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on Law and Economics |
| By: | Ding Chen; Simon Deakin |
| Abstract: | In this paper, through a case study focusing on the evolution of China’s insolvency law, we test the ‘transplant effect’ hypothesis, in particular the claim that transplanted laws can only work when there is local or ‘endogenous’ demand for them in the ‘host’ or ‘receiving’ state. We also test claims made for a ‘co-evolutionary’ understanding of the law-economy relationship in the context of China’s development. In addition to documentary sources, this paper draws on interviews we carried out in Wenzhou in September 2017 and December 2018. Our study shows that aspects of the transplant and coevolution hypotheses are in need of some modification if they are to explain China’s legal and economic development. The embedding of legal transplants is less a response to the demands of business actors, and more the result of proactive interventions on the part of judges and officials. The study also suggests that formal rules can be operationalised at the level of practice once they are seen as legitimate. While this is a process which takes time, a period of crisis provides opportunities for the learning process around the use of formal rules and procedures to be accelerated. |
| Keywords: | Law and economic growth, transplant effect, co-evolution, Wenzhou curb crisis, China’s insolvency law, judicial innovation |
| JEL: | G28 K12 K22 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:cbr:cbrwps:wp548 |
| By: | Arto Arman; Andreas Beerli; Aljosha Henkel; Michel André Maréchal |
| Abstract: | We study how incarceration experience shapes preferences for criminal justice policies. In collaboration with a newly opened prison, we conducted a randomized field experiment that offered citizens the opportunity to experience up to two days of incarceration, closely replicating the real-life journey of inmates. Providing citizens with a chance to gain firsthand incarceration leads to a significant shift in punitive attitudes, with participants becoming less supportive of harsh criminal justice policies and donating more money to organizations advocating more moderate justice policies. Although individuals overestimated the wellbeing of actual prisoners, the intervention did not alter these beliefs. This suggests that the observed changes in policy preferences are driven more by personal experience than by revised beliefs about the burden of confinement. By randomizing institutional exposure outside the laboratory, our study highlights the causal role of personal experience in the formation of policy preferences. |
| Keywords: | Incarceration, field experiment, personal experience, criminal justice policy, punitive attitudes, prison |
| JEL: | C93 D83 K14 P37 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:485 |
| By: | De Hoop, Jacobus Joost; Tribin Uribe, Ana Maria; Velásquez, Andrea |
| Abstract: | This study explores the gendered impacts of violent crime on economic opportunities in Latin America and the Caribbean. While both men and women experience violent crime, their exposure to violent crime and the consequences they suffer differ. Women are disproportionately affected by intimate partner violence, sexual harassment, and mobility restrictions, all of which limit their labor market participation and economic independence. Through a review of the literature, the study identifies six primary mechanisms through which violent crime affects women’s economic outcomes: sectoral segregation, fear of victimization, mobility constraints, intra-household bargaining power shifts, increased intimate partner violence, and disruptions to human capital accumulation. By analyzing these gendered dimensions, the study highlights how violent crime may contribute to inequality and restrict women’s access to economic opportunities. Policy responses must go beyond general crime reduction strategies and incorporate gender-sensitive interventions, including stronger legal protections, labor market reforms, and investments in childcare and financial inclusion. Addressing violent crime from a gendered perspective is essential for fostering economic resilience and reducing inequalities in Latin America and the Caribbean. |
| Date: | 2026–01–12 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11294 |
| By: | Ghosh, Anupam (University of Nebraska-Lincoln) |
| Abstract: | Little causal evidence exists regarding the long-term impacts of natural disasters on crime. Using a balanced panel of county-level crime data spanning 1980–2020, this paper estimates the short- and long-run effects of hurricanes of varying intensities that affected U.S. counties between 1990 and 2010. Findings indicate that while minor hurricanes have little effect on crime, major hurricanes cause significant increases in property crime. In the decade following exposure to major hurricanes, property crime rates rise by 8.5% relative to the baseline mean, imposing an estimated per-capita social cost of $120 on treated counties. These effects are largely driven by evacuation orders and selective out-migration in the short run and by declining per-capita incomes in the long run. Furthermore, hurricane effects are disproportionately larger for counties with less disaster experience and lower incomes, which risk losing 1.4% and 2.2% of per capita GDP, respectively, due to hurricane-induced crime. Overall, the findings underscore the need for greater resource allocation toward vulnerable communities and increased investment in disaster resilience measures to mitigate the economic and social consequences of climate change. |
