Investment Decisions of Nonprofit Firms: Evidence from Hospitals

Manuel Adelino Duke’s Fuqua School of Business

manuel.adelino@duke.edu

Katharina Lewellen Tuck School at Dartmouth k.lewellen@dartmouth.edu

Anant Sundaram

Tuck School at Dartmouth anant.sundaram@dartmouth.edu

First draft: February 2012 Current draft: October 2012

We thank Chuck McLean and Arthur Schmidt of the GuideStar USA for their extensive help in providing the data. The paper greatly benefited from conversations with Robin F. Kilfeather-Mackey, Gary Husband, and Peter Martin of Dartmouth-Hitchcock, Dianne Ingalls and Scott Frew of Dartmouth College, Michael Pagliaro and Nikki Kraus of Hirtle, Callaghan, & Co, Rick Steele of Longmeadow Capital, LLC., and Diane Daych of Marwood Group. We also thank seminar participants at Dartmouth College, Heitor Almeida, Andrew Bernard, Ken French, Robert Hansen, Leonid Kogan, Joshua Schwartzstein, and Jonathan Skinner for their helpful comments and suggestions.

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Abstract

More than 20% of U.S. firms are nonprofit, yet this organizational form has received little attention in corporate finance. This paper takes a step towards closing this gap by examining investment choices of nonprofit hospitals. Most hospitals hold large financial assets, and hospital-specific shocks to the performance of these assets are likely unrelated to investment opportunities. We use this setting to test how shocks to cash flows affect hospital investment. We find that capital expenditures increase, on average, by 10-23 cents for every dollar received from financial assets. The sensitivity is similar to that found earlier for shareholder owned corporations, and it is driven by spending on buildings and medical equipment. We find little evidence that hospital executives are “paid for luck”, or that spending on perks responds to cashflow shocks. Hospitals with an apparent tendency to overspend do not exhibit higher investment-cash flow sensitivities. However, the sensitivities are higher for hospitals that appear financially constrained.

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1 Introduction

About 20 percent of U.S. corporations are not-for-profit entities, yet this organizational form has

received almost no attention in corporate finance. Nonprofits dominate the healthcare space, which

constitutes more than 15% of the U.S. economy (Philipson and Poser (2006)), and the sector has been at

the center of the political debate on how to make healthcare more efficient. Hospitals are part of this

debate as they account for about 30 percent of all expenditures in healthcare.1 However, understanding

investment decisions of a nonprofit hospital is challenging because standard finance theories and most

empirical research deal primarily with shareholder owned corporations, and their insights are not

necessarily applicable to nonprofits.

The key question underlying much finance research on corporate investment is whether firms’

investment choices are aligned with shareholders’ interests and to what extent they are distorted by

financing constraints. These questions are highly relevant in the context of nonprofit firms. For example,

nonprofits do not have owners in the sense corporations do, so any agency conflicts between insiders

and capital providers–whether they are donors or taxpayers–are likely magnified.2 The standard approach

taken in the literature to identify the effects of governance or financing frictions on investment has been

to focus on the sensitivity of investment to cash flows. This framework relies on a prediction from Q

theory, which is that cash flow shocks that are unrelated to a firm’s investment opportunities should

have no impact on investment. Using this insight, researchers have interpreted the observed investment-

cash flow sensitivity as evidence of free-cash flow problems or financing constraints.

This paper contributes to this extensive literature in three ways. First, our analysis sheds light on the

nonprofit healthcare sector which has been largely neglected in corporate finance in spite of its economic

importance. Second, we use returns from endowments as a novel source of variation for identifying the

causal effect of cash flows on investment. Third, we use unique data on hospital (over)spending to study

the mechanisms that drive investment-cash flow sensitivities.

The investment-cash flow sensitivity literature has traditionally faced two major challenges. First, it

is difficult to identify shocks to cash flows that are unrelated to investment opportunities (e.g., Gilchrist

1 CMS National Health Expenditure Projections for 2009 (http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/downloads/proj2009.pdf). 2 These arguments are discussed, for example, in Easily and O’Hara (1983), Fama and Jensen (1985), Glaeser and Shleifer (2001), and Fisman and Hubbard (2005).

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and Himmelberg, 1995; Kaplan and Zingales, 1997; Erickson and Whited, 2000; Blanchard, Lopez-de-

Silanes and Shleifer, 1994; Lamont, 1997; Moyen, 2004; Rauh, 2006; Bakke and Whited, 2011). Second,

the literature has struggled to distinguish between the different types of frictions that may cause

investment to vary with cash flows. The two most common explanations–financing constraints and

agency conflicts–are fundamentally different but are difficult to distinguish empirically (Stein, 2003).3

As we point out above, our setting allows us to address these difficulties in a new way. First,

nonprofit hospitals are exposed to significant exogenous and measurable cash flow shocks because these

firms–in contrast to most corporations–own large financial assets in the form of endowments. Gains and

losses from these financial assets make up a large fraction of hospital profits and have a potential to

affect capital and other expenditures. Importantly, cross-sectional variation in returns on these assets is

unlikely to be correlated with hospitals’ investment opportunities or with any other aspect of a hospital’s

production function, so we can use returns on financial portfolios to measure the impact of cash flow

shocks on various types of expenditures.

Second, hospitals publish detailed information on their capital investments and spending. In order

to receive Medicare funding, each hospital must provide detailed cost reports on different types of

capital spending, including expenditure on major hospital equipment (such as CT scan, MRI, and other

major surgical and diagnostic equipment), buildings, and minor equipment (such as surgical instruments).

In addition, in annual filings with the Internal Revenue Service hospitals report other types of

expenditures, including executive salaries and travel and conference expenses. We combine these

different data sources to obtain a detailed picture of the types of expenditures that respond to cash flow

shocks.

Third, spending by hospitals has been the subject of a large literature in healthcare economics, and

extensive research focuses specifically on identifying and measuring excessive spending (see review in

Skinner, 2012). This allows us to explore directly the role of overspending as an explanation for

investment-cash flow sensitivity, which is an opportunity rarely available in other contexts. One

influential approach to identify overspending has been to measure hospital-specific attitudes to treatment

at the end of patients’ lives, including the use of life sustaining treatment, chemotherapy, or surgical

interventions. Following this approach, we explore how hospitals’ attitudes to medical spending relate to

3 The literature has also questioned some of the assumptions of the Q theory, pointing out that violations such as the presence of monopoly power or the absence of adjustment costs would affect its implications for how investment should respond to cash flows (e.g., Gomes (2001), Alti (2003), Abel and Eberly (2011)). 

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their investment patterns, and in particular how hospitals with different spending philosophies invest in

financially good vs. bad times.

Fourth, some hospital investments are subject to government regulation, and the degree and nature

of this regulation varies by states. Specifically, 36 states require hospitals to obtain state approval for

major investment projects as part of their Certificate of Need (CON) laws. The final contribution of this

paper is to explore how direct government oversight interacts with hospitals’ propensity to change their

investment plans in response to financial performance.

We start our empirical analysis by establishing the baseline estimate of how capital expenditures of

nonprofit hospitals respond to cash flow shocks. We find that hospitals invest, on average, 10 cents

more in a given year for every additional dollar returned by investment securities in the previous year.

This impact is magnified to 23-27 cents for every dollar when we consider capital expenditures over two

years following the return on financial investments. These estimates are substantially higher than the

sensitivities implied by formal spending rules used internally by non-profit organizations.4 Interestingly,

however, the estimates are similar to those found in prior studies for shareholder owned–and arguably

better governed–corporations (e.g., Gilchrist and Himmelberg (1995), Cleary (1999), Rauh (2006), and

Lewellen and Lewellen (2012)).

The remaining tests focus on gaining a better understanding of the mechanism driving investment-

cash flow sensitivities. We follow the previous literature in considering financing constraints and agency

problems–and specifically, overspending by hospital insiders–as the two key alternative explanations. To

examine the effect of constraints, we follow the standard approach in the literature and divide the sample

into groups of hospitals that appear more or less severely constrained based on their levels of debt and

financial assets.5 Kaplan and Zingales (1997, 2000) show analytically that although constraints should

affect investment-cash flow sensitivities, the relation between the degree of constraints and sensitivity

may be non-monotonic. We find that in our sample, more severe ex-ante constraints are associated with

a stronger responsiveness of investment to cash flows. For example, hospitals with above median

4The rules can be viewed as a non-profit’s internal governance mechanism. They determine the amount of funds that can be taken out from an endowment for spending each year, and the amount is typically linked to the value of the organization’s endowment. The actual spending in any given year can be either higher or lower than the amount implied by the spending rule. For example, a not-for-profit can save the appropriated amount or finance its investment from non-endowment sources. 5These tests are similar in spirit to those in Fazzari, Hubbard and Petersen (1988), Almeida, Campello and Weisbach (2004), and Rauh (2006), among others, in the context of corporations. 

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constraints–when constraints are measured as net debt or net debt minus financial investments in

proportion to fixed assets–spend 14-30 cents more per dollar of cash flows than those with below

median constraints.

The importance of free cash flow problems as an explanation for investment-cash flow sensitivity

has received less attention in the literature, likely because an ex-ante tendency to overspend is more

difficult for researchers to measure (Stein, 2003). We attempt to shed light on this explanation in two

ways. Our first approach is to examine categories of expenditures that might be especially prone to

excessive spending in a nonprofit firm, such as executive salaries, total salaries, or travel and conference

expenses. We find little evidence that hospitals increase these expenditures when their financial assets do

well. For example, hospital executives do not appear to be “paid for luck” (at least not significantly so),

contrary to findings in Bertrand and Mullainathan (2001) for for-profit firms. In a similar spirit, we

examine investment spending on equipment vs. buildings and land. Glaeser (2003) and others argue that

spending on smaller ticket items with potential private benefits to doctors–such as medical equipment–

might be particularly susceptible to overspending. Again, our tests show little evidence that spending on

equipment increases more strongly per dollar of cash flows than spending on buildings (though spending

on land is generally unresponsive to cash flow shocks). Overall, these tests cast some doubt at the

hypothesis that insiders’ spending of free cash flow on perks or favored projects is responsible for the

investment-cash flow sensitivities we observe.

Our second approach follows research in healthcare economics focused on identifying hospitals

with an ex-ante tendency to overspend on medical treatment. The approach relies on measuring the

intensity with which different hospitals treat severely ill patients at the end of their lives. Consistent with

the previous tests, we find no evidence that hospitals with a higher preference for spending increase

investment more strongly when cash flow is unexpectedly high. In fact, several specifications yield the

opposite result, raising questions about how overspending and investment patterns might be linked. It is

possible, for example, that better governed organizations (and those more concerned about

overspending) are also more financially prudent in the sense that they cut expenditures when financial

performance is poor to avoid financial distress. Taking this perspective, higher sensitivities might indicate

insiders’ financial conservatism rather than free-cash flow problems, which is contrary to how the tests

have been traditionally interpreted.

The paper proceeds as follows. Section 2 describes a theoretical framework to analyze investment

decisions of nonprofit firms. Section 3 discusses how spending of nonprofits’ financial income is

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regulated. Section 4 describes data and sample selection, and Section 5 presents the main evidence on

investment-cashflow sensitivities. Section 6 examines how sensitivities depend on a hospital’s attitude to

spending, and Section 7 analyzes the effect of hospital regulation. Finally, Section 8 concludes.

2 Investment decisions of nonprofit firms

In this section, we summarize the theories of nonprofit firms developed in prior literature and

discuss their implications for nonprofit investment, and especially for how investment should respond to

cash flow shocks. We start with a frictionless benchmark view of a nonprofit firm–analogous to the Q-

theory for for-profits–in which the firm is financed by donors and its objective is to maximize donor

utility. We then discuss alternative theories that emphasize conflicts of interests between the donors (or

the public) and the nonprofit insiders.

2.1 Frictionless Benchmark: Nonprofits Acting in Donors’ Interests

The altruistic view of nonprofits has been discussed, for example, in Fama and Jensen (1985), Rose-

Ackerman (1996), and Fisman and Hubbard (2005). According to this view, altruistic agents (donors)

derive utility from providing certain goods or services to others and are thus willing to subsidize firms

specializing in the production of these goods. To survive, such firms must attract donations, so their

choices are guided by the preferences of the donors. To capture this idea, the models assume that the

objective function of a nonprofit firm is to maximize the utility of its donors. Alternatively, the donor is

sometimes replaced by a representative taxpayer that provides subsidies to the nonprofit.

This framework provides a useful frictionless benchmark for thinking about investment choices of

nonprofits that is analogous to the Q-theory for for-profit firms. We highlight its implications in a simple

example described in detail in Appendix A. The example abstracts from conflicts of interest between

donors (or taxpayers) and nonprofit insiders to match the assumptions of the Q-theory. (In practice,

such conflicts of interest could be significant and we discuss their implications in Section 2.2.) The main

insight from this model is analogous to that of the Q-theory in that, without frictions, investment-cash

flow sensitivity for nonprofit firms should be close to zero.

The intuition for this result is simple. If a donor derives utility from charity and other (non-charity)

consumption, a shock to the nonprofit’s cashflows affects the donor’s wealth as it changes the total

amount of funds available to the donor for consumption. In a frictionless world, the donor can

substitute charity for non-charity consumption by reducing his current or future donations. Similarly, he

can substitute current for future charity consumption by investing in capital markets. As long as

consumption of the good produced by the nonprofit that experienced the cashflow shock is an

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insignificant fraction of the donor’s overall consumption, a one-dollar shock to the nonprofit’s cashflows

will have an insignificant effect on the nonprofit’s optimal output and expenditures (in the appendix, we

derive conditions under which the sensitivity is exactly zero).6

Moving away from the frictionless benchmark, it is interesting to consider the effects of the

nondistribution constraint on hospital spending. (We assume that without frictions, the constraint does

not bind as donors can make optimal donations costlessly each period.) In the presence of a binding

constraint, the nonprofit is unable to pay out excess cash to donors. Thus, a positive cash flow shock

experienced by a nonprofit may shift the donor’s optimal consumption towards hospital charity (relative

to what it would be without the non-distribution constraint). However, donors may be able to scale back

future donations in response to nonprofit cash flow shocks, so that some of the cash flow can still be

used towards spending on other goods. Moreover, if donors prefer to smooth charity consumption over

time, they will require that nonprofits save some of the unexpected cash flow for future spending so that

current spending would be less affected.

