To investigate the impact of HSR connection on technological diversity in China, city-level panel data from 2004 to 2017 is collected. The cities in this study include four provincial and 15 subprovincial cities, 16 provincial capitals, and 249 ordinary prefecture-level cities. The data includes three parts. First, a city-level statistical dataset, obtained from China City Statistical Yearbooks. Price-relevant indicators are transformed into constant 2000 pricing using a province-level price index. The data for the province-level price index are from China Statistical Yearbooks. Second, information regarding whether a city is connected to HSRs is from the Chinese Research Data Services Platform. Third, the invention patent applications dataset, including basic information for each patent, such as name, types, applicants, address, IPC code, application date, public announcement date, and other relevant details, which comes from the China National Intellectual Property Administration.

The dependent variable is technological diversity. Referencing previous research (Choi & Lee, 2021; Kim et al., 2016; Zabala-Iturriagagoitia et al., 2020), we measure it using the Shannon–Wiener diversity index, calculating technological diversity as follows:

The patent stock is calculated by applying the perpetual inventory method to the patent count of four-digit industries referencing Park and Park (2006). The patents of four-digit industries in each city and year are obtained by merging the four-digit industry codes and the invention patent applications dataset using the recently issued Table of Reference Relationship between International Patent Classification and National Economic Industry Classification (2018). A higher diversity index value indicates more even distribution of patents across four-digit industries, thus the higher the technological diversity.

One of the advantages of the Shannon–Wiener index as compared to other diversity indices, such as Simpson's index (Chiu et al., 2008; Garcia-Vega, 2006), is that it avoids the influence of scale effects. More specifically, the minimum of TDit is zero if all patents are concentrated in a specific industry, and the maximum of TDit is lnN when patents are evenly distributed across four-digit industries, where N is the number of four-digit industries. However, the Simpson's diversity index is measured by subtracting the Herfindahl–Hirschman Index (HHI) from 1, where HHI will continuously decrease with the increase in the number of industries.

Fig. 2 presents the spatial distribution of technological diversity in China. Note that the number of red blocks increases over time. In addition, yellow blocks are always located in western China from 2004 to 2017, but red blocks are primarily located in eastern China. A lot of blue blocks in central China convert into red blocks. These results indicate that the technological diversity between four-digit industries is roughly increasing over time and cities with relatively high technological diversity are along HSR lines. However, whether HSR connection leads to a rise or decline in technological diversity remains unclear, because it requires further comparison of the trends between the HSR group and the non-HSR group.

Fig. 2.

The dynamics of technological diversity in China

Note: (a) is the map of technological diversity in 2004; (b) is the map of technological diversity in 2008; (c) is the map of technological diversity in 2012; (d) is the map of technological diversity in 2017, and the map of HSR lines in 2016 is from Li (2016).

(1.13MB).

The independent variable of interest is HSR connection. Referencing Gao and Zheng (2020), Chen (2021), and Hanley et al. (2022), HSR equals 1 if a city has an HSR station and 0 otherwise, which is described as follows:

Fig. 3 presents the changes of technological diversity based on HSRs. The technological diversity (TDit) in HSR cities is higher than that in non-HSR cities, but the upward trend in non-HSR cities is more rapid than that in HSR cities.

Fig. 3.

The changes of technological diversity.

(0.1MB).

Finally, some city-level characteristics that may affect technological diversity and HSRs are considered. Referencing Gao et al. (2020), Wang and Cai (2020), Kuang et al. (2021), and Chen (2021), the control variables include (1) population size, which is measured by the residential population; (2) population density, which is measured by the residential population per square kilometer; (3) financial development, which is measured by the ratio of bank credit to GDP; (4) foreign investment, which is measured by the ratio of foreign direct investment to GDP; (5) public expenditure, which is measured by the ratio of government expenditure to GDP; (6) industry structure, which is measured by the share of tertiary industry value-added; (7) road infrastructure, which is measured by road length; and (8) industry scale, which is measured by the employment of manufacturing and the value-added of secondary industry. To avoid contemporaneous correlation and heteroskedasticity problems, a one-year lag for all control variables is taken, with logarithm form is taken for some variables.

The descriptive statistics of all variables are reported in Table 1.

