Financial Disclosure Management by Nonprofit Organizations 1

Transcription

1 Financial Disclosure Management by Nonprofit Organizations 1 Ranjani Krishnan, Michelle H. Yetman, Robert J. Yetman* Eli Broad College of Business, Michigan State University, East Lansing, MI Tippie College of Business, The University of Iowa, Iowa City, IA Abstract This paper examines how nonprofit organizations respond to incentives to manage their publicly available financial information. Prior research identifies two operating ratios donors commonly use to evaluate the efficiency and effectiveness of nonprofits (i.e., the program service ratio, defined as the fraction of total expenses committed to advancing the charitable mission of the organization, and the fundraising ratio, defined as the ratio of fundraising expenses to donations revenue). Nonprofit managers have an incentive to over-report the expenses classified as program services and under-report the expenses classified as administrative and fundraising in order to improve these ratios. We examine whether nonprofits respond to these incentives, and we find evidence consistent with opportunistic cost shifting to improve the program service and fundraising ratios. Additional analysis finds that smaller nonprofits that are more reliant on donations revenue manipulate their operating ratios to a greater extent. JEL classification: M4; L3 Key words: Nonprofit organizations, earnings management, disclosure, hospitals. *Corresponding author. Tel.: (319) ; fax (319) ; robert_yetman@uiowa.edu 1 We thank Ashiq Ali, Ramji Balakrishnan, Leslie Eldenburg, Lil Mills, Shiva Sivaramakrishnan, and workshop participants at the University of Arizona and Texas A&M University for their helpful comments.

2 1. Introduction This paper examines how nonprofit organizations respond to incentives to manage their publicly available financial information through accounting expense allocations. Although nonprofit organizations do not have an observable stock price, there are nonetheless significant contractual incentives for financial disclosure management. In particular, implicit contracts with donors provide nonprofits with an incentive to appear efficient in raising their donations and in allocating their resources to charitable, rather than administrative, outputs. Typically, two ratios are used to measure a nonprofit organization s donative and operational efficiency. Nonprofit financial statements aggregate all expenses into one of three categories (i.e., program services, fundraising, and administrative), and the ratios are based on these expense categories. The first ratio, known as the program service ratio, is program service expenses divided by total expenses. Because program service expenses are those directly related to the nonprofit s primary charitable output (as opposed to either administrative or fundraising expenses), this ratio measures a nonprofit s operating effectiveness. The second ratio, known as the fundraising ratio, is fundraising expenses divided by donations revenues and captures a nonprofit s fundraising efficiency. Numerous industry watchdog groups suggest that donors use these two ratios to determine which charities are worthy of receiving donations, and a growing body of empirical research finds evidence consistent with donors responding to information contained in these ratios (Weisbrod and Dominguez 1986; Posnett and Sandler 1989; Callen 1994; Tinkelman 1999; Okten and Weisbrod 2000; Yetman and Yetman 2002; Baber, Daniel and Roberts 2002). To the extent that nonprofit managers believe that donors respond to these ratios, there is an incentive to allocate expenses out of the fundraising and administrative categories and into the 1

3 program services category in order to improve reported operating ratios. Although these kinds of accounting expense allocations have long been suspected, they have never been empirically documented (Khalat and Hueslein 1992). For example, an article in Forbes states, Moreover from a public relations standpoint, charities have every incentive to maximize the charitable commitment figure and minimize reported fundraising expenses. Dubious accounting isn t rare. (Barret, 1999). Our paper provides empirical evidence consistent with nonprofits using accounting expense allocations to overstate their program service expenses and understate their fundraising expenses, thereby making themselves more attractive to potential donors. Further analysis estimates the magnitude of these accounting expense allocations, and examines the effects of political costs and reliance on donations as a revenue source on a nonprofit s propensity to make expense allocations. This research is of potential interest for several reasons. First, nonprofits provide a natural setting (free of price-related incentives) in which to examine the effects of contractual incentives on financial reporting choices. Second, the effects of disclosure management on the quality and decision usefulness of publicly available financial information generally has not been addressed in the nonprofit setting. Nonprofit organizations, which constitute over 10 percent of national gross domestic product, are an important part of the United States economy (Independent Sector 2001). Annual donations received by nonprofits total over $200 billion (American Association of Fundraising Council Trust for Philanthropy, 2002). To the extent that donors make resource allocation decisions based on reported financial information and are unable to disentangle disclosure management, economic resources are potentially misallocated. Third, a small but growing body of research uses these ratios for empirical analysis without considering the implications of managerial manipulation (Baber, Roberts, Visvanathan 2001; Yetman 2001; 2