In Section 5.3.1, we use the nonprofits’ internal spending rules to approximate investment-cash flow

sensitivities that might be required by donors in the presence of nondistribution constraints. These rules

are put in place by nonprofit boards, and in some cases directly by donors, and they determine the

amount of funds that nonprofits can take out from endowments for spending each year. The rules

provide a useful benchmark in our context because they link nonprofits’ targeted expenditures to the past

performance of their endowments.

2.2 The Power of Nonprofit Insiders

The discussion above abstracts from conflicts of interest between nonprofit insiders (managers and

employees) and outsiders such as donors or the society at large. Much of the existing literature on

nonprofits argues that these conflicts of interest are potentially severe, suggesting higher investment-cash

flow sensitivities than those targeted by donors.

6 The assumption that charitable giving to a particular hospital represents a small fraction of donors’ overall consumption seems to fit well in our setting, and Glaser and Shleifer (2001) and Hubbard (2005) make similar assumptions in their models of nonprofits. For example, Andreoni (2006) shows that, as of 1995, families spent somewhere between 1.3 percent and 3 percent of household income on charitable donations, and the percentage spent on hospital-related charitable giving is likely much smaller. In addition, of all hospital-related charitable giving, donors may wish to spend an even smaller amount on charitable giving to a particular hospital. Finally, it is worthwhile to note that the relationship between income and the percentage of income donated to charity is not linear – it starts at 3 percent for income below USD 20,000, is reduced to 1.3 percent for households making just below USD 50,000 and then rises again to 3 percent for the highest earners.

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Many authors take the view that nonprofit insiders are subject to less scrutiny that corporate

managers. The discipline imposed by the past and the potential donors is likely weak and leaves more

room for insider discretion (Fisman and Hubbard, 2005). Consistent with this prespective, Newhouse

(1970) assumes that hospitals maximize the quantity and the quality of their services subject to a zero-

profit constraint (see also Feldstein, 1971). This is because quantity and quality increase the prestige of

hospital trustees, administrators, and doctors. Pauly and Redisch (1973) model the hospital as a

physician’s cooperative where physicians or the hospital staff enjoy de facto control of the hospital.

Similarly, Glaeser (2003) argues that nonprofits’ managers and boards have “an unmatched degree of

autonomy” over their organizations, and that they are likely to cater to their most influential workers,

such as doctors in the case of hospitals. He describes the evolution of hospitals from donor-driven

charitable institutions of the 19th century to mostly doctor-controlled organizations of the 1980s, but

argues that the power of doctors may have eroded more recently because of the increased competitive

pressures in the industry.7 Chang and Jacobson (2010) argue that the evidence from a large exogenous

shock to hospitals’ fixed costs is most consistent with a view of hospitals as perquisite maximizers.

Easily and O’Hara (1983) and Glaeser and Shleifer (2001) and others show that, in spite of the

potentially significant agency conflicts, nonprofits can be more efficient than for-profits. Specifically, in

industries in which customers have a hard time judging product quality (or cannot easily punish firms for

poor quality), shareholder owned firms may have incentives to shirk on quality to increase profits. The

nonprofit’s nondistribution constraint is a way to diminish these incentives and improve product quality.

The undesirable consequence of the constraint is, however, that nonprofits do not have residual

claimants and are, thus, fully controlled by managers. Since managers cannot divert profits directly, they

resort to implicit payouts such as salary increases and perks.

Arrow (1963) argues that the healthcare sector is particularly prone to product-related information

problems because of the nature of its output: medical advice is highly complex, its quality is difficult for

patients to assess, even ex post, and bad advice can be prohibitively costly. As a result, the softer

incentives inherent to nonprofit firms may deliver socially better outcomes. Hansmann (1980) points

out, however, that information problems may be less severe in the hospital sector than in healthcare

7 One important change occurred in 1983 when Medicare changed the way it reimbursed providers for their services. Specifically, Medicare switched from reimbursing providers on a cost-plus basis (that is, paying for costs plus a certain percentage) to paying fixed prices per patient based on the so-called “diagnostic related group” (DRG) determined primarily by the diagnosis and the severity of the case. Another important development was the emergence of the Health Maintenance Organizations (HMOs) which replaced the traditional forms of insurance and put additional pressure of hospitals to reduce prices. (Glaeser 2003, p. 27).

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overall, and that private and public subsidies to hospitals may have contributed to the importance of

nonprofits in this sector. Both authors emphasize the prominent role of insiders in nonprofit firms, and

the potential distortions such power implies.

A direct implication of this literature in our context is that investment decisions of nonprofit firms

should reflect, in part, the preferences of their insiders. If insiders have a stronger preference for current

spending than donors, nonprofits would spend too much of cash windfalls on current projects.

Assuming that excessive spending is more difficult to finance from external sources such as new

donations or debt, the observed investment-cash flow sensitivities of nonprofit firms should be higher

than those demanded by donors.

3 Legal Rules Governing the Use of Investment Income in Nonprofits

Our tests focus on how nonprofit hospitals spend income from their financial investments, and this

section describes how this spending is regulated. Financial investments of nonprofits can be broadly

divided into funds that can be used without any limitations by management, and funds that have some

type of restrictions as to their use (usually referred to as “endowment” funds).8 These restrictions are put

in place by the donors at the time of the donation and the state attorney-generals monitor the adherence

of the organizations to these rules.

The restrictions usually specify that the donated funds be held in perpetuity by the nonprofit (in the

case of “permanently restricted” funds), or they limit the use of these funds to a certain purpose or for a

pre-determined period of time (in the case of “temporarily restricted” funds). The restrictions typically

apply only to the original principal amount of the donation. The use of income earned from those

investments is governed by the Uniform Prudent Management of Institutional Funds Act (UPMIFA)

which, as of June of 2011, had been adopted by all 50 states.9 This act sets guidelines regarding what is

considered “prudent” investment and spending of endowment funds. The current law replaces its

predecessor–the Uniform Management of Institutional Funds Act (UMIFA)–that was in effect from

1972 to 2006 and that covers the years in our sample. Below we describe the provisions included in both

acts to highlight what is and what is not required by the law.

8 The equivalent to shareholders’ equity in not-for-profit corporations is usually referred to as “Net Assets”, and this account is subdivided into restricted, temporarily restricted and unrestricted net assets. While there is a correspondence between restricted and temporarily restricted financial investments and the equivalent net asset accounts, our discussion here focuses on the classification of financial assets, i.e. it refers to the left-hand side of the balance sheet.. 9 In some cases, donors also specify a desired use for income earned from investments, in which case the restrictions apply to both the original principal amount, as well as to any income received from the assets (see Ruppell, 2002 for a more complete discussion).

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The state-level regulations are relevant to our paper to the extent that they affect the investment

policy of nonprofits or their ability to spend income derived from their endowments. With respect to

investment policies, the law leaves ample space for the nonprofits to make asset allocation decisions. The

prudence standard in the current law simply includes “the duty of care, the duty to minimize costs, and

the duty to investigate” when making investments, so it makes no specific recommendations in terms of

asset classes or maturity of the investments. The previous act (which covers the years in our sample) put

even fewer constraints on the allocation decisions of nonprofits (Gary, 2007).

Regarding the spending of income from financial investments, the UMIFA (that covers the period of

our sample) put one important constraint on nonprofits. They were not allowed to spend any income

while the endowment was “underwater”, i.e., whenever the market value of the endowment was below

the dollar amount of the original gift (“historic dollar value”). Some nonprofits interpreted this provision

as limiting only their ability to spend gains on investments, while others in this situation did not spend

any form of income, including dividends and interest (National Conference of Commissioners on

Uniform State Laws, 2006). There were no other notable restrictions on spending income derived from

investments in UMIFA, including no suggested spending caps or floors (“spending rules”). The more

recent UPMIFA removed the restriction on spending income when an endowment fund is underwater,

but it added a “presumption of imprudence” clause which states that spending over seven percent of the

three-year rolling average value of an endowment fund in a single year would be considered imprudent

by the state. There was no such clause in place during our sample period.

As discussed in Section 2, in addition to the state laws it is common for nonprofit organizations to

adopt internal spending rules as part of their self-imposed governance mechanisms. For example, for

universities and colleges, the NACUBO-Commonfound study of Endowments 2010 survey finds that

the majority of the surveyed organizations have spending rules expressed as a percentage of the moving

average value of their endowment funds.10 The rules determine the amount of funds that an organization

is allowed to appropriate for spending from its endowment in a given year. A more detailed discussion of

these internal spending rules and how they relate to our results is in Section 5.3.

10 NACUBO is the National Association of College and University Business Officers and their publications are available on http://www.nacubo.org/.

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4 Sample and data

4.1 Data sources

Our main dataset on nonprofit hospitals comes from IRS filings by tax-exempt nonprofit

organizations referred to as Form 990. The filing is required annually from most organizations exempt

from federal taxes, and it contains detailed information on each organization’s finances, mission, and

programs.11 The main dataset was provided to us by GuideStar USA Inc., an information service on U.S.

nonprofits. The Guidestar dataset includes balance sheet and income statement items from Form 990 for

all Hospitals and Primary Medical Care Facilities as classified by the National Center for Charitable

Statistics from 1999 through 2006.12 We supplement it with hand-collected information on unrealized

gains and losses from investments in securities, which is reported in the attachment to Form 990.13 Data

on treatment intensity at the end of life is provided by the Dartmouth Atlas of Health Care. We describe

these datasets in more detail in Section 6.

Apart from the IRS Form 990 data, we also use hospital financial statement information provided by

the Healthcare Cost Report Information System (HCRIS). HCRIS contains information from cost

reports submitted annually to the Center for Medicare and Medicaid Services (CMS) by all Medicare-

certified institutional providers, including hospitals. The reports contain detailed data on facility

characteristics, utilization, and cost, and it also include financial statement information, which we use in

our tests.

The key variables for our analysis–i.e., capital expenditures and financial income measures–can be

constructed using either the IRS data or the HCRIS data. We use IRS data for our main analysis because

the IRS reporting of investments in securities is more reliable than reporting in HCRIS. In particular,

some hospitals choose to report their securities in HCRIS at historical cost rather than mark them to

market, and the database does not identify these cases. However, the HCRIS dataset has two advantages.

First, it contains a breakdown of fixed assets into land, buildings, and equipment, which allows us to

11 The tax exempt organizations that are not required to file Form 990 are churches and state institutions. 12 We follow Guidestar in using the NTEE Classification System created by the National Center for Charitable Statistics. Codes starting with an “E” refer to Health-related organizations and we focus on codes E21, E22 and E24 that together make up the “Hospitals and Primary Medical Care Facilities” subgroup. 13 Unrealized gains and losses from investments in securities are not reported in the income statement filed with the IRS for the period we analyze: the income statement includes only dividend and interest income and realized gains and losses. However, investments in securities are recorded at market value on the Form 990 balance sheet, and unrealized gains and losses must be included as part of Line 20 of Form 990 (“Other changes in net assets of fund balances”). The line summarizes all changes in the organization’s equity (or fund balances in case of not-for-profits) that do not flow through the income statement. The breakdown of Line 20 into different types of adjustments is reported in the supporting statement to Form 990, and we extract unrealized gains and losses manually from this attachment.

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examine each investment category separately. Second, hospital identifiers in HCRIS correspond to those

on Dartmouth Atlas (the source of data on hospitals’ end-of-life treatment intensity), which ensures

consistent matching. For these reasons, we use the HCRIS dataset for a subset of tests that either require

a breakdown of investments into spending on land, buildings, and equipment, or require matching with

Dartmouth Atlas.14

4.2 Sample

Our main sample consists of 1,352 hospitals and 5,269 hospital-year observations from 1999 to

2006. To construct the sample, we start with 3,555 hospitals. Each is characterized by distinct Employer

Identification Number EIN assigned by the IRS and has at least one million dollars in assets and at least

one million dollars in service revenue.15 Of the initial 3,555 hospitals, 1,521 report having some

investments in securities and also report valid information on investment income, the key variable for

our tests (this corresponds to 6,354 hospital-years). Investment income includes realized gains and losses,

unrealized gains and losses, and interest and dividends from securities, and we require that all three

variables are available for each hospital-year in our sample. Our final filter eliminates observations with

extreme values in our main variables: investment income divided by lagged fixed assets, investment

income divided by lagged securities value, change in fixed assets divided by lagged fixed assets, operating

income, revenues and growth in revenues. These extreme observations seem to be caused by large

changes in hospital assets such as mergers or by hospital closures. To minimize the influence of such

events, for our main tests we exclude observations in the top or bottom one percent of the sample based

on any of the three variables. This last step eliminates 169 hospitals, resulting in 1,352 hospitals in the

final sample.

The HCRIS dataset is constructed similarly to the Form 990 dataset. We start with 20,345 hospital-

year observations for 2,324 nonprofit hospitals with the minimum of one million dollars in assets and

one million dollars in service revenues. When we limit the sample to observations with non-missing

investment income and financial variables, we are left with 10,457 observations and 2,061 hospitals. The

14 As an alternative, we matched the HCRIS and the Form 990 datasets using hospital names and repeated the tests on the matched sample. Because coverage differs across the two databases, the matching procedure reduces the sample considerably relative to the main analysis. After performing the match, we still find significant discrepancies in the values of balance sheet statement of activities items reported on HCRIS and on Form 990 and impose additional data filters to reduce incorrect matches. Based on conversations with experts on hospital accounting, the most likely reason for the discrepancies is that hospitals sometimes include different entities in the consolidated reports provided to Medicare vs. the IRS. The tests using the matched IRS-HCRIS sample are not reported, but they yield similar results as the main tests. 15 These requirements eliminate 1,958 organizations in the Guidestar database that appear to be either small hospitals, holding companies for hospital systems with no significant service revenue, or miscoded observations.

