Unemployment in Russia during COVID-19: Teleworkability and Face-to-Face context

The aggregate data, scripts, and Supplementary Tables are an additional material to the paper Kotyrlo E. "The Impact of Anti-COVID-19 Restrictions and Transitory Unemployment Insurance Policies on Unemployment in Russia" Journal of Economic Studies, 2023, doi: 10.1108/JES-12-2022-0615. Aggregate data are composed on the base of 5 mln monthly administrative records, January 2019 – December 2020, on individuals registered as unemployed by the Russian Public Employment Service. Individual data are received through the Research Data Infrastructure (INID) (https://www.data-in.ru/). Aggregate data account for individuals with a period of less than 6 months between application for unemployment and their latest working month. They are aggregated by teleworkability (TW) and face-to-face (F2F) indices as proposed by Dingel and Neiman (2020) and Sostero et al. (2020) into monthly data. Data report 149 latest professional occupations that are categorized by the TW index and a F2F score. Data are aggregated by TW index, by 5 categories (0, 2, 4, 6, 8) of face-to-face intensity and, in addition, by gender and three levels of education (primary, secondary and higher). Stata do files provide a code for two-way fixed effects estimates and plots (TWFE.do) of TW (F2F) effects and Wald and change-in-change DID (fuzzy-did.do) effects proposed by de Chaisemartin and D’Haultfoeuille (2020). R script provides a code for doubly robust ATT estimates and plots of TW (F2F) effects as proposed by Callaway and Sant’Anna (2021). Bibliography Callaway, B. and Sant’Anna, P.H.C. (2021), “Difference-in-Differences with Multiple Time Periods”, Journal of Econometrics, Vol. 225 No. 2, pp. 200-230, https://doi.org/10.1016/j.jeconom.2020.12.001. De Chaisemartin, C., and d’Haultfœuille, X. (2020), “Two-way fixed effects estimators with heterogeneous treatment effects”, American Economic Review, Vol. 110 No. 9, pp. 2964-96. Dingel, J.I. and Neiman, B. (2020), “How Many Jobs Can Be Done at Home?”, Journal of Public Economics, Vol. 189, 104235. doi.org/10.1016/j.jpubeco.2020.104235 Sostero, M., Milasi, S., Hurley, J., Fernandez-Macías, E., and Bisello, M. (2020), “Teleworkability and the COVID-19 crisis: a new digital divide?” No. 2020/05, JRC Working Papers series on Labour, Education and Technology. http://hdl.handle.net/10419/231337.聚合数据、脚本与附表为本论文的补充材料：Kotyrlo E. 所著《新冠防疫限制与临时性失业保险政策对俄罗斯失业状况的影响》，发表于《经济研究期刊》(Journal of Economic Studies)，2023年，DOI: 10.1108/JES-12-2022-0615。 聚合数据基于2019年1月至2020年12月期间，由俄罗斯公共就业服务局登记的500万份月度行政记录构建而成。个体数据通过研究数据基础设施（Research Data Infrastructure, INID）获取，网址：https://www.data-in.ru/。本聚合数据仅涵盖自失业登记申请至最近一个工作月份间隔不足6个月的个体。数据按照丁格尔与内曼（Dingel and Neiman, 2020）、索斯特罗等人（Sostero et al., 2020）提出的远程工作能力（teleworkability, TW）指数与面对面工作（face-to-face, F2F）指数进行聚合，生成月度数据集。数据集涵盖149种最新职业，均按照TW指数与F2F得分进行分类。数据分别按TW指数、按F2F强度的5个等级（0、2、4、6、8）进行聚合，此外还按性别与三个教育层次（初等、中等与高等教育）进行分组聚合。 Stata do文件包含用于双向固定效应估计及绘图的代码（TWFE.do），可实现TW（F2F）效应的相关分析，以及德谢马尔坦与多尔特福伊（de Chaisemartin and D’Haultfoeuille, 2020）提出的Wald检验与双重差分变化（change-in-change DID，模糊双重差分fuzzy-did.do）效应分析。R脚本则包含用于双重稳健平均干预效应（Average Treatment Effect on the Treated, 简称ATT）估计及绘图的代码，可实现卡拉韦与桑塔安娜（Callaway and Sant’Anna, 2021）提出的TW（F2F）效应分析。 参考文献 1. 卡拉韦（Callaway, B.）与桑塔安娜（Sant’Anna, P.H.C.）（2021），《多期双重差分法》，《计量经济学期刊》(Journal of Econometrics)，第225卷第2期，第200-230页，https://doi.org/10.1016/j.jeconom.2020.12.001。 2. 德谢马尔坦（De Chaisemartin, C.）与多尔特福伊（d’Haultfœuille, X.）（2020），《存在异质性干预效应的双向固定效应估计量》，《美国经济评论》(American Economic Review)，第110卷第9期，第2964-2996页。 3. 丁格尔（Dingel, J.I.）与内曼（Neiman, B.）（2020），《多少岗位可在家完成？》，《公共经济学期刊》(Journal of Public Economics)，第189卷，第104235号，doi.org/10.1016/j.jpubeco.2020.104235。 4. 索斯特罗（Sostero, M.）、米拉西（Milasi, S.）、赫尔利（Hurley, J.）、费尔南德斯-马西亚斯（Fernandez-Macías, E.）与比塞洛（Bisello, M.）（2020），《远程工作能力与新冠疫情危机：新的数字鸿沟？》，编号2020/05，欧盟联合研究中心（Joint Research Centre, JRC）劳动、教育与技术领域工作论文系列，http://hdl.handle.net/10419/231337。