4 Baber, Daniel, and Roberts 2002;). By documenting the extent and method of nonprofit disclosure management, we provide valuable information for future research. We conduct our analysis using two databases. The first database contains matched sets of IRS 990s and state regulatory reports for a pooled sample of 719 observations from California hospitals for the years 1994 to The IRS 990, which is freely available for all nonprofits, is considered to be the principal publicly available source of financial information for nonprofit organizations (Joint Committee on Taxation, 2000). The state regulatory reports are available from the California Office of Statewide Health Planning and Development and are referred to as OSHPD reports. For reasons discussed later, we believe that the OSHPD reports are not likely to be used by donors to evaluate a particular hospital s operating efficiency or effectiveness and therefore managers have less incentive to manipulate their OSHPD reports. We compare the amounts of program service expenses reported by the hospitals on their OSHPD reports with the amounts reported on their publicly available IRS 990 financial statements. Results of this analysis find that, although nonprofit hospitals in California report the same amounts of total expenses (i.e., the sum of program service, administration, and fundraising) on their OSHPD and IRS 990 forms, they report an average of $13.9 million more program service expenses (and $13.9 million less administrative and fundraising expenses) on their IRS 990 forms than on their OSHPD reports. These expense allocations increased the average program service ratio from 68 percent to 83 percent, causing the average organization to appear to be more operationally effective in that a larger proportion of total expenses are for charitable purposes. This result is consistent with nonprofit managers understating the amounts of program expenses reported on their publicly available financial reports (i.e., the IRS 990s) in order to appear more operationally efficient to potential donors. 3

5 The advantage of our first empirical test is that it measures disclosure management by comparing data on two sets of matched financial reports as self-reported by the nonprofits (i.e., each organization acts as its own control). The disadvantage of our first test is its limitation to a relatively small sample (i.e., 719) of California hospital observations. To overcome this limitation, we conduct a second empirical analysis using a second database of over 100,000 organization-year observations from 1982 to 1998, which includes educational, medical and charitable nonprofits in all 50 states (plus the District of Columbia). Because the only publicly available data for this larger sample is the IRS 990, we employ an estimation technique to determine expected expenses, and then compare this expected amount with the amounts reported on the IRS 990s, with the difference representing accounting expense allocations. Our estimation technique uses a regression model based on prior research to derive nonprofit-level estimates of fundraising expenses. We examine only fundraising expenses with this larger sample (rather than program services or administrative expenses) because prior research supplies models that can be used to estimate fundraising expenses. Results of our second analysis show that educational, medical, and charitable nonprofits under-report an average of $1,656 thousand, $129 thousand, and $227 thousand, respectively, in fundraising expenses on their IRS 990s. These expense allocations decreased the average fundraising ratios for educational, medical, and charitable nonprofits by 16 percent, 3 percent, and 7 percent, respectively, causing the average organization to appear to be more efficient in raising donations. Our third set of empirical tests is based on our observation that almost 40 percent of all nonprofits that report earning some amount of private donations (i.e., from individuals or corporations) report zero fundraising expenses. We investigated these nonprofits further because 4

6 it seems unlikely that such a large percentage of nonprofits could earn substantial private donations (average of $1.8 million each) with zero fundraising effort. To conduct this analysis, we randomly selected a sample of 10 percent of nonprofits that report zero fundraising expenses yet earn private donations and searched their Internet web pages for evidence of fundraising activities. Of the 349 organization web pages searched, we found evidence of fundraising activities for 130 organizations (37%). The instructions to the IRS 990 require that any and all expenses related to fundraising activities, such as those we found on the internet, be reported as fundraising. The results of this analysis imply that a substantial portion of nonprofits that report zero fundraising expenses actually do incur some amount fundraising expenses. This analysis corroborates the findings of our two previous tests that nonprofits allocate costs among various accounting categories so as to appear more operationally effective and efficient to potential donors. In the fourth and final portion of our analysis, we test the prediction that accounting expense allocations are negatively related to political costs (as measured by size) and positively related to the relative importance of donations as a revenue source. Results generally support our predictions. Our results have at least two implications. First, as with earnings management by for-profit entities, nonprofit financial disclosure management accomplished through accounting manipulations has potential resource allocation implications. Second, the results suggest that future nonprofit research using the program service or fundraising ratios must account for possible managerial manipulation. Prior research examined earnings (i.e., net income) management by nonprofit hospitals and finds that they smooth their net income and do not avoid reporting losses (Leone and Van Horne 5

7 2001). We differentiate our research in several ways. First, we focus on the manipulation of operating ratios rather than on net income because, in the nonprofit setting, operating ratios are a more commonly used evaluation metric. Prior research suggests that, since a nonprofit s value is not reflected in a price metric, nonprofit objective functions generally do not include net income maximization, but rather include maximizing charitable output and meeting donor expectations (Rose-Ackerman 1996). Second, we consider all kinds of nonprofits, not just hospitals. Finally, we develop and test predictions about the characteristics associated with disclosure management; while prior research does not. The next section reviews the nonprofit financial disclosure environment. Section III discusses our data; Section IV presents our empirical analyses; Section V provides robustness tests, and the final section our conclusion. 2. Nonprofit Financial Disclosure Environment 2.1. The Agency Problem in Nonprofit Organizations Public demand for nonprofit financial statement data is at least partially driven by the inherent agency conflict within the nonprofit setting. The economics literature typically assumes that not-for-profit firms have different objectives and behave differently from for-profit firms. Much of the work in this area draws on the seminal research of Arrow (1963), who suggests that nonprofit hospitals arise in response to the asymmetry of information between patients and providers of healthcare. Fama and Jensen (1983) argue that nonprofits arise for particular forms of organizations such as charities because unrestricted donations pose agency problems for any organization with residual claimants. In order for donors to be assured that residual claimants do not consume their resources, the nonprofit form arises, which has no alienable residual claims. 6

