IDEAS home Printed from https://ideas.repec.org/p/een/camaaa/2024-19.html
   My bibliography  Save this paper

Business Cycle and Health Dynamics during the COVID-19 Pandemic: A Scandinavian Perspective

Author

Listed:
  • Hilde C. Bjornland
  • Malin C. Jensen
  • Leif Anders Thorsrud

Abstract

We use a unique measure of daily economic activity and manually audited non-pharmaceutical intervention indexes for Norway and Sweden to model the dynamics between COVID-19, policy, health, and business cycles within a SVAR framework. Our analysis documents large measurement errors in commonly used containment policy measures, significant endogeneity between the model’s variables, and a strong health-economy trade-off following both policy shocks and precautionary actions. We further document that a large share of the variation in containment policies is driven by news innovations and quantify via counterfactual simulations the output cost per life saved from following stricter versus softer policies.

Suggested Citation

  • Hilde C. Bjornland & Malin C. Jensen & Leif Anders Thorsrud, 2024. "Business Cycle and Health Dynamics during the COVID-19 Pandemic: A Scandinavian Perspective," CAMA Working Papers 2024-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2024-19
    as

    Download full text from publisher

    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2024-03/19_2024_bjornland_jensen_thorsrud_0.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    2. Brookes, L G, 1972. "More on the Output Elasticity of Energy Consumption," Journal of Industrial Economics, Wiley Blackwell, vol. 21(1), pages 83-92, November.
    3. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    4. Brantley Liddle and Hillard Huntington, 2020. "Revisiting the Income Elasticity of Energy Consumption: A Heterogeneous, Common Factor, Dynamic OECD & non-OECD Country Panel Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 207-230.
    5. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    6. Robert K. Kaufmann, 2004. "The Mechanisms for Autonomous Energy Efficiency Increases: A Cointegration Analysis of the US Energy/GDP Ratio," The Energy Journal, , vol. 25(1), pages 63-86, January.
    7. Cai, Zongwu & Li, Qi & Park, Joon Y., 2009. "Functional-coefficient models for nonstationary time series data," Journal of Econometrics, Elsevier, vol. 148(2), pages 101-113, February.
    8. Bierens, Herman J. & Martins, Luis F., 2010. "Time-Varying Cointegration," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1453-1490, October.
    9. Zsuzsanna Csereklyei, M. d. Mar Rubio-Varas, and David I. Stern, 2016. "Energy and Economic Growth: The Stylized Facts," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    10. Jakob, Michael & Haller, Markus & Marschinski, Robert, 2012. "Will history repeat itself? Economic convergence and convergence in energy use patterns," Energy Economics, Elsevier, vol. 34(1), pages 95-104.
    11. Chang, Yoosoon & Kaufmann, Robert K. & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2020. "Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate," Journal of Econometrics, Elsevier, vol. 214(1), pages 274-294.
    12. Park, Joon Y. & Hahn, Sang B., 1999. "Cointegrating Regressions With Time Varying Coefficients," Econometric Theory, Cambridge University Press, vol. 15(5), pages 664-703, October.
    13. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    14. Richmond, Amy K. & Kaufmann, Robert K., 2006. "Is there a turning point in the relationship between income and energy use and/or carbon emissions?," Ecological Economics, Elsevier, vol. 56(2), pages 176-189, February.
    15. Ruth A. Judson & Richard Schmalensee & Thomas M. Stoker, 1999. "Economic Development and the Structure of the Demand for Commercial Energy," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 29-57.
    16. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2021. "Forecasting regional long-run energy demand: A functional coefficient panel approach," Energy Economics, Elsevier, vol. 96(C).
    17. Brantley Liddle, 2022. "What Is the Temporal Path of the GDP Elasticity of Energy Consumption in OECD Countries? An Assessment of Previous Findings and New Evidence," Energies, MDPI, vol. 15(10), pages 1-12, May.
    18. Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
    19. Inglesi-Lotz, R., 2011. "The evolution of price elasticity of electricity demand in South Africa: A Kalman filter application," Energy Policy, Elsevier, vol. 39(6), pages 3690-3696, June.
    20. Burke, Paul J. & Csereklyei, Zsuzsanna, 2016. "Understanding the energy-GDP elasticity: A sectoral approach," Energy Economics, Elsevier, vol. 58(C), pages 199-210.
    21. Lin, Chien-Fu Jeff & Terasvirta, Timo, 1994. "Testing the constancy of regression parameters against continuous structural change," Journal of Econometrics, Elsevier, vol. 62(2), pages 211-228, June.
    22. Kenneth B. Medlock III & Ronald Soligo, 2001. "Economic Development and End-Use Energy Demand," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 77-105.
