Analysis of Government Roles in Funding Public Education Expenditure and Resource Appropriation to Optimise Education Sustainability in China

doi:10.21203/rs.3.rs-1957784/v1

Download PDF

Research Article

Analysis of Government Roles in Funding Public Education Expenditure and Resource Appropriation to Optimise Education Sustainability in China

https://doi.org/10.21203/rs.3.rs-1957784/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The assessment of a country's education quality lies in the productivity level of public education in relation to the country's development through education. Generally, the government conscientiously provides education to the citizens by providing sufficient resources to build human capital development. The study reveals Chinese government funds the education expenditures of all 31 provinces, including the municipalities.

The result indicates that each year China government budgets more money for education. The correlation analysis proves that there are (0.9311), i.e., 93% of total expenditure (TE) on education from 1992 to 2017. This implies seriousness in fostering sustainability in investing in education across the country by China's leadership. In addition, government appropriation expenditure (GAE) and public expenditure (PE) are significantly and strongly correlated at 0.9990 (99.9%) and 0.9983 (99.8%), respectively. The summary of returns to education was qualitatively reviewed in tabular form to highlight the phases and progress which China's education has recorded. Though the literature buttresses the gradual progress to education returns, remarkable achievements have been made in the present administration in his plans on education. Besides, the study highlighted other possible means for education expenditure and funding, such as research, tuition, donations from organizations and agencies, tax revenues.

Other Economics

Education Expenditure

Government Education Funding

Public Education

China

Does government adequately fund education in the country? Is there any connection or relationship between education expenditure and the output of the invested expenditure in education? These questions have been haphazardly addressed in the previous scholarly literature, numerous strands of education economics literature that explore the former argumentative question. However, the latter remains barely unexplored. In nexus to the understudied phenomenon, Houghton, Bagranoff, and Jubb, 2021 raised similarly related questions in their study; one of the established research questions is how much will it require to provide public education to the management and business faculties in Australian Universities? (Houghton, Bagranoff, & Jubb, 2021).

Education funding is instantiated from the possible investment targeted to human capital development (Hatsor, 2015). The logical comprehension in funding education creates an avenue to alleviate the burden of the citizens in acquiring excellent and quality education, perceived as an expensive and essential commodity that can change an individual's life and that of the country's (Maria & Bleotu, 2014).

Education is categorized into two strata (i) public education (ii) private education. These two education systems function differently with two various funding institutions. But have similar learning objectives in building human capital for the country's well-being and progress.

Education investment is primarily the task of the country's federal government to make education attainment affordable for all citizens. Government caters solely to the expenditures of public education (Gerrard, Savage, & O’Connor, 2017). Houghton et al. (2021) highlighted the recent education policy reform by the Australian Parliament in supporting Higher Education; government focuses on the research productivity of the higher education students (Houghton et al., 2021).

The cheaper rate tendency of public education is linked to the government involvement in subsidizing learning fares and prices compared to private education. Public education is coordinated in the public institutions built and funded by the government (Blanden, Del Bono, McNally, & Rabe, 2016; Maria & Bleotu, 2014). Public schools remain the prioritized institutions by the government in creating human capital and resources that could contribute to the country's progress (Houghton et al., 2021). The administrative leadership's duty in education is to instill the nation's objectives, morals, and cultures in the education structure, inscribing them in the school learning curriculum (Mitra, 2020).

The discovery from the existing literature in education economics and education funding, numerous studies focused on the rate of returns to education on the premise of gender, age, education levels, quality of education, etc (Asadullah & Xiao, 2020). But no study has explored the degree of financial allocation appropriated to education enhancement by the government. And the relationship between education expenditure and returns to education in public education in China. On this aspect, this study focuses on understudying the funding capacity of the Chinese government on public education in China and explore the research questions of the study (i) Does the government provide enough financial resources to public education expenditures? and (ii) what is the influence of returns to education to budget appropriation to education expenditure?

In responding to the first research question, Fan and Zhang (2015) emphasized a non-direct response on whether government provides sufficient funding for education, in fairness to government struggles in balancing every sector in the country to pursue a measurable speed of growth(Fan & Zhang, 2015).

Extant literature's opinions on the second research question delineate that the positive impacts of education on individual and the ability to apply the knowledge for society's betterment are considered the returns to education. The benefits of education and its returns can be visualized on its potency to create financial returns and talent skill creation in learners. In furtherance, numerous education economists expounded that the amount of years of schooling determines the amount of returns to education to the individual, which is beneficial at the labour market (Hongbin Li, Liu, & Zhang, 2012). No matter how many years of education and tuition paid to the school can match up with the appropriated budget to cover education expenditure.

In affirmation to the Fan and Zhang, 2015 perception, Rothbart, 2020 attested that the provision of standard education remains the priority of Xi Jinping's leadership administration, including the other sector of Chinese society (Rothbart, 2020). Funding education activities is highly costly, given the constant changes in education, and expectations of meeting up to those changes financially, technically no government has provided more than the required funds Chinese education has attained about 80% of funding by the government (Hongbin Li, Ma, Meng, Qiao, & Shi, 2017)

Nevertheless, the second question examines the influencing power of education returns on government investment in education. The concept of profits and loss apply to every economic venture, having established the baseline argument of the government as chief sponsor of public education, and public schools visualize education as a business enterprise for marginal profits and losses. Checks and balances are determinants of assessing public education's growths, failures, achievements, and struggles (Windle, 2014). There is a close relationship between investment and returns to investment; every business entity implements the ideology of a business tracking system, assessing the progress of the business, thereby mapping out the investment plans in response to the output of the inputted resources (funding) (Zipin, 2009). Nonetheless, public education and schools are not buying and selling enterprises, but proper scrutiny of their output is essential.

