Does Mandatory Measurement and Peer Reporting Improve ... Files/16-018_7ea64939-452… · 369...

Does Mandatory Measurement and Peer Reporting Improve Performance?

Susanna Gallani Takehisa Kajiwara Ranjani Krishnan

Working Paper 16-018

Working Paper 16-018

Copyright © 2015, 2017 by Susanna Gallani, Takehisa Kajiwara, and Ranjani Krishnan

Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author.


Susanna Gallani Harvard Business School

Takehisa Kajiwara Kobe University - Japan

Ranjani Krishnan Michigan State University


SUSANNA GALLANI Harvard Business School

369 Morgan Hall 15 Harvard Way, Boston MA 02163

Ph: 617-496-8613 Fax: 617-496-7363 [email protected]

TAKEHISA KAJIWARA

Kobe University Graduate School of Business Administration

2-1 Rokkodai, Nada-ku, Kobe, 657-8501, Japan Ph: +81-78-803-6988 Fax: +81-78-803-6988

[email protected]

RANJANI KRISHNAN Michigan State University

N207 North Business College Complex The Eli Broad College of Business

632 Bogue St., East Lansing, MI 4884 Ph: 517-353-4687 Fax: 517-432-1101

[email protected]

Acknowledgments: We thank for their valuable comments and suggestions Jeff Biddle, Clara Chen, Leslie Eldenburg, Matthias Mahlendorf, Melissa Martin, Pam Murphy, Steve Salterio, Greg Sabin, Daniel Thornton, Stephanie Tsui, Jeff Wooldridge, workshop participants at the 2015 “Patient-Centric Healthcare Management in the Age of Analytics” conference, University of Arizona, Erasmus University, Michigan State University, Queen’s School of Business, and Wilfrid Laurier University. We appreciate the help we received in gathering and interpreting information about the Japanese healthcare industry from Nobuo Sato and Mayuka Yamazaki at the Harvard Business School Research Center in Tokyo, and for the precious insights on the institutional settings shared with us by Kanoko Oishi. We thank Kenji Yasukata, Yoshinobu Shima and Chiyuki Kurisu for their support in the collection of the data used in this study.

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ABSTRACT We examine the effect of mandated measurement and peer disclosure of new information on the persistence of performance improvements in a setting without performance incentives. Value of information (VOI) theory posits that information can improve the accuracy of posterior beliefs and thereby have a decision facilitating effect. These effects are more pronounced when the information is new versus an update. Using data from the Japanese National Hospital Organization, we analyze performance trends following regulation requiring standardized measurement and peer disclosure of absolute and relative patient satisfaction performance. After controlling for ceiling effects and regression to the mean, mandatory patient satisfaction measurement and peer disclosure introduce positive and significant mean shifts in performance with larger improvements for poorly performing hospitals. The largest positive effects occur when the information is new. Our study provides empirical evidence of the decision facilitating value of information without confound from its decision influencing value. Keywords: Value of information, Patient Satisfaction, Mandatory performance measurement, Healthcare. JEL codes: I10, L30, M14, M41 Data Availability: Data used in this study can be obtained from the Japanese National Hospital Organization.

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1. Introduction

There is little debate that accounting information is useful both for belief revision and

performance evaluation (Narayanan and Davila [1998], Baiman and Demski [1980]). The belief

revision (or “decision facilitating”) role refers to the use of information to improve the manager’s

action choice, in that it allows for improved mapping between actions and outcomes. The

performance evaluation (or “decision influencing”) role refers to the use of information for

managerial compensation to motivate managerial performance, exercise control, and facilitate

risk sharing by improving the mapping of outcomes to rewards. Analytical and practitioner

research posit that, while an information signal has value in each of these roles independent of its

value in the other, the decision influencing use of a signal could encourage managerial

manipulation and reduce its decision facilitating value (Kaplan and Norton [1992], Narayanan

and Davila [1998], Simons [2013]). Moreover, while the decision influencing use of an

information signal automatically makes it available for decision facilitating purposes, the reverse

is not true. That is, a signal could have decision facilitating role but not a decision influencing

role if it is not included in performance measurement. Data limitations stymie archival study of

the value of information in each of these roles independently; consequently the bulk of archival

research has focused on the decision influencing role of information. There is a dearth of archival

research that examines the decision facilitating role, absent any confound from the decision

influencing role. Our study seeks to address this lacuna and aims to examine whether new

nonfinancial performance information has a decision facilitating value in a setting bereft of

explicit pecuniary benefits or penalties.

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Our empirical setting uses health care quality data. Improving healthcare outcomes,

quality, and cost are topics of high policy interest in countries around the world. Systematic

collection and disclosure of reliable, consistent, and comparable healthcare outcomes

information is a popular policy tool that has been proposed in many countries. For example, in

the U.S., the Center for Medicare and Medicaid Services (CMS) and the Agency for Health Care

Research and Quality (AHRQ) developed a patient satisfaction survey titled Hospital Consumer

Assessment of Healthcare Providers and Systems (HCAHPS) with an aim to produce and

publicly disseminate “comparable data on quality outcomes that allow objective and meaningful

comparisons between hospitals on domains that are important to consumers”, to “create

incentives for hospitals to improve their quality of care”, and “to enhance public accountability

in healthcare by increasing the transparency of the quality of hospital care provided in return for

the public investment” (http://www.hcahpsonline.org/home.aspx).1

Such regulations generate information that was previously unavailable to the decision

maker. The effectiveness of quality disclosure regulations on actual quality improvements

depends, in large part, on the extent to which the information contained in these disclosures is

useful to decision makers within the organizations subject to those provisions. Considerable

research has examined the effectiveness of public quality reporting on health quality outcomes

with equivocal results. Some studies find improvements in medical outcomes, such as hospital

mortality following public disclosures, while others find no effects (Fung et al. [2008], Lamb et

al. [2013]). One factor driving the mixed results is that public reporting can lead to a variety of

hospital dynamics that are unrelated to actual quality changes. The requirement for public

disclosure may cloud the value of the new information for improved internal decision-making. 1 Since 2007, U.S. hospitals that admit Medicare patients must collect and submit HCAHPS data to receive their full payment.

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Decision makers may become so highly focused on the public’s response to quality information

that they could exert more effort to manipulate reported quality performance rather than focus on

the actual drivers of performance (Dranove and Jin [2010]). Some of these public reporting

settings confound disclosure with performance evaluation by including compensation-based

incentives for quality performance, which impose pressures on hospital managers to manage

reported quality performance. Mandatory requirements for new information that are bundled

with public disclosure and incentive compensation do not allow to decouple the assessment of

the value of the new information for the decision maker from that of the rewards or penalties

arising from the public disclosure. We use value of information theory to examine the decision

facilitating role of mandatory nonfinancial information disclosure in a setting where the decision

influencing role is not present.

Economists and decision theorists use Bayesian methods to define the value of new

information (VOI) as the difference between the expected utility of an action based on the

posterior probability given a new information set, and the expected utility of the action given

only the prior information set (Pratt et al. [1995]). However, outside of a laboratory, one rarely

encounters situations where access to new information is not confounded with other factors that

influence the decision maker’s use of the new information. In this study, we examine the value of

new information using a unique, quasi-experimental, Japanese hospital setting where new

regulation requiring standardized measurement and peer disclosure of patient satisfaction was

introduced. The new information (1) was not previously collected by the hospital or any other

agencies, (2) was not tied to incentive compensation or other pecuniary payoffs, and (3) could

not be gamed by individual managers to improve reported but not actual performance. We study

two effects of new information: an information effect that arises from the value of the new

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information about the firm’s own performance, and a referent performance effect that arises from

the value of the new information signal about the firm’s performance relative to the peer group.

Our empirical setting is the Japanese hospital industry, which introduced regulation in

2004 that requires hospitals belonging to the Japanese National Health Organization (NHO) to

collect patient satisfaction performance data using a standardized survey. A neutral external

agency surveys inpatients and outpatients about their satisfaction with a number of aspects of

their hospital experience, including medical treatments and procedures, physician and staff

behavior and attitudes, and hospital infrastructure. The results of the survey containing

performance information on the level as well as relative rank of individual member hospitals are

disseminated to all hospitals within the NHO.

We analyze patient satisfaction panel data from all 145 NHO-member hospitals over a

period of eight years (2004-2011). We first conduct a factor analysis of the survey responses and

identify main inpatient (outpatient) satisfaction constructs, which we label as staff /treatment,

logistics/infrastructure (staff /treatment, administrative procedures, and location). We then

examine whether the patient satisfaction information results in an improvement in performance

on each construct in subsequent years, and whether there are differences in the extent of

improvement based on initial relative performance. Our analyses control for ceiling effects, i.e.,

hospitals with a lower performance baseline have more opportunities to improve than better

performing hospitals, and for regression to the mean, i.e., hospitals that have low (high)

performance on patient satisfaction in a particular year could have high (low) performance in

future years simply because of the nature of the distribution rather than actual changes in

performance (Cook and Campbell [1979]).

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Results indicate that mandatory measurement of patient satisfaction has an information

effect. Controlling for hospital-fixed effects and for the existence of a pre-existing trend, our

analyses indicate an overall increase in both inpatient and outpatient satisfaction following the

mandatory measurement. We also find that the performance improvement is of larger magnitude

in the year immediately following the first release of the survey results, when the information is

new, compared to subsequent years. The improvement is found for all hospitals and not merely

for the poorly performing hospitals (defined as hospitals in the lowest quartile of performance in

2004). This evidence supports our hypothesis that patient satisfaction information has decision

facilitating value. We infer that information has highest value when it is new, whereby

subsequent improvements to performance follow a path of diminishing returns. We also find

evidence of the referent performance effect - poorly performing hospitals have greater

improvements in performance following the release. We conclude that the new information

about relative performance facilitates improved goal-directed effort.

