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Cartel Damages to the Economy: An Assessment for Developing Countries

Part of the International Law and Economics book series (ILEC)

Abstract

The competition policy implementation and enforcement, including cartel deterrence and detection, require substantial investments. Therefore, it is important to understand to which extent these investments are compensated in terms of prevented damages to consumers. Answer to this question is especially important for developing countries for which decision to create or reinforce an antitrust authority largely depends on associated costs, while the sufficient and robust quantitative evaluation of potential benefits is still missing. The present study aims at providing the missing evidence by assessing the aggregate economic harm caused by cartels in developing countries. We find that economic damage of cartels already detected in developing countries is substantial—in terms of affected sales related to GDP the maximal rate reaches up to 6.38 %, while excess profits resulting from unjustified price overcharges reach up to 1 % when related to GDP. Furthermore, if one wants to take into account cartels that were not detected, the total damage appears at least four times larger.

Keywords

  • Hard-core cartel
  • Developing countries
  • Cartel damages
  • Antitrust
  • Cartel deterrence
  • Price overcharge

JEL Classifications

  • L12
  • L42
  • K22
  • B14
  • F29

This research project is funded by the CEPR PEDL program. It is as well recognized by the UNCTAD RPP initiative. Any opinions expressed here are those of the author(s) and not those of the CEPR or the UNCTAD.

This chapter is also published in the World Bank - OECD book edited by Georgiana Pop and Martha Licetti, “A Step Ahead: Competition Policy for Shared Prosperity and Inclusive Growth,” Washington: World Bank Group forthcoming 2016.

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Fig. 1

Notes

  1. 1.

    Collusive behavior could be granted an exemption by the competition authority if it is shown to be beneficial for consumers or to be necessary for firms’ survival in given economic conditions. This was, for instance, the case of the mixed concrete industry cartel in South Korea in 2009.

  2. 2.

    We have used the list of developing countries from the International Monetary Fund's World Economic Outlook Report, April 2010.

  3. 3.

    “Private International Cartels” database by John M. Connor, Purdue University, Indiana, USA (March 2009).

  4. 4.

    We wish to thank for a fruitful cooperation competition authorities from Brazil, Chile, Colombia, Indonesia, Peru, South Africa, Russia, Mexico, Ukraine, Pakistan, Zambia and South Korea and Mauritius, as well as UNCTAD RPP initiative coordinators.

  5. 5.

    Median values are more convenient to consider because the data are skewed and contain a few outliers with number of cartel participants more than 200 and duration of more than 150 months that renders mean values uninformative.

  6. 6.

    We understand that in some cases this can result in a slightly overestimated estimate of excess profits as output effect is not taken into account. Output effect refers to either reduction in sold quantities of the good due to the overall hike in market prices in presence of a cartel, or deliberate limitation of quantities by cartel members in order to increase prices.

  7. 7.

    We calculate the penalty-excess profits ratio without taking into account the money depreciation, which would render the values even lower.

  8. 8.

    Recall that margin constant for all cartel participants is one of the basic assumptions of the methodology. Keeping this in mind, when market shares and prices are known, it is easy to recover average cartel margin from the gross individual margins, and vice versa: \( AM={\displaystyle \sum_{j=1}^J{\overline{S}}_j^{cartel}}\frac{\left({p}_j^{cartel}-{c}_j\right)}{p_j^{cartel}}=\Big(\underset{cons\; \tan\;t\;for\; all\;j}{p_j^{cartel}-{c}_j\Big)}{\displaystyle \sum_{j=1}^J\frac{{\overline{s}}_j^{cartel}}{p_j^{cartel}}} \)

  9. 9.

    Marginal costs are calculated from margins, either average for the cartel or firm-specific ones.

  10. 10.

    For example, if a cartel was fined for US$100 and the maximal penalty rate is 10 % of cartel’s sales, then minimal bound for cartel’s sales can be estimated as 100/0.1 = US$1000. Because percentage penalty rule is sometimes applied to company’s total sales, we have employed, where needed and where possible, a coefficient that corresponds to the share of sales on the relevant market in total company sales.

  11. 11.

