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  • Published: 21 January 2021

Why do inequality and deprivation produce high crime and low trust?

  • Benoît De Courson 1 , 2 &
  • Daniel Nettle 2  

Scientific Reports volume  11 , Article number:  1937 ( 2021 ) Cite this article

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  • Human behaviour

Humans sometimes cooperate to mutual advantage, and sometimes exploit one another. In industrialised societies, the prevalence of exploitation, in the form of crime, is related to the distribution of economic resources: more unequal societies tend to have higher crime, as well as lower social trust. We created a model of cooperation and exploitation to explore why this should be. Distinctively, our model features a desperation threshold, a level of resources below which it is extremely damaging to fall. Agents do not belong to fixed types, but condition their behaviour on their current resource level and the behaviour in the population around them. We show that the optimal action for individuals who are close to the desperation threshold is to exploit others. This remains true even in the presence of severe and probable punishment for exploitation, since successful exploitation is the quickest route out of desperation, whereas being punished does not make already desperate states much worse. Simulated populations with a sufficiently unequal distribution of resources rapidly evolve an equilibrium of low trust and zero cooperation: desperate individuals try to exploit, and non-desperate individuals avoid interaction altogether. Making the distribution of resources more equal or increasing social mobility is generally effective in producing a high cooperation, high trust equilibrium; increasing punishment severity is not.

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Introduction

Humans are often described as an unusually cooperative or ‘ultrasocial’ species 1 . The truth is more complex: humans from the same society can cooperate for mutual benefit; or they can simply co-exist; or they can actively exploit one another, as in, for example, crime. A theory of human sociality should ideally predict what mix of these alternatives will emerge under which circumstances. Comparing across industrialised societies, higher inequality—greater dispersion in the distribution of economic resources across individuals—is associated with higher crime and lower social trust 2 , 3 , 4 , 5 , 6 , 7 . These associations appear empirically robust, and meet epidemiological criteria for being considered causal 8 . However, the nature of the causal mechanisms is still debated. The effects of inequality are macroscopic phenomena, seen most clearly by comparing aggregates such as countries or states. It is their micro-foundations in individual psychology and behaviour that still require clarification.

There are, broadly, two classes of explanation for how inequality, a population-level phenomenon, could influence individual-level outcomes like crime or trust. The first class of explanation is compositional: in more unequal societies, the least fortunate individuals are absolutely worse off than in more equal societies of the same average wealth, exactly because the dispersion either side of the average is greater. Some individuals are also better off too, at the other end of the distribution, but if there is any non-linearity in the function relating individual resources to outcomes—if for example the poor becoming absolutely poorer has a larger effect on their propensity to offend than the rich becoming absolutely richer has on theirs—this can still change outcome prevalence in the population 9 , 10 , 11 . In line with compositional explanations, across US counties, the association between inequality and rate of property crime is fully mediated by the prevalence of poverty, which is higher in more unequal counties 2 . Moreover, changes in rates over time track changes in the economic prospects of people at the bottom end of the socioeconomic distribution 12 , 13 . The second class of explanation is psychosocial: individuals perceive the magnitude of social differentials in the society around them, and this affects their state of mind, increasing competitiveness, anxiety and self-serving individualism 8 , 14 . In this paper, we develop an explanatory model for why greater inequality should produce higher crime and lower social trust. Our model provides a bridge between compositional and psychosocial explanations. Its explanation for the inequality-crime association is compositional: individuals offend when their own absolute level of resources is desperately low, and the effect of increasing inequality is to make such desperation more prevalent. On the other hand, the model’s explanation for the inequality-trust association is more psychosocial: all individuals in high-inequality populations end up trusting less, regardless of their personal resource levels.

To provide a micro-foundation in individual behaviour for the macro-level effects of inequality on crime, we must start from explanations for why individuals commit crimes. Economic 15 , 16 and behavioural-ecological 17 approaches see offending as a strategic response to specific patterns of incentive. Economic models predict that offending should be more attractive when the payoffs from legitimate activity are low. This principle successfully explains variation in offending behaviour both within and between societies 12 , 16 . It can also explain the relationship between crime levels and inequality, in compositional manner, because unequal societies produce poorer legitimate opportunities for people at the lower end of the socioeconomic spectrum 2 . However, these models are generally taken to predict that making punishments for crime more severe should reduce the prevalence of offending, because harsher punishment should reduce the expected utility associated with the criminal option. Empirical evidence, though, does not clearly support the hypothesis that increasing punishment severity reduces offending 18 , 19 . There is more evidence for a deterrent effect of increased probability of punishment, though even this effect may be modest 18 , 19 .

Becker 15 pointed out that the puzzle of the weak deterrent effect of punishment severity would be solved if offenders were risk-preferring. The decision to offend is risky in that it has either a large positive payoff (if not caught) or a large negative one (if caught and punished). An individual who prefers risk might thus choose to offend even if the expected utility of offending is negative due to a possible severe punishment. Thus, the question becomes: why would some people—those who commit crime—prefer risk, when people are usually averse to it? To address this question, our model incorporates features of classic risk-sensitive foraging theory from behavioural ecology 20 (for a review in the context of human behaviour, see Ref. 21 ). Risk-sensitive foraging models incorporate a desperation threshold: a level of resources below which it is disastrous to fall, in the foraging case because of starvation. The models show that individuals in sufficient imminent danger of falling below this threshold ought to become risk-preferring. If a risky option is successful, it will allow them to leap back over the threshold; and if not, their prospects will be no more dire than they were anyway. Our model is novel in explicitly incorporating a desperation threshold into decisions about whether to cooperate (analogous in our model to participating in legitimate economic activity) or exploit others (analogous to committing an acquisitive crime).

The desperation threshold is the major theoretical innovation of our model. We justify its inclusion on multiple grounds. First, the ultimate currency in our model is fitness, a quantity with a natural biological interpretation that must necessarily be zero if the individual lacks the minimal resources to subsist and function socially. Thus, it is reasonable that expected fitness should be related to resource levels, but not linearly: there should be a point where, as resources deplete, expected fitness rapidly declines to zero. Our threshold assumption produces exactly this type of function (see Supplementary Sect.  2.1 , Supplementary Fig. S1 ). Second, in experimental games where gaining a payoff is subject to a threshold, people do switch to risk-proneness when in danger of falling below the threshold, as risk-sensitive foraging theory predicts 22 . Although this does not show that such thresholds are widespread or important in real life, it does show that people intuitively understand their implications when they are faced with them, and respond accordingly. Third, there are ethnographic descriptions of ‘disaster levels’, ‘crisis levels’, or ‘edges’ that affect the risk attitudes of people facing poverty 23 , 24 . For example, writing on Southeast Asia, Scott 23 describes the spectre of a “subsistence crisis level—perhaps a ‘danger zone’ rather than a ‘level’ would be more accurate…a threshold below which the qualitative deterioration in subsistence, security and status is massive and painful” (p. 17), as an ever-present factor in people’s decisions. Thus, including a desperation threshold is a simple but potentially powerful innovation into models of cooperation and exploitation, with potential to generate new insights.

In our model, agents repeatedly decide between three actions: foraging alone, foraging cooperatively, or exploiting a cooperative group. Foraging cooperatively is analogous to legitimate economic activity, and exploitation is analgous to acquisitive crime. Agents have variable levels of resources, and their behaviour is state-dependent. That is, rather than having a fixed strategy of always cooperating or always exploiting, each agent, at each interaction, selects a behaviour based on their current level of resources, the behaviour of others in the surrounding population, and background parameters such as the probability and severity of punishment, and the likelihood of resources improving through other means. Agents seek to maximize fitness. We assume that fitness is positively related to resource levels, but that there is a threshold, a critically low level of resources below which there is an immediate fitness penalty for falling. Our investigation of the model has two stages. We first compute the optimal action policy an individual should follow; that is, the optimal action to select for every possible combination of the situational variables. Second, we simulate populations of individuals all following the optimal action policies, to predict population-level outcomes for different initial resource distributions.

To explain the model in more detail, at each time point t in an indefinitely long sequence of time steps (where one time step is one economic interaction), agents have a current level of resources s. They can take one of three actions. Foraging alone costs x units of resources and is also guaranteed to return x. Thus, foraging alone is sufficient to maintain the agent but creates no increase in resources. It is also safe from exploitation, as we conceptualise it as involving minimal interaction with others. Alternatively, agents can team up with n-1 others to cooperate . As long as no other group member exploits, cooperation is mutually beneficial, costing x units but producing a payoff of \(\alpha x \left( {\alpha > 1} \right)\) to each group member. Finally, agents can exploit : join a cooperating group and try to selfishly divert the resources produced therein. If this exploitation is successful, they obtain a large reward β, but if they fail, they receive a punishment π. The probability of being punished is γ. The punishment is not administered by peers: we assume that there is a central punitive institution in place, and both the size and probability of punishment are exogenous. In our default case, the expected payoff for exploitation is zero (i.e. \(\left( {1 - \gamma } \right)\beta = \gamma \pi\) ), making exploitation no better than foraging alone on average, and worse than cooperating. However, the reward for a successful exploitation, β, is the largest payoff available to the agent in any single time step.

