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The Chinese Inland-Coastal Inequality: The Role of Human Capital and the 2007–2008 Crisis Watershed


We investigate the role of human capital (HC) in the Chinese inland-coastal inequality and, related to this, how the consequences of the 2007–2008 crisis may induce China to re-focus its development path on HC. We compare panel data analyses for two periods (1998–2008 and 2009–2017) for two diverging groups of provinces (the richer/coastal and the relatively poor/inland areas). In the first period, the economic strengths that influenced the Chinese take-off and the dualism are confirmed. However, the results show that an evolution in local economic endowments is taking place: first, HC has a more evident economic effect after the crisis only in the inland provinces; second, the development path of the inland area is changing, with an evolution towards more productive sectors which can favor higher returns to HC.


Two forms of inequality affect the Chinese territory. The first form, the best known and studied, is the one between urban and rural areas (Lu and Chen 2006; Sicular et al. 2007): for instance, by 2017 in the rural areas the per capita disposable income was around 39% the one in urban areas (2017 data from National Bureau of Statistics of China). The second form of inequality, studied in this article, is between coastal and inland provinces (Yang 2002). In this case, we observe a per capita GDP (Gross Domestic Product) in the poorest provinces (Yunnan, Gansu) being less than 30% of the richest ones (Beijing, Shanghai) (2017 data from National Bureau of Statistics of China). These two types of inequality affect the national Gini index: by 2016, it was as high as 38.5 (World Bank data), positioning China above many Western countries and halfway between the BRICS countries.Footnote 1

With respect to these forms of inequality, there are at least two important qualifications. The first is that while urban–rural inequality appears to be quite stable, the coastal-inland one has risen strongly in recent years (Kanbur and Zhang 1999), thus deserving in-depth studies. The second aspect concerns the relationship between inequality and economic growth: this is indeed very controversial, with the literature being still inconclusiveFootnote 2 (see for instance the literature review in Chen 2010).

Excessive inequality, among people or between areas, could be a limit to development due, respectively, to observed and perceived disparities inducing behaviors unfavorable to economic growth (see Barro 2000) or, as in this study, to the delay in the long-term development process of a part of the country (e.g., Capello 2016).

For the second type of inequality, we refer to the different local economic strengths that shape the diverging development paths and thus imply the inland-coastal dualism in the Chinese case. In this sense, a “positive” stimulus to change could result from a wider exogenous “negative” event.

In particular, considering the rapid evolution of the Chinese economy and the changes forced by the international crisis, we wonder if these events can lead to rethinking local development paths, fostering greater attention on the diffusion of (advanced) education. We expect that the consequences on HC may in turn have consequences on the dualism: how did these two macro areas respond to the 2007–2008 crisis, how the role of HC could have changed according to different development paths?

The focus on HC is due to the fact that the growing differences between inland and coastal areas can be explained, at least in part, by the different local endowments. In this respect, HC may play a major role and should be thoroughly investigated for at least three important aspects. First, HC is a crucial determinant of regional economic performance, in the present as well as in the past (e.g. Felice 2012). Second, a large part of the within-country inequality in income is influenced by education (Acemoglu and Dell 2010). Third, the economic contribution of HC could be more important in the less-developed areas of a country (Gitto and Mancuso 2015; Felice 2018), which means that in the case of China it should be an important lever for the “poorest”.

In addition, we expect that the response of the Chinese economy to the widespread of advanced education could still be controversial in the analyzed period. On the one hand, the rapid Chinese economic growth would presuppose the contribution of HC as a key economic strength; on the other hand, the China’s development level may not be yet able to fully exploit capabilities of educated workers as an economic strength. In fact, the diffusion of primary and middle school education was necessary for the rapid development of the Chinese economy until the early 2000s, while, more in general, the local labor force does not yet have the characteristics of those of high-income economies, characterized by high productivity and continuous innovation (Li et al. 2017a).

The focus on the crisis period is influenced by the fact that China has not suffered from the serious consequences of the 2007–2008 events observed elsewhere, despite a sharp decline in exportsFootnote 3 (Wen and Wu 2019). Nonetheless, we may expect a change in postcrisis economic strengths: for example, a stronger focus on a resource capable of creating innovation and value in the long run, such as HC, as opposed to an export-led strategy based on low wages. This change could favor provinces not uniformly, following differences in past and current investments in education and training.

Two comparisons can help us to answer our questions, considering the 20-years period 1998–2017. First, a comparison of panel data analysis on the richer and on the poorer provinces (the first-level administrative division) allows us to identify the differences in local economic strengths between these two contexts, by observing their effects on both the variation and on the absolute values of provincial GDP. We expect that a different role of HC (e.g., a greater effect of educated workers in the more advanced provinces) should help explain disparities in economic performance, although its effects may not be observable in the short term (Chang and Shi 2016). Second, the analysis is applied to two periods, before and after the 2007–2008 crisis (1998–2008 and 2009–2017 years). This comparison serves to studying whether or not recessionary events have affected the role of HC—proxied through enrollment in advanced education – at the local level.

It should be stressed that, thus far, the results of the literature on the relationship between local economic endowments and regional inequality in China are controversial. For example, concerning the 1982–2005 years, Hao and Wei (2009) found that physical capital intensity and total factor productivity are relevant in regional income inequality, while a small role is played by HC. On the contrary, Wang and Yao (2003) found a relevant economic role and a huge potential (Wang and Yao 2003: for the 1978–1999 period) and an increasing role in fostering economic development (Zhang and Zhuang 2011; 1997–2006 period) for HC.

So far, however, in the studies about the evolution of the Chinese regional dualism two key aspects are still missing: the role at the local level of other conditioning factors, those characterizing the inland/coastal divide, and the consequences of the 2007–2009 international crisis.

In this sense, our article contributes to the literature in proposing an “extended” framework of the policies to improve advanced HC—which are, to our knowledge, those more useful for economic development (those with expected higher returns, Barro and Lee 2013). Our measure of HC includes—in addition to the well-established indicator of enrollment in higher education (e.g., Asteriou and Agiomirgianakis 2001)—also the level of enrollment in senior secondary education (i.e., the second level of secondary education, after the end of compulsory school, see Bush et al. 1998).

In studying the Chinese economy (e.g., Tuan et al. 2009), the secondary and tertiary enrollment levels have been aggregated and tested as HC proxies. Our new proxy, however, represents an extended level of advanced HC. It could have an important role for a fast-growing economy, which is not yet able to take full advantage of the so-called knowledge workers, as the Western countries are.

Finally, we take into account the endogeneity issue that stems from the reverse causality between HC and GDP, and we also test for the presence of spatial effects among provinces.

The paper is organized as follows. In Sect. 2, we present an outline of China’s modern economic development, taking into account the events that could influence the differences between the coastal areas and the hinterland ones, with a focus on the role of higher education (Sect. 2.1). In Sect. 3, we explain the models used in the analysis and their application to two groups of provinces. The variables, proposed by the economic literature, are presented in Sect. 4, followed by the results in Sect. 5. In the concluding Section, we put forward some policy implications.

