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What Do We Learn from Cross-Regional Empirical Estimates in Macroeconomics?

Author

Listed:
  • Adam Guren
  • Alisdair McKay
  • Emi Nakamura
  • Jón Steinsson

Abstract

Recent empirical work uses variation across cities or regions to identify the effects of economic shocks of interest to macroeconomists. The interpretation of such estimates is complicated by the fact that they reflect both partial equilibrium and local general equilibrium effects of the shocks. We propose an approach for recovering estimates of partial equilibrium effects from these cross-regional empirical estimates. The basic idea is to divide the cross-regional estimate by an estimate of the local fiscal multiplier, which measures the strength of local general equilibrium amplification. We apply this approach to recent estimates of housing wealth effects based on city-level variation, and derive conditions under which the adjustment is exact. We then evaluate its accuracy in a richer general equilibrium model of consumption and housing. The paper also reconciles the positive cross-sectional correlation between house price growth and construction with the notion that cities with larger price volatility have lower housing supply elasticities using a model in which housing supply elasticities are more dispersed in the long run than in the short run.
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(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Adam Guren & Alisdair McKay & Emi Nakamura & Jón Steinsson, 2021. "What Do We Learn from Cross-Regional Empirical Estimates in Macroeconomics?," NBER Macroeconomics Annual, University of Chicago Press, vol. 35(1), pages 175-223.
    • Handle: RePEc:ucp:macann:doi:10.1086/712321
    DOI: 10.1086/712321
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    3. Wall, Howard, 2023. "The Great, Greater, and Greatest Recessions of US States," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 53(1), January.
    4. Burchardi, Konrad & Terry, Stephen & Chaney, Thomas & Tarquinio, Lisa & Hassan, Tarek, 2020. "Immigration, Innovation, and Growth," CEPR Discussion Papers 14719, C.E.P.R. Discussion Papers.
    5. Alisdair McKay & Christian K. Wolf, 2023. "What Can Time‐Series Regressions Tell Us About Policy Counterfactuals?," Econometrica, Econometric Society, vol. 91(5), pages 1695-1725, September.
    6. Hyunseung Oh & Choongryul Yang & Chamna Yoon, 2024. "Land development and frictions to housing supply over the business cycle," Finance and Economics Discussion Series 2024-010, Board of Governors of the Federal Reserve System (U.S.).
    7. Luigi Marattin & Tommaso Nannicini & Francesco Porcelli, 2022. "Revenue vs expenditure based fiscal consolidation: the pass-through from federal cuts to local taxes," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(4), pages 834-872, August.
    8. Diego Daruich & Julian Kozlowski, 2023. "Macroeconomic Implications of Uniform Pricing," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(3), pages 64-108, July.
    9. Jeremy Majerovitz & Karthik Sastry, 2023. "How Much Should We Trust Regional-Exposure Designs?," Working Papers 2023-018, Federal Reserve Bank of St. Louis.
    10. Quincy, Sarah, 2022. "Income shocks and housing spillovers: Evidence from the World War I Veterans’ Bonus," Journal of Urban Economics, Elsevier, vol. 132(C).
    11. Ozan Güler & Mike Mariathasan & Klaas Mulier & Nejat G. Okatan, 2021. "The real effects of banks' corporate credit supply: A literature review," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1252-1285, July.
    12. Kieran P. Donaghy, 2021. "Implications for Regional Science of the “Rebuilding Macroeconomic Theory Projectâ€," International Regional Science Review, , vol. 44(3-4), pages 363-384, May.
    13. Timothy G. Conley & Bill Dupor & Rong Li & Yijiang Zhou, 2023. "Decomposing the Government Transfer Multiplier," Working Papers 2023-017, Federal Reserve Bank of St. Louis, revised 17 Nov 2023.
    14. Yui Kishaba & Tatsushi Okuda, 2023. "The Slope of the Phillips Curve for Service Prices in Japan: Regional Panel Data Approach," Bank of Japan Working Paper Series 23-E-8, Bank of Japan.

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    More about this item

    JEL classification:

    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand

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