A Diffusion Network Event History Estimator
Bibliographic Data
| ID | 6384641 |
|---|---|
| Authors | Jeffrey J Harden (0000-0001-5337-7918, University of Notre Dame), Bruce A Desmarais, Bruce Desmarais (0000-0002-3031-8883, Pennsylvania State University), Mark Brockway (Syracuse University), Frederick J Boehmke (0000-0003-3309-0885, University of Iowa), Scott James Lacombe (0000-0003-0653-4006, Smith College), Fridolin Linder (0000-0002-0499-0676, Pennsylvania State University), Hanna Wallach (0000-0003-3395-7186, Microsoft Research (United Kingdom)) |
| Year | 2023 |
| Volume | 85 |
| Issue | 2 |
| Pages | 436-452 |
| Publication date | 2023-04-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | The Journal of Politics (JOURNAL) |
| Journal identifiers | ISSN: 0022-3816 • E-ISSN: 1468-2508 |
| Publisher | University of Chicago Press (PUBLISHER • US) |
| DOI | 10.1086/723804 |
| OpenAlex | W4317379380 |
| Language | EN |
| Citations received | 7 |
| References cited | 33 |
Research on the diffusion of political decisions across jurisdictions typically accounts for units’ influence over each other with (1) observable measures or (2) by inferring latent network ties from past decisions. The former approach assumes that interdependence is static and perfectly captured by the data. The latter mitigates these issues but requires analytical tools that are separate from the main empirical methods for studying diffusion. As a solution, we introduce network event history analysis (NEHA), which incorporates latent network inference into conventional discrete-time event history models. We demonstrate NEHA’s unique methodological and substantive benefits in applications to policy adoption in the American states. Researchers can analyze the ties and structure of inferred networks to refine model specifications, evaluate diffusion mechanisms, or test new or existing hypotheses. By capturing targeted relationships unexplained by standard covariates, NEHA can improve models, facilitate richer theoretical development, and permit novel analyses of the diffusion process
Covariate · Data mining · Data science · Diffusion · Econometrics · Economics · Estimator · Event (particle physics · Inference · Machine learning · Statistics · Artificial Intelligence · Computer Science · Electoral Systems and Political Participation · Mathematics · Policy Transfer and Learning · Political Influence and Corporate Strategies
Global Human Rights and State Sovereignty
State Policy Innovativeness Revisited
The Initiative Process and Policy Innovation in the American States
Four Ways We Can Improve Policy Diffusion Research
Interest Group Influence in Policy Diffusion Networks
Politics and Morality in State Abortion Policy
Policy Diffusion and the Pro-innovation Bias
The Ties that Bind Us
Replications in Context
What Can We Learn from Predictive Modeling
Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap
Policy Innovation Adoption Across the Diffusion Life Course
Spid
Text as Policy
Who Are Your Neighbors? The Role of Ideology and Decline of Geographic Proximity in the Diffusion of Policy Innovations
Interdependent and Domestic Foundations of Policy Change
Why International Organizations Commit to Liberal Norms
Policy Inventing and Borrowing among State Legislatures
The Mechanisms of Policy Diffusion
Into the Words
States as Policy Laboratories
The Diffusion of Policy Diffusion Research in Political Science
Empirical Modeling of Policy Diffusion in Federal States
Ideology and Learning in Policy Diffusion
Persistent Policy Pathways
Innovation in the States
State Lottery Adoptions as Policy Innovations
Targeted for Diffusion? How the Use and Acceptance of Stereotypes Shape the Diffusion of Criminal Justice Policy Innovations in the American States
The Diffusion of Innovations among the American States
| Unique citing works | 7 |
|---|---|
| Citations per year | 3,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 7 |