IDEAS home Printed from https://ideas.repec.org/p/sef/csefwp/590.html
   My bibliography  Save this paper

The Covid-19 Crisis and Consumption: Survey Evidence from Six EU Countries

Author

Abstract

Using new panel data from a representative survey of households in the six largest euro area economies, the paper estimates the impact of the Covid-19 crisis on consumption. The panel provides, each month, household-specific indicators of the concern about finances due to Covid-19 from the first peak of the pandemic until October 2020. The results show that this concern causes a significant reduction in non-durable consumption. The paper also explores the potential impact on consumption of government interventions and of another wave of Covid-19, using household-level consumption adjustments to scenarios that involve positive and negative income shocks. Pandemic-related financial concerns induce a significant reduction (increase) in the marginal propensity to consume in response to a positive (negative) income shock, an effect consistent with models of precautionary saving and liquidity constraints. These results are robust to endogeneity problems through the use of panel fixed effects models as well as partial identification methods that account also for time-varying unobservable variables, and provide informative identification regions of the average treatment effect of the financial concern due to Covid-19 under weak assumptions.

Suggested Citation

  • Dimitris Christelis & Dimitris Georgarakos & Tullio Jappelli & Geoff Kenny, 2020. "The Covid-19 Crisis and Consumption: Survey Evidence from Six EU Countries," CSEF Working Papers 590, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy, revised 18 Dec 2020.
    • Handle: RePEc:sef:csefwp:590
    as

    Download full text from publisher

    File URL: http://www.csef.it/WP/wp590.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    2. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    3. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444606, September.
    4. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    5. Christelis, Dimitris & Dobrescu, Loretti I., 2020. "The causal effect of social activities on cognition: Evidence from 20 European countries," Social Science & Medicine, Elsevier, vol. 247(C).
    6. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    7. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444590, September.
    8. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    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. Giorgio Brunello & Dimitris Christelis & Anna Sanz‐de‐Galdeano & Anastasia Terskaya, 2024. "Does college selectivity reduce obesity? A partial identification approach," Health Economics, John Wiley & Sons, Ltd., vol. 33(10), pages 2306-2320, October.
    2. Charles F. Manski & John Newman & John V. Pepper, "undated". "Using Performance Standards to Evaluate Social Programs with Incomplete Outcome Data: General Issues and Application to a Higher Education Block Grant Program," IPR working papers 00-1, Institute for Policy Resarch at Northwestern University.
    3. Charles F. Manski & John Newman & John V. Pepper, 2002. "Using Performance Standards to Evaluate Social Programs with Incomplete Outcome Data," Evaluation Review, , vol. 26(4), pages 355-381, August.
    4. Grafova, Irina B. & Freedman, Vicki A. & Lurie, Nicole & Kumar, Rizie & Rogowski, Jeannette, 2014. "The difference-in-difference method: Assessing the selection bias in the effects of neighborhood environment on health," Economics & Human Biology, Elsevier, vol. 13(C), pages 20-33.
    5. Michael Lechner & Blaise Melly, 2010. "Partial Idendification of Wage Effects of Training Programs," Working Papers 2010-8, Brown University, Department of Economics.
    6. Manski, Charles F., 2000. "Identification problems and decisions under ambiguity: Empirical analysis of treatment response and normative analysis of treatment choice," Journal of Econometrics, Elsevier, vol. 95(2), pages 415-442, April.
    7. Charles F. Manski, 2003. "Identification Problems in the Social Sciences and Everyday Life," Southern Economic Journal, John Wiley & Sons, vol. 70(1), pages 11-21, July.
    8. Christelis, Dimitris & Messina, Julián, 2019. "Partial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection," IDB Publications (Working Papers) 9520, Inter-American Development Bank.
    9. James J. Heckman & Edward J. Vytlacil, 2000. "Instrumental Variables, Selection Models, and Tight Bounds on the Average Treatment Effect," NBER Technical Working Papers 0259, National Bureau of Economic Research, Inc.
    10. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    11. Michael Lechner & Blaise Melly, 2007. "Earnings Effects of Training Programs," University of St. Gallen Department of Economics working paper series 2007 2007-28, Department of Economics, University of St. Gallen.
    12. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    13. C, Loran & Eckbo, Espen & Lu, Ching-Chih, 2014. "Does Executive Compensation Reflect Default Risk?," UiS Working Papers in Economics and Finance 2014/11, University of Stavanger.
    14. Vazquez-Alvarez, R. & Melenberg, B. & van Soest, A.H.O., 1999. "Nonparametric Bounds on the Income Distribution in the Presence of Item Nonresponse," Other publications TiSEM d37fb6a5-2075-42b2-b0b4-5, Tilburg University, School of Economics and Management.
    15. Fan, Yanqin & Park, Sang Soo, 2014. "Nonparametric inference for counterfactual means: Bias-correction, confidence sets, and weak IV," Journal of Econometrics, Elsevier, vol. 178(P1), pages 45-56.
    16. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, vol. 44(1), pages 81-109, February.
    17. Vikesh Amin & Jere R. Behrman & Jason M. Fletcher & Carlos A. Flores & Alfonso Flores-Lagunes & Hans-Peter Kohler, 2022. "Does Schooling Improve Cognitive Abilities at Older Ages: Causal Evidence from Nonparametric Bounds," PIER Working Paper Archive 22-016, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    18. Manski, Charles, 1994. "Simultaneity with Downward Sloping Demand," SFB 373 Discussion Papers 1994,29, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    19. Monique De Haan & Edwin Leuven, 2020. "Head Start and the Distribution of Long-Term Education and Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 38(3), pages 727-765.
    20. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Covid-19; Consumption; Income Shocks; Marginal Propensity to Consume; Financial concerns; Fiscal policies.;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:sef:csefwp:590. 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: Dr. Maria Carannante (email available below). General contact details of provider: https://edirc.repec.org/data/cssalit.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.