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final HCRIS dataset, after eliminating extreme observations, consists of 1,895 hospitals and 8,847

observations.

4.3 Summary Statistics

Table 1 shows summary statistics for the 5,269 observations (1,352 hospitals) in the IRS sample

included in the main regressions. The left panel shows the full sample, and the right panel shows sub-

samples split at the median based on the size of fixed assets or based the size of financial assets relative

to fixed assets. The average hospital in the full sample has 189 million in revenue (the median is 117

million) and 83 million in net fixed assets, defined as net fixed assets minus accumulated depreciation

(the median is 48 million). Financial investments constitute, on average, 82% of net fixed assets (the

median is 71%), which reflects the importance of financial assets for the hospitals in our sample. Net

income is on average 9% of lagged fixed assets, while operating income–computed as program service

revenue minus program service expenses, excluding management and general expenses–is on average

36% of lagged fixed assets.16 For an average hospital, service revenue grows at an annual rate of 8%,

which matches the average growth rate reported by Moody’s (2011) for U.S. nonprofit hospitals during

1999-2006. The average net debt to net fixed assets (calculated as bonds and other notes payable minus

cash and short term investments) is 49%, and about 64% of hospitals have tax-exempt bonds.

The first row of Table 1 shows that the mean of our main cash flow variable, income from

investments scaled by lagged net fixed assets, is 3% and the median is 2%. When investment income is

scaled by the lagged value of financial investments, the mean and median are 4% and 3%. For

comparison, the average value-weight NYSE / AMEX / Nasdaq return is 6.5% during our 1999-

2006sample period. The lower average returns for the hospitals in our sample are consistent with the

nonprofits holding significant fixed-income investments, which we confirm in conversations with several

hospital executives and financial advisors. The last three lines of Table 1 show the breakdown of

investment income into its three components. Dividend and interest income make up about 40% of the

total return, with realized and unrealized gains and losses making up the remaining 17% and 42%,

respectively. Our main outcome variable–the change in net fixed assets scaled by lagged net fixed assets–

has a mean of 4% and a median of 2%.

16 Program service revenue is revenue derived from activities that form the basis for the tax exemption. In case of hospitals, these are primarily medical services.

14

Table 1 also shows descriptive statistics for subsamples of our data, in particular small and large

hospitals (based on median net fixed assets each institution), as well as hospitals with low and high

fractions of financial assets as a proportion of net fixed assets. The descriptive statistics for those

subsamples are shown in the last four columns of Table 1.

Descriptive statistics for the HCRIS sample are in Table 2. The entities in HCRIS are, on average,

smaller than those in the IRS sample (e.g., the mean service revenue is $135 million compared to $178

million in Table 1) and have smaller financial assets in proportion to fixed assets (mean of 0.50

compared to 0.82 in Table 1), consistent with the differences in reporting of financial assets discussed in

Section 4.2. Investment income in proportion to fixed assets–the key variable for our tests–has the same

mean and median in both datasets. The average net income and the average growth rates for revenues

and fixed assets are also similar across the two samples.

5 Returns from Securities and CAPEX

5.1 Empirical Specification

Our main regressions estimate the response of capital expenditures and other hospital expenses to

income from investments in securities. As explained in the previous section, investment income is the

sum of dividend and interest income and realized and unrealized gains and losses from securities. We run

OLS regressions of the following form:

∆ ,. ,

,

,

,,

. ,

, , , ,

where ∆ , is the change in the left-hand side variable used in each specification from t to

t+k. We consider one-year and two-year changes in fixed assets and one-year changes in other left-hand

side variables. Flow variables are measured between t-1 and t. We also run an alternative specification in

which investment income is scaled by securities rather than net fixed assets.

We control for hospital size using the logarithm of total revenue and also for the size of hospital

investments in securities as a proportion of fixed assets. We discuss these controls in more detail in the

next section. In the absence of a clean measure of marginal Q, especially for private companies, we

include growth in service revenue and operating income as a fraction of lagged fixed assets to account

for changes in hospital investment opportunities. A perfect control for marginal Q is less critical as long

15

as returns on financial investments are uncorrelated with Q, which is an assumption we discuss in detail

below. We run regressions with and without hospital fixed effects, i, to account for the possibility of a

small-sample bias (Stambaugh, 1999; see discussion in Lewellen and Lewellen, 2012). Year fixed effects,

t, are included in all regressions. Standard errors are heteroskedasticity robust and clustered at the

annual level.

5.2 Identification

The key identifying assumption for all our tests is that returns obtained from securities portfolios are

uncorrelated with hospital investment opportunities. There are three ways this assumption could be

violated. First, aggregate stock market returns could be correlated with investment opportunities in the

hospital sector. To address this, we include year dummies in each regression, so that the tests rely only

on the within-year cross-sectional variation in hospital returns. As a robustness test, we decompose a

hospital’s total return into the market and the idiosyncratic component and show that both components

are associated with capital spending (this test is included in the Supplementary Appendix). A related

concern is that a hospital’s investment opportunities could be linked to the local rather than market-wide

economic conditions. If hospital investment portfolios are tilted towards local firms, the idiosyncratic

component of a hospital return could be associated with local hospital investment opportunities. Our

regressions are robust to the inclusion of state-year fixed effects, so that regional economic conditions

do not seem to drive the results.

Second, hospitals may differ with respect to the stock picking skills of their investment managers or

investment advisors. If hospitals with higher skills (and thus higher average returns) are also better at

managing capital investments, they may exhibit higher capital spending on average. In this spirit, Lerner,

Schoar and Wang (2009) show that university endowment returns are correlated with the quality of the

student body and the prestige of the institution. We control for hospital specific skill using hospital fixed

effects, so any remaining skill effect would have to come from the time varying hospital quality. Based

on Lerner, Schoar, and Wang (2009) this is not a significant concern. They argue that skill comes mostly

from the top institutions’ ability to gain access to unique financial investments, and such ability is likely

to persist over time. Also, in general, finance literature finds little evidence of stock picking skill among

mutual fund managers (see, for example, Fama and French (2010)), suggesting that skill is unlikely to

drive realized returns in our sample.

The final concern might be that hospitals tailor their portfolios so that portfolio returns are high at

times when investment opportunities in the healthcare sector are also high, and when hospitals need

16

funds to invest. To achieve such correlation, hospitals may choose to tilt their portfolio towards

investments in the healthcare sector. One potential objection to this argument is that this strategy would

magnify the variability of a hospital’s overall income, and thus would not be desirable to managers. In

fact, the notion that investments would be tailored to be high during times of high investment

opportunities was consistently dismissed by the hospital executives we talked to. Also, the inclusion of

year fixed effects should absorb most of the variation coming from industry-wide shocks, given that our

sample includes hospitals only. A related issue is that hospitals are likely to adjust their asset allocation

towards safer investments at times when they anticipate capital expenditures to be high. Such behavior

would generate negative correlation between portfolio returns and capital investments and thus would

bias our tests against finding significant cash flow-investment sensitivity. To address these issues, we

perform a robustness test where we split the investment income variable into a market and an

idiosyncratic component, and we show that both components are correlated with future capital spending

(Table A9 in the Supplementary Appendix). This mitigates the concern that our results are driven by the

fact that financial portfolios are tailored to match capital needs.

5.3 Sensitivity of CAPEX to Returns on Securities

Table 3 shows our main results. We find that one additional dollar of investment income leads to an

increase of 10 cents in net fixed assets in the subsequent year, and this estimate is not affected by the

inclusion of hospital fixed effects. This means that a one standard deviation change in investment

income scaled by lagged fixed assets (which is six percentage points) translates into a 0.6 percentage

point change in asset growth (the average growth is four percentage points). In the last two columns of

the left panel, we scale investment income by the lagged holdings of securities rather than lagged fixed

assets. This variable measures the actual return on financial investments, and it is also positively and

significantly associated with future capital spending. A one standard deviation change in investment

return translates into a 10 percent change in the asset growth rate (or 0.4 percentage points).

In the right panel of Table 3, we consider longer-run effects of the cash flow shocks from securities

on capital investments. The goal is to account for the fact that investments may take time to implement.

Specifically we examine asset growth over two years following the investment return. We find that the

coefficients on investment income are larger and statistically more significant than in the one-year

analysis. One additional dollar of investment income is associated with a 23 cents increase in fixed assets

over the two subsequent years. This means that a one standard deviation change in investment income

scaled by lagged fixed assets translates into a 1.3 percentage point increase in the two-year growth rate of

17

fixed assets. The results are similar when we scale investment income with lagged holdings of securities

instead of lagged fixed assets. We repeat these tests using HCRIS data in Table A1 of the Supplementary

Appendix and find similar results.

5.3.1 Interpreting the magnitudes

The tests in Table 3 reject the frictionless benchmark model of a nonprofit firm in Section 2.1 that

predicts investment-cash flow sensitivities close to zero. An alternative benchmark is the investment-

cash flow sensitivity implied by the nonprofits’ internal spending rules. The rules are typically set by the

organizations’ boards of trustees, and they determine the amount of funds the organization is allowed to

appropriate for expenditure from its endowment each year. The rules offer an interesting reference point

because they can be interpreted as an indication of what trustees (and thus donors and taxpayers)

typically consider to be the appropriate level of spending as a function of the endowment’s performance.

Taking this perspective, we ask how the nonprofits’ actual spending patterns compare to those implicit

in their spending rules.

Although we do not have systematic evidence on spending rules for hospitals, the NACUBO-

Commonfound Study of Endowments reports such evidence for a large sample of U.S. universities and

colleges. According to conversations with industry specialists, the most common rules described in the

study appear to be also used by hospitals. According to the 2010 NACUBO study, 75% of surveyed

organizations define the amount of funds available for appropriation each year as a fraction (typically 3-

4%) of the three-year moving average of the endowment’s market value. For example, a strictly applied

four-percent rule implies that a dollar increase in the value of the endowment in year t-1 would increase

spending by one-third of four cents, i.e., by 1.33 cents, in year t (this is because the endowment value in

year t-1 has a weight of one-third in the moving average). In contrast, the regressions in Table 2 show

that actual spending on fixed assets in year t increases by approximately 10 cents for each additional

dollar of financial income in year t-1, implying a substantially higher sensitivity. Thus hospitals appear to

increase investment more strongly in response to cash flow shocks than the typical rules imply.

In practice, a hospital’s investment could deviate in a number of ways from the amount obtained by

a direct application of the spending rule (i.e., in the example the amount equal to 4% of the moving

average of the past endowment values). First, the spending rule determines the amount of funds that can

be taken out of the endowment in a given year, but the actual spending in that year could be higher or

lower than that amount. For example, an organization could finance its expenditures from funds

available outside of the endowment, or it could choose to save the appropriated funds for future

expenditures. Second, the rule defines the amount of endowment funds available for total spending

18

rather than for spending on fixed assets alone. Thus in the example, a strict application of the rule to

total spending suggests that investment-cash flow sensitivity may be below the 1.33 estimate (if some of

the 1.33 cents is used for non-CAPEX spending). Finally, spending rules can be changed or overruled by

nonprofits’ boards of trustees, which gives the organization additional flexibility to adjust its spending

rate. For example, Sedlacek and Jarvis (2010) report that in 2009, 18% of organizations surveyed by

NACUBO used special appropriations of endowments funds, that is, appropriations in excess of those

permitted by the spending rule.17

As a final comparison, it is informative to relate the cash flow-investment sensitivities in Table 2 to

those obtained in large-sample studies of for-profit firms. Based on this comparison, the estimate of 10

cents per dollar of income does not appear high. For example, Cleary (1999) and Baker, Stein, and

Wurgler (2003) report values of $0.05–0.15 for for-profits. Using alternative methods to correct for

measurement error in Q, Gilchrist and Himmelberg (1995) estimate adjusted investment-cash flow

sensitivities of $0.17, Ericson and Whited (2001) of zero, and Lewellen and Lewellen (2012) of $0.18.

Rauh (2006) reports an estimate of $0.11 but also shows that firms cut investment by $0.60-0.70 in

response to a dollar of mandatory pension contributions. Thus based on these studies, the

responsiveness of investment to cash flow shocks is similar for for-profit and nonprofit firms. To the

extent that the literature is successful at identifying the effects of frictions, this comparison suggests that

the impact of frictions on investment does not differ substantially across the two organizational forms. 18

5.3.2 What types of spending respond to cash flow shocks?

Table 4 shows how the changes in fixed assets are allocated among their different components. The

tests are based on the HCRIS data described in Section 4.1 for which we have a breakdown of fixed

assets into different categories.19 The regressions are similar to those in Table 3, except that investment is

measured separately for three types of fixed assets: land, buildings and equipment. The equipment

variable includes five sub-categories (Graning, 1967): fixed equipment (built in items such as cabinets,

17 Based on evidence in Sedlacek and Jarvis (2010), the special appropriations appear countercyclical: the percentage of organizations reporting special appropriations is close to 15% from 2005 to 2007 but it increases to 19% in the crisis year of 2008 and remains high at 18% in 2009. 18 Most estimates in the literature use investment measured over one year and are thus more comparable with the $0.10 estimate in this paper (rather than the $0.30 estimate investment measured over two years). A direct comparison of the longer-term sensitivities for for-profit and not-for-profit firms are beyond the scope of this paper. We cannot rule out, however, that such comparison would reveal a higher sensitivity for not-for-profits, which could be the case if not-for-profit investment takes longer to respond to cash flow shocks. 19 As discussed earlier, HCRIS contains a less reliable measure of investment income compared to the IRS dataset. In spite of this drawback, the basic investment-cash flow sensitivity regressions conducted using the HCRIS data yield results consistent with those reported in Table 3 (see Table A1 in the Online Appendix).

