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Analysis of the Patient Protection and Affordable Care Act
(H.R. 3590)
O
n December 24, 2009, the Patient Protec-
tion and Affordable Care Act (H.R. 3590)
was passed in the U.S. Senate by a vote
of 60–39. R AND undertook an analysis
of the provisions of this bill related to expand-
ing coverage for people who currently do not
have health insurance. Using the COMPARE
microsimulation model, R AND estimated the
potential effects between 2010 and 2019 of H.R.
3590 on changes in the number of uninsured, the
costs to the federal government and the nation,
revenues from penalty payments, and consumers’
health care spending. Although the bill would
be enacted in 2010, many of the coverage-related
provisions would not take effect until 2014. We
also estimated the effects associated with dif-

ferent scenarios for expanding coverage. These
scenarios provide insights into how the various
provisions of the bill (i.e., the individual man-
date, employer fees for not offering insurance,
and Medicaid expansion) contribute to the
estimated changes in coverage and spending.
Moreover, these analyses illustrate how sensitive
the estimates are to key design choices (e.g., the
size of the penalty associated with an individual
mandate) or modeling assumptions (e.g., admin-
istrative costs for insurance offered through the
Health Benefit Exchanges).
Summary of Major Findings
Coverage
• The bill would reduce the number of unin-
sured to 25 million by 2019, a 53% decrease
compared with status quo projections.
• Those who are uninsured after implementa-
tion of the legislation would be younger,
healthier, and wealthier than those unin-
sured in the status quo.
• Among those who would be uninsured under
the policy change, 38% would be eligible for,
but not enrolled in, Medicaid.
• By 2019, 28 million people would purchase
insurance through an Exchange; 14% of
these enrollees are projected to be in fair or
poor health, compared with 6% in the exist-
ing nongroup market.
Spending

• Between 2014 and 2019, cumulative per-
sonal health care spending would increase
2% compared with status quo projections,
primarily as a result of increased utilization
among the newly insured.
• Government spending would increase by
$899 billion compared with the status quo;
$499 billion is attributed to increased spend-
ing in Medicaid and $400 billion to subsi-
dies for premiums and cost-sharing.
• New revenues from penalties paid by individ-
uals who do not obtain health insurance, and
employers who do not offer health insurance
and whose employees obtain subsidies to
purchase insurance, would total $87 billion.
• In 2019, premiums paid by individuals
obtaining coverage through the employer-
sponsored market are projected to be 2%
lower than in the status quo, primarily
because of changes in the composition of the
population purchasing insurance.
• Unsubsidized premiums in the most com-
mon Exchange plans1 are projected to be
3.7% lower than status quo average non-
group premiums because of a combination
of changes in the benefit packages being
purchased, lower administrative costs, and
changes in the composition of the population
purchasing insurance in this market.
Consumer Financial Risk

• Those who become newly insured would
C O M PA R EPolicy Brief
1 Most people would select the Bronze or Silver plans.
http://www.rand.org
http://www.randcompare.org/
spend more on health care than they did previously but
would face a lower risk of very high expenditures and
would use more services. Overall, this results in a net
benefit to the newly insured population from the policy
change. The greatest benefit accrues to those obtain-
ing coverage through an Exchange and enrolling in the
Silver plan or enrolling in Medicaid.
Effects of Alternate Design Choices and Assumptions
on Results
• The individual mandate has the largest independent
effect on increasing coverage; if enacted alone, it would
reduce the number of uninsured in 2019 to 31 million.
• In the absence of a penalty for noncompliance with the
individual mandate, 10 million more people would be
uninsured.
• In the absence of subsidies to help people purchase
insurance, 13 million more people would be uninsured,
and 14 million fewer people would purchase insurance
through an Exchange.
• If the penalty provisions for employers who do not offer
insurance were removed from the legislation, 700,000

fewer people would be insured, but cumulative new gov-
ernment spending would increase by $98 billion because
more people would obtain subsidized policies through an
Exchange.
• Changing the income eligibility threshold for Medicaid
has a greater effect on the number of persons newly enroll-
ing in Medicaid than it does on the number of uninsured.
• Changing the allowed premium differential from a
ratio of 3:1 to 2:1 increases premiums and results in 1.8
million more people being uninsured. We find that 3
million fewer people would obtain insurance through an
Exchange; the proportion of people aged 50–64 obtain-
ing insurance through an Exchange would increase by
4.4 percentage points.
Provisions of H.R. 3590 Included in This Analysis
H.R. 3590 includes provisions to address a wide range of
issues related to improving the functioning of the health care
system. These include, but are not limited to, developing a
national prevention and health promotion strategy, estab-
lishing a value-based purchasing program for hospitals and
reducing payments to physicians who do not report measures
under the physician quality reporting initiative, establishing a
patient-centered outcomes research institute, improving rural
access protections, and authorizing and supporting nonprofit
member-run insurance cooperatives. We focused this analysis
on the provisions of H.R. 3590 that are designed to increase
the number of people who are insured. Specifically, these
provisions of the bill do the following:
• Prohibit health plans from rescinding coverage from
enrollees once they have been covered.
• Require qualified health plans to sell insurance to anyone

who wishes to purchase it, including those with preexist-
ing medical conditions, generally referred to as “guaran-
teed issue.”
• Require that premiums vary only on the basis of fam-
ily size, geography, age, and tobacco use. Moreover, the
difference in premiums by age for adults may not exceed
a ratio of 3 to 1—that is, the highest price charged for a
policy can be no more than 3 times higher than the low-
est price charged for the same policy, typically referred to
as “rate banding.”
• Require that everyone have insurance through either pri-
vate sources or public programs, known as an individual
mandate.
• Expand the insurance options available to potential new
purchasers by creating Health Benefit Exchanges at the
state level. The Exchanges would offer four plans with
different levels of cost-sharing (Bronze, Silver, Gold, and
Platinum) for a standard benefit package.2
• Make subsidies available to persons in lower-income
households (defined as 100% to 400% of the federal
poverty level [FPL]) to offset the costs of purchasing
health insurance.
• Allow for a penalty to be charged to anyone who does
not comply with the mandate; the penalty is equal to the
greater of a flat fee ($95 in 2014, $495 in 2015, $750 in
2016, and indexed thereafter) or 2% of income up to a
cap of the national average Bronze plan premium.
• Impose a penalty on employers with more than 50
full-time employees who do not offer coverage and have
at least one full-time employee receiving the premium

assistance tax credit. The penalty payment starts at $750
per full-time employee and is indexed to the growth rate
in premiums.
• Establish an excise tax on high-cost employer-sponsored
coverage, often referred to as “Cadillac plans.” The core
provision specifies a tax of 40% on the “excess pre-
mium,” defined as the amount by which single and fam-
ily policies exceed the thresholds of $8,500 and $23,000,
respectively.
• Expand Medicaid eligibility to include everyone in
households with income below 133% of FPL.
– 2 –
2 H.R. 3590 also includes a catastrophic plan for those under 30
who can
claim that they do not have access to affordable coverage
(hardship exemp-
tion). We do not model this plan because the population is
extremely small
(less than 500,000), and we do not have adequate sample size to
create a
separate risk pool.
What We Found
We report our findings in three major sections: the effect of
H.R. 3590 as passed on coverage, spending, and consumer
financial risk; the relative contribution of different options
for expanding coverage on the overall results; and the sensi-
tivity of the estimated effects to different design options and
model assumptions.

The Estimated Effects of H.R. 3590 on Coverage
The Number of Uninsured Decreases by 53%
Under H.R. 3590, the number of uninsured in 2019 would
be 25 million. This represents a reduction of 28 million, or
53%, relative to the number of uninsured expected if no
law is passed (53 million). (See Table 1 and Figure 1.) The
increase in coverage comes through several different sources.
Compared with the status quo, the number of people
enrolled in employer-sponsored insurance (ESI) would
increase by 6 million, the number of insured in the nongroup
market (including the Exchanges) would increase by 10 mil-
lion, and the number covered by Medicaid would increase by
12 million (see Figure 2).
Four Million Children Will Gain Coverage
Under H.R. 3590, an additional 4 million children would
have insurance coverage compared with the status quo. The
increase in coverage for children comes entirely through
increases in the number enrolled in ESI (2.2 million) and
Medicaid (2.2 million). These increases are offset in part by
a reduction of 400,000 in the number of children covered in
the nongroup market.
Those Uninsured in 2019 Are Younger, Healthier, and
Wealthier
The composition of the uninsured population under H.R.
3590 differs in important ways from what we estimate would
occur in the absence of the legislation. For example, under
H.R. 3590, on average, those who are not insured would be
younger, be in better health, and have higher incomes than
– 3 –
Table 1
Projected Changes in Insurance Coverage (millions) Under H.R.

