Víctor Sancibrián

Often, datasets cover much of the population under study — think of censuses of firms or workers, or state-level panels. Yet, standard practice remains to treat the sample as drawn from a hypothetical superpopulation, and classical finite-population adjustments are of limited use since they rule out unobserved heterogeneity. In this paper, I study settings where interest is in population averages over a latent characteristic, and the data only provides noisy, repeated measurements. I show that conventional standard errors are generally too large, and propose Finite Population Corrections (FPCs) that guarantee non-conservative inference. FPCs are very simple to implement via covariance restrictions. I apply these to (i) predicting lethal police encounters using data from all U.S. police departments and (ii) studying labor misallocation from a census of Indonesian firms. FPCs yield standard errors that properly combine uncertainty from measurement and from sampling — and lead to confidence intervals that are up to 50% shorter in these applications.

We study panel data regression models when the shocks of interest are aggregate and there are omitted macro and micro-level shocks of any relative size. This speaks to a large empirical literature that targets impulse responses via panel local projections. We show how to interpret the estimated coefficients when responses are heterogeneous and that a simple recipe leads to uniformly valid inference over the macro–micro composition of the errors: including lags as controls and then clustering at the time level. Finally, we use our methods to reassess the role of firm financial frictions in shaping the transmission of monetary policy.

We develop a methodology for modeling perceived household income processes when subjective probabilistic assessments of future income are available. This allows us to flexibly estimate conditional cdfs directly using elicited individual subjective probabilities, and to obtain empirical measurements of subjective risk and subjective persistence. We then use two longitudinal surveys collected in rural India and rural Colombia to explore the nature of perceived income dynamics in those contexts. Our results suggest linear income processes are rejected in favor of more flexible versions in both cases; subjective income distributions feature heteroskedasticity, conditional skewness and nonlinear persistence.

For a long time, the majority of economists doing empirical work relied on choice data, while data based on answers to hypothetical questions, stated preferences or measures of subjective beliefs were met with some skepticism. Although this has changed recently, much work needs to be done. In this paper, we emphasize the identifying content of new economic measures. In the first part of the paper, we discuss where the literature on measures in economics stands at the moment. We first consider how the design and use of new measures can help identify causal links and structural parameters under weaker assumptions than those required by approaches based exclusively on choice data. We then discuss how the availability of new measures can allow the study of richer models of human behavior that incorporate a wide set of factors. In the second part of the paper, we illustrate these issues with an application to the study of risk sharing and of deviations from perfect risk sharing.