regions with high economic development levels.Figure 5
Security ability and GDP per capita.The study found that “basic protection” and “flexibility” are the main factors that lower the security ability index of the EIUR in each province. In practice, this is reflected primarily in the low treatment level, lack of flexibility in the system adjustment mechanism, and low contribution level of citizens who have not participated in other endowment insurance; these factors have, in turn, led to an increase in financial dependence. (According to the “China Pension Fund Report 2018,” between 2011 and 2017, the proportion of personal contributions to the income fund of the basic pension insurance of urban and rural residents decreased from 38.79% to 24.52%.) Because it is a pay-as-you-go system, the increasing financial burden year by year will force the local government to devote most of its energy to maintaining the regular operation of the current system rather than increasing the level of treatment, thereby resulting in the financially supported payment subsidies, and basic pensions have been at a low level for a long time, and it will not effectively attract residents whose living consumption level is increasing with the economic development to actively participate and choose higher-level payment, andt. In addition, increased financial subsidies and low personal contributions will cause personal contributions to income funds to decrease year by year. Such repeated cycles will eventually lead to a situation in which the system’s security ability does not coincide with the economic development of the province.The fundamental reason for the above is that the system is ineffective in regard to guiding residents to choose a higher level of contribution. It is not difficult for residents to afford a higher level of contribution. However, due to the system’s defect in the setting of government subsidies for individual contributions, the individual account pension return rate of the lowest contribution level for each province is the highest, and the proportion of government subsidies for individual contributions shows a downward trend as the contribution level increases. Therefore, the system does not encourage insured persons to increase their contribution levels. Whether the residents make “smart choices” or follow the herd mentality after making their cost-benefit calculations, they are more inclined to choose a lower contribution level.


## 4.1. Low Level of Security Ability of the EIUR

As shown in Table2, the security ability of EIUR in China is currently low. First, Hubei, which ranks first in the national security ability index, has only 0.752. The security ability indexes for Jilin, Tianjin, and Anhui are all below 0.6–0.597, 0.586, and 0.561, respectively—and much lower than that of Hubei. There are only nine provinces with a security ability index of between 0.5 and 0.6; however, there are 21 provinces with an index below 0.5, accounting for 67.74%. In addition, four provinces (autonomous regions)—Inner Mongolia, Tibet, Guangdong, and Zhejiang—have a security ability index below 0.4. Among them, Zhejiang has an index of only 0.321, which is the lowest in the country.Table 2
EIUR: security ability index and rank in 2018.
RegionTargetGoalRankFull coverageBasic protectionFlexibilitySustainabilityFairnessHubei0.5170.450.6680.8600.3000.7521Jilin0.4160.3550.5260.6270.290.5972Tianjin0.3140.4320.5580.3910.3880.5863Anhui0.4660.5230.3000.8930.3100.5614Xinjiang0.6900.3940.2170.8130.3880.5455Hainan0.5350.4790.2160.9600.3560.5446Ningxia0.4240.4010.3430.7830.3370.5437Heilongjiang0.7360.3720.2630.6850.3170.5398Guizhou0.7700.3740.2310.6090.2750.5079Fujian0.5370.3060.3160.6810.2550.50210Shanghai0.2620.7040.1670.2340.9460.49811Yunnan0.5420.5090.2340.6470.3000.49412Shanxi0.4910.4190.2570.6950.3030.49113Beijing0.4610.5960.280.4030.3490.48514Liaoning0.4290.3440.3680.4370.2990.48215Qinghai0.6820.3370.1240.7850.3300.46716Shandong0.4920.3110.2110.7870.3070.46617Gansu0.4750.3720.1880.8040.2780.45618Jiangxi0.2470.4350.2350.9310.2280.45519Hebei0.4910.3010.2120.7170.2970.4520Henan0.4880.3460.2150.7590.2040.44621Chongqing0.4320.3410.2420.6360.2850.44422Sichuan0.4470.3690.2110.5560.3910.44423Shaanxi0.5300.3540.1860.6810.2850.44424Guangxi0.3030.4500.2100.6570.3150.42725Hunan0.3970.3650.2000.6900.2780.42626Jiangsu0.3880.2410.2600.5550.3110.42127Inner Mongolia0.4860.3430.1510.4690.2960.38028Tibet0.2340.3250.1530.6650.350.37129Guangdong0.3990.3420.2060.3990.2470.37030Zhejiang0.3280.2600.1500.3520.3300.32131Second, from a security level perspective (Figure2), a few provinces have a higher level of security ability while others have medium and low levels. Specifically, Hubei is the only high-level province, and there are only seven higher-level provinces, nine medium-level provinces, and then other provinces that are at a lower level and below, accounting for 45.16%.There are several reasons for the low level of security ability. First, target completion is poor. As shown in Table2, regarding the five targets, the provinces facing “certain risks” and “serious challenges” account for a high proportion: 87.10%, 93.55%, 90.32%, 67.74%, and 96.77%. Second, none of the provinces have been able to implement the targets. For example, Hubei, which ranks first in the country in regard to the security ability index, has only two targets—“flexibility” and “sustainability”—that have been “basically achieved,” while the “fairness” target is still facing “serious challenges.” Inner Mongolia, Guangdong, and Zhejiang, which have lower security ability index rankings, have the lowest target completion. Four targets in each province face “serious challenges,” and none can achieve “basic implementation.”


