In Indonesia, illegal deforestation is widespread and has challenged law enforcement over the country’s forests. Nowhere is this more visible than in Riau province, which has seen declines of over 65% in forest cover in recent years. The persistence of illegal logging has long been attributed to networks of powerful brokers, private sector entrepreneurs, and local political heads who extract timber resources for personal gain. But what are these networks and who participates in them? How do they function, and whose interests do they serve? How might they ultimately be disrupted?

Social network analysis (SNA), derived from graph theory, promises a new way of approaching and visualising corrupt networks. I conducted network analysis using legal archives from Indonesia’s Corruption Eradication Commission pertaining to its successful conviction of the head of Pelalawan district in Riau for corruption leading to deforestation. SNA helped identify the main actors in the network and illustrate how their relational structures enabled corruption. 

In the Pelalawan scheme, pulpwood capitalists developed vertically integrated timber suppliers through fictitiously ‘independent’ shell companies. This corrupt network was centred around the district head, who was later imprisoned on corruption charges, and his right-hand man, the whistle-blower. These two figures were at the centre of a sprawling network of state and private sector actors who played a variety of roles in facilitating the scheme and who benefited to varying extents. My analysis of the investigative files yielded a total of 201 individual nodes (actors), 92 organisational or company nodes, and 919 interactions between these nodes from 2000 through 2007.

I found that the network structure amplified the visibility of the principal perpetrators, indicating that they were largely unconcerned by the threat of law enforcement. This impunity reflects the privileged role that forestry capitalism has historically played and continues to play in the development of the Indonesian economy. The case study of Pelalawan illustrates how corrupt networks reflect the wider organisation of political and economic power within a society and a sector. Analysing a corrupt network in this context is a first step towards understanding its resilience as well as the potential pathways for disrupting its activities.

While SNA analysis largely confirms the decisions of corruption investigators in the Pelalawan case, it also highlights areas that they might have probed further to better uncover the full dimensions and main beneficiaries of the network. Such extended investigation could in turn provide a basis for more effective prosecutions.

In particular, investigation can benefit from a more thorough examination of private sector actors. Forestry corruption networks have large numbers of actors, organised in clusters of association with unevenly distributed gains. State actors control key resources, but these networks are dominated by private sector forestry actors, whose roles need to be better understood. In the Pelalawan case, investigators rightly targeted the major architects, the district head and his right-hand man. However, social network analysis reveals the importance of the many enablers of the scheme, including directors of pulp supply companies as well as lawyers, land surveyors, accountants, and other professionals.

Investigators should follow the money trail to expose the raft of peripheral figures who enable a corrupt network to persist and flourish. Corrupt money circulates far beyond its immediate beneficiaries, but investigators rarely pursue this beyond first- or second-tier transactions. Following the trail of dirty money is critical to expanding investigators’ understandings of network enablers and beneficiaries.

These actors, many of them in the private sector, typically escape prosecution. Policy makers should work with corruption investigators to develop a framework for corporate sanction beyond criminal prosecution that can be applied to private sector actors. Such sanctions could include, for example, public condemnation, demotion, resignation, nullification of operating licences, indemnity, and compensation.

Finally, given the usefulness of network analysis, anti-corruption agencies, law enforcement, and donors should consider providing support for the opening of legal archives to seed collaborations between academia and law enforcement. These could spearhead new innovations in anti-corruption efforts, though it should be kept in mind that results may be subject to data bias.