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Lockdown, Infection, and Expressed Happiness in China

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Abstract

This paper exploits data from Sina Weibo posts to study the effect of the Wuhan lockdown on expressed happiness during the initial COVID-19 outbreak. By applying the difference-in-differences method to a city-level expressed happiness index generated by Weibo posts, we find that the announcement regarding human-to-human transmission significantly lowered expressed happiness in Wuhan relative to Chinese cities outside Hubei province, while the subsequent Wuhan lockdown had protective effects during the first 12 days. The effects on expressed happiness remained significant in the medium run toward the end of Wuhan lockdown. However, our results also suggest that the protective effects of the Wuhan lockdown declined as the lockdown continued and the risk of infection became lower. Our findings are robust to the use of alternative control groups and sample periods, different expressed happiness measures, and the potential censorship of Weibo.

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Notes

  1. Our study covers municipalities, deputy-provincial-level cities, prefecture-level cities, and prefectures. There are different types of cities in mainland China, characterized by official ranks in the administrative hierarchy. There are 4 major cities as municipalities, specifically Beijing, Shanghai, Tianjin, and Chongqing. They have equal standing to a province, under the governance of central government directly. The next level is called deputy-provincial-level cities, which are major cities in their province. These cities enjoy administrative dependence though they are still governed by their province. Among 15 of them, 10 are provincial capitals. For example, Wuhan as a deputy-provincial-level city, is the provincial capital of Hubei province. The next level is prefecture-level cities. These cities are governed directly by their provincial government. Some of them are provincial capitals. There are also prefectures which have the same administrative level as prefecture-level cities, mostly located in less developed areas. Below prefecture-level city and prefecture, there are county-level cities or counties. Our study excludes them due to their relatively smaller sizes. For simplicity, we will just use “city” to refer to these three types of cities and prefectures in the rest of the paper.

  2. Other cities in Wuhan province are excluded from our analysis to mitigate the potential spillover effect.

  3. They excluded China from their analysis on impacts of lockdowns on expressed happiness.

  4. https://www.reuters.com/article/us-china-health-who-idUSKBN1ZM1G9. Assessed on August 24, 2021.

  5. March 18 was the first date with no new infections, but later there were some new infections (one for about 2 days).

  6. Since we controlled for date fixed effects in our models, the common information (both on the infection risks and the lockdown) shock should have already captured. Unless we believe the information effects were quite different on the treatment and control groups over time, the lockdown effect we estimated should be mostly the actual lockdown effect not the information effect.

  7. The falsification test follows the specification as below:

    $${y}_{jt}={\beta }_{0}+{\beta }_{1}Pseudo{Lockdown}_{t}\times {Wuhan}_{j}+{{\varvec{X}}}_{{\varvec{j}}{\varvec{t}}}^{\boldsymbol{^{\prime}}}{\varvec{\gamma}}+{{\varvec{\theta}}}_{{\varvec{t}}}+{{\varvec{\mu}}}_{{\varvec{j}}}+{\varepsilon }_{jt}$$

  8. The event study specification is as follows, with interaction terms between the Wuhan indicator and date indicators:

    $${y}_{jt}={\beta }_{0}+\sum_{t=-22}^{t=12}{{\beta }_{t}\theta }_{t}\times {Wuhan}_{j}+{X}_{jt}^{\mathrm{^{\prime}}}\gamma +{{\varvec{\theta}}}_{{\varvec{t}}}+{{\varvec{\mu}}}_{{\varvec{j}}}+{\varepsilon }_{jt}.$$