Our study contributes to the literature in several ways. First, our unique, quasi-

experimental design enables the assessment of the value of new information, which to our

knowledge has not been studied in an archival health care setting insofar. Second, we study the

value of nonfinancial information in the absence of confounding factors such as incentive

compensation or performance pressure arising from public disclosures. That is, our setting

isolates the decision facilitating role of information without any confound from the decision

influencing role. Prior research finds that public disclosures of health care quality can motivate

hospitals and providers to improve quality. For example, Evans et al. [1997] find that mandated

public disclosures of hospital mortality performance lead to subsequent improvements in

mortality for hospitals that were performing poorly during the initial period. Lamb, et al. [2013]

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find that voluntary reporting of quality of ambulatory care motivated physician groups to

improve quality. In both these aforementioned studies, the hospitals or physicians were already

collecting the quality information and the only change was to disclose it to the public. That is,

the information was not new to the decision maker but only the disclosure was new. Another

point of departure is that these studies focus on medical quality rather than satisfaction quality.

Improvements in medical technology could mistakenly produce results in favor of the hypothesis

that information improves quality. Third, our empirical method attempts to disentangle the value

of information about individual performance and the value of information about relative

performance. Fourth, research in accounting has explored the importance of nonfinancial

measures, such as satisfaction, in driving future financial performance (e.g. Ittner and Larcker

[1998], Nagar and Rajan [2005]). These studies have stressed the importance of nonfinancial

information due to its role as a leading indicator of future financial performance, and its use in

managerial compensation (Banker et al. [2000]) rather than the value of such information in

improving decision making. Our evidence suggests that organizations would benefit from

designing planning and feedback systems where decision makers obtain information that is

disassociated from evaluation or rewards. Finally, our study has policy implications. The Patient

Protection and Affordable Care Act of 2010 requires that hospitals collect and report patient

satisfaction, which increases the pressure on hospitals to improve their quality of care. Our

results indicate that such regulation requiring mandatory collection of patient satisfaction

information has the potential to improve hospital performance on the reported measures, even in

the absence of incentives.

2. Prior literature and hypotheses development

2.1 INSTITUTIONAL BACKGROUND OF JAPANESE NHO

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The Japanese National Health Organization (NHO) is the oversight agency for Japanese

government hospitals. Headquartered in Tokyo, it comprises of 145 hospitals, which represent

3.5% of the total number of hospitals in Japan. Like other Japanese Independent Administrative

Institutions (IAI), the NHO is the result of the separation between political and operational

responsibilities for public services. NHO hospitals are grouped into two categories: general

hospitals, which have discretion on the type of service they provide, and sanatoriums, which, in

addition to offering services similar to general hospitals, are required to supply particularly

expensive and risky medical services. Both types of hospitals provide inpatient as well as

outpatient treatment.2 Funding for NHO hospitals comes from three sources: patients’

copayments for service rendered, reimbursements by the National Health Insurance or

Employees’ Health Insurance, and public funding through government grants and subsidies.3

Patient copayments are received directly by the hospital, while insurance reimbursements are

received through a claims process. Public funding allocation to each hospital within the NHO is

dependent upon periodic performance evaluations of medical outcomes (e.g. mortality and

morbidity) and assessment of the hospital’s need for resources. Prices for healthcare services are

determined nationwide by the Japanese government. Price is, therefore, not a driver of patients’

choice of healthcare provider.

In 2004 the NHO introduced an annual patient satisfaction survey for every hospital

within the NHO. Patients treated in NHO hospitals are required to complete a standardized

2 Government hospitals in Japan are regarded as high quality providers of healthcare services, and tend to be preferred to private hospitals by many patients. 3 Health Insurance in Japan is compulsory for all citizens and can be obtained either through the employer (Employees’ Health Insurance) or, in the case of self-employed individuals and students, through the National Health Insurance system. Special insurance programs are in place for elderly citizens (over 75 years). Patients pay about for 30% of the cost of medical services, with the remaining 70% being reimbursed to the hospital by the insurer or the government. Medical costs exceeding the equivalent of $600 in a month are fully reimbursed by the insurer or the government. Other than minor cost of living adjustments, these numbers have been steady since the year 2000.

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questionnaire, which assesses their satisfaction with many aspects of their hospital experience,

including medical treatment, the behavior of the staff, and the quality of the infrastructure. The

survey is conducted by an independent university research agency, which is unrelated to the

NHO or its hospitals. The university research agency compiles and analyzes the results of the

annual survey, and feedback reports are disseminated to all member hospitals. The feedback

reports include the average scores of each hospital on the various categories, and each hospital’s

ranking within the NHO. There is no explicit monetary incentive tied to patient satisfaction

performance.4

Consistent with standard models of health economics beginning with the seminal

research of Arrow [1963] and the models of Kolstad [2013] we assume that health care providers

derive utility from non-financial outcomes such as patient satisfaction, independent of monetary

compensation. We further partition the utility from patient satisfaction into two parts: one that

arises from the hospital’s absolute performance level, and the other that arises from performance

relative to peers. We then test whether the new information provided by the patient satisfaction

survey has value, in that it affects the choice of actions leading to improvements in subsequent

performance.

2.2 INFORMATION EFFECT OF PATIENT SATISFACTION INFORMATION

Adopting an expected value of information framework (Bromwich [2007], Demski

[1972]), suppose a decision maker is considering an action choice from a vector of potential

actions given by A={a}, and a potential set of uncertain states S={s}. The utility of a particular

action is U(s, a), where Θ 𝑠 is the probability specification based on the decision maker’s

likelihood judgment. The decision maker chooses the action that maximizes expected utility, that 4 Physicians and medical staff at the NHO are compensated on a fixed wage basis and are not provided performance-contingent bonuses. Physicians and staff obtain raises based on general macro-economic conditions. Section 4 examines physician compensation at NHO hospitals in greater detail.

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is 𝐸 𝑈 𝑎∗ = 𝑚𝑎𝑥!∈! 𝑈(𝑠,𝑎)Θ(𝑠)𝑑𝑠! . The relationship between the action a and the expected

outcome is based on subjective probability distributions related to past information (Feltham

[1968]). Suppose the decision maker obtains an additional information signal y from an

information system ƞ. The information signal y allows for an improved assessment of the state

and appropriate action choices, and it has value if it affects the decision that would be made

without the signal, thus leading to greater utility. The expected utility including the new

information signal is 𝐸 𝑈 𝑦, ƞ,𝑎!∗ = 𝑚𝑎𝑥!∈! 𝑈(𝑠,𝑎)Θ 𝑠 𝑦, ƞ! where Θ 𝑠 𝑦, ƞ is the

revised probability distribution after the receipt of the new information signal y. The expected

value of signal y is the difference between 𝐸 𝑈 𝑦, ƞ,𝑎!∗ and 𝐸 𝑈 𝑎∗ . 5 If this expected value is

positive, then the new information has value to the decision maker.

We first assess whether the patient satisfaction measure provides new information. NHO

sources indicated that before 2004, neither the NHO nor individual member hospitals were

collecting information on patient satisfaction. Without this information, hospitals only had noisy

priors about their own performance. Because of the tendency for individuals to be overconfident

about their ability and overestimate their effort levels (Benoit and Dubra [2011], Camerer and

Lovallo [1999], Kruger and Dunning [1999], Kruger and Dunning [2002]), hospitals likely

concluded that their patient satisfaction performance was (a) on the higher end of an absolute

scale, and (b) above average on a relative scale. Additionally, hospitals were unable to accurately

assess the payoffs from their efforts to increase patient satisfaction. Thus, on average, hospitals

are less likely to have engaged in targeted strategies aimed at increasing patient satisfaction.

With the introduction of the mandatory patient satisfaction survey, hospitals received an

5 Although VOI is sometimes interpreted rather narrowly as the amount a decision maker would be willing to pay for higher quality information, analytical VOI models are generic and refer to “value” in a flexible sense that allows for non-pecuniary interpretations (Bromwich [2007], Demski [1972], [Raiffa 1968]).

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additional information system (corresponding to ƞ referred to earlier). ƞ contains two new signals

- absolute patient satisfaction level (y1) and patient satisfaction relative to peer hospitals (y2).

These two new signals are based on data collected systematically by an independent university-

based research center using a scientific, standardized, survey instrument. The new signals enable

hospitals to revise their priors and obtain more accurate posterior beliefs about the relation

between their actions and patient satisfaction, which influence future effort allocations and

decisions, i.e. to move from utility function 𝐸 𝑈 𝑎∗ to 𝐸 𝑈 𝑦, ƞ,𝑎!∗ .6 The information on

individual hospital performance level (y1) provides a more precise signal of performance, which

allows for guided effort that is better suited to the circumstances (Bandura and Jourden [1991],

Ederer [2010], Morris and Shin [2002]). The positive weight associated with patient satisfaction

within the healthcare provider’s utility function drives the extent to which the information is

internalized and used in the subsequent decision making process. This leads to the following

prediction about the information effect of nonfinancial performance information:

H1. Introduction of mandatory measurement of patient satisfaction improves subsequent

performance.

