Show simple item record

dc.contributor.advisorVargas, Juan Fernando 
dc.contributor.advisorVillamizar, Mauricio 
dc.creatorRestrepo Tamayo, Sara 
dc.descriptionLa reducción de la delincuencia urbana es una preocupación política primordial en todo el mundo. Los gobiernos han implementado una amplia variedad de programas para controlar el crimen, que van desde la vigilancia policial de los puntos calientes hasta los trabajos de transición para ex convictos. En las últimas décadas, la economía colaborativa, donde las personas comparten o alquilan bienes personales como automóviles o casas, ha ganado importancia como uno de los caminos más accesibles para que los trabajadores poco cali ficados tengan un ingreso regular. Este documento examina el impacto de una de las compañías de economía colaborativa más grandes de América Latina (Rappi) en el crimen urbano en Bogotá. Al usar un modelo dinámico de diferencias en diferencias, se encuentra evidencia sugerente de que la llegada de Rappi condujo a una disminución en los robos. Este trabajo contribuye a la literatura sobre las externalidades de la economía colaborativa.
dc.description.abstractThe reduction of urban crime is a paramount policy concern worldwide. Governments have implemented a wide variety of programs to control crime, ranging from hot spots policing to transitional jobs for ex-convicts. Over the past few decades, the sharing or peer-to-peer economy-where individuals share or rent personal goods like cars or houses-has gained importance as one of the most accessible paths for low skilled workers to have a regular income. This paper examines the impact of one of Latin America's largest sharing economy company (Rappi) on urban crime in Bogota. Using a dynamic differences-in-differences model, I fi nd suggestive evidence that the arrival of Rappi led to a decrease in robberies. This work contributes to the literature on the externalities of the sharing economy.
dc.sourceinstname:Universidad del Rosario
dc.sourcereponame:Repositorio Institucional EdocUR
dc.subjectEconomía colaborativa
dc.subject.lembEconomía colaborativa
dc.subject.lembPrevención del delito
dc.titleThe impact of sharing economy on urban crime
dc.publisherUniversidad del Rosario
dc.creator.degreeMagíster en Economía
dc.publisher.programMaestría en Economía
dc.publisher.departmentFacultad de Economía
dc.title.alternativeEl impacto de la economía colaborativa en el crimen urbano
dc.subject.keywordSharing economy
dc.subject.keywordPeer-to-peer economy
dc.type.spaTesis de maestría
dc.rights.accesoAbierto (Texto Completo)
dc.source.bibliographicCitationAbraham, S., & Sun, L. (2018). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects.
dc.source.bibliographicCitationAguiar, L., & Waldfogel, J. (2016). Streaming reaches ood stage: Does spotify stimulate or depress music sales? no. w21653. National Bureau of Economic Research.
dc.source.bibliographicCitationBaudains, P., Braithwaite, A., & Johnson, S. D. (2013). Target choice during extreme events: A discrete spatial choice model of the 2011 london riots. Criminology, 51 (2), 251-285.
dc.source.bibliographicCitationBecker, G. S. (1968). Crime and punishment: An economic approach. In The economic dimensions of crime (pp. 13-68). Springer.
dc.source.bibliographicCitationBernasco, W. (2010). A sentimental journey to crime: Effects of residential history on crime location choice. Criminology, 48 (2), 389-416.
dc.source.bibliographicCitationBernasco, W., Johnson, S. D., & Ruiter, S. (2015). Learning where to offend: Effects of past on future burglary locations. Applied Geography, 60 , 120-129.
dc.source.bibliographicCitationBernasco, W., & Kooistra, T. (2010). Effects of residential history on commercial robbers crime location choices. European Journal of Criminology, 7 (4), 251-265.
dc.source.bibliographicCitationBernasco, W., & Nieuwbeerta, P. (2004). How do residential burglars select target areas? a new approach to the analysis of criminal location choice. British Journal of Criminology, 45 (3), 296-315.
dc.source.bibliographicCitationBlattman, C., & Annan, J. (2016). Can employment reduce lawlessness and rebellion? a eld experiment with high-risk men in a fragile state. American Political Science Review, 110 (1), 1-17.
dc.source.bibliographicCitationBlattman, C., Green, D., Ortega, D., & Tobón, S. (2018). Place-based interventions at scale: the direct and spillover effects of policing and city services on crime. Working Paper 23941 .
dc.source.bibliographicCitationBoivin, R. (2018). Routine activity, population(s) and crime: Spatial heterogeneity and conflicting propositions about the neighborhood crime-population link. Applied geography, 95 , 79-87.
