The spatial pattern and growth performance of higher education in China: A longitudinal study in China from 2006 to 2020

The spatial pattern and growth performance of higher education in China: A longitudinal study in China from 2006 to 2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The spatial pattern and growth performance of higher education in China: A longitudinal study in China from 2006 to 2020 Tingting Yu, Yingzi Gui, Rong Ah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7732642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract China has entered a new era of mass higher education, and the growth performance of higher education is uncertain. This paper aims to determine the economic performance associated with the quantity and quality of higher education resources in China and whether there is regional heterogeneity. An econometric model incorporating temporal and special aspects is applied to a unique data set involving thousands of universities from 31 provinces over 15 years. The findings show that the quantity of higher education institutions restrains economic growth, and the quality of higher education promotes growth. The growth performance of higher education in the eastern, central and western regions is in line with that in China as a whole, while the strength and significance of the variables’ effects are different. Possible explanations and policy implications are given based on the data analysis. higher education expansion spatial pattern growth performance regional heterogeneity China Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The driving factors of China's economic development are gradually shifting from factors and investment to innovation. Innovation-driven development is a more advanced growth mode that relies mainly on intangible elements, such as knowledge capital, human capital and incentive innovation systems, to create new growth factors (Porter 1997) . The core factor of innovation is talent, and higher education provides society with critical thinkers, researchers, scholars, innovators and responsible citizens (Oazi, Raza and Jawaid 2014) . Consequently, higher education is an important requirement for developing countries, such as China, to promote rapid and sustainable economic growth. In China, higher education has expanded rapidly since 1999, and great changes have taken place. The most marked change is that higher education transitioned from the elite stage to the mass stage in just 17 years (Jia 2021) . In 2021, the gross enrollment rate of higher education in China was 57.8%, and the total number of students in higher education reached 44.3 million (according to the website of the Ministry of Education of China as of December 3, 2020 , http://www.moe.gov.cn/fbh/live/2020/52717/ ). Thus, China has become one of the countries with mass higher education and established the world's largest higher education system (Huang and Ding 2022). However, international comparison shows that the development of higher education in China is relatively advanced compared with the country’s economic development level, which is reflected mainly in the imbalance between the quantity and quality, supply and demand, and structure and function of higher education (Li 2017). There are many Chinese higher education institutions, but their quality is relatively low. Against this backdrop, it is essential to understand the quantitative and qualitative characteristics of China's higher education resources and to estimate their economic performance. China covers 9.6 million square kilometers and has 31 provinces. There are major differences in natural conditions and socioeconomic development across regions; therefore, the government formulates differentiated development policies for three distinct regions, namely, the eastern, central, and western regions. The eastern region comprises eleven provinces, the central region comprises eight provinces, and the western region comprises twelve provinces. Regional heterogeneity, if not addressed, will obviously lead to biased or incorrect analysis results. This paper contributes to the literature in three main ways. First, it characterizes the development of higher education by the two dimensions of quantity and quality, which enables us to effectively identify the main factors of countries or regions that influencing the economic effect of higher education. Second, regional heterogeneity is fully considered in this paper. Generally speaking, there are often development differences within regions. Comparative analysis of higher education development models and economic performance can provide an empirical basis for government decision-making specific to different regions. Third, this paper combines spatial and temporal views of geography with the new economic geography theory of economics, and we analyze in depth the spatiotemporal evolution of the quantity and quality of higher education resources in China. Therefore, this interdisciplinary study enriches the fields of higher education and economic geography. The rest of the paper is organized as follows. Section 2 reviews the relevant literature. Section 3 presents the data, variables and research methods. Section 4 presents the main empirical results. Section 5 discusses the results. Section 6 outlines the policy implications of our findings. Literature Review Economic development and regional sustainable growth are more and more dependent on the qualified workforce and the economy’s ability to increase the number of scientists, technical personnel and to improve the scientific and technical quality( Lilles and Rõigas 2017) . As the higher education institutions, universities have the fundamental role of supplying China with highly qualified labour force. Higher education must meet the needs of the country and society in different times. The backward development of tertiary education generates hold ups in the knowledge-intensive economic sectors and leads to reductions in productivity, innovation andcompetitiveness( http://ec.europa.eu/europe2020/pdf/themes/28_tertiary_education.pdf ). The contribution of education to economic growth has been the subject of much study. Human capital theory, proposed by Schultz (1961) and Becker (1975) , provides a theoretical basis for theoretical and empirical research on how education affects and supports economic growth. Many studies address the relationship between human capital and economic growth. Many argue that human capital is an essential factor and engine of economic growth (Tsaurai and Ndou 2019; De la Fuente and Doménech 2006; Mankiw, Romer and Weil 1992) , as it denotes the level of workforce efficiency and productivity (Sara, Saputra and Utama 2021, Jibir, Abdu and Buba 2022) . In particular, in developing countries with slow economic growth, human capital is considered the key to escaping the middle-income trap (Glawe and Wagner 2018) . Similarly, the development and popularization of China's higher education has been supported by the theory of human capital (Wang and Liu 2011) . The number of people who have completed higher education is often used as a measure of human capital. The enrollment of higher education in China increased from 1.6 million in 1999 to 10,422,200 in 2023, a more than fivefold increase in 25 years. Since 1999, the gross enrollment rate has increased by an average of 1.54 percent annually, indicating that China has entered the era of mass higher education ( https://www.tsinghua.edu.cn/info/1874/74528.htm ). The expansion of higher education can increase more educational opportunities. However, it remains uncertain how the tertiary level of education influences economic growth and whether higher education attainment can promote economic growth. Scholars who believe that higher education can promote economic development believe that an increase in the number of universities, research institutions and college graduates can have a positive effect on the local human capital stock and innovation (Canal Domínguez 2021; Fahim et al. 2022; Agasisti and Bertoletti 2022; Castelló-Climent and Hidalgo-Cabrillana 2012; Cooke 1992; Etzkowitz and Klofsten 2005) . Meulemeester and Rochat (1995) found that if the content of higher education reaches a certain level, it can promote growth; moreover, the social, political and economic structure of the education system, as well as the technological level of society, enable graduates to make practical use of the knowledge they have accumulated. Abel and Deitz (2012) explored this relationship in metropolitan areas in the United States and found that the number of graduates had a small but positive impact on the local human capital stock. The authors attributed the weakness of this link to large interregional migratory flows. Similarly to Abel and Deitz, Lille and Roigas (2017) investigated the relationship between economic growth and the human capital produced by local universities in Europe, and their findings suggested that human capital had a limited and lagging effect on economic growth within the region. Santoalha, Biscaia and Teixeira (2018) investigated the relationship between the heterogeneity of higher education and local human capital and found that diversification among universities plays an important role in generating diverse human capital. In contrast, some scholars are skeptical of the positive effect of higher education on economic development. Some scholars hold the view that higher education can only play its economic effects under certain conditions. Wolf (2002) did not believe that higher education is the engine of economic growth, but he agreed that higher education can promote economic development by training intellectuals and supporting technological innovation. Other scholars have argued that the economic effects of the scale and investment of higher education vary across disciplines and countries (Vandenbussche, Aghion and Meghir 2006; Murphy, Shleifer and Visrtnv 1991; Lin 2004; Li and Liu 2021; Di Liberto 2008) . However, some scholars have rejected the correlation between higher education and economic growth. Hanushek and Woessmann (2011) revealed that tertiary attainment is not significantly associated with long-run growth differences across OECD countries when cognitive skills are accounted for. Homels (2013) was skeptical about the existence of a causal relationship between the expansion of higher education and economic growth and emphasized that there is little concrete evidence to support the causal effects of mass higher education on economic growth. Morimoto and Tabata (2020) stated that subsidy policy for individuals pursuing higher education has a negative effect on the long-run economic growth rate, and mass higher education does not necessarily lead to greater economic growth. On the whole, the relationship between higher education and economic growth is uncertain and complicated. Scholars have come to different conclusions in the context of different time scales and regions. The interaction mechanism between higher education and economic growth and its effectiveness are influenced by the social system, economy, development mode, regional policies, etc. Existing research has the following defects. First, few studies have explored the mechanism and path of influence of higher education on the regional economy from the perspective of geographical and spatial differences. Second, research on the economic effects of higher education has focused mainly on the expansion of higher education in terms of quantitative aspects, such as the number of universities and colleges, research institutions and students, without considering the quality of higher education, which can lead to biased estimates. To address these gaps in the research, we explore the relationship between higher education and economic growth against the backdrop of the rapid expansion of Chinese higher education. We employ long-term panel data to examine the relationship between the quantity and quality of higher education and economic growth, fully considering regional heterogeneity. Research data, variables, and methods Data The research in this paper covers 31 provinces in mainland China over a time span of 15 years (2006-2020). The year 2006 was chosen as the starting year because some provinces were missing data on higher education before that year and Chinese higher education had already entered a stage of rapid expansion. The data were collected and integrated from multiple data sources. The data needed to measure the quantity and quality of higher education and real-time data regarding the research background were obtained from the Ministry of Education of the People’s Republic of China (http://www.moe.gov.cn/). Economy-related data were obtained from the National Bureau of Statistics (http://www.stats.gov.cn/) and the China Statistical Yearbook (2007-2021). Variables The new economic growth theory clarifies the importance of human capital and government policies to the economic growth of developing countries. A remarkable feature of the new economic growth theory is its emphasis on the internal forces of economic growth, which brings new ideas regarding the growth of developing countries. Based on this theoretical framework and drawing on previous research, we eliminated the variables that greatly reduced the R 2 values of