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as (referable to both allocation of quantities that each firm was assigned to supply and of market shares); ii) exclusive territories (and, closely related, the home-market principle); and iii) customer allocation.

Given the decision to allocate the market using sales quotas, first firms would reach a shared assessment of market size and then agree on the allocation of quota. Though, due to changes in non-cartel supply, cartel member’s share of the total market can change over time, cartel member’s share of total cartel supply would be highly stable under collusion. This leads to another collusive marker: there is a subset of firms for which each firm’s share of total supply for that subset of firms is highly stable over time. If cartel is all-inclusive then market shares are highly stable over time.

Another scheme broadly used is exclusive territory allocation on the basis of the 'home-market principle'. The idea underlying this principle is that cartel members reduce supply in each other’s home markets, to hopefully reach the point where each cartel member take control of their home market and only the global demand that was not part of any cartel member’s home market is shared. This mechanism can be associated to the combination of price increases and imports decrease.

2.2   Top-down approaches

This approach basically relies on statistical structural analysis and provides aggregate top down analyses that screen several sectors in the search for cartel activities. The idea is to test whether specific industry characteristics are good statistical predictors of the presence of cartels. This encompasses two stages. First a statistical model uses the economic independent variables to explain the level of cartelization in an industry, by using successful cases as a measure of where cartels are located across industries. Second, given the specific values of the economic independent values in an industry, the model can then be used to predict the probability that a cartel exists in that industry. On such basis, it is finally possible to rank industries on the likelihood that there are cartels yet to be uncovered.

Grout and Sonderegger (2005) essentially use the incidence of cartels detected by EC and US competition authorities as a measure of cartelization of an industry and then try to explain the level of cartelization by various industry specific variables (such as industry turnover, cost measures, concentration measures, measures of entry barriers or volatility measures) via regression analysis. The regression analysis allows the authors to derive a probability of collusion for industries that have not been suspected of collusion in the past, thereby providing a tool for competition authorities to focus their enforcement priorities.

Interestingly, the authors isolated four factors that appeared to be particularly important in their statistical analysis. Variability in demand growth was shown to have a negative impact on cartels; growth in turnover and high industry concentration had a positive impact on cartel formation, and finally, the cost disadvantage ratio, a measure for economies of scale in the industry, was the sole entry barrier found to be significant in explaining cartel presence.

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