Nudges, Identity and Covid-19
Welcome to Plugging the Gap (my email newsletter about Covid-19 and its economics). In case you don’t know me, I’m an economist and professor at the University of Toronto. I have written lots of books including, most recently, on Covid-19. You can follow me on Twitter (@joshgans) or subscribe to this email newsletter here. (I am also part of the CDL Rapid Screening Consortium. The views expressed here are my own and should not be taken as representing organisations I work for.)
While I keep refreshing statistics from India, I haven’t been writing new newsletters. I wanted to highlight just a couple of things today that were of interest recently.
Vaccine Nudges
An impressive study was released that conducted a randomised control trial to examine various interventions to improve vaccination rates. The study didn’t look at the Covid vaccines but the vaccine regimen we routinely give younger children, so there are some differences in how these results might translate — both negative (the Covid vaccine was developed quickly) and ‘positive’ (with Covid raging in India, the incentives to be vaccinated are higher). Nonetheless, here is the abstract:
We evaluate a large-scale set of interventions to increase demand for immunization in Haryana, India. The policies under consideration include the two most frequently discussed tools—reminders and incentives—as well as an intervention inspired by the networks literature. We cross-randomize whether (a) individuals receive SMS reminders about upcoming vaccination drives; (b) individuals receive incentives for vaccinating their children; (c) influential individuals (information hubs, trusted individuals, or both) are asked to act as “ambassadors” receiving regular reminders to spread the word about immunization in their community. By taking into account different versions (or “dosages”) of each intervention, we obtain 75 unique policy combinations. We develop a new statistical technique—a smart pooling and pruning procedure—for finding a best policy from a large set, which also determines which policies are effective and the effect of the best policy. We proceed in two steps. First, we use a LASSO technique to collapse the data: we pool dosages of the same treatment if the data cannot reject that they had the same impact, and prune policies deemed ineffective. Second, using the remaining (pooled) policies, we estimate the effect of the best policy, accounting for the winner’s curse. The key outcomes are (i) the number of measles immunizations and (ii) the number of immunizations per dollar spent. The policy that has the largest impact (information hubs, SMS reminders, incentives that increase with each immunization) increases the number of immunizations by 44 % relative to the status quo. The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1%.
These are the types of studies we need to conduct now and in the future so that we have information on, say, “how does pausing a vaccine impact on short and longer-term vaccine hesitancy?”
Data, identity and pandemic information
I was part of a panel recently on “The Perils and Promise of Big Data.” I had to pre-record my initial comments and I decided to focus on Covid-19 and the importance of being able to identify people who, say, are being tested so we can work out if they are being regularly tested — a key goal if we want to make workplaces safer. The video is below.