the model, and finally selected the following variables. The dependent variable is per capita GDP, which shows obvious spatial and temporal heterogeneity. To clearly show this heterogeneity, Figure 1 and Figure 2 visually present the differences in the level of per capita GDP across regions and growth over time, respectively. The bar chart in Figure 1 clearly shows that GDP per capita has increased annually, but GDP per capita growth has decreased significantly in recent years, especially in 2008 and 2009 under the impact of the financial crisis. Figure 2 indicates that there are obvious regional differences in the level of economic development across China. The eastern and central regions have higher economic development levels than the western region, and coastal areas are more developed than inland areas. The two key independent variables are the number of enrolled students( ENR ) and the number of teachers with senior titles in universities( TES ). In addition to the two key variables indicating the quantity and quality of higher education, four control variables are selected for the regression model to ensure the accuracy of the measurement results. The four control variables are salary ( SAL ), unemployment ( UNE ), industrial structure ( IND ) and government budget ( BUD ). ENR: Universities are labour-intensive enterprises (Yen and Ong 2015) that generate direct and indirect demand for local goods and services, encouraging the creation of new businesses in the area (Tartari and Stern 2018) . Therefor, We use enrollment in tertiary education as a proxy for the scale of a university, denoted as ENR . TES: The professional title structure of teachers is crucial to the development of higher education institutions (Sax and Linda 2002) . This is not only reflected in the teaching quality, but also has an impact on the scientific research output of higher education institutions (Ding and He 2021) .The number of teachers with senior titles represents the quality of higher education and is denoted as TES. SAL: Areas with high wages tend to attract a high level of labor(Sjaastad 1970; Arntz 2010), which can effectively improve regional human capital levels and thus promote economic development( Belton, et al, 2010) . Salary level was included as one of the control variables, denoted as SAL . UNE: Okun's law indicates that unemployment means inadequate use of production factors , and that the rise in unemployment partly accompanies a decline in GDP, which has been confirmed in numerous studies(Kaufman and Roger 1988; Ball, et al, 2017; Gil, et al, 2020). The unemployment was one of the control variables, denoted as UNE . IND:The technological progress generated by upgrading industrial structure helps to enhance the added value of products and becomes a necessary condition for the transformation of economic growth(Wu and Liu 2021). We used the proportion of the added value of tertiary industry (SE) to express the industrial structure. BUD: The amount of the government budget for higher education affects its economic performance. Budget shortages, caused or exacerbated by fiscal austerity, increase competition for public funding, reduce access to and eroded the quality of public higher education(Crookston and Hooks 2012). The government's fiscal budget for higher education was included as another control variable( BUD ). Methodology Most fixed-effect models refer to individuals, and the intercept represents the heterogeneous characteristics of individuals that do not change with time and cannot be observed. Correspondingly, the time-fixed effect model varies with time but not with individuals. In this paper, a bidirectional fixed effects model is established considering individual and time fixed effects to reduce the deviation of the estimation results caused by the omission of variables. The descriptive statistics for the variables included in the econometric model are reported in Table 1. The average GDP per capita is 43,601.81, and there is a large variance, which reflects regional heterogeneity. Similarly, there is a large variance in university enrollment, which indicates that the quantity of higher education institutions varies by region. Table 1 Descriptive statistics Variables Abbr. Obs Mean Var. Max Min Per capita GDP Pgdp 465 43601.81 27233.58 164158 6103 Enrollment in University ENR 465 23.08 14.97 86.61 0.80 Number of teachers with senior title TES 465 1.95 1.23 6.25 0.04 The average salary SAL 465 53311.42 27127.49 178178 15370 The unemployment rate UNE 465 25.74 14.61 73.90 1.00 Ratio of added value of the tertiary industry in GDP IND 465 47.21 9.33 83.73 29.79 The public budget expenditure BUD 465 3845.73 2859.09 17430.79 174.54 Results Analysis of spatial patterns To analyze the spatial differentiation and evolution of the quantity and quality of higher education, we present a spatial visualization of the number of enrollments and teachers with senior titles in each province. The visualization results are shown in Figure 3 and Figure 4. As shown in Figure 3, the number of university enrollments increased gradually from 2006 to 2020, which confirms that Chinese universities are constantly expanding. From the perspective of quantitative measurements, China's higher education is unbalanced. The number of enrollees in the eastern, central and western regions has grown to varying degrees, with the expansion rate in the western region being higher than that in the eastern and central regions. From 2006 to 2020, the growth rates of enrollment in the eastern, central and western regions were 61.56%, 69.60% and 122.48%, respectively. According to new data from the Ministry of Education of the People’s Republic of China, in 2021, the average general public budgets for higher education per student in the eastern, central and western regions were 312,368 yuan, 138,207 yuan and 309,458 yuan, respectively. Compared to the other two regions, the western region is an underdeveloped region in China. Since 2000, the Chinese government has implemented a series of policies to support the development of the central and western regions, which has led to a great increase in investment in higher education in these two regions. Figure 4 shows that the number of teachers with senior titles also has spatial heterogeneity, and the eastern region has more teachers with senior titles than the central and western regions. Statistics show that from 2006 to 2020, the percentage of teachers with senior titles increased by 113.19% in western China, by 91.29% in eastern China and by 78.15% in central China. This is mainly due to the small number of teachers with senior titles in the western region in 2006 and the support policies implemented by the government, which led to a faster growth rate in the western region than in the eastern and central regions from 2006 to 2020. For the eastern region, due to its high level of economic development, there are competitive supporting policies for talent, such as high wages, children's schooling, housing, and medical care. Although universities in the western region have adopted policies to attract talent, they have suffered from brain drain and poor ability to attract talent because of the limited strength of policy implementation and locational disadvantages. Panel regression results Model selection and validation for 31 provinces The test results in Table 2 show that the p values of the F test and LM test are 0.0000 (<0.05), individual effects are considered to exist, and the original hypothesis of mixed effects is rejected. According to the Hausman test, the null hypothesis of random effects is rejected, and a fixed effects model should be selected. Table 2 Model selection for 31 provinces Item Null hypothesis statistic P value Test results F test Mixed effects 63.24 0.0000 Fixed effects LM test Mixed effects 1676.06 0.0000 Random effects Hausman test Random effects 37.76 0.0000 Fixed effects We establish a bidirectional error correction model and test the model. The test results, shown in Table 3, indicate that the model has cross-sectional dependence, heteroscedasticity and autocorrelation, so a two-way fixed effects model is applied in this paper. In addition, we use the xtscc module in Stata to calculate Driscoll–Kraay standard errors for the panels. Table 3 Model verification Item Null hypothesis Test methods statistic Threshold/ P value Test result Cross sectional dependence There is no cross sectional dependence Frees-test 6.68 0.2838 (Critical value) Refuse Heteroscedasticity There is no heteroscedasticity chi-square test 6963.14 0.0000 (P value) Refuse Auto-correlation There is no Auto-correlation F test 353.02 0.0000 (P value) Refuse Model selection and validation for the eastern, central and western regions We conduct the F test, LM test and Hausman test for the eastern, central and western regions (Table 4). The test results suggest the selection of the same model for the three regions as for the 31 provinces, namely, a two-way fixed effect model. As shown in Table 5, the models applied for these three regions have the same cross-sectional dependence, heteroscedasticity and autocorrelation as that used for China overall. Therefore, Driscoll–Kraay standard errors are adopted to establish the bidirectional fixed effect model. Table 4 Model selection for eastern, central and western regions Region Item Null hypothesis Statistics P value Test results Eastern region F test Mixed effects 35.20 0.0000 Fixed effects LM test Mixed effects 347.89 0.0000 Random effects Hausman test Random effects 24.96 0.0000 Fixed effects Central region F test Mixed effects 16.73 0.0000 Fixed effects LM test Mixed effects 93.60 0.0000 Random effects Hausman test Random effects 15.04 0.0354 Fixed effects Western region F test Mixed effects 58.48 0.0000 Fixed effects LM test Mixed effects 619.20 0.0000 Random effects Hausman test Random effects 35.50 0.0000 Fixed effects Table 5 Model selection tests for eastern and central regions Region Item Test methods Statistics Critical/P value Test results Eastern region Cross sectional dependence Frees test 2.59 0.2262 (Critical value) Refuse Heteroscedasticity Chi-square test 297.53 0.0000 (Pvalue) Refuse Auto-correlation F test 199.26 0.0000 (Pvalue) Refuse Middle region Cross sectional dependence Frees test 0.983 0.2262 (Critical value) Refuse Heteroscedasticity Chi-square test 140.47 0.0000 (Pvalue) Refuse Auto-correlation F test 42.65 0.0003 (Pvalue) Refuse Westen region Cross sectional dependence Frees test 2.275 0.2262 (Critical value) Refuse Heteroscedasticity Chi-square test 306.79 0.0000 (Pvalue) Refuse Auto-correlation F test 224.33 0.0003 (Pvalue) Refuse The empirical results for 31 provinces and three regions based on the two-way fixed effect model are shown in Table 6. The R 2 is 0.9540, which indicates that the model has satisfactory fitting results. The coefficients of the two key variables are found to be significant in the model for China but inconsistent in the models for the three regions. Table 6 Empirical results Variable China Eastern region Central region Western region Enrollment in University -0.06 *** (0.0000) -0.04 *** (0.0002) -0.01 (0.2277) -0.04 *** (0.0092) Number of teachers with senior title 1.10 *** (0.0000) 0.80 *** (0.0000) -0.14 (0.4131) 0.40 (0.0434) The average salary 0.62 *** (0.0000) 0.71 *** (0.0000) 0.78 *** (0.0000) -0.04 (0.5668) The unemployment rate -21.26 (0.7053) -37.68 (0.6135) -121.30 (0.1367) 200.45 (0.1970) Ratio of added value of the tertiary industry in GDP -128.10 (0.1208) -741.81 ** (0.0117) -227.19 ** (0.0295) -662.52 *** (0.0000) The public budget expenditure 1.08 ** (0.0152) 0.14 (0.7623) 4.41 *** (0.0010) 1.55 (0.1001) N 465 165 120 180 R 2 0.9540 0.9658 0.9895 0.9633 Note: Parenthetical values are standard errors. *** indicates significance at the 1% level, ** at the 5% level and * at the 10% level. The model used to estimate the results for China indicates that the number of university enrollments and the number of teachers with senior professional titles have opposite effects in economic terms, such that the former has a negative effect and the latter a positive effect. These results may be due to the following two reasons. First, with the increase in university enrollment, the level of education consumption increases, while other types of consumption are reduced; that is, there is a “crowding-out effect” of education consumption. Second, as the expansion of higher education takes place in China, a developing country, the shortage of education funds in many mediocre and lower-tier universities is addressed by increasing the number of enrolled individuals without ensuring a corresponding increase in expenditure on teaching resources and management. In addition, China's promotion system regarding teachers' professional titles in the stage of mass higher education has changed over time. Since university teachers’ scientific research level has become crucial to their evaluation, teachers often spend less time preparing for teaching and more time writing papers and applying for projects. Therefore, the overall academic level of graduates is relatively low, which is undoubtedly detrimental to the high-quality growth of China's economy. Among the control variables, both wages and government budgets pass the significance test and have a positive effect on