8 Fama and Jensen (1983) conclude that the absence of residual claims avoids the donor-residual claimant agency problem and explains the dominance of nonprofits in donor-financed activities. However, the absence of residual claimants does not automatically ensure that nonprofit managers will use donations in the most efficient manner. Managers can consume more resources than efficient production requires for various reasons. For example, managers can consume excessive perquisites and expend resources in activities peripheral to the stated mission of the organization. Also, in some cases efficient production requires additional effort by the manager who would otherwise be unwilling to expend that effort in the absence of high-powered incentive contracts. Donors have an implicit contract with nonprofits in that donors supply net cash flow in exchange for the nonprofit efficiently and effectively providing some charitable output with the provided funds. Accounting information can assist donors in monitoring their implicit contracts by providing a means (i.e., the fundraising and the program services ratios) for donors to evaluate whether the nonprofit is using their donations in the most efficient and effective manner. The following sections discuss the financial disclosure requirements for nonprofit organizations that facilitate financial analysis of nonprofits by donors IRS 990 Financial Disclosures Federal regulations require that nonprofits annually prepare and disclose their IRS 990, which contains typical financial statements, including a statement of revenues and expenses and a balance sheet, as well as a substantial amount of other information related to the nonprofit s charitable purpose and activities. IRS 990 data on most nonprofits is freely available on the Internet at Congressional reports suggest that the intent of the IRS 990 is to 7

9 provide the public with the necessary information to evaluate the performance of a nonprofit and that the IRS 990 is the primary source of publicly available nonprofit financial information (Joint Committee on Taxation 2000). Nonprofit financial statements on the IRS 990 group all expenses into one of three accounting categories: program service, administrative, and fundraising expenses. Instructions to the IRS 990 indicate that fundraising expenses are any and all incurred either directly or indirectly to generate, maintain, or keep track of government grants or donations, both from the general public and from feeder organizations such as the United Way. Program service expenses are those incurred to further the charitable mission of the nonprofit, such as professors salaries at nonprofit universities, direct patient care at nonprofit hospitals, and specimen acquisitions at nonprofit zoos and aquariums. Expenses that are not properly classified as program service or fundraising are considered to be administrative. Overhead expenses, which are shared among the three categories of expenses, have to be allocated using a reasonable method of cost allocation. Although nonprofits can elect to undergo financial statement audits, there is no requirement that these audited financial statements be made public, nor is there any IRS requirement that the IRS 990 conform to audited financial statements. Although most accounting rules for both the IRS 990 and for financial statements presented in accordance with Generally Accepted Accounting Principles coincide, there are some differences. For example, the IRS 990 does not use materiality thresholds, any amount of fundraising must be separately stated. Industry watchdogs use information from the IRS 990s to compute the program service and fundraising ratios and make annual recommendations to the public via printed publications and the World Wide Web. The largest watchdog groups include the American Institute of 8

10 Philanthropy (AIP), the Philanthropy Advisory Service (PAS) of the Council of Better Business Bureaus (CBBB), and the National Charities Information Bureau (NCIB). In addition to these agencies, the popular press (e.g., Forbes, Money, and The Wall Street Journal) utilize IRS 990 data in news articles and/or in annual lists comparing nonprofits relative program service and fundraising ratios. 2, OSHPD Financial Disclosures In addition to IRS disclosure requirements, hospitals providing medical care in the state of California must provide additional disclosures to state regulatory authorities. The California Office of Statewide Health Planning and Development (OSHPD), a department of the California Health and Human Services Agency, collects, analyzes, and disseminates data on hospitals licensed in California. The information collected by OSHPD includes financial reports, patient statistics, and usage statistics. Expenses on the OSHPD report are provided in the following format: The OSHPD data include totals (and some details) for general service, administrative, and program service expenses, but does not generate a separate total for fundraising expenses. General services include expenses such as printing, non-patient food services, grounds, security, parking, accounting, and data processing. Administrative expenses comprise hospital administration, 2 For example see Giving Smartly: A Guide to Charities 2001 in 3 For example, Barrett (1999) in a Forbes article titled Look Before you Give reports, The Cancer Research Institute funneled 89% of its budget to scientific inquiry, much greater than the 62% of the far larger American Cancer Society, or the 56% of the smaller Cancer Fund of America. Much of the difference had to do with fundraising. Only 3 cents of every dollar raised by the Cancer Research Institute are spent on generating the donations. At the Cancer Fund, 24 cents go out the door that way, and at the American Cancer Society, 30 cents. 9

