Does flattening the government hierarchy improve corporate innovation? Evidence from China

ABSTRACT

We examine the impact of flattening the government hierarchy on corporate innovation. Leveraging the change from province–city–county to province–managing–county (PMC) administrative structures in China, we use a different-in-differences research design to show that the PMC reform enhances corporate innovation. The results are robust to a battery of checks. We report that political connection, state-ownership of firms and improved government services mediate the effects of the PMC reform on corporate innovation. Moreover, in the cross-sectional analysis, we find that the impact of the PMC reform is more salient for non-state-owned firms or when a province has new leadership.

KEYWORDS:

• flattening government
• corporate innovation
• province–managing–county
• delayering

JEL:

• H77
• L26
• L88

ACKNOWLEDGEMENTS

We acknowledge the helpful comments from two anonymous reviewers and an anonymous associate editor.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. For instance, a government can build a science park (a form of infrastructure) to subsidize the occupancies of innovative firms in the park (a form of subsidy). At the end of the year, the government allows these firms to deduct R&D spending (a form of tax relief) to lower their taxable income.

2. A unique feature of the government structure in China is its Hukou system. Essentially, the system imposes hard restrictions on people’s movement. For instance, an individual cannot work, live or study in elementary and high schools in a location (e.g., a city) without the Hukou of the location. However, the Hukou system has been a soft system since the economic reform in the early 1980s. Due to a general labour shortage in the developed regions (e.g., large cities), individuals can work or live in a location without the Hukou. These individuals without Hukou do not get the full convenience as those with the Hukou, such as the right to buy a house. Nonetheless, individuals without the Hukou can fully move to work and live in other locations.

3. Our argument does not negate the possibility that reducing red tape improves the productivity and efficiency, and that external resources improve economic performance (such as more revenues and higher market shares).

4. We also examine a subsample of manufacturing firms in the sample by including R&D investment as an additional control variable for the robustness check. We justify this robustness check for two reasons. First, firms in the manufacturing industry make up approximately 71% of the sample. Second, manufacturing firms typically have the discretion to engage in R&D. If they do not report R&D investments, it is very unlikely that they do not have R&D activities. Specifically, we use three metrics for robustness: (1) per capita R&D investment (RDP), which is the R&D investment per employee; (2) per unit sale R&D investment (RDS), which is the ratio of R&D investment to sales; and (3) per unit asset R&D investment (RDA), which is the ratio of R&D investment to total assets. We present the findings in Appendix B in the supplemental data online. Except for column (2) of panels A and B, the coefficients of DIRECT in panels A–C of Appendix B are similar to those of Table 4. Hence, after including R&D investment as a control variable, the baseline results remain intact.

5. An alternative approach to conducting the PSM is by matching counties. We consider the firm-level PSM superior to county-level PSM for two reasons. First, we examine corporate innovation, not county-level aggregate innovation. Hence, it is better to match by firms because matching a treatment firm (located in a PMC county) with a control firm (located in a non-PMC county) always implies we are comparing a PMC county with a non-PMC county. Second, if we match counties, there is no guarantee that other firm characteristics are matched. There may be firm characteristics that affect a firm’s choice to locate in a PMC county and also affects innovation. Thus, if we apply a PSM county match, we cannot fully mitigate the endogeneity due to missing firm level variables.

6. We also use patent citations as an alternative metric for robustness. In addition, we use the patent approval rate (patents approved/patents applied) and inventive patent approval rate (patents approved/inventive patents applied) as alternative metrics (Rickard et al., 2016). The unreported results are consistent with the baseline results in Table 4.

 

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 71702194]; the Humanities and Social Sciences of Ministry of Education Planning Fund of China [grant number 17YJC630089]; and the Fundamental Research Funds for the Central Universities, South-Central Minzu University [grant number CSQ22006]. The usual caveats apply.