2.3 REFERENT PERFORMANCE EFFECT OF PATIENT SATISFACTION INFORMATION

The second signal contained in the new information system ƞ is patient satisfaction

relative to peer hospitals (y2). Economic theory recognizes referent performance as an important

driver of individuals’ and firms’ utility functions (Kolstad [2013], Sugden [2003]).7 Referent

performance is particularly important for strategically interdependent competitive organizations

6 This assumes that hospitals are Bayesian, i.e., they use new information to update their prior beliefs, which is a standard assumption in decision theory (Pratt, et al. [1995]). 7 In a reference-dependent utility model, preferences between decisions are influenced both by the expected outcome of the decision and by a reference point, which could be performance of a peer or competitor (Sugden [2003]).

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(Lant and Hewlin [2002]). If a valuable relative performance signal indicates poorer

performance relative to a referent group, it prompts organizations to increase effort as well as

search for new strategies that can enhance relative performance (Bandura and Jourden [1991]),

especially if decision makers have flexibility to respond to the new information (Abernethy and

Bouwens [2005]). Organizational theory posits that decision makers pay more attention to

activities that fail to meet targets compared to those that succeed (Levitt and March [1988]).

Evidence indicates that poor relative performance is a higher motivator of performance than

good performance. For example, Ramanarayanan and Snyder [2012] find that information

disclosure in the dialysis industry is associated with reduction in mortality for poorly performing

organizations, but do not find comparable effects for highly performing ones. Casas-Arce and

Martinez-Jerez [2009] find analytical and field evidence in a retail setting that introduction of a

relative performance evaluation system (i.e., a contest) induces higher effort. They find that

participants who are in leading positions obtain confidence in their performance and reduce their

effort. Trailing participants exert additional effort to catch up with the others.

The relative performance signal y2 generated by the new information system ƞ eliminates

idiosyncratic uncertainty creating a level field to assess performance. The new information signal

y2 increases the accuracy of the posterior belief function about the mapping between effort and

output relative to the organization’s peer group. The noise reduction value of relative feedback is

higher for poorly performing hospitals because they likely expected to be above average in the

pre-regulatory period, and therefore the relative information represents an unpleasant surprise.8

This serves as a motivation for poorly performing hospitals to increase effort to improve

8 Prior literature finds that in the absence of information, individuals and firms tend to hold optimistic beliefs about their ability and therefore are overconfident about their performance relative to competitors (Kahnemann et al. [1982]).

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performance (Ederer [2010]). Based on the above, we predict that firms with lower initial

relative performance on patient satisfaction will have greater future performance improvements.

H2. Lower initial performance on patient satisfaction measures is associated with higher

magnitude of subsequent improvements.

2.4 VALUE OF FIRST INFORMATION VERSUS SUBSEQUENT INFORMATION

In this section we explore whether information has greater value when it is first introduced,

versus when it represents an update from the previous period. VOI theory posits that value of

information depends on the prior distribution that decision makers use to represent the current

situation (Raiffa [1968]). Before the availability of any standardized information about own and

peer performance, decision makers are likely to (a) systematically over-estimate own

performance, (b) exhibit overconfidence about own performance, and (c) produce probability

distributions that are far too tight than the actual distributions (Alpert and Raiffa [1982],

Hammitt and Shlyakhter [1999], Lichtenstein et al. [1982], Morgan et al. [1992]). This implies

that before the first release of information, the expected value of the performance signal is less

likely to be representative of the true value of the signal, whereas after the release of the first

information, subsequent information is assessed relative to a well-calibrated distribution of the

signal. Subsequent information would cause decision makers to update their beliefs in a

Bayesian fashion (Kolstad [2013]) and these subsequent updates would be smaller in magnitude

relative to their initial updates when they went from a no-information regime to a quality

information regime. In sum, the first disclosure of information causes managers to gain more

information about their own performance and relative performance, which allows them to

improve significantly more. This leads to the following hypotheses:

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H3. Mandatory patient satisfaction measurement drives greater improvements when the

performance information is first released compared to subsequent releases.

H4. Mandatory relative patient satisfaction measurement drives greater improvements when the

performance information is first released compared to subsequent releases.

3. Data and analyses

3.1 DATA AND DESCRIPTIVE STATISTICS

The sample includes data from the entire population of 145 NHO hospitals for the period

2004 to 2011. The standardized patient satisfaction survey is administered every year during the

months of June and July in all NHO hospitals. There are two types of NHO hospitals - general

hospitals and sanatoriums. General hospitals are similar to private hospitals and are allowed

greater discretion in the choice of healthcare services they offer. Sanatoriums are expected to

provide not only the services that are provided by general hospitals, but, in addition, they are

required to provide special services “that cannot be dealt with properly by Non-National Hospital

Organizations due to historical and social reasons.”9 These include treatment of expensive, long-

term, risky, or communicable ailments such as tuberculosis, AIDS, Alzheimer’s, ALS, complex

mental illnesses, and invasive or terminal cancer. Table 1 reports descriptive statistics about the

population of hospitals.

----- Insert Table 1 here -----

Separate surveys are administered to inpatients and outpatients and contain 15 questions

(outpatients) and 19 questions (inpatients) respectively (Appendix A). The surveys contain

multiple items related to quality of medical treatment, behavior of the staff, quality of

infrastructure and facilities, waiting periods, etc. Ten additional questions capture the

9 National Hospital Organization (Independent Administrative Institution) page 1; http://www.mof.go.jp/english/filp/filp_report/zaito2004e-exv/24.pdf.

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comprehensive assessment of the patient’s overall satisfaction. All the questions use a 5-point

Likert-type scale, where 1 indicates “strong dissatisfaction” and 5 indicates “strong satisfaction.”

Data are collected and processed by a university research center, which is unrelated to the NHO.

Feedback reports are subsequently distributed to each member hospital. These reports contain the

average score for each of the questions included in the questionnaires and the ranking of the

hospital within the NHO based on the overall satisfaction results.10

3.2 VARIABLE REDUCTION

Patient satisfaction is a multidimensional construct (Chen [2009]). To obtain a measure

of the underlying dimensions, we conduct a factor analysis using principal component analysis

with oblique rotation.11 Untabulated results show that for inpatients (outpatients) the items load

on two (three) factors. All factor loadings are greater than 0.6 and no variable cross-loads on

more than one factor. Based on the items’ loadings we identify two constructs for inpatients:

staff /treatment, and logistics/infrastructure. Outpatients load on three factors, which we label as

staff /treatment, administrative procedures, and location. Cronbach’s alpha values are greater

than 0.9 for each factor for both inpatients and outpatients. The factor analysis of the ten

questions related to overall satisfaction yields a single factor for inpatients (Cronbach’s alpha >

0.95) and outpatients (Cronbach’s alpha > 0.97) respectively. Since location is not controllable

by hospital managers, we drop it from subsequent analysis.

3.3 ANALYSIS OF INPATIENTS AND OUTPATIENTS

Inpatients and outpatients may weigh the importance of each factor differently in

10 Conversations with NHO hospital managers confirmed that prior to 2004, hospitals were not systematically collecting patient satisfaction information. 11 The oblique rotation method allows for the possibility that the factors are correlated.

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formulating their assessments of overall satisfaction with the hospital.12 Therefore, we examine

the extent to which each of the component factors influences overall inpatient and outpatient

satisfaction using OLS regressions with heteroskedasticity-robust standard errors clustered by

hospital. We estimate the following model:

Satisfaction= α+β1*Staff and Treatment+β2*Logistics and Infrastructure

+β3*Administrative Procedures+ β4*Size+β5*Competition (1)

+ β6*Hospital +ε

where Staff and Treatment, Logistics and Infrastructure, and Administrative Procedures

are measured by the corresponding factor scores. We control for hospital Size (measured as the

number of beds in thousands), hospital type (Hospital =1 if the site is a general hospital, 0 if the

site is a sanatorium), and competition intensity (Competition), which is the number of private and

public hospitals per 100,000 people in the geographic area (prefecture) where each NHO hospital

is located. We estimate separate models for general hospitals and sanatoriums. Results in Table 2

indicate that while both Staff and Treatment and Logistics and Infrastructure are significant

drivers of overall patient satisfaction for all patients, inpatients of general hospitals and

sanatoriums weigh satisfaction with Staff and Treatment more than Logistics and Infrastructure

(all p-values of difference in coefficients < 0.10). For outpatients of sanatoriums, Staff and

Treatment is a larger driver of overall satisfaction than are Administrative Procedures (p-value of

difference in coefficients < 0.10), while the difference in the contribution to overall satisfaction

12 A recent survey conducted by the Japanese Ministry of Health, Labor and Welfare explored the major drivers of hospital choice for inpatients and outpatients. The sample consisted of more than 150,000 respondents, randomly selected from the patient population of all Japanese Hospitals. Overall, outpatients (inpatients) identified the following drivers of hospital choice: 38% (34.9%) prior experience at the same hospital, 37.6% (29.9%) physical closeness to their residence, school or place of work, 33.2% (49%) recommendation by doctors, 31.4% (34.7%) kindness of doctors and nurses, and 28.7% (25.5%) size/technology of the hospital. (Japanese Ministry of Health, Labour and Welfare. (2011). Patients Behavior Survey. from http://www.mhlw.go.jp/english/new-info/2012.html).

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of the two factors is not significant for outpatients of general hospitals.


3.4 TEST OF H1: INFORMATION EFFECT OF PATIENT SATISFACTION

H1 predicts a positive effect of patient satisfaction information. That is, the release of

patient satisfaction information improves satisfaction performance in subsequent years due to the

incremental value of the information signal (improved mapping between actions and outcomes).