At every time step, each agent’s resource level is updated according to the outcomes of their action. In addition, resource levels change by a disturbance term controlled by a parameter r , such that the mean and variance of population resources are unchanged, but the temporal autocorrelation of agents’ resource levels is only 1 −  r . If r is high, individuals whose current resources are low can expect they will be higher in the future and vice versa, because of regression to the mean. If \(r = 0\) , resources will never change other than by the agent’s actions. We consider r a measure of social mobility due to causes other than choice of actions.

In the first stage, we use stochastic dynamic programming 25 , 26 to compute the optimal action policy. Fitness is a positive linear function of expected resource level s in the future. However, in computing the fitness payoffs of each action, we also penalize, by a fixed amount, any action that leaves the agent below a desperation threshold in the next time step (arbitrarily, we set this threshold at s  = 0). The optimal action policy identifies which one of the three actions is favoured for every possible combination of the factors that impinge on the agent. These include both their own current resource state s , and features of their social world, such as the severity of punishment π, the probability of punishment γ, and the level of social mobility r. A critical variable that enters into the computation of the optimal action is the probability that any cooperating group in the population will contain someone who exploits. We denote this probability p . We can think of 1 −  p as an index of the trustworthiness of the surrounding population. Computing the optimal policy effectively allows us to ask: under what circumstances should an individual forage alone, cooperate, or exploit?

In the second stage, we simulate populations of agents all following the optimal policies computed in the first stage. We can vary the starting distributions of resources (their mean and dispersion), as well as other parameters such as social mobility and the probability and severity of punishment. During the simulation stage, each agent forms an estimate of 1 −  p , the trustworthiness of others, through observing the behaviour of a randomly-selected subset of other individuals. We refer to these estimates as the agents’ social trust, since social trust is defined as the generalized expectation that others will behave well 27 . Social trust updates at the end of each time step. Agents’ social trust values are unbiased estimates of the current trustworthiness of the surrounding population, but they are not precise, because they are based on only a finite sample of other population members. The simulation stage, allows us to ask: what are the predicted temporal dynamics of behaviour, and of social trust, in populations with different starting distributions of resources, different levels of social mobility, and different punishments for exploitation?

Each of the three actions is optimal in a different region of the space formed by current resources s and the trustworthiness of others 1 −  p (Fig.  1 a). Below a critical value of s , agents should always exploit, regardless of trustworthiness . In the default case, this critical value is in the vicinity of the desperation threshold, though it can be lower or higher depending on the value of other parameters. With our default values, exploitation will not, on average, make the agent’s resource state any better in subsequent time steps. However, there is a large advantage to getting above the threshold in the next time step, and there is a region of the resource continuum where exploitation is the only action that can achieve this in one go (intuitively, it is the quickest way to ‘get one’s head above water’). Where s is above the critical value, cooperation is optimal as long as the trustworthiness of the surrounding population is sufficiently high. However, if trustworthiness is too low, the likelihood of getting exploited makes cooperation worse than foraging alone. The shape of the frontier between cooperation and foraging alone is complex when resources are close to the desperation threshold. This is because cooperation and foraging alone also differ in riskiness; foraging alone is risk-free, but cooperation carries a risk of being exploited that depends on trustworthiness. Just above the exploitation zone, there is a small region where cooperation is favoured even at low trustworthiness, since one successful cooperation would be enough to hurdle back over the threshold, but foraging alone would not. Just above this is a zone where foraging alone is favoured even at high trustworthiness; here the agent will be above the threshold in the next time period unless they are a victim of exploitation, which makes them averse to taking the risk of cooperating.

figure 1

Optimal actions as a function of the individual’s current resources s and the trustworthiness of the surrounding population, 1 −  p . ( A ) All parameters at their default values. This includes: α = 1.2, r = 0.1, π = 10, and γ = 1/3 (see Table 1 for a full list). ( B ) Effect of altering the efficiency of cooperation α to be either lower (1.05) or higher (1.30) than ( A ). Other parameter values are as for ( A ). ( C ) Effects of varying social mobility, to be either high (r = 0.8), or complete (r = 1.0; i.e. resource levels in this time period have no continuity at all into the next). Other parameter values are as for ( A ). ( D ) Effect of increasing the severity of punishment for exploiters to π = 15 and π = 20. Other parameter values are as for ( A ). ( E ). Effects of altering the probability of punishment for exploiters to γ = 2/3 and γ = 9/10. Other parameter values are as for ( A ).

We explored the sensitivity of the optimal policy to changes in parameter values. Increasing the profitability of cooperation (α) decreases the level of trustworthiness that is required for cooperation to be worthwhile (Fig.  1 B; analytically, the cooperation/foraging alone frontier for \(s \gg 0\) is at \(\left( {1 - p} \right) = 1/\alpha\) ; see Supplementary Sect.  2.2 ). A very high level of social mobility r moves the critical value for exploitation far to the left (i.e. individuals have to be in an even more dire state before they start to exploit; Fig.  1 C). This is because with high social mobility, badly-off individuals can expect that their level of resources will regress towards the mean over time anyway, lessening the need for risky action when faced with a small immediate shortfall.

The optimality of exploitation below the critical level of resources is generally insensitive to increasing the severity of punishment, π (Fig.  1 D), even where the expected value of exploitation is thereby rendered negative. This is because a desperate agent will be below the threshold in the next time step anyway if they forage alone, cooperate, or receive a punishment of any size. They are so badly off that it is relatively unimportant how much worse things get, but important to take any small chance of ‘jumping over’ the threshold. The exploitation boundary is slightly more sensitive to the probability of punishment, γ, though even this sensitivity is modest (Fig.  1 E). When γ is very high, it is optimal for agents very close to the boundary of desperation to take a gamble on cooperating, even where trustworthiness is rather low. Although this is risky, it offers a better chance of getting back above the threshold than exploitation that is almost bound to fail. Nonetheless, it is striking that even where exploitation is almost bound to fail and attracts a heavy penalty, it is still the best option for an individual whose current resource level is desperately low.

We also explored the effect of setting either the probability γ or the severity π of punishment so low that the expected payoff from exploitation is positive. This produces a pattern where exploitation is optimal if an agent’s resources are either desperately low, or comfortably high (see Supplementary Fig. S2 ). Only in the middle—currently above the threshold, but not by far enough that a punishment would not pull them down below it–should agents cooperate or forage alone.

We simulated populations of N  = 500 individuals each following the optimal policy, with the distribution of initial resources s drawn from a distribution with mean μ and standard deviation σ. Populations fall into one of two absorbing equilibria. In the first, the poverty trap (Fig.  2 A), there is no cooperation after the first few time periods. Instead, there is a balance of attempted exploitation and foraging alone, with the proportions of these determined by the initial resource distribution and the values of π and γ. The way this equilibrium develops is as follows: there is a sufficiently high frequency of exploitation in the first round (about 10% of the population or more is required) that subsequent social trust estimates are mostly very low. With trust low, those with the higher resource levels switch to foraging alone, whilst those whose resources are desperately low continue to try to exploit. Since foraging alone produces no surplus, the population mean resources never increases, and both exploiters and lone foragers are stuck where they were.

figure 2

The two equilibria in simulated populations. ( A ) The poverty trap. There is sufficient exploitation in the first time step ( A1 ) that social trust is low ( A2 ). Consequently, potential cooperators switch to lone foraging, resources never increase ( A3 ), and a subgroup of the population is left below the threshold seeking to exploit. Simulation initialised with μ = 5.5, σ = 4 and all other parameters at their default values. ( B ) The virtuous circle. Exploitation is sufficiently rare from the outset ( B1 ) that trust is high ( B2 ) and individuals switch from lone foraging to cooperation. This drives an increase in resources, eventually lifting almost all individuals above the threshold. Simulation initialised with μ = 5.5, σ = 3 and all other parameters at their default values.

In the second equilibrium, the virtuous circle (Fig.  2 B), the frequency of exploitation is lower at the outset. Individuals whose resources are high form high assessments of social trust, and hence choose cooperation over foraging alone. Since cooperation creates a surplus, the mean level of resources in the population increases. This benefits the few exploiters, both through the upward drift of social mobility, and because they sometimes exploit successfully. This resolves the problem of exploitation, since in so doing they move above the critical value to the point where it is no longer in their interests to exploit, and since they are in such a high-trust population, they then start to cooperate. Thus, over time, trust becomes universally high, resources grow, and cooperation becomes almost universal.

Each of the two equilibria has a basin of attraction in the space of initial population characteristics. The poverty trap is reached if the fraction of individuals whose resource levels fall below the level that triggers exploitation is sufficiently large at any point. With the desperation threshold at s = 0, his fraction is affected by both the mean resources μ, and inequality σ. For a given μ, increasing σ (i.e. greater inequality) makes it more likely that the poverty trap will result, because, by broadening the resource distribution, the tail that protrudes into the desperation zone is necessarily made larger.

