motivation

Measuring Competition Policy and its EffectivenessPaolo Buccirossi (LEAR)Lorenzo Ciari (European University Institute & LEAR)Tomaso Duso (DICE – Henrich-Heine University)Giancarlo Spagnolo (University of Rome Tor Vergata, SSE, & CEPR)Cristiana Vitale (LEAR)

ACLE Conference – Competition Policy for Emerging Economies: When and How?May 20, 2011

Measuring Competition Policy and its Effectiveness 20 May 2011 | ACLE| 2

Tomaso Duso (DICE)

Motivation¤ Is competition policy socially beneficial?

Crandall and Winston’s (JEP 2003) provocative paper claims that competition policy is ineffective

Baker (JEP 2003) and Werden (JEP 2003) disagree and point to the social benefits of competition policy as a deterrence mechanism

¤ The key empirical question is how to measure competition policy (deterrence) and how to identify the effectiveness of such a broad policy

We propose a way to quantify the quality of competition policy

We provide a framework to measure the effectiveness of competition policy in providing higher efficiency and productivity

We analyze the interactions and complementarities between competition policy and legal institutions

1. Measuring the quality of competition policy


Buccirossi, P., L. Ciari, T. Duso, G. Spagnolo, and C. Vitale (2011), “Measuring the Deterrence Effect of Competition Policy: The Competition Policy Indexes,” Journal of Competition Law and Economics, 7, 165-204

Tomaso Duso (DICE)

Competition Policy: Definition and Objective¤ The term competition policy refers to:

Competition legislation: A set of prohibitions and obligations including merger control provisions that firms have to comply with

Its enforcement: An array of tools for policing behavior and punishing any violation

¤ Competition policy should deter firms from undertaking any behavior that reduces social welfare by distorting competition, while not frightening any behavior that improves social welfare (no over-deterrence)


Tomaso Duso (DICE)

Deterrence: Competition Policy Variables¤ The optimal level of deterrence is determined by 1) the size of

the sanctions 2) the (perceived) probability of detection and conviction, and 3) the (perceived) probability of errors (Becker, JPE 1968; Polinsky and Shavell, JEL 2000)

¤ The following policy variables affect these three factors: the formal independence of the CA with respect to political

or economic interests the degree of separation between the adjudicator and the

prosecutor the quality of the law on the books the level of loss (sanctions) that firms (and their employees)

can expect to suffer as a consequence of a conviction the type of investigative powers held by the CA the amount and quality of the CA’s financial and human

resources (the budget and skills of the CA’s staff)


Tomaso Duso (DICE)

The Competition Policy Indexes (CPIs)¤ We submitted a set of tailored questionnaires to the CAs in 13

jurisdictions and integrated them with information from the OECD country studies and from the CAs’ own websites

¤ We obtained information on each of the six policy variables identified as determinants of deterrence, separately for each type of possible competition law infringement (hard-core cartels, abuses, other infringements) and for mergers over the years from 1995 to 2005

¤ Each piece of information at each step of the aggregation process was assigned a score/weight on a scale of 0-1 against a benchmark of generally agreed best practice

¤ We tested the sensitivity of this weighting scheme to alternative ones using 1) equal weights, 2) random weights, and 3) factor analysis


Tomaso Duso (DICE)

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The CPI: Discussion¤ The CPIs embody both formal and practical aspects of a competition

policy regime by combining key information on the legal framework, the institutional settings, and the enforcement tools

¤ This information is evaluated against a benchmark of best practices and then aggregated

¤ There is room for further refinement of the CPIs Cover a longer time period, as well as more (developing!)

countries Include more detailed data on the enforcement features (more

extensive information on the level of the sanctions that are effectively imposed)

¤ Exercise serves as a tool of transparency and accountability for public enforcement agencies, as well as a starting point for an assessment analysisMeasuring Competition Policy and its Effectiveness 20 May 2011 |

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2. Measuring the effect of competition policy


Buccirossi, P., L. Ciari, T. Duso, G. Spagnolo, and C. Vitale (2011), “Competition Policy and Productivity Growth: An Empirical Assessment,” CEPR DP 7470

Tomaso Duso (DICE)

Theoretical Background¤ The aim of Competition Policy is to protect social welfare by

deterring anti-competitive behavior¤ By making markets more competitive, competition policy affects

static and dynamic efficiency through a variety of channels (e.g., Aghion and Schenkerman, EJ 2004; Aghion et al. QJE 2004, ReStat2009). The causal link is thus:

Competition Policy Competition Efficiency / Productivity¤ Given our main research focus, we look at the direct link between

competition policy and efficiency to identify and estimate the causal effect of the policy (e.g. Pavnick, RES 2002; Nicoletti and Scarpetta, EP 2003)

¤ The deterrence effect of competition policy also depends on external factors such as the institutional environment (Aghion and Howitt, JEEA 2006)

We look at the complementarities between the policy and the judiciary systemMeasuring Competition Policy and its Effectiveness 20 May 2011 |

ACLE| 11Tomaso Duso (DICE)

The Empirical Application: The TFP Model¤ As a measure of efficiency/productivity we use Total Factor