economic growth. Notably, government budgets matter more than wages. In recent years, China's public budget has gradually increased, and these funds can be used to stabilize economic growth by expanding domestic demand and promoting industrial restructuring. The variables present different effects in the estimations for the three regions. University enrollment has a negative effect in the eastern and western regions, as it does at the national level, which indicates that the expansion of higher education in these two regions does not lead to economic growth. The possible reasons are consistent with the explanation regarding the country overall, namely, the “crowding-out effect” of education consumption, the lagging construction of supporting institutions accompanying education expansion, and the mechanism of title promotion. In contrast, the number of teachers with senior professional titles has a positive effect in the eastern region, while the effect in the central and western regions does not pass the significance test. The above estimation results may be attributed to two reasons. First, as China is a socialist country, its government regulates regional development strategies, which significantly impacts each region's development mode and the establishment of universities. China's economic development strategy is the fundamental reason for the differences in talent introduction among universities in the eastern, central and western regions. From 1979 to 1990, the Chinese government valued the efficiency of development and promoted a strategy of regional “unbalanced development,” that is, prioritizing the development of the eastern coastal areas. The plan aimed to make the eastern region an economic “growth pole” for China and drive the development of surrounding areas. The implementation of this policy attracted various production factors and investments to the eastern region. With the development of the economy, several high-level universities, such as Fudan University, Nanjing University, and Zhejiang University, developed rapidly. Although the Chinese government shifted its development policy to regional “balanced development” after 1991, economic development in the central and western regions was slow due to differences in economic foundation and location conditions, which had an impact on the number of colleges and universities established and their financial input. Of the 2,738 universities in China in 2020, nearly 40% were in the eastern region, and 61 of China's top 100 universities were in the eastern region. Second, universities in China are stratified among regions. Studies estimating the quality of higher education in each province according to the university rankings released by China show that the eastern region has the highest quality universities, followed by the central region and finally the western region (Li and Wei 2018) . As discussed in the previous section, given China’s vast territory, different regions have different history and culture, geographical conditions, and economic development, which determines the input of education resources and influences the locational choices of talented individuals. In the past, provinces with traditional advantages, such as Henan, Hunan, and Anhui, had the greatest quantity and highest quality of higher education resources in China. Talents usually expect to go to high-level universities to obtain more development opportunities. In recent years, talented individuals have been increasingly attracted to the eastern region, especially the eastern coastal region, not only because of its distinguished history, culture, and geographical location but also because of its high level of economic development. In particular, the Pearl River Delta and Yangtze River Delta regions have invested more funds in introducing talent and perfecting supporting facilities. This attracts recent graduates, and many high-skilled workers have flowed from inland cities to coastal areas. Therefore, due to the ‘talent war’ among universities, the pattern of large-scale talent flow to traditionally advantaged provinces persists. For the control variables, the industrial structure has a negative effect in the eastern, central and western regions. The industrial structure of the eastern region is similar to that of the central and western regions, and complementary advantages have not formed between regions. In addition, industrial development is still dominated by an extensive development model characterized by high input, high energy consumption, high pollution and low added value. Robustness of the results To test the robustness of the estimation results, the methods of changing estimation methods, substituting variables and shortening the considered period are applied. Specifically, for the method, we test the random effects model instead of the fixed effects model; for variables, we replace GDP per capita with GDP; and for the study period, we reduce the period from 15 years to 12 years. The test results are shown in Table 7. Table 7 Robustness test Variables Method change Substitution of explained variable Shorten the year Fixed effects model Random effects model Pgdp GDP Fifteen years Twelve years Enrollment in University -0.06 *** (0.0000) -0.06 *** (0.0026) -0.06 *** (0.0000) -0.03 ** (0.0150) -0.06 *** (0.0000) -0.06 *** (0.0000) Number of teachers with senior title 1.10 *** (0.0000) 1.19 *** (0.0000) 1.10 *** (0.0000) 1.09 *** (0.0000) 1.10 *** (0.0000) 1.14 *** (0.0000) The average salary 0.62 *** (0.0000) 0.67 *** (0.0000) 0.62 *** (0.0000) -0.11 ** (0.0136) 0.62 *** (0.0000) 0.57 *** (0.0000) The unemployment rate -21.26 (0.7053) -32.57 (0.7483) -21.26 (0.7053) 58.73 (0.1395) -21.26 (0.7053) 13.58 (0.7937) Ratio of added value of the tertiary industry in GDP -128.10 (0.1208) -10.93 (0.9609) -128.10 (0.1208) -367.45 *** (0.0011) -128.10 (0.1208) -76.76 (0.3878) The public budget expenditure 1.08 ** (0.0152) 0.85 (0.2271) 1.08 ** (0.0152) 4.17 *** (0.0000) 1.08 ** (0.0152) 1.10 ** (0.0268) Cross -section fixed Yes Yes Yes Yes Yes Yes Period fixed Yes Yes Yes Yes Yes Yes R 2 within 0.9541 0.9537 0.9540 0.9198 0.9540 0.9425 Parenthetical values are standard errors. ***p < 1%. **p < 5%. *p < 10%. The results show that the key explanatory variables of the number of enrolled students and the number of teachers with senior titles do not change significantly with the use of a different method. For instance, the coefficient of enrollment is estimated to be -0.06 and passes the significance test in both the fixed effect model and the random effect model. Similarly, the coefficients of the number of teachers with senior titles in the two models are 1.10 and 1.19, respectively, and both pass the significance test. The coefficients of the two variables are also similar according to the other two robustness test methods. Moreover, the value and significance of the control variables do not change significantly with the change in estimation methods. Therefore, we believe that the selected model is robust and that the estimated results are reliable. Discussion This paper analyses the spatiotemporal evolution characteristics of China's higher education and its contribution to economic growth from the perspectives of quantity and quality. It fills the research gap regarding the analysis of the temporal and spatial evolution of China's higher education in the quantitative and qualitative dimensions. Different from existing studies, it also accounts for regional heterogeneity in the performance of higher education. To address these limitations of prior research, this paper draws on the intersection of economic geography and educational economics to propose an empirical framework for comprehensive modeling. The model considers the quantity and quality of higher education as well as regional heterogeneity. The robustness tests of the panel data model show that the model estimation results are reasonable. The empirical results lead to three interesting observations in particular. First, Chinese higher education is unbalanced in terms of quantity and quality, not only among the eastern, central and western regions but also within each region. This indicates that there is an imbalance of resources in and unequal access to higher education, which contradicts the characteristics of mass higher education. Education expansion occurs in a different context in China than in other countries, where it is usually accompanied by decreasing education costs and diversified selection criteria (Wu and Zhang 2020) . In China, education is expanding in a context of social transformation from a planned economy to a socialist market economy. This transformation increases the accessibility but also the cost of higher education and makes the entry requirements more stringent (Li 2010) . In addition, unlike in market-oriented countries, the expansion of higher education in China is centrally regulated and strictly controlled by the government; in other words, it is mainly guided by government policies. Relatedly, access to higher education opportunities has a certain relationship with individuals’ place of household registration (Qian and Walker, 2015). In this way, access to university education resources and admission opportunities is unequal between economically developed areas and less developed areas and between individuals with urban and rural registration in China. Second, the quantity and quality aspects of higher education have different impacts on economic performance. Specifically, the quantity of higher education has a certain inhibitory effect on economic growth, while the quality of higher education has the opposite effect. This suggests that the expansion of China's higher education has not caused corresponding economic growth, which is worthy of further exploration. Chinese higher education has expanded within a short period and at high speed, and the mechanism meditating expansion is mainly supply-driven rather than demand-driven (Wu and Zhang 2020) . Because universities tend to imitate each other, different types of universities tend to comprehensively expand their internal institutions (Tian 2016). Such expansion can greatly reduce the input‒output efficiency of educational resources, resulting in resource waste. In addition, due to the rapid expansion and supply-driven nature of higher education, the structure and quality of higher education have not been well coordinated with the scale of higher education, and the education structure does not match the market demand. This expansion pattern does not support the transformation of higher education outputs into positive economic impacts. Third, higher education quality can be effectively translated into economic growth mainly at the national level and in the eastern region. The eastern region invests more funds in attracting talent and improving teachers’ teaching skills than the less developed regions do thanks to its sound economic foundation and location advantages. Introducing talent is crucial for universities to improve their quality and serve society, mainly by cultivating high-level skills and promoting technological innovation. Studies have shown that higher education quality promotes economic growth mainly through increasing the employment rate and improving total factor productivity and human capital quality (Hanushek and Woessmann 2016, Castelló-Climent and Hidalgo-Cabrillana 2012) . From this perspective, scale expansion should not be blindly pursued, but rather the quality of higher education should be improved in the stage of mass higher education. The limitations of this study are mainly related to the indicators available to estimate the economic effect of higher education. The lack of data restricted our choices of variables for inclusion in the model. This limitation is particularly relevant for the measurement of higher education quality. In this paper, we consider this dimension only in terms of the number of teachers with senior titles; however, further research should incorporate educational output (e.g., graduate employment rate) into the model as a variable representing educational quality. In addition, similarly to many developing countries, China has shifted from a stage in which higher education is a privilege of the elite to a stage of mass higher education. Findings in this context cannot reflect the characteristics of the transformation of higher education under the system of developed economies. Further studies can examine the case of developed countries for comparative analysis. Policy implications Since 1999, Chinese higher education has expanded rapidly and had major impacts. The main purpose of the expansion of higher education is to produce more well-educated citizens and promote economic development (Li and Min 2001) . Therefore, the issue of the economic effect of higher education has attracted increasing attention among researchers and educators. Studies have examined the relationship between higher education and economic growth. However, because studies address different areas, cover different time periods and use different variables, the research results vary. The findings of this study indicate that neither the quantitative nor the qualitative expansion of higher education has contributed to economic growth. In the three regions of China, the quantity