Earnings management in U.S. hospitals.

Dong, Gang Nathan

ABSTRACT

Objective: This paper examines the hospital management practices of manipulating financial earnings within the bounds of generally accepted accounting principles (GAAP).

Study Design: We conduct regression analyses that relate earnings management to hospital characteristics to assess the economic determinants of hospital earnings management behavior.

Method and Data: From the CMS Cost Reports we collected hospital financial data of all U.S. hospitals that request reimbursement from the federal government for treating Medicare patients, and regress discretionary accruals on hospital size, profitability, asset liquidity, operating efficiency, labor cost, and ownership.

Results: Hospitals with higher profit margin, current ratio, working capital, days of patient receivables outstanding and total wage are associated with more earnings management, whereas those with larger size and higher debt level, asset turnover, days cash on hand, fixed asset age are associated with lower level of earnings manipulation. Additionally, managers of non-profit hospitals are more likely to undertake some form of window-dressing by manipulating accounting accruals without changing business models or pricing strategies than their public hospital counterparts.

Conclusions: We provide direct evidence of the use of discretionary accruals to manage financial earnings among U.S. hospitals and the finding has profound policy implications in terms of assessing the pervasiveness of accounting manipulation and the overall integrity of financial reporting in this very special public and quasi-public service sector.

JEL Code: M4, G34, H25, H26

Keywords: Earnings management, hospitals

"HealthSouth's fraud represents an appalling betrayal of investors...
HealthSouth's standard operating procedure was to manipulate the
company's earnings to create the false impression that the company was
meeting Wall Street's expectations."
                                                   --Stephen M. Cutler
                                         S.E.C. Director of Enforcement
                                 The New York Times, March 20, 2003 (1)

Policymakers have been concerned with growing health care costs since the 1970s and seeking to contain costs by adopting new regulations and advocating for market-based health care systems to contain the rising cost of health care (Davis & Rowland, 1990; Antel et al., 1995). Faced with decreasing government payments and subsidies and intense market competition, hospitals had to resort to other cost cutting measures to increase earnings to avoid financial insolvency (Zwanziger & Melnick, 1988). Hospital managers who want to avoid losses can cut costs when revenues decrease and limit the cost increases when revenues increase because they are evaluated in part on their ability to a non-financial objective (e.g., quality of care) subject to a zero-profit constraint (Leone & Van Horn, 2005). Hospital managers often use a new accounting technique, earnings management which enables managers to improve their ability to cope with uncertainties in revenue and competition, to control the rising cost of health care provision or the windfall of profits.

Earnings management occurs when managers use judgment in financial accounting and in structuring transactions to alter financial reports to meet external benchmarks or to assure that they will have working capital on their accounting statements temporarily (Healy & Wahlen, 1999), especially when facing short-term uncertainties in revenue and competition. This phenomenon of earnings management is pervasive not only in investor-owned for-profit corporations but also in non-profit organizations (Petrovits 2006). It is regarded as inappropriate when a misallocation of economic resources arises from managerial opportunism or when non-compliance with accounting regulations is camouflaged (Ballantine et al., 2007).

A range of incentives for managers of for-profit business to exercise discretion in financial reporting has been identified and includes capital market incentives that affect equity value (Erickson & Wang, 1999; Teoh, Welch and Wong, 1998; Teoh, Wong and Rao, 1998), managerial incentives to meet earnings targets (Burgstahler & Eames, 2003; Kasznik & McNichols, 2002), and contract-based incentives relating to executive compensation and other influences (Ahmed et al., 1999; Collins et al., 1995). The challenge in studying firms' strategic decisions in financial reporting is to disentangle the market, industry, and firm-specific characteristics leading to the observed accrual manipulation behavior. To do so, it is helpful to focus on a single sector rather than comparing firms in different industries. In other words, we can better control for the impact of sector or industry factors on strategic accounting choice. In addition, having a narrowed focus on a single sector can ensure a high level of internal validity. In this paper, we study the U.S. hospital industry to answer the question, "Is earnings management commonplace or relatively infrequent among hospitals?"

Hospitals are different from other for-profit business because they provide some services for which they do not expect to receive full payment, often referred to as charity care. Most hospitals are not subject to profit-maximization pressures by shareholders, but still they require substantial earnings to maintain financial viability, and they are limited in how far they can pass on rising costs in higher prices to customers whose income are constrained by their own earning power (Himmelweit, 2007). Therefore, hospital administrators perceive the ability to diversify revenue sources (Clement, 1987) and to cut labor costs by resisting wage rises and reducing staffing levels (Mullaney, 1989) as vital to the financial viability of their hospitals. Unfortunately, there are not many studies in the accounting and finance literature that have examined the pervasiveness of the use of earnings management in this very special sector that plays an important role in providing public goods (e.g., charity care). In an early study to understand whether hospitals attempt to achieve a target level of earnings that satisfies the budget constraint, Hoerger (1991) finds that nonprofit hospitals minimize the variance in reported earnings. More interestingly, nonprofit hospitals have no incentive to avoid reporting earnings decreases as long as current period earnings are above zero. The author attributes this phenomenon to the lack of monitoring by the equity market because for-profit firms always manage their earnings to avoid market punishment; however, this market does not exist in the nonprofit and public healthcare sectors. Leone & Van Horn (2005) investigate both discretionary spending and accruals of U.S. not-for-profit hospitals and report significant use of discretionary accruals to meet earnings objectives.

