Hi! I’m Dylan Balla-Elliott;

pdf | arXiv

Balla-Elliott, Dylan (2025). "Identifying Causal Effects in Information Provision Experiments." arXiv:2309.11387
Revise and Resubmit, ReStat

Abstract

Information treatments often shift beliefs more for people with weak belief effects. Since standard TSLS and panel specifications in information provision experiments have weights proportional to belief updating in the first-stage, this dependence attenuates existing estimates. This is natural if people whose decisions depend on their beliefs gather information before the experiment. I propose a local least squares estimator that identifies unweighted average effects in several classes of experiments under progressively stronger versions of Bayesian updating. In five of six recent studies, average effects are larger than–in several cases more than double–estimates in standard specifications.

Code [Under Construction]

An R package is under construction. Please email me for example code in R and Stata in the meantime!

pdf

Balla-Elliott, Dylan, Isaac Norwich (2025). "Cohort‑Chained DiD: Long‑Run Effects with Limited Pre‑Treatment Data". Working Paper.

Abstract

Heterogeneity robust difference-in-differences methods typically require control units that remain untreated throughout the entire post-treatment window. This prevents the identification of long-run effects when researchers observe fewer pre-treatment periods than post-treatment periods. We show that cohort-stacked estimators identify long-run effects by chaining together successive not-yet-treated controls. This approach uses overlapping cohorts to extend identification under standard common trends assumptions. We apply this to the earnings effects of parenthood. In a setting where direct methods identify effects only four years post-birth, chaining extends identification to eight years.

pdf | JPAM (ungated)

Balla-Elliott, Dylan, Zoë B. Cullen, Edward L. Glaeser, Michael Luca, and Christopher Stanton (2022). "Determinants of Small Business Reopening Decisions After Covid Restrictions Were Lifted". Journal of Policy Analysis and Management 41.1, pp. 278–317. DOI: 10.1002/pam.22355

Abstract

The COVID-19 pandemic led to dramatic economic disruptions, including government-imposed restrictions that temporarily shuttered millions of American businesses. We use a nationwide survey of thousands of small business owners to establish three main facts about business owners’ decisions to reopen at the end of the lockdowns. First, roughly 60 percent of firms planned to reopen within days of the end of legal restrictions, suggesting that the lockdowns were generally binding for businesses—although nearly 30 percent expected to delay their reopening by at least a month. Second, decisions to delay reopenings did not seem to be driven by concerns about employee or customer health; even businesses in high-proximity sectors with the highest health risks generally reported intentions to reopen as soon as regulations allowed. Third, pessimistic demand projections primarily explain delays among firms that could legally reopen. Owners expected demand to be one-third lower than before the crisis throughout the pandemic. Using experimentally induced shocks to perceived demand, we find that a 10 percent decline in expected demand results in a 1.5 percentage point (8 percent) increase in the likelihood that firms expected to remain closed for at least one month after being legally able to open. We use follow-up surveys to cross-validate expectations with realized outcomes. Overall, our results suggest that governments set more stringent guidelines for reopening than what many businesses would have selected, suggesting that governments may have internalized costs of contagion that businesses did not.