11 personnel, library, staff administration, and public relations. Program service expenses 4 as per the OSHPD data include daily hospital services such as medical/surgical intensive and acute care, coronary care, pediatric care, obstetrics, and psychiatric care; ambulatory services such as emergency room, medical transportation, and home health; ancillary services such as radiology, occupational therapy, and renal dialysis; research expenses; education costs; insurance and malpractice; and other operating overhead and miscellaneous costs not classified as administrative or general expenses. There are two primary users of the OSHPD reports: First, the Health Planning and Policy (HPP) Division of OSHPD, which conducts research on issues related to healthcare cost containment, access to needed services, and improving the quality of care, demands a great amount of accurate, detailed information. Second, health care researchers and other medical policy makers also rely on the data. Therefore, OSHPD reporting guidelines are very detailed (the instruction manual is about 1,000 pages) and specific for each line item. We believe that hospitals are less likely to manipulate their operating ratios on the OSHPD reports as compared with their IRS 990s for four reasons. First, the OSHPD data have to be purchased at a cost of $375 per year; while the IRS 990s are freely and easily available to potential donors. Second, the OSHPD reports contain large amounts of complex data (hundreds of accounts) that must be aggregated across various categories and cost centers in order to compute a nonprofit s performance ratios. In contrast, the IRS 990 contains only three categories of expenses which can be easily used to construct the operating ratios (all data necessary to calculate the two ratios are clearly presented on page one of the IRS 990). Third, 4 The OSHPD database does not specifically refer to these expenses as program service expenses. However, consistent with the IRS 990 classification, all expenses other than administrative, fundraising, and general are program service expenses. 10

12 the popular press and all watchdog agencies use IRS 990 data in forming their recommendations. Finally, the OSHPD data are available only for California hospitals; while the IRS 990 data are available for all types of nonprofit organizations located throughout the country. Donors are likely to be interested in comparing a variety of nonprofits in a number of locations before making decisions. It is also possible that, rather than overstating the amount of program service expenses on the IRS 990, California hospitals are actually understating their program service expenses on their OSHPD reports, although we are not aware of any incentive to manipulate the OHSPD reports in this manner. To the contrary, it is possible that there are incentives, such as expense reimbursement policies or state regulatory scrutiny, for hospitals to overstate, rather than understate, the program expenses on their OSHPD reports. The effect of overstated OSHPD program expenses is to bias our tests against finding results. The differing opportunities and incentives for earnings management between the IRS 990 and the OSHPD reports guide our interpretation that the reported expenses on the OSHPD data are less managed compared with the financial information on the IRS 990. However, to the extent that hospitals do simultaneously overstate their program services on both their OSHPDs and IRS 990s, our estimates of expense allocations will be biased downward, conservatively influencing our results. 11

13 3. Data 3.1. Sample Selection Sample of California Nonprofit Hospitals Our pooled cross-sectional sample of California hospitals is based on the intersection of two databases: the OSHPD Hospital Financial data and the IRS 990 data from the Statistics of Income (SOI) files. The SOI files contain data on all 501(c)(3) organizations with more than $10 million in assets plus a random sample of approximately 4,000 smaller organizations and include most financial variables on the IRS 990. The SOI data files were obtained from the National Center for Charitable Statistics (NCCS). The NCCS, which is a project of the Center on Nonprofits and Philanthropy (CNP) of the Urban Institute, is the national repository of data on the nonprofit sector in the United States ( We matched each nonprofit hospital in the OSHPD database with each nonprofit hospital in the IRS 990 database. Since the two databases have different firm identifier numbers (a unique 9-digit hospital number in the OSHPD data and a unique 9-digit Employer Identification Number (EIN) in the IRS 990s), we matched by hospital name and zip code. Because external changes in the hospital industry make the analysis of a large panel of data noisy, we restrict our analyses to the most recent five years for which data are available ( ). The matching process produced 719 hospital-year observations. The number of hospitals per year ranges from 167 in 1994 to 122 in Although there are about 262 nonprofit hospitals in California, not all hospitals are represented in the IRS 990 database. Our sample is less likely to include very small hospitals (under 25 beds), because the IRS 990 data only contains a random sample for hospitals with less than $10 million in assets. 12