19

counters or elevators); Cars and Trucks; Major Movable Equipment (large equipment items that can be

moved but are typically stationary, such as X-ray or MRI machines); and two categories for minor

equipment (like surgical instruments or sheets and linen) broken down by whether it can be depreciated

or not.

Prior studies suggest that spending on equipment may be particularly prone to overspending

because it involves small ticket items with potentially large private benefits for doctors (e.g., Glaeser

(2003)). If overspending drives investment-cash flow sensitivity in our sample, we may observe especially

high sensitivity for this category of expenditures. The regressions in Table 4 provide no support for this

hypothesis. For example, in regressions without hospital fixed effects, the coefficients on investment

income are 0.05 for equipment and 0.06 for buildings, with t-statistics of 2.02 and 1.2, respectively.

Including hospital fixed effects weakens the results on equipment spending. There is no evidence that

investment income is associated with spending on land.

Table 5 tests whether investment income affects other categories of spending reported on the IRS

form. We consider four different types of expenses: salaries of officers and directors, other salaries paid,

conference expenses, and travel expenses. We observe only total executive and other salaries, so we

cannot distinguish the effects of cash flow shocks on the number of employees vs. on salaries per

employee. The dependent variable in each regression is the growth rate in expenses during the year

following the measurement of cash flows. As a robustness test, we also use the expense item scaled by

lagged fixed assets with similar results.

In the executive compensation and other salaries regressions, the coefficients on investment income

are positive but are not statistically significant. For example, in the regressions without hospital fixed

effects, the coefficients are 0.17 and 0.02, respectively (t-statistics are 1.01 and 0.09). Inclusion of

hospital fixed effects further reduces statistical significance in both regressions. There is no evidence that

conference or travel expenses increase after positive shocks to investment income: the coefficients in

these regressions switch signs depending on specification and are not statistically significant.

The fact that salaries (or perks) in hospitals are not significantly sensitive to cash flow shocks has

two potential interpretations. First, even though these expenditures directly benefit insiders, the items

might be relatively easy for boards to monitor, so that excessive spending is not a significant concern.

Glaeser and Shleifer (2001) make a similar argument and suggest that private benefits in nonprofits

might take a less tangible form than direct pay. Interestingly, however, Bertrand and Mullainathan

20

(2001) find that CEOs of for-profit firms appear to be “paid for luck”, suggesting excessive

executive pay even in the presence of shareholders. In the context of hospitals, Brickley, Van Horn,

and Wedig (2010) find evidence of excessive CEO pay in a sample of hospitals with weak boards.20

An alternative interpretation of our findings is that hospital salaries (and perks) are influenced by

insiders, but that insider influence does not necessarily make spending more sensitive to cash flows. The

traditional argument in favor of higher sensitivity is based on the assumption that skimming is easier for

insiders to implement when internal cash flow is high, in part, because it is easier to conceal. However,

this assumption is difficult to verify directly. More generally, it is possible that some expenditures are

persistently too high, or that excessive spending is independent of financial performance. We explore

this possibility further in Section 6.

5.3.3 Financing constraints

Another potential explanation for investment-cash flow sensitivities in the literature is the presence

of financing constraints, and this is the explanation we focus on in this section. Specifically, we test

whether financing constraints contribute to the sensitivity of investment to cash flows documented in

Table 3. To do so, we compare investment-cash flow sensitivity for sub-samples formed based on ex-

ante measures of constraints. We use three measures of financial slack: the level of net debt, the level of

financial investments, and the level of net debt minus financial investments. Each variable is measured

one year before the measurement of cash flows, and it is scaled by the contemporaneous net fixed assets.

In Table 6 we pool the entire sample and test whether investment-cash flow sensitivities differ across the

different hospital groups. We also report a specification that includes hospital fixed effects in Table A2

of the Supplementary Appendix, as well as separate investment regressions for the different samples split

by financing constraints in Table A3 in the Supplementary Appendix.

Overall, the tables show that investment-cash flow sensitivities increase significantly with the level

of ex-ante constraints. For example, based on Table 6, firms in the top tercile formed based on the level

of net debt exhibit significantly higher sensitivity of capital spending to investment income than firms in

the bottom tercile. The difference corresponds to 26 cents on each dollar of investment income, and the

corresponding t-statistic is 3.27. When we split the sample into terciles based on the level of financial

investments rather than net debt, we obtain a difference in sensitivity between the top and the bottom

20 Specifically, they find that boards with insiders as members pay higher CEO salaries. They also show that in hospitals with insider boards, executive salaries respond positively to operating profits.

21

tercile corresponding to -16 cents with a t-statistic of -1.27. Finally, using a split based on net debt minus

financial investments yields a differential effect of 27 cents with a t-statistic of 1.92. Overall, the analysis

in Table 6 (and Tables A2 and A3 of the Supplementary Appendix) suggests that financing constraints

are an important driver of the investment-cash flow sensitivity documented in Table 3.

6 Treatment intensity and overinvestment

This section re-examines the link between investment-cash flow sensitivity and agency problems, and

specifically overspending by insiders, using a new hospital-specific data source. There are few direct tests

of overspending (or overinvestment) in the corporate finance literature because excessive spending is

difficult for researchers to identify. Hospitals offer a unique setting to explore these issues. This is

because a large literature in healthcare economics focuses on identifying and measuring excessive

spending by hospitals, and insights from this research can be used to tests whether overspending and

cash flow-investment sensitivity are related. We start with a brief overview of the relevant literature (an

extensive survey can be found in Skinner, 2012).

6.1 Variation in treatment intensity: background

Research on the nature and efficiency of medical spending spans many decades with the oldest

studies dating back to the 1930s. One of its key insights is that medical spending varies strongly across

geographic regions, and that much of the variation cannot be explained by observable factors such

patient population, income level, market structure, and other patient and hospital characteristics.21

Numerous studies document this pattern for a wide variety of diagnoses, time periods, and geographic

areas. As an example, Wennberg and Birkmeyer (1999) document large unexplained variation in the use

of beta blockers for 200,000 heart attack patients across the U.S. in 1994-1995. The variation is especially

puzzling given that nearly all patients in their sample should have received the treatment.22 Similar

patterns have been reported, for example, for tonsillectomy (Glover, 1938), Wennberg and Gittelsohn

(1973), Suleman et al., 2010), Cesarean section (Gruber and Owings (1996), Epstein and Nicholson,

2009)), PSA testing, including for men over 80 (Bynum et al., 2010), and other types of medical

interventions.

21 E.g., Sutherland et al. (2009), Gottlieb et al. (2010), and Zuckerman et al. (2010) 22 The actual treatment rates varied substantially across regions (e.g., it was 5% in McAllen, Texas and 90% in Albany, Georgia). This is in spite of the fact that the effectiveness of beta blockers had been well documented at the time, and that the treatment was relatively inexpensive.

22

The literature attempts to identify reasons for the unexplained variation in treatment intensity, and a

prominent view is that the variation is driven, to a large extent, by regional and hospital-specific norms

and doctor preferences, or are caused by differences in excess capacity. Norms and preferences are

especially important in cases when benefits of treatment to patients are small or not well understood (so-

called Category III treatments, Skinner, 2012): absent clear clinical evidence in favor of one treatment vs.

another (or no treatment at all), doctors are more likely to rely on personal views, prevailing norms or

rules of thumb.

A prominent example of Category III treatments in the literature–and one that we focus on in this

paper–are certain treatments administered to terminally ill patients at the end of their lives. These

treatments are classified as Category III because there is no clear-cut clinical evidence that more

treatment–at levels typical in the U.S.–is better for patients, either in terms of prolonging life or

improving quality of life. Still, there is substantial variation in the use of these treatments across

hospitals, including the very best ones (Wennberg, Fisher, Stukel, et al., 2004). Moreover, research on

patient choices at the end of life suggests that patient preferences are unlikely to explain this cross-

sectional variation, and that preferences are often trumped by hospital-specific practices.23

6.2 Hypotheses

In this section, we consider differences in cash-flow investment sensitivity of hospitals with different

levels of per-patient spending (or treatment intensity) at the end of life. To interpret the tests, we assume

that high-intensity hospitals spend too much per patient, relatively to what is optimal from the perspective

of taxpayers or donors. This assumption could be reasonably debated, and there is no perfect way to

identify ‘unwarranted’ vs. ‘justified’ medical spending (see, for example, Doyle, Graves, Gruber and

Kleiner, 2012 and Doyle, 2011 for settings in which higher-cost interventions are associated with better

outcomes for patients). However, three arguments speak in favor of our approach. First, given the

frictions in the market for hospital services, it is reasonable to assume that individual hospitals

systematically over- or under-supply treatment.24 Second, we identify overspending by focusing on

Category III treatments for which the benefits to patients are clinically least well established, and where

23 For example, a study of dying Medicare patients during the period of 1992-1993 asks patients where they prefer to die, in the hospital or at home, and then studies the determinates of the actual place of death. It finds considerable regional variation in the propensity to die in the hospital. Patient preferences, socioeconomic factors or clinical factors have no explanatory power in explaining this variation, and the strongest determinant is the availability of hospital beds in the region (Pritchard , Fisher, Teno et al. (1998)). See also Anthony, Herndon, Gallagher et al. (2009) and Barnato, Herndon, Anthony, et al. (2007). 24 Most importantly, consumers do not pay for services directly, and providers face a range of conflicting incentives, including loyalty to patients, monetary incentives, and malpractice risks.  

23

idiosyncratic factors are most likely to drive supply. Finally, the measures themselves account for

differences in race, gender and primary chronic condition, and our tests control for observable

differences across hospitals–including size, case mix (i.e., the average complexity of a case), or hospital

type–and thus focus on hospital specific unexplained variation in intensity.

Our goal is to establish empirically whether a hospital’s tendency to overspend–as measured by

spending intensity on end-of-life (EOL) treatments–is associated with a higher investment-cash flow (or

spending-cash flow) sensitivity. Finding such pattern would support the free-cash flow explanation, i.e. it

would suggest that organizations with a higher preference for spending tend to increase expenditures

more strongly in response to cash flow shocks. This would be consistent with the argument in Jensen

(1986) and Stulz (1990) that external capital providers–such as banks or donors–impose extra constraints

on excessive spending.

An alternative hypothesis is that high-expenditure hospitals spend cash regardless of their financial

performance. Because of their higher preference for spending, such organizations may be more willing to

raise debt or tap into their endowments to cover financing shortfalls. This attitude would make their

expenditures less tightly linked to cash flow than expenditures of their low-spending counterparts. Such

finding would be interesting as it would challenge the idea that overspending (or overinvestment) and

cash flow-investment sensitivity are linked.

6.3 Data and descriptive evidence

Data on the use of end-of-life (EOL) treatments by hospital comes from The Dartmouth Atlas of

Health Care based at The Dartmouth Institute for Health Policy and Clinical Practice. The database

contains information from the Center for Medicare and Medicaid Services (CMS) research files which

include detailed data on Medicare claims and beneficiaries. We rely on the sample constructed by the

Dartmouth Atlas and used commonly in studies on EOL treatment. The original population includes

4,732,448 Medicare beneficiaries who (1) died over the five-year period from 2003 to 2007; (2) were

hospitalized in an acute care hospital at least once during the last two years of life; (3) had at least one of

nine chronic illnesses associated with a high probability of death25; (4) were 67 to 99 at the time of death.

The patients are assigned to the hospitals they used most frequently in the last two years of life

(except for the capacity utilization measures described below for which patients are grouped by place of

25 The primary chronic conditions are Malignant Cancer/Leukemia, Congestive Heart Failure, Chronic Pulmonary Disease, Dementia, Diabetes with End Organ Damage, Peripheral Vascular Disease, Chronic Renal Failure, Severe Chronic Liver Disease and Coronary Artery Disease.

24

residence). For each measure, we only have the hospital-level average over the considered time period.

To minimize noise, Dartmouth Atlas includes only hospitals with a minimum of 80 deaths in the sample.

An important feature of the EOL measures on Dartmouth Atlas is that they are backward-looking, that

is, they condition on patients’ death and thus exclude severely ill patients that survive, possibly as a result

of treatment. Skinner, Stager, and Fisher (2010) report the Atlas measures are highly correlated with

similar forward-looking variables, with correlations close to 90%.26

Table 7 shows descriptive statistics for the EOL measures by hospitals, and in Table A4 of the

Supplementary Appendix we show a correlation matrix of these measures, as well as hospital-level

financial and quality characteristics. All measures are adjusted for age, sex, race, primary chronic

condition, and whether patients had more than one of the nine chronic conditions. The first three

variables measure the percentage of patients that during their last six months of life (1) saw more than 10

different physicians; (2) received medical and surgical intervention; and (3) received care in an intensive

care unit. These measures are available for between 1,286 and 1,476 hospitals in our sample, depending

on the exact measure. In addition, we also use the percentage of cancer patients that received life

sustaining interventions during the last one month of life. This measure is available for a subset of 311 or

180 hospitals in our sample, respectively. In addition to the EOL measures, the table shows descriptive

statistics for a number of hospital characteristics included in the Atlas database, such as the hospital case

mix index, Medicare quality score, and dummy variables for teaching hospitals and National Cancer

Institute (NCI) cancer centers.27 It also includes two capacity variables measuring the number of beds

and the number of specialist per 1,000 residents in the hospital’s Hospital Service Area (HSA; a

geographic unit constructed by Dartmouth Atlas using Medicare discharge data).

