The Impact of Social Security Expenditure on Human Common Development: Evidence from China’s Provincial Panel Data

Abstract

With the rapid development of the social economy, human development has been greatly improved. However, the gap between rich and poor still exists, which restricts the further development of society. In order to study the relationship between social security expenditure and human common development, we constructed, for this paper, the Human Common Development Index, and made a regression analysis between social security expenditure and human common development. The results showed that, in China, obvious and remarkable progress has been made in human common development. There were differences between regions—high in the east and low in the west—but the gap between regions was narrowing. There was an inverted U-shaped relationship between social security expenditure and human common development. At present, social security expenditure per capita in most provinces has not reached the inflection point. The Chinese government should pay attention to the important role of social security, optimize social security expenditure, and improve the accuracy and effectiveness of social security expenditure. In addition, the Chinese government should also improve the urbanization rate and promote economic development.

1. Introduction

The real purpose of social development is human development, enabling people to live fulfilling and creative lives, and to develop their potential [1]. With the improvement of people’s living standards, people’s concerns have shifted to good health, knowledge acquisition and a decent standard of living. It is no longer sufficient to use GDP to measure social development [2,3]. In 1990, the United Nations Development Program (UNDP) published its first Human Development Report, and proposed the Human Development Index (HDI), upending the long-standing system of measuring only material growth [4].

In recent years, the HDI in all provinces of China has been on the rise [5]. However, the contradictions brought about by unbalanced and inadequate development are affecting the further development of the Chinese economy and Chinese society [6]. The Chinese government is aware of the problem, and has put forward its Outline of the 14th Five-Year Plan (2021–2025) for National Economic and Social Development and Vision 2035 of the People’s Republic of China in 2021, emphasizing the realization of common prosperity for all its people. The current HDI, and its expansion, ignore the positive contribution of common development to human development. Because the essence of common prosperity for all is human common development, we constructed, for this paper, the HCDI, to measure human development and common development at the same time. The higher the index was, the higher human development and common development were.

When income inequality is high, the public expects its government to increase social spending [7]. Social security expenditure provides financial support for the poor, and pursues fairness, which is regarded as an important means of income redistribution [8]. However, social security expenditure accounts for a large proportion of total government social expenditure, and continues to grow, raising concerns about fiscal sustainability [9]. Social security and employment expenditure in China increased from 510.45 billion yuan in 2007 to 3.14 trillion yuan in 2020, an increase of 516.09%. As it is not advisable to increase social security expenditure without limit, how to optimize social security expenditure to promote human common development has become an important issue which, until the present paper, had not yet been studied. In order to solve the problem, the following questions had to be addressed:

(1)

How is human common development progressing in the various provinces of China?

(2)

What is the relationship between social security expenditure and human common development?

In order to solve the above questions, this paper employed the Human Common Development Index (HCDI), which we constructed by adding the dimension of common development to the HDI, and adopting the latest statistical data to measure provincial development in China. Then, we put social security expenditure and human common development into the same analytical framework, and empirically tested the relationship between them, from the provincial level.

The rest of this paper is organized as follows: Section 2 reviews the earlier studies; Section 3 introduces the construction of the HCDI, and measures the HCDI of various provinces; Section 4 introduces the methods and data; Section 5 presents the empirical results and discussion; Section 6 concludes the study with important findings, and puts forward the policy implications.

2. Literature Review

2.1. Social Security Expenditure

Social security originated with the Social Security Act of the United States in 1935 [10]. Every country which has established a social security system has done so in the form of legislation, which means that the social security system is the responsibility of the government [11]. Social security is a public product, with positive externalities, and is an important basis for establishing political identity and legitimacy, which means that it should meet people’s survival and development needs. Social security is controlled by the government, and provides basic assistance to people who have lost the ability to work, or who are threatened with poverty [8]. The government uses public power to tax residents in accordance with the law, and then provides help to individuals in the form of social security, ultimately achieving the purpose of public income redistribution [12]. Social security expenditure refers to the expenditure of public finance for social security, which is the foundation and core of the social security system, and directly reflects social security development in a region. Social security expenditure is an important part of fiscal expenditure, which is an important aspect of the government’s role in restraining and guiding social development [13].

