Economic Returns and Postconflict Crime

How variations in economic conditions impact civil conflict (Dube and Vargas Reference Dube and Vargas2013) and crime (Dix-Carneiro, Soares, and Ulyssea Reference Dix-Carneiro, Soares and Ulyssea2018) has been widely studied. The theoretical motivation for this effect can be traced back to classical economic studies of crime, according to which agents engage in criminal activities after performing a calculation of the cost and benefits that includes the opportunity costs of legal sector employment (Becker Reference Becker1968; Dal Bó and Dal Bó Reference Dal Bó and Dal Bó2011).Footnote ¹ The effect of a commodity price shock will depend on many factors, including whether the commodity is legal or illegal, capital or labor intensive, or seasonal or not.Footnote ² There is evidence that changes in the economic returns to the production and distribution of illegal commodities are associated with crime and violence (Dell Reference Dell2015; Dube and Vargas Reference Dube and Vargas2013; Mejia and Restrepo Reference Mejia and Restrepo2013; Millán-Quijano Reference Millán-Quijano2020; Sviatschi Reference Sviatschi2022). When illegal profits change, rates of criminality also change, for example, because of variations in market shares or because the number of people involved in the illicit market varies.

Profits from illegal markets can fuel criminality in general and recidivism of ex-combatants in particular. To study variations in the ex-combatant’s opportunity cost, I analyze municipal-level changes to illegal gold production. This commodity is at the core of the individual constraints that bind delinquent behavior, particularly in the developing context of Colombia, characterized by low state capacity and limited labor market opportunities. Especially relevant for our case study, the fact that the product is illegal gives former combatants a comparative advantage in this market. Ex-combatants possess knowledge of the functioning of illegal operations and the know-how required to make a profit from engaging in such activities (Ortiz-Riomalo and Rettberg Reference Ortiz-Riomalo and Rettberg2018; Valencia and Riano Reference Valencia and Riano2017).

To simplify my argument, I hypothesize that ex-combatants’ participation in criminal activities changes in response to changes in the economic returns to labor-intensive illegal commodities.

Peer Effects and Postconflict Crime

Several scholars in sociology and criminology underscore the effect of social networks in crime decisions (Papachristos Reference Papachristos2014; Warr Reference Warr2002). According to this explanation, individuals participate in criminal activities as a result of the interaction with other delinquents. This approach suggests that delinquents have friends who have participated in crimes and that social ties are a means by which an actor is influenced to commit a crime (Sarnecki Reference Sarnecki2001). In the same vein, following the seminal work of Glaeser, Sacerdote, and Scheinkman (Reference Glaeser, Sacerdote and Scheinkman1996), recent theoretical approaches in economics relate crime to social networks. Several papers find that, in fact, crime and delinquency are related to positions in social networks.Footnote ³

These studies suggest that properties of peer associations should be taken into account in order to better understand the relationship of social pressure and delinquent behavior in the aftermath of conflict and to inform delinquency-reducing policies targeted at ex-combatants. The reason to focus on wartime peers is threefold. First, recent studies show that different aspects of the social connections of an ex-combatant are important in explaining recidivism (Daly, Paler, and Samii Reference Daly, Paler and Samii2020; Kaplan and Nussio Reference Kaplan and Nussio2018b; Themnér Reference Themnér2011; Reference Themnér2015). Second, wartime bonds lay the foundation for one of the most important social networks of ex-combatants following demobilization and during reintegration. Militancy often requires that combatants break links with family and friends, which in most cases implies a complete separation of military and civil life that is not always easy to bridge after conflict (Themnér and Karlén Reference Themnér and Karlén2020; Wood Reference Wood2008). The bonds created during a conflict are an important source of information (Parkinson Reference Parkinson2013; Staniland Reference Staniland2014) and many ex-combatants actually retain their identity long after a conflict has ended (Daly Reference Daly2016). Third, the stigma against ex-combatants in postconflict societies (Kaplan and Nussio Reference Kaplan and Nussio2018a; McMullin Reference McMullin2013) may make them look to maintain contact and alliances with former peers. Consequently, although reintegration paths may differ, ex-combatants have in common at least one set of “weak ties”: their former fellow combatants. These weak ties derive from bonds formed during wartime through socialization for long periods of time in small units while being exposed to constant threats.

