Journal
of Accounting
and Economics
Are overhead
to activity?
17 (1994) 255-278.
North-Holland
costs strictly proportional
Evidence from hospital
service departments*
Eric Noreen
INSEAD, Fontainebleau, France
University of Washington, Seattle,
Naomi
WA 98195. USA
Soderstrom
Universiry of Washington,
Received October
Seattle,
WA 98195, USA
1991, final version
received July 1992
Using cross-sectional
data from hospitals in Washington
State, we test whether overhead costs are
proportional
to overhead activities. This assumption
is at the heart of nearly all cost accounting
systems, which implicitly assume that marginal cost is equal to average cost. Empirically,
the
proportionality
hypothesis can be rejected for most of the overhead accounts. On average across the
accounts, the average cost per unit of activity overstates marginal costs by about 40% and in some
departments
by over 100%. Thus, the average cost per activity should be used with a great deal of
caution in decisions.
Key words: Management
functions
accounting;
Cost behavior;
Overhead;
Activity-based
costing;
Production
1. Introduction
Cost accounting
such as products
Correspondence
USA.
systems typically assign overhead
or customers
using an averaging
to: Naomi
Soderstrom,
DJ-10,
University
costs to costing objects
process. Turney (1992,
of Washington,
Seattle,
WA 98195,
*We would like to thank the staff of the Washington
State Department
of Health for their able
assistance without which this research would have been impossible. We would also like to thank
participants
in the University of Washington,
University of Oregon, and University of British
Columbia annual workshop, and Bob Bowen, Jim Jiambalvo, Robert Kaplan, Lauren Kelly, Susan
Moyer, Ken Merchant, Chris Stinson, and particularly
Dave Burgstahler
for their comments.
0165-4101/94/$06.00
0
1994-Elsevier
Science Publishers
B.V. All rights reserved
256
pp.
E. Noreen and N. Soderstrom,
58-59) provides
an example
Overhead
costs and activity
in an activity-based
costing
setting:
The cost of the purchasing activity is traced to part numbers via the number
of purchase orders per part . . . . A volume of 6,000 purchase orders and an
activity cost of $450,000 [for purchasing]
yields a cost per purchase order
of $75.
The cost per purchase order of $75 is clearly an average cost - it is obtained by
dividing the total cost of $450,000 by the total activity of 6,000 purchase orders.
If this cost of $75 per purchase order is subsequently
used in decisions such as
whether to drop a product, the implicit assumption
is that each reduction in
aggregate purchase orders will decrease total purchasing costs by $75. In other
words, a decision that reduces total activity by x% will result in a reduction of
the associated costs by x%. This is a stronger assumption
than linearity; it
requires that costs be strictly proportional
to activity.’
The proportionality
assumption is in conflict with what is commonly believed
about economies
of scale - average cost should at first decline as volume
increases.* Despite the central importance in cost accounting of the assumption
that cost is strictly proportional
to activity, and its apparent
conflict with
conventional
wisdom in economics, we know of no attempt to empirically test
whether the assumption
is valid.3
The purpose of this paper is to take the first step in empirically
testing
whether costs are really strictly proportional
to activity in a specific industry.
We use a data base compiled by the Washington
State Department
of Health
(WSDOH)
from reports submitted
by Washington
hospitals.
The reports
contain expense and activity data for predefined overhead cost pools. Our
’ This is not the only assumption
about cost behavior that is made when costs generated by
typical cost accounting systems are used in decision-making.
Noreen (1991) demonstrates
that fully
allocated costs -even in activity-based
costing systems ~ are relevant costs in decisions if and only if
the following conditions are satified: 1) all costs can be partitioned into pools, each of which is solely
a function of a measured activity; 2) the amount of cost in each cost pool varies in direct proportion
to its activity; and 3) all activities can be attributed
to products in the sense that if a product is
dropped then the activities associated with that product will be avoided. In this paper only the
second assumption
is examined. Marais (1990) makes similar observations.
’ If average cost decreases with activity, we label this as increasing returns to scale. If average cost
is constant and does not depend upon activity, we label this as ‘constant returns to scale’. These
definitions are slightly different from the definitions commonly used by economists.
3 Foster and Gupta (1990) examine the relations between overhead spending and various possible
measures of activity across 38 facilities of one electronics firm. However, they do not test the
proposition
that overhead costs are strictly proportional
to activity. Kaplan’s (1987) graphs of
overhead costs versus various activity measures might be misinterpreted
as evidence that there are
no fixed costs in the particular firm he studied. However, these are plots of the percentage change in
cost veruss the percentage change in activity. There should be no intercept in these plots even if there
are substantial fixed costs. On the other hand, if cost is strictly proportional
to the activity measures
selected, the plots should lie strictly along the diagonal, and most do not.
E. Noreen and N. Soderstrom,
Overhead costs and activity
251
cross-sectional
analyses of the data indicate that most of the overhead cost pools
exhibit statistically and economically
significant returns to scale; that is, average
cost declines with activity.
There is an extensive literature
concerned
with estimating
hospital cost
functions.4 However, all of these studies have examined the behavior of aggregate hospital costs or of particular medical services. None has dealt with the cost
behavior of individual
overhead activities. Moreover, these studies have not
resulted in any consensus regarding whether there are returns to scale in the
provision of medical services in hospitals. Some studies find significant returns
to scale and others find none.
2. Testing the proportional cost model
Using average costs in decision-making
is often justified by the assertion that
‘in the long run all costs are variable’.5 Presumably
those who rely on this
justification
do not necessarily expect spending to be proportional
to activity in
the short run. Since cost functions
estimated with cross-sectional
data are
interpreted as long-run cost functions [Johnston (1960, pp. 29930)], we base our
analysis on cross-sectional
data.‘j Thus, we assume that a cost function estimated with cross-sectional
data represents the cost expansion path a hospital
overhead account would take as activity changes.
There are a number of reasons why this might not be true. Activities carried
out by a particular
overhead function may not really be the same across
hospitals. Different costs may reflect differences in quality. Production functions
may differ across hospitals, depending upon what fixed capital was acquired and
when it was acquired. Input prices may differ, particularly wage rates in different
4 For reviews of this literature
(1969) Mann and Yett (1968).
see Cowing,
Holtman,
and Powers (1983), Feldstein
(1974), Hefty
s See, for example, Greenwood and Reeve (1992, p. 3 1) and Shank and Govindarajan
(1989. p. 29).
This justification for fully allocating costs appears to be based on a misinterpretation
of economists’
discussions of the production
function. In economists’ discussions, the statement that ‘in the long
run all costs are variable’ defines what is meant by the long run and is not a statement of fact about
the way costs behave. That is, the short run is a period in which the levels of some inputs are fixed
and cannot be adjusted and the long run is defined to be a period in which the levels of all inputs can
be adjusted. Moreover, a variable input is simply one that can be adjusted. There is not any
implication in the economists’ discussions that variable costs are strictly proportional
to activity.