Meanwhile, government evaluates its expenditures to education through human capital productivity, economic impact, contributions to the solution of the social issues, the literacy level of the nation per academic year, and labor market satisfaction (Bush, 2009). A clear difference between public and private education is the endearment strength for the no financial gain mindset. Contrastingly, private education concentrates on profit-making and provides education for the highest bidders (individuals with high social-economic status)(Dar & Lee, 2014). The government's responsibility is to foster equality, equity, and balanced treatment for both wealthy and privileged individuals in the education environment (Messacar & Frenette, 2019). And that's the aim of every government in public education.

The novelty of this current study is to assess the growing level of resources needed for public education sustainability and the percentage level of funding and expenditures allocated by the Chinese government to satisfy the school needs. Secondly, it evaluates the profit level of china's public education and its influence on the government's decisions by investing more in education. Thirdly, it explores the government's budget allocation to all the provinces and municipalities in China.

The dataset for this research was extracted from the National Bureau of Statistics China of Statistical Yearbook, 2017. The dataset contains the core variables tested and analyzed on the national level contribution to the general education development of the country.

1.1 Brief Insights from the Extant Literature

School affairs cannot be effectively run without support, financial provisions, and funding. School projected expenditures and goals are clearly stated to the reputable units in the education ministry before the financial disbursement (Aziz, Shuib, Aziz, Tawil, & Nawawi, 2013). Chinese government evaluates the education culture of the schools and the achievement rate by assessing the students' annual performance and teachers' teaching styles and behaviours (Ghafoori, Ip, & Kabátek, 2021). Hence, the expenditure tracking systems and supervisory personnel are established to ensure that the financial and learning resources are justly utilized to maintain credibility and transparency (Hagood, 2019). Before entrusting new funds to school leadership, the assessment of previously assigned monies and the outputs are considered.

The present successes of China's education are holistic efforts of leaders at both national and local levels, teachers, school leaders, and parents ensuring that the government investments in education are materialized (Bleotu & Maria, 2014). The citizens enjoin with the government as the watchdogs, ensuring that schools effectively deliver quality education as resources are being provided(Maria & Bleotu, 2014). Arguably, the level of funding appropriated for education determines the quality of education (FrØlich & StrØm, 2008). Illustratively speaking, countries that budget insufficient for education expenditures experience negative growth in the human capital and redundant society(Mitra, 2020). In addition, a shaky education system's poor funding can be correlated with undesirable outcomes in the students (Bleotu & Maria, 2014).

In general, education experiences hiccups when the government inadequately funds it due to corruption and negligence (Ryn, 2017). The education funding in the United States of America is entangled with the resourcefulness of the departments or faculties in meeting the social demands like medical-related education departments primary and K-12 education, which are the most funded section, thereby creating unequal financial distribution to other academic units (Dar & Lee, 2014).

Tax revenues remain the major income generation channels for funding education (Potrafke, 2011) evinces that budget allocation to education expenditures anchors on the national education policy.

Education financial aid dissipates education inequality and increases inclusion (equity) in the school system, giving a second chance to economically disenfranchised learners (Kenno, Lau, Sainty, & Boles, 2021). A wide disparity between public and private education is the social-economic status differentiation (SES) lies on the financial background of the learner (private schools) and acceptance of learners from any social status background (Ghafoori et al., 2021). Also, private education has no existing scholarship package for individuals from a poor economic background. Nevertheless, provincial and national scholarship programs are provided to various public schools as a medium means to encourage more outstanding academic excellence (Messacar & Frenette, 2019).

Similarly, the Australian education system has the semblance of China's education system regarding the legislative policy influences the funding decisions on the generalized education systems(Gerrard et al., 2017). The Australian Gonski report on the conception of public and private education in public policy formulation and adjustments themed into (i) prismatic political procedures, (ii) perceptions on public and private relations, (iii) nexus between the concerns on government schools orientation of equity and the public need (Gerrard et al., 2017). The education expenditures comprise libraries, experimental laboratories, books, desks, whiteboards, chairs, research, teachers' service delivery, well-being, etc. The three core principles of education funding are (i) to enhance efficiency, (ii) maintain equity, and (iii) adequacy (Furtado, 2009)

Conversely, the education funding and investment success rate are measured on the returns to education(Fan & Zhang, 2015). Notably, the percentage of financial allocation varies to levels of education, likewise returns to education. The returns of kindergarten education are different from secondary and higher education, respectively. Li et al. (2012) analyzed the discoveries of Mincer 1974 and Zhang et al. (2005) on the returns to education in China; the former results show the overall percentage of 2–4% returns to education from the 1980s and 1990s China urban data. While the latter deduced his findings from the 1988–2001 national bureau of statistics china dataset recorded a dramatic increase of 4% in 1988 to higher than 10% in 2001(Hongbin Li et al., 2012).

