The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprofit hospitals in Taiwan

ADIAC-00418; No of Pages 11 Advances in Accounting xxx (2019) 100418

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The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprofit hospitals in Taiwan Yi-Chieh Wen a, Pinghsun Huang b, Hsiu-Chu Shen c,e,f,⁎, Yan Zhang d a

Department of Accounting Information, National Taichung University of Science and Technology, Taichung 404, Taiwan Graduate Institute of Finance, National Cheng Kung University, Tainan 701, Taiwan Center for Geriatrics and Gerontology & Division of Neurology, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan d School of Management, Binghamton University, State University of New York, Binghamton, NY 13902, USA e Department of Business Management, National Sun Yat-sen University, Kaohsiung 80424, Taiwan f Department of Physical Therapy, Shu-Zen Junior College of Medicine and Management, Kaohsiung 82144, Taiwan b c

a r t i c l e

i n f o

Article history: Received 15 October 2018 Received in revised form 20 April 2019 Accepted 20 April 2019 Available online xxxx Keywords: Healthcare quality Hospital management Ownership type Real activity management

a b s t r a c t Using a sample of 319 Taiwan's nonprofit hospital-years over the period of 2005–2010, we uncover evidence that nonprofit hospitals engage in real activity manipulation to meet earnings benchmarks through both core operating- and non-revenue-generating- expenditures. More importantly, we provide novel insight into the role of organizational forms in real earnings management for the nonprofit hospital sector. While private hospitals manipulate expenditures significantly upward or downward to achieve specific net income benchmarks, public hospitals, which are characterized by weaker financial incentives, manipulate earnings less extensively. We further find that, among private hospitals, real activity management is more pronounced in the nonreligious group, suggesting that entity-level religiosity is likely to deter undesirable behavior. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction Earnings management has been considered one of the most important financial reporting issues facing both for-profit and nonprofit firms (Cohen & Zarowin, 2010; Healy & Wahlen, 1999; Lee & Masulis, 2009; Leuz, Nanda, & Wysocki, 2003; Merchant & Rockness, 1994; Roychowdhury, 2006; Schipper, 1989; Xie, Davidson, & DaDalt, 2003). While earnings management has been found widespread in for-profit firms, scant attention has been paid to nonprofit firms (Eldenburg, Gunny, Hee, & Soderstrom, 2011; Leone & Van Horn, 2005). In particular, empirical evidence on real earnings management is surprisingly sparse for the nonprofit sector. Along this line of inquiry, extant research has not yet examined whether organizational forms play an instrumental role in earnings management through real activity manipulation.1

⁎ Corresponding author at: Division of Neurology, Department of Medicine, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan. E-mail addresses: [email protected] (Y.-C. Wen), [email protected] (P. Huang), [email protected] (H.-C. Shen), [email protected] (Y. Zhang). 1 In this paper, we use “real activity manipulation”, “real activity management”, and “real earnings management” interchangeably.

To fill in this research gap, we examine whether real activity manipulation varies significantly with the ownership type of Taiwanbased nonprofit hospitals. Our research question is motivated by Fama and Jensen (1983a, 1983b) that managerial incentives are largely influenced and shaped by organizational forms. Moreover, recent studies (e.g., Guiso, Sapienza, & Zingales, 2004, 2006) emphasize that customary beliefs and value systems that a particular entity shares can impact firm behavior. The analysis of nonprofit hospitals is especially interesting, because, in contrast to for-profit firms, nonprofit hospitals comprise a large number of state-owned and religious entities. Further, nonprofit hospital managers have strong incentives to manipulate income upward to avoid possible terminations or downward to protect their tax-exempt status (Brickley & Van Horn, 2002; Leone & Van Horn, 2005). We focus on nonprofit hospitals in Taiwan because emerging market entities, which are characterized by potentially extreme moral hazard problems, provide an excellent laboratory to explore unethical management (La Porta, Lopez-de-Silanes, Shleifer, & Vishny, 1998). Further, the Taiwanese government-sponsored system of universal health coverage (National Health Insurance, NHI) can exacerbate managerial incentives to engage in real activity manipulation (Dongtao, 2013; Johnson, 2009; Roy, 2012; Zakaria, 2012). The NHI has reported deficits on an increasing scale ever since its introduction in 1995 because of stunningly low insurance premiums, which are under the strong influence of politicians. To avoid the bankruptcy of the NHI program, the National Health

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Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

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Insurance Administration administers a global budget system and reduces service payment for each service performed.2 Moreover, the NHI's great deficits can induce the government to review the taxexempt status of the nonprofit hospital sector more rigorously, when hospital profits hit a certain threshold. Taken together, Taiwanese nonprofit hospitals face strong incentives to manipulate earnings upward or downward to meet earnings benchmarks. Consequently, an exploration of Taiwan's nonprofit hospitals provides a unique opportunity to investigate the effect of organizational forms on the extent of real activity manipulation. In a sample of 319 hospital-years in the period of 2005–2010, we observe the discontinuity of earnings distribution and detect the possibility of earnings management.3 We also investigate the channels through which nonprofit hospitals engage in real activity management to meet their earnings benchmarks. As expected, we find that nonprofit hospitals achieve net income thresholds not only via non-revenue-generating expenditures, but also via core operating expenditures. Equivalently, these types of expenditures rise when projected income is above the benchmark area. Such expenses, however, decline when expected earning is below the benchmark range. We further examine whether such real earnings management depends on the ownership types of nonprofit hospitals. Taiwan's nonprofit hospitals can be broadly classified into two groups — publicly- and privately-owned hospitals. As Eldenburg, Hermalin, Weisbach, and Wosinska (2004) point out, poor financial performance is related to CEO turnover for all hospitals, except government hospitals. The intuition is that taxpayers provide substantial support to state-owned hospitals and that the terms of public hospital CEOs are generally guaranteed by explicit contracts. In contrast, CEOs of privately-owned hospitals, whose operations rely heavily on patient fees, are particularly sensitive to losses and thus have a strong motive to manage earnings upward. In addition, private hospitals, which largely face rigorous scrutiny from regulators about their tax-exempt status, have a strong incentive to manage earnings downward when they have substantial profits.4 Further, private individuals or groups are likely to run non-profitable hospitals in an effort to gain a socially responsible reputation for the sake of concealing their self-dealing activities (Cennamo, Berrone, & Gomez-Mejia, 2008; Jensen & Meckling, 1976; McWilliams & Siegel, 2000). This provides another rationale for privately-owned hospitals to manipulate earnings downward to meet the community's expectations. Hence, we posit that private nonprofit hospitals are more likely to engage in real activity management to boost or lower their accounting performance vis-à-vis public nonprofit hospitals. Interestingly, we find that religious hospitals, which are characterized by high charity-care levels and strong religious beliefs, account for half of private hospitals. As Smith (1790) indicates, religious morality is an important driver of ethical behavior. Iannaccone (1998) adds that customary beliefs and values in religion can affect economic attitudes and activities of individuals, groups, and firms. Supporting this notion, Guiso, Sapienza, and Zingales (2003) and Barro and McCleary (2003) provide evidence that individual misbehavior decreases significantly with the level of religiosity. Consequently, we expect that religious hospital managers, relative to managers in non-religious private hospitals, are less likely to engage 2 In response to the NHI's increasingly lower service fee schedule and a global budget system that places a ceiling on hospital claims, hospitals can cut core operating expenditures to make up for the loss of core operating revenues. For example, hospitals can switch from brand-name drugs to generic drugs, delay or skip investment in modern medical equipment, lower bonuses and other benefits of medical staff, freeze hiring, reduce the amount of overtime pay, or reduce the quality of clinical sessions. Given that these expenditures are subject to managerial discretion, we expect that the tendency to reduce expenditures, including core operating expenditures, is greater for hospitals with predicted earnings below the benchmark. 3 We do not examine branch hospitals because earnings management decisions are frequently consolidated and determined by headquarters. 4 The favorable tax treatment is typically assured for public hospitals, because of their mission to perform government-assigned tasks, as noted in Eldenburg et al. (2004)

