Analysis of Spillover Effects in Randomized Experiments

This dissertation studies identification, estimation, inference and experimental design for analyzing causal spillover effects in randomized experiments. Chapter II provides a nonparametric framework based on potential outcomes to define spillover effects in a setting in which units are clustered and their potential outcomes can depend on the treatment assignments of all units within a group. Using this framework, I provide conditions for identification of average direct and spillover effects when the treatment is randomly assigned. I then study identification under three estimation strategies that are commonly employed in empirical work: a regression of an outcome on a treatment indicator, which calculates a difference in means between treated and controls, a regression that controls for the proportion of treated peers, and a regression exploiting variation in treatment probabilities in two-stage designs. Chapter III analyzes estimation and inference for spillover effects. I start by illustrating the results from Chapter II using two empirical applications. I then study nonparametric estimation and inference for spillover effects in a setting in which both the number of groups and the group size are allowed to grow. This setting allows me to understand the effect of the number of parameters on the asymptotic properties of the proposed nonparametric estimators. Finally, I discuss the implications of these findings for the design of experiments. Chapter III discusses some key issues related to the empirical implementation of the results from the previous chapters: the inclusion of covariates, identification of spillover effects in experiments with imperfect compliance and optimal design of experiments.PHDEconomicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144008/1/gvazquez_1.pd

The Regression Discontinuity Design

This handbook chapter gives an introduction to the sharp regression discontinuity design, covering identification, estimation, inference, and falsification methods

Public Credit Programmes and Firm Performance in Brazil

Credit rationing is a common phenomenon faced by firms, one that has negative implications for longâ term investments. In Brazil, public credit plays a key role in supporting firms: stateâ owned banks account for almost half of the outstanding credit. Public credit programmes aim at reducing credit restrictions, increasing competitiveness and job creation for small and medium enterprises. This article analyzes the effectiveness of the credit lines managed by two main public institutions in Brazil. Results show that access to public credit lines has a significant positive impact on firmsâ employment growth and exports, while no effect was found on wage differential. The impact on exports is driven by the increase in volumes among exporting firms rather than the probability of becoming an exporter.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138401/1/dpr12250_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138401/2/dpr12250.pd

Power calculations for regression-discontinuity designs

In this article, we introduce two commands, rdpow and rdsampsi, that conduct power calculations and survey sample selection when using local polynomial estimation and inference methods in regression-discontinuity designs. rdpow conducts power calculations using modern robust bias-corrected local polynomial inference procedures and allows for new hypothetical sample sizes and bandwidth selections, among other features. rdsampsi uses power calculations to compute the minimum sample size required to achieve a desired level of power, given estimated or user-supplied bandwidths, biases, and variances. Together, these commands are useful when devising new experiments or surveys in regression-discontinuity designs, which will later be analyzed using modern local polynomial techniques for estimation, inference, and falsification. Because our commands use the communitycontributed (and R) package rdrobust for the underlying bandwidths, biases, and variances estimation, all the options currently available in rdrobust can also be used for power calculations and sample-size selection, including preintervention covariate adjustment, clustered sampling, and many bandwidth selectors. Finally, we also provide companion R functions with the same syntax and capabilities

Inference in regression discontinuity designs under local randomization

We introduce the rdlocrand package, which contains four commands to conduct finite-sample inference in regression discontinuity (RD) designs under a local randomization assumption, following the framework and methods proposed in Cattaneo, Frandsen, and Titiunik (2015, Journal of Causal Inference 3: 1–24) and Cattaneo, Titiunik, and Vazquez-Bare (2016, Working Paper, University of Michigan, http://www-personal.umich.edu/∼titiunik/papers/ CattaneoTitiunikVazquezBare2015 wp.pdf). Assuming a known assignment mechanism for units close to the RD cutoff, these functions implement a variety of procedures based on randomization inference techniques. First, the rdrandinf command uses randomization methods to conduct point estimation, hypothesis testing, and confidence interval estimation under different assumptions. Second, the rdwinselect command uses finite-sample methods to select a window near the cutoff where the assumption of randomized treatment assignment is most plausible. Third, the rdsensitivity command uses randomization techniques to conduct a sequence of hypothesis tests for different windows around the RD cutoff, which can be used to assess the sensitivity of the methods and to construct confidence intervals by inversion. Finally, the rdrbounds command implements Rosenbaum (2002, Observational Studies [Springer]) sensitivity bounds for the context of RD designs under local randomization. Companion R functions with the same syntax and capabilities are also provided

Design of two-stage experiments with an application to spillovers in tax compliance

We set up a framework to conduct experiments for estimating spillover effects when units are grouped into mutually exclusive clusters. We improve upon existing methods by allowing for heteroskedasticity, intra-cluster correlation and cluster size heterogeneity, which are typically ignored when designing experiments. We show that ignoring these factors can severely overestimate power and underestimate minimum detectable effects. We derive formulas for optimal group-level assignment probabilities and the power function used to calculate power, sample size, and minimum detectable effects. We apply our methods to the design of a large-scale randomized communication campaign in a municipality of Argentina to estimate total and neighborhood spillover effects on property tax compliance. Besides the increase in tax compliance of individuals directly targeted with our mailing, we find evidence of spillover effects on untreated individuals in street blocks where a high proportion of taxpayers were notified.Fil: Cruces, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; ArgentinaFil: Tortarolo, Dario. University of Nottingham; Estados UnidosFil: Vazquez Bare, Gonzalo. University of California; Estados Unido