2.2. Human Development

The HDI, based on Amartya Sen’s theory of viable capability, contains three aspects: life expectancy, adult literacy rate and per capita GDP [14]. It has been used to measure human development in many countries [15,16,17]. The HDI has been praised by scholars for its simple operation and intuitive advantages in evaluating, comparing and analyzing the development of countries around the world [18]. With the development of society, many factors have been combined with the HDI to construct a more appropriate measurement index, such as environment [19,20], gender [21], and political and civil liberties [22].

Human development is influenced by many factors: economic growth is one of the most important. Specifically, human development is affected by per capita income [23], GDP [24,25], international trade [26,27] and other economic factors. Fiscal spending is another important factor affecting human development. Qureshi took Pakistan as a sample for analysis, and showed that human development was strongly correlated with public expenditure, especially public expenditure on education and health [28]. Sofilda, et al. found that public expenditure seriously affected human development and its spatial distribution in Indonesia [29]. Agarwal empirically analyzed the relationship between social sector expenditure and human development in Indian states, and found that the share of social sector expenditure in development expenditure was positively correlated with the human development [30]. Some scholars have analyzed the influencing factors of human development from other dimensions. Yonehara found that educational resources, such as child health workers and primary school teachers, played an important role in human development [31]. Sims et al. demonstrated a negative correlation between corruption and human development in 68 countries, and found that the strength of the relationship depended on aspects of power distance and individualism in national culture [32]. Social capital is another important factor affecting human development [33]. Scholars have also used various indicators of global fertility rate and cultural transition to prove that demographic transition has a significant impact on HDI [34]. Nourou used the relevant data of 74 developing countries from 1980 to 2012 to estimate the effects, and concluded that life expectancy decreased when food prices soared, but that HDI might not be significantly affected when food prices collapsed [35].

2.3. Social Security Expenditure and Human Common Development

Social security can provide security and assistance to residents, promoting resident employment and improving residents’ income, which is closely related to human development [36]. The government can restrain and guide social development through social security expenditure. Specifically, social security expenditure can affect people’s income [37,38], health [39], and education [38,39]. In addition, social security has the natural property of fair distribution, which plays an important role in poverty reduction and narrowing the income gap [40,41,42,43]. Therefore, social security expenditure is one of the main tools and basic supports with which the government promotes social stability and development [13], which is helpful for promoting human common development.

However, social security expenditure may also hinder human common development. Being government expenditure, social security expenditure comes from the tax collected by the government from the public [12]. Social security contributions, which fund social security programs, make up a large portion of total tax revenue. Excessive social security expenditure will reduce people’s disposable income, thus affecting people’s health and education. At the same time, social security may worsen ex post intragenerational inequality of lifetime income [38]. Yu and Li found that the increase of social security expenditure widened the income gap between urban and rural residents in the long run [8].

Therefore, the relationship between social security expenditure and human common development has not been fully explained. In order to explore the relationship between the two, this paper proposes the following research hypothesis.

Hypothesis H1.

There is an inverted U-shaped relationship between social security expenditure and the HCDI.

In addition, the increase or decrease of human common development may have long-term effects, and there may be a trend of inertia. Therefore, this paper proposes the following research hypothesis:

Hypothesis H2.

The human common development of the current year is correlated with the value of the previous year.

3. Construction of the HCDI

3.1. Index Selection

For this chapter, we constructed the HCDI, and measured the human common development of each province in China. Centering on the idea of people-centered development, we integrated common development into the HDI, to construct the HCDI. Based on the principles of pertinence, simplicity, transparency, ease of operation and representativeness, we chose HDI’s income, health and education as the three dimensions by which to measure human development, and we chose the Gini coefficient to measure common development among people. The Gini coefficient is the most direct and important aspect by which to measure the balance of social development. The composition, selection and meaning of the indicators are shown in

Table 1. Data description.

Total Index	Sub-Index	Definition	Specification
Human common development	Income	Disposable income per capita.	Logarithm of disposable income per capita.
	Life	Life expectancy.	Number of years of life lived by people born in the same age group, with mortality rates held constant in all age groups.
	Education	Years of education per capita.	Total number of years of education (including adult education) in a region divided by the population.
		Expected years of education per capita.	Average number of years a five-year-old can expect to receive formal education over his or her lifetime, which is calculated by summing up the net enrolment rates of the population over the age of five.
	Common development	Income gap among residents.	Gini coefficient.