in real earnings management to present a misleading picture of their true earnings capacities. Consistent with our expectation, we find that private hospitals undertake real activities via non-revenue-generating- and core operating- expenditures more extensively to achieve a particular accounting performance vis-à-vis public hospitals. Our results indicate that greater financial incentives induce private hospital managers to mislead their stakeholders, such as community and government, about the underlying economic performance of their entities by aggressively engaging in real activity manipulation. Within the class of private hospitals, we find that earnings manipulation through real activities predominately exists in non-religious hospitals. Our findings suggest that hospital culture affected by religious faiths can significantly influence hospital behavior by reducing the level of unethical practices (e.g., real activity earnings management). On the whole, our primary contributions are in four aspects. First, to the best of our knowledge, this study is the first attempt to explore the impact of organizational forms on potentially unethical management along the dimension of real activity manipulation in the nonprofit setting. We find that private hospitals exploit both core- and non-core activities to manipulate earnings, whereas the extent of real earnings management is far less for public hospitals. We further show that real activity management is remarkably more pronounced in non-religious private hospitals. Second, our paper contributes to a growing literature arguing that religion or corporate culture matters for corporate decision making. Hilary and Hui (2009) find that risk exposure is smaller for firms that are headquartered in counties with higher levels of religiosity. Grullon, Kanatas, and Weston (2010) report that firms that are located in more religious counties are less likely to engage in unethical or undesirable behavior, such as backdating options and practicing accrual management. Han, Kang, Salter, and Yoo (2010) show that risk aversion and aspects of individualism in national culture explains earnings management behavior. While these studies provide important insights into the implications of local culture for a firm's decision making, they focus on county- or national-level value systems in lieu of entity-level beliefs and values. In contrast to extant research, we identify a new channel (i.e., real activity manipulation) through which firm culture matters. We illuminate the fact that firm-level religiosity, which is generally overlooked in prior research, per se influences firm decision making. Third, our findings enrich the understanding of recent research on real earnings management practices for the nonprofit sector (Jones & Roberts, 2006). In particular, we add to Eldenburg et al. (2011), which features California-based nonprofit hospitals, by providing new evidence that real activity manipulation is prevalent for nonprofit hospitals in Taiwan. While Tan (2011) also provides important insights into earnings management in Taiwan's non-profit hospitals, she focuses her analysis on accrual-based earnings management. Our study contributes to this strand of literature by presenting evidence on real earnings management of nonprofit hospitals in Taiwan, which has received scant attention to date.5 Consistent with the premise that unethical management is more likely for emerging markets (La Porta et al., 1998; Pinkowitz, Stulz, & Williamson, 2006), we find that Taiwanese nonprofit hospitals manipulate not only non-core spending, but also core operating expenditures for real earnings management. Fourth, our results provide important implications to stakeholders of nonprofit hospitals. Customers, suppliers, and medical staff of hospitals should recognize that healthcare institutes have an incentive to achieve 5 Although Tan uncovers that religion-based foundation hospitals are less likely to report their earnings in the interval of (0, 0.02) than enterprise-based foundation hospitals, she finds no evidence on the link between organizational forms and accrual management. We contribute to this line of inquiry by shedding light on the devices (e.g., real activity management) through which nonprofit hospitals characterized by a spectrum of organizational forms in public, private, religious, and non-religious types can achieve their earnings benchmarks

Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

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their earnings benchmarks by cutting core operating expenditures potentially at the expense of healthcare quality. This incentive is likely to be reinforced by the underfunded National Health Insurance, whose cost-containment measures prevent hospitals from charging market prices on their healthcare provisions and thus put substantial pressures on their earnings. Consequently, it is imperative for policymakers in Taiwan to introduce substantive reforms to strike a balance between healthcare costs and medical quality for the long-term sustainability of the healthcare system. Regulators and Taxpayers should also be aware that the pressure to maintain a tax-exempt status can create a motive for hospitals to manage real activity expenditures upward potentially through inefficient resource allocation. Moreover, our results call stakeholders' attention to the role of organizational forms in nonprofit hospitals' real earnings management. The remainder of this paper is organized as follow. Section 2 reviews the related literature and develops hypotheses. Section 3 describes the sample selection procedure and presents research design. Section 4 reports empirical results. Section 5 concludes the paper. 2. Extant research and hypothesis development 2.1. Institutional background and earnings management The National Health Insurance (NHI) program in Taiwan, by its nature, is similar to the Affordable Care Act (ACA) or ObamaCare in the United States, whose goal is to grant its citizens access to affordable, quality health insurance and to control health care spending. In contrast to ObamaCare, the single-payer NHI system, however, is like the model of a planned economy in which the government is solely responsible for administrating the insurance program: the administration determines the rates, collects the premiums, establishes the standards for service quality and fees, and pays for medical services (Dongtao, 2013; Zakaria, 2012). Thus far, the NHI has provided affordable medical insurance to the community; in effect, it has extended insurance coverage to 99.6% of the total population since its implementation in 1995 (Department of Health, 2011). Consequently, the sources of revenues for Taiwan's hospitals mainly derive from the NHI plan for the medical services performed. However, the rapid growth of health care expenditures and a low-premium policy have resulted in severe financial deficits of this insurance program. The insurance premiums stay flat despite strong demands for health insurance reforms. In response to the incurring financial problems in the health insurance plan, the government has taken actions to achieve macro-efficiency. For instance, the administration has instituted a global budget system, which is the most significant supply-side costcontainment mechanism and sets global budget caps for hospitals and primary care. The vast majority of Taiwanese hospitals are nonprofit institutions with social objectives. All nonprofit hospitals are expected to deliver medical care of high quality in return for tax exemptions. Nevertheless, there exist conflicts of interest between the hospital sector and the government because hospital managers are frequently evaluated based on their financial performance (Yan, Hsu, Yang, & Fang, 2010). The hospital's accounting information is presumed to assist its donors and other stakeholders with monitoring its implicit contracts and the allocation of resources (Krishnan, Yetman, & Yetman, 2006). However, hospital managers are likely to engage in earnings management to distort accounting information to maximize their private benefits. Moreover, imperfect monitoring from the regulatory agencies and the severe deficits of the NHI program can exacerbate moral hazard problems. As Johnson (2009) points out, the financial problems of the universal health care program can adversely influence the quality of medical care because core operating expenditures are subject to managerial discretion. For example, hospitals can place ceilings on drug expenditures or cut spending on diagnostic equipment. It is also likely that the government with great financial deficits from the NHI has an incentive to