In confirmation to Li et al., 2012 results, Zhong, 2011 opined that a country's GDP growth and budget allocation to the education sector have a positive relationship. The amount of funding to education depends on the growth and its returns to the economy (Zhong, 2011). He further underscored that china's GDP increased exponentially with sufficient funds than in the 1980s-2000s. As shown in Fig. 1 below.

The estimated variables are derived from the established datasets from the national bureau of statistics in education funding and expenditure. The parameters for the analysis are as follow (i) government appropriation for education, (ii) public expenditure on education, (iii) funds from the runners of private schools, (iv) donations and fundraising for running schools, (v) income from teaching research and other auxiliary activity, (vi) tuition and miscellaneous fees, (vii) other educational funds.

Where Yexp = year of expenditure, TE = total monetary expenditure and funding by the government (Federal and Local/ provincial government), GA = government appropriation for education, PE = Chinese government public education expenditure and budget allocation, FPS = Funds from runners of the private school, DFFS = Donation, and fundraising for running education in the schools, ITR = income from research education and auxiliary activity, TMF = tuition from miscellaneous fees, OEF = other educational fundings.

This model is justified with the characteristics of the education production function proposed by EA Hanushek. Hanushek's theory implies that a school's standard education can be determined based on funding and resource generation for effective education

Fig 2 illustrates clearly the functionalities of resources in the educational atmosphere. The upper division of the framework succinctly depicts the resource distribution and its end goals.

Stage 1: Resource generation focuses on the availability of educational materials. Resource Allocation: appropriating the generated resources to institutions. Utilization is the application of the generated and disbursed resources to achieve the purpose of the resources.

Stage 2: The (resource) generation is linked with finance, connecting with the three distinctive levels, (i) the macro-level, (ii) meso-level, and (iii) micro-level, on the three considerable approaches of making efficient usage of the resources such as input, production, and output. The crucial endpoint lies in the output of the allocated resources to achieve the desired goals.

2.1 The Concept Hanushek Education Production Function in Education Expenditure

One of the criteria for the continuous funding of the education expenditures and funding by the government, appropriate funders and donors are associated with the output of the invested resources in bringing excellent performance in students with positive academic achievements (Deller & Rudnicki, 1993). The current study aimed at the Chinese government sponsoring education expenditures on different levels. The government maintains equity and equality in education through funding for all the students.

γ= f(x₁, . . . . . xg, xg+1, . . . . .,xn)…………………….2

y indicates the output variable which could also term students' academic success, or achievement, (x1, . . . . .,xg) is an input vector of the production process under the supervision of the school administration and (xg+1, . . . ., xn) is the input vector of the external production process with the supervision of the institution management and administration(Deller & Rudnicki, 1993). Monk 1989 defines education production function as the relationship between input and output. This definition corroborates Hanushek's perception of the concept(Monk, 1989). This function is highly pivotal in this study as the Chinese government funding education expenditure centers on this concept (Asadullah & Xiao, 2020)

2.2 Empirical Data Analysis

Qualitative Analysis of Returns to Education in China

The second question of the study says to what extent can returns to education influence the government's funding decisions. Arbitrarily, the partial focus is cast on the returns to education as it is widely researched in various forms by numerous Chinese education economists on the premises of gender, school types, levels of education, years of schooling, rural and urban as evidenced in (Churchill & Mishra, 2018; Dai, Cai, & Zhu, 2021; Fan & Zhang, 2015; Wang, Fleisher, Li, & Li, 2014; Zhong, 2011) studies. In the review of the existing scholarship……

