The current study analyzed the determinants of the Peruvian coca illegal plantations in the period 2003-2019. Hence, the DEVIDA database variables were gathered at first. Then, a machine learning-based technique is employed to select the most relevant variables for the study. That technique, Lasso, selected as accurate variables eradication of coca plantations and pasta base. Both OLS and VAR are employed to analyze the relevance of the selected variables. OLS finds that eradication was negatively related to the dependent variable. Nonetheless, pb confiscation had a positive relationship with illegal coca crops. Furthermore, VAR encounters that only pb confiscation affected the dependent variable. Supplementary tests are carried to ensure the accuracy of the results. In consequence, it is concluded that eradication policies by themselves were not enough to discourage the coca plantations. Farmers should get instruction about alternative crops and financial help. Furthermore, it has been claimed that pb confiscation generates scarcity of the drug, which elevates its price. Thus, coca farmers are more motivated to plant coca because of the higher prices. Therefore, as long as the international demand, which is disposed to pay high prices, the coca illegal crops and its illicit products will exist.

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, lowering cocaine prices might attract more users. Therefore, educational activities are also required.

Investors’ decision-making are influenced by certain biases as reported in literature. Fundamental analysis is based on the assumption that investors think rationally, but in practice, things may be different. This study captures the impact of herding behavior and overconfidence biases on the investors’ decision-making in Pakistan. The proposed study collects the necessary data through questionnaires distributed among 150 respondents who were active in stock market and manage to process 100 completed ones. The relationships between investors’ decision-making and herding behavior as well as overconfidence biases were empirically tested using Ordinary Least Square (OLS) method. The results show that Pakistani investors’ decisions were significantly influenced by both herding behavior and overconfidence biases.