Shen Yan Shun, Lucas

Empirical Economist


Currently a PhD candidate in Economics at the Nanyang Technological University. I use machine learning and natural language processing within the standard applied econometric analyses. I use Stata for standard applied econometrics and Python for everything else.

Working papers

Measuring political media slant using textual data: Evidence from Singapore

[Draft, Data appendix]

This paper explores a novel and objective measure of political media slant using the aid of natural language processing and machine learning. Implementing this approach in the institutional context of Singapore, the paper finds that The Straits Times, the flagship daily, is less accurate when quoting the parliamentary speeches of opposition politicians.

The promise of board gender diversity?

In this paper, I exploit the institutional context of Singapore to estimate the effect of board gender diversity on firm performance using a sample from 2000–2017. To address endogeneity concerns, I instrument the number of women on board using the increase in women political participation and private firms' differential links to the government. The evidence suggest that having more women in the boardroom does not affect profitability, firm value, or systematic risk.

Women's wave or the blue wave: The Me Too movement and the 2018 U.S. midterm elections

In this paper I download all tweets containing the meToo hashtag in the year 2018 leading up to the midterm elections in November, and parse the geolocation tags of the tweeters. With the county level density measure of the tweets in 2018, I test whether the meToo movement can explain the historic performance of women candidates in the 2018 U.S. House elections.


I use Python for my research work, and have benefitted extensively from open-source libraries in the Python ecosystem. As a tiny contribution I wrote Leixcal richness, which I use to generate proxies for language sophistication in my research.