Authors	Title	Instrument	Focus	Results %	Findings	Conclusion
(Kang & Peng, 2012)	Selected analysis of Returns to Education in china	China Health and Nutrition Survey (CNHS) 1989-2009	Adult workers in Nanjing	Their study showed the returns to education for an extra one year of education increased from 2.6% in 1989 to 7.9% in 2009.	There was a rapid decrease with labour market experience for one extra year of education.	The decline of additional schooling with labour market experience in 1989 till 2009 occurred during the cultural revolution, and few individuals are privileged to attend to education based on political favours.
(Asadullah & Xiao, 2020)	Wage Returns to Education in Post Reform China	China General Social Survey 2010-2015	Fresh young graduate workers in rural and urban labour markets	The returns to education in 2010 were 6.7% and 6.2% in 2015. Returns to education declined among women in coastal and urban cities.	The study reveals that graduates who work in China's urban and coastal regions, including individuals with market understanding and necessary skills, earn higher than the graduate workers in rural cities.	The reduction in returns to education is caused by demand and supply factors that led to the increase in high-skilled workers and a slow productivity rate.
(Fan & Zhang, 2015)	Returns to higher engineering education in China	Chinese General Social Survey (CGSS 2003, 2008)	Students that completed high school education, college education, and University Education.	The returns to higher engineering education were 10.6% in 2003 and 14.7% in 2008. Engineering was at the intermediate positions 7^th in 2003, 6^th 2008.	Gender differences played a significant role in the returns to engineering education in china. Male students have a high rate of returns, while female students recorded low returns to engineering education. Female returns increased from 9.7% in 2003 to 14.3% in 2008.	To improve engineering education to optimize human capital development in the country, more financial funding is highly essential to improve the present engineering education.
(Zhong, 2011)	Returns to higher education in China: What is the role of college quality	National Bureau of Statistics, and Chinese Household income project (CHIP) 2002	Individuals at the age of 16 to 60 living in the urban regions were employed in 2002.	The study highlights that the university returns to education higher than primary education at 113%, and college graduates at 78%.	The earning gap of students from low-quality colleges and technical or vocational schools reduced drastically over time. For the cohort that entered the labour market after 199, their 3-year college return to education was 8.2% greater than vocational or technical schools.	The summarized that returns to education are based on the quality of schools the cohorts graduated from. The graduates who entered the workforce recently attained quality schools and received a better education than the previous cohorts. Suggested that government should invest more in technical and vocational schools and poor quality higher institutions.
(Haizheng Li, 2003)	Economic transition and returns to education in China	Chinese Income Household Project 1995	The study centers on the comparison of private-sector graduate workers and state-owned sector graduate workers.	Comparatively, the result shows higher returns to education than the previous studies done on this title. The total returns are 5.4, 4.3% male workers, and 6.9% female workers.	The findings clearly show that returns to education from these two sectors increase by 21% for higher education.	The increase in returns to education is the hourly wage system while the previous years' works used annual earnings. The second factor is the expansion returns are higher due to dramatic growth in China's GDP. Annual returns to above primary education level are higher. Private sector graduate workers earn higher returns than ones in state-owned sectors.

Data

The data for this study was derived from the National Bureau of Statistics of China (NBS). This is a nationally represented survey carried out yearly. The adopted survey on the Chinese government funding education expenditure in all the schools across the nation covered from 1992 to 2017. The annual study on this theme aims to properly assess the quality of education provided by the government through financial support to the academic institutions in the country. The adopted variables remain the constant indicators used to examine the survey by the statistics personnel. The crucial part of the survey is that it recorded information through the significant stages of education transformation in China (Haizheng Li, 2003).

However, the financial resource allocation to all the school provincial regions and municipalities, cities, and variables was encapsulated in the dataset. It, therefore, gives insight into how money is distributed to different cities. Although, the current study tries to understand whether the government adequately finances education expenditures through the annual appropriations of money. In other to comprehend the support of education by the government and donation by reputable stakeholders and private organizations, a qualitative summary of the returns to education was elaborated, as shown in Table 1. Evaluating returns to education on this study examined by equating the expended resources and comparing with the education output regarding the investment. Extensive studies have been done on the returns to education in many forms, but the focus of the current study is novel and gives new perspectives on education financing. The first step of the result presentation was the descriptive analysis, correlation matrix, and correlation significance.

Summary of Statistics

Table 2 illustrates the overview of the result, the total expenditure of funding by the government (TE) has the highest Mean=1.7e+08 million yuan, SD =1.39e+08, followed by the government appropriation expenditure with the Mean of 1.36e+08 (million yuan), SD of 1.16e+08, mean of public expenditure (PE) equals to 1.22e+08, standard deviation. Su (2006) positioned that is the responsibility to promote the quality of the education of the country, Kenno, Lau, Sainty, and Boles (2021) established the amount of money provided by the government for education expenditures determines the growth of the country and human capital development level of the students. Supporting education expenditures in cash and providing learning resources such as a laboratory, library, project units, adequate classrooms, and books enhances high learning performance(Kenno et al., 2021). In most cases, funding education is not restricted to the administrative government; education stakeholders and reputable organizations enter into a partnership to provide public and subsidized education to the citizens through a public-private partnership agreement (PPP)(Su, 2006).

Other corporate donors and funders of education besides administrative government are categorized as the DFFS (Donations and funding for supporting learning institutions), with a mean = 957995.1, SD =265493.8. As it is established in the literature that education is funded from the tax generated revenues as proposed by (Deller & Rudnicki, 1993; Furtado, 2009; Houghton et al., 2021; Quiddington, 2010), other recorded means through which income is generated for education teaching services such as a research program, is being represented in the study as the ITR (income from research and other education programs) with a mean value of 3.26e+07, and standard deviation = 2.11e+07, Tuition revenue (TMF), mean = 2.38e+07, SD = 1.67e+07, other educational funds (OEF), mean = 4901218, SD = 2985870

Table 2: Descriptive Statistics

Variable	Observation	Mean	Std.Dev	Minimum	Maximum
Year	20	2007	6.859492	1992	2017
TE	22	1.76e+08	1.39e+08	8670491	4.26e+08
GAE	22	1.36e+08	1.16e+08	7287506	3.42e+08
PE	22	1.22e+08	1.02e+08	5649364	2.99e+08
FPS	22	1684550	1376030	0	5490583
DFFS	22	957995.1	265493.8	359636	1628414
IT TMF OF	22 22 22	3.26e+07 2.38e+07 4901218	2.11e+07 1.67e+07 2985870	0 439319 0	6.96e+07 5.29e+07 1.09e+07

Note: 1.76e+08= 17,600,000,000.00(yuan), TE= Total Expenditure, GAE= Government Appropriated Expenditure

3.1 Correlation Matrix

The Pearson correlation matrix Table 3 shows the associations of the tested variables to determine their relationships. The total expenditure of 0.9311 is strongly and positively correlated with the year. This indicates that China's government prioritized education by providing sufficient finances to suffice the education expenditures across the provinces. Kang and Peng (2012) revealed the progress of China's education has significantly improved of 2009 till date compared to the 1800s -1900s, one indicator they used in the assessment of the education development of China is the increased returns to education, and the willingness of China's administration is investing hugely to learning.