The DID method is a widely used tool for estimating the treatment effect of HSR connection (e.g. Chen, 2021; Gao et al., 2020; Gao & Zheng, 2020; Hanley et al., 2022; Hiroyasu et al., 2017; Wang & Cai, 2020). As with previous research, we take HSR connection as a quasi-natural experiment. The treated group is cities with at least one HSR station, and the control group is cities that have no HSR station from 2004 to 2017. The econometric model is as follows:

A parallel trends assumption is a necessary precondition for causal inference using the DID method. Referencing Beck et al. (2010), Gao et al. (2020), and Chen (2021), we test whether there are common pretrends between the HSR and non-HSR groups as follows:

To address the endogeneity issues caused by the nonrandom selection of HSR stations, we employ IV regressions to estimate the causal effect of HSR connection on technological diversity. The method includes two-stage regressions, which are as follows:

Referencing previous research (Banerjee et al., 2020; Chen, 2021; Gao et al., 2020; Hornung, 2015), the straight-line strategy is used to construct the IV for HSR connection. In 2004, the HSR blueprint consisting of Four North–South Lines and Four East–West Lines was planned, in which 30 cities were selected as HSR nodes, which is the foundation for constructing the IVs for HSR connection. While the planned HSR blueprint is correlated with the actual HSR lines, it does not directly affect present outcomes; thus, a potential HSR connection (PHSR) could be constructed by the planned HSR blueprint, taking 1 if a city is passed through by the planned HSR blueprint and 0 otherwise. The IV for HSR connection is as illustrated by Fig. 4.

Fig. 4.

The IV for HSR connection.

(0.63MB).

To capture the time-variation of HSR connection, we construct a pseudo opening time using the actual opening time of the nearest planned route, where 1 is taken if both node cities of the nearest planned route have HSR stations and 0 otherwise. For example, the actual opening time of Changsha and Guiyang was in 2009 and 2014, respectively. The straight-line of Changsha–Guiyang passes through Loudi and Huaihua; thus, the pseudo opening time of the two cities is 2014.

Table 2 presents the baseline regression results. Column (1) is the regression result excluding the control variables in Eq. (4), revealing that the treatment effect of HSR connection on technological diversity is −0.118. Columns (2)–(4) present the regression results when adding the control variables stepwise. The treatment effect of HSR connection declines as more control variables are added into the model, but the coefficients of HSR connection remain statistically significant at the 1% level. As shown in column (4), the coefficient of HSR connection declines when further controlling for the share of tertiary industry value-added, road length, the number of manufacturing employees, and the value-added of secondary industry. The adjusted treatment effect of HSR connection on technological diversity is −0.063, indicating that HSR connections lead to a reduction in technological diversity in HSR cities.

The regression coefficients from Eq. (5) are presented in Fig. 5, revealing that the pretrends are not statistically significant at the 5% level. The results indicate that there are no systematic differences in technological diversity between HSR cities and non-HSR cities before connecting to HSRs. Meanwhile, technological diversity decreases annually after connecting to HSRs. The downward trend in coefficients means that the negative effect of HSR connections on technological diversity strengthens over time.

Fig. 5.

The changes of technological diversity before and after connecting to HSRs.

(0.11MB).

Overall, the baseline regression results indicate that HSR connection has a significant role in explaining the changes in technological diversity between HSR cities and non-HSR cities. Our results confirm the technological specialization effect of HSR connection. As argued by Caviggioli et al. (2020), specialty matching of highly skilled migrants is an important mechanism in explaining why migration leads to technological specialization. Accordingly, a possible explanation for our findings may be that HSR connection accelerates intra-industry knowledge spillovers, such as highly skilled teamwork, talent migration, and collaborative innovation.

To comprehensively validate the path in Fig. 1, we also estimate the impacts of HSR connection on innovation and knowledge spillovers. The results are presented in Table 3. Columns (1) and (2) are the regression results of changes in granted invention patents per capita and invention patent applications per capita based on HSR connection. Connecting to HSRs leads to a 0.230 increase in granted patents and 1.021 patent applications per 10,000 persons. Columns (3) and (4) report the result of changes in industrial agglomeration and changes in co-authored patent applications per capita based on HSR connection. The HHI of two-digit industry employment significantly increased by 0.007 and the number of co-authored patent applications per 10,000 persons significantly increased by 0.444. The results of Table 3 validate that HSR connection has positive effects on innovation and knowledge spillovers.

The coefficient of HSR connection may be biased due to outlier samples and measurement errors. Table 4 reports various robustness check results. First, we exclude four provincial cities from the samples. As shown in column (1), the treatment effect of HSR connection on technological diversity is −0.059, which is very close to the coefficient in the baseline regressions. Second, we employ a market potential approach to estimate the impact of HSR connection on technological diversity. As shown in column (2), the effect of increased market potential caused by HSR connection on technological diversity is also statistically significant. Third, we measure the concentration of patent stock across four-digit industries by HHI. As shown in column (3), the treatment effect of HSR connection on technological concentration is statistically positive. Finally, we re-calculate the technological diversity by Shannon–Wiener diversity index using two-digit industry patent data, and column (4) shows that HSR connection significantly reduces technological diversity by −0.031. These robustness check results support the basic findings of baseline regressions.