6 We use the term ‘cost function’ differently than an economist would. An economist views the cost
function as a mapping between output and the least costly means of attaining that output given the
production
technology and input prices. We view the cost function for an overhead cost pool as
simply the empirically observed relation between total cost and its measure ofactivity. This could be,
but is not necessarily,
the minimum
cost to attain that activity level. Nevertheless,
it seems
reasonable to suppose in most cases that whoever controls a hospital would prefer to run efficient
overhead activities such as printing and duplicating since money saved on such secondary activities
can be put to use on primary activities.
258
E. Noreen and N. Soderstrom,
Overhead costs and activity
locations. Some hospitals may appear to have abnormally
high or low average
costs because of transitory
abnormal
volume. Hospitals are likely to have
different patient populations.
And, there are likely to be differences among
managers in how they adjust to changes in the level of activity. Despite such
problems, cross-sectional
empirical analysis of cost behavior is at least a starting
point in addressing the fundamental
question of whether costs are proportional
to activity.
Given that cross-sectional
data will be used, the next question is the functional form used to estimate the cost function. The proportional
cost model
assumes a very simple cost function in which cost is strictly proportional
to
a single measure of activity and there are no other explanatory
variables. If Cj is
the total cost for hospital j and qj is the activity measure at hospital j for
a particular overhead account, the proportional
cost model assumes
cj
=
(1)
Pj'qj3
where pj is a positive constant for hospital j. To simplify cross-sectional
empirical testing, we temporarily
impose the restriction
that pj is the same for all
hospitals, or
Cj
=
(2)
p’qj.
A simple test of the proportional
cost model would involve regressing Cj on qj
and then testing whether the intercept is zero. However, as we shall see later, this
specification of the cost function results in heteroscedastic
residuals. Fortunately, this econometric problem largely disappears if the following logarithmic form
is used to estimate the cost function:
In (Cj) = In(p) + p In (qj).
(3)
This cost function is consistent with the generalized Cobb-Douglas
production
function [Heathfield and Wibe (1987, p. 84)]. The slope coefficient p is the ratio
of marginal to average cost.7 And, roughly speaking, p quantifies how much of
a given percentage change in volume translates into a percentage change in cost.
In this logarithmic form the test of the proportional
cost model reduces to a test
of whether the slope coefficient /I is 1. A slope coefficient of 1 is consistent with
the proportional
cost model. A slope of less than 1 is consistent with increasing
returns to scale.
’ If C = pq8. then average
Or, /I= MC/AC.
cost is AC = C/q = pq8-
’ and marginal
cost is MC =
apqBm’= PAC.
E. Noreen and N. Sodersrrom.
Overhead COGS and activit)
259
3. The data
The Washington
State Department
of Health (WSDOH) collects standardized cost and operating data from over 100 hospitals located within the state.
The standard
chart of accounts
established
by the WSDOH
includes
36
predefined overhead accounts. Twenty-two of these overhead accounts are used
in this study.’ Each of these accounts is described in the appendix
using
language taken from the WSDOH
Accounting and Reporting Manual. The
Standard Unit of Measure (i.e., unit of activity) chosen by the WSDOH for each
account is also identified in the appendix.
The Standard Units of Measure have been selected by the WSDOH. In most
cases we believe that a cost system designer would have difficulty identifying
a priori any better single summary
measure of activity that can be easily
measured. For example, the number of admissions
is the Standard
Unit of
Measure for Admitting and it would appear to be a natural choice for an activity
measure in a cost system. (And indeed the subsequent analysis indicates a very
strong statistical relation between admissions
and admitting
costs.) The accounts where the choices of activity measures are most questionable
a priori are
Data Processing (Gross Patient Revenue), Hospital Administration
(Number of
FTE Employees), and Public Relations (Total Revenue). Even so, better activity
measures for these departments
are not obvious without breaking them down
into finer cost pools.
Hospital controllers have indicated to us that they use the WSDOH chart of
accounts and the units of service selected by the WSDOH in budgeting and
controlling
overhead costs.
The WSDOH collects data concerning
both actual and budgeted expenses
and Standard Units of Measure. One research design issue is whether we should
analyze actual or budgeted data. We are concerned with the accuracy of product
and other costs that are computed in typical cost accounting
systems. Some
firms rely on actual cost and activity data to compute product and other costs
and some use budgeted cost and activity data. It appears most common,
however, to use budgeted
costs and activity levels to distribute
costs to
costing objects - particularly
in ABC systems. We analyze .both actual and
budgeted data in this study, using budgeted data from 1990 and actual data
from 1987.
*We were not able to use all 36 accounts for a variety of reasons. Seven of the accounts were not
used by any hospital. Five accounts were used by such a small number of hospitals that meaningful
cross-sectional
analysis was impossible. In addition, the relation between costs and units of service in
two accounts,
Purchasing
and Nursing Inservice Education,
were so erratic that they are not
reported here. In the case of Purchasing,
it appears the problem is that the activity measure was
defined to be Total Gross Noncapitalized
Purchases rather than a measure such as number of
purchase orders processed. We have no explanation for the erratic behavior of the Nursing Inservice
Education costs.
260
E. Noreen and N. Soderstrom, Overhead costs and activity
We used budgeted data from 1990 because prior to 1989 hospitals in Washington were subject to revenue regulation which created incentives to manipulate budgeted figures.’ The regulations are described in Blanchard, Chow, and
Noreen (1986). Briefly, the total allowable revenue for a hospital was its budgeted total cost for the year (plus an allowance for profits in the case of for-profit
hospitals), adjusted for the difference between budgeted and actual volume. The
adjustment
in the allowable revenue assumed that the hospital’s variable costs
were a specified percentage of total budgeted costs. Hospitals could effectively
escape revenue capping by manipulating
the budgeted figures they reported to
the WSDOH (then called the Washington
State Hospital Commission).
The
evidence in Blanchard, Chow, and Noreen is consistent with such manipulation
of budgeted figures. And the staff of the WSDOH
has told us that by the
mid-1980s they were aware that hospitals were biasing their budgeted data.
Actual data is reported to the WSDOH well over a year after the budgeted
data is reported. Consequently,
there were not enough observations
in the most
recent version of the database to use actual data from 1990. As a result, we went
back to the year with most observations
- which was 1987. While the form of
revenue regulation used in the state did not explicitly take into account actual
cost data, the staff of the WSDOH reports that by 1987 they were using actual
data to check on the budgeted data submitted by hospitals. So it is possible that
regulation may have affected actual cost and activity data ~ although it is far
from obvious what those effects would have been. At any rate, as we shall see,
the results of our tests are remarkably consistent across the 1987 actual data and
the 1990 budgeted data samples. If revenue regulation
did affect actual costs
reported to the state, the effects were not major in terms of the phenomenon
we
are studying here.
Coding and keying errors are a recurrent problem with hospital databases.
We screened the data for these sorts of irregularities in two stages. Although the
final analysis concerns only budgeted data for 1990 and actual data for 1987,
data from 1973 through 1992 were used for screening purposes. In the first stage,
the average cost per unit of activity was calculated for every hospital in every
year for which data was available.
