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The Productivity of Public Capital: A Meta-analysis

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Infrastructure Productivity Evaluation

Part of the book series: SpringerBriefs in Economics ((BRIEFSECONOMICS,volume 1))

Abstract

The paper measures the contribution of public capital to private output using a simple meta-analysis and a meta-regression analysis based on panel data. We find an output elasticity of public capital of 0.14 in the random effects model, which is substantially smaller than the simple arithmetic average value of 0.20. Reported estimates of the output elasticity of public capital show considerable heterogeneity. We identify the type of public capital, the level of aggregation of the public capital data, the country type, the econometric specification, and publication bias as sources of variation.

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Notes

  1. 1.

    Mera (1973) was the first study that estimated for nine Japanese regions a production function including some form of public capital, which he refers to as “social capital.” For example, transportation and communications facilities, soil and water conservation, health and educational facilities. The work of Mera was followed by two papers by Ratner (1983) and Da Costa et al. (1987).

  2. 2.

    Only a small number of studies have been reported. More details on the output elasticities of public capital can be found in Table 2.1 below.

  3. 3.

    Meta-analysis has a long-standing tradition in psychological and medical research. Environmental and transport economists were the first to apply meta-analysis in economics in the 1980s. Since then, it has been picked up by researchers in other fields in economics such as labour economics (e.g., Card and Krueger 1995), industrial organization (e.g., Button and Weyman-Jones 1992), and international economics (e.g., De Mooij and Ederveen 2003).

  4. 4.

    See the studies by Munnell (1991, 1992), Gramlich (1994), Pfahler et al. (1996), Button (1998), Sturm et al. (1998), Button and Rietveld (2000), Mikelbank and Jackson (2000), IMF (2004), and Romp and De Haan (2007).

  5. 5.

    Button’s (1998) analysis is basically a cross-sectional approach given the limited number of observations.

  6. 6.

    The surveys of Sturm et al. (1998) and Romp and De Haan (2007) identify 54 studies employing some form of production function approach. In 2005, the other three approaches feature the following number of papers: 21 studies concern VAR studies; 26 studies deal with cost or profit functions; and 12 studies use growth regressions.

  7. 7.

    Hicks-neutral public capital enters the production in such a way that the average and marginal products of all factors increase in the same proportion.

  8. 8.

    Early adopters of the translog specification are, amongst others, Merriman (1990), Pinnoi (1994), and Dalamagas (1995).

  9. 9.

    Here, it is assumed that public capital is remunerated based on its marginal productivity. Aaron (1990) has argued that in the presence of government pricing inefficiencies and the absence of markets, this is not a very realistic assumption.

  10. 10.

    Munnell (1990), Eisner (1991), Garcia-Milà and McGuire (1992), and Evans and Karras (1994), and Holtz-Eakin (1994).

  11. 11.

    Some authors argue that spillover effects are likely to be positively related to the population size and the openness of regions, which is not reflected in the above equation.

  12. 12.

    For example, Aschauer (1989), Hulten and Schwab (1991a), and Sturm and De Haan (1995) were early adopters of this specification.

  13. 13.

    See, for example, Aaron (1990), Hulten and Schwab (1991a), and Tatom (1991).

  14. 14.

    Clarida (1993), Otto and Voss (1996), Batina (1998), Flores de Frutos et al. (1998), Pereira and Roca Sagales (1999), Ligthart (2002), and Pereira and Roca Sagales (2003) have employed a VAR approach amongst others.

  15. 15.

    Many VAR studies were not considered for our potential database because they neither reported standard errors nor disclosed any output elasticities.

  16. 16.

    Studies reporting output elasticities of public capital but not their standard errors are the following: Clarida (1993), Pinnoi (1994), Crihfield and Panggabean (1995), Wylie (1996), Lau and Sin (1997), Mamatzakis (1999), Pereira and Flores de Frutos (1999), Pereira and Roca Sagales (2001), Ashipala and Haimbodi (2003), Pereira and Roca Sagales (2003), and Everaert and Heylen (2004). These studies, however, have been included in the meta-regression analysis of Sect. 2.4.

  17. 17.

    We could not get a hold of some of the early unpublished papers, thereby making the sample of unpublished papers less representative.

  18. 18.

    This is still a controversial issue in the literature. Bijmolt and Pieters (2001) claim that all available measurements need to be included, whereas Stanley (1998) believes that only one measurement per study should be selected.

  19. 19.

    No routines are available yet to measure and correct for this problem.

  20. 20.

    The causes of heterogeneity can be assessed by means of a meta-regression analysis. See Sect. 2.2.4.

  21. 21.

    See Hedges (1994) for an exposition of how this terminology differs from that used in the panel data literature.

  22. 22.

    In the meta-regression analysis, we include both types of study characteristics.

  23. 23.

    Note that authors may not report unsatisfactory results, which, of course, cannot be measured by a meta-analysis.

  24. 24.

    The cross-country studies are as follows: Evans and Karras (1994), Canning and Bennathan (2000), Nourzad (2000), Vanhoudt et al. (2000), and Dodonov et al. (2002).

  25. 25.

    Of course, we could have also used the standard errors in weighting the observations. To maximize the number of observations, we decided against this. Any references to “fixed effects” and “random effects” pertain to the standard panel data methods rather than the terminology as employed by meta-analysts.

  26. 26.

    The F test amounts to \( F(54,217)=1.91,\)which exceeds the critical value.

  27. 27.

    We could not find any evidence of autocorrelation in the residuals. We have performed a likelihood ratio (LR) test to check for the presence of cross-panel heteroscedasticity. The LR test is based on the difference between the unrestricted model, which allows for heteroscedasticity, and the restricted model, which assumes a constant variance of the residuals. The LR test in Table 2.3 shows that the unrestricted model performs better, implying that the error structure is heteroscedastic.

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Authors and Affiliations

  1. Department of Economics and Center, Tilburg University, 90153, 5000 LE, Tilburg, The Netherlands

    Jenny E. Ligthart

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  1. Jenny E. Ligthart
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  2. Rosa M. Martin Suárez
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Correspondence to Jenny E. Ligthart .

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  1. TNO (Dutch Organization for Applied Scie, PO Box 49, Delft, 2600 AA, Netherlands

    Wouter Jonkhoff

  2. TNO (Dutch Organization for Applied Scie, PO Box 49, Delft, 2600, Netherlands

    Walter Manshanden

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Ligthart, J.E., Suárez, R.M.M. (2011). The Productivity of Public Capital: A Meta-analysis. In: Jonkhoff, W., Manshanden, W. (eds) Infrastructure Productivity Evaluation. SpringerBriefs in Economics, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8101-1_2

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