dc.contributor | Grupo de investigaciones. Facultad de Economía. Universidad del Rosario | |
dc.creator | Saavedra, Santiago | |
dc.date.accessioned | 2022-05-02T13:06:48Z | |
dc.date.accessioned | 2022-09-22T14:20:54Z | |
dc.date.available | 2022-05-02T13:06:48Z | |
dc.date.available | 2022-09-22T14:20:54Z | |
dc.date.created | 2022-05-02T13:06:48Z | |
dc.date.issued | 2022-04-29 | |
dc.identifier | https://repository.urosario.edu.co/handle/10336/34088 | |
dc.identifier | https://doi.org/10.48713/10336_34088_ | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3437964 | |
dc.description.abstract | New monitoring technologies can help curb illegal activities by reducing information asymmetries between enforcing and monitoring government agents. I created a novel dataset using machine learning predictions on satellite imagery that detects illegal mining. Then I disclosed the predictions to government agents to study the response of illegal activity. I randomly assigned municipalities to one of four groups: (1) information to the observer (local government) of potential mine locations in his jurisdiction; (2) information to the enforcer (National government) of potential mine locations; (3) information to both observer and enforcer, and (4) a control group, where I informed no one. The effect of information is relatively similar regardless of who is informed: in treated municipalities, illegal mining is reduced by 11% in the disclosed locations and surrounding areas. However, when accounting for negative spillovers --- increases in illegal mining in areas not targeted by the information --- the net reduction is only 7%. These results illustrate the benefits of new technologies for building state capacity and reducing illegal activity. | |
dc.language | eng | |
dc.publisher | Universidad del Rosario | |
dc.publisher | Facultad de Economía | |
dc.relation | https://ideas.repec.org/p/col/000092/020078.html | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | Abierto (Texto Completo) | |
dc.source | instname:Universidad del Rosario | |
dc.source | reponame:Repositorio Institucional EdocUR | |
dc.subject | Minería ilegal en Colombia | |
dc.subject | Tecnologías de monitoreo | |
dc.subject | Actividades ilegales | |
dc.subject | Predicciones de machine learning | |
dc.subject | Nuevas tecnologías para aumentar la capacidad estatal | |
dc.title | The response of illegal mining to revealing its existence | |
dc.type | workingPaper | |