  9. Data on infections during the initial outbreak are under-reported but can indeed represent perceived infection risks.

  10. Our original data only consist of posts by active Weibo users. Active users were defined and collected by Hu et al. (2020) by the following four rules: (1) follows > 50; (2) fans > 50; (3) tweets > 50; and (4) recent post < 30 days. Based on this definition, active users account for 8% of all users. The reasons for including only the active users are mainly to bypass the limitation of Weibo search interface and cost of traversing all Weibo users during the COVID-19 outbreaks and to facilitate the efficiency and timeliness of data collection. Given the rapid spread of COVID-19 and abrupt lockdown measures during the sample period of this study, we believe this dataset provides a reliable collection of Weibo posts and happiness measurement, which is more real-time and less subject to censorship. We observe that about 70% of active Weibo users are self-identified as female. However, there is no user id to link the dataset of Weibo posts to the user dataset, and therefore, we are not able to distinguish individual users from institutional users. We also dropped re-tweeted posts, which mostly consisted of news and posts representing other users’ sentiment. We further dropped abnormal Weibo posts with fewer than 3 Chinese characters or more than 170 characters, to improve the efficiency of the sentiment analysis.

  11. DXY started to publish COVID 19 data only after the Wuhan lockdown on January 23, 2020.

  12. The information was retrieved from http://news.ifeng.com/c/7u8Wvj66XuS on March 1, 2021.

  13. Alternatively, we used the daily mean of AQI calculated by the Chinese standard using six atmospheric pollutants—AQI, CO, NO2, O3, PM10, PM2.5, and SO2—and the results appear to be consistent.

  14. All meteorological variables are inverse-distance-weighted mean.

  15. On average, we have 2,760.59 Weibo active users in Wuhan and 776.34 Weibo active users in other cities posting 3181.70 Weibo posts with geographical information in Wuhan and 867.05 in other cities per day during January 1 and April 30, 2020. The (average) GDP per capita was 135,136 RMB (Chinese Yuan) for Wuhan and 64,554.43 RMB for other cities.

  16. Summary statistics of the two variables are reported in Panel A of Appendix Table 6.

  17. The complete list of keywords is available upon request.

  18. Using pretreatment outcome variables as the predictors are crucial to control for unobserved covariates (Abadie, 2021; Abadie & Vives-i-Bastida, 2022). We include pre-treatment outcome variables on January 19, 18, and 17 in the model, however, our results are robust to including more pre-treatment outcome variables.

  19. We also conducted the analysis with propensity score weighting. The results are similar to our baseline ones, and they will be available upon requests.

  20. Results are essentially the same if we use all cities, as in column 5.

  21. Summary statistics of the set of lockdown and unlocked variables are reported in Panels B (for column 1 in Table 5) and C (for column 1 in Table 5) of Appendix Table 6 respectively.

  22. From March 4th, our control cities mostly had no government-implemented restrictions. However, we acknowledge the possibility of self-protection. If the self-protection of the control group cities has a net positive influence on their expressed happiness, our estimated lockdown effect was a lower bound.

References

  • Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391–425. https://doi.org/10.1257/jel.20191450

    Article  Google Scholar 

  • Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505. https://doi.org/10.1198/jasa.2009.ap08746

    Article  Google Scholar 

  • Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque country. American Economic Review, 93(1), 113–132. https://doi.org/10.1257/000282803321455188

    Article  Google Scholar 

  • Abadie, A., & Vives-i-Bastida, J. (2022). Synthetic Controls in Action. arXiv. https://doi.org/10.48550/arXiv.2203.06279

  • Abouk, R., & Heydari, B. (2021). The immediate effect of COVID-19 policies on social-distancing behavior in the United States. Public Health Reports, 136(2), 245–252. https://doi.org/10.1177/0033354920976575

    Article  Google Scholar 

  • Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2020). Inequality in the impact of the coronavirus shock: Evidence from real time surveys. Journal of Public Economics, 189, 104245. https://doi.org/10.1016/j.jpubeco.2020.104245

    Article  Google Scholar 

  • Agüero, J. M. (2021). COVID-19 and the rise of intimate partner violence. World Development, 137, 105217. https://doi.org/10.1016/j.worlddev.2020.105217

