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

Jaepil Han, Deockhyun Ryu and Robin Sickles

A chapter in Spatial Econometrics: Qualitative and Limited Dependent Variables, 2016, vol. 37, pp 259-294 from Emerald Group Publishing Limited

Abstract: This paper aims to investigate spillover effects 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 inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

Keywords: Public capital; spillover effect; stochastic frontier model; spatial panel model; time-varying spatial weights; C23; D24; O47 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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Working Paper: How to Measure Spillover Effects of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320160000037017

DOI: 10.1108/S0731-905320160000037017

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