Replication Data for: The Reputational Dividends of Collaborating with a Highly Reputable Agency:  The Case of the FDA and Its Domestic Partner Agencies
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Utilizing a dataset covering 30 U.S. federal agencies over a period of 34 years (1980–2013), we estimate the short and long-term reputational effects of interagency collaboration. Collaboration is measured by the number of memorandums of understanding (MOUs) in effect between each agency and the FDA, while agency reputation is assessed using an automated measure of media-coverage valence (positive/negative tone) for each agency-year. To account for potential reverse and reciprocal causality, we utilize cross-lagged fixed-effects models. We find evidence of moderate rises in reputation due to increased collaboration with the FDA. These effects persist significantly for two years, before decaying to null after four years. Employing similar analyses, we furthermore estimate reversed causality – of reputation on the level of consequent coll' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["FDA","interagency collaboration","reputaion"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Utilizing a dataset covering 30 U.S. federal agencies over a period of 34 years (1980–2013), we estimate the short and long-term reputational effects of interagency collaboration. Collaboration is measured by the number of memorandums of understanding (MOUs) in effect between each agency and the FDA, while agency reputation is assessed using an automated measure of media-coverage valence (positive/negative tone) for each agency-year. To account for potential reverse and reciprocal causality, we utilize cross-lagged fixed-effects models. We find evidence of moderate rises in reputation due to increased collaboration with the FDA. These effects persist significantly for two years, before decaying to null after four years. Employing similar analyses, we furthermore estimate reversed causality – of reputation on the level of consequent coll' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Utilizing a dataset covering 30 U.S. federal agencies over a period of 34 years (1980–2013), we estimate the short and long-term reputational effects of interagency collaboration. Collaboration is measured by the number of memorandums of understanding (MOUs) in effect between each agency and the FDA, while agency reputation is assessed using an automated measure of media-coverage valence (positive/negative tone) for each agency-year. To account for potential reverse and reciprocal causality, we utilize cross-lagged fixed-effects models. We find evidence of moderate rises in reputation due to increased collaboration with the FDA. These effects persist significantly for two years, before decaying to null after four years. Employing similar analyses, we furthermore estimate reversed causality – of reputation on the level of consequent coll
FDA; interagency collaboration; reputaion