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Predicting bankruptcy in the Texas nursing facility industry

Author

Listed:
  • Knox, Kris Joseph
  • Blankmeyer, Eric C.
  • Trinidad, José A.
  • Stutzman, J.R.

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.

Suggested Citation

  • Knox, Kris Joseph & Blankmeyer, Eric C. & Trinidad, José A. & Stutzman, J.R., 2009. "Predicting bankruptcy in the Texas nursing facility industry," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 1047-1064, August.
  • Handle: RePEc:eee:quaeco:v:49:y:2009:i:3:p:1047-1064
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    References listed on IDEAS

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    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Rousseeuw, Peter J. & Christmann, Andreas, 2003. "Robustness against separation and outliers in logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 43(3), pages 315-332, July.
    3. O. David Gulley & Rexford E. Santerre, 2003. "The Effect of Public Policies on Nursing Home Care in the United States," Eastern Economic Journal, Eastern Economic Association, vol. 29(1), pages 93-104, Winter.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    5. David C. Grabowski, 2001. "Does an Increase in the Medicaid Reimbursement Rate Improve Nursing Home Quality?," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 56(2), pages 84-93.
    6. John J. McConnell & Ronald C. Lease & Elizabeth Tashjian, 1996. "Prepacks As A Mechanism For Resolving Financial Distress: The Evidence," Journal of Applied Corporate Finance, Morgan Stanley, vol. 8(4), pages 99-106, January.
    7. Estrella, Arturo, 1998. "A New Measure of Fit for Equations with Dichotomous Dependent Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 198-205, April.
    8. Kris Knox & Eric Blankmeyer & J. Stutzman, 1999. "Relative economic efficiency in Texas nursing facilities: A profit function analysis," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 23(3), pages 199-213, September.
    9. Yotopoulos, Pan A & Lau, Lawrence J, 1973. "A Test for Relative Economic Efficiency: Some Further Results," American Economic Review, American Economic Association, vol. 63(1), pages 214-223, March.
    10. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    11. Kris Knox & Eric Blankmeyer & J. Stutzman, 2007. "Technical efficiency in texas nursing facilities: A stochastic production frontier approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 31(1), pages 75-86, March.
    12. Lau, Lawrence J & Yotopoulos, Pan A, 1971. "A Test for Relative Efficiency and Application to Indian Agriculture," American Economic Review, American Economic Association, vol. 61(1), pages 94-109, March.
    13. Croux, Christophe & Haesbroeck, Gentiane, 1999. "Influence Function and Efficiency of the Minimum Covariance Determinant Scatter Matrix Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 161-190, November.
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    Cited by:

    1. Abbas, Qaiser & Rashid, Abdul, 2011. "Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan," MPRA Paper 28161, University Library of Munich, Germany.

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