Wirtschaft. Politik. Wissenschaft

Competition Policy and Macroeconomic Outcomes

Tomaso Duso DIW Berlin, BCCP, & DICE Düsseldorf

LOOKING BEYOND THE DIRECT EFFECTS OF THE WORK OF COMPETITION AUTHORITIES: DETERRENCE AND MACROECONOMIC IMPACT

Brussels, September 17 2015

Introduction

Large consensus on the welfare-enhancing properties of competition

Achievement of allocative, productive, and dynamic efficiency it increases

productivity and growth (e.g., Aghion et al., REStat 2009)

Several studies provide measurement of other competition-enhancing policies

and their effectiveness

Privatization (Megginson, JEL 2001), trade liberalization (Pavcnik, RES 2002)

(de)regulations (Nicoletti and Scarpetta, EP 2003; Bourles et al, REStat 2013)

Market specific & focused instruments (e.g. entry regulations, price caps..)

Little evidence on whether competition policy is socially beneficial

Broad policy with many different tools affecting all markets simultaneously

Some authors conclude that competition policy is ineffective (e.g. Crandall and

Winston, JEP 2003)

Others claim that it is difficult to measure it because competition policy is a

deterrence system (e.g. Baker, JEP 2003 and Werden, JEP 2003)

Two Examples: Both based on research projects we undertook for the European

Commission (DG EC Fin, DG COMP)

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Tomaso Duso 2 Competition Policy and Macroeconomic Outcomes

The Impact of Competition Policy on TFP Growth Study commissioned by DG Economic & Financial Affairs

Published in Buccirossi et al. (JCLE 2012) and Buccirossi et al. (REStat 2013)


2

Competition Policy– Measurement Issues

How to define competition policy?

Competition legislation: set of prohibitions and obligations

Its enforcement: array of tools for policing and punishing violations

Institutions play an important role

Competition policy institutions –the procedures and rules– might affect enforcement (Duso

et al. JLE 2007, EER 2011, EJ 2013)

Competition policy & institutions are embedded in and interact with a system of other

institutions, in particular legal institutions (e.g., Buccirosssi et al. REStat 2013)

How to measure competition policy?

Competition policy has to be mostly seen as a deterrence system

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, 2000)

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Measuring Competition Policy: Optimal Deterrence

The following policy variables affect these three factors:

Formal independence of the CA with respect to political or economic interests

Degree of separation between the adjudicator and the prosecutor

Quality of the law on the books

Level of loss (sanctions) that firms (and their employees) can expect to suffer as a

consequence of a conviction

Type of investigative powers held by the CA

Amount and quality of the CA’s financial and human resources (the budget and skills

of the CA’s staff)

2


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 various information on 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) 1,000 sets of random weights, and 3) factor analysis

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Tomaso Duso 6

Measuring Competition Policy: Optimal Deterrence


Impact of Competition Policy on Growth: The Framework

To assess the effectiveness of competition policy we build on a model of

endogenous growth (e.g., Aghion et al., 2009)

Laggard industries try to catch up with the technological frontier by innovating

Leader industries, try to escape competition by innovating and pushing forward the

technological frontier

We analyze the following causal links:

Competition Policy [Competition] Efficiency

As a measure of efficiency we choose TFP (and LP) growth

We control for all major drivers of TFP growth and estimate the following model:

3

Tomaso Duso 7

1 1 1

ijt

ijt it Ljt ijt ijt it ij t ijt

Ljt

TFPTFP CPI TFP X Z u

TFP


Impact of Competition Policy on TFP Growth: 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 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

3


Impact of Competition Policy on TFP Growth: Results

Competition policy has a positive impact on TFP and LP growth, which is

statistically significant at the 1% level

The impact is economically significant: e.g. the actual improvement of the CPI in the

UK in 2001-2002, is responsible for 22.1% of the increase of TFP growth in that year

(in “food products” 0.7% out of 5.2%)

The institutional dimension of the policy –more than its enforcement— and

the antitrust policy –more than the merger control— seem to have a stronger

impact

We provide evidence that the measured effect is causal and it is not driven by

omitted factors

3


Impact of Competition Policy on TFP Growth: Results

We analyze dimensions of heterogeneity of this causal effect

1. We explore the interactions between legal institutions and competition policy.

CPI has a significantly larger impact in countries with better legal institutions

With low cost for the enforcement of contracts (Doing Business),

With high rule of law (Fraser)

2. CPI 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-post antitrust scrutiny

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The Impact of Competition Policy Enforcement on the Functioning of Energy Markets

Study commissioned by DG Competition


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Competition Policy in energy Market

To study the long-term market-wide effects of overall policy enforcement – both

national and EU-wide – in energy sectors

Case studies focus on short-term effects of specific interventions

We postulate a relationship between policy enforcement – i.e., competition

policy and regulatory interventions— and market outcomes

4


Competition Policy Enforcement: Data

EU competition policy enforcement: detailed dataset on EU competition policy

enforcement between 2005 and 2013 in energy markets (NACE D.35)

200 merger decisions, 17 antitrust decisions (16 abuses and 1 cartel), and 203 state

aid decisions

As energy markets still largely function at the national level, the EU data are

transformed to match the national level of analysis proposed in this study, i.e. the

member state/year unit of observation

Measurement of ‘Enforcement’

Mergers: remedies (in phase 1 or phase 2) and merger withdrawals during phase,

relative to total number of notifications

State aid: the decision to initiate a formal investigation, relative to total number of

notifications

Abuses and cartels: the number of cases (all led to remedies or fines)