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actively review their preferred tax status, when nonprofit hospitals hit a certain level of profits. Accordingly, Taiwan's hospitals have strong incentives to manipulate earnings upward or downward. Equivalently, managers are likely to manage earnings upward for their incumbency rents when income levels are below the benchmark. In a similar fashion, nonprofit hospital managers have an incentive to manipulate income downward for the sake of favorable tax treatments when earnings are above the earnings threshold. Such undesirable or unethical management behaviors are also documented in academic research, which suggests that nonprofit hospitals and for-profit institutions are similarly concerned about financial performance. Eldenburg et al. (2004) document that accounting performance affects CEO turnover across all types of hospitals, except government hospitals. Nonetheless, external pressures from regulators or donors may constrain nonprofit hospitals' earnings, so that they must balance their profit against their costs. Vansant (2011) suggests that nonprofit hospitals can reduce scrutiny and avoid the potential loss of tax exemptions by decreasing profits. Hence, they are likely to manage earnings toward a long-run economic zero profit. Burgstahler and Dichev (1997) identify a discontinuity of earnings distribution as evidence of earnings management. Similarly, Degeorge, Patel, and Zeckhauser (1999) indicate that threshold-based earnings management is a significant discontinuity around zero in the earnings distribution. For nonprofit hospitals, Leone and Van Horn (2005) provide evidence that managers use discretionary spending and discretionary accruals to meet a range just above zero profit. Leone and Van Horn (2005) argue that nonprofit hospitals face costs and pressures from reporting losses and profits. Nonprofit hospitals have conflicting incentives to break even. The CEOs or managers may impair their reputations from reporting losses or profits. When nonprofit hospitals report a loss, the managers will face the possibility of dismissal. Therefore, they have incentives to improve their accounting performance by decreasing expenditures. In order to avoid scrutiny from media, regulators, and third-party sponsors, managers also have incentives to increase expenditures in the circumstance under which earnings are high. Given that Taiwan's nonprofit hospitals face fairly strong incentives to manage earnings, partly arising from the mounting deficits of the universal health insurance plan, we expect a discontinuity around zero in their earnings distribution.

2.2. Earnings management through real activity manipulation As Merchant and Rockness (1994) put it, “earnings management practices probably raise the most important ethical issues facing the accounting profession.” Arthur Levitt, a former SEC chairman, emphasizes that any earnings management behavior is undesirable, because it frequently obscures the facts that stakeholders need to know, leaving them in the dark about the true picture of the entity (Kaplan, 2001). As in Roychowdhury (2006) and Cohen, Mashruwala, and Zach (2010), earnings management via real activity manipulation is accomplished by changing operating decisions through discretionary spending, such as R&D expenditures and selling, general, and administrative (SG&A) expenses. Abnormal production costs and abnormal operating cash flows usually result from excessive price discount. Dechow and Sloan (1991) find that CEOs take advantage of the opportunistic reduction of R&D expenditures to increase short-term earnings. Bens, Nagar, and Franco Wong (2002) add that managers repurchase stock to avoid EPS dilution by reducing R&D spending opportunistically. In a sample of California-based hospitals, Eldenburg et al. (2011) find that non-core expenditures are the greatest amount of real earnings management. These expenditures do not directly affect the mission of the hospital sector or the quality of healthcare services. When nonprofit hospitals have earnings above (below) their benchmarks, managers tend to decrease (increase) profit through increasing (reducing) noncore expenditures. In contrast, core operating expenditures, which are

Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

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associated with medical quality, are less likely to be the sources of real earnings management. As La Porta et al. (1998) and Pinkowitz et al. (2006) point out, moral hazard problems are particularly severe for emerging market firms. Further, financial incentives that are closely tied to the deficits of the NHI are notably strong for Taiwan's nonprofit hospitals. Given that policymakers have been reluctant to impose growing medical costs fully on users of the insurance program, underfunding has been a constant challenge for the execution of the NHI policy. To deal with the financial deficit and thus balance the NHI budget, the government has imposed cost-containment measures (e.g., global budget system) primarily on the supply side of healthcare. Within the global budget, medical services providers are often paid through a mix of case-payment and fee-for-service. Under the case-payment scheme, providers are reimbursed medical procedures (e.g., appendectomy, C-section, kidney transplants, gynecological surgery) at a fixed number of points. Given that the case-payment scheme determines reimbursement for medical and other expenses based on a predetermined number of points, itemized fees for medical care, medication, material, and ward care are generally not allowed for additional claims.6 Moreover, the point values used to pay for services are largely below market values regardless of adopting the case-payment or fee-for-service scheme (Cashin, Bloom, & Bhatt, 2015; Dongtao, 2013; Roy, 2012). This payment system ensures that total payments stay under global budget caps. Because the global budget payment system is putting substantial pressures on the earnings of healthcare providers, hospitals have an incentive to cut their core operating expenditures. This incentive is expected to intensify when the projected income is below the benchmark range. The reduction in core operating expenditures can be achieved by, for example, shifting the current replacement of obsolete equipment to the future period or switching from branded drugs to generic drugs. In turn, this can create a material effect on healthcare quality if the phenomenon persists for a long time. The fact that core operating expenditures are the major source of hospital spending is also likely to explain why managing core operating expenses downward or upward can play a pivotal role in meeting a hospital's benchmark range. Our summary statistics in Table 2 indicate that the change in core operating expenditures, on average, accounts for more than 4% of lagged total assets, whereas the corresponding changes in other expenditures are slightly above zero. Accordingly, manipulating core operating spending upward or downward potentially through overinvestment or underinvestment in healthcare activities is more likely to have a material effect on the ratio of projected income to lagged assets and then achieve the goal of earnings management. Non-revenue-generating expenditures constitute the second largest resource of hospital expenditures for our sample (see Table 2). Article 46 of Taiwan's Medical Law requires hospitals to reserve more than 10% of annual operating income to engage in non-revenue-generating activities related to (1) research and development, (2) employee training and health education, and (3) community and other social services. Regulators evaluate the compliance of hospitals with this mandatory requirement. However, non-operating expenditures (e.g., rent, maintenance) are quite trivial, relative to hospital size. Therefore, we expect that nonprofit hospital managers tend to rely on core operating- and nonrevenue-generating expenditures, in lieu of non-operating expenditures, to achieve earnings benchmarks. We state our first hypothesis as follows: H1. Nonprofit hospitals are more likely to manage earnings through core operating expenses and non-revenue-generating expenditures than through non-operating expenditures.