For this purpose, costs were inflationadjusted using the American Hospital Association’s
Hospital Market Basket
Index. Within each overhead account, observations
that were more than 1.645
standard deviations from the mean of the inflation-adjusted
average cost were
flagged. We then examined the raw time-series data for each hospital to judge
whether the flagged observations
were obvious data errors that should be
deleted or simply extreme observations
that should remain in the sample. An
example of a time-series flagged for investigation
is provided in table 1. This
example concerns the actual 1987 data for hospital 84 and account 8670,
9 Interestingly,
cost and activity
it is largely at the insistence of hospitals that the WSDOH has continued
data even after the demise of revenue regulation in 1989.
to collect
E. Noreen and N. Soderstrom,
Table
Example
of an apparent
coding
1
error: hospital
Year
Expense
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1131
1221
1343
1216
23925
27652
30614
35826
37102
40009
44000
4090 1
53479
57071
61595
“Flagged
261
Ooerhead costs and actiaitr
84, account
8670, 1987 actual
data.
Units of service
47311
47317
43147
48416
53825
57346
57017
52556
48447
46885
44433
44473
280”
49698
48751
as outlier.
Chaplaincy,
which had been flagged as an outlier on the basis of an unusually
large average cost. Perusal of the time series of the expenses and units of service
for this hospital made it obvious that the 1987 units of service had been coded or
keyed incorrectly and the observation
was dropped. In the second stage of the
screening process, we plotted expenses against units of service (i.e., activity) for
each account for 1987 and 1990. The few observations
that in our judgement
were extremely unusual in the cross-section plots were flagged. For each of these
observations,
we returned to the time-series data and only deleted the observation if it appeared to us to be clearly inconsistent
with other data for that
hospital and therefore likely to be a coding or keying error. The numbers of
observations
eliminated via these two screening processes are listed in table 2 for
each account. Overall, slightly in excess of 3% of the observations,
or 77 in total,
were deleted due to apparent coding and keying errors. While not reported in
the table, 64 of these were deleted in the first stage of the screening and only 13 in
the second stage of screening.
Apart from outright coding and keying errors, there is a potential problem
with inconsistencies
across hospitals in the definition of the Standard Unit of
Measure. The WSDOH does sometimes allow hospitals to modify the definition
of the Standard Unit of Measure to suit the hospital’s circumstances;
although
we have been assured by the WSDOH staff that this is a rare occurrence for the
accounts examined in this paper. Nevertheless, inconsistencies
in the definitions
of the Standard Unit of Measure across hospitals may have caused some of the
apparent anomalous observations
we noticed and may be responsible for some
of the remaining variance in average costs across hospitals.
4
1
I
I
6
I
0
3
0
1
3
4
1
1
1
1
2
1
0
2
1
4
.
30
72
47
70
58
73
62
70
55
61
60
55
73
51
57
41
32
23
74
43
60
72
8310:
8320:
8330:
8350:
8360:
8430:
8460:
8510:
8520:
8530:
8540:
8560:
8610:
8630:
8650:
8660:
8670:
8680:
8690:
8700:
8710:
8720:
Printing and Duplicating
Dietary
Cafeteria
Laundry and Linen
Social Services
Plant
Housekeeping
Accounting
Communications
Patient Accounts
Data Processing
Admittmg
Hospital Administration
Public Relations
Personnel
Auxiliary Groups
Chaplaincy Services
Medical Library
Medical Records
Medical Staff
Health Care Review
Nursing Administration
Deleted
1987 actual
number
Table 2
and keying errors:
Original
for coding
Account
Results of screening
30
70
46
66
57
72
56
69
55
58
60
54
70
47
56
40
31
22
72
42
56
71
Remaining
of observations
28
68
39
62
48
70
52
67
50
56
57
55
68
47
57
38
31
28
65
37
60
59
Original
I
0
2
4
2
4
4
2
0
2
2
2
I
1
0
0
1
I
0
4
3
2
Deleted
1990 budgeted
before and after screening.
27
68
39
61
47
70
48
64
48
55
56
55
66
43
55
34
27
26
65
35
58
57
Remaining
:
z2
4
a
$
9
r%
f:
z.
2
\
F?
R
z
3
3
;
2
h
2
p
%
3
E. Noreen and N. Soderstrom.
Overhead costs and activity
263
4. The results
The first test consist of examining the relation between average cost and the
level of activity. If costs are strictly proportional
to activity, then average cost
should be constant and independent
of the level of activity. If there are increasing returns to scale, average cost should decline as the level of activity increases.
An example of a plot of average cost versus units of service (i.e., activity) is
displayed in fig. 1. This figure illustrates the relation between average cost and
units of service for account 8530, Patient Accounts, for 1990 budgeted data.
There is an unmistakable
negative relation between average cost and units of
service; average cost declines as the activity level increases. This pattern is
confirmed by the Spearman rank correlation between average cost and units of
service, which is reported in tables 3a and 3b. The null hypothesis of no relation
between average cost and the level of activity is rejected at the 0.05 level for 10
out of 22 of the overhead accounts for the 1987 actual data and for 13 out of 22
for the 1990 budget data.
The next tests involve estimating
cost functions for each of the overhead
accounts. However, cross-sectional
plots of expenses versus units of service
Table 3a
Descriptive
statistics
and Spearman
rank correlations
between
1987 actual data.
Average
Account
N
Mean
Std. dev.
8310
8320
8330
8350
8360
8430
8460
8510
8520
8530
8540
8560
8610
8630
8650
8660
8670
8680
8690
8700
8710
8720
30
70
46
66
57
72
56
69
55
58
60
54
70
47
56
40
31
22
72
42
56
71
14.85
7.14
3.33
0.50
20.91
6.44
10.36
772.94
524.96
18.86
14.19
54.92
2325.01
10.80
456.92
2.48
2.49
239.74
45.93
798.45
20.28
2230.24
10.94
3.06
1.60
0.16
33.25
2.46
3.52
751.85
184.63
9.76
5.99
30.09
1459.11
8.37
191.24
2.54
“One-tailed
probability
associated
1.53
142.62
18.19
632.00
12.16
1124.37
average
cost
Min.
Spearman
rank corr.
i-s
Prob(rs)
Max.
0.6 1
1.36
1.23
0.25
1.56
3.20
2.36
63.52
129.79
8.24
4.61
15.46
629.94
1.oo
117.51
0.53
0.50
64.94
12.48
63.56
5.27
502.34
with the alternative
cost and units of service:
42.03
18.76
9.05
1.23
184.88
19.14
19.91
427 1.65
890.64
54.69
31.42
149.80
8709.94
36.12
9 13.05
16.73
6.32
544.80
88.45
2515.51
61.26
5510.83
hypothesis
~
-
-
0.48
0.36
0.14
0.39
0.66
0.03
0.05
0.64
0.21
0.57
0.28
0.21
0.48
0.13
0.04
0.47
0.25
0.01
0.19
0.18
0.35
0.34
that rs c: 1.
