City Reputation and Stock Price Crash Risk: Evidence from China’s National Civilized City Award

ABSTRACT

As a top honor for the overall civilization of cities in mainland China, the National Civilized City (NCC) award reflects good city reputation of the award-winners. This study investigates whether and how NCC awards affect stock price crash risk. Using a staggered difference-in-differences model, we find that the NCC award significantly reduces local firms’ future crash risk. This effect is more pronounced among firms with a better corporate reputation, inferior internal governance, and weaker external monitoring. Mechanism tests show that the NCC award reduces crash risk by mitigating management opportunism and alleviating information opacity.

KEYWORDS:

• National civilized city
• city reputation
• stock price crash risk

JEL:

• G12
• G30
• R50
• Z20

Disclosure Statement

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

Notes

1. The China Central Civilization Commission is the core department of the CPC Central Committee that guides the civilization process of the entire nation. The department is responsible for the implementation of the NCC award campaign, including the formulation of the NCC Evaluation System and the evaluation and recognition of the NCC award. The evaluation and recognition of the NCC award are held every two or four years. The NCC Evaluation System covers eight major projects namely a clean and efficient government environment, a democratic and fair legal environment, a fair and honest market environment, a healthy and upward humanistic environment, a social/cultural environment conducive to the healthy growth of teenagers, a comfortable and convenient living environment, a safe and stable social environment, and a sustainable ecological environment. The China Central Civilization Commission evaluates the performance of NCC award candidate cities in the projects mentioned above by listening to reports, reviewing materials, questionnaire surveys, online surveys, on-site investigations, and overall observations.

2. Guiso, Sapienza, and Zingales (2004) and Jiang et al. (2020) highlight the importance of reputation at the firm level. Distinct from their studies, we extend the managerial implication of reputation to the city level and find that city reputation can also act as a social norm to reduce stock price crash risk.

3. Note that our samples are matched at the prefecture city level. As shown in Appendix A, although a considerable number of NCCs are county-level cities, most of them participated in the competition for the NCC award after their prefecture level cities had won the NCC title. Moreover, only 167 firm-year observations among 24 unique firms are from the county-level cities, who won the NCC awards earlier than their prefecture-level cities. We drop these observations to ensure robust results.

4. For example, for a given city that won the NCC award in 2005, we set its NCC_t to 0 over the period between 2001 and 2004, and 1 over the period 2005 through 2020 in our sample period. Meanwhile, if a city never won the NCC award during our sample period, we set its NCC_t to a constant value equal to 0.

5. Consistent with previous research that examines regional level effects (Ji et al. 2021; Li, Wang, and Wang 2017), we do not control firm fixed effects in our baseline analysis. This is because the NCC award is quite persistent over time. Since most firms do not change their headquarters location during our sample period, we examine variations between firms located in different cities and calculate the standard errors clustered at the city level.

6. Given the lagged term of the dependent variable included in control variables, we also follow an anonymous reviewer’s suggestion to employ the GMM model to conduct robustness tests. We control for firm, year, and city fixed effects in the GMM model and find robust results consistent with our main conclusion. To save space, we do not report this set of tests.

7. Following an anonymous reviewer’s suggestion, we add more control variables in the regression model to enhance the robustness of our main results. The additional control variables are associated with three aspects namely (i) ownership structure, such as the shareholding ratio of the top ten shareholders, the shareholding ratio of the controlling shareholders, the management shareholding ratio and the equity nature identified by state-owned enterprises, (ii) internal governance, including duality, board size and the proportion of independent directors, and (iii) information environment, such as institutional investors’ shareholding, media coverage, and analyst following. The regression results are consistent with our main conclusion. To save space, we do not report this set of tests.

8. DIB internal control index is constructed by Shenzhen DIB Enterprise Risk Management Technology Ltd. It is a composite index based on the listed firm’s internal control disclosure, internal control assessment, internal control weaknesses, and auditing/assurance reports. More details can be found through the link below: http://www.dibdata.cn/#/product/1/ic.

9. Untabulated results show that the effect of the NCC award on crash risk does not show significant heterogeneity among state-owned enterprises vs. non-state-owned firms, firms with vs. without a politically connected chairperson or CEO, and firms receiving more vs. fewer government subsidies. These findings establish that the influence of the NCC award on future crash risk is not driven by pressure from the local authorities. We do not report the results to save space. However, these are available upon request.

10. Following an anonymous reviewer’s suggestion, we also examine whether the NCC award makes it more difficult for managers to hoard bad news by raising analysts’ attention, thereby reducing firms’ future crash risk. Untabulated results show that the NCC award is not associated with increased analyst following or reports. This finding excludes the potential channel that the NCC award may affect crash risk through attracting more analysts. To save space we do not report the results. However, these are available upon request.

Additional information

Funding

Cheng Xiang acknowledges financial support from the National Social Science Fund of China [22BGL295], the National Natural Science Foundation of China [Grant No. 71973018], and the Talents Plan of Chongqing Social Science Planning Project, China [Grant No. 2021YC040]. Wenwu Cai acknowledges financial support from the National Natural Science Foundation of China [Grant No. 72202151]. Any errors that remain are our own.