Can the innovative-city-pilot policy promote urban innovation? An empirical analysis from China

ABSTRACT

Based on the data of 281 cities from 2005 to 2016, we constructed difference-in-difference (DID) models to study the impact of national innovative-city-pilot policy on urban innovation from the three dimensions of dynamic effect, heterogeneity, and influence path. The research found that: (1) The innovative-city-pilot policy has significantly improved the level of urban innovation. This conclusion is still valid after a series of robustness tests. (2) The policy does not have a lag effect, but it has a certain dynamic and continuous effect. (3) This policy is more conducive to innovation in provincial capitals or sub-provincial cities, and has little effect on innovation in other cities; there is no significant difference in the policy effects on eastern, mid-western cities, but the average policy effect in eastern cities is slightly higher than in the mid-western cities. (4) The impact mechanism test shows that the pilot policy affects urban innovation through government fiscal expenditures, the degree of urban industrial agglomeration, and the level of human capital.

Acknowledgments

For this research, the first author (Yunlei Zhou) was supported by both the Key Projects of Social Science Planning in Anhui Province of China under grant AHSKZ2019D018 and Project of Anhui Ecological and Economic Development Research Center of China under grant AHST2019011. The second author (Shengsheng Li) was supported by the National Natural Science Foundation of China under grant 71761015. The authors are very grateful for the comments of the anonymous reviewers and the editors for their hard work. The authors are particularly grateful to Dr. Kirby for his careful reading and comments.

Disclosure statement

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

Notes

1. https://baijiahao.baidu.com/s?id=1653873696746697187&wfr=spider&for=pc

2. New York in the United States, Berlin in Germany, and Tokyo in Japan may not be considered service innovation cities in their own countries, but the Chinese government is targeting these cities as catch-up cities and defining them as service innovation cities. Chinese think tanks or research reports target these cities to develop a catch-up template for Chinese cities. Of course, service innovative cities may not have this term in countries like Europe and the U.S.

3. In the Caragliu and Bo (2019) paper, they used four patent numbers to measure urban innovation. They use four patent counts, with a general-to-particular approach: total patents, high-tech patents (including all IPC classes listed in EUROSTAT, 2016), ICT patents (EUROSTAT, 2017), and Smart City patents. And our paper is mainly about urban innovation, so it uses the number of invention patents that can better reflect urban innovation activities.

4. China is upgrading its industrial structure, emphasizing the development of the tertiary industry (finance, tourism, and other service industries) and the high-end manufacturing industry (second industry) in the secondary industry. For this reason, China has formulated two strategies for technology finance and high-end manufacturing. In this context, China’s patents in cities have increased year by year, and the innovation brought by the tertiary industry has been continuously improved. Therefore, this paper takes the advanced industrial structure (advan) as the control variable, and uses the tertiary industry to account for the proportion of the secondary industry to construct the industrial structure (the proportion of the secondary industry to the tertiary industry is also possible), mainly to verify whether the ratio of the third industry and the second industry increases year by year in China really promotes the urban innovation, and then as a control variable.

5. The opening up of Chinese cities can introduce foreign advanced technology and management experience, which can be transformed into their own innovation through digestion and absorption, which is conducive to overall urban innovation.

6. For example, in 2012, China started to implement the Smart City Policy, which emphasizes the construction of a sustainable urban innovation ecology characterized by user innovation, open innovation, mass innovation, and collaborative innovation through the methodological application of Innovation 2.0 for the knowledge society, and this policy may not have an impact on urban innovation either. Considering the existence of cities with the same smart city policy and innovation city pilot policy, it is necessary to identify whether the innovation city pilot policy is the main policy that affects urban innovation. For example, Wuxi and Zhengzhou started piloting the smart city policy in 2012, but Wuxi implemented the innovation city pilot policy first in 2009, and Zhengzhou was under both the innovation city pilot policy and the smart city policy in 2012. Therefore, we constructed dummy variables for cities under both policies to test whether the innovative-city-pilot policy can still obtain the conclusion that the innovative-city-pilot policy has a significant contribution to urban innovation under the influence of the smart city policy.

7. Our review of China’s financial statistical yearbooks also shows that provincial capitals or sub-provincial cities receive more financial support.

8. The variable finan is measured as the proportion of local fiscal expenditures on science to the total local fiscal expenditures.

9. The calculation method for each industry is: $agg{l}_{i,t}=(E_{i,m,t}/E_{i,t})/(E_{k,m,t}/E_{k,t})$, where $E_{i,m,t}$ is the number of employees in a certain industry in city i in year t, $E_{i,t}$ is the number of employees in all industries in city i, $E_{k,m,t}$ is the number of employees in a certain industry in China in year t, and $E_{k,t}$ is all in year t Number of industry employees. After calculating the agglomeration of various industries, the entropy method is used to obtain the comprehensive industrial agglomeration level of city i in year t.

10. The variable human is measured by the proportion of people with a college degree or above in the total population of the city that year.

11. The innovation city pilot policy effect is an average treatment effect for all provincial or sub-provincial cities, and the innovation for central and mid-western cities is also an average effect for all cities in this classification. For example, many cities belong to non-provincial and sub-provincial cities, but the policy may have a significant contribution to innovation in Wuxi, which is a non-provincial and sub-provincial city, but the overall sample of lower tier cities participating in the regression may not be statistically significant.

12. According to the administrative division of Chinese cities, China has one city as the capital city of the province (including four municipalities directly under the central government), for example, Shenyang is the capital city of Liaoning Province, and Beijing, Tianjin, Shanghai, and Chongqing are municipalities directly under the central government, and they have the highest administrative level. And there are 15 sub-provincial cities composed of Guangzhou, Wuhan, Harbin, Shenyang, Chengdu, Nanjing, Xi’an, Changchun, Jinan, Hangzhou, Dalian, Qingdao, Shenzhen, Xiamen, and Ningbo. Among them, Shenzhen, Dalian, Qingdao, Ningbo, and Xiamen are planned cities, while the others are provincial capitals. The above cities have a high administrative status in China, while the others are non-provincial capitals and non-sub-provincial cities. In general, non-provincial capitals and non-sub-provincial cities receive less support, which is consistent with our empirical results. If China wants to improve the overall innovation competitiveness of cities, the innovation city pilot policy should be appropriately tilted to these cities with lower administrative status.Eastern, central and western cities are divided mainly based on the degree of economic development. Eastern cities mainly include cities in the following provinces: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; central and western include Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, and Guangxi. It is important to note that the four Chinese municipalities have the same status as provinces, while the empirical evidence in this paper is at the city level and the policy is only piloted in one of the four municipalities, so the sample of municipalities is excluded to ensure the robustness of the empirical results. Of course, Hong Kong, Macau, and Taiwan are not included in the sample.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71761015]; Key Projects of Social Science Planning in Anhui Province of China [AHSKZ2019D018]; and Project of Anhui Ecological and Economic Development Research Center of China [AHST2019011].

Notes on contributors

Yunlei Zhou

Yunlei Zhou is a researcher at the Center for Ecological Economics, Anhui University, and completed her PhD at Anhui University in June 2021. She is a lecturer in the School of Finance and Public Administration at Anhui University of Finance and Economics, with research interests in finance and regional economics.

Shengsheng Li

Shengsheng Li is a PhD student in the School of Economics at Anhui University. His research area is econometrics, and his main research interests are policy evaluation, urban economy, and data mining.