The boundaries of the basin of attraction of the poverty trap are also affected by severity of punishment, probability of punishment, and the level of social mobility (Fig.  3 ). If the severity of punishment π is close to zero, there is no disincentive to exploit, and the poverty trap always results. As long as a minimum size of punishment is met, further increases in punishment severity have no benefit in preventing the poverty trap (Fig.  3 A). Indeed, there are circumstances where more severe punishment can make things worse. When the population has a degree of initial inequality that puts it close to the boundary between the two equilibria, very severe punishment (π = 20 or π = 25) pushes it into the poverty trap. This is because any individual that once tries exploitation because they are close to threshold (and is unsuccessful) is pushed so far down in resources by the punishment that they must then continue to exploit forever. Increasing the probability of punishment γ does not have this negative effect (Fig.  3 B). Instead, a very high probability of punishment can forestall the poverty trap at levels of inequality where it would otherwise occur, because it causes some of the worst-off individuals to try cooperating instead, as shown in Fig.  1 E. Finally, very high levels of social mobility r can rescue populations from the poverty trap even at high levels of inequality (Fig.  3 C). This is because of its dramatic effect on the critical value at which individuals start to exploit, as shown in Fig.  1 C.

figure 3

Equilibrium population states by starting parameters. ( A ) Varying the initial inequality in resources σ and the severity of punishment π, whilst holding constant the probability of punishment γ at 1/3 and social mobility r at 0.1. ( B ) Varying the initial inequality in resources σ and the probability of punishment γ whilst holding the severity of punishment constant at π = 10 and social mobility r at 0.1. ( C ) Varying the initial inequality in resources σ and the level of social mobility r whilst holding constant the probability of punishment γ at 1/3 and the severity of punishment π at 10.

Though the equilibria are self-perpetuating without exogenous forces, the system is highly responsive to shocks. For example, exogenously changing the level of inequality in the population (via imposing a reduction in σ after 16 time steps) produces a phase transition from the poverty trap to the virtuous circle (Supplementary Fig. S3 ). This change is not instantaneous. First, a few individuals cross the threshold and change from exploitation to foraging alone; this produces a consequent change in social trust; which then leads to a mass switch to cooperation, and growth in mean wealth.

Results so far are all based on cooperation occurring in groups of size n  = 5. Reducing n enlarges the basin of attraction of the virtuous circle (Supplementary Sect.  2.5 , Supplementary Fig. S4 ). This is because, for any given population prevalence of exploitation, there is more likely to be at least one exploiter in a group of five than a group of three. Reducing the interaction group size changes the trustworthiness boundary between the region where it is optimal to cooperate and the region where it is better to forage alone. Thus, there are parameter values in our model where populations would succumb to the poverty trap by attempting to mount large cooperation groups, but avoid it by restricting cooperation groups to a smaller size.

In our model, exploiting others can be an individual’s optimal strategy under certain circumstances, namely when their resource levels are very low, and cannot be expected to spontaneously improve. We extend previous models by showing that it can be optimal to exploit even when the punishment for doing so and being caught is large enough to make the expected utility of exploitation negative. Two conditions combine to make this the case. First, exploitation produces a large variance in payoffs: it is costly to exploit and be caught, but there is a chance of securing a large positive payoff. Second, there is a threshold of desperation below which it is extremely costly to fall. It is precisely when at risk of falling below this threshold that exploitation becomes worthwhile: if it succeeds, one hurdles the threshold, and if it fails, one is scarcely worse off than one would have been anyway. In effect, due to the threshold, there is a point where agents have little left to lose, and this makes them risk-preferring. Thus, our model results connect classic economic models of crime 15 , 16 to risk-sensitive foraging theory from behavioural ecology 20 . In the process, it provides a simple answer to the question that has puzzled a number of authors 18 , 19 : why aren’t increases in the severity of punishments as deterrent as simple expected utility considerations imply they ought to be? Our model suggests that, beyond a minimum required level of punishment, not only might increasing severity be ineffective at reducing exploitation. It could under some circumstances make exploitation worse, by pushing punishees into such a low resource state that they have no reasonable option but to continue exploiting. Our findings also have implications for the literature on the evolution of cooperation. This has shown that punishment can be an effective mechanism for stabilising cooperation 28 , 29 , but have not considered that the deterrent effects of punishment may be different for different individuals, due to variation in their states. Our findings could be relevant to understanding why some level of exploitation persists in practice even when punishment is deterrent overall.

Within criminology, our prediction of risky exploitative behaviour when in danger of falling below a threshold of desperation is reminiscent of Merton’s strain theory of deviance 30 , 31 . Under this theory, deviance results when individuals have a goal (remaining constantly above the threshold of participation in society), but the available legitimate means are insufficient to get them there (neither foraging alone nor cooperation has a large enough one-time payoff). They thus turn to risky alternatives, despite the drawbacks of these (see also Ref. 32 for similar arguments). This explanation is not reducible to desperation making individuals discount the future more steeply, which is often invoked as an explanation for criminality 33 . Agents in our model do not face choices between smaller-sooner and larger-later rewards; the payoff for exploitation is immediate, whether successful or unsuccessful. Also note the philosophical differences between our approach and ‘self-control’ styles of explanation 34 . Those approaches see offending as deficient decision-making: it would be in people’s interests not to offend, but some can’t manage it (see Ref. 35 for a critical review). Like economic 15 , 16 and behavioural-ecological 17 theories of crime more generally, ours assumes instead that there are certain situations or states where offending is the best of a bad set of available options.

As well as a large class of circumstances where only individuals in a poor resource state will choose to exploit, we also identify some—where the expected payoff for exploitation is positive—where individuals with both very low and very high resources exploit, whilst those in the middle avoid doing so. Such cases have been anticipated in theories of human risk-sensitivity 21 . These distinguish risk-preference through need (e.g. to get back above the threshold immediately) from risk-preference through ability (e.g. to absorb a punishment with no ill effects), predicting that both can occur under some circumstances 32 . This dual form of risk-taking is best analogised to a situation where punishments take the form of fines: those who are desperate have to run the risk of incurring them, even though they can ill afford it; whilst those who are extremely well off can simply afford to pay them if caught. When we simulate populations of agents all following the optimal strategies identified by the model, population-level characteristics (inequality of resources, level of social mobility) affect the prevalence of exploitation and the level of trust. Specifically, holding constant the average level of resources, greater inequality makes frequent exploitation and low trust a more likely outcome. Thus, we capture the widely-observed associations between inequality, trust and crime levels that were our starting point 2 , 3 , 4 , 5 , 6 . Note that our explanation for the inequality-crime nexus is basically compositional rather than psychosocial. Decisions to offend are based primarily on agents’ own levels of resources; these are just more likely to be desperately low in more unequal populations. Turning these simulation findings into empirical predictions, we would expect the association between inequality and crime rates to be driven by more unequal societies producing worse prospects for people at the bottom end of the resources distribution, who would be the ones who turn to property crime. Inequality effects at the aggregate level should be largely mediated by individual-level poverty. There is evidence compatible with these claims for property crime 2 , 12 , 13 . This is the type of crime most similar to our modelled situation. Non-acquisitive crimes of violence, though related to inequality, do not appear so strongly mediated by individual-level poverty, and may thus require different but related explanations 2 , 36 .

However, the other major result of our population simulations—that more unequal populations are more likely to produce low trust—is not compositional. In our unequal simulated populations, every agent has low trust, not just the ones at the bottom of the resource distribution. This is compatible with empirical evidence: the association between inequality and social trust survives controlling for individual poverty 6 . Thus, our model generates a genuinely ecological effect of inequality on social relationships that fits the available evidence and links it to the psychosocial tradition of explanation 37 . Indeed, the model suggests a reason why psychosocial effects should arise. For agents above the threshold, the optimal decision between cooperation and foraging alone depends on inferences about whether anyone else in the population will exploit. To know that, you have to attend to the behaviour of everyone else, not just your own state. Thus, the model naturally generates a reason for agents to be sensitive to the distribution of others’ states in the population (or at the very least their behaviour), and to condition their social engagement with others on it.

In as much as our model provides a compositional explanation for the inequality-crime relationship, it might seem to imply that high levels of inequality would not lead to high crime as long as the mean wealth of the population was sufficiently high. This is because, with high mean wealth, even those in the bottom tail of the distribution would have sufficient levels of resources to be above the threshold of desperation. However, this implication would only follow if the location of the desperation threshold is considered exogenous and fixed. If, instead, the location of the desperation threshold moves upwards with mean wealth of the population, then more inequality will always produce more acquisitive crime, regardless of the mean level of population wealth. Assuming that the threshold moves in this way is a reasonable move: definitions of poverty for developed countries are expressed in terms of the resources required to live a life seen as acceptable or normal within that society, not an absolute dollar value (see Ref. 36 , pp. 64–6). Moreover, there is clear evidence that people compare themselves to relevant others in assessing the adequacy of their resources 38 . Thus, we would expect inequality to remain important for crime regardless of overall economic growth.

In addition to the results concerning inequality, we found that social mobility should, other things being equal, reduce the prevalence of exploitation, although social mobility has to be very high for the effect to be substantial. The pattern can again be interpreted as consistent with Merton’s strain theory of deviance 31 : very high levels of social mobility provide legitimate routes for those whose state is poor to improve it, thus reducing the zone where deviance is required. Economists have noted that those places within the USA with higher levels of intergenerational social mobility also have lower crime rates 39 , 40 . Their account of the causality in this association is the reverse of ours: the presence of crime, particularly violent crime, inhibits upward mobility 39 . However, it is possible that social mobility and crime are mutually causative.