3.4.1 Univariate analysis

Table 3, Panel A provides information on the mean inpatient and outpatient satisfaction

for each year subsequent to the information release, and partitioned by the level of hospital

satisfaction performance during the first year of the release (2004). Panel B provides

corresponding means for change in satisfaction.13 It can be seen that both inpatient and outpatient

satisfaction improved each year after 2004, with the exception of the year 2009. Top performers

(hospitals that ranked in the top quartile with respect to overall satisfaction in 2004) did not

significantly decrease their performance in 2005 despite the pressure faced by other hospitals

following the patient satisfaction information release, nor did the mean of top performers drop

below the full sample mean during any year. Indeed, the top performers continued to perform

significantly above the mean during the entire period (all p-values for the difference between top

performers and the whole sample are > 0.05).14


13 For expositional clarity and ease of interpretation, overall satisfaction in Table 2 is calculated as the average of the ten questions assessing overall satisfaction for inpatients and outpatients respectively. Univariate analyses performed using the factor scores for overall satisfaction yielded equivalent results (untabulated). 14 This provides evidence that regression to mean is not a problem with the data. If regression to the mean was occurring, top performers would have statistically significantly deteriorated in the subsequent years and eventually reached the sample mean. Formal analysis of regression to the mean is provided in Section 4.

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3.4.2 Multivariate analysis

We test H1 using the following model:

Satisfaction = α+ β1Size+ β2Competition+ β3Hospital + β4-‐10Year +𝜀 (2)

We control for hospital type (Hospital), hospital size (Size), and competition intensity

(Competition). We estimate six models, one each for the overall satisfaction score for inpatients

and outpatients respectively, one for each of the two satisfaction factors i.e., staff/treatment and

logistics/infrastructure for inpatients, and staff/treatment, and administrative procedures for

outpatients. Satisfaction results for 2004 are used as baseline and therefore 2004 is the omitted

case for Year. The model controls for hospital fixed effects and calculates heteroscedasticity-

robust standard errors clustered by hospital.

Table 4 reports the estimation results, which indicate that the introduction of the patient

satisfaction assessment tool has, on average, a positive effect on hospital overall patient

satisfaction for inpatients and outpatients alike in each of the seven subsequent years following

the first year of the patient satisfaction information release. The coefficients estimated for the

two satisfaction factors for inpatients and the staff and treatment factor for outpatients similarly

indicate that relative to 2004, satisfaction performance is higher in each subsequent year. This is

consistent with the prediction of the information effect (H1). That is, patient satisfaction

information improves performance, even after controlling for time invariant characteristics and

unobservable hospital level factors. General hospitals had higher levels of overall satisfaction for

inpatients as well as satisfaction with staff and treatment and logistics and infrastructure.

However, sanatoriums had higher levels of overall outpatient satisfaction and satisfaction with

staff and treatment and administrative services.


20

3.5 TEST OF H2: REFERENT PERFORMANCE EFFECT OF PATIENT SATISFACTION

H2 predicts that a lower initial performance on patient satisfaction is associated with

higher magnitude of subsequent nonfinancial performance improvements, arising from the value

of the relative performance signal. Univariate results in Table 3, Panel B, indicate that the mean

patient satisfaction change from 2004 to 2005 for hospitals in the lowest performance quartile in

2004 (poor performers) is higher than the change for the full sample for both inpatients (0.317

versus 0.087, p<0.01) and outpatients (0.204 versus 0.088, p<0.01).

To test H2, we estimate the following model using OLS with heteroskedasticity-robust

standard errors clustered by hospital and controlling for hospital fixed effects:

Change in Satisfaction = α + β1Trend + β2Poor Performer + β3Time Trend*Poor Performer (3) + β4Hospital + β5Size+ β6Competition+𝜀 where Change in Satisfaction is defined as the yearly change in patient satisfaction (Yeart –

Yeart-1), Trend is a discrete variable, which takes the value of 1-8 corresponding to the years

2004-2011, Poor Performer Dummy is an indicator variable that takes the value of 1 if the

hospital was in the lowest quartile of performance in the first year of the information release

(2004), and Competition and Hospital are as defined previously.

Results presented in Table 5, Panel A indicate a positive coefficient for Poor Performer

for outpatients and inpatients alike and for each of the factors. These results indicate that

hospitals that had lower satisfaction scores during the first year of mandatory performance

measurement (2004) had higher increases in satisfaction. These results indicate support for the

referent performance value of information, consistent with H2. To account for the non-linearity

of the changes and for the effects of compression at the top (that is, improvements are harder to

obtain when hospitals are already performing well), we re-estimate equation 2 using a scaled

21

version of the dependent variable, defined as ((Satisfactiont – Satisfactiont-1)/ (5 –

Satisfaction2004)). Table 5, Panel B, shows that even considering for the smaller opportunities for

improvement available for higher performers, hospitals that had lower satisfaction scores in 2004

(poor performers) obtained a larger scaled improvement in satisfaction, which further supports

H2. Additionally, the trend variable reduces the influence of diminishing marginal returns arising

from the ceiling effect.

----- Insert Table 5 Here -----

3.6 TEST OF H3 AND H4: VALUE OF FIRST INFORMATION VERSUS SUBSEQUENT

INFORMATION

The results in Table 5 can help examine if information has a higher value when it is new.

For inpatients, the results reveal that the average rate of improvement in overall satisfaction

decreases over time, captured by the significant β1 coefficient of -0.038 for unscaled changes and

-0.013 for scaled changes (p<0.01). For the inpatient factors, the decreasing trend is observed for

the staff and treatment factor (unscaled). The overall outpatient satisfaction improvement has a

similar decreasing trend (-0.020 unscaled), which is also found for the staff and treatment factor

(-0.074 unscaled and -0.018 scaled) and the administrative procedures factor (-0.006 scaled).

These results are generally consistent with H3 and indicate that greater improvements in

performance occur in earlier years relative to later years. That is, the information effect of the

performance signal is higher when the signal is first introduced. Results are also consistent with

H4, that is, the referent performance effect of the higher rate of increase in performance for

initial poor performers even accounting for the ceiling effect also decreases over time. The

Trend*Poor Performer interaction has a negative coefficient for overall inpatient and outpatient

satisfaction (-0.072 unscaled for overall inpatient satisfaction, -0.085 unscaled and 0.012 scaled

22

for overall outpatient satisfaction). Although hospitals that were performing poorly in the first

year of patient satisfaction had significantly higher rates of increase in performance as discussed

in the test of H2, there was a declining rate of increase in performance for these hospitals for the

overall inpatient satisfaction (unscaled) and outpatient satisfaction (unscaled and scaled), as well

as the two inpatient and outpatient satisfaction factors (unscaled and scaled). This indicates that

the value of new information for referent comparisons follows a declining rate of improvement.

Results for the control variables indicate that general hospitals had higher rate of increase in

inpatient performance (unscaled and scaled) and correspondingly, sanatoriums had lower rates of

increase in performance (captured by the significant positive β5 coefficient of 0.066 for unscaled

changes and 0.025 for scaled changes).

3 Robustness Analysis and Alternative Explanations

In this section, we explore whether the results can be attributed to regression to the mean

or the continuation of a pre-existing trend. We also examine if the physician or administrators’

incentive system drives the improvement in satisfaction performance.

4.1 REGRESSION TO THE MEAN

All the hypotheses relate to change in patient satisfaction subsequent to the introduction

of the new measurement system. Unobserved hospital-level characteristics may bias the results.

That is, hospitals whose initial patient satisfaction measure is high are likely to keep performing

well due to characteristics other than the patient satisfaction. A regression analysis of the

satisfaction scores for each year on the satisfaction measure of the previous year confirms the

presence of significant first-order hospital fixed effects (untabulated). Consequently, all the

analyses in this study control for hospital fixed effects and estimate robust standard errors

23

clustered by hospital. This reduces the likelihood of unobserved hospital-level characteristics

driving the results.

Because of correlations between repeated measures, and standard deviations that decrease

over time, it is necessary to consider the extent to which results may be a manifestation of

regression towards the mean rather than actual improvements in patient satisfaction. That is,

poorly performing hospitals may exhibit higher performance simply because of the nature of the

behavior of extreme values in a statistical distribution rather than an actual improvement.15 We

are able to reject the hypothesis of regression to the mean based on the following. First, Table 3,

Panel A shows that the overall mean inpatient and outpatient satisfaction measure increases over

time (except for the 2009 year). This rules out aggregate mean stability, a necessary condition for

regression to the mean (Cook and Campbell [1979], Zhang and Tomblin [2003]). Second, the

overall satisfaction never decreases significantly below the initial (2004) levels in aggregate or

for the hospitals in the highest quartile of performance, which is further evidence against the

aggregate mean stability condition. Indeed, the mean inpatient satisfaction is substantially higher

in 2011 than 2004 for the aggregate (4.448 in 2011 versus 4.194 in 2004, p<0.01), for hospitals

in the lowest quartile (4.26 in 2011 versus 3.78 in 2004, p<0.01), as well as for hospitals in the

highest quartile (4.558 in 2004 versus 4.424 in 2004, p<0.01). Similar conclusions can be drawn

for outpatient overall satisfaction, where average scores are is statistically higher in 2011 than

2004 for the pooled sample (4.119 in 2011 versus 3.951 in 2004, p<0.01), for the lowest initial

quartile (3.999 in 2011 versus 3.664 in 2004, p<0.01) and for the highest initial quartile (4.170 in

2011 versus 4.244 in 2004, p<0.10). Third, we estimate the correlation between subsequent 15 Note that regression to the mean is primarily an issue when the analysis consists of only two observations, such as two variables measured on one occasion (e.g. control and treatment group in an experiment) or one variable measured on two occasions (e.g. pre-test post-test comparison after an experimental intervention). Regression to the mean is not a phenomenon that is relevant to multiple observations over time (Nesselroade et al. [1980]).

