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The Reliability of COVID-19 Data in the Shadow of Anti-Pandemic Measures’ Cancellation

Year 2022, Volume: 31 Issue: 1, 91 - 114, 15.04.2022

Abstract

COVID-19 pandemic necessitates taking measures that may be very costly from an economic standpoint and likely to make the mass public discontent. If an anti-pandemic regimen does not accomplish its goals, its costs become even harder to justify. We argue that, under such circumstances, cancellation of an anti-pandemic regimen would decrease the reliability of health data because rank-in-file policymakers and bureaucrats have incentives to present more optimistic statistics to signal their competence and politicians would further pressure them to report statistics that appear to agree with the cancellation of restrictions and give legitimacy to taking the measures. Our empirical analyses suggest that closeness to the restrictions’ cancellation date is associated with lower reliability of COVID-19 daily cumulative cases and deaths data. Being robust to several sensitivity and robustness checks, this finding is alarming from the perspective of representative democracy and for those who have to survive in these turbulent times.

References

  • Adiguzel, F. S., Cansunar, A., & Corekcioglu, G. (2020). Truth or Dare? Detecting Systematic Manipulation of COVID-19 Statistics. Journal of Political Institutions and Political Economy, 1(4), 543-557.
  • Agence France-Press. (2020). Russia Admits to World's Third-Worst Covid-19 Death Toll. Guardian.
  • Anran Wei, & Vellwock, A. E. (2020). Is COVID-19 Data Reliable? A Statistical Analysis with Benford’s Law. Working Paper.
  • Beck, N., & Katz, J. N. (1995). What to do (and not to do) with Time-Series Cross-Section Data. The American Political Science Review, 89(3), 634-647.
  • Benford, F. (1938). The Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 78(4), 551-572.
  • Coppedge, M., John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, D. A., Michael Bernhard, . . . Ziblatt, D. (2020). V-Dem Country-Year Dataset v10.
  • Doyle, A. A., Friedlander, M. S. H., Li, G. D., Marble, W., Smith, C. J., Baronia, N., . . . Srinivasan, M. (2020). The Evidence and Tradeoffs for a 'Stay-at-Home' Pandemic Response: A Multidisciplinary Review Examining the Medical, Psychological, Economic and Political Impact of 'Stay-at-Home' Implementation in America. Working Paper.
  • Epstude, K., & Roese, N. J. (2008). The Functional Theory of Counterfactual Thinking. Personality and Social Psychology Review, 12(2), 168-192.
  • Falk, G., Romero, P. D., Nicchitta, I. A., & Nyhof, E. C. (2020). Unemployment Rates During the COVID-19 Pandemic: In Brief. Congressional Research Service.
  • Galea, S., Merchant, R. M., & Lurie, N. (2020). The Mental Health Consequences of COVID-19 and Physical Distancing: The Need for Prevention and Early Intervention. JAMA Internal Medicine, 180(6), 817-818.
  • Gilbar, O., & Hevroni, A. (2007). Counterfactuals, Coping Strategies and Psychological Distress Among Breast Cancer Patients. Anxiety, Stress, & Coping, 20, 383 - 392.
  • Grammatikos, T., & Papanikolaou, N. I. (2021). Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry. Journal of Financial Services Research, 59(1), 115-142.
  • Hamadani, J. D., Hasan, M. I., Baldi, A. J., Hossain, S. J., Shiraji, S., Bhuiyan, M. S. A., . . . Pasricha, S.-R. (2020). Immediate Impact of Stay-at-home Orders to Control COVID-19 Transmission on Socioeconomic Conditions, Food Insecurity, Mental Health, and Intimate Partner Violence in Bangladeshi Women and Their Families: An Interrupted Time Series. The Lancet Global Health, 8(11), e1380-e1389.
  • Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing 'Maddison': New Income Comparisons and the Shape of Long-Run Economic Development.
  • Kahneman, D., & Tversky, A. (1982). The Simulation Heuristic. In Daniel Kahneman, Paul Slovic, & A. Tversky (Eds.), Judgment Under Uncertainty: Heuristics and Biases (pp. 201-208). New York: Cambridge University Press.