From the CMS Cost Reports, we obtain financial statements of all U.S. hospitals that request reimbursement from the federal government for treating Medicare patients. Several unique features of this data set facilitate the current study. First, the sample includes hospitals of public, not-for-profit and for-profit ownership. (2) Second, the financial information in the Cost Reports is more comprehensive and accurate than that of survey data. Third, the sample period from 1997 to 2010 covers two economic recessions (March 2001-November 2001 and December 2007-June 2009), (3) which enables us to study the earning management phenomenon not only across hospitals but also over time.

Using this unique data set we attempt to examine the determinants of hospitals' strategic financial reporting of earnings, including hospital size, the use of debt, profitability, asset liquidity, operating efficiency, labor cost and ownership type. Table 1 lists the hypothesized effects of these determinants on earnings management and the variables that we use to proxy for these factors.

Overall, we find that hospitals often use a negative discretionary accruals strategy, especially during the recent financial crisis in 2008 and 2009, to book a negative accrual in order to bring down the net income when actual earnings are above target in the hope of being able to reverse the accrual in a subsequent year when actual earnings are below target. In addition, we identified several important factors that affect hospital earning management activities: size, profitability, asset liquidity, operating efficiency, labor cost, and ownership. The findings reported in this paper can inform regulators that earnings management is pervasive not only in publicly-traded and for-profit firms but also in not-for-profit and public hospitals. They can help policymakers to improve the understanding and increase the effectiveness of public policies that finance health care provision while relying on hospital themselves to monitor their financial reporting process.

DATA AND METHOD

Annual financial statements of U.S. hospitals are obtained from the CMS Medicare Cost Reports between 1997 and 2010. According to Magnus & Smith (2000), the CMS Medicare Cost Reports is the most comprehensive database of hospital financial accounting data because every year virtually all hospitals in the U.S. are required to file a cost report in order to receive reimbursement from the federal government for treating Medicare patients. This financial accounting dataset represents the entire hospital industry and provides highly detailed financial accounting data by hospital department and function. After excluding hospitals with incomplete financial accounting information, we end up with a sample of 42,573 hospital-years. Table 2 lists the number of hospitals in each state and year. Across all years, California, Florida, New York, Ohio, and Texas are the top five states in terms the number of hospitals in the sample. It should be noted that we drop some observations from the dataset if the financial statements are not available or incomplete. This could be due to the transition to the new reporting formats and other reporting requirements in 2010 (Gray & Schlesinger, 2009). For example, Maryland has only one observation in 2010 comparing to 15 observations in 2009, and we will control for this using state fixed-effects.

We first need to construct a variable that measures a hospital's earnings management to determine the extent to which hospitals executives are manipulating their financial performance. Prior studies of earnings management examine the use of discretionary accruals to produce financial reports that may over- or under-state a company's business activities and financial position. The models used in these studies range from the simple, in which the change in total accruals is used as a measure of discretionary accruals to the relatively sophisticated, which decompose accruals into discretionary and non-discretionary components using regression analysis. Managers cannot alter non-discretionary accruals to manage earnings because they reflect the fluctuation of business operations. Healy (1985) proposes a simple method to estimate non-discretionary accruals by comparing mean total accruals (scaled by lagged total assets) across the earnings management partitioning variable. Similarly, DeAngelo (1986) computes first differences in total accruals and assumes that the first differences have an expected value of zero under the null hypothesis of no earnings management. It is noted that both Healy (1995) and DeAngelo (1986) are built on the assumption that non-discretionary accruals are constant. Jones (1991) relaxes this assumption by controlling for the effects of changes in a firm's economic circumstances on non-discretionary accruals. Discretionary accruals are calculated as the residual of the difference between total accruals and the predicted level of non-discretionary accruals. In this paper we will use the method proposed by Jones (1991) to estimate discretionary accruals.

To study the determinants of hospital earnings management, we include the following determinant variables that measure hospital size (Natural log of Total Assets), debt level (Financial Leverage), profitability (Total Margin), asset liquidity (Current Ratio, Working Capital To Total Assets, and Days Cash On Hand), operating efficiency (Asset Turnover, Days of Patient Receivables Outstanding, Fixed Asset Age), and labor costs (Salary to Revenue).

This paper focuses upon assessing the economic determinants of hospital earnings management behavior by conducting pooled cross-sectional OLS regressions that relates discretionary accruals to various hospital financial characteristics. The regression model takes the following form:

Discretionary[Accruals.sub.i,t] = [[beta].sub.0] +[[beta].sub.1][Size.sub.i,t] +[[beta].sub.2][Leverage.sub.i,t] +[[beta].sub.3][Profitability.sub.i,t]+[[beta].sub.4][Liquidity.sub.i,t] +[[beta].sub.5][Efficiency.sub.i,t] +[[beta].sub.6]Government[Own.sub.i,t] +[[beta].sub.7] Non[Profit.sub.i,t] +[[epsilon].sub.i,t]

The dependent variable is the discretionary accruals of hospital i in year t, Here the discretionary accruals is estimated by the Jones (1991) model. The main predictor variables are Natural log of Total Assets, Financial Leverage, Total Margin, Asset Turnover, Current Ratio, Working Capital To Total Assets, Days Cash On Hand, Days of Patient Receivables Outstanding, Fixed Asset Age, and Salary to Revenue. It is well known that managers in for-profit, public and non-profit hospitals have different incentives to avoid negative net income (Eldenburg et al., 2011). Earnings management behavior in for-profit hospitals is simply driven by contractual and capital market pressures, whereas it is more likely for reputation concerns among public hospitals. Non-profit hospitals are special because they do not have a profit-maximization objective and they do not receive government funding. Their motivation for earnings management is mainly for tax-avoidance and financial sustainability (Leone & Van Horn, 2005). To control for this hospital ownership effect, we create two dummy variables: Government-owned and NotProfit. The value of Government-owned is one for public hospitals and zero otherwise. Similarly, the value of NotProfit is one for non-profit hospitals and zero otherwise. This will imply that the values of both variables (Government-owned and NotProfit) are zero for for-profit hospitals.

