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Local Job Multipliers in the United States: Variation with Local Characteristics and with High-Tech Shocks

Author

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  • Timothy J. Bartik

    (W.E. Upjohn Institute for Employment Research)

  • Nathan Sotherland

    (W.E. Upjohn Institute for Employment Research)

Abstract

This paper provides new estimates of local job multipliers, the ratio of total jobs generated to some initial number of jobs created from a demand shock. Multipliers greatly affect benefits versus costs of local job-creation policies. These new estimates rely on improved methodology and data. The methodology better captures dynamic effects of demand shocks, specifies the model so that demand shocks are more comparable, and is more general in the types of demand shocks that are considered. The data has more industry detail than that used in previous studies. The local job multipliers estimated tend to be about one-quarter lower than typically estimated local multipliers, closer to 1.5 than to 2.0. In addition, demand shocks to all industries matter, not just to tradable industries. Multipliers are similar across different types of geographic areas, with county multipliers being only one-quarter below commuting zone multipliers and state multipliers only one-quarter above commuting zone multipliers. Multipliers are not larger for larger commuting zones, but they increase in commuting zones that have lower initial employment to population ratios. Multipliers are higher for high-tech industries, particularly in commuting zones with a larger initial high-tech share. In such high-tech local economies, high-tech multipliers may be close to 3. While our high-tech multipliers are greater than for other industries, our estimated high-tech multipliers are less than in some prior studies.

Suggested Citation

  • Timothy J. Bartik & Nathan Sotherland, 2019. "Local Job Multipliers in the United States: Variation with Local Characteristics and with High-Tech Shocks," Upjohn Working Papers 19-301, W.E. Upjohn Institute for Employment Research.
    • Handle: RePEc:upj:weupjo:19-301
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    Cited by:

    1. Rickman, Dan S. & Wang, Hongbo, 2022. "Industry Aggregation and Assessment of State Economic Development from Motion Picture and Television Production Incentives," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 52(1), August.
    2. Greg Howard & Jack Liebersohn, 2023. "Regional Divergence and House Prices," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 49, pages 312-350, July.
    3. Pahontu, Raluca L., 2022. "Divisive jobs: three facets of risk, precarity, and redistribution," LSE Research Online Documents on Economics 111593, London School of Economics and Political Science, LSE Library.
    4. Freedman, Matthew & Khanna, Shantanu & Neumark, David, 2023. "Combining rules and discretion in economic development policy: Evidence on the impacts of the California Competes Tax Credit," Journal of Public Economics, Elsevier, vol. 217(C).
    5. Bartik, Timothy J., 2024. "Long-run effects on county employment rates of demand shocks to county and commuting zone employment," Regional Science and Urban Economics, Elsevier, vol. 105(C).
    6. Kalee Burns & Julie L. Hotchkiss, 2023. "The Role of Social Costs in Response to Labor Market Opportunities: Differences across Race," FRB Atlanta Working Paper 2023-7, Federal Reserve Bank of Atlanta.
    7. Rickman, Dan & Wang, Hongbo, 2020. "Assessing State Economic Development from Motion Picture and Television Production Incentives: Standardizing the Industry for Analysis," MPRA Paper 104052, University Library of Munich, Germany.
    8. Richard K. Mansfield, 2024. "Contrasting the Local and National Demographic Incidence of Local Labor Demand Shocks," Working Papers 24-36, Center for Economic Studies, U.S. Census Bureau.
    9. Rickman, Dan S. & Wang, Hongbo, 2022. "Estimating the Economic Effects of US State and Local Fiscal Policy: A Synthetic Control Method Matched Regression Approach," MPRA Paper 112575, University Library of Munich, Germany.
    10. Taner Osman & Tom Kemeny, 2022. "Local job multipliers revisited," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 150-170, January.
    11. Karen Chapple & Jae Sik Jeon, 2021. "Big Tech on the Block: Examining the Impact of Tech Campuses on Local Housing Markets in the San Francisco Bay Area," Economic Development Quarterly, , vol. 35(4), pages 351-369, November.

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    More about this item

    Keywords

    Multipliers; agglomeration economies; congestion effects; high-technology industries;
    All these keywords.

    JEL classification:

    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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