24

satisfaction measures after controlling for hospital fixed effects and other determinants of

satisfaction. Untabulated results show non-significant correlations coefficients, thus rejecting the

possibility that the improvement over time is purely a result of regression towards the mean

(Cook and Campbell [1979], Zhang and Tomblin [2003]). Finally, all analyses of satisfaction

change include the initial (2004) satisfaction rates, which is standard practice for controlling for

regression to the mean (Evans, et al. [1997], Kolstad [2013]). Consequently, we conclude that

regression towards the mean has a non-significant influence on the results of this study.

4.2 PRE-EXISTING TREND

It is possible that the change in patient satisfaction is merely due to the continuation of a

pre-existing trend in all the important firm-level variables. The absence of any systematic data

collection with respect to patient satisfaction prior to 2004 makes it impossible for us to perform

a true pre-post analysis. However, by including a time trend variable in equation 3, we control

for unobserved factors that might contribute to the increase in patient satisfaction due to the mere

passage of time (Wooldridge [2012]). General learning trends are likely to emerge and drive

better performance as the expertise of the members of the organization grows over time. By de-

trending the data in equation 3, we eliminate the concern that the significance of the regression

coefficients reported in Table 5 is due to spurious correlations with pre-existing drivers of patient

satisfaction over time. However, for robustness, we conduct three additional analyses. First, we

analyze the patterns of change in total costs. If the behavior of patient satisfaction was simply a

pre-existing trend, we should observe a similar trend with cost. We estimate equation 3, using

changes in total operating costs as the dependent variable. Estimation results (untabulated)

indicate, (a) no evidence in support of a declining rate of change in operating costs (on the

contrary – costs tend to accelerate over time), (b) no statistical significance for the poor

25

performer dummy, and (c) no statistical significance of the Trend*Poor Performer interaction,

both for inpatients and outpatients. Recall that for the cost variable, there is no new information

obtained. The results indicate that the patterns of cost changes are not similar to the patterns of

patient satisfaction changes, which reinforces the conclusion that the change in patient

satisfaction results are driven by the value of new information for decision making.

Second, using data from 2004-2011, we computed a hospital-specific annual cost trend.

We added the hospital-specific annual cost trend to equation 3 to control for the cross sectional

variation in idiosyncratic cost behavior. We assume that if there is a pre-existing trend for each

hospital, this trend would be correlated among the important economic variables for the hospital.

We estimate equation 3 augmented by this additional hospital level control variable and using

changes in patient satisfaction (unscaled and scaled) as the dependent variable. Even after

controlling for the cost trend, untabulated results continue to support hypotheses H2, H3, and H4

with equivalent degree of statistical significance. We conclude that the information effect and the

referent performance effect are not due to the continuation of a pre-existing trend in hospital-

level outcome variables.

4.3 COMPENSATION PRACTICES AT NHO AS A DRIVER

In a departure from accounting studies that examine the role of information for planning,

performance evaluation, and control, we are interested in studying the effect of the decision

facilitating role (planning role) of information unconfounded by the decision influencing role

(performance evaluation and control role). While we do not disagree that “both types of

information gathering and dissemination are used by the principal for the same purpose, to

exercise control (broadly construed) over the agent's decision making” (Baiman and Demski

[1980], 185), we believe that calibrating the value of information in the decision facilitating role

26

is important. The vast majority of rank and file workers receive minimal incentive compensation

and it is important to examine if improving their information set can improve decisions.

Prior research by Banker, et al. [2000] finds improvement in nonfinancial performance

following the implementation of an incentive plan that includes nonfinancial performance

measures. In their setting, although customer satisfaction measures were tracked before the

implementation of an incentive plan that includes such measures, these measures did not have a

performance effect until it was used explicitly for incentive purposes. Similarly, Kelly [2007]

demonstrates experimentally that providing feedback on nonfinancial measures alone improves

managerial decisions over time, but providing feedback and incentives attached to the measures

that were provided in the feedback is the key to improving performance. In (Evans, et al. [1997]),

even though the hospitals previously collected mortality data, performance improvements were

only observed when the mortality measures were disclosed to the public. This suggests that in

these studies, the decision facilitating role of information was present in conjunction with the

decision influencing role. Whether or not increased patient satisfaction information improves

performance in absence of an explicit link to incentive compensation, and when the information

is disseminated to internal as opposed to external stakeholders is our empirical question. Our aim

is to isolate the value of information unfettered by the effect of financial incentives.

We examine physician compensation practices at NHO hospitals using field and archival

data to determine if there is any link between patient satisfaction performance and compensation.

If this link is indeed present, we cannot rule out the possibility of confound between decision

influencing and decision facilitating role of satisfaction information, which is the bane of

compensation studies.

4.3.1 Field evidence of compensation practices at NHO

27

We conducted interviews with hospital administrators at the NHO headquarters to glean

information about physician compensation practices. These interviews revealed that there is no

explicit link between physician or administrative officer compensation and patient satisfaction

performance. Essentially, Japanese NHO physicians and administrators are government

employees. Each physician, nurse, paramedic, and administrator is classified into a particular

grade based on a hierarchy, and each grade is provided compensation as per a government salary

schedule. The typical compensation package includes: a monthly salary, allowances for cost of

living, overtime and travel. Appendix B contains information on employment, compensation,

and promotion systems at NHO. For example, a trauma surgeon who has the highest level of

seniority than can be attained would earn a monthly salary of 572,900 Yen (Employment Grade

10, Step 21). Adding a 20% allowance for cost of living and housing, the total compensation

package is 687,480 Yen ($6,027 per month in current U.S. Dollars).

4.3.2 Archival evidence of compensation practices at NHO

To empirically examine whether there is any link between patient satisfaction and

physician compensation, we estimated the following model of physician salary and bonus as a

function of patient satisfaction:

𝐶𝑜𝑚𝑝𝑒𝑛𝑠𝑎𝑡𝑖𝑜𝑛i,t = α + β1Overall Inpatient Satisfactioni,t-1+ β2Overall Outpatient Satisfactioni,t-1 + 𝜕k𝐻𝑜𝑠𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠!+ 𝛾!Year+𝜀 (4)

where the dependent variable Compensation is operationalized either as Salary (total

compensation paid to physicians by hospital i in year t including cash, bonus, and other benefits)

or Bonus (the total annual bonus payout by hospital i to physicians in year t). We include k

control variables for size, competition, hospital type, medical revenue, educational revenue, and

R&D revenue, and indicator variables for each of the 8 years included in our sample (j = 2,…,8;

28

2004 is dropped as the base case). Table 1 provides the descriptive statistics for the variables

used in the analyses.

If patient satisfaction were taken into consideration in salary or bonus payouts, the

coefficient on 𝛽! and 𝛽! would be positive. We use lagged satisfaction scores because the

satisfaction scores for year t are only released to member hospitals in the following year and

therefore likely to only be incorporated into the following year salary or bonus payments. The

results of estimating equation 4 (Table 6, Columns 1 and 2) indicate no significant association

between salary or bonus and either inpatient or outpatient satisfaction. These results suggest that

patient satisfaction is not taken into consideration in the determination of salary or bonus

payments. Thus, we conclude that improvements in patient satisfaction are not driven by the

motive to increase compensation.16 Both salary and bonus payments are, however, positively

associated with hospital size and medical revenue.


4.4 GRANT REVENUES

It is possible that patient satisfaction is driven by the desire to increase grant revenue for

NHO hospitals. If so, although there is no explicit tie of satisfaction performance and incentive

compensation, hospitals may have a pecuniary benefit to improving satisfaction. To test this, we

estimate equation 4 with Grant Revenue as the dependent variable. In addition to the controls

listed in the previous section we include R&D expenses, Facility Cost, Material Cost, and Total

Cost to control for the relation between grants awards and their potential uses. Estimation results

are provided in Table 6 (Column 3) and indicate a negative association between inpatient

satisfaction and hospital grant revenue. Our conversations with NHO administrators revealed that

16 We re-estimated equation 4 using two-and three-year lags and continued to find non significant coefficients for salary and for bonus.

29

hospital grants are based on the research output of the hospitals rather than patient satisfaction.17

We speculate that a reason for the negative association between grant revenue and patient

satisfaction is that hospitals with a higher research emphasis receive greater grants, but have a

lower focus on the patient satisfaction scores.

4.5 PATIENT REVENUES

Prior research finds a positive relationship between customer satisfaction and subsequent

revenues (Chen et al. [2009], Hallowell [1996], Ittner and Larcker [1998]). The mechanisms by

which customer satisfaction influences revenues include customer attraction, customer loyalty,

and word of mouth (Rust et al. [2002], Szymanski [2001]), which also operate in the non-profit

health care industry (Gemme [1997], Stizia and Wood [1997]). When patients are satisfied with

the hospital, they tend to return. Additionally, their opinion influences others’ choices (Gemme

[1997]).18 A successful patient satisfaction program can therefore result in increased revenues.

To test the relationship between patient revenues and satisfaction, we estimate equation 4 with

hospital revenue as the dependent variable. We use lagged values of satisfaction in order to allow

for word of mouth and other mechanisms to take place. We find a statistically significant

association between patient satisfaction and lagged revenues for both inpatients and outpatients

(Table 6, Columns 4 and 5), which indicates that patients respond to hospitals’ efforts to improve

patient satisfaction.19

4 Conclusions

17 We re-estimated equation 4 using three-, four- and five- year lags and did not find significant results. Similar analysis of changes in grant revenues did not yield significant results. 18 Gemme [1997] reports survey results indicating that 90% of patients’ choice of health care provider are influenced by other patients’ opinions, and that 40% of surveyed subjects had consulted a patient who had used the service of the organization they had chosen to use. 19 We re-estimated equation 4 using contemporaneous patient satisfaction data and found similarly significant associations with patient revenue.

