A. Platas-López, A. Guerra-Hernández, Francisco Grimaldo. On the Macroeconomic Effect of Extortion: An Agent-Based Approach. Journal of Artificial Societies and Social Simulation 24(1):3. January 2021. DOI: 10.18564/jasss.4496, ISSN 1460-7425 | JASSS
Abstract. This work proposes an agent-based approach to study the effect of extortion on macroeconomic aggregates, despite the fact that there is little data on this criminal activity given its hidden nature. We develop a Bottom-up Adaptive Macroeconomics (BAM) model that simulates a healthy economy, including a moderate inflation and a reasonable unemployment rate, and test the impact of extortion on various macroeconomic signals. The BAM model defines the usual interactions among workers, firms and banks in labour, goods and credit markets. Subsequently, crime is introduced by defining the propensity of the poorest workers to become extortionists, as well as the efficiency of the police in terms of their probability of capturing these extortionists. The definition of BAM under Extortion Racket Systems (BAMERS) model is completed with a threshold for the firms rejecting extortion. These parameters are explored extensively and independently. Results show that even low propensity towards extortion is enough to find considerable negative effects such as a marked contraction of Gross Domestic Product and increased unemployment, consistent with the little known data of the macroeconomic effects of extortion. The effects on consumption, Gini index, inflation and wealth distribution are also reported. Interestingly, our results suggest that it is more convenient to prevent extortion, rather than combat it once deployed, i.e., no police efficiency level achieves the healthy macroeconomic signals observed without extortion.