RESULTS

The summary statistics of all variables are shown in Table 4. The average discretionary accruals measured by the Jones (1991) model is -0.04 with the minimum and maximum values being close to -1.0 and 1.0.

We break down the sample based on hospital location and plot the distribution of discretionary accruals by state in Figure 1. The histograms show that hospitals in most states, except Montana and Puerto Rico, adopted a negative earnings management strategy to report lower profits. This can well be the case of "saving today for a better tomorrow". Still, it is likely that hospitals use negative earnings management as a way to deflect the public's attention as in the case of politically connected institutions (Guay, 2010), or simply to signal a high cost of providing charity care (to retain tax-exempt status). There are also a number of patterns in aggregate discretionary accruals over time (Figure 2). The prevalence of this accounting strategy is lowest during the boom period of 2002-2006 (except in 2005) and highest immediately following the financial crisis in 2008-2010. (4) This result is in sharp contrast to the decline in accrual based earnings management in for-profit firms after the enactment of Sarbanes-Oxley Act (SOX) in 2002, as evidenced in the recent financial accounting literature (e.g., Cohen et al., 2008).

[FIGURE 1 OMITTED]

The average hospital size is $32.6 million with the largest being $5.9 billion in total assets. The total liabilities of an average hospital is about 59% of its total assets. The highest financial leverage of 366% suggests that some hospitals in our sample are in severe financial distress. On average, the total profit margin is 3% with the most profitable hospital making $30 net income out of $100 revenue. Interestingly, labor cost constitutes only a relatively small portion, roughly 14%, of the total revenue. The average current ratio is 2.89 and the average working capital is 14.1% of the total assets. It takes about 42 days for an average hospital to exhaust all of its cash and 65 days to collect its patient service revenue, and the average fixed asset age is 14 years.

The Pearson's correlations are reported in the lower-left triangle of Table 5. An examination of the correlation matrix indicates that correlations between independent variables are generally small. This low correlation among the covariates helps prevent the problem of multicollinearity that causes high standard errors and low significance levels when both variables are included in the same regression. However, there are four pairs of variables having correlations above or close to [+ or -]0.5: Government-owned and Not-for-profit (-0.61), Current Ratio and Working Capital to Total Assets (0.52), log(Total Assets) and Asset Turnover (-0.51), and Financial Leverage and Working Capital to Total Assets (-0.49). The Spearman's correlation matrix in the upper-right triangle of Table 5 confirms the strong correlations of the first and second pairs of independent variables. To be cautious, we will exclude Asset Turnover and Working Capital to Total Assets in some of the regression specifications to avoid potential multicollinearity problems.

Table 6 provides the results of the coefficient estimates for the statistical relationship between earnings management and hospital characteristics with year and state fixed effects. The dependent variable in all specifications is the hospital's discretionary accruals which is measured by the Jones (1991) model. In specifications (1) and (2), hospitals with higher profit margin, current ratio, days of patient receivables outstanding, and wage cost are associated with higher discretionary accruals, whereas those with larger asset size, financial leverage, days cash on hand, fixed asset age are associated with lower discretionary accruals. Nonprofit hospitals are more likely to manage earnings and public hospitals are less likely to do so. In specifications (3) and (4) we add a new variable Asset Turnover which indicates how efficiently the hospital generates patient service revenue on each dollar of total assets and its coefficient estimate is negative in both regression models. In specifications (5) to (6), we add another variable Working Capital to Total Assets that measures the amount of current assets required to run the daily operations and often serves as a predictor for financial distress or bankruptcy. The positive coefficient estimate suggests that earnings management is more prevalent at hospitals with better financial health.

Together, these results provide direct evidence that asset size, profitability, asset liquidity, operating efficiency, labor cost, and ownership are important economic factors of hospital earnings management. The Variance Inflation Factor (VIF) is calculated for each independent variable to determine if these variables display collinearity amongst themselves. The mean VIFs (ranging from 5.30 to 5.40) reported at the bottom of Table 6 are below the cut-off point of ten (Myers 2000), suggesting no problem with multicollinearity in our regressions.

In additional sensitivity tests, we use alternative measures of earnings management and hospital characteristics in our analyses. The Jones (1991) model implicitly assumes that revenues are non-discretionary and therefore extracts the discretionary components of accruals; however, this assumption biases the estimate toward zero earnings management. Recognizing this limitation, Dechow, et al. (1995) modifies the Jones Model to eliminate the estimation error by deducting account receivables from revenues. We construct a new measure of discretionary accruals using the so-called Modified Jones Model and report the Pearson's and Spearman's correlations of both earnings-management measures in the Section A of Table 7. The strong correlation (0.82 in Pearson's and 0.83 in Spearman's) is not really surprising, given the fact that the only major difference between the Jones Model and the Modified Jones Model is the consideration of the change in receivables while calculating the change in revenues.