and quality aspects of higher education have the same economic effects but to different degrees and at different levels of significance. The results offer some interesting policy implications. First, in view of the shortage of investment in higher education and talent subsidies in the central and western regions, the government should implement policies to increase educational funds. To attract and retain talented individuals for work in universities, the government should provide preferential services, such as professional title evaluation, housing security, medical treatment, endowment insurance, medical insurance and children's enrollment. The government should supervise the implementation of these policies to ensure their effectiveness. Second, to improve the academic quality of graduates, there is an urgent need to change the evaluation mechanism for teachers’ title promotion. The current evaluation system focuses mainly on the quantity of teachers’ scientific research achievements and pays less attention to the quality of their teaching. Therefore, students' academic performance and evaluation of teaching effectiveness can be included in teachers’ title assessments. For example, at the end of each semester, questionnaires can be used to gather information about students' evaluation of teachers in aspects such as teacher literacy, curriculum content design, curriculum achievement, and cultivation of students' innovation consciousness. Finally, to make full use of educational resources, the structure of specialties and universities’ enrollment plans should be market oriented. In other words, universities should conduct sufficient market research before setting up majors and determining the number of students. In addition, it is necessary to maintain a market orientation to allow the dynamic adjustment of majors that are not adapted to the current market context. Declarations Author Contribution Tingting Yu: Writing original draft, Conceptualization, Supervision . Yingzi Gui: Methodology, Software, Investigation, Methodology. Rong Ah: Visualization, Writing -Reviewing and Editing. 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Journal of Economic Growth, 2006; 11: 97–127. doi:10.1007/sl0887-006-9002. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7732642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531219325,"identity":"7ecf1bb9-6082-4827-b945-009e96a0fee6","order_by":0,"name":"Tingting 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1","display":"","copyAsset":false,"role":"figure","size":83095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGDP per capita and growth in GDP per capita from 2006 to 2020\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7732642/v1/ede558a0a25270042c231a21.png"},{"id":93945770,"identity":"35243356-c869-419e-b10f-96a45823e2dc","added_by":"auto","created_at":"2025-10-20 14:22:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":635319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGDP per capita per region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: the map reports the mean over years of the GDP per capita\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7732642/v1/a635cda164863dce35c557e6.png"},{"id":93945774,"identity":"561a288e-13d4-4994-a8a7-961987040433","added_by":"auto","created_at":"2025-10-20 14:22:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":840084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial pattern evolution of the number of new students enrolled\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7732642/v1/b4be88db981c42ad4c57d713.png"},{"id":93945780,"identity":"3f84288f-ceee-41d7-91a3-1bd6e1420c7c","added_by":"auto","created_at":"2025-10-20 14:22:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":878281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial pattern evolution of teachers with senior titles\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7732642/v1/3449a6937a9d8227d7ab2818.png"},{"id":96854716,"identity":"55ac78a4-7692-4f1e-a287-2ca5ec60a27b","added_by":"auto","created_at":"2025-11-26 18:53:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3755731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7732642/v1/a727f689-b016-46e8-84c0-c620a76daf7c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The spatial pattern and growth performance of higher education in China: A longitudinal study in China from 2006 to 2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe driving factors of China's economic development are gradually shifting from factors and investment to innovation. Innovation-driven development is a more advanced growth mode that relies mainly on intangible elements, such as knowledge capital, human capital and incentive innovation systems, to create new growth factors \u003cb\u003e(Porter 1997)\u003c/b\u003e. The core factor of innovation is talent, and higher education provides society with critical thinkers, researchers, scholars, innovators and responsible citizens \u003cb\u003e(Oazi, Raza and Jawaid 2014)\u003c/b\u003e. Consequently, higher education is an important requirement for developing countries, such as China, to promote rapid and sustainable economic growth.\u003c/p\u003e\u003cp\u003eIn China, higher education has expanded rapidly since 1999, and great changes have taken place. The most marked change is that higher education transitioned from the elite stage to the mass stage in just 17 years \u003cb\u003e(Jia 2021)\u003c/b\u003e. In 2021, the gross enrollment rate of higher education in China was 57.8%, and the total number of students in higher education reached 44.3\u0026nbsp;million \u003cb\u003e(according to the website of the Ministry of Education of China as of December 3, 2020\u003c/b\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.moe.gov.cn/fbh/live/2020/52717/\u003c/span\u003e\u003cspan address=\"http://www.moe.gov.cn/fbh/live/2020/52717/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Thus, China has become one of the countries with mass higher education and established the world's largest higher education system \u003cb\u003e(Huang and Ding 2022).\u003c/b\u003e However, international comparison shows that the development of higher education in China is relatively advanced compared with the country\u0026rsquo;s economic development level, which is reflected mainly in the imbalance between the quantity and quality, supply and demand, and structure and function of higher education \u003cb\u003e(Li 2017).\u003c/b\u003e There are many Chinese higher education institutions, but their quality is relatively low. Against this backdrop, it is essential to understand the quantitative and qualitative characteristics of China's higher education resources and to estimate their economic performance.\u003c/p\u003e\u003cp\u003eChina covers 9.6\u0026nbsp;million square kilometers and has 31 provinces. There are major differences in natural conditions and socioeconomic development across regions; therefore, the government formulates differentiated development policies for three distinct regions, namely, the eastern, central, and western regions. The eastern region comprises eleven provinces, the central region comprises eight provinces, and the western region comprises twelve provinces. Regional heterogeneity, if not addressed, will obviously lead to biased or incorrect analysis results.\u003c/p\u003e\u003cp\u003eThis paper contributes to the literature in three main ways. First, it characterizes the development of higher education by the two dimensions of quantity and quality, which enables us to effectively identify the main factors of countries or regions that influencing the economic effect of higher education. Second, regional heterogeneity is fully considered in this paper. Generally speaking, there are often development differences within regions. Comparative analysis of higher education development models and economic performance can provide an empirical basis for government decision-making specific to different regions. Third, this paper combines spatial and temporal views of geography with the new economic geography theory of economics, and we analyze in depth the spatiotemporal evolution of the quantity and quality of higher education resources in China. Therefore, this interdisciplinary study enriches the fields of higher education and economic geography.\u003c/p\u003e\u003cp\u003eThe rest of the paper is organized as follows. Section 2 reviews the relevant literature. Section 3 presents the data, variables and research methods. Section 4 presents the main empirical results. Section 5 discusses the results. Section 6 outlines the policy implications of our findings.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eEconomic development and regional sustainable growth are more and more dependent on the qualified workforce and the economy\u0026rsquo;s ability to increase the number of scientists, technical personnel and to improve the scientific and technical quality( \u003cstrong\u003eLilles and R\u0026otilde;igas 2017)\u003c/strong\u003e. As the higher education institutions, universities have the fundamental role of supplying China with highly qualified labour force. Higher education must meet the needs of the country and society in different times. The backward development of tertiary education generates hold ups in the knowledge-intensive economic sectors and leads to reductions in productivity, innovation andcompetitiveness(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ec.europa.eu/europe2020/pdf/themes/28_tertiary_education.pdf\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe contribution of education to economic growth has been the subject of much study. Human capital theory, proposed by Schultz \u003cstrong\u003e(1961)\u003c/strong\u003e and Becker \u003cstrong\u003e(1975)\u003c/strong\u003e, provides a theoretical basis for theoretical and empirical research on how education affects and supports economic growth. Many studies address the relationship between human capital and economic growth. Many argue that human capital is an essential factor and engine of economic growth \u003cstrong\u003e(Tsaurai and Ndou 2019; De la Fuente and Dom\u0026eacute;nech 2006; Mankiw, Romer and Weil 1992)\u003c/strong\u003e, as it denotes the level of workforce efficiency and productivity \u003cstrong\u003e(Sara, Saputra and Utama 2021, Jibir, Abdu and Buba 2022)\u003c/strong\u003e. In particular, in developing countries with slow economic growth, human capital is considered the key to escaping the middle-income trap \u003cstrong\u003e(Glawe and Wagner 2018)\u003c/strong\u003e. Similarly, the development and popularization of China\u0026apos;s higher education has been supported by the theory of human capital \u003cstrong\u003e(Wang and Liu 2011)\u003c/strong\u003e. The number of people who have completed higher education is often used as a measure of human capital.\u003c/p\u003e\n\u003cp\u003eThe enrollment of higher education in China increased from 1.6\u0026nbsp;million in 1999 to 10,422,200 in 2023, a more than fivefold increase in 25 years. Since 1999, the gross enrollment rate has increased by an average of 1.54 percent annually, indicating that China has entered the era of mass higher education (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tsinghua.edu.cn/info/1874/74528.htm\u003c/span\u003e\u003c/span\u003e). The expansion of higher education can increase more educational opportunities. However, it remains uncertain how the tertiary level of education influences economic growth and whether higher education attainment can promote economic growth. Scholars who believe that higher education can promote economic development believe that an increase in the number of universities, research institutions and college graduates can have a positive effect on the local human capital stock and innovation \u003cstrong\u003e(Canal Dom\u0026iacute;nguez 2021; Fahim et al. 2022; Agasisti and Bertoletti 2022; Castell\u0026oacute;-Climent and Hidalgo-Cabrillana 2012; Cooke 1992; Etzkowitz and Klofsten 2005)\u003c/strong\u003e. Meulemeester and Rochat \u003cstrong\u003e(1995)\u003c/strong\u003e found that if the content of higher education reaches a certain level, it can promote growth; moreover, the social, political and economic structure of the education system, as well as the technological level of society, enable graduates to make practical use of the knowledge they have accumulated. Abel and Deitz \u003cstrong\u003e(2012)\u003c/strong\u003e explored this relationship in metropolitan areas in the United States and found that the number of graduates had a small but positive impact on the local human capital stock. The authors attributed the weakness of this link to large interregional migratory flows. Similarly to Abel and Deitz, Lille and Roigas \u003cstrong\u003e(2017)\u003c/strong\u003e investigated the relationship between economic growth and the human capital produced by local universities in Europe, and their findings suggested that human capital had a limited and lagging effect on economic growth within the region. Santoalha, Biscaia and Teixeira \u003cstrong\u003e(2018)\u003c/strong\u003e investigated the relationship between the heterogeneity of higher education and local human capital and found that diversification among universities plays an important role in generating diverse human capital.