This shows that since 1992 China's government has invested about 93% of the revenues to service education expenditures. Appropriation of expenditure (GAE) is strongly correlated with year and TE at 0.9144 and 0.9989 with a positive relationship. Budgeting and allocating monies to schools increase annually as new development and evolution occur in the educational sector. However, financial appropriation to education covers so many education concerns such as research development, the building of classrooms, provision of books, teachers' remunerations, and teachers' well-being, building vocational, entrepreneurship, and innovation framework in different levels of education (Ahmed, Chandran, Klobas, Liñán, & Kokkalis, 2020).

Table 3: Correlation Matrix

Variables	Year	TE	GAE	PE	FPS	PDF	1TR	TMF	OF
Year	1.0000
TE	0.9311	1.0000
GAE	0.9144	0.9989	1.0000
PE	0.9267	0.9990	0.9984	1.0000
FPS	0.1660	-0.0006	-0.0276	-0.0284	1.0000
PDF	-0.4126	-0.4588	-0.4535	-0.4439	-0.2905	1.0000
ITR TMF OEF	0.9794 0.9661 0.9773	0.9778 0.9888 0.9645	0.9670 0.9811 0.9514	0.9758 0.9880 0.9609	0.0739 0.0350 0.1237	-0.4715 -0.4538 -0.4792	1.0000 0.9971 0.9950	1.0000 0.9889	1.0000

PE, ITR, TMF, and OEF all show strong and positive associations to the expenditure appropriated by the government, year, and total allocated funds. While DFFS is negatively associated with GAE and TE, also shows a very weak relationship with GAE. This implies that the donation and fundraising of the school's running are lesser than what the government budgeted to enhance sophisticated education (Mou, Atkinson, & Marshall, 2019).

Table 4: Correlated Significance among the variables

VARIABLES	Year	TE	GAE	PE	FPS	DFFS	IT	TMF	OF
TE	0.9311*
GAE	0.9144*	0,9990*
PE	0.9267*	0.9989*	0.9983*
FPS	0.1660	0.0854	0.0594	0.0556
DFFS	-04126	-0.3454	-0.3501	-0.3317	-0.1059
ITR	0.9794*	0.9797*	0.9698*	0.9776*	0.1558	-0.3240
TMF OEF	0.9661* 0.9773*	0.9889* 0.9646*	0.9891* 0.9528*	0.1323 0.9597*	-0.3099 0.1815	0.9956* -0.3594	0.9923*

Significance at 1% level

Table 4 shows the relationship of the correlated variables and their level of significance; this aligns with the result in Table 3.

3.2 Discussion

In connection to the returns to education is a precursor to assess the conditions of education. However, the existing scholarships have shown that returns to education to different levels of education vary from year to year, so as the money invested to fund education in the country (Haizheng Li, 2003). National Bureau of Statistics of China survey shows that public education is being funded from different approved channels to promote the quality of education (Hongbin Li et al., 2012).

Conversely, education provides services to the learners; the study shows that other sources of funding education expenditures other than the government, for instance, schools can generate incomes to fund some internal school expenditures, such as teaching research and other auxiliary activities (ITR)(Hongbin Li et al., 2012). The pertinent argument on public education discourse is that students who enjoy public education do not pay for academic services? In responding to that, in the United States of America, education is visualized as one of the major sources of revenue for the government (Rothbart, 2020), which invariably cannot be fully funded by the government.

The American loan system responds to the argument raised on public education students. Students from middle or low-income households are offered loans to complete their education and pay up later when joined the labour market (Chatterji, Kim, & McDevitt, 2018). Like the USA, China practiced a loan system in the previous years(Hongbin Li et al., 2017). Another difference between public education and private education is the subsidizing effects of public education, although, in recent times, this practice is unpopular in present China's education institutions because of the progressive society and growth in the economy (Haizheng Li, 2003). The clear difference between public education and private education lies in the funding possibility provided by the central government and local or provincial government, and individual parent funding the education of their children independently. Table 5 illustrates the descriptive overview of the central and local government funding education expenditure.

Table 5: Comparison of the Central Government and Local Government

Variables	Obs	Mean	Std.Dev	Min	Max	S.E
CG	8	1.22e+07	1.43e+07	0	3.91e+07	5070662
LG	8	1.38e+08	1.61e+08	490338	3.87e+08	5.71e+07

Note: CG= Central government, LG= Local government

CG	39119918	27180885	16197501	0	359636	9069779	3143195	2509617
LG	386500151	314896661	283000337	2250061	490338	60505954	49789620	8357137

The CG mean= 1.22e+07, SD= 1.43e+07, while LG's mean and SD are 1.38e+08, and 1.61e+08 respectively. Both central government and local government fund education expenditure. China is a country of about 31 provinces and 5 municipalities, and resources are provided to all the regions in the country. However, the central administrative government provides an annual financial budget to suffice the services and expenditure of education across the country. The provincial or local government superintend the region's education that the objectives of the education issued by the central government are achieved (Wang et al., 2014).