Like any model, ours simplifies social situations to very bare elements. Interaction groups are drawn randomly at every time step from the whole population. Thus, there are no ongoing personal relationships, no reputation, no social networks, no kinship, no segregation or assortment of sub-groups. The model best captures social groups with frequent new interactions between strangers, which is appropriate since the phenomena under investigated are documented for commercial and industrial societies. A problem in mapping our findings onto empirical reality is that our population simulations generate two discrete equilibria: zero trust, economic stagnation and zero cooperation, or almost perfect trust, unlimited economic growth and zero exploitation. Although we show that the distribution of resources determines which equilibrium is reached, our model as presented here does generate the continuous relationships between inequality, crime, and trust (or indeed inequality and economic growth 41 ) that have been observed in reality. Even the most unequal real society features some social cooperation, and even the most equal features some property crime; the effects of inequality are graded. We make two points to try to bridge the disconnect between the black and white world of the simulations and the shades of grey seen in reality. First, our model does predict a continuous relationship between the level of inequality and the maximum size of cooperating groups. A highly unequal population, containing many individuals with an incentive to exploit, might only be able to sustain collective actions at the level of a few individuals, whereas a more equal population where almost no-one has an incentive to exploit could sustain far larger ones. Second, we appeal to all the richness of real social processes that our model excludes. In unequal countries, although social trust is relatively low, people can draw more heavily on their established social networks and reputational information; more homogenous sub-groups can segregate themselves; people can use defensive security measures, to keep cooperative relationships ongoing and protected; and so forth. Investment in these kinds of measures may vary proportionately with inequality and trust, thus maintaining outcomes intermediate between the stark equilibria of our simulations. Our key findings also depend entirely on accepting the notion that there is a threshold of desperation, a substantial non-linearity in the value of having resources. As we outlined in the Introduction, we believe there are good grounds for exploring the implications of such an assumption. However, that is very different from claiming that the widespread existence of such thresholds has been demonstrated. We hope our findings might generate empirical investigation into both the objective reality and psychological appraisal of such thresholds for people in poverty.

Limitations and simplifications duly noted, our model does have some clear implications. Large population-scale reductions in crime and exploitation should not be expected to follow from increasing the severity of punishments, and these could conceivably be counterproductive. Addressing basic distributional issues that leave large numbers of people in desperate circumstances and without legitimate means to improve them will have a much greater effect. Natural-experimental evidence supports this. The Eastern Cherokee, a Native American group with a high rate of poverty, distributed casino royalties through an unconditional income scheme. Rates of minor offending amongst young people in recipient households decline markedly, with no changes to the judicial regime 42 . Improving the distribution of resources would also be expected to increase social trust, and with it, the quality of human relationships; and this, for everyone, not just those in desperate circumstances.

The model was written in Python and implemented via a Jupyter notebook. For a fuller description of the model, see Supplementary Sect.  1 and Supplementary Table S1 .

Computing optimal policies

We used a stochastic dynamic programming algorithm 25 , 26 . Agents choose among a set of possible actions, defined by (probabilistic) consequences for the agent’s level of resources s . We seek, for every possible value of s and of p the agent might face, and given the values of other parameters, the action that maximises expected fitness. Maximization is achieved through backward induction: we begin with a ‘last time step’ ( T ) where terminal fitness is defined, as an increasing linear function of resource level s . Then in the period T  − 1 we compute for each combination of state variables and action the expected fitness at T , and thus choose for the optimal action for every combination of states. This allows us define expected fitness for every value of the state variables at T  − 1, repeat the maximization for time step T  − 2, and so on iteratively. The desperation threshold is implemented as a fixed fitness penalty ω that is applied whenever the individual’s resources are below the threshold level s  = 0. As the calculation moves backwards away from T , the resulting mapping of state variables to optimal actions converges to a long term optimal policy.

Actions and payoffs

Agents choose among three actions:

Cooperate The agent invests x units of resource and is rewarded α · x with probability 1 −  p ( p is the probability of cooperation being exploited, and 1 −  p is therefore the trustworthiness of the surrounding population), and 0 with probability p . The net payoff is therefore x · ( α  − 1) if there is no exploitation and −  x if there is. We assume that α  > 1 (by default α  = 1 . 2), which means that cooperation is more efficient than foraging alone. For the computation of optimal policies, we treat p as an exogenous variable. In the population simulations, it becomes endogenous.

Exploit An agent joins a cooperating group, but does not invest x, and instead tries to steal their partners’ investments, leading to a reward of β if the exploitation succeeds and a cost π if it fails. The probability of exploitation failing (i.e. being punished) is γ .

Forage alone The agent forages alone, investing x units of resource, receiving x in return, and suffering no risk of exploitation.

Payoffs are also affected by a random perturbation, so the above-mentioned payoffs are just the expected values. A simple form such as the addition of \(\varepsilon \sim N\left( {0, \sigma^{2} } \right)\) would be unsuitable when used in population simulations. As the variance of independent random variables is additive, it would lead to an ever increasing dispersion of resource levels in the population. To avoid this issue, we adopted a perturbation in the form of a first-order autoregressive process that does not change either the mean or the variance of resources in the population 43 :

Here, µ is the current mean resources in the population and σ 2 the population variance. The term \(\left( {1 - r} \right) \in \left[ {0, 1} \right]\) represents the desired correlation between an agent’s current and subsequent resources, which leads to us describing r as the ‘social mobility’ of the population. The perturbation can be seen as a ‘shuffle’. Each agent’s resource level is attracted to µ with a strength depending on r , but this regression to the mean is exactly offset at the population level by the variance added by the perturbation, so that the overall distribution of resources is roughly unchanged. If r  = 1, current resources are not informative about future resources.

The dynamic programming equation

Let I be the set of actions ( cooperate , exploit and alone ), which we shorten as I  = { C,H,A }. For i   ∈   I , we denote as \(\phi_{t}^{i} \left( {s, .} \right)\) the probability density of resources in in time step t if, in time step t  − 1, the resource level is s and the chosen action i . The expressions of these functions were obtained through the law of total probability, conditioning on the possible outcomes of the actions (e.g. success or failure of exploitation and cooperation), and with the Gaussian density of the random variable.

We can now write the dynamic programming equation, which gives the backward recurrence relation to compute the payoff values (and the decisions) at the period t from the ones at the period t  + 1.

Here, \(E_{i}\) is the conditional expectation if action i is played. The optimal action for the time step t is \({\text{argmax}}_{i \in I} E_{i} (f_{t} )\) . The resource variable s was bounded in the interval [− 50, 50], and discretized with 1001 steps of size 0 . 1.

For any given set of parameters (summarised in Table 1 ), we can therefore compute the optimal decision rule. Note that we can distinguish two types of parameters:

‘Structural parameters’, i.e. those defining the ‘rules’ of the game (the payoffs for the actions and the level of social mobility r , for example). In the subsequent simulation phase, these parameters will be fixed for any run of the simulations.

‘Input parameters’, such as p and s . In the simulation phase, these will evolve endogenously.

Optimal policies rapidly stabilize as the computation moves away from T . We report optimal actions at t  = 1 as the globally optimal actions.

Population simulations

We begin each simulation by initializing a population of N  = 500 individuals, whose resource levels are randomly drawn from a Gaussian distribution with a given mean µ and variance σ 2 . At each time step, interaction groups of n  = 5 individuals are formed at random, and re-formed at each time step to avoid effects of assortment. There is no spatial structure in the populations. Each individual always follows the optimal policy for its resources s and its estimate of p (see below). Varying N has no effect as long as N  >  n and 500 is simply chosen for computational convenience.

To deal with the case where several members of the same interaction group choose to exploit, we choose one at random that exploits, and the others are deemed to forage alone (in effect, there is nothing left for them to take). Also, when there is no cooperator in the group, all exploiters are deemed to forage alone.

Rather than providing each individual with perfect knowledge of the trustworthiness of the rest of the population 1 −  p , we allow individuals to form an estimate (their social trust ) from their experience. Social trust is derived in the following way. Each agent observes the decision of a sample of K individuals in the population, counts the number k of exploiters and infers an (unbiased) estimate of the prevalence of exploiters in the population: \(k^{\prime} = \frac{k}{K}N\) (rounded). The size of the sample can be varied to alter the precision with which agents can estimate trustworthiness. Unless otherwise stated we used K  = 50. Since p is the probability that there will be at least one exploiter in an interaction group, it is one minus the probability that there will be zero exploiters. Each agent computes this from their k’ by combinatorics.

An intentional consequence of social trust being estimated through sampling is that there is some population heterogeneity in social trust, and therefore in decisions about which action to take, even for agents with the same resources s . Note also that agents infer trustworthiness not from observing the particular individuals in their current interaction group, but rather, from a cross-section of the entire population. Thus, the estimate is genuinely social trust (the perception that people in society generally do or do not behave well).

Code availability

The Jupyter notebook for running the model is available at: https://github.com/regicid/Deprivation-antisociality . This repository also contains R code and datafiles used to make the figures in the paper.

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Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No AdG 666669, COMSTAR). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors thank Melissa Bateson, Juliette Dronne, Ulysse Klatzmann, Daniel Krupp, Kate Pickett, and Rebecca Saxe for their input.

