English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Social interactions determine many economic behaviors, but information onsocial ties does not exist in most publicly available and widely used datasets.We present results on the identification of social networks from observationalpanel data that contains no information on social ties between agents. In thecontext of a canonical social interactions model, we provide sufficientconditions under which the social interactions matrix, endogenous and exogenoussocial effect parameters are all globally identified. While this result isrelevant across different estimation strategies, we then describe howhigh-dimensional estimation techniques can be used to estimate the interactionsmodel based on the Adaptive Elastic Net GMM method. We employ the method tostudy tax competition across US states. We find the identified socialinteractions matrix implies tax competition differs markedly from the commonassumption of competition between geographically neighboring states, providingfurther insights for the long-standing debate on the relative roles of factormobility and yardstick competition in driving tax setting behavior acrossstates. Most broadly, our identification and application show the analysis ofsocial interactions can be extended to economic realms where no network dataexists.

Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition