Predicting Financial Vulnerability in Nonprofits

1. Predicting Financial Vulnerability in Nonprofits Roger A. Lohmann, Ph.D. Nancy Lohmann, Ph.D. Division of Social Work School of Applied Social Science Eberly College of Arts & Sciences West Virginia University

2. Age of Financial Uncertainty for Nonprofits • Cutbacks of programs • Machinations of accountability • Refusal of many funding sources to acknowledge need for long term operating support

3. Situation in WV Getting Worse • NP Crises in Kanawa Valley • Multi-CAP scandal/bankruptcy • Shawnee Hills bankruptcy • Etc. • DHHR De-funding Juvenile Retention • DHHR Contract Terminations • 70 agencies • 5-600 jobs

4. Is Prediction of Vulnerability Possible? • No cure for mismanagement • Multi-CAP Officer said: After 26 years in the business, he’d never heard you couldn’t commingle funds! • If any of you are in doubt; You can’t!! • No present way to predict actions of others • Will DHHR cancel more contracts?

5. Some Vulnerability is Predictable • NP’s are part of a fairly stable political economy • Deliberate introduction of risk • Managers feel vulnerable much of the time

6. Background • Small group of accounting researchers working on this problem. • First, in commercial settings • More recently, in nonprofits • Produced and tested a predictive model of estimating financial vulnerability that should be of interest to all nonprofit administrators.

7. Background: Accounting Statements • Three standard nonprofit financial statements relevant to prediction: • Balance Sheet (Position) • Statement of Income and Expenditures (Performance Over Time) • Changes in Fund Balance (Changes in Position Over Time)

8. Background: Expenditure Types • NASB recognizes three main types of expenditures • Administrative • Fund Raising • Program

9. Background: Revenue Sources • IRS 990 recognizes five types of revenues • Contracts, gifts and grants • Program revenues (earnings) • Membership dues • Sales of unrelated goods (UBITs) • Investment Income

10. General Approach: Ratio Analysis • Built from standardized information from financial statements • “Ratios” are just Fractions • Numerator and denominator from different places on financial statements • E.g. “current ratio” is • current liabilities / current assets • Normally, 1 or less • Greater than 1 should raise questions about long-term asset coverage of debt.

11. Three kinds of Financial Distress ratios: • Seat of the pants (practice- or experience-based wisdom) • Practice wisdom validated by empirical research • Reliability, validity and generalizability • Can you trust the measure? • When does it apply? • How widely does it apply? • Published Industry Standards

12. Defining Financial Vulnerability • ( Beaver, 1966) = financial vulnerability is probability of filing for bankruptcy. • Gilbert, et. Al. (1990) found many vulnerable companies do not file for bankruptcy. • Franks & Torous (1989): Companies that file may not be vulnerable (may be due to labor disputes, etc.)

13. Underlying Idea: • Financial Vulnerability = ability to recover from sudden financial shocks. • Sudden and unexpected loss of income • Sudden and uncontrollable increase in expenditures • Examples • Loss of a grant/contract • Sudden decrease (or increase) in clients • Discovery by EPA that your building is full of asbestos

14. Research Measures of Financial Vulnerability • Actual or anticipated filing for bankruptcy • Three consecutive years of net losses (negative net income) • (Nonprofit) Reduced program expenses

15. The Tuckman-Chang Model • Financially vulnerable nonprofit: Likely to reduce its program services following a financial shock. • Study of 4,730 501(c)3 organizations filing IRS 990’s in 1983. Howard Tuckman and Cyril Chang. A Methodology for Measuring the Financial Vulnerability of Charitable Nonprofit Organizations. Nonprofit and Voluntary Sector Quarterly. Winter, 1991. 445-460 FOR MORE INFO...

16. Tuckman-Chang Ratios • Inadequate Equity Balances • Revenue Concentration • Low Administrative Costs • Low Operating Margins

17. Inadequate Equity Balances • Ratio of total equity (fund balances) to total revenues • Lower ratio means less able to replace lost revenues following a financial shock • Lower ratio = greater vulnerability • Negative ratio unlikely (Neg. total revenues means real trouble!)