3590 Based on the COMPARE Microsimulation Model
Insurance
Coverage
Year
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Status Quo
Medicaid/SCHIP 35 35 36 36 36 37 37 37 37 38
Employer-sponsored
insurance
153 153 154 154 154 155 155 155 156 156
Nongroup 17 17 17 17 17 17 17 17 17 17
Other 15 15 15 15 16 16 16 16 16 16
Exchanges 0 0 0 0 0 0 0 0 0 0
Uninsured 49 50 50 51 51 52 52 52 53 53
Under H.R. 3590
Medicaid/SCHIP 35 35 36 36 41 45 49 49 50 50
Employer-sponsored
insurance
153 153 154 154 154 157 161 161 161 162
Nongroup 17 17 17 17 5 1 0 0 0 0

Other 15 15 15 15 16 16 16 16 16 16
Exchanges 0 0 0 0 16 24 27 27 28 28
Uninsured 49 50 50 51 44 32 24 24 25 25
Difference
Medicaid/SCHIP 0 0 0 0 4 9 12 12 12 12
Employer-sponsored
insurance
0 0 0 0 0 3 5 5 5 6
Nongroup 0 0 0 0 –12 –16 –17 –17 –17 –17
Other 0 0 0 0 0 0 0 0 0 0
Exchanges 0 0 0 0 16 24 27 27 28 28
Uninsured 0 0 0 0 –8 –19 –28 –28 –28 –28
NOTES: Projections do not include the elderly. SCHIP = State
Children’s Health Insurance Program.
in the status quo. Under the policy change, we find that 29%
of the uninsured would be under age 17, compared with 21%
in the status quo (see Figure 3). Similarly, under H.R. 3590,
66% of the uninsured would report being in excellent health,
compared with 61% in the status quo (see Figure 4). Finally,
we estimate that 40% of the uninsured would have incomes
over 300% of FPL, compared with 30% in the status quo

(see Figure 5).
A Substantial Portion of Those Who Remain Uninsured
Are Eligible for Medicaid
We assume in our analysis that people’s understanding and
perception of the Medicaid program are unchanged by the
reform, which means that the rates at which newly and cur-
rently eligible persons enroll in Medicaid would be similar
to what we observe today. As a result, we find that of the 25
Figure 1
Senate Bill Would Reduce the Number of Uninsured by
53% Relative to Status Quo
0
10
20
30
40
50
60
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Status quo
COMPARE
Congressional Budget Office

Year
N
u
m
b
e
r
o
f
u
n
in
su
re
d
(
m
il
li
o
n
s)
Figure 2
Newly Insured Obtain Coverage Through Employers,
Medicaid, Exchanges

Status quo
H.R. 3590
0
Number of persons (millions)
ESI
Nongroup
Medicaid
Other
Uninsured
50 100 150 20025 75 125 175
Figure 3
Uninsured in 2019 Are Younger Than Projected Under
Status Quo
0 5 10 15 20 25 30 35 40
Proportion of uninsured population (%)
A
g
e
(
y
e

a
rs
)
0–17
18–34
35–49
50–64
Status quo
H.R. 3590
Figure 4
Uninsured in 2019 Are Relatively Healthier Than
Projected Under Status Quo
Status quo
H.R. 3590
0
Excellent or
very good
Good
Fair or poor
10 30 50 7020 40 60
S

e
lf
-r
e
p
o
rt
e
d
h
e
a
lt
h
s
ta
tu
s
Figure 5
Uninsured in 2019 Are “Wealthier” Than Projected Under
Status Quo
Status quo
H.R. 3590
0

<150% FPL
150–300% FPL
>300% FPL
10 20 30 455 15 25 35 40
– 4 –
million people who remain uninsured in 2019 under H.R.
3590, approximately 9 million (38%) are eligible for Medic-
aid. There may be opportunities to further increase coverage
by conducting outreach efforts to improve the take-up rate of
Medicaid among those that are newly eligible. Because these
activities will be left to the states to implement, and therefore
are likely to be variable, we do not estimate any additional
enrollment that might result from these efforts.
By 2019, 28 Million People Would Purchase Insurance
Through Health Benefit Exchanges
H.R. 3590 expands the insurance options available to poten-
tial new purchasers by establishing Health Benefit Exchanges.
The Exchanges will offer four basic plans with different
actuarial values: Bronze (60%), Silver (70%), Gold (80%),
and Platinum (90%). The actuarial value of a health plan is
defined as the percentage of the “allowed charges” of a medi-
cal bill that the health plan will pay, on average, for a stan-
dardized population. Plans with higher actuarial values have
higher premiums and lower deductibles and copayments.
We estimate that by 2019, 28 million people will pur-
chase insurance through such exchanges. We find that people

choosing to purchase through an Exchange are distributed
across all plan types but are concentrated in the Bronze and
Silver plans. In 2019, we estimate that, driven by available
subsidies, 36% would choose Bronze, 46% Silver, 5% Gold,
and 13% Platinum.
Premium and cost-sharing subsidies are available to
people in lower-income households who choose to purchase
coverage through an Exchange. Of the 28 million people
participating in an Exchange in 2019, we estimate that 15
million will be receiving a subsidy. Among those, 33% would
be enrolled in the Bronze plan, 54% in Silver, 3% in Gold,
and 10% in Platinum. As a result, the subsidized population
constitutes 50% of the Bronze plan enrollees, 65% of Silver
plan enrollees, 37% of Gold plan enrollees, and 40% of Plati-
num plan enrollees.
People Purchasing Through an Exchange Are Sicker
Than in the Status Quo
Under H.R. 3590, people who purchase insurance through
an Exchange (i.e., the nongroup market) are more likely to
report being in fair or poor health than those who would
purchase through the nongroup market in the status quo.
Specifically, under the legislation, we estimate that 14% of
the nongroup market would report being in fair or poor
health, compared with 6% in the status quo. This is not sur-
prising, given that the mandate, coupled with premium tax
credits and insurance market reforms (i.e., guaranteed issue
with removal of health condition underwriting, community
rating), will enable a broader range of people to participate in
the nongroup market. Currently, insurance coverage in the
nongroup market for people in poor health is very expensive
or, in most cases, not available.
The Estimated Effects of H.R. 3590 on Spending

U.S. Personal Health Care Spending Would Increase by 2%
National health expenditures would increase as a result of
the insurance coverage policies contained in H.R. 3590. The
increase in health expenditures arises as previously unin-
sured people take up coverage and increase their utilization
of health care services. We estimate that the additional U.S.
personal health care expenditures associated with the imple-
mentation of H.R. 3590 would be $548 billion, cumulatively
from 2010 to 2019 (see Table 2). According to the projections
of National Health Expenditure Accounts (NHEA) from the
Centers for Medicare and Medicaid Services (CMS), cumula-
tive U.S. personal health care spending will be $22.5 trillion
over the same period. Therefore, under H.R. 3590, U.S.
personal health care spending will increase by 2% over the
period 2010–2019 (see Figure 6).
Government Spending Would Increase by $899 Billion
New government spending associated with the insurance
coverage provisions of H.R. 3590 would increase by $899
billion between 2014 and 2019. This spending comes from
increased federal and state Medicaid expenditures associ-
ated with the eligibility expansion ($499 billion) and federal
payments of premium and cost-sharing subsidies to eligible
people participating in the Exchanges ($400 billion). Gener-
ally, in this analysis we have focused only on those provi-
sions that are related to increasing insurance coverage. One
coverage-related cost that is not included in our estimate is
the small business tax credits associated with the employer
mandate. The Congressional Budget Office (CBO) estimates
that the cumulative cost of the small business tax credits will
be $38 billion between 2010 and 2019 (CBO, 2009).
We did not estimate costs associated with the bill’s pro-
visions that do not directly affect coverage. These costs are
described in CBO’s analysis of the bill and stem from a range
of programs, such as reinsurance for early retirees ($5 bil-

lion), early childhood home visiting programs ($1.5 billion),
establishment of the Prevention and Public Health Fund
($12.9 billion), and the startup and administration of Health
Benefit Exchanges ($2 billion) (CBO, 2009).
Medicaid Spending. H.R. 3590 expands eligibility for
Medicaid to all people in households with income below 133%
of FPL. We project a cumulative increase of $499 billion, or
19%, in Medicaid expenditures at the state and federal level
between 2014 and 2019 resulting from this expansion (see
Figure 7). Approximately 95% of this increased spending is for
adult beneficiaries and is driven by the increase in number of
– 5 –
people covered and not by higher utilization among the newly
insured. The average spending per beneficiary remains rela-
tively constant.
Premium and Cost-Sharing Subsidies. H.R. 3590
offers premium and cost-sharing subsidies for low-income
people purchasing insurance through an Exchange. Cumu-
lative subsidy payments would total $400 billion for 2014
through 2019. Premium subsidies account for 96% of these
expenditures. In 2019, we estimate that 15 million people
will be living in households that are receiving some premium
subsidies. In these households, the average subsidy is $7,240,
which corresponds to about 73% of the unsubsidized pre-
mium.3 The cost-sharing subsidy does not lead to any signifi-
Table 2
Impact of H.R. 3590 on Spending ($ billions), by Category,
Based on COMPARE Microsimulation Modeling