Sent a template/questionnaire to all national competition authorities

We construct similar variables as for EU competition policy

Average numbers are quite low, but substantial variation across countries

4


Competition Policy Enforcement: Data

OECD regulatory energy markets indices

Most used and most complete source of regulatory data in network industries

Used as an aggregate explanatory variable

To divide sectors into high-regulation and low-regulation industries

Assign sectors and firms to high-regulation (low-regulation) sub-sample if regulation index in

that country and year is higher (lower) than the median over all countries and time periods

Use sub-samples based on this definition rather than interactions between continuous indexes

to better exploit the limited variation in the data

Several additional sources to construct control variables

Institutional and governance data sourced from the World Bank

Information on the energy mix and energy imports

Macro factors, e.g., GDP/capita and population growth, from the OECD databases

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Outcomes

All measures based on Amadeus data (Nace D35.1 and D35.2 – very large firms)

Investment (Grajek and Röller, JLE 2009)

Monetary measure of investment: change in fixed assets owned by the firms

𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑖 𝑡 = 𝑓𝑖𝑥𝑒𝑑𝑎𝑠𝑠𝑖𝑡 − 𝑓𝑖𝑥𝑒𝑑𝑎𝑠𝑠𝑖𝑡−1

Total factor productivity

The firm-level residual between its value added (VA) and an industry-specific, trans-log

production function f , which depends on capital (K), labour (L) and Material expenses (M)

𝑇𝐹𝑃𝑖𝑡 = 𝑉𝐴𝑖𝑡 − 𝑓(𝐾𝑖𝑡 , 𝐿𝑖𝑡 ,𝑀𝑖𝑡)

Productivity dispersion (Syverson, JEL 2011)

Standard deviation of TFP at sector/country/year level of observation

Elasticity of profits wrt. costs (Boone, RJE 2007)

Increased competition should reallocate profits towards more efficient firms

We estimate this relationship between firm-level (i) relative profits (RPD) and relative costs

(RCE) at sector (s)/country (c)/year (t) level of observation

𝑙𝑛𝑅𝑃𝐷𝑖𝑠𝑐𝑡 = 𝛼 + 𝜷𝒔𝒄𝒕𝑙𝑛𝑅𝐶𝐸𝑖𝑠𝑐𝑡 + 𝜇𝑠𝑐 + 𝜇𝑡 + 휀𝑖𝑠𝑐𝑡

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Emprical Framework

Market-level competition

𝑌𝑠𝑐𝑡 = 𝛼 + 𝛾𝑗𝐸𝑈𝐸𝑛𝑓_𝑡𝑜𝑜𝑙𝑐𝑡−1

𝑛

𝑗=1

+ 𝛿𝑗𝑁𝑎𝑡𝐸𝑛𝑓_𝑡𝑜𝑜𝑙𝑐𝑡−1

𝑚

𝑗=1

+𝛽𝑅𝑒𝑔𝑐𝑡−1 + 𝑍𝑐𝑡−2 + 𝜔𝑠 + 𝜔𝑡 + 휀𝑠𝑐𝑡,

𝑌𝑠𝑐𝑡 outcome variable of sector s in country c at year t

𝐸𝑈𝐸𝑛𝑓_𝑡𝑜𝑜𝑙𝑐𝑡 EU competition policy enforcement affecting country c in year t

𝑁𝑎𝑡𝐸𝑛𝑓_𝑡𝑜𝑜𝑙𝑐𝑡 national competition policy enforcement in country c in year t

𝑅𝑒𝑔𝑐𝑡 intensity of regulation in a given national market c in year t

𝑍𝑐𝑡 time-varying country-specific factors

𝜔𝑠 and 𝜔𝑡 sector and time fixed-effects

휀𝑖𝑐𝑡 error, correlated among observations within the same sector-country

Same equation for firm-level outcomes (but with 𝑌𝑖𝑐𝑡 𝑎𝑛𝑑 𝜔𝑖)

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Wrap-up of the Main Results

Clear overall picture: EU merger policy enforcement is consistently and

significantly related to better outcomes, especially in low-regulated sectors

Lowers both Boone’s beta and productivity dispersion in these sectors, indicating that

national energy sectors became more competitive after these interventions

Leads to higher investment and higher total factor productivity in these sectors

Interpretation: EU merger policy actions—through the channel of competition—

induce energy firms to invest more, which ultimately leads to a higher

productivity

Competition policy is mostly effective there where the competitive process is not

affected by a high regulation

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Wrap-up of the Main Results & Caveats

Other interventions

Other policy enforcement measures show a more patchy impact, i.e. show more

limited correlation pattern with market outcomes

But this cannot conclusively be seen as a lack of their effectiveness

Possible that their low frequency of happening did not allow us to empirically identify

consistent relationships

Careful with causal inferences

Tried to deal with reverse causality bias (by lagging policy variables)

Tried to deal with omitted variable bias (by using fixed effects and other controls)

But our identification strategy is not based on a clear source of exogenous variation

Virtually impossible to find valid exogenous variation in such a broad and

heterogeneous framework

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Tomaso Duso 19

Source: Buccirossi, Ciari, Duso, Spagnolo, & Vitale (JCLE 2012)

Source: Buccirossi, Ciari, Duso, Spagnolo, & Vitale, Journal of Competition Law and Economics, 2012)

Measuring the Determinants of Deterrence


The Competition Policy Indexes 2

Tomaso Duso 20

.3.4

.5.6

.7.3

.4.5

.6.7

.3.4

.5.6

.7

1995 2000 2005 1995 2000 2005 1995 2000 2005 1995 2000 2005

Can Cze Fra Ger

Hun Ita Jap Net

Spa Swe UK USA

CPI Aggregate CPI Aggregate with EU component

year

Graphs by country

Source: Buccirossi, Ciari, Duso, Spagnolo, & Vitale, Journal of Competition Law and Economics, 2012)


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