18. Tuckman-Chang Ratios • Inadequate Equity Balances • Revenue Concentration • Low Administrative Costs • Low Operating Margins

19. Revenue Concentration • Sum of revenue sources / total revenues squared • (24/328,000) = .000073 • .000073*.000073 = .0000000053 • Organizations with fewer revenue sources are more vulnerable to financial shocks in any one of them • Fewer sources = greater vulnerability

20. Tuckman-Chang Ratios • Inadequate Equity Balances • Revenue Concentration • Low Administrative Costs • Low Operating Margins

21. Low Administrative Costs • Ratio of Administrative Expenses/Total Revenues • Contrary to conventional wisdom: Lower ratios = greater vulnerability • Lower administrative costs almost always translate into decreased flexibility. • Diminished ability to reduce administrative costs in a crisis • More limited management problem solving capabilities • E.g., fewer mgrs., supervisors or support personnel to draft to 1)solve problem or 2) provide services in meantime. • Reminder: Conclusion based on random sample study of 4,730 nonprofits!

22. Tuckman-Chang Ratios • Inadequate Equity Balances • Revenue Concentration • Low Administrative Costs • Low Operating Margins

23. Low Operating Margins • Ratio of (total revenues less total expenses) / Total Revenues • Lower ratios = greater vulnerability • $300,000 in revenues and $200,000 in expenses • 30-20 = 10 • 10/30 = .33 • $500,000 in revenues and $200,000 in expenses • 50-20 = 30 • 30/50 = .60

24. Tuchman and Chang standards • Any score in second quintile “at risk” • Lowest quintile on all four variables “severely at risk” • Quintile ratios for four measures • Equity Balances • Revenue Concentration • Administrative Cost • Operating Margins

25. Tuchman and Chang ‘Insights’ • Inverse relationship --> revenues and risk • Low equity levels an indicator of risk • Higher long-term debt to long-term assets ratios another sign of trouble • Vulnerable nonprofits are less liquid (current ratios) • Higher program service reliance --> greater vulnerability

26. Two Major Research Problems • Extent of Program Services not fully captured by accounting system. • Difficult to determine independently which nonprofits experience financial shocks

27. Prediction Equation • Greenlee and Trussel (2001) develop a prediction equation • Useful for exact estimation of financial vulnerability within a set of norms for interpreting it. • Useful for comparing vulnerabilities

28. Greenlee-Trussel Equation • Yields the probability of financial vulnerability • Probability greater than 10% is a strong indication of financial vulnerability • Probability less than 7% is a strong indication of no vulnerability • Probability between 7-10% are indeterminate.

29. Greenlee-Trussel Equation • 1/(1+e-z) where • Z=Constant (3.0610) + EQUITY + CONCENT + ADMIN + MARGIN • The four ratios of Tuckman-Chang

30. Almost Ready for Prime Time • In the absence of other information, this approach is solid enough that nonprofit managers might begin to make use of it to test their hunches about the financial vulnerability of their organizations. • Probably not a good idea to rely on totally. • Certainly better than anything else currently existing.

31. Some general guidelines for more intuitive use of ratios • Greater the body of data you have more meaningful it will be. • Year-to-year comparisons are more momentous than month-to-month • Cross-organizational comparisons of programs with similar names can be very risky. (E.g., All home health programs are not created equal).

32. Different Approaches • Self-Norming • Compare a single organization at different periods • Pick most secure and most vulnerable periods and compare • Peer-Comparisons • Compare Groups of related organizations • Compare community systems • Information on ranges

33. Limitations of the Model: I • Not all nonprofits file 990’s • Risky to generalize about non-filers. • Ratios limited to data IRS 990 collects • E.g., IRS doesn’t collect data on outputs

34. Limitations of the Model: II • The Greenlee-Tressel model (GTM) only tested on organizations four or more years old. • Could be that newer organizations (1-3 years old) behave differently. • The GTM only tested on charitable organizations to date. • Further research needed using alternative definitions of financial vulnerability.