In terms of other measures of hospital size besides the natural log of total assets, we collect the total patient days and visits (in thousands), total number of discharges (in thousands), and total number of beds (in thousands) from the CMS Cost Reports. Again, the correlations among four different measures of hospital size are above 0.70 in both Pearson's and Spearman's coefficients. We further adjust the profit margin and salary expenses by total assets and total patient visits respectively to increase the cross-sectional comparability of hospital profitability and labor costs, and report the regression results in the Section B of Table 7. Discretionary accruals are estimated using the Jones Model in specifications (1) to (3) and the Modified Jones Model in specifications (4) to (6). Most coefficient estimates are broadly consistent with those of our previous results in Table 6, and the effect of financial leverage on earnings management remain positive across all six specifications and the negative effect of fixed assets ages turns statistical significant.

DISCUSSION AND CONCLUSION

The annual financial statement data of hospitals that filed Medicare Cost Reports between 1997 and 2010 show that hospitals often adopted a negative discretionary accruals strategy that is to book a negative accrual to bring down the net income when actual earnings are above target in the hope of being able to reverse the accrual in a subsequent year when actual earnings are below target. This is in contrast to the common practice of using discretionary accruals to maintain earnings momentum in for-profit firms.

An important feature of the present study is the inclusion of hospital characteristics that may influence the choice of accounting methods. We find that hospital size, profitability, asset liquidity, operating efficiency, labor cost, and ownership appear to be important economic factors of earnings management. Specifically, hospitals with higher profit margin, current ratio, working capital, days of patient receivables outstanding, and total wage are associated with higher discretionary accruals, whereas those with larger asset size, financial leverage, asset turnover, days cash on hand, fixed asset age are associated with lower discretionary accruals. More interestingly, nonprofit hospitals are more likely to manage earnings and public hospitals are less likely to do so. Together, these results provide direct evidence of the use of discretionary accruals to manage earnings among U.S. hospitals. It is worth emphasizing the subtle difference between manipulating discretionary accruals within the bounds of generally accepted accounting principles (GAAP) and committing accounting fraud (e.g., overstatement of earnings via revenue, expenses, or accounts receivables, as the case of HealthSouth quoted at the beginning of this article), and there is evidence showing the predictive power of earnings management in detecting actual cases of fraudulent and restated earnings. (5)

The findings reported in this study have profound policy implications in terms of assessing the pervasiveness of accounting manipulation and the overall integrity of financial reporting in this very special sector that provides public and quasi-public services. Still, this paper leaves us with an open question: to what extent will the hospital executive compensation contract affect earnings manipulation as the stock options seem to have done to fraudulent accounting practices in publicly traded for-profit companies and large financial institutions? However, because most of the hospitals in our sample are not-for-profit and public, the answer will depend on what role the bonus schemes rather than stock options are playing in accounting procedure and accrual decisions. (6) Of course, to answer this question would involve the massive and difficult task of collecting executive compensation data from various data sources. We will leave such issues for future research.

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GANG NATHAN DONG (*)

Columbia University

(*) Dept. of Health Policy & Management, Mailman School of Public Health, Columbia University. 722 W 168th Street, New York, NY 10032. Tel: 212-342-0490. E-mail: gd2243@columbia.edu. No potential conflict of interest relevant to this article was reported.

(1) Excerpt from Freudenheim (2003). On March 19th, 2003, the U.S. Securities and Exchange Commission (S.E.C.) filed a civil against HealthSouth and its CEO Richard Scrushy alleging the company had falsified earnings. This Birmingham, Alabama-based hospital chain is the largest owner and operator of inpatient rehabilitative hospitals, operating in 28 states across the country and in Puerto Rico. According to this S.E.C. lawsuit, HealthSouth chief executive manipulated expenses, assets and liabilities to overstate at least $2.7 billion worth of profit between 1996 and 2002. Richard Scrushy was charged with violating the Sarbanes-Oxley corporate governance law that penalizes executives for certifying false financial statements. See Matulich & Currie (2008) for more details of this case.

(2) For the purposes of this study a "public" hospital is defined as a hospital operated and supported by a city, county, special district, state, or federal government. The use of "public" is different from the definition of publicly traded firms listed on the stock market.

(3) Business cycle expansion and contractions is retrieved from NBER: http://www.nber.org/cycles/cyclesmain.html

(4) We reverse the direction of Y axis of so that higher values of discretionary accruals correspond to more negative earnings management.

(5) For example, see Jones et al.(2008).

(6) See Healy (1985) and Kaplan (1985) for more details on this subject.

Table 1. Determinants of hospital earnings management

Control          Sign      Hypothesis             Proxy Variables

                           Large hospitals
                           are more likely
                           be targeted for
                           tax-status
Hospital Size    Negative  scrutiny due
                           to high patient
                           revenues and
                           government subsidies;  Natural log
                           therefore, managers    of Total Assets
                           are likely
                           to use discretionary
                           accruals to
                           lower income
                           (Morrisey,
                           Wedig and
                           Hassan 1996).
                           A hospital with
                           high risk of
                           bankruptcy (financial
                           distress) is
                 Positive  likely to "tune
                           up" its profit
                           for future borrowing
                           and investment
Use of Debt                (Wedig et al
                           1988).                 Financial
                 Negative  Limited borrowing      Leverage
                           (debt) capacity
                           reduces the
                           need for high
                           profit (Widig
                           et al 1996).
Profitability    Positive  This is a statistical  Total Margin
                           control
                           because higher level
                           of discretionary
                           accruals is
                           likely to be
                           associated with
                           high profit.
                           Hospitals with
                           more liquid
                           assets are more
                           likely to obtain
Asset liquidity  Positive  external financing
                           for capital
                           investment due
                           to higher
                           probability of
                           repayment (Shleifer
                           and Vishny 1992),      Current Ratio
                           therefore              Working Capital
                           managers are           to total Assets
                           likely to report       Days Cash On Hand
                           larger positive
                           earnings.
                           Excessive labor
                           costs in the
                           form of compensation
                           and benefits
Labor cost       Positive  reduce profits
                           (Sloan 2000,
                           Sloan and Steinwald
                           1980). There is        Salary to
                           the need to            Revenue
                           revise up earnings
                           to maintain financial
                           viability.
                           Managers may engage
                           in upward
                           earnings
                           manipulation to
Operating        Positive  avoid losses when
inefficiency               slack resources,
                           wasteful capacity,     Days of Patient
                           dysfunctional          Receivables
                           operation and          Outstanding
                           organizational chaos   Fixed Asset
                           lower hospital         Age
                           income.