\u003c/p\u003e\n\u003cp\u003eIn contrast, some scholars are skeptical of the positive effect of higher education on economic development. Some scholars hold the view that higher education can only play its economic effects under certain conditions. Wolf \u003cstrong\u003e(2002)\u003c/strong\u003e did not believe that higher education is the engine of economic growth, but he agreed that higher education can promote economic development by training intellectuals and supporting technological innovation. Other scholars have argued that the economic effects of the scale and investment of higher education vary across disciplines and countries \u003cstrong\u003e(Vandenbussche, Aghion and Meghir 2006; Murphy, Shleifer and Visrtnv 1991; Lin 2004; Li and Liu 2021; Di Liberto 2008)\u003c/strong\u003e. However, some scholars have rejected the correlation between higher education and economic growth. Hanushek and Woessmann \u003cstrong\u003e(2011)\u003c/strong\u003e revealed that tertiary attainment is not significantly associated with long-run growth differences across OECD countries when cognitive skills are accounted for. Homels \u003cstrong\u003e(2013)\u003c/strong\u003e was skeptical about the existence of a causal relationship between the expansion of higher education and economic growth and emphasized that there is little concrete evidence to support the causal effects of mass higher education on economic growth. Morimoto and Tabata \u003cstrong\u003e(2020)\u003c/strong\u003e stated that subsidy policy for individuals pursuing higher education has a negative effect on the long-run economic growth rate, and mass higher education does not necessarily lead to greater economic growth.\u003c/p\u003e\n\u003cp\u003eOn the whole, the relationship between higher education and economic growth is uncertain and complicated. Scholars have come to different conclusions in the context of different time scales and regions. The interaction mechanism between higher education and economic growth and its effectiveness are influenced by the social system, economy, development mode, regional policies, etc. Existing research has the following defects. First, few studies have explored the mechanism and path of influence of higher education on the regional economy from the perspective of geographical and spatial differences. Second, research on the economic effects of higher education has focused mainly on the expansion of higher education in terms of quantitative aspects, such as the number of universities and colleges, research institutions and students, without considering the quality of higher education, which can lead to biased estimates. To address these gaps in the research, we explore the relationship between higher education and economic growth against the backdrop of the rapid expansion of Chinese higher education. We employ long-term panel data to examine the relationship between the quantity and quality of higher education and economic growth, fully considering regional heterogeneity.\u003c/p\u003e"},{"header":"Research data, variables, and methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research in this paper covers 31 provinces in mainland China over a time span of 15 years (2006-2020). The year 2006 was chosen as the starting year because some provinces were missing data on higher education before that year and Chinese higher education had already entered a stage of rapid expansion. The data were collected and integrated from multiple data sources. The data needed to measure the quantity and quality of higher education and real-time data regarding the research background were obtained from the Ministry of Education of the People\u0026rsquo;s Republic of China (http://www.moe.gov.cn/). Economy-related data were obtained from the National Bureau of Statistics (http://www.stats.gov.cn/) and the China Statistical Yearbook\u003cem\u003e\u0026nbsp;\u003c/em\u003e(2007-2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe new economic growth theory clarifies the importance of human capital and government policies to the economic growth of developing countries. A remarkable feature of the new economic growth theory is its emphasis on the internal forces of economic growth, which brings new ideas regarding the growth of developing countries. Based on this theoretical framework and drawing on previous research, we eliminated the variables that greatly reduced the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003evalues of the model, and finally selected the following variables.\u003c/p\u003e\n\u003cp\u003eThe dependent variable is per capita GDP, which shows obvious spatial and temporal heterogeneity. To clearly show this heterogeneity, Figure 1 and Figure 2 visually present the differences in the level of per capita GDP across regions and growth over time, respectively. The bar chart in Figure 1 clearly shows that GDP per capita has increased annually, but GDP per capita growth has decreased significantly in recent years, especially in 2008 and 2009 under the impact of the financial crisis. Figure 2 indicates that there are obvious regional differences in the level of economic development across China. The eastern and central regions have higher economic development levels than the western region, and coastal areas are more developed than inland areas.\u003c/p\u003e\n\u003cp\u003eThe two key independent variables are the number of enrolled students(\u003cem\u003eENR\u003c/em\u003e) and the number of teachers with senior titles in universities(\u003cem\u003eTES\u003c/em\u003e). In addition to the two key variables indicating the quantity and quality of higher education, four control variables are selected for the regression model to ensure the accuracy of the measurement results. The four control variables are salary (\u003cem\u003eSAL\u003c/em\u003e), unemployment (\u003cem\u003eUNE\u003c/em\u003e), industrial structure (\u003cem\u003eIND\u003c/em\u003e) and government budget (\u003cem\u003eBUD\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eENR: Universities are labour-intensive enterprises\u003cstrong\u003e(Yen and Ong 2015)\u003c/strong\u003e that generate direct and indirect demand for local goods and services, encouraging the creation of new businesses in the area\u003cstrong\u003e(Tartari and Stern 2018)\u003c/strong\u003e. Therefor, We use enrollment in tertiary education as a proxy for the scale of a university, denoted as \u003cem\u003eENR\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTES: The professional title structure of teachers is crucial to the development of higher education institutions\u003cstrong\u003e(Sax and Linda 2002)\u003c/strong\u003e. This is not only reflected in the teaching quality, but also has an impact on the scientific research output of higher education institutions\u003cstrong\u003e(Ding and He 2021)\u003c/strong\u003e.The number of teachers with senior titles represents the quality of higher education and is denoted as \u003cem\u003eTES.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSAL: Areas with high wages tend to attract a high level of labor(Sjaastad 1970; Arntz 2010), which can effectively improve regional human capital levels and thus promote economic development(\u003cstrong\u003eBelton, et al, 2010)\u003c/strong\u003e. Salary level was included as one of the control variables, denoted as \u003cem\u003eSAL\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eUNE: Okun\u0026apos;s law indicates that unemployment means inadequate use of production factors , and that the rise in unemployment partly accompanies a decline in GDP, which has been confirmed in numerous studies(Kaufman and Roger 1988; Ball, et al, 2017; Gil, et al, 2020). The unemployment was one of the control variables, denoted as \u003cem\u003eUNE\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIND:The technological progress generated by upgrading industrial structure helps to enhance the added value of products and becomes a necessary condition for the transformation of economic growth(Wu and Liu 2021). We used the proportion of the added value of tertiary industry (SE) to express the industrial structure.\u003c/p\u003e\n\u003cp\u003eBUD:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe amount of the government budget for higher education affects its economic performance. Budget shortages, caused or exacerbated by fiscal austerity, increase competition for public funding, reduce access to and eroded the quality of public higher education(Crookston and Hooks 2012). The government\u0026apos;s fiscal budget for higher education was included as another control variable(\u003cem\u003eBUD\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethodology\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost fixed-effect models refer to individuals, and the intercept represents the heterogeneous characteristics of individuals that do not change with time and cannot be observed. Correspondingly, the time-fixed effect model varies with time but not with individuals. In this paper, a bidirectional fixed effects model is established considering individual and time fixed effects to reduce the deviation of the estimation results caused by the omission of variables.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"blob:https://wordtohtml.net/2069522b-61dc-494a-bb15-0f3c720ffbf7\" width=\"755\" height=\"282\" 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics for the variables included in the econometric model are reported in\u0026nbsp;Table 1.\u0026nbsp;The\u0026nbsp;average GDP per capita is 43,601.81, and\u0026nbsp;there is\u0026nbsp;a\u0026nbsp;large\u0026nbsp;variance, which reflects regional heterogeneity. Similarly, there is a large variance in\u0026nbsp;university\u0026nbsp;enrollment, which indicates\u0026nbsp;that\u0026nbsp;the quantity of higher education institutions varies by region.\u003c/p\u003e\n\u003cp\u003eTable 1 Descriptive statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003eAbbr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003eVar.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003eMax\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003ePer capita GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003e\u003cem\u003ePgdp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003e\u003cem\u003e465\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003e43601.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003e27233.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003e164158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003e6103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003eEnrollment in University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003e\u003cem\u003eENR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003e\u003cem\u003e465\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003e23.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003e14.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003e86.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003eNumber of teachers with senior title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003e\u003cem\u003eTES\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003e\u003cem\u003e465\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003eThe average salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003e\u003cem\u003eSAL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003e\u003cem\u003e465\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003e53311.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003e27127.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003e178178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003e15370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003eThe unemployment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003e\u003cem\u003eUNE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003e\u003cem\u003e465\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003e25.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003e14.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003e73.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003eRatio of added value of the tertiary industry in GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003e\u003cem\u003eIND\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003e\u003cem\u003e465\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003e47.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003e83.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003e29.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.9373%;\"\u003e\n \u003cp\u003eThe public budget expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9151%;\"\u003e\n \u003cp\u003e\u003cem\u003eBUD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8856%;\"\u003e\n \u003cp\u003e\u003cem\u003e465\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1292%;\"\u003e\n \u003cp\u003e3845.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4391%;\"\u003e\n \u003cp\u003e2859.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7306%;\"\u003e\n \u003cp\u003e17430.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9631%;\"\u003e\n \u003cp\u003e174.