Does partisan conflict affect US innovation?

ABSTRACT

This article explores the influence of partisan conflict on innovation using panel data of 48 mainland US states over the period from 1992 to 2013. A novel indicator of the US partisan conflict, first proposed by Professor Marina Azzimonti, is employed. The nonlinear nexus is tested by accommodating cubic polynomial functional forms. Results show that the relationship between partisan conflict and innovation presents an inverted N-shape with two turning points. That is, the effect might change with partisan conflict. To further study the heterogeneous impact of partisan conflict across different economic development levels, this article divides these states into high- and low-income groups. Subsample analysis concludes that the relationship between partisan conflict and innovation is inverted N-shaped in both high- and low-income groups. A comparative analysis of the turning points between these two groups is conducted. The findings contribute to previous studies in identifying the role of partisan conflict in innovation.

KEYWORDS:

• Partisan conflict
• innovation
• panel data
• high- and low-income states

JEL CLASSIFICATION:

• C33 - Models with Panel Data
• G18 - Government Policy and Regulation

Acknowledgments

We are grateful to the anonymous referee for valuable comments and helpful suggestions. The authors are grateful for the financial support from the China Postdoctoral Science Foundation (No. 2020M670471), the Ministry of Education of Humanities and Social Science Project of China (No. 19YJC630206), and the Natural Science Foundation of Fujian Province under grant (No. 2019J01215). All remaining errors are ours.

Disclosure statement

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

Notes

¹ The statistical caliber of education level data changed in around 1992 (when the Bachelor’s degree data is added). And the PCI is up to 2013.

² We sincerely thank the anonymous referee for giving us much good advice on the model specification.

Additional information

Funding

This work was supported by - China Postdoctoral Science Foundation [2020M670471]; Natural Science Foundation of Fujian Province [2019J01215]; Ministry of Education of Humanities and Social Science Project of China [19YJC630206].