Table 2. Number of hospitals in each state and year

Year   1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008
State

AK      10    11    12    14    13    11    12    10     8     8    12
AL      76    72    79    79    74    71    74    65    65    60    66
AR      54    61    63    60    68    70    63    60    55    48    44
AZ      31    35    27    33    35    35    33    35    37    37    38
CA     251   266   261   258   227   239   260   233   224   225   233
CO      40    44    39    45    53    53    47    48    51    53    50
CT      30    32    30    28    31    32    32    32    32    31    30
DE       2     3     4     4     5     5     7     8     7     6     5
FL     115   126   126   134   140   138   134   129   130   130   132
GA     105   114   100   97    109   108   107   104   105   110   106
HI      11    10    17    19    17    16    19    19    20    19    20
IA      83    86    79    71    64    65    72    75    73    86    93
ID      31    30    34    34    38    30    29    36    37    38    37
IL     106   107   114   102   101   102   105   109   106   104   105
IN      71    73    66    54    62    63    61    72    72    80    75
KS      91    84    89    96   106    98   105   114   117   118   117
KY      62    66    64    65    67    71    69    68    70    70    75
LA      82    84    85    87    93    96    96    94    98    97   102
MA      50    49    42    48    53    55    58    61    60    57    56
MD      22    29    28    26    22    20    20    21    19    17    19
ME      30    28    27    28    25    30    28    25    24    24    26
MI      92    85    95    95   101    94    89    86    87    89    86
MN      89    88    93    93    76    80    91    85    83    83    82
MO      76    71    78    75    74    74    75    73    71    79    84
MS      59    70    59    70    69    71    72    76    71    73    76
MT      38    43    43    42    39    40    43    43    44    46    43
NC      65    56    61    65    68    66    64    68    71    72    72
ND      34    31    34    31    26    27    35    37    36    36    36
NE      77    65    50    44    62    64    61    65    73    69    76
NH      21    23    23    16    16    18    18    21    22    21    23
NJ      60    62    68    60    66    58    61    68    67    63    60
NM      28    26    23    22    20    23    27    30    30    32    28
NV      15    19    17    19    18    17    18    16    19    26    32
NY     138   137   142   141   145   148   144   137   143   150   144
OH     116   135   129   117   116   122   124   124   124   129   127
OK      62    64    63    67    65    54    49    59    75    83    91
OR      38    36    35    38    41    40    35    40    46    46    47
PA     129   134   141   149   145   144   136   138   142   142   142
PR      23    23    19    21    26    31    31    26    23    28    27
RI       6     6     8     9     8     7     8     7     6     6     8
SC      33    33    33    38    39    42    46    50    49    49    50
SD      27    26    36    35    41    41    43    35    34    37    39
TN      77    74    80    78    82    88    84    89    82    80    90
TX     236   233   227   250   246   249   244   271   265   253   251
UT      34    31    30    29    33    33    34    36    36    35    36
VA      67    68    61    62    61    56    61    58    59    64    63
VT      13    11    11    12    11    13    13    12    13    13    13
WA      77    81    73    68    73    66    74    76    80    84    84
WI      82    92    88    76    72    77    85    82    85    95    84
WV      45    50    46    39    36    43    48    41    41    43    41
WY      14    17    13    13    19    17    18    15    14    15    17

Year   2009  2010
State

AK      15     7
AL      71    54
AR      47    30
AZ      43    25
CA     241   131
CO      53    48
CT      31    32
DE       7     3
FL     128    92
GA     106    58
HI      20     3
IA      89    16
ID      36    30
IL     112    64
IN      70    45
KS     118    99
KY      78    39
LA     110    76
MA      61    58
MD      15     1
ME      33    20
MI      83    57
MN      87    70
MO      82    50
MS      72    65
MT      43    14
NC      73    70
ND      34    16
NE      74    23
NH      21    16
NJ      60    58
NM      27    15
NV      32    21
NY     147   141
OH     124   105
OK     103    50
OR      47    31
PA     142    20
PR      27    24
RI       8     9
SC      52    45
SD      41    17
TN      89    36
TX     251   181
UT      37    29
VA      63    42
VT      12    12
WA      79    56
WI      77    60
WV      40    27
WY      16     5

Table 3. Variable definitions

Variable Name             Definition

Discretionary Accrual     Jones (1991)
(Jones Model)
Natural log of Total      log (Total Assets)
Assets
Financial Leverage        Total Liabilities / Total Assets
Total Margin              Net Income / Revenue
Asset Turnover (Sales to  Revenue / Total Assets
Assets)
Current Ratio             Current Assets / Current Liabilities
Working Capital To        (Current Assets - Current
                          Liabilities) / Total
Total Assets              Assets
Days Cash On Hand         (Cash + Cash Equivalents)
                          x 365 / Operating
                          Expenses
Days of Patient           (Accounts Receivable - Allowances for
Receivables Outstanding   uncollectible) x 365 / Revenue
Fixed Asset Age (Year)    Accumulated Depreciation / Annual
                          Depreciation
                          Expense
Salary to Revenue         Salary Expense / Revenue
Government-owned          1 for government owned hospitals
                          and 0 otherwise
Not-for-profit            1 for nonprofit hospitals and 0 otherwise

