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Proximity still matters in research collaboration! Evidence from the introduction of new airline routes and high-speed railways in China

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Abstract

This paper examines the importance of spatial proximity in research collaborations, focusing on China’s transportation infrastructure advancements. Analyzing academic papers published by Chinese institutions over three decades, we find that collaboration probability is positively correlated with institutions’ research output and negatively correlated with geographic distance. When considering other dimensions of proximity, administrative proximity and social network proximity exhibit the most substantial influence. Employing staggered difference-in-differences methods, we then establish causal inferences by investigating the effects of direct flight routes and high-speed rail (HSR) connections. Findings show that both modes of transportation contribute to enhanced academic collaborations. Specifically, the introduction of flight routes leads to an increase in collaborations of at least 10.66%, while the establishment of HSR connections results in an increase by at least 32.65%. Flight routes are advantageous for facilitating collaborations in medium to long distances, while HSR primarily benefits medium to short-distance collaborations. Efficient public transportation connections, by reducing travel time, can significantly enhance collaboration across broader spatial areas within the knowledge production sector.

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Notes

  1. According to the “Statistical Analysis of Chinese Scientific Papers in 2020” released by the Ministry of Science and Technology of China, in 2020, China published 553 thousand SCI papers, accounting for 23.7% of the global total. This proportion was only 3.2% in the year 2000.

  2. The data was last updated at 2023-03-28.

  3. Different schools or departments within a same university are treated as a single institution. It’s worth noting that we identified the primary affiliation of each author for every article based on the authorship information provided in the papers. We allow for the possibility that the same author could have different affiliations at different time.

  4. The article’s DOI is: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.191803. It's challenging to determine whether there is research collaboration between all these authors. Including such articles in the analysis would introduce more noise than useful information, potentially interfering with the measurement of inter-institutional collaboration to some extent.

  5. The number of institutions is not more than the number of authors in a paper, e.g., 4 authors in 2 institutions.

  6. Cities in this article refers to (a) prefecture-level regions, and (b) county-level regions under direct provincial control.

  7. We have provided the information regarding city pairs with collaborations over the years in the appendix.

  8. The term “distance” mentioned in this paper, including the subsequent variable dist, refers to spherical distances calculated using the latitude and longitude of institutions.

  9. It’s calculated from 337 prefecture-level administrative regions, including 293 prefecture-level cities, 7 regions, 30 autonomous prefectures, 3 leagues, and 4 municipalities.

  10. For instance, Marek et al. (2017) measured cognitive proximity based on the similarity of regional industry structures, while Bergé (2017) used the similarity of research portfolios, essentially representing technological proximity. These measures do not necessarily capture true cognitive proximity in its purest sense.

  11. We determine whether two cities are in the same dialect region based on the information recorded in the Chinese Dialect Dictionary. In cases where a region has multiple dialects, we select the dialect with the highest coverage as the representative one.

  12. We define institutional pairs that have collaborated more than 5 times as having long-term collaborators.

  13. In a total of 8,796,915 bilateral pairs formed by 4195 institutions, academic collaboration occurred in only 4.04%.

  14. It’s essential to emphasize that the aggregation at the city-pair level is not a simple summation of institution-pair level data. This is because situations can arise where an article is collaboratively authored by two institutions \({i}_{1}\) and \({i}_{2}\) situated in city \({c}_{1}\), collaborating with a single institution j located in city \({c}_{2}\). Consequently, a direct aggregation could lead to duplications, wherein collaborations (\({i}_{1}\), j) and (\({i}_{2}\), j) would be redundantly counted as collaboration between cities \({c}_{1}\) and \({c}_{2}\).

  15. Other dimensions of proximity either become inapplicable due to aggregation (e.g., organizational and social network proximity) or have negligible impact (e.g., the variable SameDialect used to measure cultural proximity).

  16. In the Logit model, we present ORs rather than MEs mainly because MEs estimated by the Logit and Probit models are nearly identical. ORs can offer added insights as they emphasize the proportional alteration in collaboration odds resulting from a one-unit shift in the independent variable. In contrast, MEs predominantly center on the actual alterations in probability of collaboration.

  17. Results not reported due to space constraints.

  18. In the appendix, we provide estimations from the linear probability model (LPM), which incorporates institutional FE. This method significantly mitigates concerns associated with omitted variables.

  19. We have also provided in the appendix the tests plotted using 95% confidence intervals. The conclusion remains valid.

  20. If a city did not have an airport during the sample period, all city pairs associated with that city will be excluded. The same procedure applies to subsequent analyses.

  21. We have provided a detailed explanation in the appendix, discussing spatial distribution and its impact.

  22. The Poisson regressions and negative binomial regressions are estimated using the xtpoisson and xtnbreg commands in Stata software, respectively.

  23. It’s worth noting that at this point, the relative magnitudes of the effects of flights and HSR remain inconclusive, possibly due to the influence of the distribution of the dependent variable on these count models.

  24. The fundamental principle of this method is to match each observation in the treatment group with observations that either never received treatment or have not yet received treatment, creating a comprehensive dataset. Subsequently, these datasets are stacked together, and a linear regression analysis is performed, incorporating group-by-individual and group-by-time FE. The stacked event study estimations are implemented conducted using the “stackedev” command in Stata.

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Author notes

  1. Xin Ma

    Present address: School of Business, Nanjing Audit University, Nanjing, China

  2. Tao Huang

    Present address: Guanghua School of Management, Peking University, Beijing, China

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  1. Guanghua School of Management, Peking University, Beijing, China

    Xin Ma

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Ma, X., Huang, T. Proximity still matters in research collaboration! Evidence from the introduction of new airline routes and high-speed railways in China. Scientometrics (2024). https://doi.org/10.1007/s11192-023-04910-3

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