Figure 6
Senate Bill Would Increase Cumulative National Health
Spending by 2% over the Status Quo
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Status quo
H.R. 3590
Year
0
1,000
500
1,500
2,000
2,500
3,000
3,500
N
a
ti
o
n
a
l

h
e
a
lt
h
s
p
e
n
d
in
g
($
b
il
li
o
n
s)
– 6 –
3 All dollar amounts are expressed in the year for which they
are reported. For
example, in Table 2 the dollar amount for 2010 is in 2010
dollars, while the
dollar amount for 2019 is in 2019 dollars. This convention is
the same found
in CBO’s analysis. Cumulative figures are computed by simply

summing
yearly dollar figures, with no correction for inflation.
Year
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total
Status Quo
Personal
health
spending*
1,623 1,722 1,834 1,962 2,104 2,264 2,440 2,631 2,841 3,066
22,488
Medicaid/
SCHIP
200 211 223 237 252 270 288 309 331 355 2,675
Subsidies 0 0 0 0 0 0 0 0 0 0 0
H.R. 3590
Change in
Spending
Personal
health
spending*
0 0 0 0 44 77 96 103 110 118 548
Medicaid/

SCHIP
0 0 0 0 48 77 94 87 94 100 499
Subsidies 0 0 0 0 40 56 68 73 78 85 400
H.R. 3590
New
Revenue
Penalty
payments
by
individuals
0 0 0 0 1 5 11 12 12 13 54
Penalty
payments
by
employers
0 0 0 0 5 5 5 6 6 6 33
*Estimates of personal health spending are based on Medical
Expenditure Panel Survey data, which only include the
noninstitutionalized
population, and thus do not include costs associated with long-
term care.
cant increase in coverage, but it does alter the distribution of
people across plans, providing a strong incentive to enroll in
the Silver plan.

Revenue from Individual and Employer Penalties Would
Total $87 Billion
Under H.R. 3590, individuals and employers are subject to
financial penalties if they do not comply with the coverage
provisions of the bill. We estimate that the cumulative pen-
alty payments from individuals who do not purchase health
insurance will total $54 billion between 2014 and 2019. In
2019, we estimate that 16.6 million people will pay a penalty,
with the average amount paid equal to $769.
Among employers who do not offer insurance, we
estimate that cumulative penalty payments would be $33 bil-
lion between 2014 and 2019. In 2019, the revenue from the
employer penalty is $6.3 billion, corresponding to a payment
of $1,070 for each of the 5.9 million workers employed by
employers with more than 50 employees that decide to pay the
penalty rather than offering insurance (4.9% of all employers).
These estimates represent an upper bound of the revenue
that may be collected from penalties; we assume that the
Internal Revenue Service (IRS) can collect all penalties that
are incurred. While it is optimistic to assume that the IRS
could collect all penalties, there is no agreed-upon proportion
that is “collectible.” Therefore, we report the upper bound.
Estimates of Revenue from the Excise Tax on
High-Cost Insurance Plans Are Highly Sensitive to
Modeling Assumptions
H.R. 3590 establishes an excise tax on high-cost insurance
plans. Specifically, the legislation levies a tax of 40% on
the “excess premium,” defined as the amount by which the
single and family premiums exceed the thresholds of $8,500
and $23,000, respectively. The thresholds are indexed to the
Consumer Price Index (CPI) plus one percentage point. The
revenue generated by this provision depends on the behav-

ioral response of employers to the tax, as well as assumptions
regarding the rate of premium growth and the CPI over time.
If we assume that employers offering high-cost plans do
not change their behavior (i.e., continue to offer the same
plans) and pay the excise tax, then the tax revenue is simply
40% of the excess premium. Alternatively, employers might
change their plan offers so that the new premiums are just
below the threshold for taxation. Economic theory suggests
that the amount saved by employers in premiums would be
passed on to workers in the form of higher wages, although
there is no consensus on how the wage increases would be
distributed across workers. In this case, the tax revenue is the
sum of the income and payroll tax paid by workers on the
portion of the excess premium that is passed on to workers
as wages. In practice, we expect to see a range of responses
between the two extremes across employers. The Joint Com-
mittee on Taxation (JCT) estimates that approximately 20%
of the revenue would be raised through direct payment of the
excise tax, while 80% of it would be paid through additional
payroll and income taxes.
The revenue generated by the tax will also depend on
assumptions regarding the premium growth rate and the CPI
over time. To illustrate this, we developed plausible ranges
for the premium growth rate and the CPI and simulated dif-
ferent combinations within those ranges (see Appendix 1
for more detail). We find that the estimated revenue is very
sensitive to the choice of premium growth rate, but less
sensitive to the choice of CPI. Holding the CPI growth rate
constant at the midpoint of the plausible range (2.6% per
year), we find that estimated revenue ranges from $63 billion
for a yearly premium growth rate of 5% to $180 billion for a
yearly net premium growth rate of 7%. Under a low pre-
mium growth rate scenario (5%), the proportion of employ-
ers offering a high-cost plan grows from 11% in 2013 to 16%

in 2019, while under a high premium growth rate scenario
(7%), the proportion of employers offering a high-cost plan
grows from 18% in 2013 to 50% in 2019.
Premiums in the Large Group Market Decline by 2%
We estimate that insurance premiums in the large group
(employer) market would decrease under H.R. 3590. In
2019, with the policy change, the average individual pre-
mium in the employer market would be $7,837, compared
with $8,011 in the status quo projection. The reduction in
premiums comes from a change in the composition of the
people purchasing insurance in the employer market. While
Figure 7
Cumulative Medicaid Spending Would Increase by
$499 BIllion, a 19% Increase over the Status Quo
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Status quo
H.R. 3590
Year
0
200
100
300
400
500

M
e
d
ic
a
id
s
p
e
n
d
in
g
(
$
b
il
li
o
n
s)
– 7 –
are subject to a different level of cost-sharing. We estimate

that average individual spending for those newly acquiring
ESI will be $416 higher under the policy change than would
otherwise be the case. Average spending among people newly
insured through an Exchange will increase by $1,818. In con-
trast, people who newly enroll in Medicaid will spend $394
less, on average. People newly enrolling in Medicaid will have
lower out-of-pocket spending because they do not pay premi-
ums and have very low cost-sharing requirements.
Those gaining insurance through ESI or an Exchange
face an important tradeoff. In return for higher average
spending, these individuals face a lower risk of having unex-
pectedly high, possibly catastrophic, health expenditures. In
addition, they also benefit from the increased consumption of
health care services. To determine whether the tradeoff gen-
erated by increasing insurance coverage makes people better
off on average, we calculate the net benefit. This calculation
compares the disutility associated with higher average out-
of-pocket expenditures with the utility gains associated with
a reduced risk of catastrophic expenditures and with having
access to more health care services.5 We calculate the net ben-
efit associated with gaining insurance separately for each type
of insurance (i.e., ESI, Exchange, Medicaid). For all groups,
the net benefit is found to be positive, though the size of the
benefit varies across groups. The largest benefit accrues to
those who gain insurance through an Exchange and choose
the Silver plan ($1,391) and those who enroll in Medicaid
($1,018). The net benefit is lower for people gaining insurance
through ESI ($669) or through the other Exchange plans
(Bronze, $636; Gold, $526; Platinum, $577).
The Relative Impact of Major Bill Provisions
We ran the microsimulation model under different scenarios to
isolate the independent effects of each of the main provisions—
the individual mandate, the employer penalty, and the
Medicaid expansion—on the results. We first estimated