0.00
0.00
0.17
0.00
0.00
0.59
0.65
0.00
0.06
0.00
0.99
0.06
0.00
0.19
0.61
0.00
0.08
0.51
0.06
0.12
0.00
0.00
E. Noreen and N. Soderstrom. Overhead costs and activity
264
70 60-
El
El
50-m
z
8
40.
30-
b
q
•I
1
2
0
I
0
I
I
200000
100000
300000
units of service
Fig. 1. Example
of a plot of average
cost versus units of service: account
1990 budget data.
8530 (Patient
Accounts),
Table 3b
Descriptive
statistics
and Spearman
rank correlations
1990 budgeted
Average
Account
N
Mean
Std. dev.
8310
8320
8330
8350
8360
8430
8460
8510
8520
8530
8540
8560
8610
8630
8650
8660
8670
8680
8690
8700
8710
8720
27
68
39
61
47
70
48
64
48
55
56
55
66
43
55
34
27
26
65
3s
58
57
20.08
7.74
3.65
0.52
17.63
7.22
12.91
1001.37
544.62
17.67
14.21
67.83
2596.83
10.67
537.22
2.19
3.33
379.88
59.99
1311.60
29.12
2509.78
13.51
2.86
2.00
0.12
17.76
2.25
4.45
1027.56
170.66
11.11
7.39
38.94
1590.74
6.41
240.67
1.32
2.01
542.54
21.25
1135.16
18.54
1361.48
“One-tailed
probability
associated
between
data.
average
cost
Min.
Spearman
rank corr.
YS
Prob(rs)
Max.
0.41
1.91
1.12
0.25
3.14
2.48
2.92
123.30
188.29
7.42
4.05
23.37
784.95
2.46
110.21
0.37
0.68
8.23
15.23
95.16
3.53
414.93
with the alternative
cost and units of service:
43.96
16.64
11.70
0.71
73.40
16.29
28.93
5069.25
911.3s
60.99
37.60
266.20
10494.60
34.00
1307.14
5.62
1.55
2790.90
109.17
4699.19
73.09
5885.12
hypothesis
-
-
-
0.51
0.11
0.15
0.30
0.55
0.20
0.10
0.67
0.44
0.64
0.19
0.30
0.42
0.25
0.20
0.02
0.49
0.06
0.35
0.21
0.37
0.37
that rs < 1.
0.00
0.19
0.19
0.01
0.00
0.95
0.75
0.00
0.00
0.00
0.92
0.01
0.00
0.05
0.92
0.54
0.00
0.61
0.00
0. I 2
0.00
0.00
E. Noreen and N. Soderstrom,
0
100000
Overhead costs and activit)
200000
265
300000
units of service
Fig. 2. Example
of a plot of expenses versus units of service: account
budget data.
8530 (Patient
Accounts),
1990
revealed obvious problems with heteroscedasticity.
An example of such a plot is
displayed in fig. 2. The existence of heteroscedasticity
was confirmed by running
the GoldfeWQuandt
test, the results of which are displayed in tables 4a and 4b.
The null hypothesis of homoscedastic
residuals was rejected at the 0.001 level for
every account in both 1987 and 1990. Taking the natural logs of both expenses
and units of service largely eliminated this problem. An example of a plot of log
expenses versus log units of service is displayed in fig. 3. The log transform is not
entirely ad hoc in the context of estimating cost functions; it has a long tradition
in economics
and, as previously
noted, is consistent
with a CobbPDougias
production
function.
The results of running cross-sectional
regressions using the log/log form of
eq. (3) are summarized in tables 5a and 5b. The independent
variable, In(qj), is
the log of the units of service for the particular overhead account. The dependent
variable, In(Cj), is the log of the expense for that overhead account. The
intercept in the regression is an estimate of In(p) and the slope is an estimate of/L
Recall that B is the ratio of marginal cost to average cost. If cost is proportional
to activity, the slope should be 1. If there are increasing returns to scale, the slope
should be less than 1. All but two of the 22 estimates of the slope coefficients are
less than 1 for the 1987 actual data and all but three are less than 1 for the 1990
budgeted data.
The significance level associated with a one-tailed t-test of the null hypothesis
that the slope coefficient b is 1 versus the alternative
that it is less than 1 is
displayed in the last columns of tables 5a and 5b. The null is rejected at the 0.05
level for 13 of 22 overhead accounts for the 1987 actual data and for 15 of 22
overhead accounts for the 1990 budget data.
266
E. Noreen and N. Soderswom.
11:
7
I
*
8
,
Ooerhead costs and activit)
.
9
I
10
.
I
.
11
,
.
12
,
13
ln(units of service)
Fig. 3. Example
of a plot of In(expenses)
versus In(units of service): account
1990 budget data.
8530(Patient
Accounts),
Table 4a
Goldfeld-Quandt
test of heteroscedasticity:
Untransformed
Account
8310
8320
8330
8350
8360
8430
8460
8510
8520
8530
8540
8560
8610
8630
8650
8660
8670
8680
8690
8700
8710
8720
1987 actual
data
_____.~
data.
Log data
R
Prob(R)
R
Prob(R)
32.76
38.21
19.45
34.38
74.83
81.66
246.57
8.43
46.24
13.11
157.32
11.70
9.74
5.32
37.21
2.81
323.75
5.03
116.62
76.66
11.40
32.64
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.01
0.00
0.00
0.00
0.00
2.13
1.22
0.59
0.47
1.34
1.28
1.00
1.35
0.61
0.72
0.89
0.48
0.55
0.34
1.11
0.92
12.11
0.27
1.87
3.70
0.62
0.57
0.07
0.26
0.91
0.99
0.20
0.21
0.50
0.17
0.92
0.83
0.64
0.98
0.98
1.00
0.38
0.58
0.00
0.98
0.02
0.00
0.92
0.97
E. Noreen and N. Soderstrom,
267
Overhead costs and activity
One possibility is that units of service may be partially surrogating for the size
of the hospital. Smaller hospitals tend to be located in less urban areas and to
provide different services than larger hospitals. Therefore, the cost functions of
smaller hospitals may be different from the cost functions of larger hospitals. To
check on this possibility, we ranked all of the hospitals in our sample on the
basis of available beds and created a dummy variable for size which took on
a value of 1 for hospitals above the median and a value of 0 for hospitals below
the median. The form of the regression equation to control for this factor is
In (Cj) = CI+ p In (qj) + yesize dummyj.
(4)
This is equivalent
to allowing In(p) in eq. (3) to take on two values:
smaller hospitals and one for larger hospitals:
In(p) = a
for smaller
In(p) = a + q
for larger hospitals.
one for
hospitals,
Table 4b
Goldfeld-Quandt
test of heteroscedasticity:
Untransformed
Account
8310
8320
8330
8350
8360
8430
8460
8510
8520
8530
8540
8560
8610
8630
8650
8660
8670
8680
8690
8700
8710
8720
1990 budget
data
data.