30

Information has decision value, regardless of whether it is private or public information,

or whether the information pertains to financial or nonfinancial performance. Accounting theory

posits that the decision value of information manifests itself in two primary ways. The first type

of manifestation refers to the ability of information to facilitate belief revision and improve

managerial decision making (the decision facilitating role). The second manifestation of value of

information emerges from the use of information in managerial contracts for performance

evaluation to motivate managerial effort and improve risk sharing (the decision influencing role).

However, most empirical studies related to the role of information disclosures confound the

decision influencing and the decision facilitating roles.

The decision influencing role emerges from the value of an information signal either as a

mechanism to reduce information asymmetry between the firm and external stakeholders, or

within the firm to reduce agency problems. With respect to the relationship between the firm and

external stakeholders, there is considerable research in accounting that debates whether firm

value fully or partially reflects publicly available information, and adjusts to it either

instantaneously or with a lag. Research has examined regulatory and other mechanisms that

nudge firms to disclose private information and improve the smooth functioning of financial

markets by reducing information asymmetry between the firm and the market (Healy and Palepu

[2001]). Within the firm, research has focused on designing contracts that induce managers to

reveal private information, or that reduce their motivation to shirk (Lambert [2001]). From a

practical perspective, as Narayanan and Davila [1998] state: “Most firms collect a plethora of

information for belief revision, even though only a few signals are directly linked to incentives.”

Further they note that when a signal that is useful for belief-revision is also used for performance

31

evaluation, the manager has incentives to manipulate the signal. Managerial manipulation effort

lowers the value of the performance signal in its belief revision and learning role.

Our empirical setting allows an examination of the value of information with minimal

confound from public disclosures or incentive compensation. We use patient satisfaction data for

a sample of 145 Japanese public hospitals for a period of eight years (2004-2011) to explore the

decision facilitating value of information. In our setting, hospitals obtained two new signals –a

signal about the absolute level of patient satisfaction performance, and a signal about the level of

performance relative to a referent group. We find evidence of an information effect - the new

information signal about the level of performance induces a more precise posterior distribution

of beliefs and facilitates decision making. We find a referent performance effect arising from the

value of the new information signal about performance relative to a peer group. Results also

indicate that information has higher decision facilitating value when it is new. New information

results in the maximum belief revision, compared to subsequent updates.

Mandatory performance measurement systems are generally intended to facilitate better

decisions. However, prior research suggests that when performance measurement systems are

mandated through legislative provisions, subordinate organizations are likely to comply with the

regulatory requirement, but make little use of such information for internal decisional processes

(Cavalluzzo and Ittner [2004]). Contrary to those findings, in our setting the subordinate

organizations (i.e., NHO hospitals) appear to use the new information from the mandatory

measurement. Our results support the notion that information generated by mandatory

performance measurement systems can have decision value for firms.

Prior research on mandatory nonfinancial performance measurement in the healthcare

industry has primarily used publicly disclosed medical measures of hospital quality (such as

32

mortality). The general consensus in the literature is that public disclosures of performance

influence future demand and have the potential to assist consumers make informed choices. Two

problems plague most studies in this area. First, it is difficult to disentangle real improvements in

quality from increases arising from gaming behaviors by firms to artificially boost quality

measures. In our setting, the information is collected and analyzed by an independent third party,

reducing the likelihood of gaming. Second, demand may increase because of customers’

response to rankings as opposed to response to an actual increase in service quality (Dranove and

Jin [2010]). We find an association between changes in patient satisfaction and subsequent

patient revenues even without public dissemination of performance information. We infer that

patients respond to the actual performance improvements rather than exhibiting a mechanical

response to rankings.

Substantial evidence shows that firms should not neglect nonfinancial measures such as

satisfaction because they provide leading indicators of future financial value.20 These studies

focus on the pecuniary benefits of allocating managerial attention to nonfinancial measures.

Indeed, Banker, et al. [2000] in their study of customer satisfaction in the hotel industry mention

that without tying nonfinancial incentive measures to compensation contracts, “managers did not

recognize the true benefit of allocating more effort and resources to improve customer

satisfaction, and did not do so until the change in compensation plan that focused their attention

on improving the customer satisfaction measures” (p.90). Evidence in this study builds on the

conclusion in Banker, et al. [2000] and empirically shows that nonfinancial information has the

ability to achieve improved outcomes even without including these metrics in compensation

20 For example see Baiman and Baldenius [2009], Banker, et al. [2000], Chenhall [2005], Feltham and Xie [1994], Hemmer [1996], Ittner and Larcker [1998], Kaplan and Norton [1992], Kekre et al. [1995],Krishnan et al. [1999], Nagar and Rajan [2005], Rajan and Reichelstein [2009].

33

plans. In the hospital industry, nonfinancial performance measure such as patient satisfaction is

especially important and forms a central feature of patient centered medicine (Bardes [2012]).

Our findings offer significant contributions to research, practice, and policy by showing

that in health care settings, nonfinancial performance information has a decision-facilitating role

in addition to the previously documented decision-influencing role. While it is important to

ensure that health care decision makers have access to information that facilitates decision-

making, there is debate on whether mandatory public disclosure of information has the potential

to reduce social welfare. Mandatory public disclosure can be a double-edged policy instrument

because it can cause over-reaction by the public, as well as neglect of private information by

decision makers (Morris and Shin [2002]). Results of our study indicate that, in some instances,

mandating information collection without public disclosure requirements or compensation-based

incentives can be beneficial for managerial learning.

34

Appendix A

Survey Instrument

Panel A: Overall satisfaction (Same questions asked separately for outpatients and inpatients; Scale 1 = Strongly Dissatisfied; 2 = Somewhat Dissatisfied; 3 = Neutral; 4 = Somewhat Satisfied; 5 =Strongly Satisfied

Panel B: Individualized questions for inpatients (Scale 1 = Strongly Agree; 2 = Somewhat Agree; 3 = Neither Agree nor Disagree; 4 = Somewhat Disagree; 5 =Strongly Disagree) 1. I am not satisfied with the explanation by doctors when I was hospitalized 2. I was unhappy with the procedure of medical admission 3. I was unhappy with hospital's explanation about my life during the hospital stay 4. I think that the doctors behave badly and use bad language in this hospital 5. I was worried about some doctors' skills and knowledge 6. I think that the nurses behave badly and use bad language in this hospital 7. I was unhappy with the assistance received for daily life activities 8. I think that medical staff such as doctors, nurses and other medical staff lacked teamwork 9. I did not like today's medical tests (For patients who accepted medical tests) 10. I did not like today's medical surgeries (For patients who accepted medical surgeries) 11. I did not like today's medical treatment (For patients who accepted medical treatment) 12. I did not like today's drip, injection, medicine, or prescription (For patients who had a drip, injections, medicine, or prescription) 13. I did not like today's rehabilitation (For patients who had rehabilitation) 14. I am unhappy with the toilets and bathrooms in this hospital 15. I think that passageways, stairs and elevators are inconvenient 16. I am unhappy with my room 17. I am unhappy with the food in this hospital 18. I am unhappy with the other environment such as stores, and interiors 19. I am unhappy with the hospital's explanation of my discharge

1. I am generally satisfied with this hospital 2. I am satisfied with the results of the treatment 3. I am satisfied with the period of the treatment 4. I am satisfied with treatment I have been taking 5. I am satisfied with the hospital 6. I think this hospital provides safe medical services 7. I think the explanations provided by the medical staff were very clear 8. I think the treatment I have received was acceptable 9. I generally trust this hospital 10. I would like to recommend this hospital to family members and friends

35

Panel C: Individualized questions for inpatients (Scale 1 = Strongly Agree; 2 = Somewhat Agree; 3 = Neither Agree nor Disagree; 4 = Somewhat Disagree; 5 =Strongly Disagree) 1. I felt uneasy when I came to the hospital at the initial visit 2. I think that this hospital is very inconvenient 3. I have a bad impression about this hospital 4. I am unhappy with waiting time 5. I am unhappy with the waiting room 6. I think that doctors behave badly and use bad language in this hospital 7. I was worried about some doctors' skills and knowledge 8. I think that nurses behave badly and use bad language in this hospital 9. I did not like today's medical tests (For patients who accepted medical tests) 10. I did not like today's medical treatment.(For patients who accepted medical treatment ) 11. I did not like today's drip, injection, medicine, or prescription (For patients who had a drip, injections, medicine, or prescription) 12. I did not like today's rehabilitation (For patients who had rehabilitation) 13. I am unhappy with the treatment room 14. I am unhappy with the other environment such as shops, ATM, and interiors 15. I am unhappy with the procedures for payment Notes to Appendix A: This appendix lists the questions used in the survey administered to Japanese National Health Organization (NHO) general hospitals and sanatoriums. The translation from Japanese to English aimed at maintaining the original meaning as close as possible.

36

Appendix B

Physician Compensation at NHO

Salary schedule: Each NHO post is classified into a certain grade in a salary schedule. The classification of the employee into a post is based on two factors: educational classification and experience. Most Japanese government agencies have ten grades. Within each grade employees receive raises in steps, which are based on time in grade. A sample of the pay scale for a Japanese government agency is provided below.

