The impact of Smart city construction on labour spatial allocation: Evidence from China

ABSTRACT

Recently, the prevalence of information technology represented by ChatGPT has aroused extensive discussions. Does the adoption of information technology lead to massive regional unemployment? It is a crucial and unresolved issue for the government, enterprise employees, and researchers. Combining the 2005–2020 panel and the smart city (SC) construction policy in China, this article employs the difference in differences (DID) method to study the impact of informatization construction on labour spatial allocation. We found that informatization construction significantly attracted labour and improved labour spatial allocation. After the SC construction policy’s implementation, compared with non-SCs, the average increase of SC pilots in the labour force is about 0.78 million people. Besides, stimulating economic growth, improving the environment, and enhancing public services are the mechanisms of the SC construction policy on labour spatial allocation. Furthermore, this policy effect has heterogeneous industry department and category characteristics. The Tertiary and secondary industry department receives a more significant impact.

KEYWORDS:

• Smart city
• labour spatial allocation
• informatization
• DID model

JEL CLASSIFICATION:

• J21
• J23
• J61
• J68
• J82
• O33
• O38

Disclosure statement

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

Notes

¹ Because the population statistics of the city are missing in The China City Statistics Year Book, this article refers to the method of Huan and Penghui (2019)to deduce the city’s population statistics. The regional permanent residents are selected as a representative indicator of the labour force. The resident population = city GDP/city per capita GDP. China has a large population, and the population moves very frequently. In 2020, there was more than 200 million floating population in China. Population mobility in China is reflected in employment-based mobility, and the direction of population flow demonstrates the direction of labour mobility. In this context, it is reasonable to use population mobility data instead of labour mobility data and study the impact of China’s informatization construction policy on the labour spatial allocation.

² The municipal district is the core of the city. The development and construction of the city concentrate in the municipal district. But the whole city data includes a large part of the rural areas. Unlike the usual regulatory pilot policies, the SC policy is a targeted financial subsidy from the Chinese government to improve the informatization level of pilot cities. Local governments will use resources to enhance the urban area’s informatization level. And SC policy mainly attracts labour to the urban areas, rather than the affiliated rural areas around the cities. Therefore, this article uses data for municipal districts.

³ The standard deviation of labour force is 1.15. The standard deviation of SC is 0.305. The average increase of SC construction policy pilots in the labour force equals to 0.207 × 1.15/0.305 = 0.78.

⁴ While, there was a minor fluctuant in 2011, which might drive by other policies or random factors. To ensure the robustness of the result, we employ the subsequent sensitivity and permute test (see Appendix A1.2 and Appendix A1.3).

⁵ We also divided the second and tertiary industry departments into sub-categories. The results are shown in Appendix A2.

⁶ In the robustness test, the number of employees is replaced as the dependent variable obtained by aggregating the number of employees in the primary, secondary, and tertiary industry departments.

⁷ Secondary industry departments include mining, resource supply, construction and manufacture categories. The tertiary industry departments include wholesale and retail, transportation, warehousing and postal services, accommodation and catering, water conservancy, environment and public facilities management, residential services, repairs and other services, information transmission, computer services and software, finance, real estate industry, leasing and business services, health and social work, public administration, social security and social organization, education, and culture, sports and entertainment categories.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [71974054]; Science-Technology Innovation Platform and Talents Program of Hunan Province, China [2019TP1053]; The Hunan Natural Science Foundation [2019JJ40039]; Technical Service Items of Economic and Technological Research Institute in Hunan Electric Power Company [5216A220000A]