Table 8 shows descriptive regressions of the EOL measures on various hospital characteristics.

The residuals from these regressions are then used in the main investment-cash flow sensitivity tests in

Table 9. The patterns in Table 8 are broadly consistent with prior evidence. First, there is no clear

relation between the EOL measures and various proxies for hospital quality: the coefficients on the

26 In this paper, we use the Atlas measures to identify high-intensity hospitals (i.e., hospitals with high per-patient spending). The fact that the measures are constructed using a sample of patients who die does not seem problematic for our tests. As a robustness test, we use a sample for which the selection of dying patients is least likely to be significant (cancer patients during their last few weeks of life) and obtain qualitatively similar results as in the main tests. 27 Case Mix Index (CMI) is a summary measure of the clinical complexity of Medicare cases treated in a hospital. Higher CMI indicates more complex cases. The Medicare quality scores are provided by the Hospital Quality Alliance (HQA) and are available on the CMS Hospital Compare website. The measures attempt to capture the extent to which a hospital provides timely and effective treatment for a number of specific medical conditions. The overall quality score we use in this paper summarizes measures for treating acute myocardial infarction, congestive heart failure, and pneumonia. The National Cancer Institute (NCI) is a dummy for hospitals that are NCI-designated Cancer Centers engaged in active cancer research.

25

teaching hospital dummy and case mix index switch signs depending on the specific EOL measure and

are often not statistically significant. The coefficient on the Medicare quality score is negative and

significant for four out of the five measures. Capacity is generally positively associated with intensity of

treatment. For example, the coefficient on the number of specialists per 1,000 residents in the hospital’s

HSA is positive and highly significant for all measures. Similarly, the coefficient on the number of beds

measure is positive for the three measures directly linked to hospitalization, and is significant for two out

of the three measures. Larger hospitals (where we measure size by the logarithm of total revenues) tend

to use treatments more intensively in the broader patient population, but less intensively in the

population of cancer patients. There is some evidence that more intensive hospitals have higher debt and

smaller financial assets, but the coefficients are often not statistically significant.

6.4 Main results

The results interacting EOL treatment measures and investment income are in Table 9. The

regressions are similar to our main tests in Table 3, except that they include an interaction term of

investment income with an intensity measure (as well as the intensity measure directly). The first column

for each intensity measure uses the raw variable as it is reported in Dartmouth Atlas and in the second

column the intensity measure is a residual from a regression of the raw intensity on hospital

characteristics reported in Table 8. All control variables are the same as those in Table 3.

We find that investment of higher-intensity hospitals is significantly less sensitive to cash flows. The

coefficient on the interaction term of investment income with intensity is negative for all intensity

measures, and using both the raw measures and the residuals from the regressions in Table 8. The

coefficients are statistically significant in seven out of the ten regressions. For example, in the first

column, intensity is measured using the percent of patients seeing more than ten different physicians

during the last six months of life. A one-standard deviation increase in this measure lowers cash flow

investment sensitivity by 14 cents per dollar of income (the t-statistic is -4.1), and the estimates range

from 16 cents to 4 cents for the remaining variables. The last two regressions use the percent of cancer

patients receiving life sustaining treatment within the last month of live as a measure of treatment

intensity. The regressions provide a useful robustness tests because they focus on an especially

homogenous group of patients. The table shows that, in spite of the significantly smaller sample for

which the measure is available, the results are consistent with full-sample tests. We find similar results

when we include hospital fixed effects (results are reported in Table A5 of the Supplementary

Appendix).

26

Overall, the tests in Table 9 provide no evidence that hospitals with a stronger preference for

spending–as measured by treatment intensity–exhibit higher sensitivity of investments to cash flows. In

fact, the results suggest the opposite pattern: capital expenditure of high-spending hospitals appears less

dependent on financial performance. This finding seems at odds with the standard free-cash flow

explanation, whereby excessive spending is relatively more difficult to finance from external sources, and

thus more responsive to cash flow.

As suggested earlier, one explanation might be that higher preference for spending makes hospitals

more willing to issue debt or use endowments when internal cash flow is low. More generally, the

evidence in Tables 8 and 9, combined with prior research on end-of-life treatment, casts doubt on the

idea that overspending drives hospitals’ investment-cash flow sensitivities. The results also make clear

that low sensitivities do not necessarily imply that spending is efficient–a point made formally in Kaplan

and Zingales (1997, 2000) and Stein (2003).

7 State Oversight and Hospital Investments

Our final set of tests explores the impact of regulation on hospital investment. A number of U.S.

states regulate hospital investments as part of the so-called Certificate of Need (CON) laws. The laws

require hospitals to obtain approval from state authorities for expansions of infrastructure (new buildings

or improvements to existing buildings) and, in some states, also for significant investments in equipment.

The laws were put in place mostly during the late 1960s and 1970s with the purpose of containing

healthcare costs and aligning hospital spending with regional ‘needs’. The federal law requiring states to

have CON regulation expired in 1986, and since then fourteen states dropped the laws, mostly during

the second half of the 1980s.28

The effects of CON laws have been analyzed extensively in the literature (an overview is in Conover

and Sloan (1989)). Researchers have examined, among other things, the effects on hospital costs, capital

expenditures, bed supply, and diffusion of specific technologies. The majority of the studies surveyed in

Conover and Sloan (1998) find no significant effect of CONs on these outcomes, and some studies

report contradictory findings. There is some evidence that, as a result of CON laws, hospitals substitute

less regulated categories of spending for more regulated ones (Salkever and Bice, (1976, 1979), Sloan and

28 The list of states is in the Appendix. A frequently cited reason for the decline in the popularity of CON laws is that the introduction of the Medicare’s Prospective Payment System and the growth in managed care provided alternative and, arguably, more effective mechanisms to contain healthcare costs.

27

Steiwald (1980)). In spite of the lack of conclusive evidence that CON regulation is effective, the laws

remain in place in the majority of states.

This paper investigates how hospital investment responds to shocks to financial income, and given

the prevalence of CON regulation, it seems natural to ask how this response varies across states that

regulate hospital investments vs. states that do not. Specifically, we ask whether investment responds

more or less strongly to cash flow shocks in states that directly oversee investment spending.

Conceptually, the answer to this question depends on the specific rules states use to evaluate and reject

projects, and on hospitals’ response to these rules (we discuss this in more detail below).

To answer this question empirically, we repeat the cash flow-investment sensitivity regressions in

Table 3, including an indicator variable for hospitals located in CON states and an interaction of the

CON dummy with investment income. We measure capital expenditures over one- and two-year

horizons to account for the fact that the CON approval process may delay investment, and we show

regressions with and without hospital fixed effects. The results are reported in Table 10.

The table shows that the interaction effect is negative in all regressions, implying that investment-

cash flow sensitivity is lower in regulated states. When investment is measured over one year, the

coefficients on the interaction term are -0.14 and -0.10 in regressions without and with hospital effects,

respectively (t-statistics are -2.27 and -1.36). When investment is measured over two years, the respective

coefficients are -0.51 and -0.58 (t-statistics are -4.23 and -6.24). As a robustness test, we repeat the

analysis after controlling for a number of state characteristics, including measures of healthcare spending

per capita, population age, Medicare spending, HMO penetration, hospital concentration, and share of

for-profit hospitals (the variables are described in Table A6 of the Supplementary Appendix).

Specifically, we regress the indicator variable of whether a state has a CON law on the state

characteristics, and then use the residuals from this model–instead of the raw CON dummy–in the main

tests. The results are reported in Table A7 of the Supplementary Appendix. We find that the CON

results remain significant after accounting for state attributes: the coefficients on the interaction terms of

investment income with the CON residual are -0.11 and -0.15 when investment is measured over one

year (t-statistics of -1.91 and -1.94), and they are -0.45 and -0.57 when investment is measured over two

years (t-statistics of -1.89 and -3.76). This suggests that demographic or industry structure differences

across states are not responsible for the CON effect we document in Table 10.

As an additional robustness test, we exploit the variation across states in the types of investments

they regulate. Out of the total of 39 states that have CON laws during our sample period, 21 states

regulate spending on equipment in addition to other capital expenditures. We combine this information

28

with HCRIS data on different categories of investment spending, specifically, spending on land,

buildings and equipment. This allows us to explore how laws that apply to equipment affect equipment

spending vs. other types of capital expenditures. The sample in Table A8 is limited to hospitals located in

the 39 states with CON laws, and the regressions include a dummy variable for laws that apply to

equipment (CON Equip) and an interaction of CON Equip with investment income. The table shows

regressions for the three categories of investment. We find that the coefficient on the interaction term is

negative for both buildings and equipment (it is zero or positive for land), but consistent with

expectation, the point estimates are twice as large for equipment. For example, when investment is

measured over one year, the coefficient is -0.07 for buildings (t-statistic is -1.01) and it is -0.15 for

equipment (t-statistic is -4.01). Though the difference between the coefficients is not statistically

significant, the point estimates are consistent with the different types of CON laws having a targeted

effect on investment spending.29

Overall, the tests provide an insight into how CON agencies operate. A plausible interpretation is

that the findings reflect a causal effect of CON laws on investment.30 The evidence from prior studies

suggests that the laws have no significant impact on the level of investments, so finding a significantly

negative effect on the slope (i.e., on the sensitivity of investments to cash flows) raises the possibility that

the laws constrain investment more strongly when cash flow is high. Such pattern would be consistent,

for example, with a simple rule, whereby CON agencies reject projects more often when investment–if

unregulated–would be unusually high. For financially constrained hospitals this would be the case

precisely when financial performance is good, so that projects can be financed from internal sources.

The result is also consistent with a somewhat more sophisticated state agency, i.e., one that tends to

reject (or deter) projects more often when they appear less valuable, assuming that, for financially

constrained hospitals, such instances are more likely to occur when cash flow shocks are high. In either

case, hospitals anticipating the state’s behavior might themselves smooth investment across time and

thus minimize the law’s impact on the overall investment levels (consistent with substitution across

categories of spending documented earlier in (Salkever and Bice, (1976, 1979), Sloan and Steiwald

(1980)).

29 Separating the effects of different types of CONs empirically is difficult for at least two reasons. First, investment decisions concerning different types of expenditures, such buildings and equipment are likely linked in practice (e.g., purchases of equipment may lead to expansions in buildings), and second, states with more extensive CON laws (e.g., those that also regulate equipment) may have a stricter approach to regulation overall. 30 Alternatively, the coefficients could reflect differences across states that we do not fully control for. A reversed-causality interpretation is also possible though it would require that investment cash flow sensitivity plays a role in the states’ decisions to repeal the laws, which seems less plausible.

29

Though our tests cannot identify the precise mechanism generating the lower slope in CON states,

the results suggest that CON agencies have a measurable effect on investment in some states of the

world (consistent with an insignificant effect on average found in prior studies). Indirectly, the results are

also consistent with hospitals facing financing constraints, and CON agencies having a bigger impact

when these constraints are relaxed.

8 Conclusions

This paper examines investment decisions of nonprofit hospitals. Nonprofit firms do not have

residual claimants in the usual sense, and their governance systems differ fundamentally from those of

corporations. Prior studies have argued that nonprofit insiders have more power over the resources of

their organizations than managers of for-profit firms do, and that, if unchecked, this power would lead

to self-serving behavior, including excessive spending and overinvestment. Investment of nonprofit

firms might exhibit additional distortions because of the firms’ limited access to external financing. In

this paper, we examine the potential effects of agency problems and financing constraints on hospital

investments. Our empirical approach is based on Q theory. Its key prediction is that, in the absence of

frictions and controlling for Q, a firm’s investment should be unresponsive to cashflows.

The hospital setting allows us to test this implication in a novel way, thus sidestepping some of the

empirical challenges faced by prior literature. The key challenge has been to identify cashflow shocks that

are unrelated to Q. Nonprofit hospitals are interesting in this context because, in contrast to

corporations, they typically hold large financial assets, and shocks to the performance to these assets can

be used as a source of exogenous variation in cashflows. Using this approach, we find that hospital

investment increases, on average, by 10-23 cents for every dollar received from investments in securities,

thus implying the presence of frictions. The estimates are similar in magnitude to those found earlier for

shareholder owned corporations. This is interesting because for-profit firms are, arguably, better

governed than nonprofits, and because they have access to equity financing, which should alleviate

financing constraints.

We then consider the potential mechanisms responsible for the investment-cash flow sensitivities

we observe. The precise nature of the frictions that cause investment to respond to cashflows is not well

understood (Stein (2003)), and we use the unique data on hospital spending to shed light on this

question. We find that investment-cash flow sensitivities are substantially higher for hospitals that appear

to be financially constrained–based on their levels of debt or financial assets–and that the sensitivities are

30

close to zero for unconstrained hospitals. We find no evidence that agency problems are associated with

investment being more responsive to cashflows. Our key test is based on research in healthcare

economics that developed measures of excessive spending by hospital using data on medical treatment

of patients at the end of their lives. Using these metrics, we find no evidence that hospitals that appear to

overspend have higher investment-cashflow sensitivities, and in fact, the tests suggest the opposite

pattern. Thus, our findings shed new light on the potential forces driving investment-cash flow

sensitivities and suggest that higher sensitivities are a poor indication of excessive spending.

31

Appendix A: Investment-cash flow sensitivity in a frictionless benchmark model of a nonprofit

The appendix illustrates how investment of a nonprofit firm might respond to cashflow shocks in a

world without frictions. The example follows the altruistic view of a nonprofit, described, for example, in

Fama and Jensen (1985), Rose-Ackerman (1996), and Fisman and Hubbard (2005). Based on this view,

the objective function of the nonprofit firm is to maximize the utility of its donors or the public at large.