Literature Survey: Some Aspects of the Chinese Coastal-Inland Inequality

The study of inequality among Chinese provinces is usually associated with the analysis of China’s path of economic growth: namely, with the social and economic reforms beginning in 1978, that have initiated a period of strong economic growth, followed by an increase in inequality (Fan et al. 2011). The dualism between the coast and the hinterland can be observed as a classic case of the North–South problem (Williamson 1965), due to the development of a leading sector (Pred 1965) that experiences self-sustained economic growth by attracting resources and investments from “peripheral” areas, a scheme well-known in the literature (Friedmann 1966). Broadly speaking, the different endowments and structural conditions across the provinces, in addition to the different policies adopted, have contributed to the persistence of inequalities in the long run (Ho and Li 2008). In the economic history of modern China, we observe two major phases in this sense (e.g. Herrerías et al. 2011).

The initial phase of inequality was caused by the rise of the heavy industry sector during the 1950s, which has led to a sharp decline in public interest for the traditional and less productive agricultural sector, typical of the inland areas. Inequality between provinces increased strongly in those years (Kanbur and Zhang 2005). Lack of agricultural resources (the “Great Famine” at the end of the 1950s, see Chang and Wen 1997) led to reforms in the 1970s in order to increase productivity in this sector: they allowed more freedom for farmers, whose incomes increased (resulting into a temporary decrease in inequality between provinces, Wroblowský and Yin 2016). Higher productivity reduced labor demand and, at the same time, higher incomes increased the aggregate demand (Fan et al. 2011).

As known, China has left a planned economy toward a market-oriented economy with the reforms started in 1978, which created the conditions for a period of strong economic growth. The political and economic focus on maximizing trade after the 1980s, and the subsequent adaptation to market laws during the 1990s have brought benefits to coastal provinces and caused an increase of inequality with respect to the internal areas (Fan et al. 2011). This happened, despite the implementation of policies aimed at boosting the inland provinces and exploiting the different local endowments (Chen 2010). Arguably, this increase is in line with the first part of the Kuznets curve observed in the long-run pattern of regional inequality by Williamson (1965) for the early phases of economic development, rather than with a new rise of regional inequality observed, instead, for more advanced economies (Amos 1988).

In more detail, major cities and coastal provinces exploited the positive effects of international trade, given a context of increasing globalization: they invested in productivity improvements, in various sectors, thus encouraging the location of firms and the spread of knowledge and spillovers. In the coast, not only income levels but also local endowments were diverging from the internal areas. According to Hao and Wei (2010, p. 204), “globalization, decentralization and marketization” were important in the interaction with the Chinese economic strengths. Liu and Li (2006) argued that differences in local endowments were referable, in turn, to the quality of HC, the allocation of physical capital, and the levels of available technology while Tsui (2007) focuses on the role of productivity differences at the local level; the persistence of inequalities can also be due to the fact that often the location of firms is related to the productive specializations already present in each local area (He et al. 2018).

The financial crisis that hit several Asian countries in the 1990s has prompted the central government to promote various forms of investments in the internal provinces, for several years (Yao 2009). However, obstacles to a new phase of convergence were posed by the central and local governments’ goal to maximize economic growth, with poor interest on resource redistribution in order to reduce inequality, as far as it could affect their main goal—although it is worth mentioning that, particularly in the Chinese case, there seems to be no negative effects of inequality reduction on economic growth, in the long run (Chen 2010).

The Role of Higher Education in China

In the broader context described above, the challenging role of HC in contributing to economic growth takes great relevance.

China has made considerable efforts to increase HC as a strategic economic resource. For example, Li et al. (2013)Footnote 4 estimate an average increase of 6.72% of national HC for the years 1985–2008 (with acceleration after 1994), and this is mainly due to education and urbanization processes, affecting in particular the less developed regions (Fraumeni et al. 2019). Investment in education, therefore, has a strategic function that goes beyond economic growth: provided that it is possible to reduce the unequal access to education, especially higher education (Heckman and Yi 2012), it is possible to reduce inequalities between areas (Fleisher et al. 2010).

The effect of HC has progressively increased; it has grown together with the decisive role of international trade, thanks to economic and political reforms. Since the 1950s, economic growth has remarkably influenced the development of policies related to education; however, it is from the 1970s onwards that HC has shown the first noteworthy signs of contribution to economic development (Tsen 2006). In particular, in the following decade Chinese big firms, more autonomous, have improved and exploited the rate of return to HC; however, the average productivity level in China was still significantly lower than that of advanced economies (Meng and Kidd 1997). The same trend continued during the 1990s, though positive effects of educated workers were beginning to be recorded (Heckman 2005). Education-related productivity was increasing (Wang and Yao 2003) although the benefits of higher education were still modest in those years—a period when changes and reforms were undergoing also in the education system, which remained under the control of the state (e.g., Yang 2000; Song and Wang 2005; Ngok 2008). It is precisely at the end of the 1990s that the Chinese government decided to increase the spread of higher education, with positive effects already in the early 2000s (Che and Zhang 2018). Since then, higher education in China has developed through increasing internationalization, openness to foreign universities and between national academic institutions (Yang 2010).

The relationship between advanced education and economic growth was among the main reasons beyond these reforms, which resulted into enrollments for millions of students which, however, did not diminish the risk of perpetrating inequalities (based on the different economic conditions and opportunities among students, Wan 2006). In particular, China has regarded postsecondary education as a key factor for future development and has committed considerable resources to increase the diffusion and improve the quality of it (Altbach 2009). Since the 2000s, the new economic and market conditions have confirmed the expectations. The spread of education has increased the productivity of knowledge workers (e.g., Fleisher et al. 2011), confirming the spread of higher education as a major source of economic growth across Chinese provinces, albeit with conflicting results regarding better living conditions, equality and sustainability (see Niu et al. 2010; Whalley and Zhao 2013; Lao and Xue 2016; Zhu et al. 2018). However, these changes pose new challenges to China, such as an organization of higher education different than in the past (e.g., by including private institutions, Wu and Zheng 2008), and capable to address new problems, such as unemployment for some types of workers (Wang and Liu 2011).


In order to assess the extent to which HC has an effect on economic growth, we focus on the analysis of HC following an approach derived from an augmented Solow model (Mankiw et al. 1992), and we estimate the following equation:

$$ \begin{aligned} GDP\_VAR_{it} & = \beta_{1} EDUCATION_{it - 1} + \beta_{2} lnGDP\_PC_{it - 1} + \beta_{3} TRADE_{it - 1} + \beta_{4} FIXED\_CAP_{it - 1} + \beta_{5} WAYS_{it - 1} \\ & \;\; + \beta_{6} GOVERN_{it - 1} + \beta_{7} SECTOR_{it - 1} + \mu_{i} + \tau_{t} + \varepsilon_{it} \\ \end{aligned} $$

where the dependent variable is the GDP growth rate, namely its annual variation at the provincial level (at the prices of the previous year, %); as a second step, we repeat the same analysis considering the value of GDP per capita (GDP_PC) as the dependent variable. EDUCATION is proxied through HC, and the other variables are the control ones used to test the robustness of the results—in more detail, the independent variables are described in Sect. 4 (Table 1). Furthermore, in the analysis of GDP variation, we consider GDP per capita as an independent variable. In the following analysis, when we consider GDP per capita as dependent variable, we include in the independent variables also one-lagged GDP per capita.

