Case Study: Understanding Trader Responses to Changes in Regulation with Implications for Cronyism and Cartel Formation


This ethnographic research, carried out by Principal Investigator Rahul Oka (Anthropology, University of Notre Dame), Nitesh Chawla (Computer Engineering, University of Notre Dame), and Yang Yang (Kellogg School of Management, Northwestern University) aimed at mapping the changes in structure of trade networks of traders Western and Northern Kenya with respect to changes in political stability and regulation, specifically looking at the emergence of cartels and cronyism. Most current mainstream economic models for conducting business and enhancing economic growth in emerging markets or other unstable areas stress and encourage deregulation of business practices. It is argued that deregulation would reduce the restrictions on business growth, innovation, and expansion, and hence lead to job creation and overall economic growth. Previous ethnographic research involving traders and business peoples from Asia, Africa, Europe, and the Americas suggested that deregulation might in fact lead to increased cronyism and decline in open competition within emerging markets. In other words, when local economies are deregulated, previously entrenched and politically connected traders would stand to dominate the growing markets and exclude new or smaller traders with impunity. These phenomena have also been observed to be highly correlated with post-de-regulation adjustments in (formerly) highly regulated economies.

In 2012, Oka teamed up with Chawla and Yang to examine the impacts of deregulation on the highly regulated economy through a combination of ethnographic and social network analysis. The primary challenge was to examine the relationship between trader responses (political connections, investment in trader vs. political networks, sharing of clients and markets) and changes in network structure as the economy shifted from regulated to deregulated. This research would have significant impact for policy-makers when considering the implications of deregulation as part of structural adjustment programs.


Using semi-structured interviews, focus group discussions, and structured interviews with Somali traders of Kitale, Lodwar, Kakuma, Lokichoggio, (Western and Northern Kenya) and Juba (South Sudan) (Figure 1), we elicited data on firm histories, behaviors, and network connections of wholesalers on the Kitale-Juba route. This data provided a larger network within which the commercial economy of Kakuma Refugee Camp operated (Figure 2). Between 2008 and 2012, we collected behavioral, network, and historical data on 76 traders operating in Kakuma and using repeated interviews, reconstructed the trader networks of Kakuma between 2005 and 2012. We also collected data on the political relationships maintained by traders for the years 2008, 2010, and 2012. This was to discern between traders who preferred investing in their own networks with fellow traders and the traders who opted for and invested in political elites for patronage and advantage. Using ethnographic interviews, we also gathered data on regulatory and stability conditions from the perspectives of trade-friendliness.

Figure 1: Regional Map

Figure 2: Commercial Economic Network

Our primary hypotheses were:

  1. During times of high or even predatory regulation, we would see parity between most of the traders regardless of their investment in trader versus political allies. We may even see a cartel effect emerging as traders enter into close cooperation with each other, sharing, resources, markets, customers, information, and connections, and eschewing overt displays of wealth and power. In particular, we expected to find that times of high regulation, the trader network structure would be more egalitarian with lower variation in status, influence, and centrality of individual traders, despite political connections.
  2. During times of low or even deregulation, we would see growing disparity between traders, with the politically connected traders gaining greater status, influence, and centrality within the network. These traders, protected by their political allies would be able to indulge behaviors characteristic of cronyism: anti-competitive market capture, seeking monopolies, and bringing violence against competitors, even members of family and friends.

We used the ethnographic data to generate these above hypotheses within the context of a larger model for network convergence during high regulation and network bifurcation during low regulation (Figure 3). The network data was analyzed at both node level (centrality, status, influence of key actors through Social Network Analysis) (Figure 4) and structural level (network transformation through machine learning approaches) (Figure 5). We also used machine learning approaches to identify politically connected versus trader network dependent actors. We also developed a cartel detection algorithm to see if the traders were indeed in a cartel, and another algorithm to measure changes in political patronage and cronyism over time (Figure 6).

Figure 3: Modeling Trader-Politician Relations, Regulations, and Network Transformation

Figure 4: Mapping the Change in Wholesaler Trader (Variation in Betweenness Centrality - 2005-2012)

Figure 5: Network transformation through machine learning approaches

Figure 6:Cartels and Cronyism


The ethnographic data showed that the period between 2005 and 2009 was characterized by high or even predatory regulation by local political actors, consisting of onerous rules, ad hoc informal taxation (bribes) and a large turnover of political and bureaucratic staff that ensured that traders had to continuously negotiate with different political elites to ensure business stability and continuity. The period between 2010 and 2012 was characterized by greater political stability and deregulation as Western Turkana District was split into two, more personnel were brought for longer periods of time, and the ad hoc informal taxation systems were reduced. The ethnographic data suggested that some of traders, emboldened by the deregulation after 2010, decided to indulge in anti-competitive behaviors, targeting their own kin, and increasingly depended on their political connection to insulate themselves from the repercussions of their actions.

As seen in Figures 4-6, the analysis of both the ethnographic and network data between these two-time periods showed:

  • Between 2005 and 2009, the traders of Kakuma showed great parity, with very low variation in individual node centrality, status, rank, or influence. No one trader enjoyed monopoly or significantly greater access to resources, consumers, or markets than any other. The wholesalers inadvertently formed a cartel through which goods and capital flowed between Kakuma Refugee Camp economy and the larger trader network. The network structure is characterized by high density of links, redundancy, and balance.
  • Between 2010 and 2012, the deregulation of the economy and the greater political stability is significantly correlated with increasing disparity between some politically connected traders and the other network invested traders. 2-3 traders who were already politically connected came to enjoy much greater influence over the market, and quickly created monopolistic relationships with actors in new and established markets. The network became increasingly hierarchical with the politically connected traders showing much more fluctuations and variation in their centrality, status, influence, and rank, and the network dependent traders. The algorithm was able to identify this economy as a crony capitalist economy.
  • The algorithms were able to capture network transformation (convergence and bifurcation) with ease and high degree of accuracy, and identify and distinguish politically connected portfolio capitalists from network dependent traders even without using data on political connections. These tools have enabled us to identify hidden actors with high potential to dominate or alter markets and competition, especially in the face of deregulation.


Given the high accuracy of prediction and identification of network transformation, political connection, this research is being considered by UNHCR and partners in their attempts to convert Kakuma into a self-sustaining settlement by enhancing the abilities of local business to expand in both size and efficiency and to help new and emerging businesses by ensuring fair competition and access to markets. On a larger scale, this research is being replicated in other parts of Kenya, South Sudan, and India to operationalize the impacts of over or under-regulation using both ethnographic and network approaches.