Francis Wilson Memorial Prize for Data-Driven Research 2024


We are pleased to announce the winners of the Francis Wilson Memorial Prize!

Winners of the 2024 Francis Wilson Memorial Prize for Data-Driven Research
Left to right: Prof Reza Daniels (SALDRU Director), Tim Köhler, Lindy Wilson,
Emma Whitelaw, Prof Cally Ardington (DataFirst Director) and Prof David Lam.
Photo: Nasief Manie.

Dissertation Prize – Awarded to Emma Whitelaw, PhD (UCT). Advisor, Assoc Prof Nicola Branson. The prize is awarded to Emma for her Chapter “Studying to support? Exploring graduates’ responsibilities to remit”

This chapter makes innovative use of the NIDS Wave 5 dataset to investigate new questions about interhousehold transfers – remittances – in South Africa. A large amount of literature looks at motivations to remit, and the impacts of remittances in countries worldwide. Francis Wilson’s work on South African mines highlighted, among other things, the role of remittances from mineworkers in sustaining households in rural areas. More recent and pioneering work by Dori Posel explored the role of remittances between South African households using SALDRU data. Emma builds on this tradition, by asking:

•   Do households pool income, or do graduates, who have access to higher returns in the labour market, use their income differently in making remittance decisions?
•   Are graduates more likely to remit to other households than non-graduates? And, 
•   Do graduates remit larger amounts to other households, relative to non-graduates?

She asks these questions against the backdrop of conversations around the “Black Tax” in South Africa (sometimes called the “Family Tax” in other settings). Do black graduates have to or choose to share more of their labour market returns with other households than non-graduates?  She finds that graduates are much more likely to remit incomes across households; however, they are not more likely to send larger amounts of money.
The adjudicators liked this paper because it demonstrated the creative and careful use of the NIDS data to explore remittances – an important topic in development economics and South African public policy discourse more generally. Emma’s new angle on remittances and how graduates are more likely to share their incomes post-graduation provides important input into discussions around the pros and cons of a graduate tax.


Article Prize – Awarded to Tim Köhler, PhD (UCT) and co-authors for “Lockdown stringency and employment formality: evidence from the COVID-19 pandemic in South Africa” (Köhler, Bhorat, Hill, Stanwix) published in the Journal for Labour Market Research (2023)

This paper uses publicly available QLFS data from Q1 and Q2 of 2020 to investigate how lockdown stringency affected employment outcomes in South Africa, during this very short window at the beginning of COVID. The authors construct a balanced panel dataset from the two waves of the QLFS data and follow individuals over time from before to after the onset of COVID-19. They compare job loss for this set of individuals, across industries where lockdown was strict (e.g. non-essential services) and industries where lockdown was more lenient (e.g. essential services). Perhaps not surprisingly, they find that the stricter the lockdown measures implemented for specific industries, the larger the employment losses (from before to after the start of COVID). A key finding though, is that the large job losses in strict lockdown industries seem to be driven by informal sector work, where employment protections are absent. Differential job loss by lockdown stringency of the industry is not detected for formal sector jobs.

The adjudicators liked how this paper posed a clear research question and applied the difference-in-differences research design to answer this question. We also appreciated the creative use of the panel component of the QLFS data, acknowledging that researchers worldwide struggled with getting any high-frequency data through the early COVID period. The data are not perfect, but we think striving to learn what we can from imperfect data is worth doing and doing well.

Thank you to our panel of adjudicators, Assoc Prof Taryn Dinkelman, Prof David Lam and Assoc Prof Susan Godlonton.  Warm congratulations to Emma and Tim!


See last year's winners.

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