Chetty, Friedman, and Saez on detecting knowledge differences with observational data

Models usually implicitly assume that people are aware of the incentives they face. People in a labor supply model, for example, usually make their decisions based on the actual schedule, not their subjective impression of the schedule. But many people may not even be aware of changes in income taxes schedules: how can they then respond to changes in the schedule? In current research, Raj Chetty, John Friedman, and Emmanuel Saez turn this apparent difficulty into an advantage. They estimate the causal effect of changes in an aspect of the income tax schedule on labor supply. Since the policy they study is Federal, there is not much variation to identify the effects of interest using traditional methods, but the authors show how to recover regional variation in knowledge of the schedule. The interaction of knowledge and the schedule then varies across regions and time even though the schedule only varies over time, so there’s lots more variation to identify the effect of the schedule on labor supply.

“Using Differences in Knowledge Across Neighborhoods to Uncover the Impacts of the EITC on Earnings,” which seems to exist only in the form of slides as of yet, considers the issue of estimating the effect of the Earned Income Tax Credit (EITC) on labor supply. This is a difficult problem because the EITC does not vary regionally and has recently varied only gradually at the national level, so there’s little useful variation to measure its impacts. The central argument in the paper is this: people can only respond to the EITC if they know about it, and if they know about it they will tend to heap at the kink-point in the budget constraint where the EITC refund is greatest. We can read off the data the proportion of people who seem to be exploiting the EITC by region, and we can identify labor supply effects under the assumption that a region in which no one knows about the EITC behaves exactly like it would if the EITC didn’t exist. We then have, in effect, regional variation in the EITC, since the interaction of the knowledge measure and the EITC schedule varies at the regional level.

A couple of graphs illustrate how to detect information differences across regions.

The spike in the top graph shows that in Texas about 14% of self-employed income tax filers heap at the income where the EITC payment is greatest, whereas the bottom graph shows the proportion is much lower in Kansas. We infer that there is more knowledge of the EITC in Texas than there is in Kansas, so if labor supply schedules are identical, labor supply response will be greater in Texas than in Kansas. The study uses the universe of over a billion tax claims to estimate EITC effects using this inferred variation in knowledge.

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