.

3.2. Calculation Formula

The statistical indicators of the HCDI include positive sub-indexes and inverse sub-indexes, among which the income gap among residents belongs to the negative indicator, while other sub-indexes belong to positive indicators. In order to make the calculated index comparable in this paper, we conducted dimensionless processing for these indexes, adopted different processing formulas for positive and negative indexes, and normalized all sub-indexes from 0 to 1.

Positive indicators: ${\mathrm{Y}}_{\mathrm{ij}}=\frac{x_{\mathrm{ij}}-\min \left(x_{\mathrm{ij}}\right)}{\max \left(x_{\mathrm{ij}}\right)-\min \left(x_{\mathrm{ij}}\right)}$

Negative indicators: ${\mathrm{Y}}_{\mathrm{ij}}=\frac{\max \left(x_{\mathrm{ij}}\right)-x_{\mathrm{ij}}}{\max \left(x_{\mathrm{ij}}\right)-\min \left(x_{\mathrm{ij}}\right)}$

i represented different sub-indexes; j represented different provinces; $x_{\mathrm{ij}}$ represented the original value; ${\mathrm{Y}}_{\mathrm{ij}}$ represented the standardized value; $\max \left(x_{\mathrm{ij}}\right)$ represented the maximum value of the sub-index; and $\min \left(x_{\mathrm{ij}}\right)$ represented the minimum value of the sub-index. The specific thresholds of income, life expectancy and education were set with reference to the HDI calculation notes published by the UNDP in 2018. The specific threshold of income gap among residents was set according to its range of values. The specific threshold value of the sub-indexes of the HCDI is shown in

Table 2. Threshold of sub-indexes.

Definition of Indicator	Max	Min
Disposable income per capita	73,000	4500
Life expectancy	85	20
Years of education per capita	15	0
Expected years of education per capita	18	0
Income gap among residents	1	0

.

Referring to the Human Development Report of the UNDP in 2018, the HDI was calculated using the Geometric Average Method of Income Index (II), the Life-expectancy Index (LI) and the Education Index (EI). The Geometric Average Method is a calculation method based on product, which has an invisible correction effect. Each sub-index can influence the total index, and form its own contribution. In the case of the same magnitude of change, the low-level sub-index will have a more obvious impact on the total index, which means the weight of the low-level sub-index will increase, relatively [44]. Consistent with the HDI calculation formula, the calculation formula of the HCDI in this paper was as follows:

$$\mathrm{HCDI}={(\mathrm{II}\times \mathrm{LI}\times \mathrm{EI}\times \mathrm{SEI})}^{1/4}$$

According to the definition, the $\mathrm{HCDI}\in [0,1]$ The higher the value was, the higher the human common development in the current stage was.

3.3. Calculation Results

The annual HCDI of each province from 2007 to 2020 is shown in

Table 3. HCDI of each province.

