This article contributes to the empirical literature on the impact of competition policy on labor productivity (LP) growth, focusing specifically on EU countries. We capture the quality of competition policy by the Competition Policy Indices (CPIs), proposed, constructed and used recently by Buccirossi et al. (J Compet Law Econ 7:165–204, 2011; Rev Econ Stat 95(4):1324–1336, 2013). We construct these indices also for Greece for the period 1995–2013 and use them to study the effect of competition policy on LP growth in 22 industries for a set of 10 EU countries. We find that the CPI has a positive and statistically significant effect on LP growth. Most importantly, we also investigate possible heterogeneity of this effect by separating the countries of our sample in two groups, Laggards and Leaders. We find that the effect of the CPI on LP growth for the Laggards is about three times as large as the effect estimated for all ten countries in our sample, while it is very small and statistically insignificant for the Leaders. Finally, when we estimate the effect only for Greece the coefficient increases substantially although it is estimated quite imprecisely, reinforcing our finding that gains from increasing the quality of competition policy and making product market competition more effective are to be reaped mainly by countries for which there is the greatest scope for improving the effectiveness of product market competition.
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See, for review of empirical evidence, Sect. 2.
For instance, the UK Office of Fair Trading (OFT) measures the direct benefits to consumers arising from its activities based on the 5:1 target agreed with HM Treasury, which implies that the financial benefits to consumers should be at least five times the cost for the taxpayer per year, on average, over the period 2008 to 2011 (Office of Fair Trading 2008). In the USA, the US Department of Justice is obliged, under the Government Performance and Results Act (GPRA), to estimate the savings to consumers from its actions on a yearly basis (Werden 2008; Hüschelrath and Leheyda 2010).
These are described below.
See a recent paper comparing effectiveness of competition authorities (by Meiklejohn 2014) in which authorities are ranked by the number of stars assigned by the GCR (Global Competition Review), the Staff per population ratio and the antitrust budget per GDP ratio.
On the basis of, for example, the relevant World Economic Forum indicators—see, for example, Katsoulacos et al. (2018).
See Katsoulacos et al. (2018).
Buccirossi et al. (2011) pursue robustness checks to show that the choice of the weights does not have a significant impact on their results.
For further information about the calculation of the medium- and the high-level CPIs, see “Appendix 2”.
Later Acts strengthened further the organization and the powers of the HCC (Act 2741/1999; Act 2837/2000; Act 3373/2005).
The calculation of some elements of the low-level indices involves information of the best-performing country in the sample. Such data are not available to us, and in our calculations we assume that the UK is the best-performing country in our sample. UK has been chosen using the information in a recent paper comparing effectiveness of competition authorities (by Meiklejohn 2014). In this, authorities are ranked by the number of stars assigned to them by the GCR (Global Competition Review), the Staff per population ratio and the antitrust budget per GDP ratio. The ranking on the basis of these indices justifies the use of UK as the European benchmark.
We use 1.5 as the value of this coefficient. We also experimented with values of 2 and 5 which brought changes to the CPI only at the third decimal. Our results are robust to the choice of this coefficient and are not reported in the paper for brevity.
The set of industries in our study include: agriculture, hunting, forestry and fishing; mining and quarrying; food, beverages and tobacco; textiles, textile, leather and footwear; wood and of wood and cork; pulp, paper, printing and publishing; coke, refined petroleum and nuclear fuel; chemicals and chemical products; rubber and plastics; other non-metallic mineral; basic metals and fabricated metal; machinery, not elsewhere classified; electrical and optical equipment; transport equipment; manufacturing, not elsewhere classified, recycling; electricity, gas and water supply; construction; hotels and restaurants; transport and storage; post and telecommunications; financial intermediation; real estate, renting and business services.
For further information, see Wölfl et al. (2009).
In Appendix, we report results with PMR imputed using linear regression.
We fill in the data for the intermediate years using linear interpolation when these variables are used in our estimations.
The large drop in the observations used in the estimation is due mainly to missing observations for the human capital variable used (the educational attainment in tertiary education as a percentage of total labor force, as obtained from the OECD).
See also Meiklejohn (2014).
On the basis of, for example, the relevant World Economic Forum indicators—see, for example, Katsoulacos et al. (2018).
See Aghion and Griffith (2005) for a similar finding regarding the effect of competition on innovation.
In Appendix Table 13, we present results for different model specifications as well as with different measures of human capital and a regression-based imputed PMR. In general, the results do not change much except when trade openness is dropped from the set of controls.
Buccirossi et al. (2013) use grouping of countries according to EU and non-EU membership.
The second type of instrument for PMR is dropped from all IV regressions as it turns out to be collinear with the year dummies.
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We would like to thank Paolo Buccirossi, Giancarlo Spagnolo and Cristiana Vitale for very useful discussions in relation to the data used and more generally the subject of the paper as well as for providing the data relating to the CPI. We also thank the HCC staff for productive collaboration. Alexandros Louka provided excellent research assistance. Finally, we are grateful to the three referees and the editor for insightful comments and suggestions that greatly improved the paper. This research has been co-funded by the European Union (European Social Fund—ESF) and the Greek Ministry of Education through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: ΑRISTEIA-CoLEG.
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According to the methodology by Buccirossi et al. (2011), the calculation of the medium-level indices requires the calculation of two indices—one covering the institutional features and the other the enforcement features—for each type of infringement (abuses, hard-core cartels and other agreements) and for mergers. The calculation of these eight medium-level indices is based on weighted averages of the low-level indices. Weights for these aggregations are presented in brackets in Table 17.
The high-level CPIs include four disaggregated indices—the Antitrust CPI, the Mergers CPI, the Enforcement CPI and the Institutional CPI—and the aggregate CPI. The construction of the high-level CPIs is based in weighted aggregations of the medium-level indices. Specifically, the Antitrust CPI is calculated through the weighted average of the six medium-level indices related to antitrust infringements, while the Mergers CPI of the two corresponding medium-level indices. The Institutional CPI is calculated through the weighted average of the four medium-level indices related to the institutional features and the Enforcement CPI of the other four related to the enforcement features. Combining these four disaggregated CPIs, we end up to the aggregate CPI. Weights for the construction of the high-level CPIs are presented in brackets in Table 18.
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Benetatou, K., Katsoulacos, Y., Kyriazidou, E. et al. Competition policy and labor productivity growth: some new evidence. Empir Econ 58, 3035–3076 (2020). https://doi.org/10.1007/s00181-019-01656-x