Productivity (TFP) growth¤ The proposed specification is derived from a standard neoclassical

aggregate industry production function Yijt=TFPijtFijt (Lijt, Kijt) (e.g. Griffith et al. REStat 2004). It is estimated with a three-dimensional (country, industry, time) panel data approach:

where TFPLjt is the TFP level in the country-industry on the productivity frontier, (TFPijt/TFPLjt) represents the productivity gap to the technological frontier, X and Z are sets of control variables (R&D, PMR, human capital, trade openness, and the quality of institutions) and are country-industry and time fixed effects

1 1 1ijt

ijt it Ljt ijt ijt it ij t ijtLjt

TFPTFP CPI TFP X Z u

TFP

and ij t


Tomaso Duso (DICE)

Identification¤ The identification problem derives from the possible endogeneity of

the CPIs because of omitted variables and two-way causality Aggregate the features of the competition policy regime

(institutional inertia) Lag the potentially endogenous explanatory variables Panel data allows us to control for time invariant unobserved

individual heterogeneity We include all possible controls based on the existing literature on

the determinants of TFP growth¤ We take two further steps (more formal) to tackle the problem of

potential endogeneity: Instrumental Variables (IV) approach: We use political and

institutional variables (government type and its attitudes towards regulation, economic planning, market economy) policy in neighboring countries as instruments

Look at non-linearities: CP has a stronger impact in countries with better legal institutions and in industries which are not strongly regulated


Tomaso Duso (DICE)

The Data¤ We selected 13 jurisdictions (Canada, Czech Republic, France,

Germany, Hungary, Italy, Japan, Netherlands, Spain, Sweden, the UK, EU, and US) over the years from 1995 to 2005

¤ For the countries that are part of the EU, we built a set of indexes that incorporate information on both national as well as EU competition policy regimes

¤ For each country, our sample includes 22 industries based on the definitions of the International Standard Industrial Classification (ISIC)

¤ Data on TFP growth is drawn from the KLEMS consortium and from the Groningen Growth and Development Center

¤ Other data come from the OECD Structural Analysis (STAN) database, the OECD Main Economic Indicators (MEI) database, the OECD PMR database, the OECD Analytical Business Enterprise Research and Development (ANBERD) database, and the World Bank Worldwide Governance Indicators (WGI) database


Tomaso Duso (DICE)

Results ¤ Competition policy has a positive impact on TFP growth, and this

is statistically significant at the 1% level [Table] [Figure]¤ We can reject the hypothesis of the policy being endogenous by

using political variables (government types and their ideological position) or policy in neighboring countries as instruments [Table]

¤ Using different weighting schemes for the Aggregate CPI, labor productivity instead of TFP, different lag structures, long run/aggregated effects, different sets of control variables, sub-samples, structures for the error terms does not change our qualitative results [Table]


Tomaso Duso (DICE)

¤ Controlling for institutions (contracts enforcement and quality of the judiciary/law) does not alter our results. Yet, good institutions have a positive impact on TFP growth [Table]

¤ We explore the interactions between legal institutions and competition policy. CP has a significantly larger impact in countries with:

Low cost for the enforcement of contracts (Doing Business), high rule of law (Fraser)

¤ We further look at the effect of competition policy in different industries. CP has a much stronger impact in the manufacturing sectors

Sector specific regulations in services industries (e.g. electricity, gas, water, communication, financial intermediation) may make them to a lesser extent subject to the ex-ante antitrust scrutiny

¤ Both results improve our identification strategy

Results - Heterogeneity


Tomaso Duso (DICE)

Conclusions¤ We identify important dimensions of a good competition policy,

which can be quantified using hard data. Along these dimension, CAs should be kept accountable (keep track of their activities)

Especially in developing country, clear data on institutional details should be collected

Keep track of the policy’s enforcement (cases & sanctions)! ¤ Good competition policy appears to exert a significant and positive

impact on efficiency. But, results are based on OECD countries¤ We find complementarities between good judiciary institutions and

competition policy Very important for institutional design. One size fits all is not a

good policy


Tomaso Duso (DICE)

Results - Robustness Checks [Table] ¤ To verify the robustness of our results we perform several

robustness checks with which we consistently obtain a positive and significant effect of the CPI

Employ an alternative CPI in which information is aggregated using Factor Analysis

Use 1,000 sets of random weights and generate a distribution of coefficient estimates [Figure]

Include the EU dimension of the policy Use alternative measures of productivity (non corrected for

mark-ups and labor productivity) Use long-run rates of TFP growth Test the sensitivity of our results to the exclusion of specific

countries-industries [Figure]¤ All results point to the robustness of our main findings


Tomaso Duso (DICE)

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Fig 1: The Aggregate CPIs

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Fig 1: TFP Growth and the Aggregate CPIs

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Results – Low-level indexes [Table]

¤ Both the Institutional CPI and the Enforcement CPI coefficient are positive and significant yet the former has a much larger impact

¤ Both the coefficient estimate for the Antitrust CPI and Merger CPI are positive and strongly significant, yet the former has a larger impact

¤ A positive impact on the intensity of competition is suggested for the quality of the law and for the powers held by the CAs during the investigation

¤ Also the resources held by the CAs have a positive impact on TFP growth


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motivation

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