the effects of scenarios in which we assumed that only one
of these provisions was enacted (e.g., individual mandate
without employer penalties or Medicaid expansion). We then
estimated the effects of scenarios that included two of the
three provisions. Comparing the results across these sce-
narios provides insights into the contributions of the different
model provisions and how they interact with each other to
generate changes in insurance coverage and spending.
The Individual Mandate Contributes Most to Reducing
the Number of Uninsured
Estimates from scenarios that include each major coverage
– 8 –
4 Most people would select the Bronze or Silver plans.
5 A more detailed explanation of how this metric was calculated
can be found
in Appendix 1.
the predicted reduction of 2% is modest, in practice, pre-
miums in the employer market could be further reduced by
increased competition created by the Exchanges. However,
we do not model this effect, specifically because there is not a
good empirical basis for such estimates.
Premiums in the Nongroup Market Also Decline
As H.R. 3590 is phased in, the nongroup market under-
goes major changes. The major insurance market changes
(i.e., guaranteed issue, community rating, and premium
rate banding) result in younger and healthier people in the
nongroup market facing higher premiums, giving them an
incentive to leave their plans, which in turn leads to a “death
spiral” of ever-increasing premiums for those plans. As
this happens, however, the Exchange plans start becoming
available. Helped by risk equalization, which limits adverse

selection, and lower administrative costs, these plans attract
not only those leaving the traditional nongroup market, but
also previously uninsured young and healthy people who are
responding to the requirements of the individual mandate.
Our simulation does not provide details about how this
transition would occur, but our results suggest that, eventu-
ally, the Exchange plans would replace the current nongroup
market.
Unsubsidized premiums in the most common Exchange
plans4 are projected to be 3.7% lower than status quo average
nongroup premiums because of a combination of changes in
the benefit packages being purchased, lower administrative
costs, and changes in the composition of the population pur-
chasing insurance in this market. This number hides impor-
tant distributional issues, since while younger and healthier
people face higher premiums, sicker and older individuals
have lower premiums. In addition, most Exchange plans are
somewhat more generous than the typical current nongroup
policy, which means that people may be paying more but
are also getting more in return. A more complete analysis of
the overall welfare of the population is presented in the next
section.
The Effects of H.R. 3590 on Consumer Financial Risk
Newly Insured Have Higher Average Expenses but Face
Lower Risk of Catastrophic Expenses
Under H.R. 3590, we estimate that average spending on
health care among the newly insured will increase. The
increase stems in large part from the fact that those newly
insured through ESI or the Exchanges would now pay health
insurance premiums; they would also use more health care
services than they did when they were uninsured, and they

provision separately (i.e., individual mandate, employer pen-
alties, and Medicaid expansion) indicate that the individual
mandate by itself would have the largest impact on coverage,
reducing the number of uninsured in 2019 to 31 million.
This represents a reduction of 21.5 million (41%) compared
with the status quo projection. The Medicaid expansion by
itself would reduce the number of uninsured by 8 million
relative to the status quo projection. The employer penalty by
itself would reduce the number of uninsured by 1.5 million
relative to the status quo.
Eliminating Employer Penalties Would Have a Small
Effect on Number of Uninsured but Would Increase
Government Spending
Combining the individual mandate and the Medicaid expan-
sion (i.e., eliminating employer penalties) would reduce the
number of uninsured to 25.4 million in 2019; about 700,000
fewer people than would be covered with all three provisions
combined. Although the number of people covered under
a combined individual mandate and Medicaid expansion is
slightly lower than the number covered under H.R. 3590, the
cost to the government is higher. Without the employer pen-
alty, cumulative new government spending is estimated to be
$997 billion, compared with $899 billion when all three pro-
visions are included. The expenditures are higher without the
employer penalty because more people gain coverage through
an Exchange and receive subsidies from the federal govern-
ment. Specifically, we estimate that 15 million people would
be receiving subsidies in 2019 under H.R. 3590. Without an
employer penalty, 22 million would receive subsidies, and
cumulative subsidy payments would total $573 billion (com-
pared with $400 billion under H.R. 3590).
Sensitivity of Results to Different Policy Design
Choices
Without a Penalty for Noncompliance, 10 Million More

People Would Be Uninsured
Under H.R. 3590, individuals who do not comply with
the mandate to purchase health insurance are subject to a
penalty. The penalty is equal to the greater of a flat fee ($95
in 2014, $495 in 2015, $750 in 2016, and indexed thereafter)
or 2% of income up to a cap of the national average Bronze
plan premium. To evaluate the effect of the penalty on
people’s decision about whether or not to purchase insurance,
we simulated a scenario with no penalty for noncompliance.
Without a penalty, we estimate that 10 million more people
would be uninsured in 2019 than with the penalty. The
reduction in coverage is spread across all sources of insur-
ance, with 3 million fewer enrolled in Medicaid, 5 million
fewer taking ESI, and 2 million fewer purchasing through an
Exchange.
Without Subsidies, 13 Million More People Would Be
Uninsured
The premium and cost-sharing subsidies included in H.R.
3590 for low-income families offset the cost of purchas-
ing health insurance and encourage more people to obtain
coverage through the nongroup market. Without the sub-
sidies in place, we estimate that 14 million fewer people
would purchase insurance through an Exchange (14 million
compared with 28 million under H.R. 3590), and 13 million
more people would be uninsured. The reduction in coverage
through the Exchanges when the subsidy is not offered is off-
set in part by a very small increase (approximately 700,000)
in people enrolling in ESI.
The subsidy affects both the number of people purchas-
ing insurance through an Exchange and the distribution of
enrollees across plan types. Under H.R. 3590, we estimate
that, among people purchasing through an Exchange, 36%
would choose Bronze, 46% Silver, 5% Gold, and 13% Plati-
num. When the subsidies are not available, enrollees are much

more likely to choose the Bronze plan (66%) and less likely to
choose the others (Silver, 22%; Gold, 4%; Platinum, 8%).
Changing Medicaid Income Eligibility Thresholds Has
Little Effect on the Number of Uninsured but Does Affect
Medicaid Enrollment
H.R. 3590 expands eligibility for Medicaid to all people in
households with income below 133% of FPL. The number
of people covered by Medicaid is not very sensitive to the
variations in the eligibility threshold. Under H.R. 3590, we
estimate that Medicaid would enroll 50 million people, and
25 million people would be uninsured in 2019. Increasing
the Medicaid eligibility threshold to 150% of FPL would
lead to an additional 2 million people enrolled in Medicaid
but would only reduce the number of uninsured by 600,000.
The increase in Medicaid coverage does not translate into
an equal decrease in uninsured because some of the people
who choose to take up Medicaid when eligibility is expanded
would have purchased insurance either through an employer
or through an Exchange. This phenomenon is often referred
to as Medicaid crowd-out.
Increasing the eligibility threshold from 133% to 150%
of FPL would result in a $17 billion cumulative increase in
government spending between 2013 and 2019. Although
Medicaid spending would increase by $71 billion, spending
on subsidies would be reduced by $54 billion because fewer
people would be purchasing insurance through an Exchange.
We also considered a scenario in which the eligibility
threshold for Medicaid was set at 100% of FPL. Compared
with H.R. 3590, 4 million fewer people would be enrolled
in Medicaid in 2019, and the number of uninsured would
increase by 1 million. This means that about 75% of the
– 9 –

Exchange market. With administrative costs at 20% in 2019,
the average premium in the Bronze plan would be $6,878,
compared with $6,689 with administrative costs of 12%.
Conclusion
Using the COMPARE microsimulation model, we estimated
the effects of the coverage-related provisions of H.R. 3590 on
changes in the number of people without insurance, personal
health care and government spending, and consumer finan-
cial risk. We found that, if enacted as passed, the legislation
would decrease the number of uninsured in 2019 from 53 mil-
lion to 25 million, would increase national health care spend-
ing by 2% between 2014 and 2019, and would result in a
net benefit to those who become newly insured. We found
that the individual mandate has the greatest independent
effect on reducing the number of people without insurance,
but that penalties for employers who do not offer insurance
result in lower costs for the federal government than would
otherwise occur. We tested the sensitivity of our findings to
some decisions about how to structure the legislation. We
found that the results were sensitive to the inclusion of both
penalties and subsidies as part of encouraging individuals to
purchase insurance. The results were relatively insensitive to
decisions about the threshold of income eligibility for Med-
icaid and the administrative costs of policies offered through
the Exchanges. ■
– 10 –
people who would not be eligible for Medicaid coverage
with a less generous threshold would obtain coverage either
through their employers or through an Exchange. Cumula-
tive government spending between 2014 and 2019 would