## 4.2. Uneven Development of the Targets of the EIUR

Table2 and Figure 2 show that among the five targets, “sustainability” and “full coverage” are developing well, followed by “basic protection.” In contrast, the “fairness” and “flexibility” targets are not being achieved. First, the “sustainable” target has the highest degree of completion among the five targets, and 10 provinces have reached the “basic implementation” level. The “full coverage” target is second only to the “sustainable” one, and in regard to the former, more than half of the provinces are above the national average. Second, the “basic protection” target’s performance was average, with 12 provinces above the national average and the others below. The only provinces in which “basic protection” can be “basically realized” are Shanghai and Beijing, signifying that the treatment level of the EIUR is low. Third, “fairness” and “flexibility” have the worst performance. Insurance flexibility has become the biggest shortcoming in the development of the EIUR. Regarding the “flexibility” index, the national average is only 0.261, and only nine provinces are above this threshold. “Fairness” is another target with poor performance, as 21 provinces are below the national average and are facing “serious challenges.” Moreover, there is an apparent gap in the degree of realization regarding the “fairness” target. In regard to this target, only Shanghai has achieved “basic realization,” and its index of 0.946 is 0.555 higher than that of Sichuan, which ranks second in the country.Based on the indicator calculation, we identified some reasons for the poor performance of “fairness” and “flexibility” targets: On the one hand, the low “fairness” index is due primarily to the failure of the incentives for government subsidies for individual contributions, which are reflected in the indicator such as individual account pension return rate and the financial subsidy ratio. However, we found that the lowest individual contribution level had the highest return rate, with the improvement of the individual contribution level, the pension return rate, and the financial subsidy ratio showing a downward trend. Obviously, residents will choose the lowest contribution level to pay, thereby enabling them to maximize their interests. This also explains why the individual contribution level has continued to decline in the past 5 years. (In recent years, the income funds of the basic endowment insurance system for China’s urban and rural residents have gradually increased. However, the proportion of individual contributions has gradually decreased from 38.79% in 2014 to 24.52% in 2018, indicating that the funds’ dependence on finance is higher than the income of the individual contributions.)On the other hand, the lowest “flexibility” index was due primarily to the mismatch between the individual’s contribution level and income level. Generally, with the improvement of residents’ income level and the increase in old-age awareness, the degree of residents’ participation should increase accordingly. However, the data analysis showed that a few provinces, including Jiangxi, Hainan, and Hunan, have greater flexibility in regard to personal payments. In most provinces, such as Tianjin, the individual contribution flexibility of the EIUR is deficient. It must be noted that residents’ per capita disposable income in China is increasing year by year, while individual contributions are decreasing. This means that the degree of urban and rural residents’ participation is decreasing. The question of how to reverse this trend and ensure that personal contributions are compatible with economic development and residents’ income is a significant issue in the development of the system. Moreover, the existing system design causes “flexibility” and “fairness” to influence each other. However, the system design’s contribution model is not conducive to the improvement of “flexibility” and “fairness” targets. In the future, the improvement of the system’s security ability needs to focus on “flexibility” and “fairness” targets.