6 See a Pacific Bridge Medical story entitled “Medical device reimbursement in Taiwan” for an informative discussion.

Prior research suggests that organizational forms can influence the financial incentives of hospital managers. Duggan (2000) examines how hospital ownership influences profitable opportunities, due to changes in regulatory policy. Eldenburg and Krishnan (2003) and Chang, Chang, Das, and Li (2004) emphasize that public hospitals have lower operational efficiency and poorer performance than private hospitals. It appears that public hospitals have much weaker financial incentives, because government-sponsored institutes are required to provide more charity care than their private counterparts. In particular, public hospitals receive substantial grants and subsidies from municipalities and the central government. In contrast, private hospitals depend mainly on patient fees and donations. Hence, the sensitivity of CEO turnover to financial performance tends to be greater for private hospitals than for public hospitals (see Eldenburg et al., 2004). This suggests that managers in private hospitals have greater financial incentives vis-à-vis those in public hospitals. Nonprofit hospitals and healthcare organizations with large profits are under media and governmental scrutiny. Private hospitals with substantial profits tend to be under greater pressure about their taxexempt status than are their state-owned peers, as public hospitals' special tax treatment is generally assured, as noted in Eldenburg et al. (2004). Moreover, private individuals or groups can seek a socially responsible reputation by operating non-profitable hospitals for the purpose of disguising their self-serving activities (Cennamo et al., 2008; Hemingway & Maclagan, 2004; Jensen & Meckling, 1976). This, in turn, creates another incentive for privately-owned nonprofit hospitals to bring earnings downward to a specific benchmark. Consequently, we predict that private nonprofit hospitals engage in more real activity management to meet earnings benchmark than do public hospitals. To state it more formally, our second hypothesis is presented as follows: H2. Private hospitals are more likely to engage in real activity manipulation via decreasing (increasing) core operating expenses and nonrevenue-generating expenditures than are public hospitals when their earnings are below (above) the benchmark range. As Weaver and Agle (2002) highlight, religious entities are more likely to comply with a code of ethics and social norms, as compared to non-religious entities. This suggests that the tendency to conform to the dominant values and faiths of religion has strong implications for hospital behavior. Similarly, Guiso et al. (2003) find that countries with stronger religious beliefs are less likely to display unethical behavior and are associated with higher economic growth. Hilary and Hui (2009) document that firms that are headquartered in counties with higher levels of religiosity are exposed to lower risk levels. Grullon et al. (2010) add that local religiosity discourages unethical firm behavior and significantly reduces the size of managerial compensation packages. As hospitals can engage in earnings management to cosmetically improve their income streams or conceal their superior performance, manipulating expenditures upward or downward can enable healthcare providers to present a misleading picture of their true earnings capacities (Cohen & Zarowin, 2010; DeFond & Park, 1997; Eldenburg et al., 2011; Fudenberg & Tirole, 1995; Guo, Huang, Zhang, & Zhou, 2015; Merchant & Rockness, 1994; Roychowdhury, 2006). To the extent that social norms are deeply rooted in religious organizations, faith-based hospitals should behave more ethically to meet their social missions. This perspective suggests that religious hospitals should be less likely to exploit real earnings management as a tool to artificially achieve their net income benchmarks than non-religious private hospitals. Put differently, religious hospitals are less likely to engage in real earnings management to mislead their stakeholders about the fundamental operations of their entities than are their non-religious private counterparts. Moreover, a religious-hospital's aversion to real earnings management can be driven by its mission of charity care. Religion-affiliated

Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

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hospitals are likely to value health care quality more than profitability because they are established mainly to dedicate to religious beliefs so that important tenets of their faith will be observed (Starr, 1982). In addition, Eldenburg et al. (2004) argue that religion-sponsored hospitals tend to operate patient-centered care because patients affiliated with a religious organization often exert significant influence over the operation of its faith-based hospitals. This line of reasoning suggests that religion-based hospitals are less likely to underinvest in their core patient care activities to achieve their benchmark ranges than nonreligion-based private hospitals. These discussions motivate our third hypothesis: H3. Non-religious private hospitals are more likely to engage in real activity management via decreasing (increasing) core operating expenses and non-revenue-generating expenditures than religious hospitals, when their earnings are below (above) their net income benchmarks.

3. Sample and empirical design

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Table 1 Sample selection procedure. Sample characteristics

Number of hospital-years

Nonprofit hospitals covered by Ministry of Health and Welfare Exclude: Hospitals without two consecutive annual reports for empirical analysis Hospitals with insufficient data for calculation of non-revenue-generating expenditures Final hospital-year observations

426 (47) (60) 319

This table reports the sample selection process. Our initial sample consists of 71 unique nonprofit hospitals whose financial statements in any sample year from 2005 to 2010 are available from the database compiled by the Ministry of Health and Welfare. Equivalently, we start with 426 hospital-year observations. Because our empirical analysis requires annual changes in core operating expenditures, non-operating expenditures, and non-revenue-generating expenditures, we exclude 47 hospital-year observations that lack two successive annual reports for empirical analysis. We further remove 60 hospital-years without data on annual changes in non-revenue-generating expenditures, which comprise many data items (i.e., research development and health education, general and administrative expenses, and charity care and other social service expenses). Our final sample consists of 319 hospital-years over the period from 2005 to 2010.

3.1. Data and sample selection Our initial sample consists of 71 unique nonprofit hospitals whose financial statements in any year from 2005 to 2010 are available from the database compiled by the Ministry of Health and Welfare (formerly known as the Department of Health).7 Equivalently, we start with 426 hospital-year observations. Because our model specification requires annual changes in non-revenue-generating expenditures, core operating expenditures, and non-operating expenditures, we remove 47 hospital-year observations without two consecutive annual reports for empirical analysis.8 We also exclude 60 observations with missing data on annual changes in research development and health education, general and administrative expenses, or charity care and other social service expenses, which constitute non-revenue-generating expenditures. Our final sample comprises 319 hospital-years over the period from 2005 to 2010. Table 1 summarizes our sample selection procedure discussed above. In all subsequent analyses, we winsorize all the continuous variables at the 1% and 99% levels to alleviate potential outlier problems.

3.2. Earnings distribution and earnings management Burgstahler and Dichev (1997) utilize a frequency distribution of earnings to detect earnings management. When earnings distribution has a lower (higher) frequency of just below (above) the zero earnings threshold, it displays a discontinuity around zero with a disproportionate histogram. This is referred to as threshold-based earnings management (Degeorge et al., 1999). We use the same approach to examine whether our sample exhibits a similar phenomenon. If nonprofit hospitals engage in earnings management, we should observe a discontinuous and non-normal distribution of earnings around zero. We group hospital-years into intervals based on net income scaled by lagged total assets and then construct categories of scaled earnings for bin widths. We cast light on the histogram and test statistical significance of the discontinuity by means of Z-statistic. The null states that there is no difference in the number of observations in adjacent intervals and that the earnings distribution is smooth around the threshold of zero earnings.

7 The financial statements are accessible via https://www.mohw.gov.tw/mp-2.html and/or the websites of individual hospitals. 8 47 hospital-years lack public financial statements in 2004 or 2005 possibly because the Medical Care Act on financial disclosure was less strictly enforced prior to 2006 (Hung & Lin, 2010). Specifically, no penalty was imposed on hospitals that had not filed their financial statements with the Department of Health.

3.3. Definitions of variables and empirical model Roychowdhury (2006) provides empirical measures to proxy for discretionary spending on real activity management, such as R&D, SG&A, and advertising expenditures, in for-profit firms. As Eldenburg et al. (2011) suggest, for nonprofit hospitals, real activity manipulation can involve core operating, non-revenue-generating, and nonoperating expenditures. Hence, we focus on a comprehensive set of operational activities that are measured by the hospital's income statements. Specifically, our dependent variables in regression models are the changes in expenditures for core operating- (△COE), non-revenuegenerating- (△NRGE), and non-operating (△NOE) expenditures, respectively. Akin to Eldenburg et al. (2011), we delineate NRGE as the sum of research development and health education expenses, general and administrative expenses, and community medical service and other social service expenses. As for NOE and COE, they are explicitly specified in Taiwanese hospitals' financial statements. COE are related to direct medical services, such as medical examinations, nursing care, emergency services, etc. The examples of NOE include maintenance fees, rental expenses, interest expenses, etc. for retail operations. Eq.

Table 2 Summary statistics. Variable

N

Mean

Q1

Median

Q3

Std. Dev.