14 Larger Sample of Educational, Medical, and Charitable Nonprofits The second sample is based on the pooled cross-section of all educational, medical, and charitable nonprofits contained in the IRS SOI sample files for the years 1982 to 1998, for a total of 163,093 organization-year observations. We reduce our sample by 39,383 observations which report zero donations. We further reduce our sample by 2,590 observations by trimming off the top and bottom one percent of the most extreme values of all analysis variables, although results are robust to no trimming and further trimming up to 10% of extreme observations. After these data screens, our sample contains 25,501, 37,602, and 48,017 educational, medical, and charitable nonprofit-year observations, respectively Sample Characteristics California Nonprofit Hospitals Table 1, Panel A provides descriptive statistics for the hospital sample. The average number of staffed beds is 209; the percentage of Medicare patients is percent; the percentage of Medi-Cal (i.e., the California state Medicaid program) patients to total patients is percent and the occupancy rate is percent. These data suggest that the hospitals represented in the sample are representative of the overall population of nonprofit hospitals in California (average staffed beds = 192, percentage of Medicare patients = 41, percentage of Medi-Cal patients = 21, and percentage occupancy rate=59). Hospitals in the sample provide charity care of about 2.4 percent of revenue. The average net income margin as a percentage of revenue is 3.51 percent. A large percentage of revenue is from program services (93.9 percent). Donations constitute only 2.8 percent of revenue. However, because average net income margins are only

15 percent, donations are likely to make a difference between the hospital s breaking even versus incurring a loss Educational, Medical, and Charitable Nonprofits Table 1 panel B reports descriptive statistics for our sample of 25,501, 37,602, and 48,017 educational, medical, and charitable observations, respectively. Educational nonprofits receive the largest average dollar amount of donations, followed by charitable nonprofits. Medical nonprofits average program service revenues (i.e., the sales of products and services) is over 43 times as large as average donations, suggesting that medical nonprofits rely primarily on program revenues. Charitable nonprofits earn roughly equal amounts of donations (mean of $3 million) and program revenues (mean of $4 million). 4. Empirical Analysis and Results 4.1. California Nonprofit Hospitals Expense Allocations In this section, we compare the amount of program service expenses as reported on a nonprofit s IRS 990 to those reported on its OSHPD report for a matched set of 719 California hospitals. Note that this analysis can determine whether excess expenses were allocated to program services, but it cannot determine whether those expenses were allocated from fundraising or administrative expenses because the OSHPD database aggregates both fundraising and administrative expenses into a single expense category. The program services ratio as reported on the IRS 990 is program service expenses divided by total expenses. We compute the program service ratio using the OSHPD data by aggregating expenses across the following expense centers: patient care expenses; research expenses; 14

16 education expenses; insurance, malpractice, and other operating overhead; and miscellaneous costs not classified as administrative or general expenses. Using the means from the sample, we find that the average California hospital reports $13.9 million more program service expenses on their IRS 990 than on their OSHPD report (and correspondingly $13.9 million less fundraising and administrative expenses on their IRS 990 than on their OSHPD report). The top portion of table 2 contains a two-sample matched pair t- test of the difference between reported program service ratios and program service expenses as per the IRS 990 and the OSHPD data for our sample of California Hospitals. The mean differences are highly significant across all the years (t=28.3, p<0.001), and also for each individual year (untabulated), with no discernable pattern observable across the years. These results suggest that nonprofit hospitals overstate their program service ratios on their IRS 990s by overstating their program service expenses. These expense allocations increased the average program service ratio from 67.9 percent to 82.8 percent, making the average organization appear to donors to be more effective at delivering its charitable output Educational, Medical, and Charitable Nonprofits Expense Allocations For the larger sample of nonprofit organizations, there is no publicly available data source (similar to the California OSHPD reports) that we can use as a benchmark of the amount of expenses nonprofits should report as program services, fundraising, or administrative. Therefore, we employ an alternative procedure in which we first estimate, using regression analysis, the fundraising expenses we would expect a nonprofit to report on its IRS 990, and then compare that estimate to the fundraising expenses actually reported on the IRS 990, with the difference representing our estimate of allocations out of fundraising. For this analysis, we focus 15

17 on fundraising expenses only (and not on program services or administration) because prior research provides us with a means of estimating a particular nonprofit s fundraising expenses. The model we use to estimate nonprofits expected fundraising expenses follows those used by Weisbrod and Dominguez 1986, Posnett and Sandler 1989, and Okten and Weisbrod 2000, as well as by many others. The intent of these models is to estimate the determinants of private donations to nonprofits, and they generally take the form: Private donations it = α + β 1 Fundraising expenses it-1 + β 2 Price it-1 + β 3 Age it-1 + β 4 Program revenues it-1 + ε i. (1) Unlike either program service expense or administrative expense, fundraising expense has a direct revenue counterpart (i.e., donations revenues). Because of this relationship, it is possible to use regression analysis to derive estimates of fundraising expenses. Note that a similar procedure could not be used to estimate program service expenses or administrative expenses because they have no direct revenue counterparts. The dependent variable, Private donations, is the dollar amount of donations from individuals and corporations and excludes government grants and feeder donations, such as those from the United Way. Fundraising expenses are those incurred, directly or indirectly, to generate or otherwise manage donations. Fundraising can affect donations in two ways. First, fundraising will increase donations to the extent it increases donor awareness and communicates information about the quality of the nonprofit s output (Nelson 1974). Second, excessive fundraising will reduce donations if donors are concerned with the portion of their donation that is spent on additional fundraising. The variable Price is defined as 1 / (1 - Fundraising expenses / Private donations) and captures donor sensitivity to 16