According to Wang et al. (2014), local governments sometimes budget more funds to improve the regional education standards, which will yield high dividends to the province through inbound students coming to attain education in the province and producing high returns to the regional government. The table shows that the possibility of local government funding education expenditure is higher than the central government, as it puts the region in the limelight and attracts students from different parts of China, international students included (Hillman, Tandberg, & Gross, 2014). They can be promoted to higher political positions based on their achievements in their provinces and regions. In addition, education sustainability in China is a joint effort to provide substantial learning resources and materials in building human capital development for the nation's growth (Windle, 2014).

Table 6 displays education funding in all the cities in China from 1992 to 2017. The cities are arranged in ascending order, ranging from those with the highest education funding to the least. Overall, Beijing city remains the city with the highest financial budgetary allocation to education with a total expenditure (TE) of 12512746 million-yuan, government appropriation expenditure (GAE)10863394, and public expenditure (PE) 9557007 (in yuan). Beijing is the capital of China with the best universities in the country, both in Asia and with an excellent academic ranking in the world. Other cities like Tianjin, Shanghai, provinces like Fujian, Zhejiang, Guangdong, Sichuan, Chongqing have more considerable expenditure and funding. Other provinces are relatively doing well. One could say that municipalities and provinces in the country's coastal region are more industrialized and have enough money to fund education expenditure by the local government. However, in provinces like Ningxia, Qinghai, Tibet, Xinjiang, Gansu, etc. that are located in mountainous areas with fewer habitants, the percentage of education budgets and funding are more from the central government than any other institution (Churchill & Mishra, 2018). This shows the passion of the administrative leadership to reduce education inequality to promote equity and fairness in academic institutions.

City	TE	GAE	PE	FPS	DFFS	ITR	TMF	OEF
Beijing	12512746	10863394	9557007	6368	9341	1478756	1201615	154888
Tianjin	5850624	4951367	4346123	3826	5336	804001	676659	86094
Hebei	15938479	13374769	12466288	79721	8260	2387755	2041977	87973
Shanxi	8533662	7097917	6180853	38282	2337	1333668	1092714	61458
Inner Mon	7601337	6740750	5457663	16625	5354	632425	521275	206182
liaoning	9651893	7749271	6474197	14589	1498	1830269	1476471	56265
Jilin	6586685	5512733	5038015	23997	3840	943957	816936	102159
Heilongjiang	7545432	6576669	5950720	10427	895	921928	804654	35513
Shanghai	12104556	9939601	8356465	8164	11946	1755802	1451797	389042
Jiangsu	25960645	20943564	19792749	94764	80290	4165842	3323928	676186
Zhejiang	21327866	15959821	14131448	227050	40537	4136979	3317148	963480
Anhui	13751567	11400507	10125204	91135	9585	2074927	1690256	175412
Fujian	11390975	9115032	8504673	79321	33937	1874637	1544049	288047
Jiangxi	11717849	9854977	9394189	19984	12738	1733161	1419212	96988
Shandong	23946021	19797132	18888280	128448	19939	3699432	3183679	301070
Henan	21546749	16854320	14414112	208431	27556	4143540	3515334	312903
Hubei	13821834	11041050	10370978	89220	6856	2451874	2011280	232834
Hunan	15165690	11853958	11198287	124716	8946	2872114	2321904	305955
Guangdong	38610331	28793140	25225457	357725	84318	8716596	7458697	658552
Guangxi	11891781	9915677	9119220	65841	9548	1745587	1398774	155127
Hainan	3390271	2731665	2207329	105042	933	490764	391160	61867
Chongqing	9483526	7595118	6145409	93016	21640	1547283	1212175	226469
Sichuan	19274514	15793602	6145409	132069	36822	3080108	2461467	231913
Guizhou	12488005	9790255	9066642	78135	8389	1210438	945800	1400787
Yunnan	13292088	11490455	9887455	61728	21239	1393129	1129020	325537
Tibet	2387658	2360014	2163641	162	1610	22707	16762	3164
Shaanxi	10545862	8430072	8141137	59555	4989	1838606	1421420	212639
Gansu	7087547	6372791	5673619	7987	3245	558112	452254	145412
Qinghai	2343469	2131024	1866289	8365	1477	115329	74949	87274
Ningxia	2288400	1989493	1667994	10141	4546	225951	182437	58269
Xinjiang	8462090	7876522	7217025	5227	2388	320275	233817	257677

The study shows that the Chinese government is a more prominent sponsor of China's education expenditure. From the analyzed literature, there is an obvious development to Chinese education given the accounts given by (Blanden et al., 2016; Chatterji et al., 2018; Houghton et al., 2021; Monk, 1989; Quiddington, 2010; Su, 2006; Viaene & Zilcha, 2013) on the evolution that occurred in the different stages of Chinese education progression. Funding of education cannot be expounded without referring to the returns to education, as the time of 1980s-1990s, and 2000s-2008s were relatively low (Kang & Peng, 2012), which are ascribed to many factors like the quality of education, economic downturn, etc., which have been improved in this current decade.