Log data
R
Prob(R)
R
Prob(R)
34.87
22.15
37.01
85.33
12.54
131.17
791.70
14.19
34.58
14.27
121.80
4.14
6.15
11.60
204.75
9.58
30.26
17.37
112.57
152.89
14.09
27.94
0.00
2.04
1.26
0.89
I .03
0.59
1.60
8.28
0.97
1.76
0.63
1.20
0.48
0.55
0.10
0.23
0.61
0.46
0.91
0.06
0.00
0.54
0.07
0.90
0.30
0.98
0.97
0.17
0.88
0.91
0.02
0.66
0.00
0.00
1.00
0.81
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.47
0.66
0.53
3.34
0.80
2.42
4.26
0.28
0.73
E. Noreen and N. Sodersrrom, Overhead costs and activity
268
Table 5a
Summary
of the regressions
of log expense on log units of service: 1987 actual
Ill(C,)-= @I +
data,
plll(ijj).a
Account
adjR2
sig(adjR’)
B
8310:
8320:
8330:
8350:
8360:
8430:
8460:
8510:
8520:
8530:
8540:
8560:
8610:
8630:
8650:
8660:
8670:
8680:
8690:
8700:
8710:
8720:
30.7%
81.9%
74.1%
93.1%
54.2%
91.6%
83.5%
43.9%
79.3%
80.2%
85.6%
65.0%
68.9%
44.0%
76.1%
14.8%
42.0%
71.2%
83.3%
53.0%
62.2%
75.1%
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.008
0.000
0.000
0.000
0.000
0.000
0.000
0.448
0.807
0.884
0.905
0.553
1.002
0.930
0.452
0.924
0.704
1.133
0.910
0.726
0.844
0.996
0.388
0.721
0.950
0.876
0.782
0.782
0.813
Printing and Duplicating
Dietary
Cafeteria
Laundry and Linen
Social Services
Plant
Housekeeping
Accounting
Communications
Patient Accounts
Data Processing
Admitting
Hospital Administration
Public Relations
Personnel
Auxiliary Groups
Chaplaincy
Services
Medical Library
Medical Records
Medical Staff
Health Care Review
Nursing Administration
Mean slope
t(B)
~
~
-
4.58
4.24
1.49
3.12
6.62
0.07
1.25
8.93
1.18
6.40
2.20
0.98
4.68
I.13
0.06
4.40
1.85
0.39
2.68
1.92
2.61
3.34
df
Sk(P)
28
68
44
64
55
70
54
67
53
56
58
52
68
45
54
38
29
20
70
40
54
69
0.000
0.000
0.072
0.002
0.000
0.527
0.109
0.000
0.122
0.000
0.984
0.165
0.000
0.133
0.478
0.000
0.038
0.352
0.005
0.03 I
0.005
0.001
0.797
“sig(adjR*) = significance level associated with the adjR’; r(B) = (1 - /I)/(standard
df = degrees of freedom associated with the t-test of /3; sig(b) = one-tailed significance
the null hypothesis that fl is I versus the alternative that /? is less than 1.
error of p);
level under
If cost is proportional
to activity, but the cost per unit differs for smaller
hospitals, the coefficient /I in the regression should be 1 and the coefficient
q should be nonzero. As before, however, if there are increasing returns to scale,
the coefficient b should be less than 1.
The results of the regressions using eq. (4) are reported in tables 6a and 6b.
The overall effect of including the size dummy in the regressions is to increase
the explanatory
power of the regressions and to reduce the estimates of /I. The
average value of fl declines from 0.797 to 0.685 for the 1987 actual data and from
0.825 to 0.726 for the 1990 budget data. The null hypothesis of proportionality
(i.e., /I = 1) is rejected at the 0.05 level for 14 of the 22 accounts for both the 1987
and 1990 data.
The pattern of the q coefficients is interesting. While not all of the q coefficients are significantly
different from zero (two-tailed
test), nearly all of the
coefficients are positive and all of them that are significant are positive. This
indicates smaller hospitals have lower marginal costs than larger hospitals (for
E. Noreen and N. Soderstrom,
Overhead costs and activity
269
Table Sb
Summary
of the
regressions of log expense on log units of service: 1990 budget data,
Account
adjRZ
8310: Printing and Duplicating
8320: Dietary
8330: Cafeteria
8350: Laundry and Linen
8360: Social Services
8430: Plant
8460: Housekeeping
85 10: Accounting
8520: Communications
8530: Patient Accounts
8540: Data Processing
8560: Admitting
8610: Hospital Administration
8630: Public Relations
8650: Personnel
8660: Auxiliary Groups
8670: Chaplaincy
Services
8680: Medical Library
8690: Medical Records
87001 Medical Staff
8710: Health Care Review
8720: Nursing Administration
18.6%
81.3%
76.0%
94.8%
57.4%
91.9%
85.0%
54.2%
84.0%
78.0%
83.8%
73.6%
75.8%
57.8%
84.1%
46.5%
43.2%
44.8%
84.4%
42.1%
61.0%
73.0%
Average
slope
sig(adjR’)
0.014
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
t(B)
P
0.324
0.904
0.83 1
0.930
0.657
1.039
0.928
0.47 1
0.816
0.674
1.084
0.842
0.774
0.807
1.112
1.113
0.659
0.959
0.846
0.763
0.812
0.801
-
~
5.49
1.81
2.25
2.50
4.14
1.06
1.28
9.78
3.56
6.70
1.30
2.30
4.18
1.84
1.71
0.55
2.36
0.19
3.39
1.60
2.20
3.07
df
Sk(P)
25
66
37
59
45
68
46
62
46
53
54
53
64
41
53
32
25
24
63
33
56
55
0.000
0.038
0.016
0.008
0.000
0.853
0.104
0.000
0.001
0.000
0.901
0.013
0.000
0.037
0.954
0.708
0.013
0.424
0.00 1
0.060
0.016
0.002
0.825
“sig(adjR’) = significance
level associated
with adjR*; t(p) = (1 - /I)/(standard
error of /I);
df = degrees of freedom associated with the r-test of /?; sip(p) = one-tailed significance level under
the null hypothesis that /I is 1 versus the alternative that /I is less than 1.
the same level of activity in an overhead function) and is consistent with lower
wage rates or less intensive or extensive overhead services being offered in the
smaller hospitals.
How far off are estimates of marginal costs if average costs are used? If the real
cost relation is C = pq8, then average cost overstates marginal cost by the
percentage (1 - p)/p.‘” The average estimated slope coefficient is on the order of
0.7 in tables 6a and 6b. When the slope coefficient is 0.7, average cost overstates
the change in costs resulting from a small change in activity by about 43%. Such
errors are large enough to warrant investment
in estimating more refined cost
functions than the strict proportionality
mode1 that is implicit in typical cost
accounting
systems ~ even activity-based
costing systems.
” From footnote 8, if the real cost relationship is C = pqp, then fl = MC/AC. Therefore, if average
cost is used to estimate marginal cost, the percentage error is (AC - MC)/MC or (1 - 1)/b.