Fellow ex-combatants remain an important source of information after a conflict has ended and units have demobilized. Even though these connections are not as “strong” as family and friends, they are relevant in one key domain. Granovetter (Reference Granovetter1977; Reference Granovetter2018) famously shows that weak ties are superior to strong ties in terms of providing support in getting a job.Footnote ⁴ Moreover, Patacchini and Zenou (Reference Patacchini and Zenou2008) show that weak ties are positively related with crime decisions. Criminals transmit valuable information about criminal opportunities to potential accomplices. In other words, networks of criminals amplify delinquent behavior. As information about criminal opportunities is transmitted through social networks, an increase in the level of activity of peers in turn drives an increase in individual participation in criminal activities.

To simplify the second part of my argument, I hypothesize that ex-combatants will be affected by the level of criminal activity of their wartime peers. In particular, I argue that the overall criminal activity of the wartime connections will directly affect the decision of an ex-combatant to participate in criminal activities.

Illegal Commodities and Postconflict Crime in Colombia

Studies have shown the effect of commodity price changes on conflict and criminality in Colombia.Footnote ⁶ Following Rettberg, Cárdenas, and Ortiz-Riomalo (Reference Rettberg, Cárdenas and Ortiz-Riomalo2019), gold did not play a major role during the conflict, compared to coca, coffee, and oil, but has become one of the main focuses of criminal activity since the post-paramilitary demobilization. In the words of the Colombian National Police chief, “Gold will have even more devastating effects than drug trafficking for the country” (cited in Rettberg, Leiteritz, and Nasi Reference Rettberg, Leiteritz and Nasi2014).Footnote ⁷ There are multiple opportunities for illegal groups to take advantage of mining activity: they can appropriate the value generated along the chain of gold production and even regulate the value chain in the territories where they are present.Footnote ⁸ For these activities, agents with experience in routes, the use of weapons, and engaging in violence are particularly valuable. In addition, agents with knowledge of trusted people to carry out these activities across different regions are of paramount importance.Footnote ⁹ Moreover, there are numerous documented cases of arrests related to illegal mining that spread through various municipalities where organizations consisting in part of former paramilitary members were known to operate. Section E of the Supplementary Material reports on instances of illegal gold-mining activities taking place in different municipalities and describes multiple cases in which ex-combatants were involved as a proof of concept of the mechanisms of the argument in this article.

Illegal gold mining is a widespread activity in Colombia: based on the 2010 Mining Census, more than 62% of gold mines do not have a legal permit. The economic activity related to illegal gold mining is particularly vulnerable to predation; armed groups provide security along trafficking routes and in many cases exploit the mines. As such, illegal gold mining is an important source of revenue for organized criminal bands who control deposits and transit (Valencia and Riano Reference Valencia and Riano2017). Significantly, disputes over control of the illegal activity spread beyond the locations of the mines. The actors involved are often located along trafficking routes and have developed the criminal capacity for extortion in several locations.

Wartime Connections and Postconflict Crime among Colombian Ex-Combatants

A considerable number of ex-combatants in Colombia resumed criminal activities undertaken during the conflict after demobilization, many as a result of their ongoing contact with former commanders (Daly, Paler, and Samii Reference Daly, Paler and Samii2020) and many as a result of weak family ties and the presence of illegal markets (Kaplan and Nussio Reference Kaplan and Nussio2018b).Footnote ¹⁰ Based on the number of arrests, around 15% of former paramilitary members participated in illegal activities in the years after demobilization, with their participation in crimes being spread across more than nine hundred municipalities across the country. Supplementary Figure A.1 shows the location and spread of arrests of ex-combatants in the country. Former paramilitary members participate more frequently in collective crimes.Footnote ¹¹

Three facts are worth mentioning about the collective nature of ex-combatant criminality. First, Figure 1 shows the frequency of arrest across types of crimes. The most common types of crimes are related to drug trafficking, organized crime, and possession of arms. Generally, these activities need the cooperation of several individuals to take place. Success in these activities depends on the collective participation of co-offenders (or knowing someone with connections and access to illegal markets). In fact, the National Police of Colombia uses the category of “gang-related” crime to refer to these crimes and to distinguish them from other “individual” crimes.