Total assets (NT$ million) Revenues (NT$ million) COE (NT$ million) NRGE (NT$ million) NOE (NT$ million) △COE/Total assets t-1 △NRGE/Total assetst-1 △NOE/Total assets t-1 Net Income/ Total assetst-1 CEOTurnover FinancialCrisis

319 319 319 319 319 319 319 319 319 319 319

8568 3725 3362 348 124 0.0433 0.0018 0.0002 0.0248 0.1850 0.4138

1299 583 529 52 8 0.0033 −0.0017 −0.0027 0.0025 0.0000 0.0000

2298 1707 1550 135 29 0.0209 0.0019 0.0001 0.0183 0.0000 0.0000

4896 3864 3274 501 77 0.0481 0.0064 0.0046 0.0419 0.0000 1.0000

6321 5434 5834 648 407 0.0613 0.0247 0.0355 0.0453 0.3889 0.4933

COE represents total core operating expenditures in activities, such as diagnostic tests and patient care. We delineate NRGE as the sum of research development and health education, general and administrative expenses, and charity care and other social service expenses. NOE denotes non-operating expenditures (e.g., rent, maintenance expenses for retail operations). Both NOE and COE are obtained directly from hospital income statements. CEOTurnover is defined as 1 if there is a CEO change in the given year, and 0 otherwise. FinancialCrisis takes a value of 1 if the hospital-year operated in the financial crisis period of 2008–2009, and 0 otherwise.

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(1) summarizes our model specification. ΔExpendi;t ¼ δ0 þ δ1 Increasei;t þ δ2 Decreasei;t þ δ3 NoPredi;t þ δ4 LogAsset i;t þδ5 Revenuei;t þ δ6 CEOTurnoveri;t þ δ7 FinancialCrisisi;t þ εi;t ð1Þ where i denotes hospital and t represents fiscal year. ΔExpend

= Change in non-revenue-generating expenditure (△NRGE), change in non-operating expenditure (△NOE), or change in core operating expenditure (△COE) from year t-1 to t scaled by lagged assets; △NRGE = Change in non-revenue-generating expenditure, including spending on research development and health education, general and administrative expenses, and charity care and other social service expenses deflated by lagged assets; △NOE = Change in total non-operating expenditures (e.g., rent, maintenance for retail operations) that are derived directly from income statements deflated by lagged assets; △COE = Change in total core operating expenditure (e.g., medical check-up, patient care) that are obtained directly from income statements deflated by lagged assets; Increase = 1 if projected income scaled by lagged assets is above the interval ([0, 0.02)), and 0 otherwise; Decrease = 1 if projected income scaled by lagged assets is below the interval ([0, 0.02)) and the shortage amount is less than the previous year's expenditure in the category, and 0 otherwise; Nopred = 1 if projected income scaled by lagged assets is below the interval ([0, 0.02)) and the shortage amount is greater than the previous year's expenditure in the category, and 0 otherwise; LogAsset = Natural logarithm of total assets; ΔRevenue = Change in operating revenue scaled by lagged assets; CEOTurnover = 1 if there is a CEO change in the given year, and 0 otherwise; FinancialCrisis = 1 if the hospital-year operated in the financial crisis period of 2008–2009, and 0 otherwise.

Our main variables of interest are two indicator variables (Increase and Decrease). We follow Eldenburg et al. (2011) and calculate “net income” before spending on core operating, non-operating, or nonrevenue-generating activity, and “projected income” by adding back the level of spending on core operating, non-revenue-generating, or non-operating activity in year t-1. Further, we examine whether projected income is above, below, or within the benchmark range. This range refers to net income deflated by lagged assets within the interval widths of [0, 0.02). If projected income is within the benchmark

range, then the hospital has an incentive to maintain expenditure levels similar to those in the prior year. We expect the coefficients on Increase to be positive and the coefficients on Decrease to be negative for both △COE and △NRGE. We make no prediction about the coefficient signs of Increase and Decrease for △NOE because hospitals seem less likely to rely on non-operating expenses, which are quite trivial, for real earnings management. As in Eldenburg et al. (2011), we also make no prediction about the sign of the coefficient on Nopred. While hospitals whose projected incomes are far below the benchmark range can cut expenditures to move closer to the benchmark, they cannot achieve the benchmark by decreasing their expenditures. Therefore, a negative coefficient on Nopred can simply capture the possibility that hospitals anticipating significant losses cut their expenditures to reduce the probability of financial trouble instead of meeting the goal of earnings management. A positive coefficient on Nopred can imply that hospitals with expected income far below the benchmark shift future expenditures to the current period for the sake of achieving the benchmark in the next period. Taken together, the coefficient sign of Nopred is indeterminate. We use the natural logarithm of total assets (LogAsset) to control for cross-sectional variation in hospital size. We include ΔRevenue in the model specification to adjust for the heterogeneity in ordinary operations. We add CEOTurnover to allow for the possibility that the fluctuation in hospital expenditures can be driven by a leadership change. To address the potentially adverse influence of the financial tsunami on hospital spending, we insert FinancialCrisis, which takes a value of 1 if the hospital-year operated in the financial period of 2008–2009, and 0 otherwise, in Eq. (1).

4. Empirical results 4.1. Descriptive statistics and earnings distribution Table 2 presents summary statistics of financial characteristics for the full sample. The mean and median values of total assets are NT $8568 million and NT$2298 million, respectively. While the lower quartile of revenues is NT$583 million, the upper quartile of revenues is NT $3864 million. The median of total core operating expenditures (COE) is NT$1550 million, whereas the average COE amounts to NT$3362 million. The respective standard deviations of non-revenue-generating

Fig. 1. The frequency distribution of earnings levels scaled by lagged total assets. The dotted line in this chart is a zero cutoff point and the curve is fitted using normal distribution. The histogram displays the number of observations which fall in the top part of each bin around the zero benchmark.

Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

Y.-C. Wen et al. / Advances in Accounting xxx (2019) 100418 Table 3 Test for statistical significance of discontinuity around zero earnings. Interval

Actual freq.

Freq.(%)

Expected freq.

Freq.(%)

SD

Z Stat.

−0.18 −0.16 −0.14 −0.12 −0.08 −0.06 −0.04 −0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18

2 1 0 2 1 4 5 13 84 96 44 25 17 14 4 4 1 2

0.63 0.31 0 0.63 0.31 1.25 1.57 4.08 26.33 30.09 13.79 7.84 5.33 4.39 1.25 1.25 0.31 0.63

0.5 1 1.5 0.5 3 3 8.5 44.5 54.5 64 60.5 30.5 19.5 10.5 9 2.5 3 0.5

0.16 0.32 0.48 0.16 0.95 0.95 2.69 14.1 17.27 20.29 19.18 9.67 6.18 3.33 2.38 0.79 0.95 0.16

1.5 1.22 0.614 1.766 1.364 2.596 3.89 5.023 8.937 9.251 7.393 6.176 5.011 4.431 2.662 2.55 1.318 1.658

1 0 −2.44 0.85 −1.47 0.39 −0.9 −6.27 3.3 3.46 −2.23 −0.89 −0.5 0.79 −1.31 0.59 −1.52 0.9

The Z Statistic is calculated as the difference between the actual frequency and the expected frequency divided by the estimated standard deviation. The actual frequency is from the histogram of earnings distribution in Fig. 1. The expected frequency is the average of the actual frequency in the two adjacent bins.

expenditures (NRGE) and non-operating expenses (NOE) are NT$648 million and NT$407 million. The average ratios of △COE, △NRGE, and △NOE to lagged total assets are 0.0433, 0.0018, and 0.0002, respectively. These statistics suggest that core operating expenditures, relative to other outlays, account for the highest proportion of lagged assets. Among the non-core activity expenses, non-revenues-generating expenditures are far more important than non-operating expenditures. The mean value of net income normalized by lagged assets stands at 0.02, supporting the notion that the average hospital sets its earnings benchmark near zero or slightly above zero. Nearly 20% of our observations experience a change in CEO. Approximately 42% of 319 hospital-years operated in the financial crisis period of 2008–2009. In Fig. 1, we shed light on the tendency of hospital real earnings management by examining the earnings distribution of the healthcare sector. This figure displays a plot of the frequency distribution of earnings that partitions hospital-year observations into intervals based on the ratio of net income to lagged total assets. The histogram of scaled earnings is constructed with widths of 0.02 ranging from −0.18 to +0.18. As expected, we observe a discontinuity toward zero earnings, based on the significant upward shift from the left of zero to the right.