270
E. Noreen and N. Soderstrom. Overhead COSISand activit)
Table 6a
Summary
of the regressions
of log expense on log units of service with a dummy
of the hospital; 1987 actual data,
In(Cj) = a + p In(qj) + tf size dummyj.a
Account
adjR’
a
8310
8320
8330
8350
8360
8430
8460
8510
8520
8530
8540
8560
8610
8630
8650
8660
8670
8680
8690
8700
8710
8720
32.8%
83.4%
76.9%
93.3%
64.4%
92.6%
89.7%
49.1%
79.2%
81.7%
85.4%
64.5%
71.6%
46.7%
76.6%
26.2%
40.0%
72.2%
84.7%
51.9%
61.5%
75.4%
7.773
5.778
4.041
1.343
8.798
3.376
5.788
10.616
7.185
7.273
0.713
5.110
10.498
6.303
6.946
9.003
3.670
6.970
6.112
7.698
4.390
9.045
Average
fi
P
0.372
0.670
0.758
0.845
0.342
0.881
0.703
0.348
0.852
0.578
I.177
0.862
0.545
0.609
0.870
0.189
0.718
0.709
0.752
0.751
0.812
0.729
0.685
9
0.482
0.363
0.443
0.182
0.984
0.340
0.778
0.397
0.172
0.306
0.101
0.099
0.465
0.608
0.284
0.539
0.010
0.823
0.361
0.099
0.069
0.229
r(B)
s&(P)
4.79
4.92
2.73
3.45
8.37
2.40
5.06
9.41
1.49
6.07
- 1.85
0.96
5.25
2.08
1.16
5.4 I
1.40
I.31
3.87
1.61
1.34
3.12
0.000
0.000
0.005
0.001
0.000
0.010
0.000
0.000
0.072
0.000
0.965
0.171
0.000
0.022
0.126
OX@0
0.087
0.102
0.000
0.059
0.093
0.002
variable
for the size
Sk(v)
1.36
2.69
2.53
1.78
4.10
3.34
5.83
2.80
0.96
2.36
- 0.59
0.44
2.74
1.80
1.50
2.62
0.03
1.32
2.69
0.30
- 0.27
1.27
0.186
0.009
0.016
0.080
0.000
0.001
0.000
0.007
0.342
0.022
0.558
0.662
0.008
0.079
0.144
0.013
0.976
0.200
0.009
0.766
0.788
0.210
_
“size dummyj = 1 if hospital
j’s available
beds is above
the median,
otherwise
0,
t(b) = (I - fi)/standard
error of 8; sig(/?) = one-tailed
probability
level associated
with t(b);
t(n) = n/standard
error of 9; sig(n) = two-tailed probability
level associated with t(n).
5. Limitations
Several potential limitations
should be noted. First, the costs reported by
hospitals do not include opportunity
costs. Service departments
operating at or
near capacity impose indirect costs on users in terms of waiting and service
degradation
and it is possible that total costs (including
these indirect and
opportunity
costs) are strictly proportional
to volume even though reported
overhead expense is not.”
Second, not all hospitals report overhead costs for all overhead accounts.
A hospital could omit reporting data for an overhead account either because the
I’ Balachandran
and Srinidhi (1987) analyze charge-out
rates for a service. department
with
stochastic demand. If users always have the opportunity
to go outside the firm for service and if
aggregate demand is unrelated to the charge-out
rate, a fixed charge-out
rate can effectively ration
the capacity of the service department
in a way that internalizes the externalities caused by users
crowding the system. However, the fixed charge-out
rate is not equal to the average fixed cost.
E. Noreen and N. Soderstrom. Overhead costs and activity
271
Table 6b
Summary
of the regressions
of log expense on log units of service with a dummy
of the hospital; 1990 budget data,
In(Cj) = a + /I In(qj) + q size dummyj.’
Account
adjR’
r
1,
8310
8320
8330
8350
8360
8430
8460
8510
8520
8530
8540
8560
8610
8630
8650
8660
8670
8680
8690
8700
8710
8720
25.5%
82.3%
77.9%
94.8%
65.1%
91.8%
90.2%
55.3%
83.7%
80.4%
83.5%
73.2%
76.5%
57.1%
84.0%
47.1%
42.5%
50.0%
86.5%
43.8%
60.7%
73.0%
10.244
4.700
4.181
0.501
7.876
1.745
6.176
10.504
7.552
7.799
1.822
5.794
9.938
3.422
5.998
0.956
3.017
7.871
7.056
9.238
5.575
9.260
0.142
0.774
0.756
0.91 I
0.462
I.018
0.69 I
0.385
0.798
0.534
1.069
0.808
0.655
0.878
1.043
0.973
0.801
0.586
0.683
0.558
0.726
0.715
Average
/?
‘I
0.817
0.307
0.352
0.054
0.847
0.053
0.745
0.255
0.040
0.375
0.038
0.080
0.306
- 0.163
0.171
0.375
- 0.373
1.216
0.429
0.568
0.227
0.241
r(B)
5.92
2.87
3.01
2.00
5.63
- 0.28
4.69
8.08
2.51
6.76
- 0.73
1.75
3.92
0.72
- 0.39
0.11
0.87
1.47
4.90
2.02
1.94
2.73
sig(B)
0.000
0.003
0.002
0.025
0.000
0.610
0.000
0.000
0.008
O.OQO
0.766
0.161
0.000
0.238
0.65 I
0.457
0.197
0.078
0.000
0.026
0.029
0.004
variable
for the size
f(V)
sig(rl)
1.81
2.18
2.04
0.55
3.29
0.42
5.01
1.58
0.28
2.73
0.20
0.40
1.70
- 0.55
0.82
1.16
- 0.82
1.87
3.32
1.26
0.76
1.05
0.083
0.033
0.049
0.584
0.002
0.676
0.000
0.119
0.781
0.009
0.842
0.69 1
0.094
0.585
0.416
0.255
0.420
0.074
0.001
0.217
0.450
0.298
0.726
“size dummyj = I if hospital
j’s available
beds is above
the median,
otherwise
0:
probability
level associated
with t(p);
r(b) = (I - a)/standard
error of /I; sig(/I) = one-tailed
t(q) = q/standard
error of q; sig(q) = two-tailed probability
level associated with t(q).
hospital does not carry out the activities associated with that account or because
the hospital chooses to include the costs of carrying out those activities in some
other account. This is most likely to occur with the smallest hospitals where
certain overhead functions may be too small to be reported separately. Consequently, in the accounts where these costs are reported, the total costs of the
smaller hospitals may be artificially high. This would produce the appearance of
declining average costs in those accounts. This problem is likely to be most
severe for account 8610, Hospital Administration,
which is used as a catch-all
for otherwise unassigned overhead. The other overhead accounts are generally
narrowly defined enough so that it would be unlikely that they would be used as
a place to report otherwise unassigned overhead costs.