    Article  Google Scholar 

  • Aknin, L. B., Andretti, B., Goldszmidt, R., Helliwell, J. F., Petherick, A., De Neve, J.-E., Dunn, E. W., Fancourt, D., Goldberg, E., Jones, S. P., Karadag, O., Karam, E., Layard, R., Saxena, S., Thornton, E., Whillans, A., & Zaki, J. (2022). Policy stringency and mental health during the COVID-19 pandemic: A longitudinal analysis of data from 15 countries. The Lancet Public Health, 7(5), e417–e426. https://doi.org/10.1016/S2468-2667(22)00060-3

    Article  Google Scholar 

  • Alon, T., Doepke, M., Olmstead-Rumsey, J., & Tertilt, M. (2020). The Impact of COVID-19 on Gender Equality (w26947; p. w26947). National Bureau of Economic Research. https://doi.org/10.3386/w26947

  • Armbruster, S., & Klotzbücher, V. (2020). Lost in lockdown? COVID-19, social distancing, and mental health in Germany (Working Paper 2020–04). Diskussionsbeiträge. https://www.econstor.eu/handle/10419/218885

  • Baek, C., McCrory, P. B., Messer, T., & Mui, P. (2020). Unemployment effects of stay-at-home orders: Evidence from high frequency claims data. The Review of Economics and Statistics. https://doi.org/10.1162/rest_a_00996

    Article  Google Scholar 

  • Banks, J., & Xu, X. (2020). The Mental Health Effects of the First Two Months of Lockdown during the COVID-19 Pandemic in the UK*. Fiscal Studies, 41(3), 685–708. https://doi.org/10.1111/1475-5890.12239

    Article  Google Scholar 

  • Brodeur, A., Clark, A. E., Fleche, S., & Powdthavee, N. (2021). COVID-19, lockdowns and well-being: Evidence from Google Trends. Journal of Public Economics, 193, 104346. https://doi.org/10.1016/j.jpubeco.2020.104346

    Article  Google Scholar 

  • Brodeur, A., Gray, D. M., Islam, A., & Bhuiyan, S. (2020). A Literature Review of the Economics of COVID-19. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3636640

    Article  Google Scholar 

  • Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G. J. (2020). The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. The Lancet, 395(10227), 912–920. https://doi.org/10.1016/S0140-6736(20)30460-8

    Article  Google Scholar 

  • Bullinger, L. R., Carr, J. B., & Packham, A. (2020). COVID-19 and Crime: Effects of Stay-at-Home Orders on Domestic Violence (w27667). National Bureau of Economic Research. https://doi.org/10.3386/w27667

  • Chang, S. L., Harding, N., Zachreson, C., Cliff, O. M., & Prokopenko, M. (2020). Modelling transmission and control of the COVID-19 pandemic in Australia. Nature Communications, 11(1), 5710. https://doi.org/10.1038/s41467-020-19393-6

    Article  Google Scholar 

  • China Data Lab. (2020a). Air Quality Data (NSF, Trans.; V25 ed.). Harvard Dataverse. https://doi.org/10.7910/DVN/XETLSS

  • China Data Lab. (2020b). China COVID-19 Daily Cases with Basemap (V37 ed.). Harvard Dataverse. https://doi.org/10.7910/DVN/MR5IJN

  • China Data Lab. (2020c). Meteorological Data (NSF, Trans.; DRAFT VERSION). Harvard Dataverse. https://doi.org/10.7910/DVN/TU0JDP

  • Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M. E., Longini, I. M., & Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395–400. https://doi.org/10.1126/science.aba9757

    Article  Google Scholar 

  • Coibion, O., Gorodnichenko, Y., & Weber, M. (2020). The Cost of the Covid-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending (w27141). National Bureau of Economic Research. https://doi.org/10.3386/w27141

  • Courtemanche, C., Garuccio, J., Le, A., Pinkston, J., & Yelowitz, A. (2020). Strong social distancing measures in the United States reduced the COVID-19 growth rate. Health Affairs (project Hope), 39(7), 1237–1246. https://doi.org/10.1377/hlthaff.2020.00608

