Issues in Interdisciplinarity 2020-21/Lockdown chapter

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Evidence in Effectiveness of Lockdown[edit | edit source]

Introduction[edit | edit source]

When the COVID-19 pandemic hit, a debate concerning the optimal response started. This chapter investigates the evidence disciplines of Economics, Political Science and Public Health rely upon to determine the effectiveness of the implementation of lockdowns and the role disciplinary methodological differences play in it. Lockdown is defined here as 'a series of restrictions on the social and economic life of citizens and use of public spaces'.[1]

As this debate is influencing states' decisions to pursue particular virus-containment strategies, [2] it is important to highlight the potential flaws in the arguments of each discipline and to demonstrate that an interdisciplinary approach is essential for determining the most effective measures.[3]

Disciplinary Approaches[edit | edit source]

Economics[edit | edit source]

In Economics, virus-suppression measures like lockdowns are viewed as a trade-off between economic costs and lives lost. Many researchers attempt to represent the problem as a cost-benefit analysis by constructing quantitative models that either justify the costs of lockdown or find it inefficient. [4]

Economic evaluation is based on abstract, generalised models that rely on quantitative data, estimates, theories, forecasts and other models, for example, epidemiological models which also have some degree of abstraction and error.[5][2][6] Both economic and epidemiological models have to be based on limited data obtained by observation rather than controlled randomized trials and thus may be insufficiently reliable. [2]

As a result, some economists propose mitigation strategies that are less economically severe instead of a full lockdown. [6] But others recognise the validity of strict short-term suppressive restrictions, admitting that if they help to contain the virus, it may speed up the economic recovery and lower costs in the long-term. [7][8] However, those who calculate economic costs, inclusive of deaths and harm incurred due to a recession caused by lockdowns, suggest that strict suppression measures may be less effective in less economically developed countries and that the idea of the choice between economic harm and lives lost is flawed due to the direct interconnection of the two. [3]

Therefore, a dominant opinion doesn't exist within the discipline due to a high degree of uncertainty, and the differences in questions, methodologies, and consequently, the evidence that different economists consider.

Public Health[edit | edit source]

Public Health researchers' primary focus, regarding lockdowns, is “disease trends and risk factors, outcomes of treatment or public health interventions... and health care costs and use.”[9]

Specifically for pandemics like Covid-19, the most common method used for assessing combative strategies is an ‘intervention and prevention program evaluation,’ a combination of qualitative and quantitative methodologies. [10] This method aims to determine the effectiveness of lockdowns by comparing the change in the indicators in the pre-lockdown months and post-lockdown months.[11]

Majority of the evidence in public health research consists of models of the number of cases against different factors such as geographical location, age-groups etc. Multiple national and cross-national studies have observed an overall downward trend in the number of cases after physical distancing measures (such as lockdowns) have been implemented, establishing a direct correlation between the two. [12] [13] [14] However, although India imposed a nationwide lockdown that slowed infection rate, the number of cases skyrocketed as soon as the lockdown was lifted. This was due to inadequate existing public health facilities which weren't improved during the lockdown, reversing any positive effects of it. [15]

Public health recommendations continue to evolve during the pandemic. However, overall, based on limited quantitative evidence, the public health ministries of multiple countries assert that lockdowns are the most effective preventative measures, especially when coupled with other mitigative strategies such as test and trace and mandatory quarantines.

Political Science[edit | edit source]

Political Science studies focus on the actions and attitudes of individuals, groups, and institutions on the local, national and international levels. To understand the effectiveness and consequences of lockdowns, it collects empirical evidence through surveys, questionnaires, interviews, and case studies along with field-work, ethnographic works and past experiences.[16]

In regards to the effectiveness of lockdowns, the central issue for political scientists is the impact of lockdowns on political support and behavioural attitudes towards governmental institutions and actors, especially in democratic states, due to increasing government intervention (e.g tracking systems [17]) and the undemocratic nature of lockdown depriving people of the civil liberties.[18] [19]

The findings of multiple cross-national surveys indicate an increase in diffuse political support , regarding lockdown arrangements.[20][21] [22] [23]

However, the qualitative evidence suggests that the undemocratic nature of lockdowns can worsen existing social conflicts as well as lead to a new wave of social unrest in democracies.[24] [25] Studies have illustrated several correlations: between distinct features of states and citizens’ reaction to lockdown, between political beliefs, agitation campaigns and lockdown compliance, [26] and between unemployment and opposition to the state-proposed measures. [27]

All in all, the existing evidence suggests that the short-term effects of the lockdown on political support are mostly positive as the people recognize its need. However, long-term effects are yet undetermined, but seem to be more negative as no fundamental ideological changes are happening [23] while the role of the government changes, and this can cause continuous violent discontent with undemocratic measures, thus putting the safety and well-being of people under threat.

Conflict in Evidence[edit | edit source]

Overall, multiple conflicts among these disciplines along with their most contested conclusions seem to arise from the limitations of the evidence that they rely on. The nature of their methodologies makes some disciplines consider short-term impacts rather than long-term ones and take either qualitative or quantitative factors into account, but not both.

