Abstract

Our paper examines how population aging and demographic change affect the interest-growth differential (r − g). Using overlapping generations (OLG) models, we identify three theoretical channels: first, higher life expectancy raises the savings rate, lowering the interest rate (r) and raising output growth (g) in transition. Second, lower fertility increases capital per worker, reducing (r) and (g), and third a rise in the old-age dependency ratio (OADR) lowers the aggregate savings rate, raising (r − g).  Our empirical predictions broadly align with the theory, capturing both short-run effects through panel regressions and long-run steady-state effects through VARX regressions.

Keywords

population aging, OLG models, debt sustainability

Abstract

I study how women’s secondary and tertiary educational attainment relates to economic growth using fixed-effects panel data for 141 countries from 1965 to 2010. I find that female secondary education significantly increases GDP per capita growth in low-income countries, while female tertiary education is associated with higher growth in high-income economies. The results are consistent with a U-shaped relationship between development and women’s labor force participation, as well as with broader patterns of structural transformation,  highlighting how the growth returns to women’s education evolve as economies develop.

Keywords

economic development,  U-shape hypothesis,  comparative study

Summary

This research examines how trade liberalization affected women’s labor market mobility in Mauritius. Using a Ricardo–Viner framework, I examine whether education shaped women’s ability to adjust as employment shifted across sectors, drawing on sectoral employment data and microdata. The project situates Mauritius as a small open economy experiencing both the gains and adjustment costs of global trade integration, highlighting how education mediates gendered labor outcomes during structural transformation.

Keywords

trade liberalization, female labour mobility, Ricardo–Viner model

Summary

This project is a referee-style analytical report on Autor, Dorn, and Hanson (2013), which studies the local labor market effects of rising import competition from China in the United States. Our report evaluates the paper’s empirical strategy and identification assumptions, with particular attention to the use of commuting zones as local labor markets and the interpretation of the China import shock as an exogenous, supply-driven shock. It situates the analysis within the trade and labor literature, discussing implications for employment and labor force participation. We propose an extension examining how gender, education, and sectoral reallocation shape adjustment dynamics following trade shocks.

Summary

This project applies geospatial data methods to study the relationship between natural disasters and electoral outcomes. Combining wildfire perimeter data with electoral information, the analysis constructs spatial joins to measure local exposure to natural disasters and examines how this exposure correlates with voting behavior across regions. We use panel fixed effects to account for time-invariant local characteristics and broader political trends. Our project highlights how geographically uneven shocks can be analyzed using geospatial techniques and illustrates the value of these methods for studying localized shocks in applied economic research.

Summary

This PwC Middle East publication examines the experiences of women in the Middle East and North Africa (MENA) region returning to work after career breaks. The study draws on qualitative insights from focus groups and interviews, alongside survey evidence from approximately 1,200 women across the MENA region, to document barriers to workforce reintegration and identify practical considerations for employers.I contributed to the study through involvement in research design, survey development and data analysis, as well as participation in focus group discussions related to women’s labor force participation and re-entry challenges in the region.

Keywords

female labor force participation, MENA region, workforce reintegration

Summary

This project applies Natural Language Processing (NLP) techniques to analyze lyrical trends in 6,100 Billboard Top 100 songs released between 2000 and 2017, spanning six genres: Pop, Rap, R&B, Rock, Country, and EDM. The analysis uses sentiment scoring, term frequency, and topic modeling to study linguistic and emotional changes in popular music over time. Results show that lyrics have become increasingly negative since 2000, with 2017 recording the lowest sentiment scores. The study also finds that songs released after 2010 tend to begin with more positive language rather than centering positivity in the chorus, reflecting a shift in songwriting aimed at capturing listeners’ attention earlier.

Keywords

natural language processing, sentiment analysis, topic modeling