Province	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Beijing	0.681	0.682	0.690	0.726	0.728	0.743	0.755	0.766	0.784	0.773	0.819	0.830	0.824	0.847
Tianjin	0.592	0.640	0.663	0.662	0.696	0.718	0.702	0.728	0.710	0.724	0.745	0.770	0.776	0.751
Hebei	0.462	0.498	0.517	0.517	0.555	0.594	0.608	0.611	0.614	0.618	0.651	0.651	0.665	0.690
Shanxi	0.472	0.483	0.526	0.544	0.554	0.572	0.607	0.597	0.611	0.622	0.661	0.663	0.685	0.670
Neimenggu	0.482	0.528	0.538	0.547	0.609	0.603	0.619	0.626	0.631	0.658	0.659	0.686	0.684	0.704
Liaoning	0.505	0.539	0.584	0.588	0.609	0.625	0.633	0.646	0.687	0.681	0.703	0.698	0.709	0.687
Jilin	0.489	0.509	0.541	0.563	0.593	0.581	0.597	0.627	0.635	0.648	0.644	0.639	0.674	0.688
Heilongjiang	0.470	0.499	0.513	0.547	0.585	0.603	0.621	0.645	0.652	0.653	0.654	0.661	0.687	0.657
Shanghai	0.655	0.694	0.697	0.697	0.740	0.756	0.761	0.726	0.765	0.795	0.811	0.791	0.777	0.824
Jiangsu	0.573	0.554	0.572	0.605	0.653	0.634	0.669	0.656	0.673	0.670	0.712	0.711	0.729	0.704
Zhejiang	0.599	0.607	0.619	0.619	0.666	0.659	0.702	0.709	0.697	0.692	0.696	0.724	0.732	0.719
Anhui	0.428	0.467	0.477	0.521	0.535	0.580	0.573	0.623	0.612	0.622	0.620	0.640	0.645	0.659
Fujian	0.542	0.538	0.577	0.602	0.612	0.613	0.614	0.639	0.673	0.647	0.689	0.677	0.696	0.719
Jiangxi	0.451	0.488	0.518	0.532	0.548	0.588	0.614	0.624	0.630	0.638	0.660	0.651	0.646	0.662
Shandong	0.519	0.536	0.564	0.581	0.597	0.595	0.639	0.647	0.661	0.678	0.681	0.673	0.701	0.667
Henan	0.441	0.483	0.482	0.509	0.547	0.569	0.565	0.590	0.624	0.601	0.635	0.621	0.632	0.681
Hubei	0.468	0.488	0.514	0.543	0.558	0.609	0.630	0.609	0.621	0.634	0.667	0.678	0.700	0.692
Hunan	0.452	0.494	0.511	0.523	0.577	0.594	0.593	0.635	0.650	0.647	0.640	0.654	0.665	0.671
Guangdong	0.583	0.577	0.609	0.605	0.632	0.653	0.636	0.653	0.693	0.704	0.694	0.700	0.703	0.703
Guangxi	0.440	0.457	0.492	0.501	0.531	0.568	0.569	0.608	0.627	0.614	0.635	0.642	0.657	0.658
Hainan	0.455	0.482	0.504	0.534	0.579	0.582	0.618	0.635	0.627	0.639	0.668	0.694	0.695	0.704
Chongqing	0.466	0.501	0.536	0.540	0.602	0.594	0.617	0.623	0.624	0.632	0.664	0.680	0.688	0.703
Sichuan	0.398	0.460	0.463	0.495	0.554	0.549	0.593	0.581	0.615	0.613	0.645	0.644	0.644	0.674
Guizhou	0.242	0.352	0.373	0.418	0.474	0.499	0.525	0.566	0.558	0.586	0.588	0.618	0.614	0.607
Yunnan	0.340	0.385	0.422	0.471	0.489	0.524	0.544	0.550	0.561	0.572	0.607	0.625	0.623	0.633
Xizang	0.171	0.288	0.332	0.372	0.399	0.419	0.424	0.447	0.473	0.487	0.504	0.519	0.542	0.577
Shaanxi	0.404	0.470	0.499	0.530	0.560	0.586	0.607	0.602	0.644	0.648	0.643	0.668	0.666	0.682
Gansu	0.264	0.361	0.405	0.441	0.462	0.519	0.527	0.552	0.557	0.586	0.608	0.604	0.633	0.614
Qinghai	0.376	0.414	0.444	0.475	0.484	0.516	0.565	0.573	0.554	0.587	0.595	0.612	0.610	0.643
Ningxia	0.419	0.487	0.499	0.508	0.531	0.563	0.581	0.603	0.619	0.613	0.625	0.637	0.647	0.649
Xinjiang	0.401	0.433	0.456	0.475	0.524	0.568	0.565	0.580	0.619	0.640	0.615	0.626	0.634	0.637

. Due to data availability, Tibet, Hong Kong, Macao and Taiwan were not measured. The data came from the China Statistical Yearbook, the China Education Funds Statistical Yearbook, the provincial statistical yearbook, the National Bureau of Statistics, the EPS database, etc.

3.4. Results Analysis

As shown in Figure 1, the average value of the provincial HCDIs in China from 2007 to 2020 increased significantly, showing an overall steady rise from 0.459 in 2007 to 0.683 in 2020, an increase of 48.7%. This indicates that the Chinese government has made remarkable achievements in improving human common development. However, it can also be seen that, with the continuous improvement, the improvement speed of the average value of the provincial HCDIs decreased slightly, which poses new challenges for the government, if it is to further improve human common development.