    Note that a high level of excess cartel profits related to the competition authority budget does not necessarily witness for the efficiency of the antitrust enforcement. Firstly, a low level of the ratio in question can result from a high efficiency of the competition authority if the latter focuses rather on cartel deterrence (education through mass media or higher penalties, etc.) than cartel detection. Low number of detections or lower excess profits can simply reflect the fact that there exist fewer cartels or that they are weaker. Second reason is that competition authorities can ‘free ride’ on the experience of the other ones. By ‘free riding’ we mean a situation when a cartel case already went through an examination in one of the competition authorities, and the others use this fact to trigger its own investigation or even use the already collected evidence. Therefore a competition authority can win the case without investing too much. As the collected sample demonstrates, ‘free riding’ can indeed take in place - the same cartels are often found in a large number of (often neighboring) countries. For example, this is the case of industrial gas distribution cartels in Latin America or cement cartels in Africa. Although, ‘free riding’ can potentially be considered as a sort of efficiency as it is a way of optimizing the available resources.

  12. 12.

    Because the exact duration of cartel from our database is often not known (for example, the year only was reported, but not the month or date) we take the maximal duration for each of the cartels during the known months/years. To see whether our data fit model assumption of independency and exponential distribution we performed the same testing as in Bryant and Eckard (1991). Corresponding estimation results and graphs are available upon request.

  13. 13.

    Estimates for the EU are taken from Combe et al (2008) and cover cartels prosecuted from 1969 to 2007. The maximal bound for the annual deterrence rate of 13–17 % was estimated with a similar methodology for a set of U.S. cartels (see Bryant and Eckard 1991). However these result should not be compared with the one from our study as situation in the antitrust enforcement has significantly changed since the period that was considered by authors (from 1961 to 1988).

  14. 14.

    Estimates from Boyer and Kotchoni (2014) were originally provided with respect to a ‘but-for’ prices, therefore they were recalculated with respect to the cartel price to be comparable with the other estimates in the paper.

  15. 15.

    Cartels can potentially cause a price umbrella effect as remaining firms could have more incentives to charge higher prices facing a price increase from cartel members.

  16. 16.

    Even though our model does not allow the quality characteristics to change, the degradations in quality can still appear as colluding firms may have less incentive to maintain it.

  17. 17.

    We solve the system of non-linear equations implied by proposed methodology with the use of SAS routines and procedures.

  18. 18.

    Increase in cartel’s margin decreases calibrated values of marginal costs (cartel prices are given), and also decreases calibrated price sensitivity α [see Eq. (3)]. Left-hand side of Eq. (1) remains constant, therefore, to compensate the decrease in α, δ j should decrease too. In competitive state we cannot predict whether \( \left({\delta}_j-\alpha {p}_j^c\right) \) will increase or decrease for every product, because all three parameters have lower values. Equation (1) indicates that if market shares in competitive state will be relatively higher with respect to the share of the outside option, then welfare level will be also higher, and vice versa.

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Appendices

Appendix 1: Major ‘Hard Core’ Cartels Prosecuted in Selected Developing Countries (1995–2013)

