Show simple item record

Bad News Bear(er)s: FDA Inspection Outcomes and Managers' Voluntary Disclosure Choices

dc.contributor.authorDown, Andrea
dc.date.accessioned2020-10-04T23:24:37Z
dc.date.availableNO_RESTRICTION
dc.date.available2020-10-04T23:24:37Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/2027.42/162966
dc.description.abstractThis study examines the association between bad news and managers' disclosure choices. It is empirically challenging to investigate this relationship because bad news resides in managers' private information sets, which are often unobservable and difficult to measure. To overcome this obstacle, I use a proprietary dataset that documents the dates and outcomes of FDA inspections. With the ability to detect the existence and content of news, I find that bad inspection outcomes are associated with a higher probability of disclosure and higher quantities of disclosure in the following three months. These results are stronger when inspection outcomes are more material or more severe. Further, the relation between bad news and disclosure disappears when the FDA begins publicly disclosing outcomes on a monthly basis. My timeliness tests suggest that managers accelerate, as opposed to delay, the disclosure of bad news. I also explore incentives that may influence managers' choices and provide direct evidence supporting the importance of litigation risk in managers' disclosure decisions; however, I do not find any evidence of managerial self-dealing. Collectively, these results document a significant link between private information and voluntary disclosure: managers disclose – rather than withhold – bad news, and litigation risk functions as a key motivating mechanism.
dc.language.isoen_US
dc.subjectprivate information
dc.subjectdisclosure
dc.subjectlitigation risk
dc.titleBad News Bear(er)s: FDA Inspection Outcomes and Managers' Voluntary Disclosure Choices
dc.typeThesis
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineBusiness Administration
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studies
dc.contributor.committeememberMiller, Gregory Smith
dc.contributor.committeememberMcCullough, Jeffrey Scott
dc.contributor.committeememberPurnanandam, Amiyatosh Kumar
dc.contributor.committeememberWilliams, Christopher Don
dc.contributor.committeememberYu, Gwen
dc.subject.hlbsecondlevelAccounting
dc.subject.hlbtoplevelBusiness and Economics
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/162966/1/akdown_1.pdfen_US
dc.identifier.orcid0000-0003-3485-2239
dc.identifier.name-orcidDown, Andrea; 0000-0003-3485-2239en_US
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.