Table 4. Summary statistics

Variable                        Mean     Standard    Minimum  Maximum
                                         Deviation

Discretionary Accrual (Jones   -0.0377    0.348     -1.24      1.28
Model)
Natural log of Total Assets    17.3       1.589      7.96     22.5
Financial Leverage              0.591     0.544      0.0286    3.66
Total Margin                    0.0273    0.107     -0.433     0.329
Asset Turnover (Sales to         .37      0.939      0.269     6.79
Assets)
Current Ratio                   2.89      3.12       0.148    23.5
Working Capital To Total        0.141     0.302     -1.46      0.834
Assets
Days Cash On Hand              42.4      63.7        0.00886 391
Days of Patient Receivables     4.9      34.9       12.9     299
Outstanding
Fixed Asset Age                13.9      19.7        0.146   175
Salary to Revenue               0.438     0.126      0.209     1.06
Government-owned                0.238     0.426      0         1
Not-for-profit                  0.543     0.498      0         1

Figure 2. Discretionary accruals over time

1998   .021
1999  -.054
2000  -.022
2001  -.016
2002   .129
2003   .035
2004   .094
2005  -.098
2006   .321
2007  -.036
2008  -.414
2009  -.34
2010  -.096

Note: Table made from bar graph

Table 5. Correlation matrix
The upper-right triangle is the Spearman's correlations matrix and the
lower-left triangle is the Pearson's correlation matrix.

                    Natural  Financial  Total   Asset       Current
                    log of   Leverage   Margin  Turnover    Ratio
                    Total                       (Sales
                    Assets                      to Assets)

Natural log of
Total Assets                  -0.04      0.14   -0.14        0.02
Financial Leverage  -0.18               -0.28    0.31       -0.48
Total Margin         0.18     -0.22             -0.15        0.29
Asset Turnover      -0.51      0.40     -0.02               -0.22
Current Ratio       -0.03     -0.25      0.16    -010
Working Capital
To Assets           -0.00     -0.49      0.20   -0.09        0.52
Days Cash On Hand    0.07     -0.19      0.13   -0.23        0.40
Days Receivables
Outstanding         -0.08     -0.00     -0.15   -0.15        0.07
Fixed Asset Age      0.07     -0.04     -0.05   -0.06       -0.04
Salary to Revenue   -0.27     -0.02     -0.42    0.00        0.01
Government-owned    -0.23     -0.15     -0.07   -0.06        0.09
Not-for-profit       0.31     -0.11     -0.05   -0.18       -0.08

                    Working    Days     Days of      Fixed  Salary
                    Capital    Cash     Patient      Asset  to Total
                    To Assets  On Hand  Receivables  Age    Revenue
                                        Outstanding

Natural log of
Total Assets         -0.13      0.04     -0.13        0.05   -0.31
Financial Leverage   -0.39     -0.21     -0.03       -0.14   -0.01
Total Margin          0.22      0.16     -0.10       -0.09   -0.36
Asset Turnover       -0.05     -0.43     -0.21       -0.17   -0.25
Current Ratio         0.86      0.35      0.13       -0.03   -0.12
Working Capital
To Assets                       0.39      0.16        0.02   -0.03
Days Cash On Hand     0.35               -0.01        0.15    0.14
Days Receivables
Outstanding           0.07      0.07                  0.03    0.17
Fixed Asset Age       0.01      0.07      0.22                0.26
Salary to Revenue     0.00      0.06      0.22        0.12
Government-owned      0.14      0.14      0.09        0.04    0.25
Not-for-profit        0.00      0.05     -0.08        0.13    0.03

                    Government  Not-
                     -owned     for-
                                profit


Natural log of
Total Assets         -0.24      0.32
Financial Leverage   -0.18     -0.02
Total Margin         -0.03     -0.04
Asset Turnover       -0.23     -0.14
Current Ratio         0.14     -0.09
Working Capital
To Assets             0.17     -0.09
Days Cash On Hand     0.21      0.16
Days Receivables
Outstanding           0.13     -0.10
Fixed Asset Age       0.16      0.24
Salary to Revenue     0.26      0.07
Government-owned               -0.60
Not-for-profit       -0.61

Table 6. Regressions of hospital earnings-management and financial
characteristics

The dependent variable is discretionary accrual estimated using the
Jones (1991) model. The independent variables include the natural log
of total assets, financial leverage, total margin, asset turnover
(sales to asset), current ratio, working capital to total assets, days
cash on hand, days of patient receivables outstanding, fixed asset age,
total salary to revenue, and two dummy variables of ownership: public
and nonprofit. All specifications use OLS regressions with year and
state fixed-effects. z-statistics are shown in the parentheses with
(***), (**) and (*) indicating its statistical significant level of 1%,
5% and 10% respectively.

