IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2408.13047.html
   My bibliography  Save this paper

Difference-in-differences with as few as two cross-sectional units -- A new perspective to the democracy-growth debate

Author

Listed:
  • Gilles Koumou
  • Emmanuel Selorm Tsyawo

Abstract

Pooled panel analyses often mask heterogeneity in unit-specific treatment effects. This challenge, for example, crops up in studies of the impact of democracy on economic growth, where findings vary substantially due to differences in country composition. To address this challenge, this paper introduces a Difference-in-Differences (DiD) estimator that leverages the temporal dimension of the data to estimate unit-specific average treatment effects on the treated (ATT) with as few as two cross-sectional units. Under weak identification and temporal dependence conditions, the proposed DiD estimator is shown to be asymptotically normal. The method is further complemented with an identification test that, unlike pre-trends tests, is more powerful and can detect violations of parallel trends in the post-treatment period. Empirical results using the DiD estimator suggest Benin's economy would have been 6.3% smaller on average over the 1993-2018 period had she not democratised.

Suggested Citation

  • Gilles Koumou & Emmanuel Selorm Tsyawo, 2024. "Difference-in-differences with as few as two cross-sectional units -- A new perspective to the democracy-growth debate," Papers 2408.13047, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2408.13047
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2408.13047
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    2. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    3. Ariella Kahn-Lang & Kevin Lang, 2020. "The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 613-620, July.
    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. Peppel-Srebrny, Jemima, 2021. "Not all government budget deficits are created equal: Evidence from advanced economies' sovereign bond markets," Journal of International Money and Finance, Elsevier, vol. 118(C).
    2. Wooldridge, Jeffrey M., 2023. "What is a standard error? (And how should we compute it?)," Journal of Econometrics, Elsevier, vol. 237(2).
    3. Woutersen, Tiemen & Hausman, Jerry A., 2019. "Increasing the power of specification tests," Journal of Econometrics, Elsevier, vol. 211(1), pages 166-175.
    4. Jeffrey M. Wooldridge, 2001. "Applications of Generalized Method of Moments Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 87-100, Fall.
    5. Marco Celentani & Miguel García-Posada & Fernando Gómez Pomar, 2022. "Fresh start policies and small business activity: evidence from a natural experiment," Working Papers 2210, Banco de España.
    6. Philippe Aghion, Antonin Bergeaud, Matthieu Lequien, Marc J. Melitz, 2018. "The Impact of Exports on Innovation: Theory and Evidence," Working papers 678, Banque de France.
    7. Marcelo Moreira & Geert Ridder, 2019. "Efficiency loss of asymptotically efficient tests in an instrumental variables regression," CeMMAP working papers CWP03/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    9. Wang, Honglin & Iglesias, Emma M. & Wooldridge, Jeffrey M., 2013. "Partial maximum likelihood estimation of spatial probit models," Journal of Econometrics, Elsevier, vol. 172(1), pages 77-89.
    10. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    11. Bai, Jushan & Choi, Sung Hoon & Liao, Yuan, 2024. "Standard errors for panel data models with unknown clusters," Journal of Econometrics, Elsevier, vol. 240(2).
    12. Alfredo R. Paloyo, 2011. "When Did We Begin to Spell “Heteros*edasticity” Correctly?," Ruhr Economic Papers 0300, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    13. Hideaki Matsuoka, 2022. "Debt Intolerance: Threshold Level and Composition," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 894-932, August.
    14. Roman Lanis & Grant Richardson & Brett Govendir & Gregory Pazmandy, 2021. "The effect of board of directors’ expertise and tax avoidance on corporate debt," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4475-4511, September.
    15. Dayton M. Lambert, 2020. "Dynamic panel estimation of a regional adjustment model with spatial-temporal robust covariance," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 245-265, December.
    16. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    17. Chang, Eric C.C. & Wu, Wen-Chin, 2022. "Autocracy and human capital," World Development, Elsevier, vol. 157(C).
    18. Sha Wang & Jean-Philippe Vergne, 2017. "Buzz Factor or Innovation Potential: What Explains Cryptocurrencies’ Returns?," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-17, January.
    19. Schöniger, Franziska & Morawetz, Ulrich B., 2022. "What comes down must go up: Why fluctuating renewable energy does not necessarily increase electricity spot price variance in Europe," Energy Economics, Elsevier, vol. 111(C).
    20. Entrop, Oliver & Fischer, Georg, 2019. "Hedging costs and joint determinants of premiums and spreads in structured financial products," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-34-19, University of Passau, Faculty of Business and Economics.

    More about this item

    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:arx:papers:2408.13047. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.