  • Kavakli, K. C. (2020). Did Populist Leaders Respond to the COVID-19 Pandemic More Slowly? Evidence from a Global Sample. Working Paper.
  • Lecci, L., Okun, M. A., & Karoly, P. (1994). Life Regrets and Current Goals as Predictors of Psychological Adjustment. Journal of Personality and Social Psychology, 66(4), 731-741.
  • Lührmann, A., Nils Düpont, Masaaki Higashijima, Yaman Berker Kavasoglu, Kyle L. Marquardt, Michael Bernhard, . . . Seim, B. (2020). Varieties of Party Identity and Organization (V-Party) Dataset V1.
  • Marshall, M. G., & Gurr, T. R. (2020). POLITY5: Political Regime Characteristics and Transitions, 1800-2018.
  • Mebane, W. (2006). Election Forensics: Vote Counts and Benford’s Law. Paper presented at the Summer Meeting of the Political Methodology Society, UC-Davis.
  • Mebane, W. (2008). Election Forensics: The Second Digit Benford’s Law Test and Recent American Presidential Elections. In R. Michael Alvarez, Thad E. Hall, & S. D. Hyde (Eds.), Election Fraud: Detecting and Deterring Electoral Manipulation (Vol. 162-181). Washington DC: Brookings.
  • Mellers, B. A., Schwartz, A., Ho, K., & Ritov, I. (1997). Decision Affect Theory: Emotional Reactions to the Outcomes of Risky Options. Psychological Science, 8(6), 423-429.
  • Miller, S. J., & Nigrini, M. J. (2008). The Modulo 1 Central Limit Theorem and Benford’s Law for Products. International Journal of Algebra, 2(3), 119-130.
  • Mudde, C. (2007). Populist Radical Right Parties in Europe. Cambridge: Cambridge University Press.
  • Newcomb, S. (1881). Note on the Frequency of Use of the Different Digits in Natural Numbers. American Journal of Mathematics, 4(1), 39-40.
  • Prais, S., & Winsten, C. (1954). Trend Estimators and Serial Correlation. Paper presented at the Cowles Commission Discussion Paper No. 383.
  • Roese, N. J. (1994). The Functional Basis of Counterfactual Thinking. Journal of Personality and Social Psychology, 66(5), 805-818.
  • Sambridge, M., Hrvoje Tkalcic, & Jackson, A. (2010). Benford’s Law in the Natural Sciences. Geophysical Research Letters, 37(22), 1-5.
  • Sambridge, M., & Jackson, A. (2020). National COVID Numbers - Benford’s Law Looks for Errors. Nature, 581(7809), 384.
  • Seliski, J. (2020). Interim Economic Projections for 2020 and 2021. Congressional Budget Office.
  • Shevlin, M., McBride, O., Murphy, J., Miller, J. G., Hartman, T. K., Levita, L., . . . Bentall, R. P. (2020). Anxiety, Depression, Traumatic Stress and COVID-19-related Anxiety in the UK General Population During the COVID-19 Pandemic. BJPsychOpen, 6(6).
  • Shvetsova, O., Andrei Zhirnov, Julie VanDusky-Allen3, Abdul Basit Adeel, Michael Catalano, Olivia Catalano, . . . Zhao, T. (2020). Institutional Origins of Protective COVID-19 Public Health Policy Responses: Informational and Authority Redundancies and Policy Stringency. Journal of Political Institutions and Political Economy, 1(4), 585-613.
  • Swami, V., George Horne, & Furnham, A. (2021). COVID-19-related Stress and Anxiety are Associated with Negative Body Image in Adults from the United Kingdom. Personality and Individual Differences, 170, 110426.
  • Thomas Hale, Thomas Boby, Noam Angrist, Emily Cameron-Blake, Laura Hallas, Beatriz Kira, . . . Webster, S. (2020a). Oxford COVID-19 Government Response Tracker. from Blavatnik School of Government
  • Thomas Hale, Thomas Boby, Noam Angrist, Emily Cameron-Blake, Laura Hallas, Beatriz Kira, . . . Webster, S. (2020b). Variation in Government Responses to COVID-19. Blavatnik School of Government Working Paper.
  • Tull, M. T., Keith A. Edmonds, Kayla M. Scamaldo, Julia R.Richmond, Jason P.Rose, & Gratz., K. L. (2020). Psychological Outcomes Associated with Stay-at-Home Orders and the Perceived Impact of COVID-19 on Daily Life. Psychiatry Research, 289, 113098.
  • Varian, H. (1972). Benford’s Law (Letters to the Editor). The American Statistician, 26(3), 65.
  • Wei, A., & Vellwock, A. E. (2020). Is COVID-19 Data Reliable? A Statistical Analysis with Benford’s Law. Working Paper.
  • World Bank. (2019). World Bank Development Indicators.
  • World Bank. (2021). Global Economic Prospects.