Third, it can be argued that even though cost does not appear to be proportional to activity, that is just a symptom that the measures of activity are
misspecified. This can be viewed as an errors-in-variable
problem. To the extent
that there are errors-in-variables,
the slope coefficient will be biased downward
212
E. Noreen and N. Soderstrom,
Overhead costs and activity
in the regression. There are basically two potential sources of errors of this sort.
We - or rather the WSDOH - may have selected the wrong measures of activity.
Or, the measure of activity may be correct but is measured with error.
For example, an argument
can be made that the appropriate
measure of
activity for overhead accounts should be the activity that could be supported at
capacity rather than the actual or budgeted activity. Some overhead costs are
likely to be more closely related to the amount of capacity provided than to the
amount of capacity used in a particular period. We used actual and budgeted
activity rather than activity at capacity in our tests for two reasons. First,
measures of activity at capacity were not available to us for these overhead
accounts. Second, organizations
typically assign overhead costs to products and
customers using actual and budgeted activity levels, not activity at capacity.
While Cooper and Kaplan (1991, pp. 165-171) and others have argued that
costs of providing
capacity should be allocated on the basis of volume at
capacity, this proposal appears to have been only rarely implemented
to date.
Nevertheless, it may be true that overhead costs are strictly proportional
to the
capacity provided in each overhead function. Joseph and Folland (1972) and
others have argued on the basis of queuing theory that the percentage
of
capacity utilization
in a hospital should increase with the size of the hospital.
Basically, larger hospitals should have more stable demand by virtue of the
larger population
from which patients are drawn. Therefore, larger hospitals
require porportionally
less surge capacity to deal with random fluctuations
in
demands than smaller hospitals. This prediction is borne out by the plot of the
percentage of unused capacity versus available beds displayed in fig. 4. The very
80% -
%
2
-0
s
60% -
40% 0
3
Q
200
4bo
available
6bO
8bO
beds
Fig. 4. Plot of the % of unused capacity [ = (available beds x 365 budgeted patient days)/available beds x 3651 versus available beds: 1990 budget data.
E. Noreen and N. Soderstrom, Overhead costs and activity
273
smallest hospitals (i.e., those with the smallest number of available beds) have
very large unused capacity, both absolutely and relative to the larger hospitals.
To see the problem this creates, assume that some of the costs of hospitals are
directly proportional
to available beds; for purposes of illustration,
assume it
costs $1000 per available bed to provide a particular service. If the log of this
hypothetical
cost [i.e., ln(lOOO x actual available beds)] were regressed on the
log of actual patient days from our sample, the slope coefficient would be 0.66.
While, by assumption,
the cost is proportional
to capacity provided, it is not
proportional
to the amount of the capacity used. This pattern could exist in the
overhead functions we have studied. That is, the smallest hospitals may have
proportionally
much higher unused capacity in the overhead functions than
larger hospitals. Thus, even though costs may be strictly proportional
to the
amount of capacity provided in an overhead function, a regression of the log of
cost on the log of actual or budgeted activity would yield a slope coefficient less
than 1.
Nevertheless, if we have an errors-in-variables
problem, so does anyone who
attempts to use actual or budgeted activity levels from the WSDOH chart of
accounts to make decisions. Take, for example, a hospital administrator
who
estimates future admitting costs by multiplying
estimated future admissions by
the average actual or budgeted cost of admitting per admission. Such a hospital
administrator
will tend to overestimate
total admitting costs when there is an
increase in patients admitted and to underestimate
total admitting costs when
there is a decrease in patients admitted.
The final limitation
is the practical question of how one would go about
estimating a nonproportional
long-run cost function for a particular firm in the
absence of cross-sectional
data. Perhaps account analysis or time-series analysis
can be used for this purpose. At any rate, the research agenda for management
accounting academics should include further testing of the strict proportionality
assumption
(and other assumptions
inherent in cost accounting
systems) and
developing and testing of feasible alternatives to averages as a means of estimating costs.
6. Conclusion
We use cross-sectional
data to estimate the relation between spending and
activity for a variety of overhead accounts in hospitals. The hypothesis that
overhead costs are strictly proportional
to activity, which is implicit in cost
accounting
systems, is rejected for most overhead accounts. And average costs
overstate incremental
costs by substantial
margins for many of the overhead
accounts.
It is obviously costly to build more sophisticated cost systems that recognize
nonproportional
costs structures. Nevertheless, if the hypothesis that costs are
214
E. Noreen and N. Soderstrom,
Overhead costs and activity
strictly proportional
to activity is rejected, some modification
to standard cost
accounting
methods may be advisable if costs are to be decision-relevant.
Basically, pro-rating
of costs would have to be abandoned
since it is this
mathematical
tool that requires strict proportionality.
Instead, the relations
between costs and activities could be represented by cost schedules and the cost
of a particular activity would be the incremental cost of that activity taken from
the cost schedule. This change in cost accounting procedures is not as innocuous
as it may appear at first glance. Incremental
costs are different from average
costs if costs are not strictly proportional.
Moreover, incremental cost depends
upon the level of activity if the cost function is nonlinear.
Appendix
Description of accounts, abstracted from
Health accounting and reporting manual
the Washington
The direct expenses under each account include salaries
benefits, professional
fees, supplies, purchased
services,
lease, and other direct expenses. Transfer payments from
considered as an offset to direct expenses by the WSDOH,
in this study. We are interested in the costs of providing
whether or not those costs happen to be offset by transfer
parts of the organization.
Account 8310 Printing and Duplicating
This cost center shall contain the direct expenses incurred
printing and duplicating
center.
Standard Unit of Measure: Number of reams of paper
State Department
of
and wages, employee
depreciation/rental/
other accounts, while
have been excluded
an overhead service
payments from other
in the operation
Account 8320 Dietary
This cost center contains the direct expenses incurred in preparing
ing food to patients. Also included is dietary’s share of common
cafeteria.
Standard Unit of Measure: Number of patient meals served
of the
and delivercosts of the
Account 8330 Cafeteria
This cost center contains the directly identifiable expenses incurred in preparing
and delivering food to employees and other nonpatients.
Also included is the
cafeteria’s share of common costs of dietary.
Standard Unit of Measure: Equivalent
number of cafeteria meals served
E. Noreen and N. Soderstrom.
275
Overhead COSISand aciivit]
Account 8350 Laundry and Linen
This cost center shall contain the direct expenses incurred in providing laundry
and linen services for hospital use, including student and employee quarters.
Costs of disposable linen should be recorded in this cost center.
Standard Unit of Measure: Number of dry and clean pounds processed
Account 8360 Social Services
This cost center contains the direct expense incurred in providing
to patients.
Standard Unit of Measure:
Number of personal contacts
social services
Account 8430 Plant
This cost center contains the direct expense incurred in the operation
hospital plant and equipment.
Standard Unit qf Measure: Number of gross square feet
of the
Account 8460 Housekeeping
This cost center contains the direct expenses incurred by the units responsible
for maintaining
general cleanliness and sanitation throughout
the hospital and
other areas serviced (such as Student and employee quarters).
Standard Unit of Measure: Hours of service
Account 8510 Accounting
This cost center shall include the direct expenses incurred in providing
general accounting
requirements
of the hospital.