As can be seen from Table 3, the HCDI of all provinces in China has shown an upward trend. The HCDI ranges in 2007 and 2020 were 0.171–0.681 and 0.577–0.847, respectively. There were great differences in the improvement of different provinces. Xizang exhibited the biggest improvement (0.406), increasing from 0.171 in 2007 to 0.577 in 2020, with a growth rate of 337.43%. The other four provinces with the largest increase were Guizhou (0.365), Gansu (0.350), Yunnan (0.293) and Shaanxi (0.278). Some provinces showed slow improvement, including Guangdong (0.120), Zhejiang (0.121), and Jiangsu (0.131). This was because Guangdong, Zhejiang and Jiangsu had a good economic development foundation. In 2007, they were much ahead of other provinces.

There has been regional imbalance in China’s human common development. The provinces with a high HCDI from 2007 to 2020 were mainly concentrated in the eastern region, followed by the central region, while the western region was relatively low. Unbalanced regional development is not conducive to the further development of society. Fortunately, with economic and social development, the regional gap has been alleviated. In 2007, the difference between the province with the highest HCDI (Beijing, 0.681) and the province with the lowest HCDI (Xizang, 0.171) is 0.509. In 2020, the difference between the province with the highest HCDI (Beijing, 0.847) and the province with the lowest HCDI (Xizang, 0.577) is 0.27. This illustrated that the gap in human common development among provinces was narrowing.

4. Methods and Data

4.1. Model

In order to reveal the relationship between social security expenditure and human common development, we constructed an econometric model for this paper, to analyze the effect of social security expenditure on human common development, from the provincial level. The two-step system-GMM assumes that the first differences of the instrumental variables are independent of the fixed effects, builds a system by combining the original equation and the first difference equation, and then calculates these two equations simultaneously [45]. In addition, the system-GMM can better solve problems such as endogeneity and weak instrumental variables, and the deviation of the estimated results is small [46]. Therefore, this paper used two-step system-GMM estimation to make a regression analysis between social security expenditure and human common development, as it was able to correct for potential biases through instrumenting the endogenous variables.

The increase or decrease of human common development has long-term effects, and there may be a trend of inertia. Thus, the lag term of human common development was added, to further analyze the dynamic effect of social security expenditure: on the one hand, it could reflect the sustainability and dynamics of human common development; on the other hand, it could cover other factors affecting human common development that had not been considered. For this paper, we constructed a standard dynamic panel linear regression model, as follows:

$${\mathrm{HCDI}}_{\mathrm{it}}={\alpha }_1+{\beta }_1{\mathrm{HCDI}}_{\mathrm{it}-1}+{\gamma }_1{\mathrm{SSE}}_{\mathrm{it}}+\theta X_{\mathrm{it}}+u_{\mathrm{t}}+v_{\mathrm{i}}+{\epsilon }_{\mathrm{it}}$$

(1)

In the formula: ${\mathrm{HCDI}}_{\mathrm{it}}$ represented the human common development of province i in stage t; ${\mathrm{SSE}}_{\mathrm{it}}$ represented the government social security expenditure of province i in stage t; $X_{\mathrm{it}}$ represented the control variables; $\alpha$ denoted the intercept; $\beta$, $\gamma$ and $\theta$ represented the coefficients to be estimated; $u_{\mathrm{t}}$ represented time fixed effect; $v_{\mathrm{i}}$ represented individual fixed effect; and ${\epsilon }_{\mathrm{it}}$ was a random error term.

4.2. Variables

The variables contained in the model, and their definitions are specified as follows:

(1)

Explained variable. Human Common Development Index (HCDI), as shown in Table 3.

(2)

Explanatory variable. Social Security expenditure (lnrjsse), measured by the logarithm of per capita social security and employment expenditure.

(3)

Control variables.

In addition to the core variable of social security expenditure, the HCDI is also affected by a variety of factors. In accordance with the literature review, the following control variables were selected in this paper:

The urbanization rate (lnurb), which was represented by the logarithm of the proportion of urban population in the total population. Urban residents have more job opportunities, and better access to health care and education;

The per capital GDP (lnrjgdp), which was expressed by the logarithm of the per capital GDP. Regions with better economic development generally have stronger financial strength to serve the public, and thus have higher HCDIs.