Salary per month (Yen) Grade

1 2 3 4 5 6 7 8 9 10

Steps

1 135,600 185,800 222,900 261,900 289,200 320,600 366,200 413,000 466,700 532,000 5 140,100 192,800 230,200 270,200 298,200 329,800 376,300 422,800 479,000 544,700 9 144,500 200,000 237,500 278,600 307,300 338,600 386,400 432,300 491,300 554,700

13 149,800 207,000 244,900 287,000 316,400 347,200 397,100 441,300 503,600 562,100 17 155,700 214,600 252,200 295,400 325,200 355,500 406,400 449,300 513,300 568,100 21 161,600 222,000 260,100 303,800 333,500 363,500 414,800 456,500 519,000 572,900 25 172,200 229,300 267,700 312,100 341,500 371,500 422,900 462,500 524,800 29 178,800 236,100 275,300 320,400 349,400 379,500 429,400 467,800 529,600 33 185,800 242,100 282,700 328,400 357,000 386,900 434,600 471,000 533,100 37 191,600 248,000 290,100 336,500 364,200 393,700 439,700 474,200 536,700 41 196,900 254,200 297,400 344,400 370,100 398,400 443,200 477,400 540,300 45 202,000 259,700 304,200 352,000 374,700 403,000 446,400 480,500 49 207,100 265,200 310,600 358,500 378,400 405,900 449,400 53 211,600 270,100 317,100 363,000 381,700 408,800 452,400 57 215,400 275,200 323,400 367,100 384,500 411,600 455,400 61 219,200 279,700 328,100 369,800 387,000 414,300 458,400 65 223,000 283,500 331,900 372,400 389,600 416,900 69 226,900 287,200 335,200 375,000 392,200 419,400 73 230,100 290,400 337,800 377,600 394,800 422,000 77 233,000 292,300 340,000 380,200 397,300 424,600 81 236,100 293,800 342,000 382,700 399,900 85 239,000 295,300 344,000 385,100 402,500 89 241,900 296,800 345,900 387,600 93 243,700 298,200 347,700 390,100 97 299,600 349,500

Composition of Salary: In addition to the monthly salary, government employees also get allowances averaging at about 20% of base salary. The allowances include: living expenses (cost of living adjustment), housing allowance, commuter allowance, overtime allowance, cold weather allowance, and diligence allowance (typically based on the number of months of consecutive work in the previous 6-month period). There are some compensation adjustments related to macroeconomic conditions. Individual performance-based bonuses are not commonly found.

37

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41

TABLE 1 Descriptive Statistics

Variable N Mean Median Std. Dev. Q1 Q3 Min Max

Size 1152 4.036 3.800 1.382 3.015 4.855 1.380 8.040 Competition 1152 6.986 6.300 2.620 5.100 8.300 3.400 16.400 Hospital 1152 0.403 0.000 0.491 0.000 1.000 0.000 1.000 Salary 1142 1.805 1.557 0.959 1.182 2.128 0.291 6.512 Bonus 1142 0.479 0.428 0.227 0.335 0.574 0.037 1.352 Grant 1141 0.031 0.016 0.046 0.000 0.043 0.000 0.438 Inpatient Revenue 999 4.276 3.633 2.666 2.493 5.218 0.905 15.581 Outpatient Revenue 999 0.877 0.537 0.876 0.238 1.225 0.017 5.068 Total Medical Revenue 1152 5.094 4.140 3.497 2.694 6.269 0.000 19.406 Educational Revenue 1142 0.050 0.001 0.077 0.000 0.086 0.000 0.452 R&D Revenue 1142 0.066 0.033 0.099 0.006 0.077 0.000 0.731 R&D Expenses 1142 0.002 0.001 0.003 0.000 0.003 0.000 0.029 Facility Costs 1142 0.508 0.378 0.421 0.238 0.638 0.046 2.779 Material Costs 1142 5.085 4.119 3.368 2.732 6.229 0.675 19.473 Total Costs 1142 1.230 0.782 1.242 0.329 1.631 0.005 6.768

42

TABLE 2 Regression Results: Relationship Between Factors and Overall Satisfaction

(Standard Errors in Brackets)

DV = Overall Satisfaction

Inpatients Outpatients Pooled Sample Hospitals Sanatoriums

Pooled Sample Hospitals Sanatoriums

Staff and Treatment 0.548*** 0.598*** 0.542*** 0.616*** 0.576*** 0.635*** [0.037] [0.045] [0.045] [0.036] [0.070] [0.0423]

Logistics and infrastructure

0.275*** 0.258*** 0.283*** [0.023] [0.023] [0.038]

Administrative Procedures

0.380*** 0.374*** 0.367*** [0.040] [0.070] [0.050]

Size 0.009 0.033** -0.017 0.017 0.073*** -0.033 [0.016] [0.013] [0.029] [0.022] [0.024] 0.0250]

Competition 0.043*** 0.033* 0.056*** 0.049** 0.064* 0.049** [0.014] [0.020] [0.020] [0.020] [0.033] [0.025]

Hospital 0.027 0.082 [0.026] [0.052]

Intercept 0.041* 0.049** 0.039* -0.034 0.020 -0.041 [0.022] [0.019] [0.022] [0.028] [0.053] [0.027]

N 983 433 550 1143 463 680 R2 0.777 0.755 0.769 0.769 0.798 0.754

Notes to Table 2: (1) The coefficients reported in Table 2 are estimated via OLS regression of the following model: Satisfaction= α+𝛽!*Staff and Treatment+𝛽!*Logistics and Infrastructure +𝛽!*Administrative Procedures+β4*Size+𝛽!*Competition+𝛽!*Hospital+𝜀. The model is estimated using robust standard errors clustered by hospital. (2) The factor scores are based on principal component analysis with oblique rotation. Factor scores are expressed in standardized terms. (3) Size (number of beds, in thousands) and Competition (number of hospitals per 100K people in the prefecture) are expressed in standardized terms. (4) Hospital is a binary variable assuming the value of 1 if the site is a general hospital, and 0 if the site is a sanatorium. (5) Wald tests indicate that the coefficients associated with Staff and Treatment are statistically different than the coefficients associated with Logistics and Infrastructure (for inpatients) and Administrative Procedures (for outpatients) in all cases, with the exception of general hospitals’ outpatients. (6) * = p-value <0.10; ** = p-value <0.05; *** = p-value <0.01, two-tailed.

43

TABLE 3 Satisfaction Performance

Panel A: Mean Patient Satisfaction by Year based on Initial Performance

Whole Sample Performance Quartile in 2004

Overall Inpatient Satisfaction Poor Performers High Performers

N Mean N Mean N Mean 2004 135 4.194 33 3.780 31 4.424 2005 135 4.328 33 4.143 31 4.471 2006 137 4.374 33 4.127 31 4.546 2007 137 4.393 33 4.083 31 4.536 2008 136 4.441 32 4.215 31 4.535 2009 134 4.405 31 4.031 31 4.530 2010 133 4.442 31 4.287 31 4.461 2011 135 4.448 31 4.260 31 4.558

Overall Outpatient Satisfaction 2004 144 3.951 36 3.664 36 4.244 2005 143 4.044 36 3.868 36 4.260 2006 144 4.072 36 3.907 36 4.204 2007 144 4.090 36 3.934 36 4.204 2008 143 4.115 36 3.985 36 4.215 2009 142 4.104 35 3.966 36 4.165 2010 142 4.115 36 3.986 35 4.201 2011 143 4.119 36 3.999 36 4.170

Panel B: Mean Annual Change in Yearly Satisfaction Scores

Whole Sample Performance Quartile in 2004 Overall Inpatient Satisfaction Poor Performers High Performers

N Mean N Mean N Mean 2005-2004 134 0.087*** 33 0.317*** 31 0.022*** 2006-2005 135 0.057*** 33 -0.016 31 0.074** 2007-2006 137 0.019 33 -0.044 31 -0.010 2008-2007 136 0.041*** 32 0.099 31 -0.001 2009-2008 134 -0.039* 31 -0.185** 31 -0.005 2010-2009 132 0.023 30 0.191** 31 -0.069 2011-2010 133 0.005 30 -0.054 31 0.098 Overall Outpatient Satisfaction 2005-2004 143 0.088*** 36 0.204*** 36 0.009 2006-2005 143 0.025** 36 0.039* 36 -0.060 2007-2006 144 0.018 36 0.027 36 0.000 2008-2007 143 0.024* 36 0.052* 36 0.012 2009-2008 142 -0.012 35 -0.020 36 -0.050 2010-2009 141 0.009 35 0.015 35 0.029 2011-2010 142 0.012 36 0.013 35 0.000

44

Notes to Table 3: (1) For expositional clarity and ease of interpretation of the means, these values are calculated as the average score on the ten survey questions assessing overall satisfaction for inpatients and outpatients respectively. Univariate analyses performed using overall satisfaction factor scores yielded equivalent results. (2) Hospitals are identified as poor performers (lowest quartile) and high performers (highest quartile) based on the satisfaction scores of 2004, which is the first year of the mandatory collection of satisfaction performance information. (3) In Panel B, change is defined as satisfaction in Yeart – Yeart-1. (4) Significance of the changes (year over year change is significantly different than zero) in Panel B: * = p-value <0.10; ** = p-value <0.05; *** = p-value <0.01, two-tailed.

