How long do we wait to innovate? understanding causal relationships between economic and innovation performance with temporal lags: evidence from a dynamic panel of 282 cities in China

ABSTRACT

This article examined the causal relationship between economic and innovation performance and the associated lag time within 282 prefecture-level cities in China. With the nighttime light (NTL) intensity representing economic status and 19 indicators comprehensively simulating the innovation performance index (CII), the Granger causality test was applied. The results showed that the temporal lag between economic status and innovation performance varies among cities. The innovation performance of 145 cities showed significant causal relationships with economic development in the ItoN test, and the average time lag was 2.01 years. The economic development of 137 cities also showed significance in innovation performance, with an average time lag of 3.15 years in the NtoI test. The growth rate of economic development and innovation performance has strongly impacted the temporal lag, especially in the ItoN causality test. A bidirectional causality between economic and innovation performance was found in 136 cities, but these cities complete the ‘economy-innovation-economy’ circle over a long period. Overall, this study concludes that the Granger causality test offers a useful approach to measure the time lag between economic and innovation performance, which can help better implement policies and expand research on economy and innovation.

KEYWORDS:

• Economic development
• innovation performance
• nighttime light
• Granger causality test

Acknowledgements

We highly appreciate the constructive comments and suggestions from the editors and reviewers that helped improve this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant number 42001126 and 42001135); Fujian Planning Office of Philosophy and Social Science (grant number FJ2020C035).

Notes on contributors

Xiaojun You

Xiaojun You is currently an assistant professor at Fujian Normal University, China. Her research interests include scientific and technological innovation and economic geography.

Kyle Monahan

Kyle Monahan is a Senior Data Science Specialist and Lecturer at Tufts University with interest in the application of data science and remote sensing methods to topics spanning environmental health, urban green space, pollution modelling and health impacts, innovation and the environment, and more recently enhancing resilience to extreme weather events.

Wenlong Yang

Wenlong Yang is an assistant research fellow at the Institute of World Economy, Shanghai Academy of Social Sciences with interest in geo-economic, international trade and investment.

Suqiong Wei

Suqiong Wei is a professor of the Institute of Geographical Research at Fujian Normal University with interest in economic geography.

Zuoqi Chen

Zuoqi Chen is currently an assistant research fellow at Fuzhou University, China. His research interests include urban remote sensing, nighttime light remote sensing, and the development of geographic information systems.