Dependent
Variable:             (1)                (2)                  (3)
Discretionary
Accrual

Natural log of        0.0017 (*)         0.0012            -0.0017
Total Assets         (1.71)             (1.220)           (-1.47)
Financial            -0.037 (***)       -0.036 (***)       -0.032 (***)
Leverage           (-13.85)           (-13.15)           (-11.55)
Total Margin          0.104 (***)        0.107 (***)        0.114 (***)
                     (7.38)             (7.59)             (8.06)
Asset Turnover                                             -0.010 (***)
(Sales to
Assets)                                                   (-5.41)
Current Ratio         0.015 (***)        0.015 (***)        0.015 (***)
                    (31.96)            (32.03)            (32.02)
Working Capital
To Total Assets
Days Cash On         -0.0002 (***)      -0.0002 (***)      -0.0002 (***)
Hand                (-7.03)            (-7.26)            (-7.87)
Days of Patient       0.0006 (***)       0.0006 (***)       0.0005 (***)
Receivables
                    (14.23)            (14.37)            (13.07)
Outstanding
Fixed Asset Age      -9.17e-05          -0.0001            -6.83e-05
                    (-1.34)            (-1.64)            (-0.99)
Salary to             0.039 (***)        0.035 (***)        0.038 (***)
Revenue              (3.08)             (2.68)             (2.99)
Government-          -0.0043             0.0017            -0.0061 (*)
owned               (-1.22)             (0.38)            (-1.75)
Not-for-profit                           0.0082 (**)
                                        (2.06)
Constant             -0.089 (***)       -0.087 (***)       -0.016
                    (-3.04)            (-2.96)            (-0.50)
Year Fixed-          Yes                Yes               Yes
effects
State Fixed-         Yes                Yes               Yes
effects
N                42,573             42,573             42,573
Adj. R-squared        0.324              0.324              0.325
F- Test             288 (***)          284 (***)          285 (***)
Mean VIF              5.40               5.37               5.36

Dependent
Variable:                 (4)                 (5)
Discretionary
Accrual

Natural log of         -0.0021 (*)        -0.0029 (***)
Total Assets          (-1.76)            (-2.59)
Financial              -0.031 (***)        0.033 (***)
Leverage             (-11.02)            (11.11)
Total Margin            0.117 (***)        0.094 (***)
                       (8.226)            (6.832)
Asset Turnover         -0.010 (***)       -0.026 (***)
(Sales to Assets)
Current Ratio         (-5.337)          (-14.13)
                        0.015 (***)        0.0055 (***)
Working Capital       (32.08)            (11.03)
To Total Assets                            0.283 (***)
Days Cash On                             (51.01)
Hand                   -0.0002 (***)      -0.0004 (***)
Days of Patient       (-8.05)           (-17.35)
Receivables             0.0006 (***)       0.0004 (***)

Outstanding           (13.20)             (9.54)
Fixed Asset Age
                       -8.86e-05          -2.09e-05
Salary to             (-1.28)            (-0.31)
Revenue                 0.034 (***)        0.064 (***)
Government-            (2.62)             (5.16)
owned                  -0.0007            -0.0142 (***)
Not-for-profit        (-0.15)            (-4.19)
                        0.0074 (*)
Constant               (1.86)
                       -0.015             -0.029
Year Fixed-           (-0.47)             -0.93)
effects               Yes                 Yes
State Fixed-
effects               Yes                 Yes
N
Adj. R-squared     42,573             42,573
F- Test                 0.325              0.364
Mean VIF              281 (***)          334 (***)
                        5.34               5.32

Dependent
Variable:                 (6)
Discretionary
Accrual

Natural log of         -0.0026 (**)
Total Assets          (-2.32)
Financial               0.039 (***)
Leverage              (10.80)
Total Margin            0.092 (***)
                       (6.600)
Asset Turnover         -0.026 (***)
(Sales to Assets)
Current Ratio        (-14.19)
                        0.0054 (***)
Working Capital       (10.85)
To Total Assets         0.283 (***)
Days Cash On          (51.00)
Hand                   -0.0004 (***)
Days of Patient      (-17.01)
Receivables             0.0004 (***)

Outstanding            (9.31)
Fixed Asset Age
                       -5.59e-06
Salary to             (-0.08)
Revenue                 0.067 (***)
Government-            (5.33)
owned                  -0.0183 (***)
Not-for-profit        (-4.13)
                       -0.0055
Constant              (-1.43)
                       -0.030
Year Fixed-           (-0.96)
effects               Yes
State Fixed-
effects               Yes
N
Adj. R-squared     42,573
F- Test                 0.363
Mean VIF              329 (***)
                        5.30

Table 7. Alternative measures of earnings-management and hospital
characteristics

Section A. Correlations (The upper-right triangle is the Spearman's
correlations matrix and the lower-left triangle is the Pearson's
correlation matrix.)

Alternative measure of hospital earnings management:

                          Discretionary  Discretionary Accrual
                          Accrual in     in Modified Jones
                          Jones Model    Model

Discretionary Accrual in
Jones Model                              0.83
Discretionary Accrual in  0.82
Modified Jones Model

Alternative measures of hospital size:

                        Natural log of  Patient  Number of   Number
                        Total Assets    Days     Discharges  of Beds

Natural log of Total                    0.81     0.86        0.78
Assets
Total Patient Days and  0.72                     0.93        0.86
Visits (in thousands)
Number of Discharges    0.76            0.96
(in thousands)
Number of Beds (in      0.72            0.91     0.89
thousands)

Alternative measure of hospital profitability:

                              Total Margin  Total Margin to
                                            Total Assets

Total Margin                                0.88
Total Margin to Total Assets  0.68
(in millionth)

Alternative measure of hospital labor costs:

                    Salary to Revenue  Salary to Patients
                                       Days and Visits

Salary to Revenue                      0.20
Salary to Patients
Days and Visits     0.19
(in thousands)

Section B. Regressions
The dependent variable is discretionary accrual estimated using the
Jones (1991) model in specifications (1) to (3) and discretionary
accrual estimated using the modified Jones model (Dechow et al., 1995)
in specifications (4) to (6). The independent variables include total
patient days and visits, number of discharges, number of beds,
financial leverage, total margin to assets, asset turnover (sales to
asset), current ratio, working capital to total assets, days cash on
hand, days of patient receivables outstanding, fixed asset age, total
salary to total patient days and visits, and two dummy variables of
ownership: public and nonprofit. All specifications use OLS regressions
with year and state fixed-effects. z-statistics are shown in the
parentheses with (***), (**) and (*) indicating its statistical
significant level of 1%, 5% and 10% respectively.

