His research focuses on the intersection of data science for social good, causal inference, development economics, and the use of new methods of data collection. Niall has over 10 years of experience conducting randomized evaluations and primary data collection in developing countries. During the course of his career, Niall has worked on research with financial institutions, development organizations, and online tech firms.
Other Professional Experience:
Service: IRB Board Member; Peer Review (Word Bank Economic Review, Journal of Development Economics, Journal of African Economies, National Science Foundation (NSF), IEEE/ACM Conference on Information and Communication Technologies and Development (ICTD), ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Social Informatics)
Over the past two decades, sports programs have proliferated as a mode of engaging youth in development projects. Thousands of organizations, millions of participants, and hundreds of millions of dollars are invested in sports-based development programs each year. The underlying belief that sports promote socioemotional skills, improve psychological well-being, and foster traits that boost labor force productivity has provided motivation to expand funding and offerings of sport for development (SFD) programs. We partnered with an international NGO to randomly assign 1200 young adults to a sports and life skills development program. While we do not see evidence of improved psychosocial outcomes or resilience, we do find evidence that the program caused a 0.12 standard deviation increase in labor force participation. Secondary analysis suggests that the effects are strongest among those likely to be most disadvantaged in the labor market.
We use a field experiment to show that referral-based hiring has the potential to disadvantage qualified women, highlighting another potential channel behind gender disparities in the labor market. Through a recruitment drive for a firm in Malawi, we look at men's and women's referral choices under different incentives and constraints. We find that men systematically refer few women, despite being able to refer qualified women when explicitly asked for female candidates. Performance pay also did not alter men's tendencies to refer men. In addition, women did not refer enough high-quality women to offset men's behavior.
Many critical policy decisions depend upon reliable and up-to-date information on market prices. Such data are used to construct consumer price indices, measure inflation, detect food insecurity, and influence macroeconomic policy. In developing countries, where many of these problems are most acute, reliable market price information can be hard to come by. Here, we evaluate data from Premise, a new technology for measuring price information using crowd-sourced data contributed by local citizens. Our evaluation focuses on Liberia, a country with a history of economic and political instability. Using data from Premise, which recently began data collection in Liberia, we analyze tens of thousands of individual price observations collected at hundreds of different locations in Monrovia. We illustrate how these data can be used to construct composite market price indices, and compare these constructed indices and prices for individual products to “ground truth” data from the Central Bank of Liberia and the United Nations World Food Programme. Our results indicate that the crowd-sourced price data correlates well with traditional price indices. However, we find statistically and economically significant deviations from traditional measures that require deeper investigation. We conclude by discussing how indices based on Premise data can be further improved with simple supervised learning methods that use traditional low-frequency data to calibrate and cross-validate the high-frequency Premise-based indices.