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Taking Time (and Space) Seriously: How Scholars Falsely Infer Policy Diffusion from Model Misspecification
Author(s) -
Drolc Cody A.,
Gandrud Christopher,
Williams Laron K.
Publication year - 2021
Publication title -
policy studies journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 69
eISSN - 1541-0072
pISSN - 0190-292X
DOI - 10.1111/psj.12374
Subject(s) - diffusion , econometrics , covariate , outcome (game theory) , neglect , space (punctuation) , spatial econometrics , economics , contrast (vision) , autoregressive model , empirical research , positive economics , actuarial science , computer science , statistics , psychology , microeconomics , mathematics , artificial intelligence , physics , psychiatry , thermodynamics , operating system
Scholars have long been interested in how policies and ideas spread from one observation to another. Yet, the spatial and temporal dynamics of policy diffusion present unique challenges that empirical researchers often neglect. Scholars often use temporally lagged spatial lags (TLSL)—such as the number (or percentage) of prior adopters in a neighborhood—to test various mechanisms of delayed policy diffusion but are largely unaware of two under appreciated issues. First, the effects are not limited to one time period but persist over time by changing the future value of neighboring observations. Second, minor, yet common, choices in model specification—such as omitting spatially correlated and/or autoregressive covariates—can increase the risk of falsely inferring that the outcome is a result of spatial diffusion. Indeed, we offer two applications where small changes to the model specification of an otherwise well‐specified model result in drastically different inferences about policy diffusion. We argue that scholars should avoid haphazardly including TLSLs without considerable theoretical justification, and we conclude on an optimistic note by offering straightforward solutions and new software to address these issues.