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Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data

* This paper is a replication of an original study

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  • Valerie A. Ramey
  • Sarah Zubairy

Abstract

We investigate whether US government spending multipliers are higher during periods of economic slack or when interest rates are near the zero lower bound. Using new quarterly historical US data covering multiple large wars and deep recessions, we estimate multipliers that are below unity irrespective of the amount of slack in the economy. These results are robust to two leading identification schemes, two different estimation methodologies, and many alternative specifications. In contrast, the results are more mixed for the zero lower bound state, with a few specifications implying multipliers as high as 1.5.

Suggested Citation

  • Valerie A. Ramey & Sarah Zubairy, 2018. "Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data," Journal of Political Economy, University of Chicago Press, vol. 126(2), pages 850-901.
  • Handle: RePEc:ucp:jpolec:doi:10.1086/696277
    DOI: 10.1086/696277
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    Replication

    This item is a replication of:
  • Alan J. Auerbach & Yuriy Gorodnichenko, 2012. "Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 1-27, May.
  • More about this item

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy
    • N12 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - U.S.; Canada: 1913-

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    This item is featured on the following reading lists, Wikipedia, or ReplicationWiki pages:
    1. Government Spending Multipliers in Good Times and in Bad: Evidence from U.S. Historical Data (JPE 2018) in ReplicationWiki

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