| Date: | 2026–01–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:smwtk_v1 |
| By: | Simon Deakin; Linda Shuku |
| Abstract: | The use of natural language processing (NLP) and machine learning (ML) to analyse the structure of legal texts is a fast-growing field. While much attention has been devoted to the use of these techniques to predict case outcomes, they have the potential to contribute more broadly to research into the nature of legal reasoning and its relationship to social and economic change. In this paper, we use recently developed NLP and ML methods to test the claim that judicial language is systematically shaped by economic shocks deriving from the business cycle and by long-run trends in the economy associated with technological change and industrial transition. Focusing on cases decided under the Anglo-Welsh poor law between the 1690s and 1830s, we show that the terminology used to describe the right to poor relief shifted over time according to economic conditions. We explore the implications of our results for the poor law, the theory of legal evolution, and socio-legal research methods. |
| Keywords: | Law and computation, poor law, legal evolution, natural language processing |
| JEL: | J41 K31 N33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:cbr:cbrwps:wp546 |
| By: | Gillian K. Hadfield |
| Abstract: | Most of our AI governance efforts focus on substance: what rules do we want in place? What limits or checks do we want to impose on AI development and deployment? But a key role for law is not only to establish substantive rules but also to establish legal and regulatory infrastructure to generate and implement rules. The transformative nature of AI calls especially for attention to building legal and regulatory frameworks. In this PNAS Perspective piece I review three examples I have proposed: the creation of registration regimes for frontier models; the creation of registration and identification regimes for autonomous agents; and the design of regulatory markets to facilitate a role for private companies to innovate and deliver AI regulatory services. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.01474 |
| By: | Saradindu Dolui (Capitol Technology University, 11301 Springfield Rd, Laurel, MD, 20708, USA. Author-2-Name: Leila Halawi Author-2-Workplace-Name: Embry‑Riddle Aeronautical University, 1 Aerospace Boulevard, Daytona Beach, FL, 32114, USA. Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:) |
| Abstract: | " Objective - This study aims to analyze crime dynamics in Chicago by examining the predictive relationship between crime categories and arrest frequencies using regression analysis. Methodology - A quantitative research design was applied using secondary data from the Chicago Data Portal. Regression analysis was performed to evaluate how crime categories predict arrest patterns. Findings - The results reveal a strong, positive, and statistically significant relationship between crime categories and arrest frequencies (R² = 0.613, β = 0.783, p |
| Keywords: | regression analysis; crime; Chicago; prediction model; crime prevention. |
| JEL: | C12 C35 C55 C80 Z39 Z19 |
| Date: | 2026–03–31 |
| URL: | https://d.repec.org/n?u=RePEc:gtr:gatrjs:jber270 |
| By: | Anauati, María Victoria; Romero, María Noelia; Baraldi, Lucia; Sosa Escudero, Walter; Tommasi, Mariano |
| Abstract: | Recidivism is a persistent challenge for criminal justice systems worldwide, yet evidence from Latin America remains scarce. This study addresses that gap through three contributions. First, it reviews the individual, institutional, and contextual determinants of recidivism, with special attention to Latin America. Second, it examines the potential use of AI-based prediction tools, discussing the institutional, data-related, and ethical challenges such implementation entails. Third, using two decades of administrative data from Argentinas prison system, it applies six machine learning models to predict reoffending. The analysis identifies economic offenses and age at incarceration as the strongest predictors, while geographic indicators also play a role, reflecting the spatial clustering of repeat offenders across prisons. The findings suggest that routinely collected prison-level information, often underutilized, can enable reasonably accurate risk prediction and guide effective rehabilitation and prison management strategies. |