18 nonprofits fundraising efficiency. The variable Age is the number of years a nonprofit has existed and is intended to measure a nonprofit s stock of reputation capital. We include Program revenues to control for possible crowding-in or crowding-out effects of this alternative revenue source. All variables (except for Age) are scaled by assets, although our results are qualitatively robust to alternative specifications that use total expense or total revenue scaled variables as well as using logarithmically transformed variables. All independent variables are lagged, because a donor does not have access to nonprofit financial information until the end of the year. Consistent with prior research, we interpret the coefficient estimate β 1 as the average dollar amount of donations generated by one dollar of fundraising expenses. We derive an organization-specific estimate of the fundraising expenses a nonprofit should report on its IRS 990 by dividing a nonprofit s private donations by our coefficient estimates (i.e., if the coefficient estimate were four, suggesting that one dollar of fundraising generates four dollars of donations, we would divide the nonprofit s reported amount of donations by four to derive an estimate of the fundraising expenses the nonprofit incurred). We then compare our organizationlevel estimate of fundraising expenses based on model (1) with those reported on the nonprofit s IRS 990, with the difference representing our estimate of a nonprofit s allocation of expenses out of the fundraising category. By using this estimation methodology we make several assumptions. First, we assume that nonprofits private donations revenue generation process is constant within nonprofit type (i.e., educational, medical, or charitable). Second, we assume that all fundraising expenses are incurred to generate private donations and not feeder or government grants (i.e., the dependent variable is only private donations; whereas the independent variable Fundraising expenses is all fundraising expenses). Although it is possible that nonprofits make fundraising expenditures to 17

19 raise grants, we make this assumption for two reasons. First, prior research has not recognized any link between grants and fundraising expenses, leaving us with no prior model of the relationship between fundraising and grants. Second, several institutional factors render the association between fundraising expenses and grants difficult to determine at best and tenuous at worst. 5 To the extent that fundraising expenses are incurred to generate either feeder donations or government grants, our estimates of expense reallocations are conservatively biased. Table 3 contains the results of estimating equation (1). We estimate the model controlling for a first-order auto-regressive process as well as general heteroscedasticity. 6 We find that each dollar of fundraising is associated with $1.94, $4.29, and $5.58 of private donations for educational, medical, and charitable nonprofits, respectively. The magnitude of these results are similar to those found in prior studies using similar models (Weisbrod and Dominguez 1986; Posnett and Sandler 1989; and Okten and Weisbrod 2000). Because a working supposition of our paper is that the reported amount of fundraising expenses are systematically understated, equation (1) contains a regressor with systematic measurement error. Econometrically, this measurement error is reflected in the error term, ε, and if ε is correlated with the measured variable (i.e., Fundraising expenses), then ß 1 is biased. The direction of the bias depends on the directions of the covariance between the measurement error and both Fundraising expenses and Private donations; if the direction is equal (unequal), then ß 1 5 Our discussions with grant writers at various charities and hospitals around the country suggests that feeder donation and government grant contracts usually span several years, with the majority of the fundraising expenses being front-loaded, causing the relationship between grants and fundraising expenses to be difficult to estimate. In addition, grants are usually less costly to acquire than private donations because the fundraising effort is directed at a particular agency; whereas the generating efforts for private donations are comparatively broad. 6 A Durbin-Watson tests suggests that our model error terms exhibit first order serial correlation. A Lagrange multiplier test shows that the errors exhibit non-constant variance. We use a generalized autoregressive procedure (i.e., GARCH) with a long memory process (i.e. all past squared residuals are used to estimate the current error variance). As compared with using ordinary least squares, the GARCH procedure produces higher expense allocation estimates for educational nonprofits, and qualitatively similar allocation estimates for medical and charitable nonprofits. 18

20 is biased upwards (downwards). Given that fundraising is performed to generate donations, it seems reasonable that the covariance between Fundraising expenses and Private donations is positive, biasing ß 1 upwards and our results downwards. Additionally, it seems reasonable that the larger the true fundraising expenses, the more opportunity there will be for unreported fundraising expenses, making an additional case for a positive covariance between reported fundraising and the measurement error which would again conservatively bias our results. In sum, the expected covariance structures predict an upwards bias on ß 1, causing us to derive too high an estimate of true fundraising expenses, which when compared with reported fundraising expenses on the IRS 990, would result in an understatement of the allocated fundraising expenses producing a mechanical conservative bias in our results. Although this analysis suggests that the presence of systematic measurement error in reported fundraising expenses of the type we suspect exists would cause a conservative bias in our results, we supplement that analysis with simulation results. To conduct our simulation analysis, we construct plausible models of the measurement error and directly estimate their effects on our equation (1) regression estimates. Our error models take the form: True fundraising expenses = φ Reported fundraising expenses + γ, (2) True fundraising expenses = Reported fundraising expenses λ + η, (3) where φ and λ are greater than 1, and γ and η are random errors with mean zero. We conducted analysis holding φ and λ cross-sectionally constant as well as permitting them to randomly vary across organizations. These models assume that the systematic error component is increasing [linearly in equation (2) and non-linearly in equation (3)] in the amount of true fundraising 19

