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Fiscal policy and TFP in the OECD: measuring direct and indirect effects

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Abstract

This paper analyzes the direct and indirect effects of fiscal policy on total factor productivity (TFP) in a panel of OECD countries over the period 1970–2012. Our contribution is twofold. First, when estimating the impact of fiscal policy on TFP from a production function approach, we identify the worldwide available level of technology by exploiting the observed strong cross-sectional dependence between countries instead of using ad hoc proxies for technology. Second, next to direct effects, we allow for indirect effects of fiscal policy by modeling the access of countries to worldwide available technology as a function of fiscal policy and other variables. Empirically, we propose and implement a nonlinear version of the common correlated effects pooled (CCEP) estimator of Pesaran (Econometrica 74(4):967–1012, 2006). The estimation results show that through the direct channel, budget deficits harm TFP. A shift toward productive expenditures has a strong positive impact on TFP, whereas a shift toward social transfers reduces TFP. Through the indirect channel, significant positive effects on a country’s access to global technology come from reducing the statutory corporate tax rate and from reducing barriers to trade.

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Notes

  1. These authors test the assumption of long-run homogeneity (pooled mean group estimation) versus long-run heterogeneity (mean group estimation) for the impact of fiscal policy variables on growth in a highly similar panel of 17 OECD countries for the period 1970–2004. Their Hausman test implies that the assumption of long-run homogeneity cannot be rejected (see their Table 2).

  2. The selection of countries and time coverage is driven by data availability. The included countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Italy, Japan, Netherlands, Norway, Spain, Sweden, UK and the USA.

  3. As an example, consider a government who chooses to lower the effective corporate tax rate. To do that, it has two options: (i) opting for a lower statutory corporate tax rate or (ii) choosing a smaller tax base. Both options will stimulate investment and raise profits. As a consequence, revenues from corporate taxation could rise because of more taxable profits. This means that a lower effective corporate tax rate could result in a higher macro-backward looking indicator.

  4. The overall conclusion that most variables are non-stationary does not change when changing the number of common factors or the specification of the deterministic component.

  5. Although Eq. (11) is derived, for notational convenience, under the assumption of a single factor, exactly the same augmented form is obtained for multiple common factors (see Pesaran 2006).

  6. Note that (Wan (2012), chap. 5) provides some heuristic asymptotic results for a CCEP estimator with nonlinear transformations of \(I(1)\) variables but which is still linear in the coefficients.

  7. Using the PANIC approach to testing for panel cointegration in the presence of common factors has also been suggested by Gengenbach et al. (2006), Banerjee and Carrion-i-Silvestre (2006) and Bai and Carrion-i-Silvestre (2013). The main difference between these approaches and ours lies in the estimation of the unknown coefficients in the cointegrating relation, for which we use the CCEP estimator while the above references estimate a model in first differences with the common factors and factor loadings estimated using principal components.

  8. Note that \(\widehat{\lambda }\) and \(\widehat{F}^{ca2}_t\) are only identified up to scale (see discussion in Sect. 4.2) but their product used in Eq. (19) is identified.

  9. Since \(\theta =0\) we also have \(\sigma _\varepsilon =0.02\) in this case. As the dependent variable \(\ln Q_{it}\) is log real GDP, \(\sigma _\varepsilon =0.02\) implies that 95 % of the generated error terms \(\varepsilon _{it}\) are between \(-\)4 and 4 % of real GDP.

  10. Simulation results for the CCEP estimator are available on request.

  11. Allowing for more than one common factor in the PANIC cointegration test on the CCEP composite error terms does not yield a different conclusion, i.e., setting \(r=2\) yields \(p\) values for the MW test on \(\widehat{\varepsilon }^{pc}_{it}\) equal to 0.47, 0.48, 0.85 and 0.16 in S1, S2, S3 and S4, respectively.

  12. For a correct interpretation of the results, note that the estimated coefficients are long-run elasticities. They indicate the percentage change in real output associated with a one percentage change in the share of a tax or expenditure category in GDP. To obtain the percentage change in output due to a one percentage point change in a tax or expenditure share, the estimated elasticity should be divided by the level of the tax or expenditure share. We report these shares for our sample in 2012 in “Appendix 1”, where we discuss the construction of the data

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Acknowledgments

We thank the associate editor, two anonymous referees, Sarah Balliu, Tim Buyse, Marina Emiris, Maud Nautet, Raf Wouters, Luc Van Meensel, and the members of SHERPPA for constructive comments and discussions during the development of this paper. We acknowledge support from the Flemish government (Steunpunt Fiscaliteit en Begroting - Vlaanderen) and the Belgian Program on Interuniversity Poles of Attraction, initiated by the Belgian State, Federal Office for scientific, technical and cultural affairs, contract UAP No. P 6/07. The computational resources (STEVIN Supercomputer Infrastructure) and services used in this work were kindly provided by Ghent University, the Flemish Supercomputer Center (VSC), the Hercules Foundation and the Flemish Government - department EWI.

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Authors and Affiliations

  1. Sherppa, Ghent University, Ghent, Belgium

    Gerdie Everaert & Freddy Heylen

  2. Research Department, National Bank of Belgium, Berlaimontlaan 14, 1000, Brussels, Belgium

    Ruben Schoonackers

  3. Research Department, Ghent University, Berlaimontlaan 14, 1000, Brussels, Belgium

    Ruben Schoonackers

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  1. Gerdie Everaert
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  2. Freddy Heylen
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Correspondence to Ruben Schoonackers.

Appendices

Appendix 1: Construction of data and data sources

See Table 9.

Table 9 Construction of data and data sources
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Appendix 2: Coefficients of correlation between corporate tax rate indicators

See Table 10.

Table 10 Correlation matrix
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Everaert, G., Heylen, F. & Schoonackers, R. Fiscal policy and TFP in the OECD: measuring direct and indirect effects. Empir Econ 49, 605–640 (2015). https://doi.org/10.1007/s00181-014-0872-0

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