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Competing firms can increase profits by setting prices collectively, imposing significant costs on consumers. Such groups of firms are known as cartels and because this behavior is illegal, their operations are secretive and difficult to detect. Cartels feel a significant internal obstacle: members feel short-run incentives to cheat. Here we present a network-based framework to detect potential cartels in bidding markets based on the idea that the chance a group of firms can overcome this obstacle and sustain cooperation depends on the patterns of its interactions. We create a network of firms based on their co-bidding behavior, detect interacting groups, and measure their cohesion and exclusivity, two group-level features of their collective behavior. Applied to a market for school milk, our method detects a known cartel and calculates that it has high cohesion and exclusivity. In a comprehensive set of nearly 150,000 public contracts awarded by the Republic of Georgia from 2011 to 2016, detected groups with high cohesion and exclusivity are significantly more likely to display traditional markers of cartel behavior. We replicate this relationship between group topology and the emergence of cooperation in a simulation model. Our method presents a scalable, unsupervised method to find groups of firms in bidding markets ideally positioned to form lasting cartels.

In November 2016, after 52 years of armed conflict, the Colombian government and the primary rebel group, the FARC (Fuerzas Armadas Revolucionarias de Colombia) reached a peace agreement. The agreement incorporated three changes to institutions governing forest land occupation and use: (a) the demobilization of FARC from forested places, (2) the future distribution of legal land titles and new road construction into forests, and (3) the eradication of illicit crops. However, we document unprecedented rates of forest disturbance in the months following the peace agreement in biodiversity hotspots across the country. Are the declaration of peace and the increased rates of forest disturbance related? Here, we present the first systematic assessment of the impact of the Colombian peace agreement on forest disturbance. Focusing on the Andes-Amazon Transition Belt (AATB), we used automated satellite image disturbance detection methods and ethnographic data to quantify and interpret forest cover change from 2010 to 2018 that span wartime, peace negotiation, and post-peace agreement stages. Our findings indicate that during the post-peace agreement period (2017-2018), the area of forest disturbance increased by 50% (about 238 000 ha) across the AATB in comparison with the four-year peace negotiation stage (2013-2016); these changes reflect the end of FARC-led gunpoint conservation in the region. Forest disturbance also spread deeper into the Amazon watershed and increased in area by 187% within the AATB's protected areas. We find that following the peace agreement and the withdrawal of FARC, key actors (viz. drug cartels, large landowners, campesinos and dissidents) with expectations of favorable land tenure policies swept into the region; this led to increases in large-scale cattle ranching, coca cultivation dispersal, and speculative illegal land markets each of which contributed to the widespread forest disturbance that we mapped. The rapid increase in forest disturbance occurred despite the interest of the international community in promoting forest conservation initiatives in the AATB and Colombia's existing conservation and land titling frameworks for public lands. Our findings underscore the need for conservation strategies sensitive to rapid institutional and demographic changes in the course of the peace agreement to prevent forests from becoming an unexpected casualty of premature and unstable peace.

We study the role of communication within a cartel Our analysis is carried out in Stigler's (1964) model of repeated oligopoly with secret price cuts. Firms observe neither the prices nor the sales of their rivals. For a fixed discount factor, we identify conditions under which there are equilibria with \"cheap talk\" that result in near-perfect collusion, whereas all equilibria without such communication are bounded away from this outcome. In our model, communication improves monitoring and leads to higher prices and profits.

It is conventional wisdom that transparency in cartels—monitoring of competitors’ prices, sales, and profits—facilitates collusion. However, in several recent cases cartels have instead worked to preserve the privacy of their participants’ actions and outcomes. Toward explaining this behavior, we show that cartels can sometimes sustain higher profits when actions and outcomes are observed only privately, because better information can hinder collusion by helping firms devise more profitable deviations from the collusive agreement. We provide conditions under which maintaining privacy is optimal for cartels that follow a marketsegmentation strategy.

Firms coordinate their actions with industry peers, thereby affecting product market competition. Using the cartel setting, we investigate how financial reporting transparency affects industry coordination. Economic theory predicts that transparency might either prolong cartel duration through increased contracting efficiency, or destabilize cartels due to earlier detection of deviating members. We test these predictions on firms indicted by the European Commission for anticompetitive behavior between 1980 and 2010. Using reporting under internationally recognized accounting standards (IFRS or U.S. GAAP) as our measure of reporting transparency, we find that following a transparent accounting framework decreases cartel duration. We show this finding is partly explained by transparent segment disclosure, which provides a means for the verification of agreed-upon sales for a product or region. Consistent with the view that transparent reporting leads to earlier detection of deviating members, we further show that transparency lowers cartel duration when the likelihood of cheating is high.