7

Most of the reported earnings in nonprofit hospitals are slightly above zero. This finding is consistent with the notion that nonprofit hospitals manage earnings to avoid losses and reach small profits. Similar to Burgstahler and Dichev (1997), we also examine the statistical significance of the discontinuity by using the Z-statistic. Table 3 shows the lower-than- expected frequency in the interval to the left of zero (z-statistic = −6.27) and the higher- than-expected frequency in the interval to the right of zero (z-statistics = 3.30 and 3.46 around 0 to 0.02 interval). The significant Z-statistics indicate that the actual earnings distribution of nonprofit hospitals presents a discontinuity around zero, which provides supporting evidence of earnings management. Table 4 summarizes the frequency of hospital-years by income level and types of expenditures during our sample period of 2005–2010. For non-core activities, 211 (204) observations have projected earnings above the benchmark in the category of non-revenue-generating expenditures (non-operating expenditures), 41 (29) hospital-years have projected income below the benchmark, and 26 (40) have income far below the benchmark. As for core operating activities, we find a similar pattern. Among the 319 observations, 251 (79%) are expected to increase core operating expenditures, 39 (12%) are predicted to cut such expenditures, 6 (2%) may increase or decrease the spending, and 23 (7%) are likely to maintain the levels of core service expenditures. 4.2. Analysis of real activity manipulation In this subsection, we examine whether nonprofit hospitals tend to use any particular type of expenditure to achieve their accounting performance. In Table 5, we demonstrate that the coefficient on Decrease is negative for change in non-revenue-generating expenditures (△NRGE) at the 1% level of statistical significance. This evidence suggests that nonprofit hospitals reduce their discretionary spending in non-revenue-generating activities when the projected income is below the benchmark range. Moreover, we uncover evidence that Increase is positively related to change in non-revenue-generating expenditures at the 1% level. This finding indicates that nonprofit hospitals increase their discretionary spending when the projected income is above the benchmark range. When we shift attention to changes in non-operating expenditures (△NOE), we find no discernible evidence that hospitals increase (decrease) this type of expenditures when their projected earnings are above (below) the earnings benchmark (neither significant at conventional levels). However, we document that the levels of real activity manipulations via core operating expenditures are notably higher with the magnitudes of 0.037 and 0.028, respectively, for Increase and Decrease

Table 4 Frequency of hospital years by income level and types of expenditures. Non-core operating activities

Core operating activities

Income Level of Hospitals

Expected earnings management via expenditures

Dummy variable

△Non-revenue-generating expenditures

△Non-operating expenditures

△Core-operating expenditures

Income above benchmark

Income ↓ Expenditures ↑ Income ↑ Expenditures ↓ Income? Expenditures? Income ↔ Expenditures ↔

Increase

211

204

251

Decrease

41

29

39

Nopred

26

40

6

Baseline

41

46

23

319

319

319

Income below benchmark Income far below benchmark Income within benchmark Total obs.

We define non-revenue-generating expenditures as the sum of research development and health education, general and administrative expenses, and charity care and other social service expenses. Non-operating expenditures include many items, such as rent and maintenance expenses, for retail operations. Core operating expenditures involve diagnostic tests, patient care, etc. Increase equals 1 if projected income scaled by lagged assets is above the benchmark range of ([0, 0.02)), and 0 otherwise. Decrease is set to 1 if the scaled projected income is below the benchmark range and the shortage amount is less than the previous year's expenditure in the category, and 0 otherwise. Nopred equals 1 if the scaled projected income is below the benchmark range and the shortage amount is greater than the previous year's expenditure in the category, and 0 otherwise. As in Eldenburg et al. (2011), we calculate projected income by adding back the expenditure of interest in prior year to net income before the spending of interest.

Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

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Y.-C. Wen et al. / Advances in Accounting xxx (2019) 100418

Table 5 Regression analysis on real activity manipulation through different types of expenditures for all nonprofit hospitals. Variable

Expected sign

Non-core operating activities

Intercept Increaset

+

Decreaset

−

Nopredt

?

LogAssett

?

ΔRevenuet

?

CEOTurnovert

?

FinancialCrisist

−

Observations Adjusted R2

Core operating activities

△NRGE

△NOE

△COE

−0.010 (0.629) 0.010*** (0.000) −0.024*** (0.000) 0.005 (0.302) 0.000 (0.643) −0.035*** (0.000) −0.001 (0.624) 0.002 (0.494) 319 0.22

0.054* (0.053) 0.003 (0.206) −0.006* (0.056) −0.026* (0.074) −0.003** (0.043) 0.118** (0.041) 0.005 (0.272) 0.003 (0.254) 319 0.23

0.062 (0.252) 0.037*** (0.000) −0.028*** (0.000) −0.026 (0.339) −0.003 (0.293) 0.245*** (0.002) −0.006 (0.297) −0.009* (0.086) 319 0.47

Dependent variables comprise △NRGE, △NOE, and △COE. We define △NRGE as the sum of research development and health education, general and administrative expenses, and charity care and other social service expenses deflated by lagged assets. △NOE denotes change in non-operating expenditures deflated by lagged assets. △COE is defined as change in total core operating expenditures in activities, such as diagnostic tests and nursing care, deflated by lagged assets. Increase takes the value of 1 if projected income scaled by lagged assets is above the benchmark range of ([0, 0.02)), and 0 otherwise. Decrease is set to the value of 1 if the scaled projected income is below the benchmark range and the shortage amount is less than the previous year's expenditure in the category, and 0 otherwise. Nopred is set to 1 if the scaled projected income is below the benchmark range and the shortage amount is greater than the previous year's expenditure in the category, and 0 otherwise. As in Eldenburg et al. (2011), we calculate projected income by adding back the expenditure of interest in prior year to net income before the spending of interest. LogAsset is the natural logarithm of total assets. ΔRevenue is delineated as change in operating revenues scaled by lagged assets. CEOTurnover is defined as 1 if there is a CEO change in the given year, and 0 otherwise. FinancialCrisis takes a value of 1 if the hospital-year operated in the financial crisis period of 2008–2009, and 0 otherwise. *, **, and *** denote significances at the 10%, 5%, and 1% levels, respectively, with p-values in parentheses. The p-values of coefficient estimates are based on robust standard errors clustered by hospital.