Dependent
Variable:                  (1)               (2)
Discretionary
Accrual

Total Patient Days      0.00056 (***)
and Visits            (12.40)
Number of                                   0.0032 (***)
Discharges                                (14.05)
Number of Beds

Financial Leverage      0.0119 (***)        0.0126 (***)
                       (3.46)              (3.65)
Total Margin to         0.768 (***)         0.773 (***)
Total Assets           (7.60)              (7.65)
Asset Turnover         -0.0076 (***)       -0.0075 (***)
(Sales to Assets)    (-10.34)            (-10.18)
Current Ratio           0.0064 (***)        0.0065 (***)
                      (11.00)             (11.30)
Working Capital         0.279 (***)         0.278 (***)
To Total Assets       (43.66)             (43.63)

Days Cash On           -0.0004 (***)       -0.0004 (***)
Hand                 (-14.78)            (-14.74)
Days of Patient         0.0003 (***)        0.0003 (***)
Receivables
                       (6.79)              (7.18)
Outstanding
Fixed Asset Age        -0.0003 (***)       -0.0003 (***)
                      (-4.39)             (-4.51)
Salary to Patients      0.0007              0.0006
Days and Visits        (0.52)              (0.48)
Government-            -0.049 (***)        -0.050 (***)
owned                 (-9.80)             (-9.85)
Not-for-profit          0.014 (**)          0.017 (***)
                       (3.25)              (3.86)
Constant                0.0110              0.0099
                       (0.41)              (0.36)
N                  42,573              42,573
Adj. R-squared          0.253               0.254
F- Test               205.64 (***)        206.39 (***)
Mean VIF                5.30                5.30

Dependent
Variable:                (3)                 (4)
Discretionary
Accrual

Total Patient Days                           0.00028 (***)
and Visits                                  (6.98)
Number of
Discharges
Number of Beds            0.148 (***)
                        (14.18)
Financial Leverage        0.0122 (***)       0.0235 (***)
                         (3.54)             (7.66)
Total Margin to           0.768 (***)        0.545 (***)
Total Assets             (7.60)             (6.07)
Asset Turnover           -0.0075 (***)      -0.0127 (***)
(Sales to Assets)     ( -10.18)           (-19.49)
Current Ratio             0.0064 (***)       0.0049 (***)
                        (11.10)             (9.48)
Working Capital           0.279 (***)        0.284 (***)
To Total Assets         (43.71)            (50.09)

Days Cash On             -0.0004 (***)      -0.0004 (***)
Hand                   (-14.45)           (-16.97)
Days of Patient           0.0003 (***)       0.0003 (***)
Receivables
                         (6.75)             (7.89)
Outstanding
Fixed Asset Age          -0.0004 (***)      -0.0002 (***)
                        (-4.66)            (-3.50)
Salary to Patients       -0.0003            -0.0002
Days and Visits         (-0.26)            (-0.22)
Government-              -0.049 (***)       -0.034 (***)
owned                   (-9.77)            (-7.72)
Not-for-profit            0.015 (***)        0.008 (**)
                         (3.41)             (2.13)
Constant                  0.0049             0.0040
                         (0.18)             (0.16)
N                    42,573             42,573
Adj. R-squared            0.254              0.266
F- Test                 206.50 (***)       220.18 (***)
Mean VIF                  5.30               5.30

Dependent
Variable:                 (5)               (6)
Discretionary
Accrual

Total Patient Days
and Visits
Number of                 0.0016 (***)
Discharges               (7.69)
Number of Beds                               0.0832 (***)
                                            (8.95)
Financial Leverage        0.0237 (***)       0.0237 (***)
                         (7.74)             (7.75)
Total Margin to           0.550 (***)        0.546 (***)
Total Assets             (6.12)             (6.07)
Asset Turnover           -0.0127 (***)      -0.0127 (***)
(Sales to Assets)      (-19.39)           (-19.37)
Current Ratio             0.0048 (***)       0.0049 (***)
                         (9.64)             (9.57)
Working Capital           0.284 (***)        0.284 (***)
To Total Assets         (50.04)            (50.10)

Days Cash On             -0.0004 (***)      -0.0004 (***)
Hand                   (-16.97)           (-16.73)
Days of Patient           0.0003 (***)       0.0003 (***)
Receivables
                         (8.07)             (7.95)
Outstanding
Fixed Asset Age          -0.0002 (***)      -0.0003 (***)
                        (-3.55)            (-3.72)
Salary to Patients       -0.0003            -0.0006
Days and Visits         (-0.26)            (-0.56)
Government-              -0.035 (***)       -0.035 (***)
owned                   (-7.75)            (-7.69)
Not-for-profit            0.009 (**)         0.009 (**)
                         (2.44)             (2.33)
Constant                  0.0036            -0.0004
                         (0.15)            (-0.01)
N                    42,573             42,573
Adj. R-squared            0.266              0.267
F- Test                 220.33 (***)       220.75 (***)
Mean VIF                  5.30               5.30