In the example, an altruistic donor derives utility from providing a charity good, X, to the nonprofit’s

customers. We use the term “charity good” loosely to denote any good that the donor is willing subsidize

and that requires subsidization in order to be provided. For example, subsidized education or

uncompensated healthcare services would fall in this category. Given that there are no frictions, the

donor makes an optimal donation every period, and the nonprofit maintains no endowment. The donor

consumes non-charity goods outside the nonprofit, and we denote the non-charity consumption as C.

Besides the charity good X, the nonprofit produces a good Y which generates profit V but does not

enter the donor’s utility function directly.31 For simplicity, we assume that the production functions for

X and Y are independent. Since Y has no direct effect on donor utility, the organization always chooses

Y to maximize profit because this maximizes resources available to produce X. The nonprofit’s problem

is thus:

Max , , st. and 0, (1)

with U(.) and X(.) being increasing and concave, D is donation, and W is the donor’s wealth. We

assume for simplicity that the profit V is not sufficient to satisfy the donor’s demand for X, so that the

donor chooses to make a strictly positive donation. In this case, the first-order conditions to problem (1)

imply that the nonprofit produces X up to the point when the donor’s marginal utilities from charity and

non-charity consumption are equalized:

(2)

Importantly, an exogenous shock to the non-profit’s cash flow can induce higher spending on X

because it relaxes the donor’s wealth constraint. Eq. (3) below describes this effect in more detail.

Specifically, it shows how a change in V affects the optimal level of donations, and indirectly the

production of X. Assuming that the donor’s utility is separable in X and C (i.e., UC,X = 0), one can show

that:

31 The role of revenue generating activities in nonprofits is discussed, for example, in Rose-Ackerman (1996).

32

, , (3)

where , , . Since both U and X are concave, K 0, and depending on the

curvature of U, is between -1 and 0. Thus, in equilibrium, the donor reduces his donation in response

to the cash flow shock because the nonprofit has more internal resources available for production of X.

The reduction in D typically does not totally undo the cash windfall V because the donor spends some

of the windfall on an increased consumption of X.

Overall, the example highlights how for-profit and nonprofit firms respond to cash flow shocks. For

a donor-focused nonprofit, a cash flow shock relaxes the donor’s wealth constraint, and thus can lead to

more consumption and spending on X. In contrast, a for-profit firm expands production only if it is

value-maximizing to do so. Thus a cash flow shock that is unrelated to the for-profit’s investment

opportunities should never impact spending.

The example also shows under what conditions the nonprofit’s spending becomes unresponsive to

cash flows. Specifically, eq. (3) shows that when the donor’s utility is quasi-linear in non-charity

consumption C (i.e., , 0), donations decline one-for-one in response to shocks to V ( 1).

This means that cash windfalls have no impact on production of X and instead are used entirely to

increase the non-charity consumption. The quasi-linearity assumption fits well in our setting as long as

charitable giving to a particular hospital constitutes a small fraction of its typical donor’s consumption

bundle. If this is the case, one can reasonably assume that a dollar increase in a donor’s wealth would be

spent primarily on other consumption ( , in eq. 3 is close to zero), leaving the desired charitable

giving to the hospital almost unchanged. Consequently, cash flow shocks that are unrelated to

investment opportunities have no impact on nonprofit expenditures, which matches the standard result

from Q-theory.

33

Appendix: Certificate of Need Laws

Below we show the dates for the introduction and repeal of Certificate of Need Laws by state as of

September 2011. States with CON laws affecting equipment spending are marked with an asterisk.

State or District Dates of the Program State or District Dates of the Program

Indiana 1980-1996, 1997-1999 Louisiana 1991-present

North Dakota 1971-1995 Maine* 1978-present

Pennsylvania 1979-1996 Maryland 1968-present

Arizona 1971-1985 Massachusetts* 1972-present

California 1969-1987 Michigan* 1972-present

Colorado 1973-1987 Mississippi* 1979-present

Idaho 1980-1983 Missouri* 1979-present

Kansas 1972-1985 Montana 1975-present

Minnesota 1971-1985 Nebraska 1979-present

New Mexico 1978-1983 Nevada 1971-present

South Dakota 1972-1988 New Hampshire* 1979-present

Texas 1975-1985 New Jersey 1971-present

Utah 1979-1984 New York* 1966-present

Wyoming 1977-1989 North Carolina* 1978-present

Alabama 1979-present Ohio 1975-present

Alaska* 1976-present Oklahoma 1971-present

Arkansas 1975-present Oregon 1971-present

Connecticut* 1973-present Puerto Rico 1975-present

Delaware* 1978-present Rhode Island* 1968-present

District of Columbia* 1977-present South Carolina* 1971-present

Florida 1973-present Tennessee* 1973-present

Georgia* 1979-present Vermont* 1979-present

Hawaii* 1974-present Virginia* 1973-present

Illinois 1974-present Washington 1971-present

Iowa 1977-present West Virginia* 1977-present

Kentucky* 1972-present Wisconsin 1977-1987, 1993-present

Source: National Conference of State Legislatures (www.ncsl.org/default.aspx?tabid=14373)

34

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38

Table 1

Descriptive Statistics for the main (IRS) sample, 1999-2006 The data comes from the IRS Form 990. The left panel shows descriptive statistics for hospital-years in the full sample, and the right panel shows means for subsamples split at the median based on the size of fixed assets or based on financial assets scaled by fixed assets. Investment income is income from dividends plus interest and realized and unrealized gains and losses on investments. Net Fixed Assets is gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is defined as the difference between service revenue and service expenses scaled by lagged net fixed assets. Executive compensation reflects the salaries of officers and directors. Net Debt is defined as total financial debt (bonds issued and bank loans) minus cash and temporary securities.

Full sample Means for sub-samples

Mean Med Std P5 P95 N Small Large Low

Fin. A.High

Fin. A.

Investment Incomet / Net fixed assetst-1 0.03 0.02 0.06 -0.04 0.14 5,269 0.03 0.04 0.02 0.05

Investment Incomet / Financial Investmentst-1 0.04 0.03 0.07 -0.06 0.14 5,269 0.04 0.04 0.04 0.04

Financial Investmentst / Net fixed assetst 0.82 0.71 0.55 0.14 1.93 5,269 0.81 0.83 0.46 1.18

Growth in Service Revenuet 0.08 0.08 0.08 -0.03 0.20 5,269 0.08 0.08 0.08 0.08

Net Incomet / Net fixed assetst-1 0.09 0.09 0.15 -0.15 0.35 5,269 0.08 0.10 0.06 0.13

Operating Incomet / Net fixed assetst-1 0.36 0.33 0.29 -0.05 0.90 5,269 0.39 0.32 0.33 0.38

Service Revenuet (in millions) 178.40 111.00 203.30 13.20 630.20 5,269 59.60 297.60 163.30 193.70

Total Revenuet (in millions) 189.10 116.80 216.90 14.20 665.50 5,269 62.10 316.40 172.00 206.20

Net Fixed Assetst (in millions) 82.50 48.30 97.90 5.90 287.70 5,269 22.80 142.50 77.30 87.80

Growth in Net fixed assetst 0.04 0.02 0.18 -0.10 0.32 5,269 0.03 0.06 0.03 0.06

Growth in Executive Compensationt 0.15 0.08 0.49 -0.53 0.99 4,251 0.13 0.16 0.15 0.14

Growth in Other Salaries and Wagest 0.07 0.07 0.06 -0.04 0.17 5,095 0.06 0.07 0.06 0.07

Divident and Interest Income (% of Inv. Income) 0.40 0.26 1.38 -0.85 1.84 5,265 0.34 0.46 0.41 0.40

Unrealized Gains and Losses (% of Inv. Income) 0.42 0.42 1.94 -1.44 2.15 5,265 0.51 0.34 0.46 0.38

Realized Gains and Losses (% of Inv. Income) 0.17 0.00 0.98 -0.69 1.32 5,265 0.15 0.19 0.15 0.20

Net Debtt / Net fixed assetst 0.49 0.48 0.63 -0.23 1.19 5,266 0.42 0.56 0.49 0.49

Share of Hospitals with Tax-Free Bonds 0.64 5,269 0.52 0.75 0.61 0.66

39

Table 2

Descriptive Statistics for the HCRIS Sample, 1999-2009 The data comes from HCRIS, Schedule D. Investment income comes from the Income Statement in Schedule G. Net Fixed Assets are defined as gross land, buildings and equipment minus accumulated depreciation. Equipment includes cars and trucks, major movable equipment, minor equipment and minor nondepreciable equipment. All other variables are defined in Table 1. Mean Med Std P5 P95 N

Investment Incomet / Net fixed assetst-1 0.03 0.02 0.04 0.00 0.12 8,847

Investment Incomet / Financial Investmentst-1 0.06 0.04 0.09 0.00 0.17 8,672

Financial Investmentst / Net fixed assetst 0.50 0.34 0.51 0.01 1.59 8,847

Growth in Service Revenuet 0.07 0.07 0.08 -0.05 0.19 8,847

Net Incomet / Net fixed assetst-1 0.08 0.08 0.15 -0.16 0.34 8,689

Service Revenuet (in millions) 134.7 84.1 145.2 10.8 442.6 8,847

Net Fixed Assetst (in millions) 68.0 40.3 82.7 4.3 229.9 8,847

Growth in Net fixed assetst 0.06 0.01 0.16 -0.09 0.36 8,847

Buildingst (in millions) 88.5 53.2 106.8 6.1 294.0 8,635

Equipmentt (in millions) 56.8 33.4 68.3 3.4 191.9 7,978

Landt (in millions) 3.9 1.2 11.8 0.0 13.7 7,996

Financial Debtt / Net fixed assetst 0.63 0.61 0.55 0.00 1.50 8,609

40

Table 3

Effect of Performance of Financial Investments on CAPEX The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variable is the forward looking one-year (i.e. from t to t+1) and two-year (from t to t+2) growth in fixed assets. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. Return on Securities is Investment Income in year t divided by Securities holdings in year t-1. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Net fixed assets (1-year growth) Net fixed assets (2-year growth)

Investment Incomet 0.10 0.10 0.10 0.23 0.28 0.27 (2.15) (1.78) (3.19) (4.00) (3.85) (4.40)

Return on Securitiest 0.06 0.07 0.15 0.19 (2.72) (2.72) (2.88) (4.92)

Financial Investmentst-1 0.04 0.04 0.09 0.04 0.10 0.08 0.07 0.14 0.09 0.15 (8.26) (5.72) (7.54) (8.64) (7.98) (8.77) (6.98) (7.24) (8.16) (7.28)

Growth Service Revenuet 0.19 0.17 0.02 0.19 0.03 0.31 0.28 -0.05 0.31 -0.05 (7.11) (6.91) (0.80) (7.05) (0.83) (5.65) (4.58) (-0.70) (5.77) (-0.67)

Operating Incomet 0.07 0.07 0.15 0.07 0.15 0.14 0.13 0.30 0.14 0.30 (12.56) (12.98) (10.08) (12.69) (10.13) (8.91) (7.34) (10.68) (8.91) (10.54)

Log(Total Revenuet) 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.02 0.02 (5.16) (6.18) (0.18) (5.31) (0.21) (5.30) (4.87) (0.18) (5.31) (0.23)

Hospital Fixed Effects Y Y Y Y

State-Year Fixed Effects Y Y

Year Fixed Effects Y Y Y Y Y Y Y Y

Number of Observations 5,269 5,269 5,269 5,269 5,269 4,271 4,271 4,271 4,271 4,271 R-Squared 0.06 0.13 0.47 0.06 0.47 0.08 0.16 0.58 0.08 0.58

41

Table 4

Effect of Performance of Financial Investments on CAPEX by Type of Fixed Assets The table shows OLS regressions of growth in buildings, equipment, or land on investment income and control variables for the HCRIS sample from 1999-2009. The dependent variables are the forward looking one-year (i.e. from t to t+1) change in (1) buildings, (2) equipment and (3) land scaled by net fixed assets. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income comes from the Income Statement in Schedule G of the HCRIS dataset. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Equipment includes cars and trucks, major movable equipment, minor equipment and minor nondepreciable equipment. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Buildings Equipment Land

Investment Incomet 0.06 0.11 0.05 0.02 0.00 0.00 (1.20) (1.33) (2.02) (0.36) (0.20) (0.46)

Financial Investmentst-1 0.03 0.09 0.02 0.05 0.00 0.00 (7.19) (7.01) (8.90) (6.23) (3.80) (2.99)

Growth Service Revenuet 0.06 0.01 0.05 0.00 0.00 0.00 (1.90) (0.31) (2.42) (-0.12) (1.57) (-0.04)

Operating Incomet 0.04 0.08 0.02 0.04 0.00 0.00 (4.40) (5.73) (3.37) (2.83) (2.30) (1.77)

Log(Total Revenuet) 0.00 -0.04 0.00 -0.02 0.00 0.00 (1.72) (-1.14) (2.80) (-1.17) (0.48) (0.08)

Hospital Fixed Effects Y Y Y Year Fixed Effects Y Y Y Y Y Y

Number of Observations 8,436 8,436 7,677 7,677 7,721 7,721 R-Squared 0.02 0.26 0.02 0.25 0.01 0.27

42

Table 5

Effect of Performance of Financial Investments on Selected Expenses The table shows OLS regressions of selected expenses on investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variables are growth rates in (1) compensation of officers and directors, (2) other salaries, (3) expenses with conferences and (4) travel expenses in year t+1. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Executive

Compensation Other

Compensation Conference Expenses

TravelExpenses

Investment Incomet 0.17 0.21 0.02 0.01 -0.07 -0.44 0.11 -0.03 (1.01) (0.81) (1.09) (0.30) (-0.21) (-1.10) (0.89) (-0.17)

Financial Investmentst-1 0.00 0.00 0.00 0.00 -0.04 -0.05 0.02 0.00 (-0.32) (-0.31) (3.92) (-0.99) (-3.37) (-0.93) (1.81) (0.01)

Growth Service Revenuet 0.24 0.03 0.19 0.08 1.00 0.85 0.36 0.16 (2.26) (0.18) (10.96) (2.56) (3.33) (2.33) (3.20) (1.10)

Operating Incomet 0.01 0.06 0.01 0.05 -0.12 0.04 0.01 0.11 (0.29) (1.43) (4.69) (5.89) (-2.05) (0.33) (0.56) (2.17)

Log(Total Revenuet) 0.01 0.08 0.00 -0.07 0.00 -0.21 0.00 -0.17 (1.43) (0.67) (1.56) (-4.60) (-0.28) (-1.44) (-0.62) (-2.17)

Hospital Fixed Effects Y Y Y Y Year Fixed Effects Y Y Y Y Y Y Y Y

Number of Observations 4,251 4,251 5,095 5,095 2,496 2,496 4,197 4,197

R-Squared 0.03 0.3 0.07 0.4 0.03 0.39 0.02 0.3

43

Table 6

Effect of Performance of Financial Investments on CAPEX by Level of Financing Constraints The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variable is the forward looking one-year (i.e. from t to t+1) growth in net fixed assets. We use three measures of financing constraints: Financial Investments scaled by net fixed assets; Net Debt, defined as the value of bonds outstanding and other notes payable minus cash and temporary investments scaled by net fixed assets; and Net Debt minus Financial Investments scaled by net fixed assets. The regressions use ranks formed using the tertials or the median of each measure. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Financial Constraints Measure

Fin. Investments Net Debt Net Debt – Fin. Inv.