Argentina Brazil (cont.)
Portland cement 1981–1999 Maritime hose Jun’99–May’07
Medical gases n/a–1997 Crushed rocks Dec’99–Jun’03
Healthcare services n/a Security guard services 1990–2003
Liquid petroleum gas (S.C. Bariloche) Jan’98–Dec’98 Hermetic compressors 2001–2009
Sand (Parana city) Jun’99–Jul’01 Industrial gas 1998–Mar’04
Liquid oxygen Jan’97–Dec’01 Air cargo Jul’03–Jul’05
Cable TV (Santa Fe city) Oct’97–Dec’01 Transportation Oct’97–Jan’01
Cable TV (football transmissions) Jan’96–Dec’98 Steel bars 1998–Nov’99
Brazil Construction materials (sand) 1998–Apr’03
Civil airlines Jan’99–Mar’03 Steel 1994–Dec’99
Retail fuel dealers (Goiania) Apr’99–May’02 Blood products Jan’03–Dec’03
Retail fuel dealers (Florianopolis ) 1999–2002 Toy manufacturers (imports from China) 2006–2009
Retail fuel dealers (Belo Horizonte) 1999–2002 Chile
Retail fuel dealers (Recife) Apr’99–Feb’02 Petroleum products Feb’01–Sep’02
Generic drugs Jul’99–Oct’99 Medical gases (oxygen) 2001–2004
Chile (cont.) Colombia (cont.)
Medical insurance plans 2002–2004 Milk processing n/a–2008
Medical services May’05–May’06 Health services Mar/09–Nov’11
Construction materials (asphalt) 20 Oct’06 (bid rigging) Oxygen supply May’05–Mar’11
Public transportation (bus) 2006 Road paving Aug’10–Jan’12
Public transportation (bus) Nov’07–May’08 Sugar cane remuneration rates Feb’10–Aug’11
Petroleum products Mar’08–Dec’08 Cars’ techno-mechanical and gas review Mar’10–Oct’11
Vehicles and spare parts 11 Aug’06 (bid rigging) Cars’ techno-mechanical and gas review Mar’10–Dec’11
Publishing services Mar’08–Apr’08 Feed ration service for prisons May’11–Sept’12
Pharmaceutical industry (distribution) Dec’07–Apr’08 Cars’ techno-mechanical and gas review Apr’10–Mar’12
Public transportation Oct’06–Nov’07 TV advertising market Apr’10–Apr’11
Radio transmission 2007 Egypt
Tourism (agent services) 2008 Construction (Egypt Wastewater Plant) Jun’88–Sept’96
Public transportation (maritime) 2009 Cement Jan’03–Dec’06
Public transportation (bus) Feb’07–Mar’09 El Salvador
Flat Panel TV n/a Petroleum products n/a–2007
Colombia Indonesia
Cement Feb’06–Jan’10 Mobile phone services Mar’03–Nov’05
Mobile phone services Apr’99–Aug’07 SMS Jan’04–Apr’08
Green onions Feb’07–Jan’09 School books Jan’99–Dec’00
Pasteurized milk Jan’97–n/a Cement n/a–Dec’09
Green paddy rice Jan’04–Nov’06 Airlines Jan’06–Dec’09
Chocolate and cocoa products Oct’06–Oct’09 Pharmaceuticals n/a
Private security services Feb’11–Sep’12 Poultry (day old chicken) Jan’00–Dec’00
Services of grade systematization (Bogotá District schools) Jun’08–Dec’09 Sea cargo (Jakarta-Pontianak) Jun’02–Oct’03
Indonesia (cont.) South Korea (cont.)
Sea cargo (Surabaya-Makassar) Jan’03–Sep’03 Trunked radio system devices Dec’03–Feb’06
Public transportation (city bus) Sep’01–Oct’03 Petrochemicals Sep’00–Jun’05
Salt Trade ( North Sumatra) Jan’05–Dec’05 Copy paper imports Jan’01–Feb’04
Sea Cargo (Sorong Seaport) Mar’00–Nov’08 Soft drink bottling Feb’08–Feb’09
Kazakhstan Gas (LPG) Jan’03–Dec’09
Petroleum products (brokers) 2002–2005 Elevators and escalators Apr’96–Apr’06
South Korea Toilet roll manufacturing Mar’97–Jan’98
Batteries manufacturing (auto) Jun’03–Sep’04 Coffee Jul’97–Jan’98
Beer Feb’98–May’99 Kenya
Cement Jan’02–Mar’03 Coffee producers n/a
Construction machinery (excavators) May’01–Nov’04 Fertilizers I n/a–2003
Forklifts manufacturing Dec’99–Nov’04 Beer (production) n/a–2004
Petroleum products (military, wholesale) 1998–2000 Soft drinks n/a–2004
Telecom services (local, land line) Jun’03–May’05 Transportation n/a
Telecom services (long-distance, land line) Jun’03–May’05 Mechanical engineers services n/a
Telecom services (international, landline) Jun’03–May’05 Insurance (transportation sector) n/a–2002
Broadband Internet service Jun’03–May’05 Petroleum (retail) n/a–2004
Detergent manufacturing 1998–2006 Fertilizers II n/a–2011
Telecommunications (mobile services) I Jun’04–May’06 Tea growers n/a–2004
Telecommunications (mobile services) II Jan’00–Jul’06 Sugar n/a–2004
Gasoline and diesel (refining) Apr’04–Jun’04 Port Customs Department auctions n/a
Industrial motors 1998–2006 Malawi
Polyethylene (low density) Apr’94–Apr’05 Cotton farmers n/a
Polypropylene (high density polyethylene) Apr’94–Apr’05 Tea growers n/a
Movie tickets Mar’07–Jul’07 Tobacco growers n/a
Malawi (cont.) Pakistan (cont.)