    23. Rossana Galli, 1998. "The Relationship Between Energy Intensity and Income Levels: Forecasting Long Term Energy Demand in Asian Emerging Countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 85-105.
    24. Lescaroux, François, 2011. "Dynamics of final sectoral energy demand and aggregate energy intensity," Energy Policy, Elsevier, vol. 39(1), pages 66-82, January.
    25. Webster, Mort & Paltsev, Sergey & Reilly, John, 2008. "Autonomous efficiency improvement or income elasticity of energy demand: Does it matter?," Energy Economics, Elsevier, vol. 30(6), pages 2785-2798, November.
    26. Park, Sung Y. & Zhao, Guochang, 2010. "An estimation of U.S. gasoline demand: A smooth time-varying cointegration approach," Energy Economics, Elsevier, vol. 32(1), pages 110-120, January.
    27. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2024. "Common Trends and Country Specific Heterogeneities in Long-Run World Energy Consumption," Working Papers No 01/2024, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Liddle, Brantley, 2023. "Is timing everything? Assessing the evidence on whether energy/electricity demand elasticities are time-varying," Energy Economics, Elsevier, vol. 124(C).
    3. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2016. "Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 232-243.
    4. Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
    5. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2021. "Forecasting regional long-run energy demand: A functional coefficient panel approach," Energy Economics, Elsevier, vol. 96(C).
    6. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    7. Fotis, Panagiotis & Karkalakos, Sotiris & Asteriou, Dimitrios, 2017. "The relationship between energy demand and real GDP growth rate: The role of price asymmetries and spatial externalities within 34 countries across the globe," Energy Economics, Elsevier, vol. 66(C), pages 69-84.
    8. Brantley Liddle, 2022. "What Is the Temporal Path of the GDP Elasticity of Energy Consumption in OECD Countries? An Assessment of Previous Findings and New Evidence," Energies, MDPI, vol. 15(10), pages 1-12, May.
    9. Burke, Paul J. & Csereklyei, Zsuzsanna, 2016. "Understanding the energy-GDP elasticity: A sectoral approach," Energy Economics, Elsevier, vol. 58(C), pages 199-210.
    10. Gao, Jiti & Peng, Bin & Smyth, Russell, 2021. "On income and price elasticities for energy demand: A panel data study," Energy Economics, Elsevier, vol. 96(C).
    11. Liddle, Brantley & Parker, Steven, 2022. "One more for the road: Reconsidering whether OECD gasoline income and price elasticities have changed over time," Energy Economics, Elsevier, vol. 114(C).
    12. Zsuzsanna Csereklyei & M. d. Mar Rubio-Varas & David I. Stern, 2016. "Energy and Economic Growth: The Stylized Facts," The Energy Journal, , vol. 37(2), pages 223-256, April.
    13. Galeotti, Marzio & Salini, Silvia & Verdolini, Elena, 2020. "Measuring environmental policy stringency: Approaches, validity, and impact on environmental innovation and energy efficiency," Energy Policy, Elsevier, vol. 136(C).
    14. Liddle, Brantley & Huntington, Hillard, 2021. "There’s Technology Improvement, but is there Economy-wide Energy Leapfrogging? A Country Panel Analysis," World Development, Elsevier, vol. 140(C).
    15. Shemelis Kebede Hundie & Megersa Debela Daksa, 2019. "Does energy-environmental Kuznets curve hold for Ethiopia? The relationship between energy intensity and economic growth," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-21, December.
    16. Suharno Suharno & Nurul Anwar, 2022. "The Energy Demand Elasticity in Relation to Gross Domestic Product in Indonesia: Sectoral Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 634-640, July.
    17. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    18. Fouquet, Roger, 2016. "Lessons from energy history for climate policy: technological change, demand and economic development," LSE Research Online Documents on Economics 67785, London School of Economics and Political Science, LSE Library.
    19. Liddle, Brantley & Parker, Steven & Hasanov, Fakhri, 2023. "Why has the OECD long-run GDP elasticity of economy-wide electricity demand declined? Because the electrification of energy services has saturated," Energy Economics, Elsevier, vol. 125(C).
    20. Agovino, Massimiliano & Bartoletto, Silvana & Garofalo, Antonio, 2019. "Modelling the relationship between energy intensity and GDP for European countries: An historical perspective (1800–2000)," Energy Economics, Elsevier, vol. 82(C), pages 114-134.

    More about this item

    Keywords

    COVID-19; simultaneity; expectations; business cycles;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:een:camaaa:2024-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Cama Admin (email available below). General contact details of provider: https://edirc.repec.org/data/asanuau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.