The statistical caliber of social security and employment expenditure was greatly adjusted in 2007, therefore this paper selected the data of social security and employment expenditure from 2007 to 2020 for analysis, based on the continuity and availability of data. Based on the availability of data, this paper used the data of 30 provinces in China (excluding Tibet, HK, Macau and Taiwan) from 2007 to 2020. The data in this paper came from the China Statistical Yearbook, the China Education Funds Statistical Yearbook, the provincial statistical yearbook, the National Bureau of Statistics, the EPS database, etc. In addition, the difference method was used, to supplement the few missing data in the research process. The general descriptive statistics of the variables are shown in

Table 4. Descriptive statistics of variables.

Variable	Obs	Mean	Std.	Min	Max
HCDI	434	0.599	0.0980	0.171	0.847
lnurb	434	3.982	0.265	3.068	4.495
lnrjgdp	434	1.401	0.565	−0.231	2.803
lnrjsse	434	7.063	0.689	5.273	8.721

.

5. Results and Discussion

5.1. Main Results

The regression results are shown in

Table 5. Main estimation results.

Variables	HCDI
L.HCDI	0.450 ***
	(0.0827)
lnrjsse	0.0451 *
	(0.0241)
lnrjsse2	−0.00301 **
	(0.00153)
lnurb	0.0731 ***
	(0.0181)
lnrjgdp	0.0605 ***
	(0.0107)
Constant	−0.205
	(0.130)
Observations	403
Number of id	31
Sargan test	26.34 (0.9839)
AR (1)	−4.276 (0.0000)
AR (2)	0.865 (0.3871)

Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. AR (1), AR (2), and the Sargan test gave the p-value corresponding to the statistic.

. The p value of AR (1) was less than 0.1, while the p value of AR (2) was greater than 0.3, indicating that there was no second-order autocorrelation. In addition, the Sargan test result showed that the p value was greater than 0.3, indicating that the instrumental variables used in the regression were effective. In conclusion, the test results show that the two-step system-GMM estimation method adopted in this paper was effective, and that the model setting was reasonable.

The social security expenditure’s coefficient was significantly positive, and its quadratic coefficient was significantly negative, which showed an inverted U-shaped relationship between social security expenditure and human common development. The coefficients of lnrjsse and lnrjsse2 were 0.0451 and −0.00301, respectively. According to the formula $k=-\frac{{\beta }_1}{2{\beta }_2}$, the inflection point of the inverted U-curve was 7.492. This indicated that when social security expenditure was less than 7.492, human common development increased as the social security expenditure increased; and that when social security expenditure was greater than 7.492, human common development decreased as social security expenditure increased. This was because when social security expenditure was low, the government’s security capacity was limited. Increasing social security expenditure was able to improve the government’s social security capacity, thus improving people’s income, health and education, and narrowing the income gap among people. When social security expenditure was large, the impact of increasing social security expenditure on improving the ability of the government’s social security was limited. At the same time, excessive social security expenditure increased people’s tax revenue, which in turn reduced people’s disposable income, affected people’s education and health, and may even have widened the income gap among people.

The coefficient of the first-order lag term of the HCDI was significantly positive at the 1% level, which showed that the change in human common development had obvious time continuity. It indicated that improvement in human common development was a long-term work, which required continuous effort. The coefficient of lnurb was 0.0731, and it was significant at the 1% significance level, which proved that increased urbanization increased human common development. The coefficient of lnrjgdp was 0.0605, and it was significant at the 1% significance level. This result was consistent with the reality, that the better the economic development of the region, the higher was the common development of the people.

5.2. Robustness Test

This paper adopted the method of changing the sample range to conduct the robustness test, excluding the first year (2007) and the last year (2020). The regression results are shown in

Table 6. Robustness analysis results.

