What Motivates Non-democratic Leadership - Evidence from COVID 19 Reopenings in China (Tables)

Table 1: Reopening announcements, COVID-19 infections, and proxies for economic activity (1) Dependent Variable AnyCOV ID -0.052∗∗∗ (0.008) log(1 + Reopening) City FEs Date FEs Observations R-Squared (2) ∆T raf f ic Yes Yes 7342 0.896 0.012∗∗ (0.005) Yes Yes 7342 0.894 (3) (4) -0.052∗∗∗ (0.008) 0.012∗∗ (0.005) Yes Yes 7342 0.897 -0.122∗∗∗ (0.031) Yes Yes 9606 0.229 (5) log(∆AQI) 0.019∗ (0.010) Yes Yes 9606 0.226 (6) -0.121∗∗∗ (0.031) 0.018∗ (0.010) Yes Yes 9606 0.230 (7) log(1 + Reopening) -0.011 (0.033) Yes Yes 9606 0.342 Notes: Numbers in parentheses are robust standard errors. Standard errors are clustered at the city level. The sample covers the period from Feb 10, 2020 to April 15, 2020. The dependent variable in Columns (1)-(3) is ∆T raf f ic, the daily Baidu traffic index deflated by the corresponding daily traffic index in 2019. The dependent variable in Columns (4)-(6) is log(∆AQI), the log of the daily AQI in 2020 deflated by the corresponding daily AQI in 2019. The dependent variable in Column (7) is log(1 + Reopening), the natural log of one plus the number of reopening-related reports for each day in each city. AnyCOV ID is an indicator variable which denotes that there has been at least one new coronavirus case in each city 7 days preceeding each day. Significance: * significant at 10%; ** significant at 5%; *** significant at 1%. Table 2: Baidu keywords related to dissent and city reopenings (1) Dependent Variable log(1 + BaiduDissent_7days) (2) 0.029∗∗∗ (0.011) 0.050∗∗∗ (0.019) log(1 + BaiduDissent_14days) log(1 + BaiduP olitics_7days) log(1 + BaiduEconomy_7days) City FEs Date FEs Observations R-Squared (3) Yes Yes 9562 0.345 Yes Yes 9562 0.345 0.012 (0.009) 0.017∗ (0.009) Yes Yes 9562 0.344 Notes: Numbers in parentheses are standard errors clustered by city. The sample covers the period from Feb 10, 2020 to April 15, 2020. All columns use city-date level observations. The dependent variable in all columns is log(1 + Reopening), which captures the number of reopening-related reports for each day in each city. BaiduDissent_7days and BaiduDissent_14days are the average number of searches of keywords related to social unrest and economic difficulties in the preceding 7 and 14 day period respectively. BaiduP olitics_7days and BaiduEconomy_7days are the average number of searches of keywords related to social unrest and economic difficulties, respectively, in the preceding 7 day period. Significance: * significant at 10%; ** significant at 5%; *** significant at 1%. Coefficient on last week's Baidu search -.05 0 .05 .1 Figure 1: The timing of the relationship between dissent-related keyword searches and reopening announcements 1 2 3 4 5 6 Week since Feb 10 7 8 Notes: This figure shows the coefficients from a regression that relates week-byweek Baidu keyword searches to government newspaper reopening articles. Each point indicates the point estimate from a variant on Equation (2) that allows the coefficient to vary by week. The whiskers show the 95 percent confidence interval of each coefficient estimate. See text for additional details. 9 Appendix Table A1: Summary Statistics, City-Date level variables Variable Name log(1 + Reopening) AnyCOV ID log(∆AQI) ∆T raf f ic log(1 + BaiduDissent_7days) log(1 + BaiduDissent_14days) Mean 1.340 0.228 -0.130 0.830 3.954 3.981 StdDev 0.664 0.420 0.535 0.234 1.526 1.382 Obs 9606 9606 9606 7343 9562 9562 Notes: Reopening is the number of reopening-related reports for each day in each city. AnyCOV ID is an indicator variable which denotes that there has been at least one new coronavirus case in each city 7 days preceeding each day. log(∆AQI) is daily AQI in 2020 deflated by corresponding daily AQI in 2019. ∆T raf f ic is the daily Baidu traffic index deflated by corresponding daily traffic index in 2019. BaiduDissent_7days is the average number of searches of a group of keywords that are related to social unrest and economic difficulties on each day for each city in the preceding 7 day period. BaiduDissent_14days is the average number of searches of a group of keywords that are related to social unrest and economic difficulties on each day for each city in the preceding 14 day period. Table A2: Relationship between past worker protests and Baidu keywords related to discontent Dependent Variable log(1 + U nrest_1yr) log(1 + U nrest_3yrs) log(P opulation) log(GDP P C) Province FEs Observations R-Squared (1) (2) (3) log(1 + BaiduDissent_T otal) 0.412∗∗∗ (0.085) 0.551∗∗∗ 0.634∗∗∗ (0.130) (0.133) 1.646∗∗∗ 1.533∗∗∗ 1.654∗∗∗ (0.108) (0.140) (0.156) 0.823∗∗∗ 0.795∗∗∗ 0.710∗∗∗ (0.066) (0.073) (0.087) No No Yes 212 212 211 0.814 0.814 0.885 Notes: Numbers in parentheses are robust standard errors. The sample covers the period from Feb 10, 2020 to April 15, 2020. All columns use city-level observations. The dependent variable in all columns is log(1+BaiduDissent_T otal), which captures the log of the total number of searches of keywords related to social unrest and economic difficulties during our sample period. U nrest_1yr is the number of recorded strikes and worker protests during 2019. U nrest_3yrs is the average number of recorded strikes and worker protests during 2017-19. Significance: * significant at 10%; ** significant at 5%; *** significant at 1%. Table A3: Baidu keywords related to future dissent and city reopenings Dependent Variable log(1 + BaiduDissent_7f wd) log(1 + BaiduDissent_14f wd) log(1 + BaiduP olitics_7f wd) log(1 + BaiduEconomy_7f wd) City FEs Date FEs Observations R-Squared (1) (2) (3) log(1 + Reopening) -0.012 (0.010) -0.015 (0.022) 0.003 (0.008) -0.010 (0.009) Yes Yes Yes Yes Yes Yes 9562 9562 9562 0.344 0.344 0.344 Notes: Numbers in parentheses are standard errors clustered by city. The sample covers the period from Feb 10, 2020 to April 15, 2020. All columns use city-date level observations. The dependent variable in all columns is log(1 + Reopening), which captures the number of reopening-related reports for each day in each city. BaiduDissent_7f wd and BaiduDissent_14f wd are the average number of searches of keywords related to social unrest and economic difficulties in the preceding 7 and 14 day period respectively. BaiduP olitics_7f wd and BaiduEconomy_7f wd are the average number of searches of keywords related to social unrest and economic difficulties, respectively, in the preceding 7 day period. Significance: * significant at 10%; ** significant at 5%; *** significant at 1%. Appendix Figure A1: Map of cities in the sample