Pandemi Önlemlerinin Kaldırılması Kararları Işığında Covid-19 Verilerinin Güvenilirliği Sorunu

Year 2022, Volume: 31 Issue: 1, 91 - 114, 15.04.2022

Abstract

Covid-19 pandemisi, dünyanın dört bir yanındaki hükümetleri oldukça önemli ekonomik ve sosyal sonuçları olan tedbirler almaya itmiştir. Alınan bu oldukça sert tedbirlerin pandemiyle mücadele hususunda yetersiz kaldığı yahut başarısız olduğu durumlarda, bu tedbirler en başta ekonomik olmak üzere toplumun çeşitli kesimleri üzerindeki ağır maliyetleri kamuoyu nezdinde tepkiyle karşılanabilmektedir. Çalışmamızda bu gibi durumlarda, hükümetler ve ilgili uzmanların aldıkları tedbirlerin başarısını ölçmek için referans aldığımız hasta ve vefat istatistiklerinin güvenilirliğinin önemli ölçüde azaldığı öne sürülmektedir. Zira, alınan bu sert tedbirlerin başarısız olması durumunda, seçilmişler bunların meşruluğunu ve olumlu sonuçlarını gösterecek, daha iyimser istatistikler yayımlanmasını talep etme temayülünde olacak ve ilgili istatistiklerin hazırlanmasından sorumlu uzman ve bürokratlar üzerlerinde çeşitli baskılar kuracaklardır. Nitekim, betimsel ve ampirik tahliller tam kapanma uygulamasının sona ermesinin öncesinde toplam vefat ve hasta sayılarına dair istatistiklerin daha az güvenilir hala geldiğini göstermektedir. Çalışmamızın eklerinde yer verilen alternatif model, ölçüt ve analizler de bu sonuçları destekler niteliktedir. Bu açıdan, çalışmamızın sonuçları gerek temsili demokrasi gerekse de pandemi süresince hayatlarını bu istatistiklere göre idame ettirmeye çalışan vatandaşlar için oldukça kaygı vericidir.