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalysis of spatial patterns\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the spatial differentiation and evolution of the quantity and quality of higher education, we present a spatial visualization of the number of enrollments and teachers with senior titles in each province. The visualization results are shown in\u0026nbsp;Figure\u0026nbsp;3\u0026nbsp;and\u0026nbsp;Figure\u0026nbsp;4.\u003c/p\u003e\n\u003cp\u003eAs shown in\u0026nbsp;Figure 3, the number of\u0026nbsp;university\u0026nbsp;enrollments increased\u0026nbsp;gradually from 2006 to 2020, which confirms that Chinese universities\u0026nbsp;are\u0026nbsp;constantly expanding. From the perspective of quantitative measurements, China\u0026apos;s higher education is unbalanced. The number of\u0026nbsp;enrollees\u0026nbsp;in the eastern, central and western regions\u0026nbsp;has\u0026nbsp;grown\u0026nbsp;to\u0026nbsp;varying degrees, with the expansion rate in the western region being higher than that in the eastern and central regions. From 2006 to 2020, the growth rates of enrollment in\u0026nbsp;the\u0026nbsp;eastern, central and western regions\u0026nbsp;were\u0026nbsp;61.56%, 69.60% and 122.48%, respectively.\u0026nbsp;According to\u0026nbsp;new data from the Ministry of Education of the People\u0026rsquo;s Republic of China, in 2021, the average general public\u0026nbsp;budgets\u0026nbsp;for higher education per student in the eastern, central and western regions\u0026nbsp;were\u0026nbsp;312,368 yuan, 138,207 yuan and 309,458 yuan, respectively.\u0026nbsp;Compared to the other two regions, the\u0026nbsp;western region is an underdeveloped region in China. Since 2000, the\u0026nbsp;Chinese\u0026nbsp;government has\u0026nbsp;implemented\u0026nbsp;a series of policies to support the development of the central and western regions, which has led to a great increase in investment in higher education in\u0026nbsp;these\u0026nbsp;two regions.\u003c/p\u003e\n\u003cp\u003eFigure 4 shows that the number of teachers with senior titles also has spatial heterogeneity, and the eastern region has more teachers with senior titles than the central and western regions. Statistics show that from 2006 to 2020, the percentage of teachers with senior titles increased by 113.19% in western China, by 91.29% in eastern China and by 78.15% in central China. This is mainly due to the small number of teachers with senior titles in the western region in 2006 and the support policies implemented by the government, which led to a faster growth rate in the western region than in the eastern and central regions from 2006 to 2020. For the eastern region, due to its high level of economic development, there are competitive supporting policies for talent, such as high wages, children\u0026apos;s schooling, housing, and medical care. Although universities in the western region have adopted policies to attract talent, they have suffered from brain drain and poor ability to attract talent because of the limited strength of policy implementation and locational disadvantages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePanel regression results\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel selection and validation for 31 provinces\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe test results in\u0026nbsp;Table 2\u0026nbsp;show that the p values of the F test and LM test are 0.0000 (\u0026lt;0.05), individual effects are considered to exist, and the original hypothesis of mixed effects is rejected. According to the Hausman test, the null hypothesis of random effects is rejected, and a fixed effects model should be selected.\u003c/p\u003e\n\u003cp\u003eTable 2 Model selection for 31 provinces\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1462%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5059%;\"\u003e\n \u003cp\u003eNull hypothesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.996%;\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.834%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5178%;\"\u003e\n \u003cp\u003eTest results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1462%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5059%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.996%;\"\u003e\n \u003cp\u003e63.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.834%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5178%;\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1462%;\"\u003e\n \u003cp\u003eLM test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5059%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.996%;\"\u003e\n \u003cp\u003e1676.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.834%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5178%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1462%;\"\u003e\n \u003cp\u003eHausman test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5059%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.996%;\"\u003e\n \u003cp\u003e37.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.834%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5178%;\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe establish a bidirectional error correction model and test the model. The test results, shown in\u0026nbsp;Table 3, indicate that the model has cross-sectional dependence, heteroscedasticity and autocorrelation, so a two-way fixed effects model is applied in this paper. In addition, we use the xtscc module in Stata to calculate Driscoll\u0026ndash;Kraay standard errors for the panels.\u003c/p\u003e\n\u003cp\u003eTable 3 Model verification\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5829%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0364%;\"\u003e\n \u003cp\u003eNull hypothesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0291%;\"\u003e\n \u003cp\u003eTest methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4754%;\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.0328%;\"\u003e\n \u003cp\u003eThreshold/\u003c/p\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8434%;\"\u003e\n \u003cp\u003eTest result\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5829%;\"\u003e\n \u003cp\u003eCross sectional dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0364%;\"\u003e\n \u003cp\u003eThere is no cross sectional dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0291%;\"\u003e\n \u003cp\u003eFrees-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4754%;\"\u003e\n \u003cp\u003e6.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.0328%;\"\u003e\n \u003cp\u003e0.2838\u003c/p\u003e\n \u003cp\u003e(Critical value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8434%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5829%;\"\u003e\n \u003cp\u003eHeteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0364%;\"\u003e\n \u003cp\u003eThere is no\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eheteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0291%;\"\u003e\n \u003cp\u003echi-square test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4754%;\"\u003e\n \u003cp\u003e6963.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.0328%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003cp\u003e(P value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8434%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5829%;\"\u003e\n \u003cp\u003eAuto-correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0364%;\"\u003e\n \u003cp\u003eThere is no\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAuto-correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0291%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4754%;\"\u003e\n \u003cp\u003e353.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.0328%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003cp\u003e(P value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8434%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel selection and validation for the eastern, central and western regions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conduct the F test, LM test and Hausman test for the eastern, central and western regions (Table 4). The test results suggest the selection of the same model for the three regions as for the 31 provinces, namely, a\u0026nbsp;two-way fixed effect model. As\u0026nbsp;shown in\u0026nbsp;Table 5, the models applied for these three regions have the same cross-sectional dependence, heteroscedasticity and autocorrelation as\u0026nbsp;that\u0026nbsp;used for\u0026nbsp;China\u0026nbsp;overall. Therefore, Driscoll\u0026ndash;Kraay\u0026nbsp;standard errors are adopted to establish the\u0026nbsp;bidirectional\u0026nbsp;fixed effect model.\u003c/p\u003e\n\u003cp\u003eTable 4 Model selection for eastern, central and western regions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eNull hypothesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.594%;\"\u003e\n \u003cp\u003e\u0026nbsp;Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4767%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eTest results\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eEastern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e35.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eFixed effects\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eLM test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e347.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eHausman test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e24.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eCentral region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e16.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eFixed effects\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eLM test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e93.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eHausman test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eWestern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e58.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eLM test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eMixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e619.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.6909%;\"\u003e\n \u003cp\u003eHausman test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3669%;\"\u003e\n \u003cp\u003eRandom effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.594%;\"\u003e\n \u003cp\u003e35.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4767%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1806%;\"\u003e\n \u003cp\u003eFixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5 Model selection tests for eastern and central regions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"456\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eTest methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eCritical/P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eTest results\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eEastern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eCross sectional dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eFrees test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.2262\u003c/p\u003e\n \u003cp\u003e(Critical value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eHeteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eChi-square test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e297.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003cp\u003e(Pvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eAuto-correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e199.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003cp\u003e(Pvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eMiddle region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eCross sectional dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eFrees test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.2262\u003c/p\u003e\n \u003cp\u003e(Critical value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eHeteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eChi-square test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e140.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003cp\u003e(Pvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eAuto-correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e42.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003cp\u003e(Pvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eWesten region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eCross sectional dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eFrees test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e2.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.2262\u003c/p\u003e\n \u003cp\u003e(Critical value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eHeteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eChi-square test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e306.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003cp\u003e(Pvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.9147%;\"\u003e\n \u003cp\u003eAuto-correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6302%;\"\u003e\n \u003cp\u003eF test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0044%;\"\u003e\n \u003cp\u003e224.