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economic crime research paper

Data Science perspectives on Economic Crime

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Economic crimes including corruption, fraud, collusion, and tax evasion impose significant costs to societies all around the world. Beyond their direct economic costs, these behaviors reduce mutual trust and cohesion in society. The erosion of these fundamental elements of a healthy society is hypothesized to contribute to growing inequality and the strengthening of political populism. Altogether there are significant incentives to study economic crimes. However, until recently, its investigation remained largely the preserve of law enforcement, which has resources to investigate only a tiny minority of cases.

Researchers now have more data than ever to investigate these phenomena, but face several unique challenges. The lack of unbiased ground-truth data hinders the straightforward application of machine learning. Publicly available data often contains only suggestive traces of illegal activity. Though economic crimes are increasingly international, data availability and quality varies highly across borders. Despite these difficulties, recent years have witnessed a remarkable increase in scientific activity in this area. Studying economic crime from a data science perspective offers unique insights and can inform the design of novel solutions. The results of such research are of eminent interest to governments, law enforcement, organizations, companies and civil society watchdogs. In light of this recent activity, there is a need to survey the field, to reflect on progress, shortcomings, and open problems, and to highlight promising new methods.

In this special issue, we gather research that highlights novel applications of data science to the problems and challenges of economic crime. We also welcome data-critical studies and mixed-methods papers, recognizing that data-driven methods complement rather than substitute for other approaches.

Topics of interest include, but are not limited to: - Estimating levels and trends of economic crimes using open source data - Corruption in public procurement - Collusion and cartels - Network science perspectives on economic crime - Agent-based models of economic crime - Detecting fraud in transaction data - Critical perspectives on the application of data science to economic crime (i.e. pitfalls and biases of predictive policing and profiling) - Data-driven analyses of organized crime and mafia-type groups - Tax evasion and money laundering - Terrorist financing - The political organization of economic crimes - Lobbying networks and political favoritism - Social and communication networks of criminal conspiracies - Transactions on the darknet and the role of crypto-currency in economic crime - Data-driven journalism and OSINT perspectives on economic crime - Novel datasets for measuring and tracking economic crime - Mixed-methods approaches to studying economic crime

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Organized Crime in Italy: An Economic Analysis

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Organized crime affects the socio-economic development of the areas in which it is rooted through several channels. However, analysing these effects is difficult, largely because it is extremely hard to observe and thus confidently measure the extent of mafia presence in a given area. On the basis of the most recent economic literature and with the aid of new information sources, this paper ( i ) analyses the spread of organized crime in Italy; ( ii ) describes the institutional environment that may have favoured the birth of mafias and their subsequent spread beyond traditional borders; ( iii ) examines the impact on economic growth and the different channels through which these effects occur.

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economic crime research paper

Organized and Economic Crime: A Common Problem

Obviously, the relevance of the mafia phenomenon cannot be limited to the economic dimension alone and many articles and books have been written on the origins and effects of organized crime, preferring, depending on the case, the historical, sociological or criminal approach. In this paper, however, we refer to the economic literature and to the conceptual tools of this discipline.

According to Transcrime ( 2015 ), the revenues from illegal activities would be around 26 billion euros, deriving mainly from drugs (7.7), extortion (4.8), prostitution (4.7), counterfeiting (4.5) and usury (2.2).

According to Mirenda et al. ( 2022 ) the turnover of joint-stock companies presumably infiltrated by the ‘Ndrangheta in the central-northern regions is around 2 percent of the total.

The overall unobserved economy is worth over 200 billion euros, 11.3% of the GDP (Istat 2021). Looking at its main components, the unobserved economy is attributable for almost half to the under-declaration of economic operators and for over a third to the use of irregular work, while illegal activities would represent about a tenth of the total.

See Calderoni ( 2011 ) and Transcrime ( 2015 ), among others, for similar attempts to measure the presence of mafia. Compared to the previous literature, our indicator stands out both for the use of indicators drawn from multiple sources, including firm-level surveys, and for a structuring of the indicators into domains, so as to distinguish the different ways in which the mafias act on a territory.

The Bank of Italy Survey of Industrial and Service firms is administered every year and gathers information on investments, gross sales, workforce and other economic variables relating to Italian industrial and service firms with 20 or more employees. Detailed information on the survey design is available on the Bank of Italy website.

The choice of the province as geographic unit of analysis is mostly due data availability. Information on real crimes, drawn from Istat, are at the province level whereas that on perceptions, drawn from survey of firms, are not representative at a finer partition of the territory. Moreover, even though some variables were available at the municipality level, they should be used with some caution because some events (e.g., a mafia-related homicide or a city council dismissal) are quite rare and their occurrence can significantly influence the mafia index of that territory.

As an alternative to simple averaging, we also used the principal component analysis. The first principal component explains more than 40 percent of the overall variability. This variable, however, is highly correlated (with an index of 0.96) to the simple average of the 16 indicators and we therefore preferred the latter for greater transparency and readability of the results.

See Figure 8 for a graphical representation of the correlation matrix between the 16 elementary indicators (correlation heatmap). The correlation between the elementary indicators is more accentuated in the South (Figure 9 ). This might be due to the lower frequency of mafia crimes (first domain) in the Centre-North – and, therefore, a lower ability of these variables to represent the phenomenon in that area – or to a subjective under-estimation of the phenomenon by the firms in an area of more recent infiltration.

For some reflections on the reasons that explain the persistence of the mafia phenomenon, see, among others, Lupo ( 1993 ). Such reasons are related to the ability to control the territory, to the relational capital built with the institutions and also to the ability to adapt to social and economic changes.

In this regard, Cutrera ( 1900 ) stated: «those who could not turn to the law both to respect one of its rights, or an alleged right, and to resolve some controversy, turned to the authority of people known for their influence or arrogance, or that for the energetic and violent action they could have made his judgment respected» (our translation).

The greater intensity of the mafia in the province of Palermo is explained by Cutrera ( 1900 ) in these terms: «There is no doubt that the development of the mafia in the Conca d’oro was accentuated and took over that of all the other districts of Sicily, when with the development of the trade in citrus fruits, i.e. from the beginning of this century, the culture of orange and lemon trees developed powerfully, which if on the one hand favoured the wealth of many owners of irrigable land, on the other made the feeling of mafia, due to the absolute lack of police service, and therefore the need to create private guardians, who, as we will see below, are the necessary element for the mafia to flourish luxuriantly» (our translation).

It should also be noted that the miners in the sulphur mines represented a conspicuous part of the trade union movement, both because their number had significantly grown with the increase in exports of the good and because their working conditions were very poor. This suggests that the factors analysed in the various works are not mutually exclusive but, more likely, complementary.

Pinotti ( 2015b ) analyses the origins of the Sacra Corona Unita in Apulia and Basilicata, a mafia with a more recent history with respect to Cosa Nostra, Camorra and Ndrangheta. Until the early 1970s, these regions were not characterized by a significant mafia presence. Also in this case a shock – the repositioning of smuggling flows in favour of the Balkans – and geographical factors – the presence of numerous criminals on forced stays ( confino ) and proximity to other criminal organizations – would have contributed significantly to the emergence and to the localization of the mafia phenomenon in this area of the country.

Commerce is the sector in which there is the greatest presence of «paper» firms, i.e. firms producing invoices («paper») for non-existent commercial transactions, in order to launder the proceeds of illicit activities. The construction sector is, on the other hand, traditionally considered the most profitable for criminal organizations which manage to enjoy a competitive advantage over their competitors thanks to a plurality of factors: a conspicuous availability of liquidity in a sector characterized by high financial leverage; the exploitation of irregular work and the circumvention of existing regulatory constraints with the consequent reduction of operating costs; the ability to intercept public procurement by exploiting its own coercive and corruptive power.

Buonanno and Pazzona ( 2014 ), exploring some of these channels in more detail, find that the interaction between forced stay and migrations has played an important role in favouring the rooting of the mafias in the Centre-North. On the role of forced stay, see also Scognamiglio ( 2018 ).

Similarly, according to Dipoppa ( 2022 ) the expansion of the mafias towards the North would have been favoured by the increase in demand for unskilled labour in the construction sector in the 1970s. Mafia organizations would have been able to provide this workforce thanks to their ability to control the immigrant population from the South and to circumvent the rules on labour regulation.

The evidence on social capital is also confirmed if we consider other indicators, such as blood donations (as in Guiso et al. 2004 ). However, we point out that social capital and the quality of local institutions are partially overlapping phenomena and that the analysis discussed, in addition to being purely descriptive, is also partial, not taking into account other potential socio-economic variables that they could have influenced the mafia infiltration in the Centre and North.

The use of this survey is preferable to that of other indicators, such as those based on the number of crimes, both because the latter are not yet available for 2020 and because the occurrence of the crime may not coincide with the moment in which it the criminal fact is reported by the police to the judicial authority. Perception indicators are also subject to possible distortions, linked in the first instance to an incorrect assessment by the interviewee of the relevance of the phenomenon. However, distortions of a structural nature are eliminated by exploiting the variation in the perception of the phenomenon between the two years provided by the interviewee himself.