Bakeries n/a Cement Mar’08–Aug’09
Beer n/a Gas (LPG) n/a–2009
Petroleum sector n/a Jute mills 2003–Jan’11
Mauritius High and low tension pre-stressed concrete poles Aug’09–May’11
Travel agency 2010 Poultry and egg industry 2007–Aug’10
Mexico Newspapers Apr’08–Apr’09
Gas (liquid propane) Jan’96–Feb’96 Vessels handling(ships) 2001–Mar’11
Chemicals (film development) Jan’98–Dec’00 Port construction May’09–Jul’10
Poultry Mar’10–Mar’10 Ghee and cooking oil Dec’08–Jun’11
Boiled corn and corn tortillas Mar’11–Jul’12 Accounting services Apr’07–Jan’13
Corn mass and tortillas May’10–Aug’12 LDI operators Sep’11–Apr’13
Transportation (touristic sector) Jul’09–Mar’12 GCC approved medical centers Jan’11–Jun’12
Anesthesiology (services) May’03–May’09 Banking services (1-Link Guarantee Ltd) Sep’11–Jun’12
Auto transportation (cargo) I Jan’10–Sep’11 Peru
Maritime public transportation Jun’08–Jun’12 Urban public transportation 1 Aug’08–Oct’08
Auto transportation (cargo) II Sept’08–Jun’10 Urban public transportation 2 Aug’08–Oct’08
Healthcare (medical drugs) 2003–2005 Public notaries n/a
Consulting services (real estate) Jul’03–Apr’09 Dock work Sep’08–May’09
Restricted TV signal Oct’02–Dec’08 Insurance 1 Dec’01–Apr’02
Food vouchers Aug’05–Sept’05 Insurance 2 Oct’00–Jan’03
Consulting services (real estate) II May’03–Jul’09 Poultry May’95–Jul’96
Railway transportation (cargo) Nov’05–Jun’09 Wheat flour Mar’95–Jul’95
Cable and cable products Feb’06–Mar’07 Heaters/boilers etc. manufacturing Oct’95–Mar’96
Pakistan Oxygen distribution (healthcare) Jan’99–Jun’04
Bank interest rates Nov/07–Apr’08 Freight transport Nov’04–May’09
Russia South Africa (cont.)
Fuel (gasoline and jet) Apr’08–Jul’08 Cement I 1996–2009
Laptop computer operating systems n/a Plastic pipes 1998–2009
Fuel (gasoline, Krasnodarki krai) Jan’05–Jul’05 Concrete, precast pipes, culverts, manholes, & sleepers 1973–2007
Fuel (gasoline, Rostov-on-Don) n/a–2005 Fishing n/a–2009
Airlines (flights between Nizhnevartovsk and Moscow) n/a–Dec’05 Cement II Jan’04–Jun’09
Railway transportation (Kemerovo) Oct’11–Dec’12 Construction n/a–2009
Soda cartel 2005–2012 Steel distribution n/a–2008
Polyvinylchloride cartel 2005–2009 Steel (re-bars, rods & sections) n/a–2008
Pharmaceutical cartel 2008–2009 Steel (wire, wire products) 2001–2008
Fish cartel (Norway) Aug’11–Dec’12 Crushed rock n/a–2008
Pollock cartel Apr’06–Dec’12 Bricks n/a–2008
Fish cartel (Vietnam) Jun’08–Sept’13 Steel (tinplate) Apr’09–Oct’09
Salt cartel May’10–May’13 Steel (mining roof bolts) 2002–2009
Sausage cartel Jun’09–Dec’09 Flour milling 2009–Mar’10
Military uniform supply 2010–Jun’12 Bitumen 2000–2009
South Africa Poultry 2005–2009
Fertilizers (phosphoric acid) Jan’03–Dec’07 Polypropylene plastic 1994–2009
Airlines (fuel surcharge) May’04–Mar’05 Sugar 2000–n/a
Airlines (So. Africa-Frankfurt routes) Jan’99–Dec’02 Taxi n/a
Milk (farm and retail) n/a–Jul’06 Auto dealers 2005–n/a
Bread and flour 1994–2007 Healthcare fees 2002–2007
Pharmaceuticals (wholesale distribution) 1998–2007 Pharmaceuticals n/a–2002
Tire manufacturing 1998–2007 Motor vehicle manufacturers/importers n/a–2006
Metal (scrap) Jan’98–Jul’07 Freight forwarding n/a–2007
Steel (flat) 1999–Jun’08 Energy/switchgear n/a–2008
South Africa (cont.) Zambia
Fertilizer (nitrogen) 2004–2006 Pipes, culverts, manholes and pre-stressed concrete sleepers n/a
Steel (reinforcing mesh) 2001–2008 Oil marketing 2001–2002
Soda ashes (imports) 1999–2008 Fertilizer 2007–2013
Tanzania Grain procurement and marketing (maize-meal) Mar’04–Jun’04
Beer n/a Public transport n/a
Pipes, culverts, manholes and pre-stressed concrete sleepers n/a–2009 Poultry 1998–1999
Petroleum sector n/a–2000 Panel Beating Services Sep’11–Dec’11
Turkey Zimbabwe
Daily newspapers n/a Bakeries n/a
Traffic lights n/a  
Public transportation (buses) n/a
Poultry n/a
Bakeries n/a
Beer n/a
Soft drink n/a
Maritime transport service n/a–2004
Mechanical engineers n/a
Insurance n/a–2003
Telecommunications n/a–2002
Architects’ and Engineers’ services n/a–2002
Yeast n/a
Cement n/a
Cement (Aegean region) n/a–2004
Accumulators n/a
Ukraine
Acquisition of raw timber auctions (furniture) 2011
Sale of poultry meat n/a
Sale of sugar n/a
Sale of alcohol n/a
Sale of buckwheat n/a
Individual insurance markets 2003
Market of services on sale of arrested property state 2004