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003cp\u003e(Pvalue)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9103%;\"\u003e\n \u003cp\u003eRefuse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe empirical results for 31 provinces and three regions based on the two-way fixed effect model are shown in\u0026nbsp;Table 6. The R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eis\u003csup\u003e\u0026nbsp;\u003c/sup\u003e0.9540, which indicates that the model has\u0026nbsp;satisfactory\u0026nbsp;fitting results. The\u0026nbsp;coefficients\u0026nbsp;of the two key variables are found to be significant in the model for China but inconsistent in the models\u0026nbsp;for\u0026nbsp;the three regions.\u003c/p\u003e\n\u003cp\u003eTable 6 Empirical results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7954%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4633%;\"\u003e\n \u003cp\u003eEastern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6216%;\"\u003e\n \u003cp\u003eCentral region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.112%;\"\u003e\n \u003cp\u003eWestern region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eEnrollment in University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e-0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e-0.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(0.2277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e-0.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0092)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eNumber of teachers with senior title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e1.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e0.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003cp\u003e(0.4131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003cp\u003e(0.0434)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eThe average salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e0.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e0.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e0.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003cp\u003e(0.5668)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eThe unemployment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e-21.26\u003c/p\u003e\n \u003cp\u003e(0.7053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e-37.68\u003c/p\u003e\n \u003cp\u003e(0.6135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e-121.30\u003c/p\u003e\n \u003cp\u003e(0.1367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e200.45\u003c/p\u003e\n \u003cp\u003e(0.1970)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eRatio of added value of the tertiary industry in GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e-128.10\u003c/p\u003e\n \u003cp\u003e(0.1208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e-741.81\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e-227.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e-662.52\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eThe public budget expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e1.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003cp\u003e(0.7623)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e4.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003cp\u003e(0.1001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.0077%;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.0077%;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7954%;\"\u003e\n \u003cp\u003e0.9540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4633%;\"\u003e\n \u003cp\u003e0.9658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6216%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.9895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.112%;\"\u003e\n \u003cp\u003e0.9633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Parenthetical values are standard errors. *** indicates significance at the 1% level, ** at the 5% level and * at the 10% level.\u003c/p\u003e\n\u003cp\u003eThe model used to estimate the results for China indicates that the number of university enrollments and the number of teachers with senior professional titles have opposite effects in economic terms, such that the former has a negative effect and the latter a positive effect. These results may be due to the following two reasons. First, with the increase in university enrollment, the level of education consumption increases, while other types of consumption are reduced; that is, there is a \u0026ldquo;crowding-out effect\u0026rdquo; of education consumption. Second, as the expansion of higher education takes place in China, a developing country, the shortage of education funds in many mediocre and lower-tier universities is addressed by increasing the number of enrolled individuals without ensuring a corresponding increase in expenditure on teaching resources and management. In addition, China\u0026apos;s promotion system regarding teachers\u0026apos; professional titles in the stage of mass higher education has changed over time. Since university teachers\u0026rsquo; scientific research level has become crucial to their evaluation, teachers often spend less time preparing for teaching and more time writing papers and applying for projects. Therefore, the overall academic level of graduates is relatively low, which is undoubtedly detrimental to the high-quality growth of China\u0026apos;s economy. Among the control variables, both wages and government budgets pass the significance test and have a positive effect on economic growth. Notably, government budgets matter more than wages. In recent years, China\u0026apos;s public budget has gradually increased, and these funds can be used to stabilize economic growth by expanding domestic demand and promoting industrial restructuring.\u003c/p\u003e\n\u003cp\u003eThe variables present different effects in the estimations for the three regions. University enrollment has a negative effect in the eastern and western regions, as it does at the national level, which indicates that the expansion of higher education in these two regions does not lead to economic growth. The possible reasons are consistent with the explanation regarding the country overall, namely, the \u0026ldquo;crowding-out effect\u0026rdquo; of education consumption, the lagging construction of supporting institutions accompanying education expansion, and the mechanism of title promotion. In contrast, the number of teachers with senior professional titles has a positive effect in the eastern region, while the effect in the central and western regions does not pass the significance test. The above estimation results may be attributed to two reasons. First, as China is a socialist country, its government regulates regional development strategies, which significantly impacts each region\u0026apos;s development mode and the establishment of universities. China\u0026apos;s economic development strategy is the fundamental reason for the differences in talent introduction among universities in the eastern, central and western regions. From 1979 to 1990, the Chinese government valued the efficiency of development and promoted a strategy of regional \u0026ldquo;unbalanced development,\u0026rdquo; that is, prioritizing the development of the eastern coastal areas. The plan aimed to make the eastern region an economic \u0026ldquo;growth pole\u0026rdquo; for China and drive the development of surrounding areas. The implementation of this policy attracted various production factors and investments to the eastern region. With the development of the economy, several high-level universities, such as Fudan University, Nanjing University, and Zhejiang University, developed rapidly. Although the Chinese government shifted its development policy to regional \u0026ldquo;balanced development\u0026rdquo; after 1991, economic development in the central and western regions was slow due to differences in economic foundation and location conditions, which had an impact on the number of colleges and universities established and their financial input. Of the 2,738 universities in China in 2020, nearly 40% were in the eastern region, and 61 of China\u0026apos;s top 100 universities were in the eastern region. Second, universities in China are stratified among regions. Studies estimating the quality of higher education in each province according to the university rankings released by China show that the eastern region has the highest quality universities, followed by the central region and finally the western region \u003cstrong\u003e(Li and Wei 2018)\u003c/strong\u003e. As discussed in the previous section, given China\u0026rsquo;s vast territory, different regions have different history and culture, geographical conditions, and economic development, which determines the input of education resources and influences the locational choices of talented individuals. In the past, provinces with traditional advantages, such as Henan, Hunan, and Anhui, had the greatest quantity and highest quality of higher education resources in China. Talents usually expect to go to high-level universities to obtain more development opportunities. In recent years,\u0026nbsp;talented individuals have been increasingly attracted to\u0026nbsp;the eastern region, especially the eastern coastal region, not only because of its distinguished history, culture, and geographical location but also because of its high level of economic development. In particular, the Pearl River Delta and Yangtze River Delta regions have\u0026nbsp;invested\u0026nbsp;more funds in introducing\u0026nbsp;talent\u0026nbsp;and perfecting supporting facilities. This attracts recent graduates, and many high-skilled workers have flowed from inland cities to coastal areas. Therefore, due to the\u0026nbsp;\u0026lsquo;talent war\u0026rsquo;\u0026nbsp;among universities, the pattern of large-scale talent flow to traditionally advantaged provinces persists.\u003c/p\u003e\n\u003cp\u003eFor the control variables, the industrial structure has a negative effect in the eastern, central and western regions. The industrial structure of the eastern region is similar to that of the central and western regions, and complementary advantages have not formed between regions. In addition, industrial development is still dominated by an extensive development model characterized by high input, high energy consumption, high pollution and low added value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRobustness of the results\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the robustness of the estimation results, the methods of changing estimation methods, substituting variables and shortening the considered period are applied. Specifically, for the method, we test the random effects model instead of the fixed effects model; for variables, we replace GDP per capita with GDP; and for the study period, we reduce the period from 15 years to 12 years. The test results are shown in\u0026nbsp;Table 7.\u003c/p\u003e\n\u003cp\u003eTable 7 Robustness test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 99px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMethod change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003eSubstitution of explained variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 163px;\"\u003e\n \u003cp\u003eShorten the year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eFixed effects model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eRandom effects model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003ePgdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eFifteen years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eTwelve years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eEnrollment in University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.03\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eNumber of teachers with senior title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.19\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eThe average salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.67\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.11\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eThe unemployment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-21.26\u003c/p\u003e\n \u003cp\u003e(0.7053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-32.57\u003c/p\u003e\n \u003cp\u003e(0.7483)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-21.26\u003c/p\u003e\n \u003cp\u003e(0.7053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e58.73\u003c/p\u003e\n \u003cp\u003e(0.1395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-21.26\u003c/p\u003e\n \u003cp\u003e(0.7053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e13.58\u003c/p\u003e\n \u003cp\u003e(0.7937)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eRatio of added value of the tertiary industry in GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-128.10\u003c/p\u003e\n \u003cp\u003e(0.1208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-10.93\u003c/p\u003e\n \u003cp\u003e(0.9609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-128.10\u003c/p\u003e\n \u003cp\u003e(0.1208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-367.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-128.10\u003c/p\u003e\n \u003cp\u003e(0.1208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-76.76\u003c/p\u003e\n \u003cp\u003e(0.3878)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eThe public budget expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e(0.2271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e4.