## 4.3. Significant Regional Differences in Security Ability

There are regional differences in the security ability of the EIUR in China. In the midwestern region, performance in regard to security ability is better than in the eastern region. The completion of the targets between regions varies. As Figure3 shows, there are 8 provinces in which the insurance security ability index has reached a higher level and above. Among the above 8 provinces, only Tianjin and Hainan in the eastern region have reached a higher level. Based on the rankings (Table 2), there are seven midwestern provinces in the top 10 in the country, and Hubei ranks first, with an absolute advantage of 0.752. Most provinces in the eastern region, such as Jiangsu, Guangdong, and Zhejiang, are ranked lower than those in the midwestern region, even appeared at the bottom of the rankings.Figure 3
The differences between the targets in the (a) eastern region and (b) the central and western regions.
(a)(b)There are also regional differences in the completion of targets. On the one hand, the “full coverage” target of the provinces in the eastern region is underdeveloped. As shown in Figure1, none of the eastern provinces “basically achieved” “full coverage,” and most have “certain risks.” For example, Shanghai, Tianjin, and Zhejiang still face “serious challenges” in achieving the “full coverage” target. Based on the participation rate of urban and rural residents in the provinces of the eastern region, as shown in Figure 4, in all except Beijing, the participation rate of residents aged 16–59 is lower than that of residents over 60 years old. This shows that the residents in the eastern region, especially the residents between youth and middle age, are less enthusiastic about participating than residents in the midwestern region in the EIUR. The system’s severe lack of attractiveness lowers the eastern provinces’ scores and rankings on the “full coverage” index.Figure 4
Participation of the EIUR.On the other hand, the provinces in the midwestern region are poorly developed in terms of the “basic protection” target. No provinces in the midwestern region have reached the “basic realization” level. In addition, all provinces in the midwestern region face “certain risks” and “serious challenges.” This is due primarily to the lower level of the basic endowment insurance treatment in these regions. Although most provinces in the central and western regions raised the basic pensions of the EIUR in 2018, (In May 2018, the Chinese government issued the “Notice on Raising the Minimum Standards of Basic Pension Insurance for Urban and Rural Residents in China in 2018.” Most provinces in the central and western regions—such as Shanxi, Anhui, Jiangxi, Guangxi, Chongqing, Sichuan, Guizhou, Shaanxi, Gansu, and Xinjiang—have raised the basic pension standard only slightly since 2014, and some did not adjust basic pension standards between 2015 and 2017.) they cannot change the current low level of treatment. According to the perspective of system design, the low level of treatment is directly related to individual contributions and financial subsidies. The central and western regions have no advantage in terms of individual contribution or financial subsidy ability; consequently, the government can provide only a relatively low subsidy amount due to downward economic pressure and financial affordability.