Appendix 2: Questionnaire

1.1 First Part. General Questions

  1. 1.

    Please, provide the annual budget of the competition policy enforcement unit during the period 1995–2013 (in local currency);

1.2 Second Part. Identification of Cartels

  1. 1.

    Please, provide a list of major “hard core” cartels for the period 1995–2013;

  2. 2.

    For each identified cartel, provide information on:

    1. (a)

      Relevant market (product, geography, etc.);

    2. (b)

      Names of cartel members;

    3. (c)

      Period of existence of the cartel (beginning/termination);

    4. (d)

      Date of discovery of the cartel;

    5. (e)

      Date of entry of each company in the cartel coalition, if available;

    6. (f)

      Fines applied, if any (in local currency);

    7. (g)

      Price overcharge by cartel members, if available (percentage with respect to the cartel price or money terms in local currency)

1.3 Third Part. Economic Data on Each Cartel Identified in the Second Section of the Questionnaire

  1. 1.

    At least for one period (month/year) of cartel existence indicate the market share/volume sold and price (in local currency) of the product/ products for each colluding company;

  2. 2.

    If possible, give an estimation of the average margin for the cartel = (price-marginal costs)/price;

  3. 3.

    Please, provide, whether available, the estimate of the volume of the relevant market (in local currency), if not:

  4. 4.

    According to the good that is analyzed, please provide an estimation of the total market share of the non-cartel members on the relative market;

Appendix 3: Example of the Calibration and Estimation Procedure (Brazil)

Four national airlines, namely Varig, TAM, Transbrasil and VASP, were convicted in collusive price-fixing behavior on the civil air transportation market between Rio de Janeiro (airport Santos Dumont) and San Paolo (airport Congonhas) during the year of 1999. We do not go into details concerning the evidence that the Brazilian competition authority employed to convict a cartel but will rather focus on the estimation of the economic harm to consumers caused by this anticompetitive practice.

Table 6 provides the collected data regarding the observed one-way ticket prices charged by cartel members, as well as their observed market shares based on number of tickets sold. These are the minimal data that are sufficient to implement our methodology and recover the price overcharges.

Table 6 Input data (as of July 1999)

We recognize that it would be more correct to separate leisure and business segments of the demand for air travel, which would obviously have different sensitivities to price (parameter α), however available data did not permit us to do so. Given that the share of business segment on the relevant market reaches up to 70 %, we believe that recovered market parameters will correspond mostly to this demand category.