Table 5 reports the IV regression results. The first-stage results in columns (1) and (3) show that our IVare highly correlated with HSR connection. The Kleibergen–Paap rk Wald F statistics for weak identification are greater than 10. The null hypothesis of weak IV is rejected. We also test the correlation between PHSR and technological diversity, and the coefficients of changes in technological diversity on PHSR in the preintervention period are statistically insignificant, indicating that the exclusion restriction is not violated. The results are not reported here, but available upon request.

Column (2) is the result when all cities participate in the regression, showing that the coefficient of HSR connection is greater than that in baseline regressions, but HSR connections overall have a statistically significant and negative effect on technological diversity. Column (4) is the results excluding the 30 node cities. The coefficient of HSR connection becomes smaller, but remains statistically significant and negative. Overall, the IV regression results indicate that connecting to HSRs has a causal effect on technological specialization for HSR cities. The IV regression results support the findings of our baseline regressions as well.

Does the impact of HSR connection on technological diversity differ between core and peripheral cities? To answer this question, we group cities by population size, administrative hierarchies, and geographical locations, presenting the results in Table 6.

Columns (1) and (2) show that connecting to HSRs exerts a greater reduction effect on technological diversity in large cities than small and medium-sized cities. The coefficient of HSR connection in large cities is −0.220 and statistically significant at the 1% level, but it is not significant in small and medium-sized cities.

Columns (3) and (4) present the regression results grouped by administrative hierarchies. In cities with high administrative hierarchies, which include provincial cities, subprovincial cities, and provincial capitals, the coefficient of HSR connection is −0.325 and statistically significant at the 1% level; however, the coefficient of HSR connection in ordinary prefecture-level cities is only −0.035, far less than the former.

Columns (5) and (6) are the regression results grouped by geographical location. The results show that HSR connection leads to a reduction in technological diversity by −0.121 in cities located in eastern China, but it only leads to a reduction by −0.050 in cities located in central and western China.

The results in Table 6 indicate that the technological specialization effect of HSR connection is asymmetric between core and peripheral cities. The asymmetric coefficients indicate that connecting to HSRs has greater backwash effect than spread effect. Specifically, HSR connection is conducive to intra-industry knowledge spillovers between core and peripheral cities, but the benefits core cities receive are greater. The possible explanations for this finding are twofold. First, the knowledge use capability of core cities is higher than that of peripheral cities, which results in core cities benefiting more, even if the mutual knowledge spillovers are symmetric. Second, HSR connection accelerates the movement of innovation resources (i.e., knowledge, skilled talent, and venture capital) from peripheral cities to core cities.

Does connecting to HSRs lead to competing clusters in the cities along HSR lines? To answer this question, we focus on the five longest HSR lines in China, including the Beijing–Hong Kong line, the Lanzhou–Xinjiang line, the Shanghai–Kunming line, the Beijing–Shanghai line, and the Harbin–Dalian line. Column (1) in Table 7 demonstrates that the technological specialization effect of HSR connection in the five longest lines is greater than that in other HSR cities. Specifically, as shown in columns (2)–(5), connecting to HSRs reduces the technological diversity in the cities along HSR lines, and the effect size is greater than the average (−0.063) in the four longest HSR lines. The Lanzhou–Xinjiang line, which is the longest HSR line located in western China, is an exception. The result is presented in column (6), in which the coefficient of HSR connection is statistically insignificant. Thus, the answer to the question is that HSR connection primarily strengthens technological specialization in the cities along the Beijing–Hong Kong line, the Shanghai–Kunming line, the Beijing–Shanghai line, and the Harbin–Dalian line.

Does HSR connection enhance the initial advantages of a city that is more technologically diverse? To answer this question, we test whether cities with higher or lower technological diversity in the initial year (2004) tend to be more technologically specialized after connecting to HSRs. As shown in columns (1) and (2) of Table 8, the coefficient of HSR connection in cities with low initial technological diversity is 0.036 and statistically insignificant, but is −0.189 and statistically significant in cities with high initial technological diversity. An interaction term is introduced in column (3), revealing that the coefficient of interaction of HSR connection with initial technological diversity is statistically significant and negative. The results of Table 8 indicate that HSR connection leads to more technological specialization in cities with initially high technological diversity, rather than initially specialized cities.