Investment Incomet 0.22 0.24 0.00 0.03 0.01 0.03 (1.82) (1.95) (-0.03) (0.78) (0.17) (0.60)

2nd Tercile of Constraint Measure 0.01 -0.01 0.00 (0.89) (-1.19) (0.18)

3rd Tercile of Constraint Measure 0.03 -0.01 -0.02 (2.48) (-1.80) (-2.82)

T2 * Investment Incomet -0.13 0.03 0.28 (-0.75) (0.32) (3.16)

T3 * Investment Incomet -0.16 0.26 0.27 (-1.27) (3.27) (1.92)

>P50 of Constraint Measure 0.01 -0.01 -0.02 (1.43) (-2.66) (-6.07)

>P50 * Investment Incomet -0.17 0.14 0.30 (-1.11) (2.59) (2.22)

Financial Investmentst-1 0.02 0.03 0.04 0.04 0.04 0.03 (2.73) (4.09) (8.80) (8.25) (5.52) (5.22)

Growth Service Revenuet 0.19 0.19 0.19 0.19 0.19 0.19 (7.40) (7.29) (6.99) (7.00) (6.98) (7.06)

Operating Incomet 0.07 0.07 0.07 0.07 0.07 0.07 (12.28) (12.28) (11.85) (11.73) (11.99) (11.30)

Log(Total Revenuet) 0.01 0.01 0.01 0.01 0.01 0.01 (5.05) (5.15) (5.01) (5.05) (4.48) (4.97)

Year FE Y Y Y Y Y Y

Number of Observations 5,266 5,266 5,266 5,266 5,266 5,266 R-Squared 0.06 0.06 0.06 0.06 0.06 0.06

44

Table 7

End-of-Life Treatment Measures in Dartmouth Atlas This table reports descriptive statistics for hospitals in the matched HCRIS-Dartmouth Atlas sample. Each observation corresponds to one hospital. The first four measures are averages for years 2003-2007. The teaching hospital status is as of the first year that a hospital is in the sample. National Cancer Institute status, Case Mix Index and Medicare Quality score are all as of 2007. Net revenue is the average revenue for a hospital for all years in the sample. Mean Med Std P5 P95 N

Percent of Patients Seeing > 10 Physicians (last 6m of life) 0.37 0.38 0.14 0.14 0.59 1,193

Percent Receiving Medical and Surgical Int. (last 6m of life) 0.08 0.08 0.03 0.05 0.13 1,358

Percent in Intensive Care Unit (last 6m of life) 0.04 0.03 0.02 0.01 0.08 1,358

Percent of Cancer Patients Rec. Life Sust. Int. (last 1m of life) 0.12 0.11 0.04 0.07 0.19 293

Teaching Hospital 0.32 0.00 0.47 0.00 1.00 1,837

Case Mix Index 1.36 1.31 0.26 1.01 2.00 1,482

Medicare Quality Score 93.75 94.50 3.49 87.20 98.00 831

Number of Beds per 1,000 Residents in HSA 2.65 2.48 0.93 1.52 4.00 1,466

Number of Specialists per 1,000 Residents in HSA 1.24 1.20 0.29 0.85 2.00 1,466

National Cancer Institute 0.14 0.00 0.35 0.00 1.00 514

45

Table 8

End-of-Life Treatment Intensity Measures, Hospital Quality and Financial Performance This table reports OLS regressions of the EOL treatment intensity measures on measures of hospital quality and financial performance. Each observation corresponds to a hospital in the matched HCRIS-Dartmouth Atlas sample. The EOL measures are averages for years 2003-2007. The teaching hospital status is as of the first year that a hospital is in the sample. National Cancer Institute status, Case Mix Index and Medicare Quality score are all as of 2007. All financial variables are averages for a hospital for all years that it is in the sample.

Percent Patients Seeing > 10

Physicians (6m)

Percent Patients Rec. Med. and

Sur. (6m)

Percent Patients in Int. Care Unit

(6m)

Percent of Cancer Pat. Rec. Life

Sust. (1m)

Teaching Hospital 0.83 0.47 -0.38 0.41 (0.98) (2.49) (-2.09) (0.80)

Case Mix Index -11.08 -2.48 0.07 -0.02 (-4.90) (-4.92) (0.15) (-0.01)

Medicare Quality Score 0.16 -0.07 -0.09 -0.11 (1.45) (-2.76) (-3.79) (-1.74)

Number of Beds in HSA -1.73 0.96 0.21 0.48 (-3.07) (7.67) (1.73) (1.30)

Number of Specialists in HSA 11.71 2.25 1.00 1.69 (8.66) (7.45) (3.44) (2.38)

Financial Investments -1.21 -0.23 -0.29 -0.91 (-1.65) (-1.41) (-1.87) (-2.19)

Growth Service Revenue -16.86 -2.94 -2.58 6.96 (-2.01) (-1.57) (-1.43) (1.43)

Operating Income -3.62 -1.06 0.03 -2.11 (-1.44) (-1.95) (0.06) (-1.55)

Log(Service Revenue) 7.45 0.65 0.61 -1.45 (9.33) (3.67) (3.57) (-2.86)

Financial Debt 2.8 -0.07 0.39 0.98 (3.93) (-0.43) (2.58) (2.40)

National Cancer Institute 1.38 (2.17)

Number of Observations 877 879 879 314 R-Squared 0.26 0.22 0.07 0.14

46

Table 9

Effect of Performance of Financial Investments on CAPEX by Level of EOL Treatment Intensity The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the matched HCRIS-Atlas sample from 1999-2009. The dependent variable is the forward looking one-year (i.e. from t to t+1) growth in net fixed assets. We use four measures of end-of-life treatment intensity, and these variables are averages for 2003-2007. The first column for each variable uses the raw end-of-life treatment measure, and the second column uses the residual from the cross-sectional regression in Table 8. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income comes from the Income Statement in Schedule G of the HCRIS dataset. All control variables defined in Table 3 are included in all regressions. T-statistics are in parentheses. Standard errors are clustered by year.

End-of-Life (EOL) Treatment Intensity Measure

Percent Patients Seeing > 10

Physicians (6m)

Percent Patients Rec. Med. and

Sur. (6m)

Percent Patients in Int. Care Unit (6m)

Percent Cancer Pat. Rec. Life

Sust. (1 m)

Raw Resid. Raw Resid. Raw Resid. Raw Resid.

Investment Inct-1 0.53 0.12 0.34 0.12 0.47 0.11 0.72 0.10 (2.90) (1.58) (1.71) (1.56) (3.87) (1.43) (2.98) (0.63)

EOL Measure 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (1.30) (0.27) (-1.53) (-1.46) (1.39) (1.20) (1.71) (1.72)

Investment Inct-1 * EOL -0.01 -0.01 -0.03 0.04 -0.09 -0.08 -0.05 -0.08 (-2.54) (-1.17) (-1.39) (1.86) (-3.23) (-2.64) (-2.93) (-2.99)

Controls + Year FE Y Y Y Y Y Y Y Y

Number of Observations 6,457 4,469 7,045 4,479 7,045 4,479 1,624 1,588 R-Squared 0.06 0.06 0.06 0.06 0.06 0.06 0.05 0.05

47

Table 10

Effect of Performance of Financial Investments on CAPEX: Certificate of Need Laws The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variable is the forward looking one-year (i.e. from t to t+1) and two-year (from t to t+2) growth in net fixed assets. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. CON is an indicator variable equal to 1 if a state has a Certificate of Need Law in place in a year and 0 otherwise. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Net fixed assets Net fixed assets (2 yr)

Investment Incomet 0.20 0.17 0.61 0.70 (2.49) (2.54) (4.45) (6.52)

CON 0.01 -0.10 0.02 -0.01 (4.76) (-3.15) (3.97) (-0.11)

Investment Incomet * CON -0.14 -0.10 -0.51 -0.58 (-2.27) (-1.36) (-4.23) (-6.24)

Financial Investmentst-1 0.04 0.09 0.08 0.14 (8.50) (7.58) (9.22) (7.37)

Growth Service Revenuet 0.19 0.02 0.32 -0.04 (7.07) (0.75) (5.58) (-0.56)

Operating Incomet 0.07 0.15 0.14 0.30 (12.93) (10.16) (9.09) (10.50)

Log(Total Revenuet) 0.01 0.00 0.02 0.01 (4.84) (0.15) (4.87) (0.14)

Year FE Y Y Y Y Hospital FE Y Y

Number of Observations 5,269 5,269 4,271 4,271 R-Squared 0.06 0.47 0.08 0.58

48

Supplementary Appendix: Additional Robustness Tests and Refinements

Table A1 Effect of Performance of Financial Investments on CAPEX Using HCRIS, 1999-2009 The table shows OLS regressions of growth in fixed assets on investment income and control variables for the HCRIS sample from 1999-2009. The dependent variables is the forward looking one-year (i.e. from t to t+1) and two-year (from t to t+2) growth in fixed assets. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income comes from the Income Statement in Schedule G of the HCRIS dataset. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year. Net fixed assets (1-year growth) Net fixed assets (2-year growth)

Investment Incomet 0.09 0.22 0.10 0.18 (1.80) (2.90) (1.36) (1.48)

Financial Investmentst-1 0.05 0.16 0.08 0.30 (9.75) (8.59) (13.63) (12.12)

Growth Service Revenuet 0.13 0.02 0.24 0.04 (3.89) (0.46) (5.09) (1.31)

Operating Incomet 0.10 0.15 0.17 0.27 (19.81) (8.49) (11.57) (11.35)

Log(Total Revenuet) 0.00 -0.04 0.01 -0.15 (1.73) (-1.31) (2.01) (-4.54)

Hospital Fixed Effects Y Y Year Fixed Effects Y Y Y Y

Number of Observations 8,847 8,847 7,867 7,867

R-Squared 0.05 0.36 0.07 0.48

49

Table A2 Effect of Performance of Financial Investments on CAPEX by Level of Financing Constraints The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variable is the forward looking one-year (i.e. from t to t+1) growth in net fixed assets. We use three measures of financing constraints: Financial Investments scaled by net fixed assets; Net Debt, defined as the value of bonds outstanding and other notes payable minus cash and temporary investments scaled by net fixed assets; and Net Debt minus Financial Investments scaled by net fixed assets. The regressions use ranks formed using the tertials or the median of each measure. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Financial Constraints Measure

Fin. Investments Net Debt Net Debt – Fin. Inv.