Figure 1.

Types of Crimes of Ex-Combatants, 2013–16

Second, we can approximate whether ex-combatants were arrested together. To that end, we can explore the density of arrests across different periods to see whether there is evidence of overdispersion (Glaeser, Sacerdote, and Scheinkman Reference Glaeser, Sacerdote and Scheinkman1996) or even bimodality (Gaviria Reference Gaviria2000) in the crime distribution, meaning that several offenders must have acted together. If we see a multimodal geographical distribution, this reflects the fact that most places have few arrests (as expected) and a significant number of places have many arrests, which suggests that multiple participants were involved in criminal activities together. Supplementary Figure A.2 shows the densities of arrest rates across Colombian municipalities for different years and, following Gaviria (Reference Gaviria2000), it provides suggestive evidence of collective participation in crime.

Finally, as preparation for this article, I conducted a national survey of ex-combatants in Colombia to capture some patterns of their social connections. The idea was to capture the types of connections that are more relevant during conflict and how those relations vary after demobilization. I present the results broken down by each type of armed group, because the sample includes members who participated in a collective demobilization (Paramilitary) and those who demobilized individually (Guerrilla). The description of the survey strategy and results are provided in Section K of the Supplementary Material. Supplementary Figure A.9 shows that combatants spent more time with members of their same unit and their same rank than with commanders or combatants of other ranks and units. After demobilization, the connection with ex-combatants is stronger with peers in the same unit and of the same rank than with other ex-combatants but lower than with family and close friends.

Therefore, the increasing presence of both criminal bands and illegal markets and the participation of ex-combatants in both, combined with the recent peace process that disarmed thousands of former fighters, make the study of the implications of wartime legacies on recidivism and criminality in Colombia particularly relevant.

Networks and Crime

There are many variants to models of crime and social interactions, all of which are similar to the specification that is considered in this section. Individuals are connected to each other through a social network represented by an adjacency matrix g, where $ {g}_{ij}=1 $ if i and j are linked to each other and $ {g}_{ij}=0 $ otherwise.Footnote ¹³ In Figure 4, agents i, j, and k belong to the same wartime network, while l belongs to another network.

In each period t, individuals indexed by i who are members of group g choose a level of criminal effort $ {y}_{it}\ge 0 $ and obtain an utility $ {u}_{it}(\mathrm{y}) $ that depends on the group’s criminal profile y $ =({y}_1,{y}_2,...,{y}_n) $ in the following way:

$$ {u}_{it}(\mathbf{y})=\underset{\mathrm{Benefits}-\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}{\underbrace{a_{it}{y}_{it}-\frac{1}{2}{y}_{it}^2}}\hskip0.15em +\underset{\mathrm{Social}\hskip0.3em \mathrm{C}\mathrm{o}\mathrm{m}\mathrm{p}\mathrm{o}\mathrm{n}\mathrm{e}\mathrm{n}\mathrm{t}}{\underbrace{\beta (\sum_{j\ne i}{g}_{ij}^w{y}_{it}{y}_{jt}).}} $$

The first two elements represent the benefits and costs of criminal effort $ {y}_{it} $ , where $ {a}_{it}>0 $ denotes differences in the individual’s economic benefits from criminal activity and the cost of engaging in crime is given by $ \frac{1}{2}{y}_{it}^2 $ , which increases with own effort.

The social component is included in the utility function by adding $ {\sum}_{j\ne i}{g}_{ij}^w{y}_{it}{y}_{jt} $ . The social component reflects the influence that the action or outcome (e.g., criminal activity) of other members of group g has on the utility of i. The social influence of i on j is captured by $ {g}_{ij}^w $ . The overall effect of the weighted sum of bilateral influences is captured by $ \beta $ . The idea behind this social component is that individuals derive more utility from committing a crime when their connections commit more crimes, which means that crime decisions are complements. Therefore, the parameter that we would like to estimate in the empirical section is $ \beta $ , which reflects the effect of the criminal records of the group, holding the other variables fixed.