Table 1 Data description (

Data refer to the 31 Chinese provinces (i) for two periods (t) 1998–2008 and 2009–2017. \({\beta }_{1}, \dots , {\beta }_{7}\) are the parameters estimated in Sect. 5. The source for all variables is the National Bureau of Statistics of China (our elaborations). The regression also includes a full set of time dummies \({\tau }_{t}\) which represent time-specific factors that can affect the provinces growth dynamics (i.e. macroeconomic shocks) and provincial time-invariant characteristics \({\upmu }_{\mathrm{i}}\), while \({\upvarepsilon }_{\mathrm{i},\mathrm{t}}\) is the idiosyncratic error term.

In order to perform the panel data analysis, we consider a fixed effects (FE) model where differences among the provinces are considered parametric shifts in the regression equation: in our case, this leads to more reliable results given that all the provinces are included (Wooldridge 2012). FE allows for unobserved heterogeneity when there is a small population size and the entire population is included in the analysis (Johnston and Di Nardo 1997), limiting the problems of collinearity (Baltagi 2008).

Fixed effects are commonly used in regional studies. For example, FE is used by Fleisher et al. (2010) to investigate the role of HC, among other factors, on GDP in the Chinese provinces, and by Zheng et al. (2013) to study the determinants of regional allocation of infrastructure investments among the Chinese provinces.

However, we are aware of the fact that the problem of conceptual endogeneity of many variables with respect to GDP can affect these kind of models—discussions on the endogeneity problem and possible solutions are common in the relevant literature (Barro and Lee 1994; Durlauf 2009). In our case, endogeneity is due to the fact that the GDP growth rate influences the opportunity for education, and this relationship is arguably strong in China, as stated among the others by Heckman and Yi (2012, p. 7, referring to “the current practice of having tertiary education financed mainly by families”); on the other side, a strong relationship is known between education and income, insofar as a higher level of education should guarantee a higher income, identifiable by the so-called educational returns. For this reason, in order to avoid inconsistent estimates caused by the reverse causality between provincial economic growth and the level of HC, we adopt an instrumental variable approach—two-stage least squares (2SLS) procedure—aimed at obtaining an exogenous variation in the individuals with secondary and tertiary education level by means of the first and second lags of the same variables.

Yet, the instruments need to be justified in terms of relevance and orthogonality to the error terms in Eq. (1). First, the relevance of the instruments has been verified by implementing the under-identification and weak identification tests. The former is a Lagrange multiplier (LM) test of whether the equation is identified, i.e., that the excluded instruments are “relevant”, meaning correlated with the endogenous regressors. However, when the i.i.d. assumption is dropped, as in our case where the errors are clustered by province, the LM and Wald versions of the Kleibergen and Paap (2006) rk statistics are reported instead of the Anderson LM and Cragg-Donald Wald statistics which are no longer valid. The weak identification test, instead, allows us to verify if the excluded instruments are weakly correlated with the endogenous regressors. In particular, we rely on the framework provided by Stock and Yogo (2005) who have identified critical values with which the Kleibergen-Paap Wald rk (KP) F statistic has to be compared. Finally, since the model is over-identified, we use the Hansen’s J test in order to verify the validity of the instruments.

The analysis must be applied on homogeneous groups of provinces, in particular we must define a “richer group” (high socioeconomic development and income) and a group of relatively poorer provinces representing the “inland” context. For the grouping of the provinces, Herrerias and Ordoñez (2012) found the presence of five convergence clubs based on income; if instead the productivity level is considered, two clubs can be found; alternatively, if capital intensity is considered as discriminant five other clubs are observed. These groupings, however, do not seem suitable for the study of the dualism we are expected to carry out in this work, as we are going to explain. An alternative possible partition is the “three economic belts” scheme, proposed by Li and Wei, which according to them is based on the Seventh Five-Year Plan (1986–1990) and “commonly used to analyze regional inequality in China” (Li and Wei 2010, p. 305); in this partition, the eastern group coincide with the coastal area as represented in many studies, while the central and western groups coincide with the inland area. We consider this well-established division into two groups,Footnote 5 which has been used, among the others, by Kanbur and Zhang (2005)Footnote 6 in their inland-coastal inequality analysis.

This division showed in Fig. 1 allows us to compare the eastern provinces (the object of specific policies and favored in trade) with an area composed by the central ones (predominantly agricultural) and the western ones (less developed but with natural resources), with the aim of analyzing the different degree of economic development (Fan 1997; Li and Wei 2010).

Fig. 1

Source: Authors’ elaborations

Clusters of the Chinese provinces.

In order to illustrate economic inequalities and differences over time, in Fig. 2 we show the values of GDP per capita values for the Chinese provinces, for the first and the last year of our analysis.

Fig. 2

Source: Authors’ elaborations on National Bureau of Statistics of China data

GDP per capita in the Chinese provinces (yuan/person), selected years.

According to the National Bureau of Statistics of China data, in 2016 the East group (12 provinces), i.e. the richest provinces in our analysis, accounts for 45.1% of the Chinese population and 57.8% of the GDP; the inland group (19 provinces) represents 54.9% of the population and the 42.2% of the national GDP.


As discussed above, studies of regional inequality usually refer to GDP and its growth rate. The annual variation of the provincial GDP (expressed at the prices of the previous year, see GDP_VAR in Table 1) is a well-established measure of regional performance (see the literature review in Greco et al. 2018). In addition, we consider the GDP per capita (GDP_PC) both to include a sort of control dependent variable and to observe traces of convergence among the provinces (e.g., Chatterji and Dewhurst 1996).

The independent variables represent HC, but we also control for other factors affecting inland-coastal inequalities according to the literature on the Chinese case. The relevance of HC deserves insights in China because it involves very different categories of workers (e.g., rural and urban workers, see Li et al. 2017a) that often characterize distinct economic areas, and because HC (as an economic resource) is present –therefore contributing to local development—in a highly uneven manner among the Chinese provinces (Fraumeni et al. 2019). In general, education is an important economic strength at the local level (Gennaioli et al. 2013; Crespo Cuaresma et al. 2014) and can contribute to reduce disparities (Sylwester 2002), and these facts represent a justification for spending on higher education (De Gregorio and Lee 2002). Given the relationship between enrollment at various level of education and economic growth (Asteriou and Agiomirgianakis 2001), we consider the ratio between the sum of enrollment rates in senior secondary and higher education, on the one side, and the total population, on the other (EDUCATION). In our view, such a measure of education represents a commitment to favor advanced HC, although it contributes to the rise of territorial differences due to its uneven concentration (Chi 2008). On the other hand, as anticipated in Sect. 1, this resource could display greater positive effects where it is relatively more scarce.

We show the spatial distributions of our index EDUCATION and the differences between the two periods in Fig. 3. We observe that, while in the first period a more marked polarization is observed towards the coast, in the second period the concentration shifts more towards the central area (in addition to greater heterogeneity in general).

Fig. 3

Source: Authors’ elaborations on National Bureau of Statistics of China data

Spatial distribution of the HC indicator for the averages of the two periods, precrisis (a) and postcrisis (b) (darker colors correspond to higher values).

For the other variables, we may refer to Fan et al. (2011, p. 48), who state:

“The evolution of regional inequalities in China since the revolution has been influenced by the policy stances taken by the authorities […] – the share of heavy industry in gross value of total output (a measure of the bias against agriculture and China’s comparative advantage), the ratio of trade volume to total GDP and effective tariff rate (a measure of the degree of openness), and the ratio of local government expenditure to total government expenditure (a measure of fiscal decentralization). We argue below that there is a close association between these policies and regional disparities.”

