Santiago Lacouture

We show how to use randomized participation incentives to test and account for nonresponse bias in surveys. We first use data from a survey about labour market conditions, linked to full-population administrative data, to provide evidence of large differences in labour market outcomes between survey participants and nonparticipants, differences which would not be observable to an analyst who only has access to the survey data. These differences persist even after correcting for observable characteristics. We then use the randomized incentives in our survey to directly test for nonresponse bias and find evidence of substantial bias. Next, we apply a range of existing methods that account for nonresponse bias and find they produce bounds (or point estimates) that are either wide or far from the ground truth. We investigate the failure of these methods by taking a closer look at the determinants of participation, finding that the composition of participants changes in opposite directions in response to incentives and reminder emails. We develop a model of participation that allows for two dimensions of unobserved heterogeneity in the participation decision. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods. Our results highlight the benefits of including randomized participation incentives in surveys. Both the testing procedure and the methods for bias adjustment may be attractive tools for researchers who are able to embed randomized incentives into their survey.

We analyze representativeness in a COVID-19 serological study with randomized participation incentives. We find large participation gaps by race and income when incentives are lower. High incentives increase participation rates for all groups but increase them more among under-represented groups. High incentives restore representativeness on race and income and also on health variables likely to be correlated with seropositivity, such as the uninsured rate, hospitalization rates, and an aggregate COVID-19 risk index.

We use data from a serological study that experimentally varied financial incentives for participation to detect and characterize selection bias. Participants are from neighborhoods with substantially lower COVID-19 risks. Existing methods to account for the resulting selection bias produce wide bounds or estimates that are inconsistent with the population. One explanation for these inconsistent estimates is that the underlying methods presume a single dimension of unobserved heterogeneity. The data suggest that there are two types of nonparticipants with opposing selection patterns. Allowing for these different types may lead to better accounting for selection bias.