21 expenses. 7 We consider these relationships to be plausible representations of the actual systematic error, although it is not possible to determine ex-ante the exact nature of the error. Our simulation results (untabulated), which varied the parameters φ and λ across a wide range of positive numbers, find that the errors represented by equations (2) and (3) produce coefficient estimates of ß 1 that are systematically biased upwards, in turn biasing our estimated expense allocations downward. These simulation results support our analytical error analysis above and suggest that, if one assumes that our error models are descriptive, our results contain a conservative bias of unknown magnitude. This inherent conservative bias, in conjunction with the previously discussed conservative bias caused by assuming that no fundraising expenses are incurred to generate grants, suggests that our estimations of fundraising expense allocations are a lower bound and should be interpreted accordingly. The bottom portion of table 2 reports our aggregate estimates of nonprofits fundraising expenses and compares those estimates with the fundraising expenses reported on the IRS 990. The average educational nonprofit in the full sample reports a mean of $493 thousand of fundraising expenses. Using our estimation methodology, our expectation of the mean fundraising expenses for educational nonprofits is $2,320 thousand, suggesting that educational nonprofits allocate a mean of $1,827 thousand in expenses out of the fundraising expense category. We estimate that the average medical nonprofit allocates a mean of $168 thousand of expenses out of the fundraising category; 7 It is well known that random measurement error (i.e., γ and η) biases coefficient estimates towards zero. In an attempt to control for the random portion of the measurement error in fundraising expenses, we replicated both our simulation and primary results using two-stage least-squares where the value of fundraising that enters the estimation model is the fitted value from a regression of fundraising expenses on the exogenous variables in equation (1) as well as lagged values of fundraising expenses (up to three lags were included). The general inferences of the results were not altered by the two-stage procedure. 20

22 while the average charitable nonprofit allocates a mean of $299 thousand of expenses out of the fundraising category. Median values show similar patterns, although with smaller magnitudes. We estimate that educational nonprofits reduced their average fundraising ratios using accounting expense allocations from 21.0 percent down to a reported figure of 2.8 percent. Medical nonprofits used accounting expense allocations to reduce their average fundraising ratios from 5.2 percent down to 1.5 percent; while charitable nonprofits reduced their average fundraising ratios from an estimated 13.3 percent down to a reported 3.3 percent. To the extent that our estimation methodologies are reasonable, our results are consistent with a behavior that has long been suspected by the popular press. If donors rely on the reported ratios to make their donations decisions, and are unable to disentangle managerial expense allocations, then financial disclosure management by nonprofit organizations likely results in economic misallocation of resources Analysis of Nonprofits that Report Zero Fundraising Expenses This analysis examines the portion of our samples that, despite earning some amount of private donations, report zero fundraising expenses. We focus on private donations because, as compared to feeder or government grants, they are the kind most likely to be associated with continuous fundraising efforts. Table 4 shows that 95 of 122 California hospitals in the 1998 sample report earning private donations but record zero fundraising expenses. For the larger sample, we find that 25 percent, 46 percent, and 33 percent of educational, medical, and charitable nonprofits, respectively, that earn private donations report zero fundraising expenses. The average dollar amount of private donations received by a nonprofit that reports zero fundraising expenses is over $1.8 million. 21

23 Although it is possible that a nonprofit could earn private donations without incurring any fundraising effort, it seems unreasonable that such a large proportion of nonprofits could earn such significant amounts of private donations with zero effort. In an attempt to determine if any of these nonprofits actually do incur some amount of fundraising expenses, we conducted a search of the nonprofits Internet web pages for evidence of fundraising activities. We examined web pages for all of the 95 California hospitals from the year 1998 (most recent year) that reported earning private donations but reported zero fundraising expenses. Rather than examining all 5,000 web pages of the larger set of educational, medical, and charitable nonprofits for the year 1998, we randomly selected a sample of 10 percent of the observations in the sample year 1998 (most recent year) for each of the three nonprofit types. To perform our investigation we first accessed the nonprofit s most recent IRS 990 at to make sure that it continues to earn private donations in its most recent year (i.e., a positive amount on page 1 line 1a of the IRS 990 for the year 2001) and still reports zero fundraising expenses (i.e., zero on line 15 of page 1 on the IRS 990 for the year 2001). For those nonprofits that report earning private donations and yet report zero fundraising expenses in their most recent year s IRS 990, we searched the Internet (using the Yahoo search engine) for their web pages and examined their web pages for evidence of fundraising activities. We consider evidence of fundraising activities to be either direct solicitations for donations or providing instructions on how a potential donor could contribute. Table 4 reports the results of our web page search. For the 95 California hospitals in 1998 that reported earning private donations yet reported zero fundraising expenses we found evidence of fundraising for 19 hospitals (20 percent of web sites searched). For the larger sample of nonprofits we found evidence of fundraising activities for 26 of 38 educational nonprofits, 13 of 22