## 4.4. Mismatched Security Ability and Economic Development Level

At present, there is an apparent mismatch between the security ability of the EIUR and the level of economic development. As shown in Figure5, the economically underdeveloped areas have high security abilities. For instance, Hubei, Jilin, Heilongjiang, Anhui, Ningxia, Guizhou, and Xinjiang, which have a relatively low per capita GDP, have relatively high system security abilities. In contrast, the economically developed areas, such as Shanghai, Beijing, Jiangsu, Guangdong, and Zhejiang, show low security abilities in regard to both ranking and index. The purpose of the EIUR is to protect the basic living needs of citizens in old age. However, if the security ability is too low, it cannot provide sufficient protection for citizens who have not participated in other endowment insurance, especially in regions with high economic development levels.Figure 5
Security ability and GDP per capita.The study found that “basic protection” and “flexibility” are the main factors that lower the security ability index of the EIUR in each province. In practice, this is reflected primarily in the low treatment level, lack of flexibility in the system adjustment mechanism, and low contribution level of citizens who have not participated in other endowment insurance; these factors have, in turn, led to an increase in financial dependence. (According to the “China Pension Fund Report 2018,” between 2011 and 2017, the proportion of personal contributions to the income fund of the basic pension insurance of urban and rural residents decreased from 38.79% to 24.52%.) Because it is a pay-as-you-go system, the increasing financial burden year by year will force the local government to devote most of its energy to maintaining the regular operation of the current system rather than increasing the level of treatment, thereby resulting in the financially supported payment subsidies, and basic pensions have been at a low level for a long time, and it will not effectively attract residents whose living consumption level is increasing with the economic development to actively participate and choose higher-level payment, andt. In addition, increased financial subsidies and low personal contributions will cause personal contributions to income funds to decrease year by year. Such repeated cycles will eventually lead to a situation in which the system’s security ability does not coincide with the economic development of the province.The fundamental reason for the above is that the system is ineffective in regard to guiding residents to choose a higher level of contribution. It is not difficult for residents to afford a higher level of contribution. However, due to the system’s defect in the setting of government subsidies for individual contributions, the individual account pension return rate of the lowest contribution level for each province is the highest, and the proportion of government subsidies for individual contributions shows a downward trend as the contribution level increases. Therefore, the system does not encourage insured persons to increase their contribution levels. Whether the residents make “smart choices” or follow the herd mentality after making their cost-benefit calculations, they are more inclined to choose a lower contribution level.


## 5. Conclusions and Recommendations

Upon the evaluation method and indicator system, the study found that factors such as the top-level design of the EIUR, the operating environment, and the willingness of the residents affected the EIUR in China, resulting in low security ability that does not coincide with the level of economic development. In addition, there are regional differences in security ability, and the development of various targets is uneven. To further enhance the security ability and facilitate the steady development of the security system, two areas need to be improved.First, there is a need to improve the completion of each target and to focus on strengthening the development of “flexibility” and “fairness” targets. On the one hand, it is necessary to improve the residents’ individual insurance contribution model and to establish and improve the individual contribution adjustment mechanism. The level of economic development, residents’ income, and consumption levels should also be taken into consideration, and individual contribution standards should be adjusted in a timely manner to enhance the flexibility of the EIUR. On the other hand, to promote the equitable development of the system, it is necessary to improve the incentive mechanism for government payment subsidies, adjust the payment subsidies according to financial strength in a timely manner, improve the efficiency of individual accounts, and establish and improve the investment mechanism of individual account funds.Second, to balance the development of each target between regions, the provinces in the eastern region should prioritize the stability of the system’s “full coverage” targets, while the provinces in the central and western regions should focus on improving the treatment level of the EIUR. On the one hand, the eastern provinces should focus on stimulating the efficiency of personal accounts by strengthening policy publicity and optimizing the incentive mechanism for government subsidies for individual contributions, thereby guiding and stabilizing existing insured residents and encouraging insured persons to choose a higher contribution level. On the other hand, provinces in the central and western regions should focus on basic pension standards to improve the provincial system treatment level. Supporting tax reform and exploring new local economic growth points will increase the financial strength of the provinces in the midwestern region and provide robust financial security for the adjustment of basic pension standards.



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*Source: 6244880-2022-02-16.xml*