    Article  Google Scholar 

  • Davillas, A., & Jones, A. M. (2020). The COVID-19 Pandemic and its Impact on Inequality of Opportunity in Psychological Distress in the UK (SSRN Scholarly Paper ID 3614940). Social Science Research Network. https://doi.org/10.2139/ssrn.3614940

  • Dickens, B. L., Koo, J. R., Wilder-Smith, A., & Cook, A. R. (2020). Institutional, not home-based, isolation could contain the COVID-19 outbreak. The Lancet, 395(10236), 1541–1542. https://doi.org/10.1016/S0140-6736(20)31016-3

    Article  Google Scholar 

  • Easterlin, R. A., & O’Connor, K. J. (2023). Three years of COVID-19 and life satisfaction in Europe: A macro view. Proceedings of the National Academy of Sciences, 120(19), e2300717120. https://doi.org/10.1073/pnas.2300717120

    Article  Google Scholar 

  • Fancourt, D., Steptoe, A., & Bu, F. (2020). Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19: Longitudinal analyses of 36,520 adults in England. medRxiv. https://doi.org/10.1101/2020.06.03.20120923

  • Fang, H., Wang, L., & Yang, Y. (2020). Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China. Journal of Public Economics, 191, 104272. https://doi.org/10.1016/j.jpubeco.2020.104272

    Article  Google Scholar 

  • Fayaz Farkhad, B., & Albarracín, D. (2021). Insights on the implications of COVID-19 mitigation measures for mental health. Economics & Human Biology, 40, 100963. https://doi.org/10.1016/j.ehb.2020.100963

    Article  Google Scholar 

  • Fetzer, T., Witte, M., Hensel, L., Jachimowicz, J., Haushofer, J., Ivchenko, A., Caria, S., Reutskaja, E., Roth, C., Fiorin, S., Gómez, M., Kraft-Todd, G., Götz, F., & Yoeli, E. (2020). Perceptions of an Insufficient Government Response at the Onset of the COVID-19 Pandemic are Associated with Lower Mental Well-Being. https://doi.org/10.31234/osf.io/3kfmh

  • Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Mellan, T. A., Coupland, H., Whittaker, C., Zhu, H., Berah, T., Eaton, J. W., Monod, M., Ghani, A. C., Donnelly, C. A., Riley, S., Vollmer, M. A. C., Ferguson, N. M., Okell, L. C., & Bhatt, S. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257–261. https://doi.org/10.1038/s41586-020-2405-7

    Article  Google Scholar 

  • Foa, R., Gilbert, S., & Fabian, M. O. (2020). COVID-19 and subjective well-being: Separating the effects of lockdowns from the pandemic. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3674080

    Article  Google Scholar 

  • Greyling, T., Rossouw, S., & Adhikari, T. (2021). A tale of three countries: What is the relationship between COVID-19, lockdown and happiness? South African Journal of Economics, 89(1), 25–43. https://doi.org/10.1111/saje.12284

    Article  Google Scholar 

  • Hensel, L., Witte, M., Caria, A. S., Fetzer, T., Fiorin, S., Götz, F. M., Gomez, M., Haushofer, J., Ivchenko, A., Kraft-Todd, G., Reutskaja, E., Roth, C., Yoeli, E., & Jachimowicz, J. M. (2022). Global behaviors, perceptions, and the emergence of social norms at the onset of the COVID-19 pandemic. Journal of Economic Behavior & Organization, 193, 473–496. https://doi.org/10.1016/j.jebo.2021.11.015

    Article  Google Scholar 

  • Hu, Y., Huang, H., Chen, A., & Mao, X.-L. (2020). Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo. arXiv:2005.09174 [Cs].