be $5 billion higher when the threshold is set at 100%
instead of at 133% of FPL. Although Medicaid spending
falls by $126 billion, this is more than offset by a $131 billion
increase in subsidy payments to people purchasing through
an Exchange.
Compressing the Rate Band in the Exchange Market
Increases the Number of Uninsured
Under H.R. 3590, premiums in the Exchanges may vary by
geography, family size, tobacco use, and age; the variation
between the top and bottom age groups is restricted to 3:1
(the highest price charged can only be three times higher than
the lowest price charged for the same policy). At the national
level, the effect of tobacco use on premiums is very small, so
we have not considered it. We have averaged premiums over
the United States so that the final premium schedule depends
only on age. We considered a scenario in which the age-based
rate band was compressed to 2:1. In this scenario, we find
that 1.8 million more people would be uninsured. We also
find that premiums in the Exchange market would increase.
For example, the average Silver plan premium in 2019 under
the 3:1 rate band is $7,804, compared with $8,100 with a
2:1 rate band. When the rate band is compressed, the price
paid by younger people increases and the price paid by older
people decreases. These price changes affect the number and
composition of the people who choose to purchase insurance
through an Exchange. With the compressed rate band, we
estimate that 3 million fewer people would obtain insurance
through an Exchange. Further, with the compressed rate
band, the proportion of the participants aged 50–64 increases
by 4.4 percentage points, from 32% to 36.4%.
Estimated Effects of H.R. 3590 Are Not Highly Sensitive
to Assumptions Regarding the Administrative Costs in
the Exchanges
One of the expected benefits associated with the formation

of the Health Benefit Exchanges is lower administrative
costs for companies offering policies through an Exchange.
In our estimate of the effects of H.R. 3590, we assume that
administrative costs can be reduced to 12% from 35%. We
also estimated a model assuming administrative costs of 20%
to determine if the estimated effects change if the Exchanges
are not able to achieve such large reductions in administra-
tive costs. Our estimates indicate that if administrative costs
were 20% rather than 12%, the number of insured people
in 2019 would fall by approximately 1 million. As expected,
higher administrative costs lead to higher premiums in the
– 11 –
Appendix 1:
Modeling Parameters and Assumptions
We have described in greater detail elsewhere how the
R AND COMPARE microsimulation model works (Girosi et
al., 2009). We provide a brief overview here to set a context
for our findings.
Overview of Our Modeling Approach
We constructed a dataset that represents the U.S. population
and some of the key entities involved in the private and pub-
lic market for health insurance (e.g., individuals, households,
employers, government, insurance companies). Based on a
review of the literature and our own analyses, we assigned
a set of behavioral rules to each entity in the dataset. The
rule set essentially tells an individual, employer, or insurance
company what to do if the conditions under which health
insurance are offered change. The entities in the model
respond to changes by following the rules that we assign to
them. When conditions change, entities have several oppor-
tunities to make decisions; this continues until everyone

decides that no further change is necessary to achieve their
preferences, known as a new equilibrium.
We used a utility maximization framework for our
analyses, as contrasted with a cost minimization or elastic-
ity framework used by other groups, including CBO. Under
cost minimization, the behavioral rules assigned to entities
always lead them to pick the lowest-cost option available. In
utility maximization, we create a set of rules that reflect dif-
ferences in the value of health insurance for different groups
in the population.6 For example, people who are older and
in poorer health have a higher value for health insurance in
general than people who are younger and healthier. Simi-
larly, people with higher incomes tend to have a higher
utility for health insurance. In our developmental work,
we found that the utility maximization framework better
reflects the choices that people are making currently. For
example, this method allows us to account for the 44 per-
cent of the uninsured who have an offer of coverage through
an employer or eligibility for Medicaid but who choose to
remain without insurance. For these reasons, we believe that
this method is the best basis for predicting response in the
future to changes in policy.
To see how the U.S. population might respond to a
change in health policy, we “perturb” the status quo by
introducing new alternatives or choices. The entities in the
dataset respond to the availability of these new choices and
either change their current status (for example, from being
uninsured to enrolling in a subsidized plan on an Exchange)
or stay with their current choice. We count up the changes
made and summarize the new state of the world. This tells us
both how well the policy change accomplishes its objective
and the costs associated with the new state of the world. We
examined the effects of these policy changes from 2010 (the

year the legislation is presumed to be enacted) through 2019.
Our model has undergone a rigorous process of review
by both other researchers at R AND and experts outside of
R AND. The results of this specific modeling exercise have
also been reviewed.
Summary of Design Choices and Assumptions
We used the following parameters and assumptions in
analyzing H.R. 3590.
• We modeled the existence of four Exchange plans:
Platinum (actuarial value of 90%), Gold (actuarial value
of 80%), Silver (actuarial value of 70%), and Bronze
(actuarial value of 60%).
• We assume that Exchanges operate at the state or
regional level, that administrative costs are reduced, and
that the market is robust to the introduction of very sick
people who were not able to acquire insurance prior to
the reform. We assume that administrative costs are 12%
of the premium, which is comparable to the administra-
tive cost in the group market for medium-size employers.
It is possible that the actual administrative cost would
diminish over time, as the Exchanges become more effi-
cient, but we did not model this scenario.
• Premiums in the Exchanges vary by age and family
size. The variation in premium price for the same policy
between the top and bottom age groups is restricted to
3:1 for adults. We assume that age groups are structured
as follows: 18–24, 25–29, 30–34, 35–39, 40–44, 45–49,
50–54, 55–59, and 60–64. In order to simulate the 3:1
restriction, we took the national average schedule and
“compressed” it to satisfy this constraint.

• The traditional nongroup market moves to community
rating and guaranteed issue. The move to community
rating is simulated by assuming that, after the policy
change, the slope (but not the level) of the premium-age
schedule follows the average schedule observed across the
country.7 The nongroup market has the same restrictions
6 The factors included in our utility maximization model
include current
insurance status, age, health status, income, employment status,
and employer
size (if employed).
7 The reason we rely on external data for the estimation of the
dependency of
premiums on age is that the number of agents representing
individuals in the
nongroup market is small (on the order of several thousand),
and the high
variance of medical expenditures makes the calculations of the
schedule very
imprecise.
– 12 –
as the Exchanges and so has the same 3:1 age banding.
The move to guaranteed issue is simulated by removing
the restriction that is used in the simulation of the status
quo that certain people are denied access to nongroup
insurance. The denial rate as a function of age was based
on survey results (America’s Health Insurance Plans,
2007).
• We modeled the structure of the subsidy exactly as pre-

scribed by the legislation. People living in health insur-
ance eligibility units (HIEUs, usually households) with
at least one offer of employer-sponsored coverage or who
are Medicaid-eligible were not allowed to receive subsi-
dies. In the case of families that contain some members
who are eligible for Medicaid and others who are ineli-
gible, the non–Medicaid-eligible portion of the family
receives subsidies if they qualify.
• We enacted a simple risk adjustment strategy under
which payments are made across Exchange plans so that
the ratio of the premiums is equal to the ratio of their
actuarial values. In other words, the premiums only
reflect the plans’ generosity and not their risk composi-
tion. Because of limitations in the accuracy of the models
used to determine the payments, we allow for a deviation
of 2% from the target ratio.
• The coverage provisions are scheduled to begin in 2013
in the legislation. We phase in the effects of the legisla-
tion evenly over three years so that it is fully imple-
mented by 2015. We assume that the people who enroll
initially tend to be sicker than the general uninsured
population, on average.
• We model an annual penalty for noncompliance with
the individual mandate as the greater of a flat fee ($95
in 2014, $350 in 2015, $750 in 2016, and indexed there-
after) or 2% of income up to a cap of the national aver-
age premium for a Bronze plan. Anyone with an income
below 100% of FPL is exempt from the penalty.
• We model an expansion of Medicaid eligibility to
everyone in families with total family income of less
than 133% of FPL. The legislation signals Congress’s
intent that efforts would be taken by states to make sure

that all of those who are Medicaid-eligible are actually
enrolled. In the absence of details regarding this process
and assuming variable effectiveness in responding to the
direction, we have not attempted to model this effort.
• In order to quantify the benefit from reduced financial
risk, we assign to each individual a disutility for risk that
is proportional to the expected spread of the distribution
of out-of-pocket expenditures: The higher the spread,
the higher the probability that one incurs a catastrophic
expenditure. The coefficient of proportionality is the so-
called coefficient of risk aversion, which was assumed to
be constant through the whole population.8 In calculating
the benefit of insurance, we used results from the RAND
Health Insurance Experiment to quantify how much
value, in dollars, people attribute to health care services
(Manning et al., 1987).9 Simply put, this value is about
one-third of the expected total medical expenditures.
We model the impact of the excise tax on high-cost
insurance plans independently from the larger simulation
model in order to take advantage of the most recent data.
We use the Kaiser Family Foundation/Health Research and
Educational Trust 2008 Annual Employer Health Benefits
Survey to estimate the revenue generated by this provision
of the bill. The survey allows us to identify employers offer-
ing high-cost plans and to estimate the number of single,
single-plus, and family policies affected by the provision.
The number of high-cost plans that Mercer reports in its
2008 survey is higher than the number reported in the 2008
HRET survey (Appleby, 2009). We therefore chose to use an
average of the two.
Since we found that the model results are very sensitive
to assumptions on the growth rates of premiums and CPI, we

