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Public Capital Spillovers and Growth: A Foray Downunder

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  • Timothy Kam
  • Yi-Chia Wang

Abstract

We extend the deterministic growth model of Glomm and Ravikumar (1994) to a stochastic endogenous growth model which nests both exogenous and endogenous growth factors. By introducing simple shocks to production technology, private capital and public capital investment, we can derive testable time series properties of the analytical model. The hypothesis of strict endogenous growth due to public capital spillovers cannot be statistically rejected for our Australian data set. We find further short-run evidence of public capital contributing to permanent increases in the levels of per capita income and private capital.

Suggested Citation

  • Timothy Kam & Yi-Chia Wang, 2006. "Public Capital Spillovers and Growth: A Foray Downunder," ANU Working Papers in Economics and Econometrics 2006-474, Australian National University, College of Business and Economics, School of Economics.
  • Handle: RePEc:acb:cbeeco:2006-474
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    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp474.pdf
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    References listed on IDEAS

    as
    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
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    6. Glomm, Gerhard & Ravikumar, B., 1994. "Public investment in infrastructure in a simple growth model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(6), pages 1173-1187, November.
    7. Sau‐Him Paul Lau & Chor‐Yiu Sin, 1997. "Public Infrastructure and Economic Growth: Time‐Series Properties and Evidence," The Economic Record, The Economic Society of Australia, vol. 73(221), pages 125-135, June.
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    More about this item

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • 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

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