Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is to allow individuals to query statistics without directly accessing the confidential data. This article studies the feasibility of using differentially private (DP) methods to make certain queries while preserving privacy. We also include new methodological adaptations to existing DP regression methods for using new data types and returning standard error estimates. We define feasibility as the impact of DP methods on analyses for making public policy decisions and the queries accuracy according to several utility metrics. We evaluate the methods using Internal Revenue Service data and public-use Current Population Survey data and identify how specific data features might challenge some of these methods. Our findings show that DP methods are feasible for simple, univariate statistics but struggle to produce accurate regression estimates and confidence intervals. To the best of our knowledge, this is the first comprehensive statistical study of DP regression methodology on real, complex datasets, and the findings have significant implications for the direction of a growing research field and public policy. Supplementary materials for this article are available online.

Payoff-biased cultural learning has been extensively discussed in the literature on cultural evolution, but where do payoff currencies come from in the first place? Are they products of genetic or cultural evolution? Here we present a simulation model to explore the possibility of novel payoff currencies emerging through a process of theory construction, where agents come up with “channels” via which different cultural traits contribute to some ultimate payoff and use such “channels” as intermediate payoff currencies to make trait-updating decisions. We show that theory-building as a strategy is mostly favored when the noise associated with the ultimate-level payoff is high, selective pressures are strong, and the probability of arriving at the right theory is high. This approach provides insights into both the emergence of payoff currencies and the role of cognition for causal model building. We close by discussing the implications of our model for the broader question of causal learning in social contexts.