policy diffusion analysis

This paper addresses spillover bias in treatment effects within spatial–economic networks. Methodologically, it develops the first causal identification framework grounded in continuous functional analysis: integrating heterogeneous agent aggregation, market equilibrium, and cost minimization to derive a propagation master equation; interpreting spatial–network interaction coefficients as mutual information between geographic and market coordinates; and introducing a Feynman–Kac path decomposition to disentangle inherited from cumulative effects—using “no-spillover” as a testable constraint that unifies identification and inference. Empirically, it synthesizes stochastic processes, information theory (entropy-based vulnerability diagnostics), and structural estimation. Applied to U.S. minimum wage policy, the no-spillover null hypothesis is rejected: total effects in border states reach four times the direct effect; entropy-based diagnostics improve labor-market disturbance forecasting accuracy by 56–76% over centrality measures, enabling six-month forward-looking warnings for high-risk state–industry pairs.

This paper addresses the challenge of identifying structural break points—termed “treatment effect boundaries”—where causal effects abruptly vanish across spatiotemporal dimensions. Methodologically, it introduces the first unified theoretical framework that jointly models spatial and temporal boundaries, defines structural parameters governed by shared dynamical systems, establishes rigorous identifiability conditions, and develops consistent, asymptotically normal estimators. Integrating information diffusion modeling with causal inference theory, the approach is validated via Monte Carlo simulations under heterogeneous spatiotemporal data, demonstrating robustness and statistical efficiency. Key contributions are: (1) a formal characterization of the critical threshold at which policy interventions transition from locally effective to systemically ineffective; and (2) practical, implementable tools for boundary detection and estimation, thereby providing both theoretical foundations and empirical support for institutional transition analysis and precision policy design.

This paper addresses the failure of causal identification in longitudinal panel data due to spatiotemporal interference—where an individual’s outcome is affected by others’ past treatment assignments. We propose a design-based causal inference framework that, under minimal assumptions (unknown interference structure and sequential ignorability), formally defines and identifies separable direct effects and spatiotemporal spillover effects for the first time. We demonstrate that conventional fixed-effects and difference-in-differences (DID) estimators suffer from systematic bias under interference. To overcome this, we construct a new estimator with consistency and asymptotic normality. Theoretical analysis, Monte Carlo simulations, and replications of two canonical empirical studies validate our approach: it substantially reduces estimation bias in spillover effects and effectively corrects the failure of standard panel methods under complex interference patterns.

This paper addresses causal inference in network experiments subject to interference. We propose a purely design-based, model-agnostic weighted least squares framework. Methodologically, we first establish the equivalence between the Hájek estimator and a specific inverse-probability-weighted regression coefficient. Second, we develop a bias-corrected network-robust covariance adjustment that ensures design-based validity of standard errors under arbitrary regression misspecification. Theoretically, our estimator is consistent and asymptotically normal. Simulations and empirical applications demonstrate stable confidence interval coverage exceeding 95%. Our approach balances practical implementability, flexible incorporation of covariates, and design-based robustness—offering a new paradigm for causal inference in network experiments that unifies theoretical rigor with empirical applicability.

This study addresses the inconsistency in estimating network spillover effects caused by contamination of the observed adjacency matrix due to reporting or disclosure errors. The authors propose a novel regularization framework that, for the first time, achieves consistent estimation of spillover effects under dense measurement error correlated with outcomes. The method integrates sparse and low-rank regularization, a two-stage denoising regression, and generalized method of moments (GMM) to simultaneously recover the latent network structure and estimate spillover effects, yielding faster convergence rates. Simulations demonstrate a 50–80% reduction in root mean squared error compared to conventional approaches. Empirical applications to international economic growth and U.S. interstate tax competition successfully recover Leontief stability and substantially improve estimation precision.

This study addresses the lack of theoretical guidance in specifying the functional form—such as neighborhood radius—of spillover exposure measures in estimating economic policy spillover effects. Building on an experimental design framework that leverages the randomness in treatment assignment, the paper jointly identifies both the functional form and its parameters through orthogonal moment conditions. It further develops the first asymptotic theory tailored to spatial and network-dependent structures. The proposed design-based inference approach enables data-driven selection of the exposure function and is validated through two large-scale poverty alleviation programs. In these applications, several pre-specified radii are formally rejected, and adjusting the exposure specification leads to substantively different estimates of policy effects.

This study addresses the identification and estimation of social effects in linear-in-means models with endogenous network structures. By leveraging randomized assignment combined with observed network data, and invoking the assumption that initially randomized peers exert no direct social influence, the authors construct local heterogeneity–robust moment conditions applicable to the full sample. Building on ψ-dependent network structures, they develop a generalized method of moments (GMM) estimator that admits a closed-form solution, ensuring both theoretical rigor and computational tractability. Monte Carlo simulations demonstrate strong finite-sample performance. Empirically, the analysis reveals a significant positive spillover effect: among Hong Kong high school students, being assigned by teachers to sit next to a randomly allocated peer positively influences the mathematics achievement of that peer’s study partners.