Standard Unit of Measure: Average number of hospital employees
the
Account 8520 Communications
This cost center shall include the direct expenses incurred in carrying on
communications
(both in and out of the hospital), including
the telephone
switchboard
and related telephone services, messenger activities, internal information systems, and mail services.
Standard Unit of Measure: Average number of hospital employees
Account 8530 Patient Accounts
This cost center shall include the direct expenses incurred in patient-related
billing activities and in extending credit and collecting accounts.
Standard Unit of Measure: Gross patient revenue
Account 8540 Data Processing
This cost center shall contain the costs incurred in operating an electronic data
processing center. Expenses incurred in the operation of terminals of the EDP
216
E. Noreen and N. Sodersrrom,
Overhead COSISand activily
center throughout
the hospital shall be included in the data processing cost
center. However, outside service bureau costs directly chargeable to a specific
nursing or ancillary cost center should be included in that specific cost center.
Outside service bureau costs benefiting more than one cost center shall be
included in the data processing cost center.
Standard Unit of Measure: Gross patient revenue
Account 8560 Admitting
This cost center shall include the direct expenses
general inpatient admitting offices.
Standard Unit of Measure: Number of admissions
incurred
in operating
all
Account 8610 Hospital Administration
This cost center contains the direct expenses associated with the overall management and administration
of the institution, including the office of administrative
director, governing board, and planning activities. Also expenses which are not
assignable to a particular cost center should be included here. However, care
should be taken to ascertain that all costs included in this cost center do not
properly belong in a different cost center.
Standard Unit of Measure: Number of FTE employees
Account 8630 Public Relations
This cost center contains
the direct expenses incurred
in the public relations/community
relations function and expenses associated with fund-raising.
Standard Unit of Measure: Total revenue
Account 8650 Personnel
This cost center shall be used to record the direct expenses
out the personnel function of the hospital.
Standard Unit of Measure: Number of FTE employees
Account 8660 Auxiliary Groups
This cost center contains
the direct expenses
hospital auxiliary or volunteer groups.
Standard Unit of Measure: Number of volunteer
incurred
incurred
in carrying
in connection
with
hours
Account 8670 Chaplaincy Services
This cost center contains the direct expenses incurred in providing chaplaincy
services and in maintaining
a chapel for patients and visitors. It does not include
those services as defined in ‘Social Services’.
Standard Unit of Measure: Number of hospital patient days
E. Noreen
and N. Sodersrrom,
Overhead
Account 8680 Medical Library
This cost center contains the direct expenses incurred in maintaining
library.
Standard Unit of Measure: Number of physicians
on active staff
Account 8690 Medical Records
This cost center contains
the direct expenses
medical records function.
Standard Unit of Measure: Number of inpatient
total emergency room and clinic visits
277
costs and actkit.)
incurred
admissions
a medical
in maintaining
plus one-eighth
the
of
Account 8700 Medical Staff
This cost center contains the direct expenses associated with the medical staff,
such as the salary of the chief of the medical staff, as well as the salary and other
costs associated with a house medical staff which serves in the daily hospital
services departments.
Interns’, externs’, and residents’ salaries (or stipends)
should not be included here, but rather in the applicable education cost center.
Compensation
paid to chiefs of services as well as other physicians working in
ancillary departments
should not be included here, but rather in the applicable
ancillary cost center.
Standard Unit of Measure: Number of physicians on active staff
Account 8710 Health Care Review
This cost center shall contain the costs incurred in providing peer review, quality
assurance, utilization
review, professional
standards review, and medical care
evaluation
functions.
Standard Unit of Measure: Number of inpatient admissions
Account 8720 Nursing Admiuistmtion
This cost center shall contain the direct expenses associated
with nursing
administration.
The salaries, wages, and fringe benefits paid nursing float
personnel shall be recorded in the cost center in which they work. Scheduling
and other administrative
functions relative to nursing float personnel are considered costs of nursing administration.
Standard Unit of Measure: Average number of nursing service personnel
References
Balachandran,
Kashi R. and Bin N. Srinidhi, 1987, A rationale for fixed charge application, Journal
of Accounting,
Auditing & Finance 2(N.S.), 151-183.
Blanchard, Garth, Chee Chow, and Eric Noreen, 1986, Information
asymmetry, incentive schemes,
and information
biasing: The case of budgeting under rate regulation, The Accounting Review,
Jan., l-15.
278
E. Noreen and N. Soderstrom. Overhead cosls and activirq
Cooper, Robin and Robert S. Kaplan, 1992, The design of cost management
systems: Text, cases,
and readings (Prentice Hall, Englewood Cliffs, NJ).
Cowing, T.G., A.G. Holtman, and S. Powers, 1983, Hospital cost analysis: A survey and evaluation
of recent studies, in: R. Schemer, cd.: Advances in health economics and health services research,
Vol. 4 (JAI Press, Greenwich, CT) 257-303.
Feldstein, Martin S., 1974, Econometric
studies of health economics, in: M. lntriligator
and D.
Kendrick,
eds., Frontiers
of quantitative
economics,
Vol. II (North-Holland,
Amsterdam)
337434.
Foster, George and Mahendra Gupta, 1990, Manufacturing
overhead cost driver analysis, Journal
of Accounting and Economics 12, 3033337.
Greenwood, Thomas G. and James M. Reeve, 1992, Activity-based
cost management
for continuous
improvement:
A process design framework, Journal of Cost Management
for the Manufacturing
Industry 5, no. 4, 22-40.
Heathfield,
David F. and Soren Wibe, 1987, An introduction
to cost and production
functions
(Macmillan Education
Ltd, London).
Hefty, Thomas R., 1969, Returns to scale in hospitals: A critical review of recent research, Health
Services Research 4, 2677280.
Johnston, J., 1960, Statistical cost analysis (McGraw-Hill,
New York, NY).
Joseph, Hyman and Sherman Folland, 1972 Uncertainty
and hospital costs, Southern Economic
Journal, Oct., 267-273.
Kaplan, Robert S., 1987, Regaining relevance, in: Robert Capettini and Donald K. Clancy, eds., Cost
accounting, robotics, and the new manufacturing
technology (Management
Accounting Section,
American Accounting
Association, Sarasota, FL) 27731.
Mann, Judith K. and Donald E. Yett, 1968, The analysis of hospital costs: A review article, Journal
of Business 41, 191-202.
Marais, M. Laurentius,
1990, Process-oriented
activity-based
costing, Working paper (Graduate
School of Business, University of Chicago, Chicago, IL).
Noreen, Eric, 1991, Conditions
under which activity-based
cost systems provide relevant costs,
Journal of Management
Accounting
Research, Fall, 1599168.
Shank, John K. and Vijay Govindarajan,
1989, Strategic cost analysis: The evolution from managerial to strategic accounting (Irwin, Homewood,
IL).
Turney, Peter B.B., 1992, What an activity-based
cost model looks like, Journal of Cost Management for the Manufacturing
Industry 5, 5460.