| JEL: | K40 C50 I30 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14489 |
| By: | Fernanda Sobrino (School of Government and Public Transformation, Tecnológico de Monterrey); Adolfo De Unánue T. (School of Government and Public Transformation, Tecnológico de Monterrey); Edgar Hernández (School of Government and Public Transformation, Tecnológico de Monterrey); Patricia Villa (School of Government and Public Transformation, Tecnológico de Monterrey); Elena Villalobos (School of Government and Public Transformation, Tecnológico de Monterrey); David Aké (School of Government and Public Transformation, Tecnológico de Monterrey); Stephany Cisneros (School of Government and Public Transformation, Tecnológico de Monterrey); Cristian Paul Camacho Osnay (Office of the Attorney General of the State of Zacatecas, Mexico); Armando García Neri (Office of the Attorney General of the State of Zacatecas, Mexico); Israel Hernández (Office of the Attorney General of the State of Zacatecas, Mexico) |
| Abstract: | Prosecutors across Mexico face growing backlogs due to high caseloads and limited institutional capacity. This paper presents a machine learning (ML) system co-developed with the Zacatecas State Prosecutor’s Office to support internal case triage. Focusing on the Módulo de Atención Temprana (MAT)—the unit responsible for intake and early-stage case resolution—we train classification models on administrative data from the state’s digital case management system (PIE) to predict which open cases are likely to finalize within six months. The model generates weekly ranked lists of 300 cases to assist prosecutors in identifying actionable files. Using historical data from 2014 to 2024, we evaluate model performance under real-time constraints, finding that Random Forest classifiers achieve a mean Precision@300 of 0.74. The system emphasizes interpretability and operational feasibility, and we will test it via a randomized controlled trial. Our results suggest that data-driven prioritization can serve as a low-overhead tool for improving prosecutorial efficiency without disrupting existing workflows. |
| Keywords: | Artificial intelligence, Digital government, Criminal justice, Algorithmic governance, Case prioritization, Public sector AI, Decision support systems, Mexico |
| JEL: | H83 K42 C38 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:gnt:wpaper:24 |
| By: | Omry Yoresh (London School of Economics); Weijian "Eddy" Zou (Institute for Fiscal Studies) |
| Date: | 2026–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:ifs:ifsewp:26/12 |
| By: | Simon Deakin; Kamelia Pourkermani |
| Abstract: | We report the results of an econometric analysis of the effects of labour laws in the UK and China. For data on labour laws we draw on the 2023 update of the CBR-LRI index, part of the Cambridge Leximetric Database, which codes for labour laws around the world between 1970 and 2022. The longitudinal coverage of the CBR-LRI enables us to use time-series techniques which model dynamic changes in an economy over time. We employ impulse response function analysis to estimate the effects of labour laws on indicators of efficiency (productivity, employment and unemployment) and distribution (labour’s share of national income). We find that stronger labour laws in the UK are associated with rising employment and falling unemployment, while those in China are associated with rising productivity. We also observe positive impacts of labour laws on the labour share in both countries. Breaking down our results according to particular types of labour law, the positive employment effect we see in the UK is associated with stronger working time protections, while the positive productivity effect in China is associated with more protective laws regulating flexible forms of employment and with stronger dismissal laws. Assessing our results, we suggest that they speak to the importance of labour laws for avoiding regression, in the British case, to a low-cost, low productivity economy, and, in China’s case, for helping bridge the ‘middle income gap’ to sustainable development. More generally, our findings imply the need for adjustment to standard models of the role of labour laws in the economy and to the policy advice which they generate, to the following effect: labour laws, by disciplining capital, contribute to its more productive use. |
| Keywords: | Labour law, employment, unemployment, productivity, labour share, leximetrics, UK, China |
| JEL: | K31 J83 O57 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:cbr:cbrwps:wp547 |
| By: | Stagoll, Jane M.; Fry, Tim R. L. |
| Abstract: | Data collected by the National Association for the Care and Resettlement of Offenders (NACRO) on fines default in English Magistrates' courts is analysed using a sequential probability model. It is found that an offender's previous history and employment status play a significant role in determining fines default behaviour. |
| Keywords: | Political Economy |
| URL: | https://d.repec.org/n?u=RePEc:ags:monebs:267300 |