To enhance standard education in the country, joint efforts are made by the central government and local government to provide the best resources to the schools. On the other hand, regional governments tend to provide more financial support to the schools in the regions to push the education to the greater height to attract students from different parts of the country and provinces for economic growth and development of the provinces.

The correlation matrix shows that both year and total expenditure increases as year increases, 0.9311 at 1% significance level, government appropriation expenditure is strongly correlated with total expenditure at 0.9990, public expenditure and year 0.9267* and government expenditure 0.9989* as year increases government expenditure, appropriation and public expenditure increases this indicates that China's government prioritize education and making sure that the central and local governments fully provide learning materials to the schools.

Declaration of interest statement

The authors declare no financial or any form of competing interest that could affect or influence the content of this paper.

Ahmed, T., Chandran, V. G. R., Klobas, J. E., Liñán, F., & Kokkalis, P. (2020). Entrepreneurship education programmes: How learning, inspiration and resources affect intentions for new venture creation in a developing economy. International Journal of Management Education, 18(1), 100327. https://doi.org/10.1016/j.ijme.2019.100327

Asadullah, M. N., & Xiao, S. (2020). The changing pattern of wage returns to education in post-reform China. Structural Change and Economic Dynamics, 53, 137–148. https://doi.org/10.1016/j.strueco.2020.01.010

Aziz, R. W. A., Shuib, A., Aziz, W. N. H. W. A., Tawil, N. M., & Nawawi, A. H. M. (2013). Pareto Analysis on Budget Allocation for Different Categories of Faculties in Higher Education Institution. Procedia - Social and Behavioral Sciences, 90(InCULT 2012), 686–694. https://doi.org/10.1016/j.sbspro.2013.07.141

Blanden, J., Del Bono, E., McNally, S., & Rabe, B. (2016). Universal Pre-school Education: The Case of Public Funding with Private Provision. Economic Journal, 126(592), 682–723. https://doi.org/10.1111/ecoj.12374

Bleotu, V., & Maria, T. D. (2014). Strategic Priorities for Education Funding. Procedia - Social and Behavioral Sciences, 116, 2231–2234. https://doi.org/10.1016/j.sbspro.2014.01.549

Bush, T. (2009). Editorial: Funding education to address disadvantage. Educational Management Administration and Leadership, 37(2), 155–157. https://doi.org/10.1177/1741143208100295

Chatterji, A. K., Kim, J., & McDevitt, R. C. (2018). School spirit: Legislator school ties and state funding for higher education. Journal of Public Economics, 164, 254–269. https://doi.org/10.1016/j.jpubeco.2018.05.013

Churchill, S. A., & Mishra, V. (2018). Returns to education in China: a meta-analysis. Applied Economics, 50(54), 5903–5919. https://doi.org/10.1080/00036846.2018.1488074

Dai, F., Cai, F., & Zhu, Y. (2021). Returns to higher education in China–evidence from the 1999 higher education expansion using a fuzzy regression discontinuity. Applied Economics Letters, 00(00), 1–6. https://doi.org/10.1080/13504851.2020.1871465

Dar, L., & Lee, D. W. (2014). Partisanship, political polarization, and state higher education budget outcomes. Journal of Higher Education, 85(4), 469–498. https://doi.org/10.1353/jhe.2014.0023

Deller, S. C., & Rudnicki, E. (1993). Production efficiency in elementary education: The case of Maine public schools. Economics of Education Review, 12(1), 45–57. https://doi.org/10.1016/0272-7757(93)90042-F

Fan, J. bo, & Zhang, C. G. (2015). A study of the rate of return to higher engineering education in China. International Journal of Educational Development, 42, 106–114. https://doi.org/10.1016/j.ijedudev.2015.04.010

FrØlich, N., & StrØm, B. (2008). Higher education funding and incentives: Evidence from the Norwegian funding reform. European Journal of Education, 43(4), 563–576. https://doi.org/10.1111/j.1465-3435.2008.00378.x

Furtado, M. (2009). Historical construction and Australian Catholic education: Accounting for school funding policy from the cultural politics of Australian education. Discourse, 30(2), 121–135. https://doi.org/10.1080/01596300902808999

Gerrard, J., Savage, G. C., & O’Connor, K. (2017). Searching for the public: school funding and shifting meanings of ‘the public’ in Australian education. Journal of Education Policy, 32(4), 503–519. https://doi.org/10.1080/02680939.2016.1274787

Ghafoori, E., Ip, E., & Kabátek, J. (2021). The impacts of a large-scale financial education intervention on retirement saving behaviors and portfolio allocation: Evidence from pension fund data. Journal of Banking and Finance, 130, 106195. https://doi.org/10.1016/j.jbankfin.2021.106195

Hagood, L. P. (2019). The Financial Benefits and Burdens of Performance Funding in Higher Education. Educational Evaluation and Policy Analysis, 41(2), 189–213. https://doi.org/10.3102/0162373719837318

Hatsor, L. (2015). Higher education funding: The value of information. Economics Letters, 137, 230–233. https://doi.org/10.1016/j.econlet.2015.10.031