In this statement are already introduced three control variables. Concerning the first one, the share of heavy industry on total GDP as a measure of the bias against agriculture and China’s comparative advantage, this indeed, in our days, would not yield a proper representation of the richest provinces, given the declining importance of heavy industries in advanced economies. For this reason, we recalculate this variable (SECTOR) as the (inverse of the) ratio between the value added of the primary sector and the value added of industry, thus considering the two sectors more important for the economic dualism (Zhao and Tang 2018a). We expect this variable to illustrate the relative weight, or “economic role”, between these two sectors that are still crucial in many provinces. Of course, a relative growth in the importance of the primary sector would be indicative of an economy still characterized by low added value.

For the second proxy variable, a measure of the degree of openness (TRADE), we have calculated the ratio of the sum of import and export on GDP, which is usually considered to be representative of trade openness and economic strength in international markets (see Frankel and Romer 1999). While representing one of the major Chinese economic strengths, trade, and the export in particular, contributes in a highly uneven way to the wealth of the Chinese provinces (Perkins 1997).

For the third variable, a measure of fiscal decentralization (GOVERN), we have calculated the ratio between “Local Governments General Budgetary Expenditure” and “Government Consumption Expenditure”, as a proxy of the strengths of two hierarchical levels of government. The latter variable refers to the fact that fiscal decentralization has forced local governments to cope with health, social and educational issues (Fan et al. 2011)—where also we observe strong inequalities (see Sect. 2).

Apart from these three control variables, we must consider that the infrastructure development is one of the main Chinese strengths (see the literature review and a case study on China by Bai and Qian 2010), which favors business activities and attracts foreign investment. In this framework, the presence of transport routes across China acquires great importance in economic studies (Banerjee et al. 2020). For example, the inadequacy of transport infrastructure has particularly affected the development of the western areas (He and Duchin 2009). Among the others, Fleisher et al. (2010) consider the ratio roads/area (km length per km2) as the key infrastructure in a study on HC and regional inequality in China. We consider a measure of the local transport infrastructures (WAYS), proxied by the sum of highways, railways, and waterways (calculated on the provincial area) to represent all local transports infrastructures.

Finally, we consider the well-established indicator “gross fixed capital formation” to GDP ratio (FIXED_CAP) (for the Chinese case see Ding and Knight 2011), as a measure of fixed asset investments (see the literature review by Zhang 2008).

In Table 1, we describe the variables with the definitions provided by the National Bureau of Statistics of China, source of the data. Provincial data cover the period 1998–2017 for the GDP growth rate and 1997–2016 for the regressors.

In Table 2, we present the descriptive statistics for the two groups of provinces and for China.

Table 2 Summary statistics

Table 2 suggests that despite a particularly higher average income in the coastal area, the average GDP growth rates by area are not very different, probably due to the postcrisis events that have hit the coastal provinces more exposed to the exogenous effects of the “imported” crisis. In the period considered, even the differences in enrollment are not very marked. On the contrary, the values of two important macroeconomic measure such as the GDP per capita and trade show evident differences, typical of a dualism.


The following Tables 3, 4 offer detailed 2SLS results and tests for the two macro areas also separating the years in which we expect a change due to the post-2008 economic events.

Table 3 2SLS results for the precrisis period (1998–2008) (GDP growth rate)
Table 4 2SLS results for the postcrisis period (2009–2017) (GDP growth rate)

In Table 3, the precrisis results suggest some similarities between the two areas. Both areas show a positive effect of (our proxy of) HC and fixed assets investments. The higher level of economic development of the coastal area makes it possible to exploit the more the spread of advanced education. Similarly, this area obtains a greater benefit from the increase in fixed investments, confirming investment in physical assets as a well-established factor linked to Chinese economic development (e.g., Ding and Knight 2011). For the inland area, our results are in line with Arayama and Miyoshi (2004), which have found that HC has been a decisive factor for the growth of the central/internal area during the past decades, together with the increased productivity associated with it. However, differences in the strength of these effects seem to be in favor of increasing economic dualism.

Concerning the level of (our proxy of) income, higher values (only for the coast) in the previous period tend to decrease the variations of provincial GDP. This could indicate a slowdown (at least partially, related to one area) in the process of growing economic gap.

Other variables showing divergent effects between areas suggest a growth of the dualism in the 1998–2008 period. The growth of trade has positive effects for the coast, since trade openness and export in particular represent an economic strength especially for the provinces most open to international trade (Perkins 1997). The effect is unexpectedly negative for the inland provinces. This difference could be explained by the strongest influence of the outlet to the sea, which might also help explain the non-relevance of the availability of other transport ways. In this respect, Wang et al. (2021) explain the current level of entrepreneurship in some areas of China precisely with the historic presence (for many decades) of ports. Another cause of the non-relevance can be related to the excessive investment in infrastructure, which may be less useful due to the poor management (Ansar et al. 2016).

The last two control variables also mark a divergent development path in the precrisis period. The greater possibilities of exploiting at a local level (see Zhang 2020) resources, such as taxation, seem to be useful in the less developed provinces. The increase in the weight of the primary sector (in proportion to the secondary sector) would, as expected, lead to a reduction in GDP variations in the wealthiest regions, while this does not happen in the inland area. In some provinces of the latter, the primary sector maintains a greater economic role, and it is still influenced by the strong improvements recorded in the 1990s linked to market incentives and greater autonomy for state-owned companies and for local governments (Bosworth and Collins 2008).

Table 4 shows that China has changed some fundamental factors of development after the crisis, and some of the new strengths could be useful to foster development paths of inland-coastal desirable convergence, especially by favoring the relatively poor provinces. In general, we observe a reduced but still positive contribution of HC in China after the crisis, but with territorial differences (for example, see Zhu et al. 2018 on central China). Other aspects of economic development have changed.

A primary feature is international trade, that has lost part of its strength in some years of the second periodFootnote 7 as observed in the result for both areas. In this framework, several development opportunities have changed for all the provinces. First, EDUCATION shows a stable economic role for the inland area, while becoming non relevant for the development of the coast. We must consider that the positive effect of increasing levels of education tends to run out where education has developed earlier or faster (see for instance Di Liberto 2008, on the Italian case), that is, in the coastal area. This could indicate the presence of a currently “equal or even more productive” resource in the relatively poorer provinces that could help counter the effects of the crisis, and encourage convergence in the long run. Secondly, the role of investments in fixed capital persists as an economic strength only for the coast, while it becomes non-significant for the other provinces, as well as for China as a whole. However, the effect of transport routes begins to play a distinctive positive role for the economic growth of the internal provinces, probably because, even though the outlet to the sea retains its primary importance, specific plans for the expansion of transport ways and infrastructures connected with the inland are one of the government’s objectives (e.g., Qin 2016). In addition, we propose in “Appendix” (Tables 8 and 9) the results with the variable WAYS divided into its three components (see the definition in Table 1). We observe that the positive contribution for the inland provinces derives precisely from the development of the waterways (even in the first period) and from the highways. The coast enjoys the positive effect of the waterways only in the first period, while the development of the other two transport networks becomes negative after the crisis. These effects could confirm the usefulness of such infrastructures in the less equipped provinces, even though they are already on the path to economic growth.

