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The Dynamics of Labor Force Participation: All Quiet on the Appalachian Front?

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  • Beverly, Joshua P.
  • Neill, Clinton L.
  • Stewart, Shamar

Abstract

This study examines the divergence and synchronicity of labor force participation rate (LFPR) dynamics across the USA. Using a dynamic factor model with time-varying stochastic volatility, we decompose each state’s LFPR into a national, regional, and state-specific latent factor. We find significant time variation in our factors and heterogeneous labor market responses and relative sensitivities. Our results show that, save for West Virginia, there is no strong Appalachian regional component, and instead, the national and state-specific components explain much of the variation in state LFPRs. Our results suggest the need for more targeted and localized labor market policies during periods of divergence in LFPRs (i.e., recessions and shocks) and federal policies during national economic booms or periods of recovery.
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Suggested Citation

  • Beverly, Joshua P. & Neill, Clinton L. & Stewart, Shamar, 2022. "The Dynamics of Labor Force Participation: All Quiet on the Appalachian Front?," 2024 Annual Meeting, July 28-30, New Orleans, LA 322258, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea22:322258
    DOI: 10.22004/ag.econ.322258
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    1. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
    2. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    3. Fang Cai & Yang Lu, 2013. "Population Change and Resulting Slowdown in Potential GDP Growth in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 21(2), pages 1-14, March.
    4. John B. Shoven, 2007. "New Age Thinking: Alternative Ways of Measuring Age, Their Relationship to Labor Force Participation, Goverment Policies and GDP," NBER Working Papers 13476, National Bureau of Economic Research, Inc.
    5. Dan Black & Kermit Daniel & Seth Sanders, 2002. "The Impact of Economic Conditions on Participation in Disability Programs: Evidence from the Coal Boom and Bust," American Economic Review, American Economic Association, vol. 92(1), pages 27-50, March.
    6. Jun Ma & Andrew Vivian & Mark E. Wohar, 2018. "Global factors and equity market valuations: Do country characteristics matter?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 427-441, October.
    7. John B. Taylor, 2016. "Can We Restart the Recovery All Over Again?," American Economic Review, American Economic Association, vol. 106(5), pages 48-51, May.
    8. West, Kenneth D. & Wong, Ka-Fu, 2014. "A factor model for co-movements of commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 289-309.
    9. Bian, Zhicun & Ma, Jun & Ni, Jinlan & Stewart, Shamar, 2020. "Synchronization of regional growth dynamics in China," China Economic Review, Elsevier, vol. 61(C).
    10. Bhatt, Vipul & Kishor, N Kundan & Ma, Jun, 2017. "The impact of EMU on bond yield convergence: Evidence from a time-varying dynamic factor model," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 206-222.
    11. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    12. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    13. Wullianallur Raghupathi & Viju Raghupathi, 2018. "An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health," IJERPH, MDPI, vol. 15(3), pages 1-24, March.
    14. Julie L. Hotchkiss & Fernando Rios-Avila, 2013. "Identifying Factors behind the Decline in the U.S. Labor Force Participation Rate," Business and Economic Research, Macrothink Institute, vol. 3(1), pages 257-275, June.
    15. Haroon Mumtaz & Paolo Surico, 2012. "Evolving International Inflation Dynamics: World And Country-Specific Factors," Journal of the European Economic Association, European Economic Association, vol. 10(4), pages 716-734, August.
    16. Kenneth A. Foster & Arthur M. Havenner & Allan M. Walburger, 1995. "System Theoretic Time-Series Forecasts of Weekly Live Cattle Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(4), pages 1012-1023.
    17. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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    More about this item

    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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