My thesis explored how retail Central Bank Digital Currencies (CBDCs) could address the financial inclusion gap in Latin America and the Caribbean (LAC) by evaluating four key claims: their ability to overcome accessibility barriers, promote efficiency and innovation in payment systems, improve cross-border transactions, and modernize financial regulation.

To conduct this analysis, I combined multiple datasets on fin. inclusion from the WB & the IMF, datasets from five Central Banks, and other sources to create a comprehensive view of the financial landscape in LAC.

I used R to merge and normalize databases, as well as for descriptive analytics, summarizing key metrics related to financial inclusion and employed multivariate regression techniques to assess the relationship between CBDC implementation and various measures of financial access.

https://docs.google.com/presentation/d/1a5YjZ6cSKeKx_3ww_E6R6E0BH8phr48KBQu-UQV5fKI/edit?usp=sharing

For my Master’s Thesis in Economics, I estimated the economic complexity of the Province of Buens Aires. The Economic Complexity Index (ECI) assesses the capabilities of an economy based on the diversity and sophistication of its productive activities. The ECI is calculated through an iterative method that begins with initial values reflecting a country's diversification and the ubiquity of its products, measured as z-scores. The complexity of a location is derived from the average complexity of the activities it hosts, while the complexity of a product is based on the average complexity of the locations producing it. This relationship is formalized in equations that connect the complexities of locations and activities, allowing for a comprehensive understanding of economic capacity across different regions

Description goes This study explores the impact of corruption and bureaucracy on innovation, particularly focusing on how procurement processes, influenced by corruption, affect firms' incentives for innovation. Corruption in procurement, including bribery and favoritism, undermines competition and shifts priorities away from research and development, leading to technological stagnation. Conversely, effective procurement policies, such as transparent bidding and e-procurement systems, can reduce corruption risks, encourage competition, and stimulate innovation. Given the importance of these dynamics, the study seeks to inform policy decisions aimed at mitigating corruption and fostering innovation-driven economies.

Using the 2024 World Bank Enterprise Surveys, this research applies various regression models and machine learning algorithms to analyze the relationship between corruption, procurement regulations, and innovation. XGBoost and Gradient Boosting were employed to predict the effects of corruption on innovation but demonstrated limited explanatory power. Hyperparameter tuning of the Random Forest model yielded weak results, with an R² of -0.16 and an RMSE of 14.05, indicating that the model struggled to effectively capture innovation dynamics. A Linear Mixed Model, accounting for country-level effects, found that most corruption-related predictors were statistically insignificant, with minimal effects from specific factors like gifts required for operating licenses.

The study concludes that bureaucracy and corruption alone do not strongly predict innovation across countries. The findings suggest that factors such as regulatory quality, market competition, and R&D incentives may play more significant roles in fostering innovation. Additionally, the analysis indicates that national context, reflected in group variance, is a key consideration in shaping innovation outcomes. These results highlight the complexity of innovation dynamics and the need for future research to incorporate a wider range of contextual factors beyond corruption and bureaucracy.here

https://colab.research.google.com/drive/1rxgXf6zrGMsfFxgGJevnojlWntla08c2#scrollTo=L-cUb0ZVE1qB