Other important changes in the economic structure are evident. The division of power between hierarchical levels of government reverses the sign (with a very low coefficient) becoming almost irrelevant also in the internal provinces, which therefore now conform to other provinces for what concerns the opportunity to exploit public resources. The inland area finally begins to suffer negatively from an excessive weight of the low productive primary sector,Footnote 8 also due to market forces which diminish the relevance of the agricultural sector, after the strong improvements during the 1990s (Zhao and Tang 2018b). Our finding gains in importance in light of the results of Arayama and Miyoshi (2004), who observed a substantial contribution of human capital in the western and central provinces in the years preceding ours. In this framework, they argued precisely the need to increase the role of education in central and western provinces with the aim of increasing the employment share of the secondary industry.

In order to strengthen our results, after analyzing the macroeconomic effects on GDP variations, we propose an analysis on GDP per capita in Tables 5 and 6.

Table 5 2SLS results for the precrisis period (1998–2008) (GDP_PC)
Table 6 2SLS results for the postcrisis period (2009–2017) (GDP_PC)

We observe that the value of GDP per capita is persistent over time, which means that its level of the previous period positively influences the current level. This effect is observed for all areas and is reduced in the postcrisis period. The effect of HC is similar to that recorded in Tables 3, 4 for the precrisis period, while it becomes more relevant in the second period, for all provinces. The observed effect is probably due to the growing role of productivity (via advanced education) in determining income (of which our dependent variable is a proxy).

About the control variables, a remarkable difference is observed in TRADE during the first period, as this variable positively influences the proxy of the Chinese average income in the inland area. Other control variables approximately confirm the differences between the two areas.

From the tests, we can observe that our estimates for the two areas are robust. First, the under-identification test indicates that the null hypothesis can be rejected, confirming that the excluded instruments are “relevant”, i.e. correlated with the endogenous regressor. Moreover, as the KP F-statistic values are higher than the critical values reported by Stock and Yogo (2005), we can argue that the instruments are strongly correlated with the endogenous regressors. The validity of moment conditions is confirmed by the Hansen’s J test as the null hypothesis cannot be rejected at 10% (i.e. the instruments are valid). In addition, in order to verify the cross-sectional dependence in fixed effects panel data models, we implement the Pesaran’s test (2004) which is a suitable statistic for balanced panels with small T and large N. Since the null hypothesis of cross-sectional independence cannot be rejected at 10%, we can argue that there is a substantial absence of spatial dependence in data. The tests are presented in the respective tables.

We have also investigated spatial dependence among Chinese provinces by estimating a spatial Durbin model (SDM) that includes both individual and time-period fixed effects (Table 7). The model is estimated through a Maximum Likelihood (ML; Yu et al. 2008) and a row-standardized weights matrix W is used to define geographical interactions.

Table 7 Spatial Durbin results for China (precrisis and postcrisis)

The main findings reported in Table 7 are substantially similar (both quantitatively and qualitatively) to the 2SLS results, confirming the validity and robustness of our estimates. Interesting and novel elements, instead, seem to emerge when we consider the advantages that stem from the integration among provinces; that is, the possibility of benefiting from the spatial proximity to tangible and intangible factors present in a neighbouring province in the form of growth spillovers, human capital externalities and infrastructure investment, as demonstrated by the positive and statistically significant coefficients associated to these variables.


Following the increased post-reform inequalities, one of the Chinese government’s objectives has been the «harmonious development» of all the provinces, with particular attention to the internal and rural areas (Fan et al. 2011). Our research suggests that, after the watershed of the international 2007–2008 crisis, important changes are taking place and could help reduce coastal-inland gaps. First, advanced education can actually be a factor of economic convergence between provinces, because after the crisis it shows positive effects where it is relatively more scarce, i.e. in the inland area. This opportunity means obtaining greater gains from education, where more possibilities of expanding this resource are present (see Zhu et al 2018 on central China). Second, we observe important changes in the economic characteristics of the less developed internal provinces: our findings suggest an evolution towards more productive economic sectors, as well as a greater ability to exploit both investments in fixed capital and the available infrastructures.

All these aspects related to a different possibility of exploiting HC—also in connection with the general economic development—lead us to assume a better reaction (i.e. resilience) to the crisis effects of the less developed provinces.

The comparison between the decade following the advent of the exogenous crisis and the previous decade illustrates how the weakening of some foundations of Chinese development (even if partial and temporary, e.g., exports) have damaged the most exposed provinces. On the contrary, the landlocked provinces have learned to exploit those resources that the other provinces already did (e.g., to focus on more productive economic sectors, transport infrastructure, HC), and even to do better after the economic shock.

The hinterland recovery can effectively be based on the development of the advanced and more productive HC, but policy makers must consider some limits to be addressed. We propose three goals for the proper development of HC.

First, our proxy of advanced HC confirms it as a key factor for regional growth in China (e.g. Fleisher et al. 2010), implying that obstacles to the access to advanced education as those of some rural areas (Heckman and Yi 2012), and for the offspring of underprivileged families (Qin et al. 2016), could hinder the inter-provincial and intra-provincial convergences.Footnote 9

Second, despite the efforts already underway, in the future expanding education must pursued at all levels in China (Mohrman 2008). All levels of education are needed for long-term development, and especially higher education. In fact, as Li and Wang (2018, p. 309) write on the Chinese provinces: “[…] different types of human capital affect economic growth through different channels: Basic human capital contributes to growth via the “factor-accumulation channel” and advanced human capital via the “productivity channel”, both individually and simultaneously”.

Third, better targeting and “exploitation” of advanced HC is also useful for obtaining a higher correlation between the higher investments in advanced education and the growth in labor productivity.Footnote 10 Public interventions must consider that spillovers of high skilled individuals (e.g., innovation) could take long periods to be effective, contrarily to those of individuals with lower education levels (see Chang and Shi 2016).

The reassessment of the role of advanced education and its appropriate exploitation can therefore be a strength for reducing the coastal-inland dualism after the crisis, reversing the previous results. This effect can also happen thanks to important industrial policy programs and to the broader structural reforms carried out in the first period (see Brondino 2019).

Finally, we propose a brief evolution of a scenario that Chinese policy makers might expect. A reduced importance of the agricultural sector could reduce urban–rural inequality and improve the socioeconomic condition in the internal area. The improvement in rural areas (mainly internal) should lead to the achievement of a better education among the resident population. The risk is that educated workers could choose to migrate towards more industrialized areas (Long et al. 2010) due to the high territorial polarization of innovation in the coastal provinces (Crescenzi and Rodríguez-Pose 2017), originating a so-called brain drain phenomenon. The outflow of educated workers (due to the best job opportunities in a more developed job market) could not only reduce the desirable contribution of HC development in the inland area, as highlighted in our analysis, but also influence the incentive in future enrollment levels (Suzuki and Suzuki 2016).

Against this scenario and its effects, it must be stressed, however, that the economic success of high skilled migrants can inspire the development of HC also in the places of origin, through for example the incentive to invest in education, the return migration, the creation of networks, with positive economic effects (Faggian et al. 2017).