Evidence in Public Health is limited to the short-term impact of lockdowns on health-related variables. It doesn’t account for how they may influence economic variables such as exacerbation of existing economic inequalities and negative growth in GDP, causing a recession.[2] Recessions result in increased unemployment levels and poverty, which can lead to higher long-term death rates, especially in less developed countries.[28]

The evidence in Economics considers these long-term impacts on economies, but it is based on limited quantifiable factors. Economic models tend to lack recognition of psychological influences, of some quantitatively unmeasurable uncertainty, and many aspects of the complex interdependence of society. [2][4] They calculate the effectiveness of lockdown measures based on a change in the GDP and estimated value of life and don't consider the citizens’ opinion on the ‘economic costs vs lives lost’ debate.[29] For example, the initially calculated costs of a lockdown were later reassessed when it was found that only 7% of the negative growth in economic activity was due to legal restrictions while 60% was caused by people’s fear of the severity of the virus and the anticipated crisis, highlighting the importance of consideration of human behavior.[30]

Due to the qualitative nature of the evidence it relies on, Political Science considers the effectiveness of suppressive measures in relation with human behavior. For example, due to its attention to the political climate and levels of trust in the country at the time of entering the lockdown, it addresses the likelihood of social conflicts and lack of compliance with the proposed measures and thus, chances of failure of lockdowns and loss of lives resulting from them if people are not in support of any governmental decisions.[31]

A higher degree of reliance on interdisciplinary perspectives and evidence therefore will lead to fewer dangerous policy experiments and a more effective mitigation of the pandemic and its consequences.[3]

References[edit | edit source]

  1. Lockdown definition and meaning | Collins English Dictionary [Internet]. 2020 [cited 1 December 2020]. Available from:
  2. a b c d e Manski C. Forming COVID-19 Policy Under Uncertainty. Journal of Benefit-Cost Analysis. 2020;:1-16.
  3. a b c Bavli I, Sutton B, Galea S. Harms of public health interventions against covid-19 must not be ignored. BMJ. 2020;:m4074.
  4. a b Besley T, Stern N. The Economics of Lockdown. Fiscal Studies. 2020;41(3):493-513.
  5. Department of Health and Social Care. Analysis of the health, economic and social effects of COVID-19 and the approach to tiering, GOV.UK; 2020 p. 10.
  6. a b Gray D, Islam A, Suraiya Jabeen Bhuiyan, Brodeur A. A Literature Review of the Economics of COVID-19. Institute of Labor Economics; 2020.
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  11. Milstein B, Wetterhall SF. Framework for program evaluation in public health. 1999.
  12. Islam N, Sharp SJ, Chowell G, Shabnam S, Kawachi I, Lacey B, Massaro JM, D’Agostino RB, White M. Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries. bmj. 2020 Jul 15;370.
  13. May T. Lockdown-type measures look effective against covid-19. 2020.
  14. Kharroubi S, Saleh F. Are Lockdown Measures Effective Against COVID-19?. Frontiers in Public Health. 2020;8:610.
  15. Ray D, Salvatore M, Bhattacharyya R, Wang L, Du J, Mohammed S, Purkayastha S, Halder A, Rix A, Barker D, Kleinsasser M. Predictions, role of interventions and effects of a historic national lockdown in India’s response to the COVID-19 pandemic: data science call to arms. Harvard data science review. 2020;2020(Suppl 1).
  16. Schram S, Caterino B. Making political science matter. New York: New York University Press; 2013.
  17. Tracking the Global Response to COVID-19 | Privacy International [Internet]. 2020 [cited 14 December 2020] Available from:
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  19. Jennings W, Stoker G, Valgarðsson V. POLITICAL TRUST AND THE COVID-19 CRISIS: PUSHING POPULISM TO THE BACKBURNER? A STUDY OF PUBLIC OPINION IN AUSTRALIA, ITALY, THE UK AND THE USA [Internet]. 2020 [cited 5 December 2020] Available from:
  20. Merkley E, Bridgman A, Loewen P, Owen T, Ruths D, Zhilin O. A Rare Moment of Cross-Partisan Consensus: Elite and Public Response to the COVID-19 Pandemic in Canada. Canadian Journal of Political Science. 2020;53(2):311-318.
  21. Leininger A, Schaub M. Voting at the dawn of a global pandemic. 2020;.
  22. BOL D, GIANI M, BLAIS A, LOEWEN P. The effect of COVID‐19 lockdowns on political support: Some good news for democracy?. European Journal of Political Research. 2020;.
  23. a b Kushner Gadarian S, Goodman S, Pepinsky T. Partisan Endorsement Experiments Do Not Affect Mass Opinion on COVID-19. SSRN Electronic Journal. 2020;
  24. Kluth A. Social Unrest Is the Inevitable Legacy of the Covid Pandemic [Internet]. BloombergQuint. 2020 [cited 11 December 2020] Available from:
  25. Amat F, Arenas A, Falcó-Gimeno A, Muñoz J. Pandemics meet democracy. Experimental evidence from the COVID-19 crisis in Spain. 2020;
  26. Brodeur A, Gray D, Islam A, Bhuiyan S. A Literature Review of the Economics of COVID-19 [Internet]. 2020 [cited 14 December 2020] Available from:
  27. Bavli I, Sutton B, Galea S. Harms of public health interventions against covid-19 must not be ignored. BMJ. 2020;:m4074.
  28. Burgard, S.A., Ailshire, J.A. and Kalousova, L., 2013. The Great Recession and health: People, populations, and disparities. The Annals of the American Academy of Political and Social Science, 650(1), pp.194-213.
  29. Hargreaves Heap S, Koop C, Matakos K, Unan A, Weber N. Valuating health vs wealth: The effect of information and how this matters for COVID-19 policymaking [Internet]. VoxEU, CEPR. 2020 [cited 14 December 2020]. Available from:
  30. Goolsbee A, Syverson C. Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. Journal of Public Economics. 2020 Jun;193:104-311.
  31. The territorial impact of COVID-19: Managing the crisis across levels of government [Internet]. OECD. 2020 [cited 14 December 2020]. Available from: