Codes and Data for "Impact of Technological Development on the Employment of Older Adults: Evidence from High-tech Industry Expansion in China"
Description
Technological development and population ageing have been two defining trends in recent decades. However, whether technological advancements reinstate or displace employment opportunities for older workers remains unclear. Using a nationally representative dataset from China and the Bartik instrumental variable approach, we observe that while the overall impact of technological development on employment is estimated to be negative but statistically insignificant, the negative effect intensifies with age and becomes significant beyond the age of approximately 52. We suggest that displacement effects outweigh reinstatement effects, as the negative impact is driven primarily by older adults with lower socioeconomic statuses, who are more vulnerable to job displacement. Furthermore, technological development may exacerbate the income gap between the rich and others among older adults. We cannot share the CFPS data due to the confidentiality requirements of CFPS. CFPS data is publicly available and easy to request. The CFPS data used in our paper can be requested via https://www.isss.pku.edu.cn/cfps/. To merge the CFPS data with our county-level and city-level variables, the county information of CFPS data is necessary, while it is not included in the public version of CFPS data. Researchers can apply for the restricted data, including the county information and city information, via http://isssappt.pku.edu.cn/index. We share all our codes used in this paper, including data clean code, data merge code, summary statistics code, and empirical regression code together with the Readme file in the replication package to make sure that our results can be replicated.
Files
Steps to reproduce
We are sorry that we cannot share any CFPS data publicly due to the confidentiality requirements of CFPS. But CFPS data is publicly available and easy to request, CFPS data from 2010 to 2018 can be requested at https://www.isss.pku.edu.cn/cfps/. So we only share the county-level and city-level data. “countyvar.dta” is data file of all our county-level and city-level variables used in this paper. To merge the CFPS data with our county-level and city-level variables, the county information of CFPS data is necessary, while it is not included in the public version of CFPS data. Researchers can apply for the restricted data, including the county information and city information, via http://isssappt.pku.edu.cn/index. “cfps_data_clean_code.do” is the code used to clean CFPS data from 2010 to 2018 separately. Since we clean CFPS data by year and most code are quite similar, we only explain the meaning of codes when firstly used instead of explaining them all. “data_merge.do” is the code used to merge the CFPS micro data with our county-level and city-level variables. We are forbidden to share any information about the restricted data, so the name of restricted data files are dropped in the file. “summary_statistics_code.do” is the code for summary statistics results, including Table 1 and Table 2 in our paper. “regression_cfps.do” is the code used in our empirical parts, including OLS results and IV results from Table 3 to Table 11 in our paper. “correlation.do” is the code used to calculate the correlation coefficient between our technological development indicator and the industrial robot penetration and draw the scatter plot, which is reported in Appendix C.
Institutions
- Dongbei University of Finance and EconomicsLiaoning, Dalian
- Southwestern University of Finance and EconomicsSichuan, Chengdu