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How to Measure Spillover Effects of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model

Author

Listed:
  • Han, Jaepil

    (Rice University)

  • Ryu, Deockhyun

    (Chung-Ang University)

  • Sickles, Robin

    (Rice University)

Abstract

This paper aims to investigate spillover e?ects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical ineffciency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

Suggested Citation

  • Han, Jaepil & Ryu, Deockhyun & Sickles, Robin, 2015. "How to Measure Spillover Effects of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model," Working Papers 15-015, Rice University, Department of Economics.
    • Handle: RePEc:ecl:riceco:15-015
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    Cited by:

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    2. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
    3. Sung, Bongsuk & Soh, Jin Young & Park, Chun Gun, 2022. "Comparing government support, firm heterogeneity, and inter-firm spillovers for productivity enhancement: Evidence from the Korean solar energy technology industry," Energy, Elsevier, vol. 246(C).
    4. Yuan, Lingran & Zhang, Qizheng & Wang, Shuo & Hu, Weibin & Gong, Binlei, 2022. "Effects of international trade on world agricultural production and productivity: evidence from a panel of 126 countries 1962-2014," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 25(2), March.
    5. Adjin, K. Christophe & Henning, Christian H. C. A., 2020. "Climate variability and farm inefficiency: A spatial stochastic frontier analysis of Senegalese agriculture," Working Papers of Agricultural Policy WP2020-09, University of Kiel, Department of Agricultural Economics, Chair of Agricultural Policy.
    6. Han, Jaepil & Sickles, Robin C., 2019. "Estimation of Industry-level Productivity with Cross-sectional Dependence by Using Spatial Analysis," Working Papers 19-002, Rice University, Department of Economics.
    7. Gong, Binlei, 2020. "Multi-dimensional interactions in the oilfield market: A jackknife model averaging approach of spatial productivity analysis," Energy Economics, Elsevier, vol. 86(C).

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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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