Examining the policies to fight organized crime, Fenizia and Saggio ( 2022 ) focus on the impacts on the levels of economic activity of the city councils’ dismissal due to mafia infiltration. They find that about a decade after the policy intervention, employment is about 17 percent higher.

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See Appendix Figs.

figure 8

Correlation matrix between the 16 elementary indicators of mafia presence. The elementary indicators are labelled as in Fig.  1 in the main text. For each combination of variables, the figure shows the correlation index which is red, when positive, and blue, when negative, and whose intensity is proportional to the extent of correlation (in absolute value)

figure 9

Correlation matrix by geographical areas. The elementary indicators are labelled as in Fig.  1 in the main text. For each combination of variables, the figure shows the correlation index which is red, when positive, and blue, when negative, and whose intensity is proportional to the extent of correlation (in absolute value)

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Mocetti, S., Rizzica, L. Organized Crime in Italy: An Economic Analysis. Ital Econ J (2023). https://doi.org/10.1007/s40797-023-00236-4

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The roots of crime are diverse and a discipline like economics, predicated on rational behavior, may be at something of a disadvantage in explaining a phenomenon largely viewed as irrational. The foray by economists into this area is relatively recent, dating back to Gary Becker’s pathbreaking contribution in 1968. As part of a larger model designed to explore optimal criminal justice policy, he developed the ‘‘supply of offense’’ function, which indicates the factors affecting the number of crimes a rational individual commits. Since then there has been much progress in both expanding on this important relationship and utilizing it for more theoretically grounded analyses of criminal behavior.

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A recent survey suggests that three general issues are of central concern in the economics of crime literature: the effects of incentives on criminal behavior, how decisions interact in a market setting, and the use of cost-benefit analysis to assess alternative policies to reduce crime (see Freeman, 1999a). In this research paper we will focus on the role of incentives on criminal behavior.

Crime is a major activity for young males. Crime is like basketball; it’s a young man’s game. As one researcher has observed: ‘‘Actual rates of illegal behavior soar so high during adolescence that participation in delinquency appears to be a normal part of teen life’’ (Moffit, p. 675). By the age of eighteen possibly 90 percent of young males have participated in delinquent acts and approximately half have been arrested for nontraffic offenses by the time they are thirty. Only 50 to 60 percent of young females have been involved in delinquent acts by the time they are eighteen and less than 10 percent have been arrested by the age of thirty (Witte, 1997).

Explaining the secular trend in criminal participation rates in most industrialized economies is a difficult task. Many social scientists argue that crime is closely related to work, education, and poverty and that truancy, youth unemployment, and crime are by-products or even measures of social exclusion. ‘‘Blue-collar’’ criminals often have limited education and possess limited labor market skills. These characteristics partly explain the poor employment records and low legitimate earnings of most criminals. These sort of issues originally led economists to examine the relationship between wages and unemployment rates on crime. More recently economists have also considered the benefits and costs of educational programs to reduce crime.

A related question concerns the impact of sanctions. For example, does increased imprisonment lower the crime rate? How does the deterrent effect of formal sanctions arise? Although criminologists have been tackling such issues for many years, it is only recently that economists have entered the arena of controversy. This is not surprising given the high levels of crime and the associated allocation of public and private resources toward crime prevention. The expenditure on the criminal justice system (police, prisons, prosecution/defense, and courts) is a significant proportion of government budgets. In addition, firms and households are spending increasingly more on private security.

The incentive-based economic model of crime is a model of decision-making in risky situations. Economists analyze the way in which individual attitudes toward risk affect the extent of illegal behavior. In most of the early literature, the economic models of crime are single-period individual choice models. These models generally see the individual as deciding to allocate time with criminal activity as one possible use of time. A key feature is the notion of utility; judgments are made of the likely gain to be realized (the ‘‘expected utility’’) from a particular choice of action. Individuals are assumed to be rational decision-makers who engage in either legal or illegal activities according to the expected utility from each activity. An individual’s participation in illegal activity is, therefore, explained by the opportunity cost of illegal activity (for example, earnings from legitimate work), factors that influence the returns to illegal activity (for example, detection and the severity of punishment), and by tastes and preferences for illegal activity. Economists see criminal activity as being similar to paid employment in that it requires time and produces an income. Clearly, the dichotomy between either criminal activity or legal activity is an oversimplification. For example, individuals could engage in criminal activities while employed since they have greater opportunities to commit crime; similarly, some criminals may jointly supplement work income with crime income in order to satisfy their needs. A secondary problem with the economist’s choice model, which was highlighted in our opening comments, is that young people are more likely to participate in crime long before they participate in the labor market. This observation raises questions about the appropriateness of the economic model of crime in explaining juvenile crime.

Economic models of criminal behavior have focused on sanction effects (e.g., deterrence issue) and the relationship between work and crime. In the main, these models have not directly addressed the role of education in offending. It could be argued that unemployment is the conduit through which other factors influence the crime rate. For example, poor educational attainment may be highly correlated with the incidence of crime. However, this may also be a key determinant of unemployment. Although educational variables have been included as covariates with crime rates, they have not received a great deal of attention in correlational studies.

The remainder of the research paper is organized as follows. In the next section, we outline the economic model of crime; the section following considers two extensions to the basic theory; then a section provides a brief overview of the empirical evidence; the final section examines recent work on juvenile crime and education.

Economic Model of Criminal Behavior: Basic Theory

As mentioned in the overview, the economic model of crime is a standard model of decisionmaking where individuals choose between criminal activity and legal activity on the basis of the expected utility from those acts. It is assumed that participation in criminal activity is the result of an optimizing individual responding to incentives. Among the factors that influence an individual’s decision to engage in criminal activities are (1) the expected gains from crime relative to earnings from legal work; (2) the chance (risk) of being caught and convicted; (3) the extent of punishment; and (4) the opportunities in legal activities. Specifying an equation to capture the incentives in the criminal decision is a natural first step in most analyses of the crime as work models. The most important of these gives the relative rewards of legal and illegal activity. For example, the economic model sees the criminal as committing a crime if the expected gain from criminal activity exceeds the gain from legal activity, generally work.

Just as in benefit-cost analysis, when comparing alternative strategies, interest centers on the returns from one decision vis-à-vis returns from another decision. For example, a preference for crime over work implies the earnings gap between legal and illegal activities must rise when the probability of being caught and the severity of punishment increases. Attitudes toward risk are central to economic models of criminal choice. For example, if the individual is said to dislike risk (i.e., to be risk averse) then he will respond more to changes in the chances of being apprehended than to changes in the extent of punishment, other things being equal. Becker developed a comparative-static model that considered primarily the deterrent effect of the criminal justice system. As we will see, how individuals respond to deterrent and incapacitation effects of sanctions has generated considerable theoretical and empirical interest from economists.

Any reasonable economic model has crime dependent on (1) legal and illegal opportunities; (2) the chance of being caught; and (3) the extent of sentencing; in the terminology of Freeman (1999a), they are intrinsically related . Thus, severe sentencing and improvements in legal work opportunities of criminals must be expected jointly to reduce crime. Of course, this assumes that crime and work are determined by the same factors and that higher legitimate earnings increase the probability of working. Early literature applied static one-period time allocation models to analyze criminal behavior. In other words, crime and work are assumed to be substitute activities; if an individual allocates more time to work, he will commit less crime because he will have less time to do so. The basic economic model of crime is static or comparative static in economic jargon because it does not see the potential criminal as considering more than a single time period when making his decision.

Extensions of The Basic Model

The incentive-based model of crime has experienced significant theoretical and empirical developments. The model by Becker has been developed subsequently by Ehrlich (1973). Since at least Ehrlich there has been an awareness of a correspondence between any crime-work decision and time allocation. In the 1970s and 1980s, the influential contributions of Ehrlich (1975) and Witte (1980), among others, made this connection much more precise and the awareness more widespread. For example, Ehrlich allowed for three different criminal justice outcomes, whereas Witte utilized a model in which the time allocations between legal and illegal activities entered the utility function directly. See Schmidt and Witte for a survey of these first-generation economic models of crime.

Early studies of criminal behavior by economists can be criticized for being set in a static framework. Economic models of crime are typically estimated as static models, though there are many reasons to suspect dynamic effects matter, both theoretically through habit formation, interdependence of preferences, capital accumulation, addiction, peer group effects, and so on, and empirically through improvements in fit when lagged dependent variables or autocorrelated residuals are included in the model. Labor economists have long been interested in state dependence, the fact that activities chosen in the current period may be strongly affected by the individual’s activities in the previous period (e.g., Heckman). Examples of state dependence in economic models of criminal behavior include: the effect of education today on future criminal activities; and the effect of crime in one period on future legitimate and criminal earnings. Becker and Murphy, Flinn, Grogger (1995), Nagin and Waldfogel, Tauchen and Witte, and Williams and Sickles exemplify attempts at describing a causal dynamic economic model of crime.

Flinn incorporates human capital formation in a time-allocation model. In his model, human capital is accumulated at work, not at school. Consequently, crime takes time away from work and hence diminishes the amount of human capital accumulated. The diminished human capital leads to lower future wages and hence less time spent working. Since crime and work are substitutes in his model, the decline in time allocated to work leads to increased participation in criminal activities.

