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The stay-at-home restrictions to control the spread of COVID-19 led to unparalleled sudden change in daily life, but it is unclear how they affected urban crime globally. We collected data on daily counts of crime in 27 cities across 23 countries in the Americas, Europe, the Middle East and Asia. We conducted interrupted time series analyses to assess the impact of stay-at-home restrictions on different types of crime in each city. Our findings show that the stay-at-home policies were associated with a considerable drop in urban crime, but with substantial variation across cities and types of crime. Meta-regression results showed that more stringent restrictions over movement in public space were predictive of larger declines in crime., The implementation of COVID-19 stay-at-home policies was associated with a considerable drop in urban crime in 27 cities across 23 countries. More stringent restrictions over movement in public space were predictive of larger declines in crime.

Information affecting a candidate's reputation might have significant electoral consequences. Do candidates respond to the release of information? Using Brazilian elections and audits as an exogenous source of information, I show that both incumbent and challenger increase their campaign spending when detrimental information affects the incumbent's reputation. Conversely, beneficial information decreases candidates' spending. The main channel is that information affects the expected competitiveness of elections and, therefore, candidates' spending. Only information disclosed prior to electoral campaigns impacts campaign spending. Furthermore, incumbents also adapt a conditional cash transfers program by increasing (decreasing) the beneficiaries when detrimental (beneficial) reputation shocks occur.

An independent and impartial judicial system is essential for a well-functioning democracy and the economy. Despite constitutional guarantees, elected politicians may substantially influence the legal system. This paper studies whether politicians in power get special treatment in courts when facing criminal accusations. I construct a unique panel of over 1,300 criminal cases for candidates for state legislative assemblies in India from 2004 to 2013. Using a regression discontinuity design, I compare the probability of a pending criminal case being closed without conviction at the end of a legislature for politicians who marginally won the election against those who marginally lost it. This paper uncovers significant opposite effects of winning office, depending on the political alignment with the state ruling party. Winners from the state ruling party are 17 per cent more likely to get their pending criminal cases closed without conviction during their period in office. In contrast, winners from other parties are 15 per cent less likely to get their pending cases closed without conviction during the same time-frame. The result is consistent with the misuse of attributions vested on those in power within the executive to affect the career of legal officials.

This paper introduces a two-stage winner-take-all contest model with reference-dependent preferences to study the determinants of conflict and its intensity. The existence of a Sub-game Perfect Nash equilibrium in pure strategies and characterization of the equilibrium are shown. Reference points are crucial in the decision of waging war and imply conflicts of higher intensity compared to standard models. The model delivers predictions in line with existing evidence and explains common empirical patterns in the conflict literature that previous models cannot account for. First, conflict is more likely to occur after negative income shocks since agents' current situation is perceived as a loss compared to the status quo. Second, income reduces the odds of conflict if agents are more risk-averse for gains than risk-seeker for losses.

The COVID-19 pandemic has resulted in over 2 billion people in the world affected by lockdowns. This has significant socioeconomic implications, especially in areas such as crime, where police resources are diverted from crime prevention towards enforcing lockdowns. This study analyzes the effect of lockdowns and their severity on crime and violence against women in India. A sharp regression discontinuity design is implemented harnessing the sudden introduction of a state-wide lockdown and novel high-frequency criminal case data. The results show a sharp decrease in aggregate crime produced by the lockdown. Over 60 per cent reductions are observed in diverse types of crimes such as murder, theft, and crimes against women, among others. This seems to be driven by the higher search costs faced by criminals. Finally, by exploiting geographic variation in terms of lockdowns' severity across districts, this study shows that relaxing lockdowns' initial restrictions increase crime, but the increment is lower in less restrictive lockdowns than in restrictive ones. While economically-motivated crimes increased, violent crimes were not impacted. This suggests that the economic downturn produced by the lockdown might be driving these effects.

We address a general equilibrium model with limited-recourse collateralized loans and securitization of debts. Each borrower is required to pledge physical collateral, and bankruptcy is filed against him if claims are not fully honored. Moreover, agents have a positive amount of wealth exempt from garnishment and, for at least a fraction of them, commodities used as collateral are desirable. In this context, equilibrium exists for any continuous garnishment rule and multiple types of reimbursement mechanisms.