Variables	HCDI
L.HCDI	0.461 ***
	(0.0648)
lnrjsse	0.101 ***
	(0.0214)
lnrjsse2	−0.00626 **
	(0.00137)
lnurb	0.0704 ***
	(0.0172)
lnrjgdp	0.0459 ***
	(0.000824)
Constant	−0.411 ***
	(0.120)
Observations	341
Number of id	31
Sargan test	25.62 (0.9006)
AR (1)	−4.187 (0.0000)
AR (2)	1.394 (0.1633)

Notes: ** p < 0.05, *** p < 0.01. AR (1), AR (2), and the Sargan test gave the p-value corresponding to the statistic.

.

The results of the robustness test were basically consistent with the main estimation results, except for a slight change in the value. This proved that the regression results in this paper are robust.

6. Conclusions and Policy Implications

This paper makes a certain contribution, in both theory and practice. On the one hand, this paper has expanded the connotation of human development, and constructed the HCDI, a more accurate and suitable index, which is helpful for better evaluating human common development. On the other hand, this paper constructed an analytical framework of social security expenditure and human common development, which makes up for the deficiency of quantitative analysis of the impact of social security expenditure on human common development in existing studies.

6.1. Conclusions

For this paper, on the basis of the health index, education index and income index of the HDI, the common development index was added to construct the HCDI. Then, the latest macro data, represented by the statistical yearbook, were substituted into the HCDI calculation formula, to estimate the current situation and trend of human common development in various provinces of China. Finally, this paper empirically tested the relationship between social security expenditure and human common development, from provincial panel data. The above work makes up for the deficiency of existing studies to some extent. The following conclusions were drawn:

Firstly, human common development in China is relatively high, showing a trend of steady improvement. The average HCDI of various provinces in China has improved year by year, rising from 0.459 in 2007 to 0.683 in 2020, an increase of about 48.7%. The improvement was significant, but the rate of improvement slowed down. This shows that human common development in China has entered a new stage of growth, and poses new challenges for the Chinese government, if it is to further promote human common development.

Secondly, social security expenditure plays an important role in promoting human common development. The relationship between social security expenditure and human common development is an inverted U-shape. The increase of social security expenditure before the inflection point can significantly promote human common development, and the increase of social security expenditure after the inflection point will hinder human common development. At present, the social security expenditure of most provinces has not reached the inflection point, and there is still room for improvement.

In addition, the change of human common development has obvious time continuity, which indicates that the improvement of human common development is a long-term work. Improving the urbanization rate, and regional economic development, will promote human common development.

6.2. Policy Implications

According to the above theoretical and empirical analysis, this paper puts forward the following suggestions:

Firstly, the government should pay attention to the important role of social security, and should provide policy and financial support. Social security is an important driving force for social development, and can lay a solid foundation for enhancing human common development. The government should uphold the people-oriented concept, speed up social security reform, and constantly improve the social security system, to help people achieve equal opportunities and enhanced ability.

Secondly, the government needs to optimize and adjust social security expenditure. The government should regard human common development as the core goal of social security, maximize the role of social security expenditure in promoting human common development, constantly improve human common development, and then lay a solid foundation for the comprehensive realization of common prosperity. At present, there are still many provinces whose social security expenditure has not reached the inflection point. The central government can narrow the gap of regional social security expenditure, by increasing social security investment and carrying out special financial transfer payments, so as to reach the inflection point and further promote human common development.

Thirdly, the government should enhance the ability of social security, improve the accuracy and effectiveness of social security expenditure, and fully explore the potential value of social security expenditure. The government should also improve the dynamic monitoring system of human common development, constantly monitor the current situation of human common development in each region of China, and make timely optimization. At the same time, the new generation of information technology can be used to improve the security capacity of the social security system, so as to promote the realization of common prosperity.

Finally, the government should further improve the urbanization rate, and promote economic development. Both contribute to the ascension of human common development.

6.3. Limitations

The above conclusions and policy recommendations provide a reference for China and other countries, to improve human common development. However, there are still many limitations to this paper. Firstly, due to the limitation of data, the two-step GMM method was not allowed to analyze the relationship between social security expenditure and human common development in the eastern, central and western regions, respectively. Future research could focus on the prefecture-level city level, to study whether the relationship between social security expenditure and human common development has regional heterogeneity. Secondly, this paper only measures human common development in China. In the future, the HCDI constructed for this paper could be used to measure human common development in every country.