References

  • Adiguzel, F. S., Cansunar, A., & Corekcioglu, G. (2020). Truth or Dare? Detecting Systematic Manipulation of COVID-19 Statistics. Journal of Political Institutions and Political Economy, 1(4), 543-557.
  • Agence France-Press. (2020). Russia Admits to World's Third-Worst Covid-19 Death Toll. Guardian.
  • Anran Wei, & Vellwock, A. E. (2020). Is COVID-19 Data Reliable? A Statistical Analysis with Benford’s Law. Working Paper.
  • Beck, N., & Katz, J. N. (1995). What to do (and not to do) with Time-Series Cross-Section Data. The American Political Science Review, 89(3), 634-647.
  • Benford, F. (1938). The Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 78(4), 551-572.
  • Coppedge, M., John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, D. A., Michael Bernhard, . . . Ziblatt, D. (2020). V-Dem Country-Year Dataset v10.
  • Doyle, A. A., Friedlander, M. S. H., Li, G. D., Marble, W., Smith, C. J., Baronia, N., . . . Srinivasan, M. (2020). The Evidence and Tradeoffs for a 'Stay-at-Home' Pandemic Response: A Multidisciplinary Review Examining the Medical, Psychological, Economic and Political Impact of 'Stay-at-Home' Implementation in America. Working Paper.
  • Epstude, K., & Roese, N. J. (2008). The Functional Theory of Counterfactual Thinking. Personality and Social Psychology Review, 12(2), 168-192.
  • Falk, G., Romero, P. D., Nicchitta, I. A., & Nyhof, E. C. (2020). Unemployment Rates During the COVID-19 Pandemic: In Brief. Congressional Research Service.
  • Galea, S., Merchant, R. M., & Lurie, N. (2020). The Mental Health Consequences of COVID-19 and Physical Distancing: The Need for Prevention and Early Intervention. JAMA Internal Medicine, 180(6), 817-818.
  • Gilbar, O., & Hevroni, A. (2007). Counterfactuals, Coping Strategies and Psychological Distress Among Breast Cancer Patients. Anxiety, Stress, & Coping, 20, 383 - 392.
  • Grammatikos, T., & Papanikolaou, N. I. (2021). Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry. Journal of Financial Services Research, 59(1), 115-142.
  • Hamadani, J. D., Hasan, M. I., Baldi, A. J., Hossain, S. J., Shiraji, S., Bhuiyan, M. S. A., . . . Pasricha, S.-R. (2020). Immediate Impact of Stay-at-home Orders to Control COVID-19 Transmission on Socioeconomic Conditions, Food Insecurity, Mental Health, and Intimate Partner Violence in Bangladeshi Women and Their Families: An Interrupted Time Series. The Lancet Global Health, 8(11), e1380-e1389.
  • Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing 'Maddison': New Income Comparisons and the Shape of Long-Run Economic Development.
  • Kahneman, D., & Tversky, A. (1982). The Simulation Heuristic. In Daniel Kahneman, Paul Slovic, & A. Tversky (Eds.), Judgment Under Uncertainty: Heuristics and Biases (pp. 201-208). New York: Cambridge University Press.
  • Kavakli, K. C. (2020). Did Populist Leaders Respond to the COVID-19 Pandemic More Slowly? Evidence from a Global Sample. Working Paper.
  • Lecci, L., Okun, M. A., & Karoly, P. (1994). Life Regrets and Current Goals as Predictors of Psychological Adjustment. Journal of Personality and Social Psychology, 66(4), 731-741.
  • Lührmann, A., Nils Düpont, Masaaki Higashijima, Yaman Berker Kavasoglu, Kyle L. Marquardt, Michael Bernhard, . . . Seim, B. (2020). Varieties of Party Identity and Organization (V-Party) Dataset V1.
  • Marshall, M. G., & Gurr, T. R. (2020). POLITY5: Political Regime Characteristics and Transitions, 1800-2018.
  • Mebane, W. (2006). Election Forensics: Vote Counts and Benford’s Law. Paper presented at the Summer Meeting of the Political Methodology Society, UC-Davis.