  • Jaidka, K., Giorgi, S., Schwartz, H. A., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2020). Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods. Proceedings of the National Academy of Sciences, 117(19), 10165–10171. https://doi.org/10.1073/pnas.1906364117

    Article  Google Scholar 

  • Kim, A. W., Nyengerai, T., & Mendenhall, E. (2020). Evaluating the mental health impacts of the COVID-19 pandemic: Perceived risk of COVID-19 infection and childhood trauma predict adult depressive symptoms in urban South Africa. Psychological Medicine. https://doi.org/10.1017/S0033291720003414

    Article  Google Scholar 

  • Kjell, O., Giorgi, S., Schwartz, H. A., & Eichstaedt, J. C. (2023). Towards well-being measurement with social media across space, time and cultures: Three generations of progress. In J. F. Helliwell, R. Layard, J. D. Sachs, J.-E. De Neve, L. B. Aknin, & S. Wang (Eds.), World Happiness report 2023 (11th ed.). Sustainable Development Solutions Network.

    Google Scholar 

  • Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Chieh-Hsi, W., Klein, B., Pigott, D. M., du Plessis, L., Faria, N. R., Li, R., Hanage, W. P., Brownstein, J. S., Layan, M., Vespignani, A., Tian, H., Dye, C., Pybus, O. G., & Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493–497. https://doi.org/10.1126/science.abb4218

    Article  Google Scholar 

  • Layard, R., Clark, A. E., De Neve, J.-E., Krekel, C., Fancourt, D., Hey, N., & O’Donnell, G. (2020). When to release the lockdown? A wellbeing framework for analysing costs and benefits. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3590884

    Article  Google Scholar 

  • Leslie, E., & Wilson, R. (2020). Sheltering in Place and Domestic Violence: Evidence from Calls for Service during COVID-19 (SSRN Scholarly Paper ID 3600646). Social Science Research Network. https://doi.org/10.2139/ssrn.3600646

  • Levinson, A. (2012). Valuing public goods using happiness data: The case of air quality. Journal of Public Economics, 96(9–10), 869–880. https://doi.org/10.1016/j.jpubeco.2012.06.007

    Article  Google Scholar 

  • Lu, H., Nie, P., & Qian, L. (2020). Do Quarantine experiences and attitudes towards COVID-19 affect the distribution of mental health in China? A quantile regression analysis. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-020-09851-0

    Article  Google Scholar 

  • Lucas, R. E., & Lawless, N. M. (2013). Does life seem better on a sunny day? Examining the association between daily weather conditions and life satisfaction judgments. Journal of Personality and Social Psychology, 104(5), 872. https://doi.org/10.1037/a0032124

    Article  Google Scholar 

  • Ma, M., Wang, S., & Wu, F. (2021). COVID-19 Prevalence and Well-being: Lessons from East Asia. In Helliwell, John F., Richard Layard, Jeffrey Sachs, and Jan-Emmanuel De Neve, eds. World Happiness Report 2021 (p. 57). Sustainable Development Solutions Network.

  • McCrary, J., & Sanga, S. (2020). The Impact of the Coronavirus Lockdown on Domestic Violence (SSRN Scholarly Paper ID 3612491). Social Science Research Network. https://doi.org/10.2139/ssrn.361249

  • Metzler, H., Pellert, M., & Garcia, D. (2022). Using social media data to capture emotions before and during COVID-19. In J. F. Helliwell, R. Layard, J. D. Sachs, J.-E. De Neve, L. B. Aknin, & S. Wang (Eds.), World happiness report 2022 (10th ed.). Sustainable Development Solutions Network.

    Google Scholar 

  • Miller, A., Segal, C., & Spencer, M. (2020). Effects of the COVID-19 Pandemic on Domestic Violence in Los Angeles (w28068; p. w28068). National Bureau of Economic Research. https://doi.org/10.3386/w28068

  • Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013). The geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE, 8(5), e64417. https://doi.org/10.1371/journal.pone.0064417

    Article  Google Scholar 

  • Pan, A., Liu, L., Wang, C., Guo, H., Hao, X., Wang, Q., Huang, J., He, N., Yu, H., Lin, X., Wei, S., & Wu, T. (2020). Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA, 323(19), 1915–1923. https://doi.org/10.1001/jama.2020.6130