conducted a Monte Carlo simulation to generate a distribu-
tion of results and quantify the uncertainty on the tax rev-
enue. We sampled the premium and CPI growth rates from
uniform distributions in the intervals 5%–7% and 1.6%–3.6%,
respectively, and computed the excise tax revenue for each
of these values. These intervals were chosen by selecting a
midpoint and then adding and subtracting one percentage
point to it. For the premium growth rate, the midpoint of 6%
was chosen as the average expected growth rate of medical
expenditures projected by CMS for the years 2013–2019. The
midpoint for the CPI growth rate was selected to be the aver-
age CPI growth rate for the years 1998–2008, which is 2.6%.
We find that 90% of the time the tax revenue would be
between $70 and $166 billion. The estimate of tax revenue
from the Joint Committee on Taxation, $149 billion, falls
within this range, is on the high side of our estimates, and
seems to correspond to an assumption of a fairly high pre-
mium growth rate, relative to the CPI growth rate.
8 To be precise, the disutility for risk was computed according
to the standard
expression 1/2rV, where r is the coefficient of risk aversion and
V is the vari-
ance of the out-of-pocket expenditures. The value of the
coefficient of risk
aversion was 0.000464 in 2010 dollars. It was obtained by
averaging the value
of 0.00095 (in 2001 dollars) used by Pauly and Herring (2000)
and the value
of 0.00021 (in 1995 dollars) used by Manning and Marquis
(1996).
9 We follow standard economic practice and estimate the value
of health care
services as the area under the demand curve for health

insurance. We found
that a good approximation for the value that an individual
assigns to health
care services received under a certain plan is one-third of the
expected total
medical expenditures under that plan. The factor one-third is
more conserva-
tive than the value one-half used by Pauly, Herring, and Song
(2002).
– 13 –
References
America’s Health Insurance Plans, Individual Health Insur-
ance 2006–2007: A Comprehensive Survey of Premiums,
Availability, and Benefits, December 2007. As of February 3,
2010:
http://www.ahipresearch.org/pdfs/Individual_Market_
Survey_December_2007.pdf
Appleby J, “New Survey: ‘Cadillac Tax’ Would Force
Employers To Trim Health Insurance Costs,” Kaiser Health
News, December 2009. As of February 4, 2010:
http://www.kaiserhealthnews.org/Stories/2009/December/
02/cadillac-tax-cost.aspx
Congressional Budget Office, “Estimate of the Patient
Protection and Affordable Care Act,” letter to Senator Harry
Reid, December 19, 2009.
Girosi F, Cordova A, Eibner C, et al., “Overview of the
COMPARE Microsimulation Model,” Santa Monica, Calif.,
RAND Corporation, WR–650, 2009. As of February 3, 2010:

http://www.rand.org/pubs/working_papers/WR650/
Kaiser Family Foundation/Health Research and Educational
Trust, “Kaiser Family Foundation/Health Research and
Educational Trust 2008 Annual Employer Health Benefits
Survey,” September 24, 2008. As of February 4, 2010:
http://ehbs.kff.org/2008.html
Manning W and Marquis S, “Health Insurance: The Tradeoff
Between Risk Pooling and Moral Hazard,” Journal of Health
Economics, Vol. 15, No. 5, October 1996, pp. 609–639.
Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz
A, and Marquis MS, “Health Insurance and the Demand for
Medical Care: Evidence from a Randomized Experiment,”
American Economic Review, Vol. 77, No. 3, June 1987, pp.
251–277.
Pauly MV and Herring BJ, “An Efficient Employer Strategy
for Dealing with Adverse Selection in Multiple-Plan Offer-
ings: An MSA Example,” Journal of Health Economics, Vol. 19,
No. 4, 2000, pp. 513–528.
Pauly MV, Herring BJ, and Song D, “Tax Credits, the Dis-
tribution of Subsidized Health Insurance Premiums, and the
Uninsured,” Forum for Health Economics and Policy, Vol. 5,
Article 5, 2002. As of February 3, 2010:
http://www.bepress.com/fhep/5/5
http://www.ahipresearch.org/pdfs/Individual_Market_Survey_D
ecember_2007.pdf
http://www.kaiserhealthnews.org/Stories/2009/December/02/cad
illac-tax-cost.aspx
http://www.rand.org/pubs/working_papers/WR650/
http://ehbs.kff.org/2008.html
http://www.bepress.com/fhep/5/5

rB-9514 (2010)
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Ratio Analysis
ii | P a g e
Contents
Introduction
...............................................................................................
..................................... 1
The Ratios
...............................................................................................
........................................ 2

Profitability Sustainability
Ratios.....................................................................................
...... 2
Operational Efficiency Ratios
...............................................................................................
. 5
Liquidity Ratios
...............................................................................................
........................... 7
Leverage Ratios
...............................................................................................
......................... 9
Other Ratios
...............................................................................................
............................ 10
Ratio Analysis
1 | P a g e
Introduction
A sustainable business and mission requires effective planning
and financial

management. Ratio analysis is a useful management tool that
will improve your
understanding of financial results and trends over time, and
provide key indicators of
organizational performance. Managers will use ratio analysis
to pinpoint strengths
and weaknesses from which strategies and initiatives can be
formed. Funders may use
ratio analysis to measure your results against other
organizations or make judgments
concerning management effectiveness and mission impact
For ratios to be useful and meaningful, they must be:
o Calculated using reliable, accurate financial information (does
your financial
information reflect your true cost picture?)
o Calculated consistently from period to period
o Used in comparison to internal benchmarks and goals
o Used in comparison to other companies in your industry
o Viewed both at a single point in time and as an indication of
broad trends and
issues over time
o Carefully interpreted in the proper context, considering there

are many other
important factors and indicators involved in assessing
performance.
Ratios can be divided into four major categories:
o Profitability Sustainability
o Operational Efficiency
o Liquidity
o Leverage (Funding – Debt, Equity, Grants)
The ratios presented below represent some of the standard ratios
used in business
practice and are provided as guidelines. Not all these ratios
will provide the
information you need to support your particular decisions and
strategies. You can also
develop your own ratios and indicators based on what you
consider important and
meaningful to your organization and stakeholders.
Ratio Analysis

2 | P a g e
The Ratios
Profitability Sustainability Ratios
How well is our business performing over a specific period, will
your social enterprise
have the financial resources to continue serving its constituents
tomorrow as well as
today?
Ratio What does it tell you?
Sales Growth =
Current Period – Previous Period Sales
Previous Period Sales
Percentage increase (decrease) in sales
between two time periods.
If overall costs and inflation are increasing, then
you should see a corresponding increase in
sales. If not, then may need to adjust pricing
policy to keep up with costs.