In equilibrium, each individual maximizes their own utility by choosing a level of criminal effort. The first-order condition for each individual i is

(1) $$ \begin{array}{rl}{y}_{it}={a}_{it}+\beta {\displaystyle \sum_{j\ne i}}{g}_{ij}^w{y}_{jt}.& \end{array} $$

Thus, the level of crime that an individual will show in equilibrium is determined by their own economic characteristics, $ {a}_{it} $ , and by the individual $ {i}^{\prime } $ s weighted sum of their group’s criminal efforts, $ {\sum}_{j\ne i}{g}_{ij}^w{y}_{jt} $ . Therefore, the first-order condition can also be divided into individual-level and social-level elements. On the one hand, suppose that the personal cost–benefit element, $ {a}_{it} $ , is subject to variation in measured and unmeasured factors:

$$ \begin{array}{rll}{a}_{it}={x}_{it}\eta +{\alpha}_g+{\tau}_t+{\epsilon}_{igt},& & \end{array} $$

where $ {x}_{it} $ are the observed variables that explain variation in the individual cost–benefit ratio, $ {\alpha}_g $ and $ {\tau}_t $ denote the full set of group and year effects, and $ {\epsilon}_{igt} $ represents the unobserved heterogeneity. On the other hand, in the second element of the best response, the social component, suppose that the scaled weights $ {g}_{ij}^w $ follow

$$ \begin{array}{rll}{g}_{ij}^w=\left\{\begin{array}{l}0,\hskip3em \mathrm{if}\hskip0.60em {g}_{ij}=0,\qquad \\ {}\frac{1}{\sum_{j\ne i}{g}_{ij}},\mathrm{if}\hskip0.60em {g}_{ij}=1.\qquad \end{array}\right)& & \end{array} $$

Thus, $ {\sum}_{j\ne i}{g}_{ij}^w{y}_{jt} $ in the best response is the average of the criminal record of the agents who influence individual i in g. This parametrization, known as the linear-in-means, determines that the criminal effort of i depends linearly on the mean of the criminal records of the other members of the group, $ E[{y}_{it}] $ . As a consequence, what we can study is the effect of the “average level of criminality of the group.” For example, an individual with one connection who commits two crimes is affected to the same degree as an individual with two connections who each commit two crimes.

Following Kline and Tamer (Reference Kline and Tamer2020), the elements that affect an individual’s equilibrium best response in Equation 1 can be represented in its corresponding empirical equation:

(2) $$ \begin{array}{rl}{y}_{it}={x}_{it}\eta +{E}_{gt}[{y}_{it}]\beta +{\alpha}_g+{\tau}_t+{\epsilon}_{igt}.& \end{array} $$

Because the outcomes, $ {y}_{it} $ , are simultaneously determined in Equation 2, the previous equation cannot simply be estimated in a regression. The aim of the following sections is to capture the effect of local economic conditions, $ \eta $ , separately from the effect of the peers’ criminal activity, $ \beta $ . To that end, the following sections describe the structure of the data I use as a proxy for the variables in the model and the identification strategy.

Data

Data for this project come from three different sources. The first dataset—gold mining data—provides the tools to create the treatment variable. I interact the time-series variation in the international price of gold with information on illegal gold production at the municipality level. The second dataset—conflict experience—provides individual-level information, which serves as the basis for the construction of the network of wartime connections. The third dataset—criminality—provides individual-year-level information of captures of ex-combatants after demobilization.