    Article  Google Scholar 

  • Perez-Vincent, S., Carreras, E., Gibbons, M. A., Murphy, T. E., & Rossi, M. (2020). COVID-19 Lockdowns and Domestic Violence: Evidence from Two Studies in Argentina. In Working Papers (143; Working Papers). Universidad de San Andres, Departamento de Economia. https://ideas.repec.org/p/sad/wpaper/143.html

  • Piquero, A. R., Riddell, J. R., Bishopp, S. A., Narvey, C., Reid, J. A., & Piquero, N. L. (2020). Staying home, staying safe? A short-term analysis of COVID-19 on Dallas domestic violence. American Journal of Criminal Justice, 45(4), 601–635. https://doi.org/10.1007/s12103-020-09531-7

    Article  Google Scholar 

  • Qiu, Y., Chen, X., & Shi, W. (2020). Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. Journal of Population Economics, 33(4), 1127–1172. https://doi.org/10.1007/s00148-020-00778-2

    Article  Google Scholar 

  • Ravindran, S., & Shah, M. (2020). Unintended Consequences of Lockdowns: COVID-19 and the Shadow Pandemic (Working Paper 27562; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27562

  • Sarracino, F., Greyling, T., O’Connor, K., Peroni, C., & Rossouw, S. (2023). A year of pandemic: Levels, changes and validity of well-being data from Twitter. Evidence from Ten Countries. PLOS ONE, 18(2), e0275028. https://doi.org/10.1371/journal.pone.0275028

    Article  Google Scholar 

  • Serrano-Alarcón, M., Kentikelenis, A., Mckee, M., & Stuckler, D. (2022). Impact of COVID-19 lockdowns on mental health: Evidence from a quasi-natural experiment in England and Scotland. Health Economics, 31(2), 284–296. https://doi.org/10.1002/hec.4453

    Article  Google Scholar 

  • Sibley, C. G., Greaves, L. M., Satherley, N., Wilson, M. S., Overall, N. C., Lee, C. H. J., Milojev, P., Bulbulia, J., Osborne, D., Milfont, T. L., Houkamau, C. A., Duck, I. M., Vickers-Jones, R., & Barlow, F. K. (2020). Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. The American Psychologist, 75(5), 618–630. https://doi.org/10.1037/amp0000662

    Article  Google Scholar 

  • Silverio-Murillo, A., Balmori de la Miyar, J. R., & Hoehn-Velasco, L. (2020). Families under confinement: COVID-19, domestic violence, and alcohol consumption. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3688384

    Article  Google Scholar 

  • Stockwell, S., Trott, M., Tully, M., Shin, J., Barnett, Y., Butler, L., McDermott, D., Schuch, F., & Smith, L. (2021). Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: A systematic review. BMJ Open Sport & Exercise Medicine, 7(1), e000960. https://doi.org/10.1136/bmjsem-2020-000960

    Article  Google Scholar 

  • Su, Z., Su, S., & Zhou, Q. (2021). Government trust in a time of crisis: Survey evidence at the beginning of the pandemic in China. China Review, 21(2), 87–116.

    Google Scholar 

  • Taquet, M., Luciano, S., Geddes, J. R., & Harrison, P. J. (2021). Bidirectional associations between COVID-19 and psychiatric disorder: Retrospective cohort studies of 62 354 COVID-19 cases in the USA. The Lancet Psychiatry, 8(2), 130–140. https://doi.org/10.1016/S2215-0366(20)30462-4

    Article  Google Scholar 

  • Taylor, L. (2022). Covid-19: Omicron drives weekly record high in global infections. BMJ, 376, o66. https://doi.org/10.1136/bmj.o66

    Article  Google Scholar 

  • Tubadji, A., Webber, D., & Boy, F. (2020). COVID-19: Narrative economics, public policy and mental health. VoxEU.Org. https://voxeu.org/article/covid-19-narrative-economics-public-policy-and-mental-health