Reliance on Revenue Source =
Revenue Source
Total Revenue
Measures the composition of an organization’s
revenue sources (examples are sales,
contributions, grants).
The nature and risk of each revenue source
should be analyzed. Is it recurring, is your
market share growing, is there a long term
relationship or contract, is there a risk that
certain grants or contracts will not be renewed,
is there adequate diversity of revenue sources?
Organizations can use this indicator to
determine long and short-term trends in line with
strategic funding goals (for example, move
towards self-sufficiency and decreasing reliance
on external funding).
Ratio Analysis
3 | P a g e

Profitability Sustainability Ratios continued
Operating Self-Sufficiency =
Business Revenue
Total Expenses
Measures the degree to which the
organization’s expenses are covered by its
core business and is able to function
independent of grant support.
For the purpose of this calculation, business
revenue should exclude any non-operating
revenues or contributions. Total expenses
should include all expenses (operating and
non-operating) including social costs.
A ratio of 1 means you do not depend on
grant revenue or other funding.
Gross Profit Margin =
Gross Profit
Total Sales

How much profit is earned on your products
without considering indirect costs.
Is your gross profit margin improving? Small
changes in gross margin can significantly affect
profitability. Is there enough gross profit to
cover your indirect costs. Is there a positive
gross margin on all products?
Net Profit Margin =
Net Profit
Sales
How much money are you making per every $
of sales. This ratio measures your ability to
cover all operating costs including indirect costs
SGA to Sales =
Indirect Costs (sales, general, admin)
Sales
Percentage of indirect costs to sales.
Look for a steady or decreasing ratio which
means you are controlling overhead

Ratio Analysis
4 | P a g e
Profitability Sustainability Ratios continued
Return on Assets =
Net Profit
Average Total Assets
Measures your ability to turn assets into profit.
This is a very useful measure of comparison
within an industry.
A low ratio compared to industry may mean
that your competitors have found a way to
operate more efficiently. After tax interest
expense can be added back to numerator
since ROA measures profitability on all assets
whether or not they are financed by equity or
debt

Return on Equity =
Net Profit
Average Shareholder Equity
Rate of return on investment by shareholders.
This is one of the most important ratios to
investors. Are you making enough profit to
compensate for the risk of being in business?
How does this return compare to less risky
investments like bonds?
Ratio Analysis
5 | P a g e
Operational Efficiency Ratios
How efficiently are you utilizing your assets and managing your
liabilities? These
ratios are used to compare performance over multiple periods.
Ratio What does it tell you

Operating Expense Ratio =
Operating Expenses
Total Revenue
Compares expenses to revenue.
A decreasing ratio is considered desirable
since it generally indicates increased efficiency.
Accounts Receivable Turnover =
Net Sales
Average Accounts Receivable
Days in Accounts Receivable =
Average Accounts Receivable
Sales x 365
Number of times trade receivables turnover
during the year.

The higher the turnover, the shorter the time
between sales and collecting cash.
What are your customer payment habits
compared to your payment terms. You may
need to step up your collection practices or
tighten your credit policies.
These ratios are only useful if majority of sales
are credit (not cash) sales.
Inventory Turnover =
Cost of Sales
Average Inventory
Days in Inventory =
Average Inventory
Cost of Sales x 365
The number of times you turn inventory over
into sales during the year or how many days it
takes to sell inventory.
This is a good indication of production and
purchasing efficiency. A high ratio indicates
inventory is selling quickly and that little unused
inventory is being stored (or could also mean
inventory shortage). If the ratio is low, it

suggests overstocking, obsolete inventory or
selling issues.
Ratio Analysis
6 | P a g e
Operational Efficiency Ratios Continued
Accounts Payable Turnover =
Cost of Sales
Average Accounts Payable
Days in Accounts Payable =
Average Accounts Payable
Cost of Sales x 365
The number of times trade payables turn over
during the year.
The higher the turnover, the shorter the period

between purchases and payment. A high
turnover may indicate unfavourable supplier
repayment terms. A low turnover may be a
sign of cash flow problems.
Compare your days in accounts payable to
supplier terms of repayment.
Total Asset Turnover =
Revenue
Average Total Assets
Fixed Asset Turnover =
Revenue
Average Fixed Assets
How efficiently your business generates sales
on each dollar of assets.
An increasing ratio indicates you are using your
assets more productively.

Ratio Analysis
7 | P a g e
Liquidity Ratios
Does your enterprise have enough cash on an ongoing basis to
meet its operational
obligations? This is an important indication of financial health.
Current Ratio =
Current Assets
Current Liabilities
(also known as Working Capital Ratio)
Measures your ability to meet short term
obligations with short term assets., a useful
indicator of cash flow in the near future.
A social enterprise needs to ensure that it can
pay its salaries, bills and expenses on time.
Failure to pay loans on time may limit your
future access to credit and therefore your
ability to leverage operations and growth.

A ratio less that 1 may indicate liquidity issues.
A very high current ratio may mean there is
excess cash that should possibly be invested
elsewhere in the business or that there is too
much inventory. Most believe that a ratio
between 1.2 and 2.0 is sufficient.
The one problem with the current ratio is that it
does not take into account the timing of cash
flows. For example, you may have to pay
most of your short term obligations in the next
week though inventory on hand will not be sold
for another three weeks or account receivable
collections are slow.
Ratio Analysis
8 | P a g e
Liquidity Ratios Continued
Quick Ratio =
Cash +AR + Marketable Securities
Current Liabilities

A more stringent liquidity test that indicates if a
firm has enough short-term assets (without
selling inventory) to cover its immediate
liabilities.
This is often referred to as the “acid test”
because it only looks at the company’s most
liquid assets only (excludes inventory) that can
be quickly converted to cash).
A ratio of 1:1 means that a social enterprise
can pay its bills without having to sell inventory.
Working Capital =
Current Assets – Current Liabilities
WC is a measure of cash flow and should
always be a positive number. It measures the
amount of capital invested in resources that are
subject to quick turnover. Lenders often use this
number to evaluate your ability to weather
hard times. Many lenders will require that a
certain level of WC be maintained.
Adequacy of Resources =
Cash + Marketable Securities + Accounts
Receivable

Monthly Expenses
Determines the number of months you could
operate without further funds received (burn
rate)
Ratio Analysis
9 | P a g e
Leverage Ratios
To what degree does an enterprise utilize borrowed money and
what is its level of
risk? Lenders often use this information to determine a
business’s ability to repay debt.
Debt to Equity =

Short Term Debt + Long Term Debt
Total Equity (including grants)
Compares capital invested by
owners/funders (including grants) and
funds provided by lenders.
Lenders have priority over equity
investors on an enterprise’s assets.
Lenders want to see that there is some
cushion to draw upon in case of financial
difificulty. The more equity there is, the
more likely a lender will be repaid. Most
lenders impose limits on the debt/equity
ratio, commonly 2:1 for small business
loans.
Too much debt can put your business at
risk, but too little debt may limit your
potential. Owners want to get some
leverage on their investment to boost
profits. This has to be balanced with the
ability to service debt.
Interest Coverage =
EBITDA
Interest Expense

Measures your ability to meet interest
payment obligations with business income.
Ratios close to 1 indicates company
having difficulty generating enough cash
flow to pay interest on its debt. Ideally,
a ratio should be over 1.5
Ratio Analysis
10 | P a g e
Other Ratios
You may want to develop your own customized ratios to
communicate results that are
specific and important to your organization. Here are some
examples.
Operating Self-Sufficiency =
Sales Revenue
Total Costs (Operating and Social Costs)

% Staffing Costs spent on Target Group =
Target Staff Costs
Total Staffing Costs
Social Costs per Employee =
Total Social Costs
Number of Target Employees
% Social Costs covered by Grants =
Grant Income
Total Social Costs
Financial ratio analysis
A reading prepared by Pamela Peterson Drake
O U T L I N E
1. Introduction
2. Liquidity ratios
3. Profitability ratios and activity ratios
4. Financial leverage ratios

5. Shareholder ratios
1. Introduction
As a manager, you may want to reward employees based on
their performance. How do you know
how well they have done? How can you determine what
departments or divisions have performed
well? As a lender, how do decide the borrower will be able to
pay back as promised? As a manager of
a corporation how do you know when existing capacity will be
exceeded and enlarged capacity will be
needed? As an investor, how do you predict how well the
securities of one company will perform
relative to that of another? How can you tell whether one
security is riskier than another? We can
address all of these questions through financial analysis.
Financial analysis is the selection, evaluation, and
interpretation of financial data, along with other
pertinent information, to assist in investment and financial
decision-making. Financial analysis may be
used internally to evaluate issues such as employee
performance, the efficiency of operations, and
credit policies, and externally to evaluate potential investments
and the credit-worthiness of
borrowers, among other things.
The analyst draws the financial data needed in financial analysis
from many sources. The primary
source is the data provided by the company itself in its annual
report and required disclosures. The
annual report comprises the income statement, the balance
sheet, and the statement of cash flows,
as well as footnotes to these statements. Certain businesses are
required by securities laws to

disclose additional information.
Besides information that companies are required to disclose
through financial statements, other
information is readily available for financial analysis. For
example, information such as the market
prices of securities of publicly-traded corporations can be found
in the financial press and the
electronic media daily. Similarly, information on stock price
indices for industries and for the market
as a whole is available in the financial press.
Another source of information is economic data, such as the
Gross Domestic Product and Consumer
Price Index, which may be useful in assessing the recent
performance or future prospects of a
company or industry. Suppose you are evaluating a company
that owns a chain of retail outlets.
What information do you need to judge the company's
performance and financial condition? You
need financial data, but it doesn't tell the whole story. You also
need information on consumer
Financial ratios, a reading prepared by Pamela Peterson Drake 1
spending, producer prices, consumer prices, and the
competition. This is economic data that is
readily available from government and private sources.
Besides financial statement data, market data, and economic
data, in financial analysis you also need
to examine events that may help explain the company's present
condition and may have a bearing on
its future prospects. For example, did the company recently