Identification

Recall that the identification problem arises from the presence of $ {y}_{it} $ on both sides of Equation 2. Following Manski (Reference Manski1993, 534) and provided that $ \beta \ne 1 $ , we can define the social equilibrium by considering the expectation on both sides of Equation 2:

(3) $$ \begin{array}{rl}{E}_{gt}[{y}_{it}]={E}_{gt}[{x}_{it}]\frac{\eta }{1-\beta }+\frac{\alpha_g}{1-\beta }+\frac{\tau_t}{1-\beta }.& \end{array} $$

Suppose that this equilibrium holds for the observed data in each period t. Then, substituting Equation 3 back into the individual-level model in Equation 2, we have

(4) $$ \begin{array}{rl}{y}_{it}={E}_{gt}[{x}_{it}]\theta +{x}_{it}\eta +{\alpha}_g^{*}+{\tau}_t^{*}+{\epsilon}_{igt},& \end{array} $$

where $ \theta =\beta \eta /(1-\beta ). $ The first element of Equation 4 estimates the mean of the economic change for members of the group, the second element estimates the variation in the economic returns for each individual, and $ {\alpha}_g^{*} $ and $ {\tau}_t^{*} $ are the rescaled group and time effects, respectively.

If we can (1) identify coefficients on $ {E}_{gt}[{x}_{igt}] $ and $ {x}_{igt} $ and (2) partial out the group and time effects, then we can back out $ \beta $ , which is the social interaction parameter of interest:

(5) $$ \begin{array}{rl}\widehat{\beta}=\frac{\widehat{\theta}}{\widehat{\theta}+\widehat{\eta}}.& \end{array} $$

The conditions that are sufficient to identify $ \beta $ are the following: first, to identify coefficients on $ {E}_{gt}[{x}_{it}] $ and $ {x}_{it} $ , we need that $ {E}_{gt}[{x}_{it}] $ and $ {x}_{it} $ not be confounded with respect to $ {\epsilon}_{igt} $ . We also need that $ {E}_{gt}[{x}_{igt}] $ and $ {x}_{igt} $ not be perfectly collinear. We obtain this with the variation in $ {x}_{igt} $ , which is equivalent to the effect of each shock within the groups indexed by g.

In other words, $ \beta $ will capture the effect on criminality of the criminal activity of peers who were affected by the exogenous economic variation by transforming the coefficients of the individual- and group-level estimations.

The treatment variable for the individual-level effect, $ {x}_{igt} $ , is the price of the commodity interacted with the intensity of its production in the municipality of residence of i in year t. In the case of gold, the shock is the interaction of the international price of gold with the number of illegal gold mines in the municipality. The outcome of interest, $ {y}_{it} $ , measures the number of red-handed captures of individual i in year t:

(6) $$ {y}_{it}={\displaystyle \begin{array}{l}\underset{{\mathrm{MeanGroupSchok}}_{gt}}{\underbrace{E_{gt}\left[\left(Pric{e}_t\times Intensit{y}_{ig}\right)\right]}}\theta \\ {}+\hskip2px \underset{{\mathrm{PriceShock}}_{it}}{\underbrace{\left(Pric{e}_t\times Intensit{y}_{ig}\right)}}\eta +{\alpha}_g^{\ast }+{\tau}_t^{\ast }+{\epsilon}_{igt}\end{array}} $$

To partial out the group and time effects, I take advantage of the panel data and include group ( $ {\alpha}_g^{*} $ ) and time ( $ {\tau}_t^{*} $ ) fixed effects. For reasons of robustness with fixed effects models, I use ordinary least squares to estimate the parameters and then the delta method for inference on $ \widehat{\beta} $ . That is, to estimate the peer effects, $ \beta $ , I compute the estimates and standard errors for the composite parameters estimated in the main specification in Equation 6. I cluster standard errors at the group level and provide additional results with individual and municipality clustered standard errors.

In addition to estimating the parameters of the model, I conduct several robustness checks to address potential concerns. First, in all estimations, I include municipality fixed effects. By including these fixed effects, I control for invariant differences between gold- and non-gold-producing municipalities. Second, I include time trends to control for changes in aggregate time trends across years. Third, I include region time trends and municipality-specific time trends for a set of baseline characteristics.Footnote ¹⁵ These interactions are included to control for potential differential changes across types of municipalities. More importantly, the municipal-level variables related to mobility and connectivity (distance to Bogotá and paved primary roads) enable me to control for any potential biases coming from differential trends in districts that have greater economic needs or are potentially more connected. Finally, I include a set of individual-level characteristics (age, gender, and race).