45

TABLE 4

Analysis of Patient Satisfaction over Time

Inpatients Outpatients

Overall Inpatient

Satisfaction

Staff and Treatment

Logistics and infrastructure

Overall Outpatient Satisfaction

Staff and Treatment

Administrative Procedures

Size 0.102 0.111* -0.021 -0.043 0.031 -0.207*** [0.063] [0.067] [0.067] [0.064] [0.062] [0.053]

Competition 0.187*** 0.154*** 0.144** 0.124** 0.091* 0.05 [0.056] [0.057] [0.060] [0.058] [0.054] [0.049]

Hospital 0.418*** 0.450*** 0.205* -0.382*** -0.235** -0.833*** [0.109] [0.104] [0.119] [0.111] [0.103] [0.109]

2005 0.451*** 0.310*** 0.373*** 0.414*** 0.638*** 0.021 [0.103] [0.104] [0.090] [0.083] [0.066] [0.061]

2006 0.598*** 0.564*** 0.566*** 0.437*** 1.077*** 0.017 [0.093] [0.106] [0.088] [0.094] [0.067] [0.077]

2007 0.670*** 0.630*** 0.506*** 0.525*** 1.116*** 0.02 [0.088] [0.110] [0.096] [0.095] [0.076] [0.076]

2008 0.849*** 0.718*** 0.685*** 0.651*** 1.263*** 0.179** [0.098] [0.101] [0.091] [0.102] [0.071] [0.083]

2009 0.714*** 0.730*** 0.893*** 0.595*** 1.286*** 0.101 [0.101] [0.112] [0.100] [0.102] [0.081] [0.081]

2010 0.847*** 0.767*** 0.926*** 0.654*** 1.330*** 0.05 [0.127] [0.105] [0.116] [0.101] [0.065] [0.084]

2011 0.880*** 0.777*** 0.983*** 0.669*** 1.368*** 0.072 [0.109] [0.098] [0.100] [0.118] [0.090] [0.093]

Intercept -0.848*** -0.776*** -0.707*** -0.338*** -0.916*** 0.281*** [0.140] [0.140] [0.113] [0.119] [0.095] [0.100]

N 1029 1033 1033 1145 1143 1143 R2 0.156 0.167 0.132 0.096 0.212 0.255 Notes to Table 4: (1) Results are based on OLS estimation of the following model: Satisfaction= α+β1 Size+ β2 Competition+ β3 Hospital+ β

4-10Year+𝜀 . (2) Columns 1 and 4 list

estimated coefficients for the model where the dependent variable is overall satisfaction, for inpatients and outpatients, respectively. (3) Dependent variables for the estimations in columns 2, 3, 5, and 6 are operationalized by the corresponding satisfaction factors. The factor scores are based on principal component analysis with oblique rotation. Factor scores are expressed in standardized terms. (4) Size (number of beds, in thousands) and Competition (number of hospitals per 100K people in the prefecture) are expressed in standardized terms. (5) Hospital is a binary variable assuming the value of 1 if the site is a general hospital, and 0 if the site is a sanatorium. (6) Year 2004 is the dropped dummy. (7) Models control for hospital fixed effects and use robust standard errors clustered by firm. (4) * = p-value <0.10; ** = p-value <0.05; *** = p-value <0.01, two-tailed.

46

TABLE 5 Drivers of change in patient satisfaction

Panel A: Unscaled Change in Patient Satisfaction

Inpatients Outpatients

Overall Satisfaction

Staff & Treatment

Logistics & infrastructure


Staff & Treatment

Admin Procedures

Trend -0.038*** -0.020** -0.014 -0.020** -0.074*** 0.014 [0.010] [0.008] [0.010] [0.009] [0.009] [0.009]

Poor performer 0.463*** 0.695*** 0.672*** 0.569*** 0.434*** 0.454*** [0.170] [0.191] [0.165] [0.172] [0.108] [0.106]

Trend * Poor Performer

-0.072** -0.106*** -0.081** -0.085*** -0.062*** -0.058*** [0.033] [0.033] [0.032] [0.028] [0.022] [0.020]

Hospital 0.066*** 0.008 0.021 0.036 0.028 0.01 [0.024] [0.023] [0.027] [0.030] [0.020] [0.024]

Size 0.009 0.007 -0.004 0.029** 0.028** 0.004 [0.018] [0.010] [0.015] [0.014] [0.013] [0.012]

Competition 0.011 0.009 0.005 0.006 -0.002 0.002 [0.015] [0.008] [0.012] [0.012] [0.010] [0.012]

Intercept 0.222*** 0.145*** 0.130** 0.143*** 0.518*** -0.107** [0.056] [0.042] [0.059] [0.049] [0.045] [0.048]

N 568 855 855 998 984 984 R2 0.026 0.035 0.034 0.03 0.063 0.017 Panel B: Change in Patient Satisfaction Scaled by Initial Potential for Improvement Inpatient Outpatient


Staff & Treatment

Logistics & infrastructure


Staff & Treatment

Admin Procedures

Trend -0.013*** -0.005 -0.002 -0.002 -0.018*** 0.006** [0.004] [0.005] [0.004] [0.003] [0.002] [0.003]

Poor Performer 0.082** 0.163*** 0.137*** 0.083*** 0.050** 0.092*** [0.041] [0.046] [0.038] [0.021] [0.021] [0.020]

Trend * Poor Performer

-0.013 -0.027*** -0.017** -0.012*** -0.007* -0.012*** [0.008] [0.008] [0.007] [0.004] [0.004] [0.004]

Hospital 0.025*** 0.01 0.011 0.013** 0.006 0.005 [0.008] [0.009] [0.008] [0.005] [0.005] [0.005]

Size 0.005 0.004 -0.001 0.009* 0.008* 0.006 [0.005] [0.003] [0.005] [0.005] [0.005] [0.004]

Competition 0.006 0.005* 0.001 0.005 0.001 0.002 [0.004] [0.003] [0.004] [0.003] [0.003] [0.003]

Intercept 0.076*** 0.039 0.024 0.011 0.127*** -0.044*** [0.023] [0.027] [0.022] [0.016] [0.013] [0.016]

N 568 855 855 998 984 984 R2 0.020 0.011 0.013 0.016 0.046 0.012 Notes to Table 5: (1) Panel A uses a fixed effects regression model of the following form:

47

Change in Satisfaction = α + β1Trend + β2Poor Performer+ β3Trend*Poor Performer+ β4Hospital+ β5Size+ β6Competition+𝜀. (2) In Panel A change in satisfaction is satisfaction in Yeart – Yeart-1, while in Panel B, change in satisfaction is scaled by the potential for improvement relative to 2004: ((Satisfaction in Yeart – Satisfaction in Yeart-1)/(5 – Satisfaction in 2004)). (3) Columns 1 and 4 list estimated coefficients for the model where the dependent variable is overall satisfaction, for inpatients and outpatients, respectively. Dependent variables for the estimations in columns 2, 3, 5, and 6 are operationalized by the corresponding satisfaction factors. The factor scores are based on principal component analysis with oblique rotation. Factor scores are expressed in standardized terms. (4) Size (number of beds, in thousands) and Competition (number of hospitals per 100K people in the prefecture) are expressed in standardized terms. (5) Hospital is a binary variable assuming the value of 1 if the site is a general hospital, and 0 if the site is a sanatorium (6) Models control for firm fixed effects and use robust standard errors clustered by firm. (7) * = p-value <0.10; ** = p-value <0.05; *** = p-value <0.01, two-tailed.

48

TABLE 6 Analysis of the Relationship Between Patient Satisfaction Performance and Incentive Compensation, Hospital Grant, Inpatient and

Outpatient Revenues Salary Bonus Grant Inpatient Revenue Outpatient Revenue Overall Inpatient Satisfaction(t-1) -0.012 -0.005 -0.004** 0.250**

[0.009] [0.003] [0.002] [0.097] Overall Outpatient Satisfaction(t-1) -0.009 0.000 -0.001 0.072**

[0.013] [0.004] [0.002] [0.031] Size(t-1) 0.133*** 0.049*** 0.007 1.279*** 0.334***

[0.032] [0.008] [0.008] [0.114] [0.040] Competition(t-1) -0.010 0.005 0.001 0.008 -0.027**

[0.013] [0.003] [0.003] [0.044] [0.013] Hospital(t-1) 0.068 0.000 -0.020** 2.054*** 0.985***

[0.050] [0.012] [0.009] [0.252] [0.092] Total Medical Revenue(t-1) 0.225*** 0.052*** -0.009**

[0.023] [0.005] [0.004] Educational Revenue(t-1) 0.719* 0.077 0.043

[0.370] [0.080] [0.069] R&D Revenue(t-1) 0.515 0.131 -0.093

[0.388] [0.084] [0.082] R&D Expenses(t-1) -0.746

[0.798] Facility Costs(t-1) -0.015

[0.024] Material Costs(t-1) -0.013

[0.011] Total Costs(t-1) 0.024**

[0.012] Year Fixed Effects Yes Yes Yes Yes Yes

Intercept 0.533*** 0.221*** -0.013 -1.959*** -0.748*** [0.069] [0.017] [0.020] [0.591] [0.155]

N 886 886 885 888 995 R2 0.958 0.952 0.354 0.714 0.718

49

Notes to Table 6: (1) Robust standard errors are reported in brackets for each coefficient. (2) Patient satisfaction predictors are measured by overall satisfaction factor scores lagged one period for both inpatient and outpatient cases. The factor scores are based on principal component analysis with oblique rotation. Factor scores are expressed in standardized terms. (3) All regressions use robust standard errors and control for hospital fixed effects. (4) Size (number of beds, in thousands), Competition (number of hospitals per 100K people in the prefecture) and all revenue and costs predictors are expressed in standardized terms. (5) Hospital is a binary variable assuming the value of 1 if the site is a general hospital, and 0 if the site is a sanatorium. (6) * = p-value <0.10; ** = p-value <0.05; *** = p-value <0.01, two-tailed.

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