dc.source.bibliographicCitationBonciu, F. (2016). Impact of the sharing economy on the labor market. Romanian Economic and Business Review, 11 (2), 43.
dc.source.bibliographicCitationBrantingham, P., & Brantingham, P. (1993). Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology, 13 (1), 3-28.
dc.source.bibliographicCitationBrantingham, P., & Brantingham, P. (2013). Crime pattern theory. In Environmental criminology and crime analysis (pp. 100{116). Willan.
dc.source.bibliographicCitationCamacho, A., Conover, E., & Hoyos, A. (2013). Effects of colombia's social protection system on workers' choice between formal and informal employment. The World Bank.
dc.source.bibliographicCitationCameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 90 (3), 414-427.
dc.source.bibliographicCitationCarroll, J., & Weaver, F. (1986). Shoplifters perceptions of crime opportunities: A process-tracing study. In The reasoning criminal (pp. 19-38). Springer.
dc.source.bibliographicCitationCarter, R. L., & Hill, K. Q. (1979). The criminal's image of the city. Elsevier.
dc.source.bibliographicCitationChal n, A., Hansen, B., Lerner, J., & Parker, L. (2019). Reducing crime through envorinmental design: Evidence from a randomized experiment of street lighting in new york city. .
dc.source.bibliographicCitationCohen, P., Hahn, R., Hall, J., Levitt, S., & Metcalfe, R. (2016). Using big data to estimate consumer surplus: The case of uber (Tech. Rep.). National Bureau of Economic Research.
dc.source.bibliographicCitationColonelli, E., & Prem, M. (2017). Corruption and firms: Evidence from randomized anti-corruption audits in brazil. Working paper.
dc.source.bibliographicCitationCramer, J., & Krueger, A. B. (2016). Disruptive change in the taxi business: The case of uber. American Economic Review, 106 (5), 177-82.
dc.source.bibliographicCitationDell, M., Feigenberg, B., & Teshima, K. (2018). The violent consequences of trade-induced worker displacement in mexico. American Economic Review: Insights forthcoming.
dc.source.bibliographicCitationDix-Carneiro, R., Soares, R. R., & Ulyssea, G. (2018). Economic shocks and crime: Evidence from the brazilian trade liberalization. American Economic Journal: Applied Economics, 10 (4), 158-95.
dc.source.bibliographicCitationEdelman, B. G., & Luca, M. (2014). Digital discrimination: The case of airbnb. com. Harvard Business School NOM Unit Working Paper(14-054).
dc.source.bibliographicCitationEhrlich, I. (1973). Participation in illegitimate activities: A theoretical and empirical investigation. Journal of political Economy, 81 (3), 521-565.
dc.source.bibliographicCitationFarronato, C., & Fradkin, A. (2018). The welfare e ects of peer entry in the accommodation market: The case of airbnb (Tech. Rep.). National Bureau of Economic Research.
dc.source.bibliographicCitationFreeman, R. B. (1999). The economics of crime. Handbook of labor economics, 3 , 3529-3571.
dc.source.bibliographicCitationGabor, T., Baril, M., Cusson, M., Elie, D., LeBlanc, M., & Normandeau, A. (1987). Armed robbery: Cops, robbers, and victims. Charles C Thomas Spring eld, IL.
dc.source.bibliographicCitationGarcía, J., et al. (2016). Closing the gap between formal and material health care coverage in colombia. Health and Human Rights, 18 (2), 49.
dc.source.bibliographicCitationGreenwood, B. N., & Agarwal, R. (2015). Matching platforms and hiv incidence: An empirical investigation of race, gender, and socioeconomic status. Management Science, 62 (8), 2281-2303.
dc.source.bibliographicCitationGronqvist, H. (2013). Youth unemployment and crime: Lessons from longitudinal population records. Swedish Institute for Social Research, mimeo.
dc.source.bibliographicCitationHall, J. V., & Krueger, A. B. (2018). An analysis of the labor market for uber's driver-partners in the united states. ILR Review, 71 (3), 705-732.
dc.source.bibliographicCitationHipp, J. R., & Kim, Y.-A. (2019). Explaining the temporal and spatial dimensions of robbery: Differences across measures of the physical and social environment. Journal of Criminal Justice, 60 , 1-12.
dc.source.bibliographicCitationHorton, J. J., & Zeckhauser, R. J. (2016). Owning, using and renting: some simple economics of the" sharing economy" (Tech. Rep.). National Bureau of Economic Research.
dc.source.bibliographicCitationJohnson, S. D., & Summers, L. (2015). Testing ecological theories of offender spatial decision making using a discrete choice model. Crime & Delinquency, 61 (3), 454-480.