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.10\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.0268)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eCross -section fixed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003ePeriod fixed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ewithin\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.9541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.9537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.9540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.9198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.9540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.9425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParenthetical values are standard errors. ***p \u0026lt; 1%. **p \u0026lt; 5%. *p \u0026lt; 10%.\u003c/p\u003e\n\u003cp\u003eThe results show that the key explanatory variables of the number of enrolled students and the number of teachers with senior titles do not change significantly with the use of a different method. For instance, the coefficient of enrollment is estimated to be -0.06 and passes the significance test in both the fixed effect model and the random effect model. Similarly, the coefficients of the number of teachers with senior titles in the two models are 1.10 and 1.19, respectively, and both pass the significance test. The coefficients of the two variables are also similar according to the other two robustness test methods. Moreover, the value and significance of the control variables do not change significantly with the change in estimation methods. Therefore, we believe that the selected model is robust and that the estimated results are reliable.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper analyses the spatiotemporal evolution characteristics of China's higher education and its contribution to economic growth from the perspectives of quantity and quality. It fills the research gap regarding the analysis of the temporal and spatial evolution of China's higher education in the quantitative and qualitative dimensions. Different from existing studies, it also accounts for regional heterogeneity in the performance of higher education. To address these limitations of prior research, this paper draws on the intersection of economic geography and educational economics to propose an empirical framework for comprehensive modeling. The model considers the quantity and quality of higher education as well as regional heterogeneity. The robustness tests of the panel data model show that the model estimation results are reasonable. The empirical results lead to three interesting observations in particular.\u003c/p\u003e\u003cp\u003eFirst, Chinese higher education is unbalanced in terms of quantity and quality, not only among the eastern, central and western regions but also within each region. This indicates that there is an imbalance of resources in and unequal access to higher education, which contradicts the characteristics of mass higher education. Education expansion occurs in a different context in China than in other countries, where it is usually accompanied by decreasing education costs and diversified selection criteria \u003cb\u003e(Wu and Zhang 2020)\u003c/b\u003e. In China, education is expanding in a context of social transformation from a planned economy to a socialist market economy. This transformation increases the accessibility but also the cost of higher education and makes the entry requirements more stringent \u003cb\u003e(Li 2010)\u003c/b\u003e. In addition, unlike in market-oriented countries, the expansion of higher education in China is centrally regulated and strictly controlled by the government; in other words, it is mainly guided by government policies. Relatedly, access to higher education opportunities has a certain relationship with individuals\u0026rsquo; place of household registration \u003cb\u003e(Qian and Walker, 2015).\u003c/b\u003e In this way, access to university education resources and admission opportunities is unequal between economically developed areas and less developed areas and between individuals with urban and rural registration in China.\u003c/p\u003e\u003cp\u003eSecond, the quantity and quality aspects of higher education have different impacts on economic performance. Specifically, the quantity of higher education has a certain inhibitory effect on economic growth, while the quality of higher education has the opposite effect. This suggests that the expansion of China's higher education has not caused corresponding economic growth, which is worthy of further exploration. Chinese higher education has expanded within a short period and at high speed, and the mechanism meditating expansion is mainly supply-driven rather than demand-driven \u003cb\u003e(Wu and Zhang 2020)\u003c/b\u003e. Because universities tend to imitate each other, different types of universities tend to comprehensively expand their internal institutions \u003cb\u003e(Tian 2016).\u003c/b\u003e Such expansion can greatly reduce the input‒output efficiency of educational resources, resulting in resource waste. In addition, due to the rapid expansion and supply-driven nature of higher education, the structure and quality of higher education have not been well coordinated with the scale of higher education, and the education structure does not match the market demand. This expansion pattern does not support the transformation of higher education outputs into positive economic impacts.\u003c/p\u003e\u003cp\u003eThird, higher education quality can be effectively translated into economic growth mainly at the national level and in the eastern region. The eastern region invests more funds in attracting talent and improving teachers\u0026rsquo; teaching skills than the less developed regions do thanks to its sound economic foundation and location advantages. Introducing talent is crucial for universities to improve their quality and serve society, mainly by cultivating high-level skills and promoting technological innovation. Studies have shown that higher education quality promotes economic growth mainly through increasing the employment rate and improving total factor productivity and human capital quality \u003cb\u003e(Hanushek and Woessmann 2016, Castell\u0026oacute;-Climent and Hidalgo-Cabrillana 2012)\u003c/b\u003e. From this perspective, scale expansion should not be blindly pursued, but rather the quality of higher education should be improved in the stage of mass higher education.\u003c/p\u003e\u003cp\u003eThe limitations of this study are mainly related to the indicators available to estimate the economic effect of higher education. The lack of data restricted our choices of variables for inclusion in the model. This limitation is particularly relevant for the measurement of higher education quality. In this paper, we consider this dimension only in terms of the number of teachers with senior titles; however, further research should incorporate educational output (e.g., graduate employment rate) into the model as a variable representing educational quality. In addition, similarly to many developing countries, China has shifted from a stage in which higher education is a privilege of the elite to a stage of mass higher education. Findings in this context cannot reflect the characteristics of the transformation of higher education under the system of developed economies. Further studies can examine the case of developed countries for comparative analysis.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePolicy implications\u003c/h2\u003e\u003cp\u003eSince 1999, Chinese higher education has expanded rapidly and had major impacts. The main purpose of the expansion of higher education is to produce more well-educated citizens and promote economic development \u003cb\u003e(Li and Min 2001)\u003c/b\u003e. Therefore, the issue of the economic effect of higher education has attracted increasing attention among researchers and educators. Studies have examined the relationship between higher education and economic growth. However, because studies address different areas, cover different time periods and use different variables, the research results vary. The findings of this study indicate that neither the quantitative nor the qualitative expansion of higher education has contributed to economic growth. In the three regions of China, the quantity and quality aspects of higher education have the same economic effects but to different degrees and at different levels of significance. The results offer some interesting policy implications.\u003c/p\u003e\u003cp\u003eFirst, in view of the shortage of investment in higher education and talent subsidies in the central and western regions, the government should implement policies to increase educational funds. To attract and retain talented individuals for work in universities, the government should provide preferential services, such as professional title evaluation, housing security, medical treatment, endowment insurance, medical insurance and children's enrollment. The government should supervise the implementation of these policies to ensure their effectiveness.\u003c/p\u003e\u003cp\u003eSecond, to improve the academic quality of graduates, there is an urgent need to change the evaluation mechanism for teachers\u0026rsquo; title promotion. The current evaluation system focuses mainly on the quantity of teachers\u0026rsquo; scientific research achievements and pays less attention to the quality of their teaching. Therefore, students' academic performance and evaluation of teaching effectiveness can be included in teachers\u0026rsquo; title assessments. For example, at the end of each semester, questionnaires can be used to gather information about students' evaluation of teachers in aspects such as teacher literacy, curriculum content design, curriculum achievement, and cultivation of students' innovation consciousness.\u003c/p\u003e\u003cp\u003eFinally, to make full use of educational resources, the structure of specialties and universities\u0026rsquo; enrollment plans should be market oriented. In other words, universities should conduct sufficient market research before setting up majors and determining the number of students. In addition, it is necessary to maintain a market orientation to allow the dynamic adjustment of majors that are not adapted to the current market context.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTingting Yu: Writing original draft, Conceptualization, Supervision . Yingzi Gui: Methodology, Software, Investigation, Methodology. Rong Ah: Visualization, Writing -Reviewing and Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author would like to thank the participants of the Young Economists Forum, and in particular the conveners of the forum, for their valuable and useful comments.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbel JR., Deitz R. Do colleges and universities increase their region\u0026rsquo;s human capital? Journal of Economic Geography, 2012; 12(3): 667-91. doi.org/1093.020/jeg/lbr.\u003c/li\u003e\n\u003cli\u003eAgasisti, T., Bertoletti, A.. 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Growth, distance to frontier and composition of human capital. \u003cem\u003eJournal of Economic Growth, \u003c/em\u003e2006; 11: 97\u0026ndash;127. doi:10.1007/sl0887-006-9002.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"higher education expansion, spatial pattern, growth performance, regional heterogeneity, China","lastPublishedDoi":"10.21203/rs.3.rs-7732642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7732642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChina has entered a new era of mass higher education, and the growth performance of higher education is uncertain. This paper aims to determine the economic performance associated with the quantity and quality of higher education resources in China and whether there is regional heterogeneity. An econometric model incorporating temporal and special aspects is applied to a unique data set involving thousands of universities from 31 provinces over 15 years. The findings show that the quantity of higher education institutions restrains economic growth, and the quality of higher education promotes growth. The growth performance of higher education in the eastern, central and western regions is in line with that in China as a whole, while the strength and significance of the variables\u0026rsquo; effects are different. Possible explanations and policy implications are given based on the data analysis.\u003c/p\u003e","manuscriptTitle":"The spatial pattern and growth performance of higher education in China: A longitudinal study in China from 2006 to 2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 14:21:55","doi":"10.21203/rs.3.rs-7732642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd408184-110e-4eae-921d-ad7eb46f8e78","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T23:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 14:21:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7732642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7732642","identity":"rs-7732642","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}