(both significant at the 1% level). Taken together, our findings support H1, namely, that nonprofit hospitals engage in real activity management through core operating- and non-revenue-generating expenditures to meet their net income thresholds. While the negative coefficient on Nopred is marginally significant for the model whose dependent variable is △NOE, the healthcare sector cannot count on nonoperating expenditures to achieve its earnings benchmark.

4.3. Real activity management and organizational forms Thus far, we have established evidence that nonprofit hospitals engage in real earnings management by manipulating non-revenuegenerating expenses and core operating expenditures to achieve earnings goals. In this subsection, we further investigate whether hospital organizational forms have a significant impact on real earnings management behavior.

Table 6 Real activity manipulation using non-revenue-generating expenditures for public vs. private nonprofit hospitals. Variable

Expected sign

Intercept Increaset

+

Decreaset

−

Nopredt

?

LogAssett

?

ΔRevenuet

?

CEOTurnovert

?

FinancialCrisist

−

Observations Adjusted R2

Public hospitals

Private hospitals

0.024 (0.100) 0.005** (0.011) −0.002 (0.590) 0.002 (0.166) −0.001 (0.104) 0.107* (0.059) 0.005* (0.087) −0.004* (0.071) 113 0.49

0.003 (0.902) 0.012*** (0.001) −0.027*** (0.000) 0.007 (0.101) 0.000 (0.791) −0.012*** (0.000) −0.001 (0.742) 0.002 (0.545) 206 0.33

Difference in coefficients private vs. public (p-values)

0.007** (0.015) −0.025*** (0.000) 0.005 (0.528) 0.001 (0.206) −0.119*** (0.000) −0.006 (0.404) 0.006 (0.281)

Dependent variable is change in non-revenue-generating expenditures (△NRGE). We define NRGE as the sum of research development and health education, general and administrative expenses, and charity care and other social service expenses deflated by lagged assets. Increase takes the value of 1 if projected income scaled by lagged assets is above the benchmark range of ([0, 0.02)), and 0 otherwise. Decrease is set to the value of 1 if projected income scaled by lagged assets is below the benchmark range and the shortage amount is less than the previous year's expenditure in the category, and 0 otherwise. Nopred is set to 1 if projected income scaled by lagged assets is below the benchmark range and the shortage amount is greater than the previous year's expenditure in the category, and 0 otherwise. We calculate projected income by adding back the expenditure of interest in prior year to net income before the spending of interest. LogAsset is the natural logarithm of total assets. ΔRevenue is delineated as change in operating revenues scaled by lagged assets. CEOTurnover is defined as 1 if there is a CEO change in the given year, and 0 otherwise. FinancialCrisis takes a value of 1 if the hospital-year operated in the financial crisis period of 2008–2009, and 0 otherwise. *, **, and *** denote significances at the 10%, 5%, and 1% levels, respectively, with p-values in parentheses. The p-values of coefficient estimates for each subsample are based on robust standard errors clustered by hospital. We derive the p-values in the last column from the Chow test.

Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

Y.-C. Wen et al. / Advances in Accounting xxx (2019) 100418

9

Table 7 Real activity manipulation using core operating expenditures for public vs. private nonprofit hospitals. Variable

Expected sign

Intercept Increaset

+

Decreaset

−

Nopredt

?

LogAssett

?

ΔRevenuet

?

CEOTurnovert

?

FinancialCrisist

−

Observations Adjusted R2

Public hospitals

Private hospitals

0.125** (0.001) 0.028*** (0.001) −0.001 (0.712) −0.001 (0.631) −0.006*** (0.001) 0.457** (0.015) −0.001 (0.520) 0.001 (0.593) 113 0.70

0.064 (0.428) 0.042*** (0.000) −0.047*** (0.000) −0.013 (0.350) −0.002 (0.521) 0.029*** (0.000) −0.008 (0.400) −0.015** (0.022) 206 0.31

Difference in coefficients private vs. public (p-values)

0.014** (0.048) −0.046*** (0.000) −0.012 (0.515) 0.004*** (0.007) −0.428*** (0.000) −0.007 (0.368) −0.016 (0.303)

Dependent variable is change in total core operating expenditure (e.g., medical check-up, patient care) deflated by lagged assets. Increase takes the value of 1 if projected income scaled by lagged assets is above the benchmark range of ([0, 0.02)), and 0 otherwise. Decrease is set to the value of 1 if projected income scaled by lagged assets is below the benchmark range and the shortage amount is less than the previous year's expenditure in the category, and 0 otherwise. Nopred is set to 1 if projected income scaled by lagged assets is below the benchmark range and the shortage amount is greater than the previous year's expenditure in the category, and 0 otherwise. As in Eldenburg et al. (2011), we calculate projected income by adding back the expenditure of interest in prior year to net income before the spending of interest. LogAsset is the natural logarithm of total assets. ΔRevenue is delineated as change in operating revenues scaled by lagged assets. CEOTurnover is defined as 1 if there is a CEO change in the given year, and 0 otherwise. FinancialCrisis takes a value of 1 if the hospital-year operated in the financial crisis period of 2008–2009, and 0 otherwise. *, **, and *** denote significances at the 10%, 5%, and 1% levels, respectively, with p-values in parentheses. The p-values of coefficient estimates for each subsample are based on robust standard errors clustered by hospital. We derive the p-values in the last column from the Chow test.

4.3.1. Real activity management for private versus public hospitals To test H2, we partition all nonprofit hospitals into public and private hospitals. We first examine whether private hospitals engage in more real activity management using non-revenue-generating expenditures than public hospitals. As anticipated, Table 6 illustrates that the statistically negative coefficient on Decrease, significant at the 1% level, exists only in the group of privately-owned hospitals. Not surprisingly, the coefficient of 0.012 on Increase for private hospitals is greater than the parallel coefficient of 0.005 for public hospitals. Moreover, our Chow test results validate that the differences in both Increase and

Decrease between private and public hospitals are significant at conventional levels or better. We further investigate whether private hospitals manipulate earnings more extensively via core operating activities than do public hospitals. In Table 7, we report similar findings with higher levels of earnings management via core operating activities. We present evidence that private hospitals utilize core operating expenditures to manipulate income upward and downward with the coefficients of 0.042 and − 0.047 on Increase and Decrease, respectively (both significant at the 1% level). Additionally, we find that the degree of real activity manipulation is significantly greater for private hospitals vis-à-vis their public counterparts.

Table 8 Real activity manipulation using non-revenue-generating expenditures for religious vs. non-religious nonprofit hospitals. Variable

Expected Sign

Intercept Increaset

+

Decreaset

−

Nopredt

?

LogAssett

?

ΔRevenuet

?

CEOTurnovert

?