Investment Incomet 0.32 0.30 0.02 0.08 0.00 0.04 (2.90) (2.96) (0.29) (1.39) (0.11) (1.06)

2nd Tercile of Constraint Measure 0.04 -0.01 -0.01 (3.66) (-1.43) (-1.72)

3rd Tercile of Constraint Measure 0.10 0.02 -0.04 (5.40) (1.23) (-3.57)

T2 * Investment Incomet -0.29 0.06 0.29 (-1.87) (0.40) (3.12)

T3 * Investment Incomet -0.28 0.16 0.24 (-1.99) (1.49) (1.66)

>P50 of Constraint Measure 0.05 0.01 -0.04 (3.65) (1.50) (-4.76)

>P50 * Investment Incomet -0.25 0.04 0.27 (-2.41) (0.42) (3.52)

Financial Investmentst-1 0.07 0.08 0.09 0.09 0.09 0.09 (4.76) (6.41) (7.62) (7.58) (6.67) (7.21)

Growth Service Revenuet 0.03 0.03 0.02 0.02 0.03 0.03 (1.02) (0.81) (0.65) (0.79) (0.91) (0.90)

Operating Incomet 0.14 0.14 0.16 0.15 0.15 0.15 (9.74) (9.50) (9.59) (9.77) (8.35) (9.25)

Log(Total Revenuet) 0.00 0.00 0.00 0.00 0.00 0.00 (-0.17) (0.03) (0.15) (0.16) (0.06) (0.16)

Year FE Y Y Y Y Y Y Hospital FE Y Y Y Y Y Y

Number of Observations 5,266 5,266 5,266 5,266 5,266 5,266 R-Squared 0.06 0.06 0.06 0.06 0.06 0.06

50

Table A3 Effect of Performance of Financial Investments on CAPEX by Level of Financing Constraints, 1999-2006 The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variable is the forward looking one-year (i.e. from t to t+1) growth in net fixed assets. We use three measures of financing constraints: Financial Investments scaled by net fixed assets; Net Debt, defined as the value of bonds outstanding and other notes payable minus cash and temporary investments scaled by net fixed assets; and Net Debt minus Financial Investments scaled by net fixed assets. The first three columns for each measure show results for subsamples split at the terciles, and the last two columns show results for subsamples split at the median. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Financial Investments Net Debt Net Debt – Financial Investments

T1 T2 T3 <Med >Med T1 T2 T3 <Med >Med T1 T2 T3 <Med >Med

Investment Incomet 0.26 0.06 0.07 0.25 0.07 0.07 0.05 0.17 0.09 0.11 0.01 0.28 0.28 0.05 0.29 (1.74) (0.44) (0.76) (1.46) (0.73) (0.90) (0.80) (1.63) (1.84) (1.52) (0.10) (8.24) (1.66) (0.71) (2.15)

Fin. Investmentst-1 0.03 0.06 0.01 0.04 0.03 0.02 0.05 0.05 0.02 0.05 0.03 0.05 0.04 0.02 0.05 (1.75) (2.60) (1.02) (2.09) (3.02) (1.46) (26.44) (5.82) (3.08) (6.81) (3.31) (5.19) (2.69) (2.68) (3.84)

Growth Service Rev.t 0.17 0.24 0.17 0.19 0.19 0.19 0.15 0.21 0.17 0.20 0.18 0.20 0.18 0.15 0.22 (4.06) (4.21) (2.34) (5.22) (3.54) (3.21) (4.79) (4.08) (4.13) (7.40) (2.48) (5.30) (3.99) (2.66) (6.78)

Operating Incomet 0.08 0.07 0.07 0.08 0.07 0.07 0.06 0.08 0.08 0.07 0.08 0.05 0.08 0.07 0.08 (7.82) (4.01) (5.17) (7.31) (4.17) (6.66) (4.99) (3.63) (19.14) (4.91) (7.15) (3.51) (5.98) (6.74) (11.43)

Log(Total Rev.t) 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 (3.57) (3.06) (3.08) (1.89) (5.17) (2.01) (1.88) (3.21) (2.54) (3.05) (2.47) (0.81) (2.55) (4.11) (2.00)

Year FE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

N 1,736 1,794 1,736 2,637 2,629 1,736 1,794 1,736 2,636 2,630 1,736 1,794 1,736 2,632 2,634 R-Squared 0.04 0.04 0.04 0.04 0.04 0.04 0.07 0.08 0.04 0.07 0.05 0.05 0.05 0.04 0.06

51

Table A4 Correlation Table for End-of-Life Measures This table reports correlations of End-of-Life measures and other hospital characteristics for all hospitals in the matched HCRIS-Dartmouth Atlas sample. Each observation corresponds to one hospital. The first four measures are averages for years 2003-2007. The teaching hospital status is as of the first year that a hospital is in the sample. National Cancer Institute status, Case Mix Index and Medicare Quality score are all as of 2007.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1) Percent of Patients Seeing > 10 Physicians (6m) 1.00

(2) Percent Receiving Medical and Surgical Int. (6m) 0.25 1.00

(3) Percent Receiving in Intensive Care Unit (6m) 0.53 -0.12 1.00

(4) Percent of Cancer Patients Rec. Life Sust. Int. (1m) 0.26 0.27 0.30 1.00

(5) Teaching Hospital 0.35 0.18 0.20 0.05 1.00

(6) Case Mix Index 0.36 -0.04 0.21 -0.04 0.52 1.00

(7) Overall Medicare Quality Score 0.10 -0.12 -0.10 -0.12 0.08 0.20 1.00

(8) Number of Beds per 1000 Residents in HSA -0.22 0.22 -0.03 0.13 -0.16 -0.28 -0.12 1.00

(9) Number of Specialists per 1,000 Residents in HSA 0.31 0.26 0.15 0.21 0.20 0.06 0.00 -0.04 1.00

(10) National Cancer Institute 0.21 0.19 0.12 0.16 0.30 0.39 0.02 0.13 0.29 1.00

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Table A5 Effect of Performance of Financial Investments on CAPEX by EOL Measure: Robustness The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the matched HCRIS-Atlas sample from 1999-2009. The dependent variable is the forward looking one-year (i.e. from t to t+1) growth in net fixed assets. We use four measures of end-of-life treatment intensity, and these variables are averages for 2003-2007. The first column for each variable uses the raw end-of-life treatment measure, and the second column uses the residual from the cross-sectional regression in Table 8. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income comes from the Income Statement in Schedule G of the HCRIS dataset. All control variables defined in Table 3 are included in all regressions. T-statistics are in parentheses. Standard errors are clustered by year.

End-of-Life (EOL) Treatment Intensity Measure

Percent Patients Seeing > 10

Physicians (6m) Percent Patients Rec. Med. and Sur. (6m)

Percent Patients in Int. Care Unit (6m)

Percent Cancer Pat. Rec. Life Sust. (1 m)

Raw Resid. Raw Resid. Raw Resid. Raw Resid.

Investment Inct-1 0.83 0.19 0.43 0.19 0.63 0.18 0.66 0.07 (3.45) (1.82) (1.18) (1.75) (3.81) (1.62) (1.75) (0.49)

EOL Measure -0.01 -0.03 0.04 -0.08 (-0.58) (-0.61) (0.66) (-1.06)

Investment Inct-1 * EOL -0.02 -0.01 -0.02 0.02 -0.11 -0.08 -0.05 -0.08 (-3.23) (-0.72) (-0.62) (0.38) (-3.16) (-2.50) (-1.57) (-2.98)

Controls + Year FE Y Y Y Y Y Y Y Y Hospital FE Y Y Y Y Y Y Y Y

Number of Observations 6457 4469 7045 4479 7045 4479 1624 1588 R-Squared 0.32 0.31 0.33 0.31 0.34 0.31 0.30 0.30

53

Table A6 Descriptive Statistics for Hospitals in CON and non-CON States, 1999-2006 This table reports means for all hospital-year observations split by whether they are located in a state that has Certificate of Need laws in place or not. All variables are defined as in previous tables and in the summary statistics of the paper. The bottom panel shows data for the states (rather than hospital-level data). Data for this table is obtained from Form 990.

CON Non-CON T-Stat

Investment Incomet / Net fixed assetst-1 0.032 0.029 -1.6

Investment Incomet / Financial Investmentst-1 0.040 0.037 -1.3

Financial Investmentst / Net fixed assetst 0.815 0.825 0.6

Growth in Service Revenuet 0.076 0.089 5.3

Net Incomet-1 / Net fixed assetst 0.088 0.103 3.1

Operating Incomet-1 / Net fixed assetst 0.365 0.333 -3.6

Service Revenuet 178.7 177.7 -0.2

Total Revenuet 189.5 187.9 -0.3

Net fixed assetst 82.0 83.9 0.6

Growth in Net fixed assetst 0.046 0.036 -1.8

Growth in Executive Compensationt 0.148 0.136 -0.8

Growth in Other Salaries and Wagest 0.064 0.072 4.2

Divident and Interest Income (% of Inv. Income) 0.390 0.436 1.1

Unrealized Gains and Losses (% of Inv. Income) 0.437 0.385 -0.9

Realized Gains and Losses (% of Inv. Income) 0.173 0.169 -0.2

Net Debtt / Net fixed assetst 0.469 0.539 3.7

Share of Hospitals with Tax-Free Bonds 0.627 0.666 2.7

State Level Statistics

Population (in millions) 8.55 16.01 29.7

Growth in Population 0.006 0.007 6.1

Growth in GDP 0.048 0.053 7.2

Percentage Urban Population 0.759 0.797 8.9

Percentage of Population Above 65 0.173 0.168 -9.1

Healthcare Share of GDP 0.141 0.134 -9.3

Growth in Healthcare Spending 0.072 0.072 -0.3

Healthcare Spending Per Capita 8.598 8.535 -12.0

Medicaid Share of Healthcare Spending 0.174 0.159 -10.2

Medicare Share of Healthcare Spending 0.189 0.190 1.5

HMO Penetration (Percentage of State Population) 0.189 0.250 19.6

County-Level Herfindahl Index 0.491 0.433 -5.8

County-Level Share of For-Profit Hospitals 0.055 0.111 12.3

Number of Observations 3,809 1,460

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Table A7 Effect of Performance of Financial Investments on CAPEX: CON Laws Residuals The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variable is the forward looking one-year (i.e. from t to t+1) and two-year (from t to t+2) growth in net fixed assets. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. CON is a residual from a probit regression of a CON dummy on state characteristics in Table A4. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Net fixed assets Net fixed assets (2 years)

Investment Incomet 0.09 0.10 0.23 0.29 (1.90) (3.23) (3.48) (5.97)

CON Residual 0.02 -0.02 0.03 0.10 (3.36) (-0.26) (2.65) (0.97)

Investment Incomet * CON Residual -0.11 -0.15 -0.45 -0.57 (-1.91) (-1.94) (-1.89) (-3.76)

Financial Investmentst-1 0.04 0.10 0.08 0.14 (8.90) (8.13) (8.58) (7.77)

Growth Service Revenuet 0.20 0.03 0.32 -0.02 (7.15) (1.03) (5.72) (-0.36)

Operating Incomet 0.07 0.15 0.13 0.28 (12.77) (10.79) (8.31) (10.51)

Log(Total Revenuet) 0.01 0.00 0.02 0.01 (5.08) (0.04) (5.34) (0.08)

Year FE Y Y Y Y Hospital FE Y Y

Number of Observations 5,209 5,209 4,224 4,224 R-Squared 0.06 0.48 0.08 0.59

55

Table A8 Returns from Financial Investments on CAPEX: Certificate of Need Laws on Equipment, 1999-2009 The table shows OLS regressions of growth in fixed assets on investment income and control variables for the HCRIS sample from 1999-2009. The dependent variables is the forward looking one-year (i.e. from t to t+1) and two-year (from t to t+2) change in net fixed assets, buildings, equipment and land, all scaled by lagged net fixed assets. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income comes from the Income Statement in Schedule G of the HCRIS dataset. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. CON Equipment is an indicator variable equal to 1 if a state has a Certificate of Need Law in place that impacts investments in equipment and 0 otherwise. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year.

Net fixed assets Buildings Equipment Land

1 year 2 years 1 year 2 years 1 year 2 years 1 year 2 years

Investment Incomet 0.17 0.17 0.10 -0.10 0.12 0.36 0.00 0.01 (1.63) (1.08) (1.72) (-0.54) (4.99) (2.66) (-0.45) (0.10)

CON Equipment 0.00 0.00 0.00 0.01 0.01 0.03 0.00 -0.01 (-0.21) (0.05) (-0.67) (0.76) (4.34) (3.19) (-0.03) (-2.57)

Investment Incomet * CON Eq. -0.18 -0.24 -0.07 -0.14 -0.15 -0.34 0.00 0.09 (-1.41) (-1.50) (-1.01) (-0.57) (-4.01) (-1.48) (0.10) (2.10)

Financial Investmentst-1 0.05 0.08 0.03 0.08 0.02 0.03 0.00 0.00 (7.36) (10.21) (4.69) (7.05) (12.08) (4.85) (3.23) (-0.31)

Growth Service Revenuet 0.13 0.23 0.06 0.15 0.05 0.09 0.00 0.05 (3.16) (6.43) (1.77) (1.78) (2.25) (1.48) (0.19) (1.56)

Operating Incomet 0.09 0.16 0.04 0.12 0.01 0.03 0.00 0.00 (14.12) (16.57) (3.46) (3.55) (2.20) (1.60) (3.17) (0.43)

Log(Total Revenuet) 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 (1.09) (1.33) (2.16) (-0.08) (1.86) (0.58) (-0.24) (-1.91)

Year FE Y Y Y Y Y Y Y Y

Number of Observations 6,403 5,707 6,133 5,635 5,575 5,059 5,591 5,060 R-Squared 0.05 0.07 0.02 0.01 0.02 0.01 0.01 0.01

56

Table A9 Effect of Performance of Financial Investments on CAPEX: Market vs. Idiosyncratic components The table shows OLS regressions of growth in fixed assets on measures of investment income and control variables for the main (IRS) sample from 1999-2006. The dependent variable is the forward looking one-year (i.e. from t to t+1) and two-year (from t to t+2) growth in net fixed assets. Investment incomet, operating incomet, and financial investmentst-1 are scaled by net fixed assets in year t-1. Investment income is income from dividends and interest plus realized and unrealized gains and losses on investments. The market component of investment income in year t is obtained by multiplying the S&P500 return in year t by a hospital’s financial investments in year t-1, and the idiosyncratic component is the difference between investment income and the market component. Net fixed assets are gross land, buildings and equipment minus accumulated depreciation. Service revenue includes all revenue from medical services and total revenue includes all operating and non-operating revenue. Operating income is the difference between service revenue and service expenses. T-statistics are in parentheses. Standard errors are clustered by year. Net fixed assets (1-year growth) Net fixed assets (2-yr growth)

Investment Incomet (Market) 0.32 0.31 0.55 0.19 (4.40) (2.33) (5.49) (0.63)

Investment Incomet (Idiosyncratic) 0.07 0.06 0.19 0.29 (1.48) (1.98) (2.37) (9.96)

Financial Investmentst-1 0.04 0.09 0.08 0.14 (14.61) (8.05) (10.42) (7.32)

Growth Service Revenuet 0.19 0.02 0.31 -0.05 (7.12) (0.78) (5.65) (-0.69)

Operating Incomet 0.07 0.15 0.14 0.30 (12.67) (10.05) (8.93) (10.65)

Log(Total Revenuet) 0.01 0.00 0.02 0.01 (5.18) (0.13) (5.36) (0.18)

Hospital Fixed Effects Y Y Year Fixed Effects Y Y Y Y

Number of Observations 5,269 5,269 4,271 4,271 R-Squared 0.06 0.47 0.08 0.58