This article presents an exploratory work; future research should consider different groups of provinces, such as the division into 3 areas (east-central-west) or the more specific division into 5 macro-regions, in order to better understand local socioeconomic characteristics and opportunities of resilience. A different grouping may also help explain the role of the HC proxy observed for the coastal provinces in the second period. Further research may also help overcome some limits to the present work, for instance by obtaining more data to test different proxies of HC, as well as by constructing a microeconomic framework that can better represent the education choices and chances.

Availability of data and material

All data used in this manuscript are freely accessible online.

Code availability

Not applicable.


  1. 1.

    For 2016, the values are: Brazil 53.3, Russian Federation 36.8, India 37.8 (2011), South Africa 63 (2014) (source: World Bank).

  2. 2.

    Even the explanations on the trend of provincial inequalities face with many theories in contrast (see Xiaobin 1996).

  3. 3.

    The export/GDP ratio, which was above 35% in the years before the 2007 crisis, has lost approximately 10% since 2009 (on World Bank data). This decrease contributed to the lowering of the annual GDP growth rate from 14.2% (2007) to less than 10% in subsequent years (International Monetary Fund data).

  4. 4.

    They consider the human capital stock as the discounted present value of all future incomes of individuals; its accumulation is due to formal education and on-the-job training.

  5. 5.

    The provinces in the coastal group are: Beijing, Fujian, Guangdong, Guangxi, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, Zhejiang. The provinces in the inland group are: Anhui, Chongqing, Gansu, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Inner Mongolia, Jiangxi, Jilin, Ningxia, Qinghai, Shaanxi, Shanxi, Sichuan, Tibet, Xinjiang, Yunnan.

  6. 6.

    The authors incorporate data of Hainan in Guangdong and Chongqing in Sichuan for some years.

  7. 7.

    According to IMF data, the annual percentage change in the China’s export volume was on average 20.70% in the decade before the crisis, and 6.38% in the following period until 2016.

  8. 8.

    However, appropriate aid to the agricultural sector can contribute to the growth of the industrial sector in landlocked countries (Pelloni et al. 2020): the same, arguably, can happen in the inland provinces.

  9. 9.

    Recent studies demonstrate that, in a context of education polarization, income inequality increasingly depends on the level of HC (Anderson et al. 2019). According to Heckman (2005), in order to favor convergence based on the role of education, some form of public support for families may be necessary, as well as fostering college loan schemes for poor families.

  10. 10.

    Effects of HC development could also be observed through the increase in the productivity of physical capital investments (Chang and Shi 2016). We must also consider that Whalley and Zhao (2013) found the possibility of misallocation or not full exploitation of growing human capital in Chinese growth strategies. In addition, China, with other neighboring countries, reached the peak of proportions of working-age population in 2010, with the need to find alternative ways to increase productivity in the country (Chansarn 2010).


  1. Acemoglu D, Dell M (2010) Productivity differences between and within countries. Am Econ J Macroecon 2(1):169–188

    Article  Google Scholar 

  2. Altbach PG (2009) One-third of the globe: the future of higher education in China and India. Prospects 39:11–31

    Article  Google Scholar 

  3. Amos OM (1988) Unbalanced regional growth and regional income inequality in the latter stages of development. Reg Sci Urban Econ 18(4):549–566

    Article  Google Scholar 

  4. Anderson G, Hao T, Pittau MG (2019) More unequal yet more alike, the changing patterns of family formation, generational mobility and household income inequality in China: a counter-factual analysis. J Econ Inequal 17:359–378

    Article  Google Scholar 

  5. Ansar A, Flyvbjerg B, Budzier A, Lunn D (2016) Does infrastructure investment lead to economic growth or economic fragility? Evidence from China. Oxf Rev Econ Policy 32(3):360–390

    Article  Google Scholar 

  6. Arayama Y, Miyoshi K (2004) Regional diversity and sources of economic growth in China. World Econ 27(10):1583–1607

    Article  Google Scholar 

  7. Asteriou D, Agiomirgianakis GM (2001) Human capital and economic growth: time series evidence from Greece. J Policy Model 23(5):481–489

    Article  Google Scholar 

  8. Bai C-E, Qian Y (2010) Infrastructure development in China: the cases of electricity, highways, and railways. J Comp Econ 38(1):34–51

    Article  Google Scholar 

  9. Baltagi B (2008) Econometric analysis of panel data. Wiley, Chichester

    Google Scholar 

  10. Banerjee A, Duflo E, Qian N (2020) On the road: access to transportation infrastructure and economic growth in China. J Dev Econ 145:102442

    Article  Google Scholar 

  11. Barro RJ (2000) Inequality and growth in a panel of countries. J Econ Growth 5(1):5–32

    Article  Google Scholar 

  12. Barro RJ, Lee J-W (1994) Sources of economic growth. Carn-Roch Conf Ser Public Policy 40:1–46

    Google Scholar 

  13. Barro RJ, Lee J-W (2013) A new dataset of educational attainment in the world, 1950–2010. J Dev Econ 104:184–198

    Article  Google Scholar 

  14. Bosworth B, Collins SM (2008) Accounting for growth: comparing China and India. J Econ Perspect 22(1):45–66

    Article  Google Scholar 

  15. Brondino G (2019) Productivity growth and structural change in China (1995–2009): a subsystems analysis. Struct Chang Econ Dyn 49:183–191

    Article  Google Scholar 

  16. Bush T, Coleman M, Xiaohong S (1998) Managing secondary schools in China. Compare A J Comp Int Educ 28(2):183–195

    Article  Google Scholar 

  17. Capello R (2016) What makes Southern Italy still lagging behind? A diachronic perspective of theories and approaches. Eur Plan Stud 24(4):668–686

    Article  Google Scholar 

  18. Chang X, Shi Y (2016) The econometric study on effects of Chinese economic growth of human capital. Procedia Comput Sci 91:1096–1105

    Article  Google Scholar 

  19. Chang GH, Wen GJ (1997) Communal dining and the Chinese famine of 1958–1961. Econ Dev Cult Change 46(1):1–34

    Article  Google Scholar 

  20. Chansarn S (2010) Labor productivity growth, education, health and technological progress: a cross-country analysis. Econ Anal Policy 40(2):249–261

    Article  Google Scholar 

  21. Chatterji M, Dewhurst JHL (1996) Convergence clubs and relative economic performance in Great Britain: 1977–1991. Reg Stud 30(1):31–39

    Article  Google Scholar 

  22. Che Y, Zhang L (2018) Human capital, technology adoption and firm performance: impacts of China’s higher education expansion in the late 1990s. Econ J 128(614):2282–2320

    Article  Google Scholar 

  23. Chen A (2010) Reducing China’s regional disparities: is there a growth cost? China Econ Rev 21(1):2–13

    Article  Google Scholar 

  24. Chi W (2008) The role of human capital in China’s economic development: review and new evidence. China Econ Rev 19(3):421–436

    Article  Google Scholar 

  25. Crescenzi R, Rodríguez-Pose A (2017) The geography of innovation in China and India. Int J Urban Reg Res 41(6):1010–1027

    Article  Google Scholar 

  26. Crespo Cuaresma J, Doppelhofer G, Feldkircher M (2014) The determinants of economic growth in European regions. Reg Stud 48(1):44–67

    Article  Google Scholar 

  27. De Gregorio J, Lee J-W (2002) Education and income inequality: new evidence from cross-country data. Rev Income Wealth 48(3):395–416