dc.source.bibliographicCitationKhanna, G., Medina, C., Nyshadham, A., Tamayo, J., et al. (2018). Formal employment and organized crime: Regression discontinuity evidence from colombia. Borradores de Economía; No. 1054 .
dc.source.bibliographicCitationKling, J. R. (2006). Incarceration length, employment, and earnings. American Economic Review, 96 (3), 863-876.
dc.source.bibliographicCitationKoster, H. R., van Ommeren, J., & Volkhausen, N. (2018). Short-term rentals and the housing market: Quasi-experimental evidence from airbnb in los angeles. CEPR Discussion Papers 13094, C.E.P.R. Discussion Papers.
dc.source.bibliographicCitationKroft, K., & Pope, D. G. (2014). Does online search crowd out traditional search and improve matching efficiency? evidence from craigslist. Journal of Labor Economics, 32 (2), 259-303.
dc.source.bibliographicCitationLammers, M. (2017). Co-offenders crime location choice: Do co-offending groups commit crimes in their shared awareness space? The British Journal of Criminology, 58 (5), 1193-1211.
dc.source.bibliographicCitationLammers, M., Menting, B., Ruiter, S., & Bernasco, W. (2015). Biting once, twice: The influence of prior on subsequent crime location choice. Criminology, 53 (3), 309-329.
dc.source.bibliographicCitationLi, Z., Hong, Y., & Zhang, Z. (2018). An empirical analysis of the impacts of the sharing economy platforms on the us labor market. In Proceedings of the 51st hawaii international conference on system sciences.
dc.source.bibliographicCitationLuca, M. (2017). Designing online marketplaces: Trust and reputation mechanisms. Innovation Policy and the Economy, 17 (1), 77-93.
dc.source.bibliographicCitationMaguire, M., & Bennett, T. (1982). Burglary in a dwelling: The offence, the offender, and the victim. Heinemann London.
dc.source.bibliographicCitationPinotti, P. (2015). The economic costs of organised crime: Evidence from southern italy. The Economic Journal, 125 (586), F203-F232.
dc.source.bibliographicCitationRengert, G. F., & Wasilchick, J. (1985). Suburban burglary: A time and a place for everything. CC Thomas Spring eld, IL.
dc.source.bibliographicCitationReppetto, T. A. (1974). Residential crime. Ballinger Publishing Company Cambridge, MA.
dc.source.bibliographicCitationSeamans, R., & Zhu, F. (2013). Responses to entry in multi-sided markets: The impact of craigslist on local newspapers. Management Science, 60 (2), 476-493.
dc.source.bibliographicCitationSundararajan, A. (2014). Peer-to-peer businesses and the sharing (collaborative) economy: Overview, economic effects and regulatory issues. Written testimony for the hearing titled The Power of Connection: Peer to Peer Businesses.
dc.source.bibliographicCitationWalsh, D. (2017). Victim selection procedures among economic criminals: The rational choice perspective. In The reasoning criminal (pp. 39-52). Routledge.
dc.source.bibliographicCitationWu, L., & Brynjolfsson, E. (2015). The future of prediction: How google searches foreshadow housing prices and sales. In Economic analysis of the digital economy (pp. 89-118). University of Chicago Press.
dc.source.bibliographicCitationZervas, G., Proserpio, D., & Byers, J. W. (2017). The rise of the sharing economy: Estimating the impact of airbnb on the hotel industry. Journal of marketing research, 54 (5), 687-705.
dc.rights.licenciaEL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma. PARGRAFO: En caso de presentarse cualquier reclamación o acción por parte de un tercero en cuanto a los derechos de autor sobre la obra en cuestión, EL AUTOR, asumirá toda la responsabilidad, y saldrá en defensa de los derechos aquí autorizados; para todos los efectos la universidad actúa como un tercero de buena fe. EL AUTOR, autoriza a LA UNIVERSIDAD DEL ROSARIO, para que en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, Decisión andina 351 de 1993, Decreto 460 de 1995 y demás normas generales sobre la materia, utilice y use la obra objeto de la presente autorización. -------------------------------------- POLITICA DE TRATAMIENTO DE DATOS PERSONALES. Declaro que autorizo previa y de forma informada el tratamiento de mis datos personales por parte de LA UNIVERSIDAD DEL ROSARIO para fines académicos y en aplicación de convenios con terceros o servicios conexos con actividades propias de la academia, con estricto cumplimiento de los principios de ley. Para el correcto ejercicio de mi derecho de habeas data cuento con la cuenta de correo, donde previa identificación podré solicitar la consulta, corrección y supresión de mis datos.
dc.creator.degreetypeFull time

Files in this item


This item appears in the following Collection(s)

Show simple item record