A machine learning approach to find the determinants of peruvian ccocaine local price

Descripción del Articulo

The coca leaf has many uses in the Peruvian culture. Although there are legal usages, people employ coca for illicit business. The most infamous illegal use is cocaine production. The cocaine business is highly profitable, but it harms human health. Then, what are the determinants of cocaine price?...

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Detalles Bibliográficos
Autores: Huaman Ivala, Yulisa Margoth, Flores Guillen, Alexander, Perez Evangelista, Elizabeth Yadhira
Formato: tesis de grado
Fecha de Publicación:2022
Institución:Universidad Continental
Repositorio:CONTINENTAL-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.continental.edu.pe:20.500.12394/12266
Enlace del recurso:https://hdl.handle.net/20.500.12394/12266
https://doi.org/10.5267/j.ijdns.2021.11.009
Nivel de acceso:acceso abierto
Materia:Cultivos ilegales de coca
https://purl.org/pe-repo/ocde/ford#2.02.04
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dc.title.es_ES.fl_str_mv A machine learning approach to find the determinants of peruvian ccocaine local price
title A machine learning approach to find the determinants of peruvian ccocaine local price
spellingShingle A machine learning approach to find the determinants of peruvian ccocaine local price
Huaman Ivala, Yulisa Margoth
Cultivos ilegales de coca
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short A machine learning approach to find the determinants of peruvian ccocaine local price
title_full A machine learning approach to find the determinants of peruvian ccocaine local price
title_fullStr A machine learning approach to find the determinants of peruvian ccocaine local price
title_full_unstemmed A machine learning approach to find the determinants of peruvian ccocaine local price
title_sort A machine learning approach to find the determinants of peruvian ccocaine local price
author Huaman Ivala, Yulisa Margoth
author_facet Huaman Ivala, Yulisa Margoth
Flores Guillen, Alexander
Perez Evangelista, Elizabeth Yadhira
author_role author
author2 Flores Guillen, Alexander
Perez Evangelista, Elizabeth Yadhira
author2_role author
author
dc.contributor.advisor.fl_str_mv Ruiz Parejas, Rubén Ángel
dc.contributor.author.fl_str_mv Huaman Ivala, Yulisa Margoth
Flores Guillen, Alexander
Perez Evangelista, Elizabeth Yadhira
dc.subject.es_ES.fl_str_mv Cultivos ilegales de coca
topic Cultivos ilegales de coca
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_ES.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description The coca leaf has many uses in the Peruvian culture. Although there are legal usages, people employ coca for illicit business. The most infamous illegal use is cocaine production. The cocaine business is highly profitable, but it harms human health. Then, what are the determinants of cocaine price? The current analysis aims to get the variables with the capability to explain the cocaine prices in Peru. The period analyzed is 2003-2019. The study gathered variables from DEVIDA and UNDOC databases. The Lasso technique selected the variables with the best capability to predict cocaine price. Those variables were: ENACO acquisition, coca seizures, and coca crops. OLS, VAR, and Granger analyses employed those variables to analyze the relationship between them. According to the OLS analysis, both ENACO acquisition and coca crops had adverse effects on cocaine prices, while coca seizures were positively related to the cocaine price. VAR analysis showed that only ENACO acquisition had a short-term relationship with the dependent variable. Moreover, it showed that the whole set of variables influenced the dependent variable. The Granger analysis proved that there was a cause-effect relationship between ENACO acquisition and cocaine price. Hence, the ENACO purchases expansion can rest the attractiveness of illegal groups to farmers. However, low- ering cocaine prices might attract more users. Therefore, educational activities are also required.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2023-01-13T16:54:12Z
dc.date.available.none.fl_str_mv 2023-01-13T16:54:12Z
dc.date.issued.fl_str_mv 2022
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dc.identifier.citation.es_ES.fl_str_mv Huaman, Y. Flores, A. y Perez, E. (2022). A machine learning approach to find the determinants of peruvian ccocaine local price. Tesis para optar el título profesional de Ingeniero Industrial . Escuela Académica Profesional de Ingeniería. Universidad Continental. Huancayo. Perú.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12394/12266
dc.identifier.doi.es_ES.fl_str_mv https://doi.org/10.5267/j.ijdns.2021.11.009
identifier_str_mv Huaman, Y. Flores, A. y Perez, E. (2022). A machine learning approach to find the determinants of peruvian ccocaine local price. Tesis para optar el título profesional de Ingeniero Industrial . Escuela Académica Profesional de Ingeniería. Universidad Continental. Huancayo. Perú.
url https://hdl.handle.net/20.500.12394/12266
https://doi.org/10.5267/j.ijdns.2021.11.009
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spelling Ruiz Parejas, Rubén ÁngelHuaman Ivala, Yulisa MargothFlores Guillen, AlexanderPerez Evangelista, Elizabeth Yadhira2023-01-13T16:54:12Z2023-01-13T16:54:12Z2022Huaman, Y. Flores, A. y Perez, E. (2022). A machine learning approach to find the determinants of peruvian ccocaine local price. Tesis para optar el título profesional de Ingeniero Industrial . Escuela Académica Profesional de Ingeniería. Universidad Continental. Huancayo. Perú.https://hdl.handle.net/20.500.12394/12266https://doi.org/10.5267/j.ijdns.2021.11.009The coca leaf has many uses in the Peruvian culture. Although there are legal usages, people employ coca for illicit business. The most infamous illegal use is cocaine production. The cocaine business is highly profitable, but it harms human health. Then, what are the determinants of cocaine price? The current analysis aims to get the variables with the capability to explain the cocaine prices in Peru. The period analyzed is 2003-2019. The study gathered variables from DEVIDA and UNDOC databases. The Lasso technique selected the variables with the best capability to predict cocaine price. Those variables were: ENACO acquisition, coca seizures, and coca crops. OLS, VAR, and Granger analyses employed those variables to analyze the relationship between them. According to the OLS analysis, both ENACO acquisition and coca crops had adverse effects on cocaine prices, while coca seizures were positively related to the cocaine price. VAR analysis showed that only ENACO acquisition had a short-term relationship with the dependent variable. Moreover, it showed that the whole set of variables influenced the dependent variable. The Granger analysis proved that there was a cause-effect relationship between ENACO acquisition and cocaine price. Hence, the ENACO purchases expansion can rest the attractiveness of illegal groups to farmers. However, low- ering cocaine prices might attract more users. 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