Hillman, N. W., Tandberg, D. A., & Gross, J. P. K. (2014). Performance funding in higher education: Do financial incentives impact college completions? Journal of Higher Education, 85(6), 826–857. https://doi.org/10.1353/jhe.2014.0031

Houghton, K. A., Bagranoff, N., & Jubb, C. (2021). The Funding of Higher Education: An Empirical Examination of the Cost of Education in Business Schools. Abacus, 57(4), 780–809. https://doi.org/10.1111/abac.12230

Kang, L., & Peng, F. (2012). A selection analysis of returns to education in China. Post-Communist Economies, 24(4), 535–554. https://doi.org/10.1080/14631377.2012.729307

Kenno, S., Lau, M., Sainty, B., & Boles, B. (2021). Budgeting, strategic planning and institutional diversity in higher education. Studies in Higher Education, 46(9), 1919–1933. https://doi.org/10.1080/03075079.2019.1711045

Li, Haizheng. (2003). Economic transition and returns to education in China. Economics of Education Review, 22(3), 317–328. https://doi.org/10.1016/S0272-7757(02)00056-0

Li, Hongbin, Liu, P. W., & Zhang, J. (2012). Estimating returns to education using twins in urban China. Journal of Development Economics, 97(2), 494–504. https://doi.org/10.1016/j.jdeveco.2011.05.009

Li, Hongbin, Ma, Y., Meng, L., Qiao, X., & Shi, X. (2017). Skill complementarities and returns to higher education: Evidence from college enrollment expansion in China. China Economic Review, 46(August), 10–26. https://doi.org/10.1016/j.chieco.2017.08.004

Maria, T. D., & Bleotu, V. (2014). Modern Trends in Higher Education Funding. Procedia - Social and Behavioral Sciences, 116, 2226–2230. https://doi.org/10.1016/j.sbspro.2014.01.548

Messacar, D., & Frenette, M. (2019). Education savings plans, matching contributions, and household financial allocations: Evidence from a Canadian reform. Economics of Education Review, 73(August 2018), 101922. https://doi.org/10.1016/j.econedurev.2019.101922

Mitra, R. (2020). COVID-19 is killing education budgets: are educational public-private partnerships an answer? Journal of Professional Capital and Community, 5(3–4), 255–264. https://doi.org/10.1108/JPCC-07-2020-0056

Monk, D. H. (1989). The Education Production Function: Its Evolving Role in Policy Analysis. Educational Evaluation and Policy Analysis, 11(1), 31–45. https://doi.org/10.3102/01623737011001031

Mou, H., Atkinson, M. M., & Marshall, J. (2019). Budgeting for efficiency? A case study of the public K-12 education systems of Canada. Applied Economics, 51(34), 3740–3757. https://doi.org/10.1080/00036846.2019.1584380

Potrafke, N. (2011). Public Expenditures on Education and Cultural Affairs in the West German States: Does Government Ideology Influence the Budget Composition? German Economic Review, 12(1), 124–145. https://doi.org/10.1111/j.1468-0475.2010.00507.x

Quiddington, P. (2010). The new politics of Australian higher education: Why universities get rumbled in the budget. Higher Education Research and Development, 29(4), 475–487. https://doi.org/10.1080/07294361003601867

Rothbart, M. W. (2020). Does school finance reform reduce the race gap in school funding? Education Finance and Policy, 15(4), 675–707. https://doi.org/10.1162/edfp_a_00282

Ryn, C. G. (2017). The Common Good and History. Democracy and the Ethical Life, (I), 206–231. https://doi.org/10.2307/j.ctt285092.17

Su, X. (2006). Endogenous determination of public budget allocation across education stages. Journal of Development Economics, 81(2), 438–456. https://doi.org/10.1016/j.jdeveco.2005.05.002

Viaene, J. M., & Zilcha, I. (2013). Public funding of higher education. Journal of Public Economics, 108, 78–89. https://doi.org/10.1016/j.jpubeco.2013.09.008

Wang, X., Fleisher, B. M., Li, H., & Li, S. (2014). Access to college and heterogeneous returns to education in China. Economics of Education Review, 42, 78–92. https://doi.org/10.1016/j.econedurev.2014.05.006

Windle, J. (2014). The Rise of School Choice in Education Funding Reform: An Analysis of Two Policy Moments. Educational Policy, 28(2), 306–324. https://doi.org/10.1177/0895904813513151

Zhong, H. (2011). Returns to higher education in China: What is the role of college quality? China Economic Review, 22(2), 260–275. https://doi.org/10.1016/j.chieco.2011.02.002

Zipin, L. (2009). Dark funds of knowledge, deep funds of pedagogy: Exploring boundaries between lifeworlds and schools. Discourse, 30(3), 317–331. https://doi.org/10.1080/01596300903037044

Download PDF

Version 1

posted

You are reading this latest preprint version

Analysis of Government Roles in Funding Public Education Expenditure and Resource Appropriation to Optimise Education Sustainability in China

Status:

Version 1

Abstract

Figures

1. Introduction

1.1 Brief Insights from the Extant Literature

2. Model Analysis

3. Methodology

4. Conclusion

Declarations

References

Status:

Version 1