    Article  Google Scholar 

  28. Di Liberto A (2008) Education and Italian regional development. Econ Educ Rev 27(1):94–107

    Article  Google Scholar 

  29. Ding S, Knight J (2011) Why has China grown so fast? The role of physical and human capital formation. Oxf Bull Econ Stat 73(2):141–174

    Article  Google Scholar 

  30. Durlauf SN (2009) The rise and fall of cross-country growth regressions. Hist Polit Econ 41(1):315–333

    Article  Google Scholar 

  31. Faggian A, Rajbhandari I, Dotzel KR (2017) The interregional migration of human capital and its regional consequences: a review. Reg Stud 51(1):128–143

    Article  Google Scholar 

  32. Fan CC (1997) Uneven development and beyond: regional development theory in post-Mao China. Int J Urban Reg Res 21(4):620–639

    Article  Google Scholar 

  33. Fan S, Kanbur R, Zhang X (2011) China’s regional disparities: experience and policy. Rev Dev Financ 1(1):47–56

    Article  Google Scholar 

  34. Felice E (2012) Regional convergence in Italy (1891–2001): Testing human and social capital. Cliometrica 6(3):267–306

    Article  Google Scholar 

  35. Felice E (2018) The socio-institutional divide: explaining Italy’s long-term regional differences. J Interdiscip Hist 49(1):43–70

    Article  Google Scholar 

  36. Fleisher B, Li H, Zhao MQ (2010) Human capital, economic growth, and regional inequality in China. J Dev Econ 92(2):215–231

    Article  Google Scholar 

  37. Fleisher BM, Hu Y, Li H, Kim S (2011) Economic transition, higher education and worker productivity in China. J Dev Econ 94(1):86–94

    Article  Google Scholar 

  38. Frankel JA, Romer DH (1999) Does trade cause growth? Am Econ Rev 89(3):379–399

    Article  Google Scholar 

  39. Fraumeni BM, He J, Li H, Liu Q (2019) Regional distribution and dynamics of human capital in China 1985–2014. J Comp Econ 47(4):853–866

    Article  Google Scholar 

  40. Friedmann J (1966) Regional development policy: a case study of Venezuela. MIT Press, Cambridge

    Google Scholar 

  41. Gennaioli N, La Porta R, Lopez-de-Silanes F, Shleifer A (2013) Human capital and regional development. Quart J Econ 128(1):105–164

    Article  Google Scholar 

  42. Gitto S, Mancuso P (2015) The contribution of physical and human capital accumulation to Italian regional growth: a nonparametric perspective. J Prod Anal 43(1):1–12

    Article  Google Scholar 

  43. Greco S, Ishizaka A, Matarazzo B, Torrisi G (2018) Stochastic multi-attribute acceptability analysis (SMAA): an application to the ranking of Italian regions. Reg Stud 52(4):585–600

    Article  Google Scholar 

  44. Hao R, Wei Z (2009) Sources of income differences across Chinese provinces during the reform period: a development accounting exercise. Dev Econ 47(1):1–29

    Article  Google Scholar 

  45. Hao R, Wei Z (2010) Fundamental causes of inland–coastal income inequality in post-reform China. Ann Reg Sci 45(1):181–206

    Article  Google Scholar 

  46. He L, Duchin F (2009) Regional development in china: Interregional transportation infrastructure and regional comparative advantage. Econ Syst Res 21(1):3–22

    Article  Google Scholar 

  47. He C, Yan Y, Rigby D (2018) Regional industrial evolution in China. Pap Reg Sci 97(2):173–198

    Article  Google Scholar 

  48. Heckman JJ (2005) China’s human capital investment. China Econ Rev 16(1):50–70

    Article  Google Scholar 

  49. Heckman JJ, Yi J (2012) Human capital, economic growth, and inequality in China. In: NBER Working Paper No. 18100

  50. Herrerías MJ, Ordoñez J (2012) New evidence on the role of regional clusters and convergence in China (1952–2008). China Econ Rev 23(4):1120–1133

    Article  Google Scholar 

  51. Herrerías MJ, Orts V, Tortosa-Ausina E (2011) Weighted convergence and regional clusters across China. Pap Reg Sci 90(4):703–734

    Article  Google Scholar 

  52. Ho C-Y, Li D (2008) Rising regional inequality in China: policy regimes and structural changes. Pap Reg Sci 87(2):245–259

    Article  Google Scholar 

  53. Johnston J, Di Nardo J (1997) Econometric methods. McGraw-Hill, New York

    Google Scholar 

  54. Kanbur R, Zhang X (1999) Which regional inequality? The evolution of rural–urban and inland–coastal inequality in China from 1983 to 1995. J Comp Econ 27(4):686–701

    Article  Google Scholar 

  55. Kanbur R, Zhang X (2005) Fifty years of regional inequality in China: a journey through central planning, reform, and openness. Rev Dev Econ 9(1):87–106

    Article  Google Scholar 

  56. Kleibergen F, Paap R (2006) Generalized reduced rank tests using the singular value decomposition. J Econ 133(1):97–126

    Article  Google Scholar 

  57. Lao X, Xue L (2016) The spatial distribution of China’s higher education resources and its effect on regional economic growth. J Higher Educ 37(6):26–33

    Google Scholar 

  58. Li T, Wang Y (2018) Growth channels of human capital: a Chinese panel data study. China Econ Rev 51:309–322

    Article  Google Scholar 

  59. Li Y, Wei YHD (2010) The spatial-temporal hierarchy of regional inequality of China. Appl Geogr 30(3):303–316

    Article  Google Scholar 

  60. Li H, Liang Y, Fraumeni BM, Liu Z, Wang X (2013) Human capital in China, 1985–2008. Rev Income Wealth 59(2):212–234

    Article  Google Scholar 

  61. Li H, Loyalka P, Rozelle S, Wu B (2017a) Human capital and China’s future growth. J Econ Perspect 31(1):25–48

    Article  Google Scholar 

  62. Li Z, Wu M, Chen BR (2017b) Is road infrastructure investment in China excessive? Evidence from productivity of firms. Reg Sci Urban Econ 65:116–126

    Article  Google Scholar 

  63. Liu T, Li K-W (2006) Disparity in factor contributions between coastal and inner provinces in post-reform China. China Econ Rev 17(4):449–470

    Article  Google Scholar 

  64. Long H, Liu Y, Li X, Chen Y (2010) Building new countryside in China: a geographical perspective. Land Use Policy 27(2):457–470

    Article  Google Scholar 

  65. Lu M, Chen Z (2006) Urbanization, urban-biased policies, and urban-rural inequality in China, 1987–2001. Chin Econ 39(3):42–63

    Article  Google Scholar 

  66. Mankiw NG, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Quart J Econ 107(2):407–437

    Article  Google Scholar 

  67. Meng X, Kidd MP (1997) Labor market reform and the changing structure of wage determination in China’s state sector during the 1980s. J Comp Econ 25(3):403–421

    Article  Google Scholar 

  68. Mohrman K (2008) The emerging global model with Chinese characteristics. High Educ Pol 21:29–48

    Article  Google Scholar 

  69. Ngok K (2008) Massification, bureaucratization and questing for “world-class” status: higher education in China since the mid-1990s. Int J Educ Manag 22(6):547–564

