Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition
Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net GMM method. We employ the method to study tax competition across US states. We find the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show the analysis of social interactions can be extended to economic realms where no network data exists.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularizat…
BIG-bench Machine LearningTime SeriesTime Series AnalysisLeast squares estimation in nonstationary nonlinear cohort panels with learning from experience
We discuss techniques of estimation and inference for nonstationary nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares esti…
SurveyOveridentification in Shift-Share Designs
This paper studies the testability of identifying restrictions commonly employed to assign a causal interpretation to two stage least squares (TSLS) estimators based on Bartik instruments. For homogeneous effects models …
validIdentification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models
In nonlinear panel data models, fixed effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. The common argument is that identifying AMEs requires knowledge of …
Discrete Choice ModelsLarge Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference
This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by appl…
Causal InferencecounterfactualMissing Values