FinancialCrisist

−

Observations Adjusted R2

Religious Hospitals

Non-religious Hospitals

−0.011 (0.746) 0.005* (0.064) −0.019*** (0.001) 0.008 (0.108) 0.000 (0.837) 0.106 (0.124) −0.001 (0.882) 0.002 (0.621) 104 0.19

0.035 (0.264) 0.021*** (0.004) −0.030*** (0.006) 0.006 (0.113) −0.002 (0.208) −0.062*** (0.000) −0.001 (0.901) 0.003 (0.575) 102 0.38

Difference in Coefficients Non-religious vs. Religious (p-values)

0.016** (0.039) −0.011** (0.021) −0.002 (0.828) −0.002 (0.732) −0.168 (0.173) 0.000 (0.800) 0.001 (0.693)

Dependent variable is change in non-revenue-generating expenditures (△NRGE). We define NRGE as the sum of research development and health education, general and administrative expenses, and charity care and other social service expenses deflated by lagged assets. Increase takes the value of 1 if projected income scaled by lagged assets is above the benchmark range of ([0, 0.02)), and 0 otherwise. Decrease is set to the value of 1 if projected income scaled by lagged assets is below the benchmark range and the shortage amount is less than the previous year's expenditure in the category, and 0 otherwise. Nopred is set to 1 if the scaled projected income is below the benchmark range and the shortage amount is greater than the previous year's expenditure in the category, and 0 otherwise. LogAsset is the natural logarithm of total assets. ΔRevenue is delineated as change in operating revenues scaled by lagged assets. CEOTurnover is defined as 1 if there is a CEO change in the given year, and 0 otherwise. FinancialCrisis takes a value of 1 if the hospital-year operated in the crisis period of 2008–2009, and 0 otherwise. *, **, and *** denote significances at the 10%, 5%, and 1% levels, respectively, with p-values in parentheses. The p-values of coefficient estimates for each subsample are based on robust standard errors clustered by hospital. We derive the p-values in the last column from the Chow test.

Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003

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Y.-C. Wen et al. / Advances in Accounting xxx (2019) 100418

Table 9 Real activity manipulation using core operating expenditures for religious vs. non-religious nonprofit hospitals. Variable

Expected sign

Intercept Increaset

+

Decreaset

−

Nopredt

?

LogAssett ΔRevenuet

? ?

CEOTurnovert

?

FinancialCrisist

−

Observations Adjusted R2

Religious hospitals

Non-religious hospitals

−0.008 (0.874) 0.029** (0.011) −0.025* (0.078)

0.157 (0.203) 0.050** (0.030) −0.052*** (0.000) −0.031 (0.453) −0.007 (0.258) 0.199*** (0.001) −0.009 (0.553) −0.014 (0.120) 102 0.52

0.001 (0.757) 0.245 (0.178) −0.007 (0.405) −0.015** (0.044) 104 0.16

Difference in coefficients Non-religious vs. religious (p-values)

0.021** (0.023) −0.027*** (0.008)

−0.008 (0.210) −0.046 (0.188) −0.002 (0.238) −0.001 (0.309)

Dependent variable is change in core operating expenditures deflated by lagged assets. Increase equals 1 if projected income scaled by lagged assets is above the benchmark range of ([0, 0.02)), and 0 otherwise. Decrease is set to 1 if the scaled projected income is below the benchmark range and the shortage amount is less than the previous year's expenditure in the category, and 0 otherwise. Nopred equals 1 if the scaled projected income is below the benchmark range and the shortage amount is greater than the previous year's expenditure in the category, and 0 otherwise. LogAsset is the natural logarithm of total assets. ΔRevenue is delineated as change in operating revenues scaled by lagged assets. CEOTurnover is defined as 1 if there is a CEO change in the year, and 0 otherwise. FinancialCrisis is set to 1 if the hospital-year operated in the crisis period of 2008–2009, and 0 otherwise. In the subsample of religious hospitals, we cannot add Nopred to the model because no observation is classified into the group whose scaled projected income is below the benchmark range and shortage amount is greater than its prior year's expenditure in the category. *, **, and *** denote significances at the 10%, 5%, and 1% levels, respectively, with p-values in parentheses. The p-values of coefficient estimates for each subsample are based on robust standard errors clustered by hospital. We derive the p-values in the last column from the Chow test.

In sum, our results support H2 that real earnings management through adjustment of both non-revenue-generating expenditures and core operating expenditures is more pronounced in private hospitals than that in public hospitals. 4.3.2. Real activity management for religious versus non-religious hospitals To the extent that religious hospitals meet strict social missions and maintain good relationships with their stakeholders, they should have weaker incentives to manage earnings upward or downward and obscure the facts that their stakeholders are entitled to know. To test this premise, we divide private nonprofit hospitals into religious and nonreligious subgroups and re-run our regression model. Table 8 reports regression results. We find that the coefficient on Increase (Decrease) is positive (negative) for the subsample of non-religious private hospitals at the 1% significance level. Although the coefficient of −0.019 on Decrease for religious hospitals is also statistically distinguishable from zero, its magnitude is significantly smaller than that of 0.030 for nonreligious hospitals. Substantiating H3, our coefficient difference tests verify that non-religious hospitals indeed engage in real activity management via non-revenue-generating expenditures more extensively to reach earnings benchmarks than do religious hospitals (significant at the 5% level). We further analyze the levels of real earnings management via core operating expenditures for religious and non-religious hospitals. Table 9 reports our results. Consistent with our results on non-revenuegenerating expenditures, we find that real activity via core operating expenditures tends to prevail in non-religious hospitals. Specifically, for the non-religious subsample, the coefficient on Increase is 0.050 and the coefficient on Decrease is −0.052 (both at the 5% significance level or better). In contrast, the estimate on Increase for religious hospitals is at a much smaller magnitude of 0.029. Moreover, the estimate on Decrease is only marginally significant for faith-based hospitals. Our coefficient difference tests confirm that the extent of earnings manipulation via core operating expenditures is notably lower for religious hospitals than for their non-religious peers (significant at the 5% level or better). 9

As a sensitivity test, we alternatively use the benchmark range of [0, 0.01) or [0, 0.015) and repeat all main analyses from Table 5 to Table 9 in the paper. We find qualitatively similar results.

These findings suggest that religiosity is likely to play an important role in constraining real earnings management behavior, lending support to H3. Not surprisingly, our untabulated evidence shows that public/private entities and religious/non-religious hospitals do not depend on non-operating activities to manage earnings.9 In summary, we find compelling evidence that real earnings management exists in Taiwan's nonprofit hospitals. In particular, private and non-religious private hospitals, compared with their public and religious counterparts, manage core-operating and non-revenuegenerating expenditures more aggressively. These hospitals appear not to rely on non-operating expenses for real activity manipulation, possibly because such expenditures are too trivial to meet the goal of earnings management. Rather, nonprofit hospitals tend to focus more on core operating expenditures to achieve their earnings benchmarks. This evidence seems consistent with anecdotal evidence and press reports that the implementation of the national health insurance program is likely to distort financial incentives, thereby potentially affecting healthcare quality. 5. Conclusions In this paper, we document three important findings for the nonprofit healthcare sector in Taiwan. First, we observe a discontinuous and non-normal distribution of earnings around zero. Second, we uncover compelling evidence that not only non-revenue-generating expenditures but also core operating spending are the main expense items on which nonprofit hospitals rely to achieve a specific accounting performance. Third, the level of real activity management varies significantly across the ownership types of nonprofit hospitals. While privately-owned hospitals, on average, increase or decrease both types of expenditures to meet their net income benchmarks, state-owned hospitals are less likely to exhibit a similar behavior. Furthermore, real earnings management is concentrated in non-religious private hospitals, indicating that religiosity is likely to deter unethical management. References Barro, R., & McCleary, R. (2003). Religion and economic growth. American Sociological Review, 68, 760–781.

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Please cite this article as: Y.-C. Wen, P. Huang, H.-C. Shen, et al., The role of organizational forms in nonprofit firms' real earnings management: Evidence from nonprof..., Advances in Accounting, https://doi.org/10.1016/j.adiac.2019.04.003