Becker and Murphy build on consumer demand theory and develop a model of rational addiction. Their model relies on ‘‘adjacent complementarities’’ in consumption to produce habit formation. Under their model, the marginal utility of consuming a good that is an adjacent complement is higher if the good has been consumed in the previous period. They also incorporate myopia to explain why people become addicted to harmful goods.

Grogger estimates a distributed lag model to allow arrests and prosecution to affect both current and future labor market outcomes. Using data from the California Adult Criminal Justice Statistical System, he found that arrest effects on employment and earnings are moderate in magnitude and fairly short-lived. Nagin and Waldfogel consider the effects of criminality and conviction on the income and job stability of young male British offenders. Their analysis uses a panel data set assembled by David Farrington and Donald West as part of the Cambridge Study in Delinquent Development (CSDD). The authors present results which at first sight appear somewhat paradoxical. They find that conviction increases both the job instability and legal income of young offenders. To rationalize these results Nagin and Waldfogel outline a characterization of the labor market in which young men participate. The basic idea underlying the model is that young men have two types of jobs available to them—skilled and unskilled— where wage profiles are rising in the former (due to accumulation of human capital, training and experience) and flat in the latter (no training). If discounted wages are equalized across jobs, the unskilled wage would start above and end below skilled wage. Also, human capital theory suggests that job stability will be greater in skilled sector than in the unskilled sector. Given these predictions, and assuming that a criminal conviction adversely affects prospects of getting a skilled job, it is likely that conviction is associated with higher pay and higher job instability. Note that Nagin and Waldfogel found criminal activity without conviction had no significant effect on labor market performance. They conclude that this result implies stigma, rather than withdrawal from legal work, explains the effects of conviction.

Dynamics arising from the impact of private and social programs (e.g., police treatments in cases of domestic violence) have been dealt with by including the lag of the dependent variable (actual violence) and the latent variable (Tauchen and Witte). Tauchen and Witte use data from the Minneapolis Domestic Violence Experiment to determine how police treatments in cases of domestic violence (advising the couple, separating the individuals temporarily, or arresting the suspect) affect the couple’s subsequent violence. Estimating a dynamic probit model for the probability of observing violence in the follow-up periods, the authors find that arrest is more effective than advising or short-term separation but that the differential effect is transitory.

In an interesting paper, Williams and Sickles provide an extension of Ehrlich (1973) by including an individual’s social capital stock into his utility and earnings functions. Social capital, including things like reputation and social networks, is used as a proxy to account for the effect of social norms on an individual’s decision to participate in crime. This assumes that the stigma associated with arrest depreciates an individual’s social capital stock. Williams and Sickles clarify this point further by arguing that employment and marriage create a form of state dependence, which reduces the likelihood of criminal involvement. In other words, an individual with a family, job, or good reputation has more to lose if caught committing crimes than those without such attachments. Dynamics arise from current decisions affecting future outcomes through the social capital stock accumulation process. The main result is that criminals behave rationally in the sense that they account for future consequences of current period decisions.

A Brief Sketch of The Empirical Evidence on The Supply of Crime

The motivation behind most early applications of Becker’s model was to examine the impact of legitimate labor market experiences (e.g., unemployment) and sanctions on criminal behavior. Broadly speaking, the empirical findings are that (1) poor legitimate labor market opportunities of potential criminals, such as low wages and high rates of unemployment, increases the supply of criminal activities; and (2) sanctions deter crime.

The empirical evidence on the relationship between unemployment and criminal activity has been the subject of much investigation (see literature review by Freeman, 1999a). Unemployment could be taken to influence the opportunity cost of illegal activity. High rates of unemployment growth could be taken to imply a restriction on the availability of legal activities, and thus serve to ultimately reduce the opportunity cost of engaging in illegal activities. Although theoretically well-defined, most empirical studies of the unemployment-crime relationship have provided mixed evidence.

Not all early studies used aggregate timeseries data to test the relationship between unemployment and crime. Thornberry and Christenson use individual level data from the 1945 Philadelphia cohort to find that unemployment had significant effects on crime. Farrington et al., using data from the CSDD, showed that property crime rates were higher when offenders were unemployed.

Witte and Tauchen (1994) exploit the panel data dimensions of the Philadelphia cohort used by Thornberry and Christenson. Instead of primarily focusing on crime as a function of unemployment, they use a richer set of controls, like deterrence, employment status, age, education, race, and neighbourhood characteristics. The results reported by Tauchen and Witte on the relationship between employment and crime were consistent with the previous findings of Thornberry and Christenson and Farrington. Recent work, of which Levitt and Witt et al. (1999) are representative, proceeded to use pooled timeseries cross-section data and find, inter alia, positive associations between unemployment and property crime.

One problem with most work and crime models is that they assume both activities are mutually exclusive. This may be a problematic assumption when considering disadvantaged youths (see Freeman, 1999b). The fact that a youth can shift from crime to an unskilled job and back again or can commit crime while holding a legal job means that the supply of youths to crime will be quite elastic with respect to relative rewards from crime vis-à-vis legal work or to the number of criminal opportunities.

From the 1970s through the 1990s the labor market prospects for unskilled workers in most OECD countries has deteriorated considerably. In particular, the real earnings of young unskilled men fell, while income inequality rose. This suggests that as the earnings gap widens, relative deprivation increases, which in turn leads to increases in crime. Empirical research into the relationship between earnings inequality and crime generally find that more inequality is associated with more crime. For example, in a study based on a sample of the forty-two police force areas in England and Wales, Witt et al. (1999) report a positive association between earnings inequality and crime rates for vehicle crime, theft, and burglary. For the United States, see the evidence reviewed in Freeman (1999a).

Much of the empirical work on testing the Becker model has focused on the role of deterrence in determining criminal activity. Deterrence refers to the effect of possible punishment on individuals contemplating criminal acts. Deterrence may flow from both criminal justice system actions and from social actions (i.e., the negative response of friends and associates to criminal behavior). To date, attempts to measure deterrent effects have concentrated on the effects of the criminal justice system. See Nagin (1998) for a survey of this literature.

This section discusses a variety of practical problems that arise in testing for deterrent effects. In particular, we consider three estimation issues: measurement error, endogeneity, and nonstationarity.

Models of criminal behavior are usually estimated using official reported crime statistics. Such recorded offenses are influenced both by victims’ willingness to report crime and by police recording practices and procedures. At the level of the individual police department, both administrative and political changes can lead to abnormalities in reported data or to failures to report any data. For example, the measurement error in crime rates may arise because hiring more police leads to more crimes reported. Consequently, estimates derived from regressing crime rates on the number of police (or on arrest rates) may be severely distorted by the impact of measurement error.

The potentially serious problem of simultaneity between sanctions and crime has been the subject of much debate. Here, the main point is that increases in sanctions may cause decreases in crime, but increases in sanctions may be in response to higher crime rates. Since the 1970s there has been a considerable effort to find instruments (i.e., exogenous factors) to identify the effects of sanctions on the supply of crime. For example, Levitt (1996) uses instrumental variables to estimate the effect of prison population on crime rates. Prison-overcrowding litigation in a state is used as an instrument for changes in the prison population.

In order to identify the effect of police on crime, Marvell and Moody and Levitt (1997) proposed different procedures. Marvell and Moody are concerned with the timing sequence between hiring police and crime. Using lags between police levels and crime rates to avoid simultaneity, they test for causality in the spirit of Granger. Although they find Granger causation in both directions, the impact of police on crime is much stronger than the impact of crime on police. In a recent paper Levitt (1997) uses the timing of elections (when cities hire more police) as an instrumental variable to identify a causal effect of police on crime. He finds that increases in police instrumented by elections reduces violent crime, but have a smaller impact on property crime.

A substantial problem that has been ignored in the vast majority of empirical studies is nonstationarity of crime rates. A time-series is said to be nonstationary if (1) the mean and/or variance does not remain constant over time; and (2) covariance between observations depends on the time at which they occur. In the United States, index crime rate appears strongly nonstationary, for the most part being integrated of order one with both deterministic and stochastic trends (a random variable whose mean value and variance are time-dependent is said to follow a stochastic trend). See, for example, Witt and Witte (2000). Here, the authors have attempted to estimate and test a model using linear nonstationary regressor techniques like cointegration and error correction models. The empirical results suggest a long-run equilibrium relationship between crime, prison population, female labor supply, and durable consumption.

Recent Developments: Juvenile Crime and Education

Recently some researchers have focused their attention on juvenile crime and education. Levitt (1998) and Mocan and Rees provide evidence to show that the economic model of crime applies to juveniles as well as adults. Levitt uses state-level data over the period 1978–1993 for making comparisons between the adult criminal justice system and delinquents. The dependent variable is juvenile crime (either violent or property crime) per number of juveniles. The explanatory variables include the number of juveniles or adults in custody per crime; the number of juveniles or adults in custody per juvenile or adult; economic variables, including the state unemployment rate; and demographic variables, including race and legal drinking age, and dummy variables for year and state. Levitt finds that juvenile crime is negatively related to the severity of penalties, and that juvenile offenders are at least as responsive to sanctions as adults. Interestingly, he finds that the difference between the punishments given to youths and adults helps explain sharp changes in crimes committed by youths as they reach the age of majority.