  • Mebane, W. (2008). Election Forensics: The Second Digit Benford’s Law Test and Recent American Presidential Elections. In R. Michael Alvarez, Thad E. Hall, & S. D. Hyde (Eds.), Election Fraud: Detecting and Deterring Electoral Manipulation (Vol. 162-181). Washington DC: Brookings.
  • Mellers, B. A., Schwartz, A., Ho, K., & Ritov, I. (1997). Decision Affect Theory: Emotional Reactions to the Outcomes of Risky Options. Psychological Science, 8(6), 423-429.
  • Miller, S. J., & Nigrini, M. J. (2008). The Modulo 1 Central Limit Theorem and Benford’s Law for Products. International Journal of Algebra, 2(3), 119-130.
  • Mudde, C. (2007). Populist Radical Right Parties in Europe. Cambridge: Cambridge University Press.
  • Newcomb, S. (1881). Note on the Frequency of Use of the Different Digits in Natural Numbers. American Journal of Mathematics, 4(1), 39-40.
  • Prais, S., & Winsten, C. (1954). Trend Estimators and Serial Correlation. Paper presented at the Cowles Commission Discussion Paper No. 383.
  • Roese, N. J. (1994). The Functional Basis of Counterfactual Thinking. Journal of Personality and Social Psychology, 66(5), 805-818.
  • Sambridge, M., Hrvoje Tkalcic, & Jackson, A. (2010). Benford’s Law in the Natural Sciences. Geophysical Research Letters, 37(22), 1-5.
  • Sambridge, M., & Jackson, A. (2020). National COVID Numbers - Benford’s Law Looks for Errors. Nature, 581(7809), 384.
  • Seliski, J. (2020). Interim Economic Projections for 2020 and 2021. Congressional Budget Office.
  • Shevlin, M., McBride, O., Murphy, J., Miller, J. G., Hartman, T. K., Levita, L., . . . Bentall, R. P. (2020). Anxiety, Depression, Traumatic Stress and COVID-19-related Anxiety in the UK General Population During the COVID-19 Pandemic. BJPsychOpen, 6(6).
  • Shvetsova, O., Andrei Zhirnov, Julie VanDusky-Allen3, Abdul Basit Adeel, Michael Catalano, Olivia Catalano, . . . Zhao, T. (2020). Institutional Origins of Protective COVID-19 Public Health Policy Responses: Informational and Authority Redundancies and Policy Stringency. Journal of Political Institutions and Political Economy, 1(4), 585-613.
  • Swami, V., George Horne, & Furnham, A. (2021). COVID-19-related Stress and Anxiety are Associated with Negative Body Image in Adults from the United Kingdom. Personality and Individual Differences, 170, 110426.
  • Thomas Hale, Thomas Boby, Noam Angrist, Emily Cameron-Blake, Laura Hallas, Beatriz Kira, . . . Webster, S. (2020a). Oxford COVID-19 Government Response Tracker. from Blavatnik School of Government
  • Thomas Hale, Thomas Boby, Noam Angrist, Emily Cameron-Blake, Laura Hallas, Beatriz Kira, . . . Webster, S. (2020b). Variation in Government Responses to COVID-19. Blavatnik School of Government Working Paper.
  • Tull, M. T., Keith A. Edmonds, Kayla M. Scamaldo, Julia R.Richmond, Jason P.Rose, & Gratz., K. L. (2020). Psychological Outcomes Associated with Stay-at-Home Orders and the Perceived Impact of COVID-19 on Daily Life. Psychiatry Research, 289, 113098.
  • Varian, H. (1972). Benford’s Law (Letters to the Editor). The American Statistician, 26(3), 65.
  • Wei, A., & Vellwock, A. E. (2020). Is COVID-19 Data Reliable? A Statistical Analysis with Benford’s Law. Working Paper.
  • World Bank. (2019). World Bank Development Indicators.
  • World Bank. (2021). Global Economic Prospects.
There are 40 citations in total.

Details

Primary Language English
Subjects Political Science
Journal Section Articles
Authors

Evgeny Sedashov This is me 0000-0002-1022-6375

Dr. Mert Moral 0000-0001-6674-3198

Publication Date April 15, 2022
Submission Date December 28, 2021
Published in Issue Year 2022 Volume: 31 Issue: 1

Cite

APA Sedashov, E., & Moral, D. M. (2022). The Reliability of COVID-19 Data in the Shadow of Anti-Pandemic Measures’ Cancellation. Siyasal: Journal of Political Sciences, 31(1), 91-114.