As the developed methodology implies, to perform calibration of supply and demand parameters we need to set the share of the outside alternative (s o) and average cartel margin exogenously. We use additional data on the case to set the admissible ranges for these parameters.

Considered airports are the only ones situated close to the city centers of Rio de Janeiro and Sao Paulo, which makes them especially relevant for business passengers. In addition, there are no convenient substitutes, such as sufficiently fast trains or buses. Airlines that formed the cartel performed nearly 100 % of the flights between the mentioned airports at the time. Therefore, one can assume that share of the outside alternative for the business segment cannot be too big. However, presence of the leisure segment and other airports serving the same origin and destination cities suggests that s 0 cannot be too low either. We arbitrary choose the admissible range for the share of the outside option as \( {s}_0\in \left[10\;\%,\;50\;\%\right] \).

As for the second exogenous parameter—average cartel margin, we first make use of the results of Betancor and Nombela (2001), who demonstrate that marginal costs of American and European airlines are at least equal and at most twice higher than their average costs. We assume further that Brazilian airlines’ cost structure is not much different from that in Europe and the U.S. Having extracted average costs from the annual reports of the colluding companies, we get 40 % as a maximal value for the average margin (when marginal costs are equal to average costs). Given that airlines’ activities on the relevant market include also those non-cartelized (e.g. on board sales), we assume that possible margin on the cartelized market could potentially have an upper bound above 40 %. After a final check with sign constraints for marginal costs and price sensitivity parameter α, we define a permitted range for the average cartel margin as [10 %, 45 %].

When one changes level of external parameters, then calibrated market parameters also change. Along with the minimal and maximal bounds for exogenous parameters, considering some intermediary values might be also reasonable if an analyst has an idea about their most probable values inside the chosen interval. In Table 7 we provide calibrated price sensitivity α depending on the average cartel margin and share of the outside option: for minimal, maximal and some intermediary values of external parameters. These dependencies are monotonic. We also report corresponding calibrated values of \( {\delta}_j,j=\overline{1,J} \) in Table 8.

Table 7 Calibrated price sensitivity parameter (α)
Table 8 Calibrated parameters of differentiation (δ j )

We observe that calibrated parameter α and \( {\delta}_j,j=\overline{1,J} \) decrease when the share of the outside option increases, margins being fixed. This dependence follows directly from Eqs. (1) and (4) in the main part of the chapter and can be explained as following. Lower α indicates that preferences of consumers are mostly driven by the quality rather than prices. Lower δ j , therefore, results in a higher number of consumers who preferred the outside option as its’ utility is normalized and remains fixed. α also decreases with higher cartel’s margin—when consumers are less sensitive to the price, cartel members have more incentives to charge a higher price.

For the set of calibrated market parameters we further perform the simulation of the counterfactual (competitive) state.Footnote 17 Tables 9 and 10 report the average for the cartel price overcharge rates [Eq. (8) in the main part of the chapter], and consumers’ welfare losses [Eq. (10) in the main part of the chapter] estimated for a given combination of values of exogenous parameters.

Table 9 Estimated price overcharge rate (average for the cartel)
Table 10 Estimated consumers’ welfare losses, %

Variations of the obtained estimations of price overcharges and welfare losses according to the level of external parameters are intuitive. On one hand, when cartel margin is being fixed, a high share of the outside option informs the analyst about a high elasticity of demand. In these conditions, the ability of colluding firms’ to increase their prices is rather limited. Accordingly, welfare losses are also les significant. On another hand, keeping the share of the outside option fixed, higher desired cartel margin naturally transforms into a higher price increase when compared to a competitive state of the market. Though, no definite conclusion can be made concerning the relative change in consumers’ welfare.Footnote 18

We acknowledge that variations of the estimates in Tables 9 and 10 are quite large. Price overcharge varies from 3.2 to 33.9 %, while the welfare losses estimates range from 42.2 to 78.6 %. A greater precision can be gained provided that more precise inputs concerning the relevant market are at hands.

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Ivaldi, M., Jenny, F., Khimich, A. (2016). Cartel Damages to the Economy: An Assessment for Developing Countries. In: Jenny, F., Katsoulacos, Y. (eds) Competition Law Enforcement in the BRICS and in Developing Countries. International Law and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-30948-4_3

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