Mocan and Rees estimate the economic model of crime for juveniles using individual-level data from a nationally representative sample of 16,478 students in grades 7 through 12. The data set contains rich information on offenses and deterrence measures, as well as on personal, family, and neighborhood characteristics. They find that probit estimates for young males selling drugs and assault are strongly affected by violent crime arrests (i.e., increases in arrests per violent crime reduce the probability of selling drugs and committing an assault). Violent crime arrests for females reduces the probability of selling drugs and stealing. Mocan and Rees also find higher levels of local unemployment and higher levels of local poverty associated with higher levels of crime. Family welfare status, a proxy for family poverty, has a positive impact on juvenile offending. Finally, family structure and the education of the juveniles’ parents also have an impact on delinquent behavior.

Up to now, we have primarily concerned ourselves with research on crime reduction that focuses on labor market experiences and deterrent effects. The issue of education and training has generally been neglected. It is only recently that economists have begun to explicitly model work, education, and crime. Witte (1997) reviews the literature on education and crime and discusses models that suggest possible crime-reducing effects of education. She carefully traces the various attempts made over the past two decades at a full integration of education and crime but finds that the empirical evidence regarding the effects of education on crime is limited. In recent work, using data from the National Longitudinal Survey of Youth and Uniform Crime Reports, Lochner (1999) developed and estimated a dynamic model in which all three activities—work, investment in human capital, and crime—are endogenized. He finds that education, training, and work subsidies can reduce criminal activity.

Summary and Conclusions

Most economic work on crime has focused on the deterrent effect of the criminal justice system and on the interrelationship between work and crime. Empirical work provides some, but not unambiguous support for the deterrence hypothesis. Recent work by economists suggest that the relationship between work and crime may be far more complicated than implied by economic models.

The rise in juvenile crime rates has focused increasing attention on youth crime. This has forced economists to expand their thinking to incorporate such things as education, peer group effects, and the influence of family and community.

Increasingly both theoretical and empirical work on the economics of crime has come to use dynamic models. Theoretical work is developing multi-period models of crime. Empirically economists are using both panel data techniques and modern time series techniques to examine the dynamics of criminal behavior.

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  1. The case for economic criminology

    This paper sets out the case for economic criminology. It argues 'economic criminology' is more than just a prefix that brings scholars and papers together exploring economic crime. The paper argues that crimes associated with economic criminology are a significant societal problem that deserves the attention of scholars to understand these ...

  2. Journal of Economic Criminology

    About the journal. Journal of Economic Criminology aims to be the premier outlet for peer-reviewed theoretical and empirical work on economic criminology. This interdisciplinary journal welcomes manuscript submissions on a broad array of topics, including various types of economic crime, explanations of offending …. View full aims & scope.

  3. Economic crime, economic criminology, and serious crimes for economic

    This article has two main objectives. First, to interrogate the concept and/or conception of 'economic crime' (framed as a singular thing). We argue that current policy, and subsequently, social scientific (or criminological more specifically) framings, tend to arbitrarily 'carve up' the objects of study that interest us, in turn creating a 'conceptual disorder' that has ...

  4. Why do inequality and deprivation produce high crime and low trust

    In this paper, we develop an explanatory model for why greater inequality should produce higher crime and lower social trust. ... C. Crime and economic incentives. J. Hum. Resour. 39, 958-979 ...

  5. Full article: The Changing Face of Financial Crime: New Technologies

    Financial crime is a growing crime problem throughout the world. It is a trillion-dollar industry that takes an enormous social and economic toll on the lives it touches. The primary goal of this special issue was to explore the many dimensions of financial crime from the perspectives of victims (both individual and organizational) and offenders.

  6. (PDF) What is 'economic' about 'economic crime'?

    Economic crime is that criminal behaviour whose effects disrupt or damage economic. done, for example, by means of an enumeration, as in the instructions issued. to the Swedish Economic Crimes ...

  7. Examining the Impact of Socioeconomic Factors on Crime Rates: A Panel

    Our research aims to evaluate the factors affecting crime rates using economic, demographic, and institutional variables. In this respect, for a panel of 17 countries, we examined the relationship between crime rates, real GDP, unemployment, urbanization, and the rule of law by using the fully modified least squares (FMOLS) regression.

  8. Full article: Revisiting the economic theory of crime A state-level

    The study reports that deterrence factors (probability of arrest and apprehension) have a perverse impact on India's crime rates. The paper reveals that an increase in the probability of deterrence variables does not lead to a reduction in crime rates, indicating a fundamental flaw in India's corrective mechanisms. ... Economic Research ...

  9. Risks

    It is especially important to evaluate the quality of this many research papers and to obtain valuable information. Full article (This article belongs to the Special Issue Economic and Financial Crimes) ... Economic and financial crime is closely related to the changes and the development of societies. In this paper, we question whether the ...

  10. Data Science perspectives on Economic Crime

    Studying economic crime from a data science perspective offers unique insights and can inform the design of novel solutions. The results of such research are of eminent interest to governments, law enforcement, organizations, companies and civil society watchdogs. In light of this recent activity, there is a need to survey the field, to reflect ...

  11. Identifying the Effect of Unemployment on Crime

    UNEMPLOYMENT ON CRIME* STEVEN RAPHAEL University of California, Berkeley and RUDOLF WINTER-EBMER University of Linz and Center for Economic Policy Research, London Abstract In this paper, we analyze the relationship between unemployment and crime. Using U.S. state data, we estimate the effect of unemployment on the rates of seven felony offenses.

  12. PDF Economic Crime in A Globalizing Society: Its Impact on The ...

    VISITING EXPERTS' PAPERS 71 ECONOMIC CRIME IN A GLOBALIZING SOCIETY: ITS IMPACT ON THE SOUND DEVELOPMENT OF THE STATE - AN INDIAN PERSPECTIVE Deepa Mehta* I. INDIA: THE LAND AND THE PEOPLE India is a vast sub-continent covering an area of 3, 287,590 sq. km. It extends from the snow covered

  13. Crime and justice research: The current landscape and future

    Early in 2018, I was invited by the Economic and Social Research Council (ESRC) to prepare a concise (12 page) paper - a 'think piece' - on the scope for future Research Council investments in research on crime and justice. 1 This was one of 13 such invitations. These were issued to scholars working in fields that for various reasons (in some cases, perhaps, their comparative newness ...

  14. The Economics of Crime

    The Economics of Crime. in Virtual Issues. To think of crime in terms of risk and rewards, punishment and incentives has a long lineage. Jeremy Bentham in his 1830 book The Rationale for Punishment already applied utilitarian logic to the sanctions applied to criminal offenders. In economics itself, research in earnest began with the seminal ...

  15. PDF Measures to Combat Economic Crime, Including Money-laundering

    Senior Research Fellow, Institute for Security Studies, South Africa 'These five characteristics - specialisation in market-based crimes, hierarchical and durable structures, use of ... Financial crime committed systematically could also be described as market based economic crime. This paper is confined to market based economic crimes ...

  16. PDF The Impact of Economic Opportunity on Criminal Behavior

    Working Paper 2019.012 The Center for Growth and Opportunity at Utah State University is a university-based academic research center that explores the scientific foundations of the interaction between individuals, business, and government. This working paper represents scientific research that is intended for submission to an academic journal.

  17. Economic crime research strategy: Home Office research priorities

    This research strategy represents a set of prioritised evidence needs to support the response to economic crime by better understanding the threat in key areas. This document allows anyone ...

  18. (PDF) Economics of Crime

    Economics of Crime. January 1978; Foundations and Trends® in Microeconomics 2(2) ... Law & Economics Research Paper Series. Re search Paper No. 11-1 14. Economics of Crime . Erling Eide .

  19. Economic Crime Research Papers

    Based on statistical analysis of economic crimes in Ukraine, it was determined that the number of crimes in the sphere of economic activity is 1.37% of the total number of crimes, in general, the number of economic crimes increased in 2017-2019. The National Police of Ukraine provides the investigation of the largest proportion of economic crimes.

  20. Economic Crime Research Paper

    Classical Approach to Economic Crime. The classical approach to crime originated in the Enlightenment and is evident in the writings of Thomas Hobbes, John Locke, Jean Jaques Rousseau, and others. According to this perspective, intelligence and rational thought are fundamental characteristics of people and the principal basis for their behavior.

  21. Economics of Crime

    His research focuses on the impact of social programs with particular attention to crime. Crystal S. Yang is a Professor of Law at Harvard Law School. Her research focuses on empirical law and economics, with particular focus on issues of criminal justice, such as racial bias and the operation of the bail system, and on consumer bankruptcy.

  22. Organized Crime in Italy: An Economic Analysis

    Organized crime affects the socio-economic development of the areas in which it is rooted through several channels. However, analysing these effects is difficult, largely because it is extremely hard to observe and thus confidently measure the extent of mafia presence in a given area. On the basis of the most recent economic literature and with the aid of new information sources, this paper (i ...

  23. Economic Theories of Crime Research Paper

    A recent survey suggests that three general issues are of central concern in the economics of crime literature: the effects of incentives on criminal behavior, how decisions interact in a market setting, and the use of cost-benefit analysis to assess alternative policies to reduce crime (see Freeman, 1999a). In this research paper we will focus ...