Research Experience at the Vancouver School of Economics

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                                                                                                                                                                                          05/2025 - Now  

Lead researcher: Dr. Gallipoli, Giovanni

• Collected and organized historical versions of the O*NET and predecessor datasets; compiled release years and matched them to corresponding survey questions in spreadsheet for comparative analysis
• Identified and documented exact survey questions in previous literature aligning with the research topic; created a detailed reference sheet including question identifiers, sources, and dataset metadata
• Supported literature review and assisted in proofreading the paper draft

                                                                                                                                                                                      05/2024 - 07/2024

Leading researcher: Dr. Juhász, Réka

• Collaborated with the Industrial Policy Research Group on data entry validation and coded East Asian Industrial Policy according to the existing government policy records
• Processed and analyzed international trade patterns across 10 East Asian countries (1870-2013) covering 120+ industry categories

                                                                                                                                                                                      02/2024 - 05/2024

Lead researcher: Dr. Graves, Jonathan

• Implemented instrument variable analysis and complied the guidebook in R programming and designed the R training packet for second-year economics major students
• Further evaluated the validity of using instrumental variables in causal inferences using confidential Canadian data and address its applicability in the undergraduate research

                                                                                                                                                                                      02/2024 - 05/2024

Lead researcher: Dr. Li, Hao

• Evaluated the preliminary benchmark analysis for the team in the context of how the sequence of penalty kickers results in the penalty outcome investigating the pressure and non-pressure games
• Complemented the quantitative modelling to replicate the first stage game focusing on the rule of penalty kicks

Individual expenditures reflect economic well-being and highlight socioeconomic disparities. This study identifies and quantifies these disparities using OLS and FE models, examining the effects of seven variables: age, health, life satisfaction, number of children, education, region, and GDP per capita. We found that regional disparities in expenditures are significant, with life satisfaction, number of children, and GDP per capita positively influencing expenditures across regions. Besides, age negatively impacts expenditures with a diminishing effect with age. Education is the strongest predictor after adjusting for heteroskedasticity and autocorrelation, with a 21.4% increase in expenditure linked to higher education levels.  

Link

This study investigates the relationship between economic development and fertility in China under the implementation of the Two-child Policy, using provincial-level data from 2006 to 2019. Economic development is measured through indicators including nominal GDP per capita, average wage, disposable income per capita, unemployment rate, and inflation rate. The results indicate that nominal GDP has a statistically significant positive effect on the birth rate, while disposable income and inflation rate show significant negative effects. No significant impact of the Two-child Policy on birth rates was identified.

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This study investigates whether white wine quality, which is rated on a 9-point scale, can be reliably predicted using a K-Nearest Neighbors (KNN) classification approach. Five chemical attributes (volatile acidity, chlorides, density, pH level, and sulphates) were selected through exploratory data analysis for their relevance to perceived wine quality. The project evaluates the effectiveness of KNN in modeling sensory quality from measurable chemical traits, offering insight into the potential for data-driven quality assessment in enology.

Link