    Article  Google Scholar 

  70. Niu D, Jiang D, Li F (2010) Higher education for sustainable development in China. Int J Sustain High Educ 11(2):153–162

    Article  Google Scholar 

  71. Pelloni A, Stengos D, Tedesco I (2020) Aid to agriculture, trade and structural change. Metroeconomica 71(2):345–368

    Article  Google Scholar 

  72. Perkins FC (1997) Export performance and enterprise reform in China’s coastal provinces. Econ Dev Cult Change 45(3):501–539

    Article  Google Scholar 

  73. Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. In: Cambridge Working Papers in Economics No. 0435, University of Cambridge

  74. Pred A (1965) Industrialization, initial advantage and American metropolitan growth. Geogr Rev 55(2):158–185

    Article  Google Scholar 

  75. Qin Y (2016) China’s transport infrastructure investment: past, present, and future. Asian Econ Policy Rev 11(2):199–217

    Article  Google Scholar 

  76. Qin X, Wang T, Zhuang CC (2016) Intergenerational transfer of human capital and its impact on income mobility: evidence from China. China Econ Rev 38:306–321

    Article  Google Scholar 

  77. Sicular T, Ximing Y, Gustafsson B, Li S (2007) The urban-rural income gap and inequality in China. Rev Income Wealth 53(1):93–126

    Article  Google Scholar 

  78. Song HM, Wang R (2005) The role of higher education in the growth rate of economy: contribution estimate and correlation analysis. Res Higher Educ Eng 1(1):55–58

    Google Scholar 

  79. Stock JH, Yogo M (2005) Testing for weak instruments in linear IV regression. In: Andrews DWK, Stock JH (eds) Identification and inference for econometric models: essays in honor of Thomas, Rothenberg. Cambridge University Press, Cambridge, pp 80–108

    Chapter  Google Scholar 

  80. Suzuki Y, Suzuki Y (2016) Interprovincial migration and human capital formation in China. Asian Econ J 30(2):171–195

    Article  Google Scholar 

  81. Sylwester K (2002) Can education expenditures reduce income inequality? Econ Educ Rev 21(1):43–52

    Article  Google Scholar 

  82. Tsen WH (2006) Granger causality tests among openness to international trade, human capital accumulation and economic growth in China: 1952–1999. Int Econ J 20(3):285–302

    Article  Google Scholar 

  83. Tsui KY (2007) Forces shaping China’s interprovincial inequality. Rev Income Wealth 53(1):60–92

    Article  Google Scholar 

  84. Tuan C, Ng LFY, Zhao B (2009) China’s post-economic reform growth: the role of FDI and productivity progress. J Asian Econ 20(3):280–293

    Article  Google Scholar 

  85. Wan Y (2006) Expansion of Chinese higher education since 1998: its causes and outcomes. Asia Pac Educ Rev 7:19–32

    Article  Google Scholar 

  86. Wang X, Liu J (2011) China’s higher education expansion and the task of economic revitalization. High Educ 62:213–229

    Article  Google Scholar 

  87. Wang Y, Yao Y (2003) Sources of China’s economic growth 1952–1999: incorporating human capital accumulation. China Econ Rev 14(1):32–52

    Article  Google Scholar 

  88. Wang Z, Yang H, Zhang X (2021) History matters: the effects of Chinese ports from 170 years ago on entrepreneurship today. Reg Stud 55(4):630–644

    Article  Google Scholar 

  89. Wang Y, Yao Y (1999) Sources of China’s economic growth, 1952–99: Incorporating human capital accumulation. In: The World Bank, Policy Research Working Papers, November 1999

  90. Wen Y, Wu J (2019) Withstanding the great recession like China. Manch Sch 87(2):138–182

    Article  Google Scholar 

  91. Whalley J, Zhao X (2013) The contribution of human capital to China’s economic growth. China Econ Policy Rev 2:1

    Article  Google Scholar 

  92. Williamson JG (1965) Regional inequality and the process of national development: a description of the patterns. Econ Dev Cult Change 13(4):1–84

    Article  Google Scholar 

  93. Wooldridge JM (2012) Introductory econometrics: a modern approach, 5th edn. Cengage Learning, Boston

    Google Scholar 

  94. Wroblowský T, Yin H (2016) Income inequalities in China: stylized facts vs. reality. Perspect Sci 7:59–64

    Article  Google Scholar 

  95. Wu B, Zheng Y (2008) Expansion of higher education in China: challenges and implications. In: Briefing Series—Issue 36, China Policy Institute – University of Nottingham, Nottingham

  96. Xiaobin SZ (1996) Spatial disparities and economic development in China, 1953–92: a comparative study. Dev Chang 27(1):131–164

    Article  Google Scholar 

  97. Yang R (2000) Tensions between the global and the local: a comparative illustration of the reorganisation of China’s higher education in the 1950s and 1990s. High Educ 39:319–337

    Article  Google Scholar 

  98. Yang DT (2002) What has caused regional inequality in China? China Econ Rev 13(4):331–334

    Article  Google Scholar 

  99. Yang R (2010) Soft power and higher education: an examination of China’s Confucius Institutes. Glob Soc Educ 8(2):235–245

    Google Scholar 

  100. Yao Y (2009) The political economy of government policies toward regional inequality in China. In: Huang Y, Magnoli Bocchi A (eds) Reshaping economic geography in East Asia. World Bank, Washington, DC, pp 218–240

    Google Scholar 

  101. Young A (2003) Gold into base metals: productivity growth in the People’s Republic of China during the Reform Period. J Polit Econ 111(6):1220–1261

    Article  Google Scholar 

  102. Zhang J (2008) Estimation of China’s provincial capital stock (1952–2004) with applications. J Chin Econ Business Stud 6(2):177–196

    Article  Google Scholar 

  103. Zhang W (2020) Political incentives and local government spending multiplier: evidence for Chinese provinces (1978–2016). Econ Model 87:59–71

    Article  Google Scholar 

  104. Zhang C, Zhuang L (2011) The composition of human capital and economic growth: evidence from China using dynamic panel data analysis. China Econ Rev 22(1):165–171

    Article  Google Scholar 

  105. Zhao J, Tang J (2018a) Industrial structure change and economic growth: a China-Russia comparison. China Econ Rev 47:219–233

    Article  Google Scholar 

  106. Zhao J, Tang J (2018b) Understanding agricultural growth in China: an international perspective. Struct Chang Econ Dyn 46:43–51

    Article  Google Scholar 

  107. Zheng X, Li F, Song S, Yu Y (2013) Central government’s infrastructure investment across Chinese regions: a dynamic spatial panel data approach. China Econ Rev 27:264–276

    Article  Google Scholar 

  108. Zhu TT, Peng HR, Zhang YJ (2018) The influence of higher education development on economic growth: evidence from Central China. High Educ Pol 31:139–157

    Article  Google Scholar 

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See Tables 8 and 9.

Table 8 2SLS results for the precrisis period (1998–2008) (GDP growth rate)
Table 9 2SLS results for the postcrisis period (2009–2017) (GDP growth rate)

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Felice, E., Odoardi, I. & D’Ingiullo, D. The Chinese Inland-Coastal Inequality: The Role of Human Capital and the 2007–2008 Crisis Watershed. Ital Econ J (2021).

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  • Human capital
  • China
  • Inland-coastal inequality
  • North–South problem

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  • O11
  • P25