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Returns to investment in navigation infrastructure: An equilibrium approach

Author

Listed:
  • Harry H. Kelejian

    () (Department of Economics, University of Maryland, Tydings Hall, Room 3105, College Park, MD 20742, USA The Institute of Water Resources, Casey Building, Alexandria, VA 22315-3868, USA)

  • Dennis P. Robinson

    () (Department of Economics, University of Maryland, Tydings Hall, Room 3105, College Park, MD 20742, USA The Institute of Water Resources, Casey Building, Alexandria, VA 22315-3868, USA)

Abstract

We argue that there are serious biases in public infrastructure productivity estimates which are based on a production function, or a cost function framework. These biases arise because public infrastructure has important effects on the demands, as well as prices of the factors of production. In this study we estimate the productivity effects of investment in navigation infrastructure in a systems framework. Our results strongly suggest a significant productivity effect of such investments.

Suggested Citation

  • Harry H. Kelejian & Dennis P. Robinson, 2000. "Returns to investment in navigation infrastructure: An equilibrium approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 34(1), pages 83-108.
  • Handle: RePEc:spr:anresc:v:34:y:2000:i:1:p:83-108
    Note: Received: March 1997/Accepted: October 1997
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    Cited by:

    1. David M. Drukker & Peter Egger & Ingmar R. Prucha, 2013. "On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 686-733, August.
    2. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    4. Xueting Zhao & J. Burnett, 2014. "Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 171-183, October.
    5. Mario Larch & Janette Walde, 2009. "Finite sample properties of alternative GMM estimators for random effects models with spatially correlated errors," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(2), pages 473-490, June.

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