Predicting bankruptcy in the Texas nursing facility industry
Kris Joseph Knox,
Eric C. Blankmeyer,
José A. Trinidad and
J.R. Stutzman
The Quarterly Review of Economics and Finance, 2009, vol. 49, issue 3, 1047-1064
Abstract:
Approximately 50% of nursing facilities in Texas petitioned for bankruptcy during the 1998-2004 period. Using a logit regression model tested for robustness, we find nursing facilities that are profit-seekers, chain members, pay higher than average wage rates, accept more intensive-care residents and obtain a larger than average portion of their funding from public sources are highly vulnerable to negative changes in regulatory policy decisions on Medicare and Medicaid reimbursement. Larger facilities having higher than average occupancy rates and quality of care are less susceptible to adverse decisions. The model correctly classifies a facility as either bankrupt or solvent in about 75% of cases. We also examine the duration of bankruptcy using accelerated failure-time models. It appears that the duration of bankruptcy depends on location, out-of-state ownership, length of ownership, volume of resident days supplied, total cost and proportion of revenues from Medicaid.
Keywords: Bankrupt; Nursing; facilities; Logit; regression (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1062-9769(08)00064-1
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:49:y:2009:i:3:p:1047-1064
Access Statistics for this article
The Quarterly Review of Economics and Finance is currently edited by R. J. Arnould and J. E. Finnerty
More articles in The Quarterly Review of Economics and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().