This thesis examines the complex relationship between economics and theft, more specifically how different economic factors such as GDP per capita or the Gini Index influence theft rates in different countries. For this a comparative analysis between six countries will be done (three European countries and three American countries).Through a panel analysis this research aims to identify the correlations and impacts of seven independent variables on one dependent indicator (theft). Key findings reveal that GDP per capita, unemployment rates, Gini index, population density, and cocaine seizure impact theft rates. In the results, five out of the seven are significant, having the variable Diff Unemployment as the one with more impact over the dependent variable,where a change of one percentage unit on ΔUnemployment corresponds to a change of 329,071 on Δthefts. The variable DiffGiniIndex also has a really high impact over thevariable ΔThefts, where a change in 1 point over the variable, has an impact of 185,715 in the variable Δthefts. By shedding light on the economic foundations of theft, this thesis contributes to a deeper understanding of the case.