24 91 medical nonprofits, and 72 of 125 charitable nonprofits. If our samples are representative, these results suggest that over one-third of nonprofits that report zero fundraising expenses on their IRS 990s actually do incur some amount of fundraising. A list of the nonprofits that we found evidence of fundraising for is included in Appendix A. For many of the nonprofits we found evidence of substantial fundraising activities beyond simply soliciting donations. Some web sites had links to separate offices for fundraising development while many others contained the names of individuals whose job titles included Director of Foundation and Government Grants and Director of Individual Giving. It is possible that organizations engage in other types of fundraising efforts that are not revealed by an internet web page search (such as mailings or radio announcements). If true, our analysis provides a conservative estimate of fundraising expense under reporting. These results provide additional evidence that nonprofits under-report the true extent of their fundraising activities on their publicly available financial reports Characteristics Associated with Expense Allocations The purpose of this analysis is to use the estimated expense allocations from our prior analyses as dependent variables to test the hypotheses that accounting expense allocations are decreasing in political costs and increasing in a nonprofit s reliance on donations as a revenue source. Prior research suggests that larger for-profit firms make more conservative accounting choices so as to avoid drawing unwanted attention to themselves (Watts and Zimmerman 1978), and we test this hypothesis in the nonprofit setting. The incentive to report more favorable fundraising and program service ratios is likely to be higher for those nonprofits that rely more heavily on donations as a funding source. We first conduct this analysis using the expense 23

25 allocation estimates for the sample of 719 California hospitals, and then using the allocation estimates we derived for the larger set of educational, medical, and charitable nonprofits. Although both analyses test the same research hypotheses, they include somewhat different control variables because the OSHPD dataset permits us to include an institutionally richer set of controls. Results are not qualitatively sensitive to the choice of control variables. We empirically test our predictions using the combined OSHPD and IRS 990 data on a sample of 719 California hospitals by estimating the following regression: Program allocations it = α + β 1 Size it + β 2 Margin it + β 3 Donations it + β 4 LOS it + β 5 Medicare it + β 6 Medi-Cal it + β 7 Occupancy it + β 8 Charity it + ε it, (4) where Program allocations is a nonprofit s reported program service ratio (i.e., program service expenses / total expenses) calculated using IRS 990 data less its program service ratio calculated using the OSHPD data; Size is total assets in $millions; Margin is net income divided by net revenue; Donations is donations revenue divided by total revenue; LOS is the average length of stay from admission to discharge; Medicare is the proportion of Medicare patients to total patients; Medi-Cal is the proportion of Medi-Cal patients to total patients; Occupancy is the actual patient days divided by available patient days and Charity is charity care expense divided by net revenue. Our primary variables of interest are Size and Donations, and we predict that the sign of the coefficient estimate will be negative for Size and positive for Donations. Medicare, Medi-Cal, LOS, and Occupancy are control variables. Prior health care studies suggest that these variables have an influence on hospital outcomes (e.g., for Size: Alexander and Lee, 1996; French, 1996; Mick and Wise, 1996; Robinson and Phibbs, 1989; for Medicare and Medi-Cal: 24

26 Dranove, 1988, Lynk, 1995; for LOS: Dranove, Shanley, and White, 1993; Lynk, 1995). The model includes, but does not tabulate, yearly fixed-effects. 8 To empirically test our predictions for the larger sample of nonprofits, we estimate the following regression: Fundraising allocations it = α + β 1 Size it + β 2 Margin it + β 3 Donations it + ε it. (5) Fundraising allocations is our estimate of a nonprofit s fundraising expense allocations scaled by total revenues (to be consistent with equation (4) above). Size is total assets in $ millions; Margin is net income divided by revenue and Donations is private donations revenue divided by total revenue. Once again our primary variables of interest are Size and Donations, and we predict that the sign of the coefficient estimate will be negative on Size and positive on Donations. Margin is included as a control variable. Because the IRS 990 data do not include as much institutional detail as the OSHPD reports, we are not able to include as many control variables for our sample of medical nonprofits in equation (5) as for equation (4). We estimate equation (5) separately by nonprofit type and include (untabulated) yearly fixed-effects. Table 5 contains the results from estimating equation (4); while table 6 contains the results of estimating equation (5). Results in table 5 support our prediction that hospitals with a greater reliance on donations as a revenue source have larger expense allocations into program services, but they do not support our hypothesis that expense allocations are sensitive to political costs as measured by total assets. Although not a part of our hypothesis, the results for the charity care 8 Because we use time series data, we cannot eliminate the possibility of auto-correlation. To control for this, we use a linear auto-regression model whose error term is assumed to be an autoregressive process. Our models also control for conditional heteroscedasticity (ARCH) and generalized heteroscedasticity (GARCH). 25