  • Wang, J., Fan, Y., Palacios, J., Chai, Y., Guetta-Jeanrenaud, N., Obradovich, N., Zhou, C., & Zheng, S. (2022). Global evidence of expressed sentiment alterations during the COVID-19 pandemic. Nature Human Behaviour, 6(3), 349. https://doi.org/10.1038/s41562-022-01312-y

    Article  Google Scholar 

  • Wang, Y., Wu, P., Liu, X., Li, S., Zhu, T., & Zhao, N. (2020). Subjective well-being of Chinese Sina Weibo users in residential lockdown during the COVID-19 pandemic: Machine learning analysis. Journal of Medical Internet Research, 22(12), e24775. https://doi.org/10.2196/24775

    Article  Google Scholar 

  • Wu, C., Shi, Z., Wilkes, R., Wu, J., Gong, Z., He, N., Xiao, Z., Zhang, X., Lai, W., Zhou, D., Zhao, F., Yin, X., Xiong, P., Zhou, H., Chu, Q., Cao, L., Tian, R., Tan, Y., Yang, L., … Nicola Giordano, G. (2021). Chinese citizen satisfaction with government performance during COVID-19. Journal of Contemporary China, 30(132), 930–944. https://doi.org/10.1080/10670564.2021.1893558

    Article  Google Scholar 

  • Yang, J., Chen, X., Deng, X., Chen, Z., Gong, H., Yan, H., Wu, Q., Shi, H., Lai, S., Ajelli, M., Viboud, C., & Yu, P. H. (2020). Disease burden and clinical severity of the first pandemic wave of COVID-19 in Wuhan, China. Nature Communications, 11(1), 5411. https://doi.org/10.1038/s41467-020-19238-2

    Article  Google Scholar 

  • Zheng, S., Wang, J., Sun, C., Zhang, X., & Kahn, M. E. (2019). Air pollution lowers Chinese urbanites’ expressed happiness on social media. Nature Human Behaviour, 3(3), 237–243. https://doi.org/10.1038/s41562-018-0521-2

    Article  Google Scholar 

  • Ziedan, E., Simon, K. I., & Wing, C. (2020). Effects of state COVID-19 closure policy on NON-COVID-19 health care utilization (w27621). National Bureau of Economic Research. https://doi.org/10.3386/w27621

    Article  Google Scholar 

  • Zivanovic, S., Martinez, J., & Verplanke, J. (2020). Capturing and mapping quality of life using Twitter data. GeoJournal, 85(1), 237–255. https://doi.org/10.1007/s10708-018-9960-6

    Article  Google Scholar 

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Acknowledgements

We are grateful to Yanqing Cao for outstanding research assistance. We would like to thank seminar participants at 2021 Applied Economics Workshop, SUFE, the International Society for Quality-of-Life Studies (ISQOLS) meeting, and Happiness and Age Conference at International Centre for Economic Analysis for helpful comments and suggestions. The Sina Weibo posts data were generously provided by Yong Hu, Heyan Huang and Xian-Ling Mao at Beijing Institute of Technology, and Anfan Chen at University of Science and Technology of China. Fengyu Wu gratefully acknowledges the support of the Observatoire de la Compétitivité, Ministère de l’Economie, DG Compétitivité, Luxembourg, and STATEC, the National Statistical Office of Luxembourg. Views and opinions expressed in this article are those of the authors and do not reflect those of STATEC and funding partners. All errors are our own.

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Authors and Affiliations

  1. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Mingming Ma & Shun Wang

  2. STATEC Research, National Institute of Statistics and Economic Studies (STATEC), 14 Rue Erasme, 1468, Luxembourg, Luxembourg

    Fengyu Wu

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  1. Mingming Ma
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Correspondence to Fengyu Wu.

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Appendix

Appendix

See Tables 6 and 7.

Table 6 Summary statistics for lockdown variables
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Table 7 Robustness checks for the medium-run effect of lockdown on expressed happiness
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Ma, M., Wang, S. & Wu, F. Lockdown, Infection, and Expressed Happiness in China. J Happiness Stud 25, 40 (2024). https://doi.org/10.1007/s10902-024-00752-9

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