incur some extraordinary losses? Is the
company developing a new product? Or acquiring another
company? Is the company regulated?
Current events can provide information that may be
incorporated in financial analysis.
The financial analyst must select the pertinent information,
analyze it, and interpret the analysis,
enabling judgments on the current and future financial condition
and operating performance of the
company. In this reading, we introduce you to financial ratios --
the tool of financial analysis. In
financial ratio analysis we select the relevant information --
primarily the financial statement data --
and evaluate it. We show how to incorporate market data and
economic data in the analysis and
interpretation of financial ratios. And we show how to interpret
financial ratio analysis, warning you
of the pitfalls that occur when it's not used properly.
We use Microsoft Corporation's 2004 financial statements for
illustration purposes throughout this
reading. You can obtain the 2004 and any other year's
statements directly from Microsoft. Be sure to
save these statements for future reference.
Classification of ratios
A ratio is a mathematical relation between one quantity and
another. Suppose you have 200 apples
and 100 oranges. The ratio of apples to oranges is 200 / 100,
which we can more conveniently
express as 2:1 or 2. A financial ratio is a comparison between
one bit of financial information and
another. Consider the ratio of current assets to current
liabilities, which we refer to as the current

ratio. This ratio is a comparison between assets that can be
readily turned into cash -- current assets
-- and the obligations that are due in the near future -- current
liabilities. A current ratio of 2:1 or 2
means that we have twice as much in current assets as we need
to satisfy obligations due in the near
future.
Ratios can be classified according to the way they are
constructed and their general characteristics.
By construction, ratios can be classified as a coverage ratio, a
return ratio, a turnover ratio, or a
component percentage:
1. A coverage ratio is a measure of a company's ability to
satisfy (meet) particular obligations.
2. A return ratio is a measure of the net benefit, relative to the
resources expended.
3. A turnover ratio is a measure of the gross benefit, relative to
the resources expended.
4. A component percentage is the ratio of a component of an
item to the item.
When we assess a company's operating performance, we want to
know if it is applying its assets in
an efficient and profitable manner. When we assess a company's
financial condition, we want to
know if it is able to meet its financial obligations.
There are six aspects of operating performance and financial
condition we can evaluate from financial
ratios:

1. A liquidity ratio provides information on a company's ability
to meet its short−term,
immediate obligations.
2. A profitability ratio provides information on the amount of
income from each dollar of
sales.
http://www.microsoft.com/msft/ar.mspx
3. An activity ratio relates information on a company's ability to
manage its resources (that is,
its assets) efficiently.
4. A financial leverage ratio provides information on the degree
of a company's fixed
financing obligations and its ability to satisfy these financing
obligations.
5. A shareholder ratio describes the company's financial
condition in terms of amounts per
share of stock.
6. A return on investment ratio provides information on the
amount of profit, relative to the
assets employed to produce that profit.
We cover each type of ratio, providing examples of ratios that
fall into each of these classifications.
2. Liquidity Ratios
Liquidity reflects the ability of a company to meet its short-
term obligations using assets that are

most readily converted into cash. Assets that may be converted
into cash in a short period of time
are referred to as liquid assets; they are listed in financial
statements as current assets. Current
assets are often referred to as working capital because these
assets represent the resources needed
for the day-to-day operations of the company's long-term,
capital investments. Current assets are
used to satisfy short-term obligations, or current liabilities. The
amount by which current assets
exceed current liabilities is referred to as the net working
capital.1
The role of the operating cycle
How much liquidity a company needs depends on its operating
cycle. The operating cycle is the
duration between the time cash is invested in goods and services
to the time that investment
produces cash. For example, a company that produces and sells
goods has an operating cycle
comprising four phases:
(1) purchase raw material and produce goods, investing in
inventory;
(2) sell goods, generating sales, which may or may not be for
cash;
(3) extend credit, creating accounts receivables, and
(4) collect accounts receivables, generating cash.
The operating cycle is the length of time it takes to convert an
investment of cash in inventory
back into cash (through collections of sales). The net operating

cycle is the length of time it takes to
convert an investment of cash in inventory and back into cash
considering that some purchases are
made on credit.
The number of days a company ties up funds in inventory is
determine by:
(1) the total amount of money represented in inventory, and
(2) the average day's cost of goods sold.
The current investment in inventory -- that is, the money "tied
up" in inventory -- is the ending
balance of inventory on the balance sheet. The average day's
cost of goods sold is the cost of goods
1 You will see reference to the net working capital (i.e., current
assets – current liabilities) as simply working
capital, which may be confusing. Always check the definition
for the particular usage because both are common
uses of the term working capital.
sold on an average day in the year, which can be estimated by
dividing the cost of goods sold found
on the income statement by the number of days in the year.
We compute the number of days of inventory by calculating the
ratio of the amount of inventory on
hand (in dollars) to the average day's Cost of Goods Sold (in
dollars per day):

365 / sold goods ofCost
Inventory
sold goods ofcost sday' Average
Inventory
inventory days ofNumber ==
If the ending inventory is representative of the inventory
throughout the year, the number of days
inventory tells us the time it takes to convert the investment in
inventory into sold goods. Why worry
about whether the year-end inventory is representative of
inventory at any day throughout the year?
Well, if inventory at the end of the fiscal year-end is lower than
on any other day of the year, we
have understated the
number of days of
inventory.
Indeed, in practice most
companies try to choose
fiscal year-ends that
coincide with the slow
period of their business.
That means the ending
balance of inventory would
be lower than the typical
daily inventory of the year.
We could, for example,
look at quarterly financial
statements and take
averages of quarterly
inventory balances to get
a better idea of the typical

inventory. However, here
for simplicity in this and
other ratios, we will make
a note of this problem and
deal with it later in the
discussion of financial
ratios.
We can extend the same
logic for calculating the
number of days between a
sale -- when an account
receivable is created -- to
the time it is collected in
cash. If the ending
balance of receivables at
the end of the year is
representative of the
receivables on any day throughout the year, then it takes, on
average, approximately the "number of
days credit" to collect the accounts receivable, or the number of
days receivables:
Try it!
Wal-Mart Stores, Inc., had cost of revenue of $219,793 million
for the fiscal
year ended January 31, 2005. It had an inventory balance of
$29,447 million
at the end of this fiscal year. Using the quarterly information,
Wal-Mart’s
average inventory balance during the fiscal year is $29,769.25:
Inventory balance, in millions
$28,320 $27,963

$33,347
$29,447
$24,000
$26,000
$28,000
$30,000
$32,000
$34,000
April July October January
Source: Wal-Mart Stores 10-K and 10-Q filings
Based on this information, what is Wal-Mart’s inventory
turnover for fiscal year
2004 (ending January 31, 2005)?
Solution
:
Using the fiscal year end balance of inventory:

= =
$29,447 $29, 447
Number of days inventory = 48.9 days
$219,793/365 $602.173
Using the average of the quarterly balances:
= =
$29,769.25 $29, 769.25
$219,793/365 $602.173
In other words, it takes Wal-Mart approximately 50 days to sell
its
merchandise from the time it acquires it.
= =
Accounts receivable Accounts receivable
Number of days receivables

Average day's sales on credit Sales on credit / 365
What does the operating cycle have to do with liquidity? The
longer the operating cycle, the more
current assets needed (relative to current liabilities) because it
takes longer to convert inventories
and receivables into cash. In other words, the longer the
operating cycle, the more net working
capital required.
We also need to look at the liabilities on the balance sheet to
see how long it takes a company to pay
its short-term obligations. We can apply the same logic to
accounts payable as we did to accounts
receivable and inventories. How long does it take a company, on
average, to go from creating a
payable (buying on credit) to paying for it in cash?
= =
Accounts payable Accounts payable

Number of days payables
Average day's purchases Purchases / 365
First, we need to determine the amount of an average day's
purchases on credit. If we assume all
purchases are made on credit, then the total purchases for the
year would be the Cost of Goods Sold,
less any amounts included in this Cost of Goods Sold that are
not purchases.2
The operating cycle tells us how long it takes to convert an
investment in cash back into cash (by
way of inventory and accounts receivable):
Number of days Number of days
Operating cycle
of inventory of receivables
= +
The number of days of purchases tells us how long it takes use
to pay on purchases made to create
the inventory. If we put these two pieces of information

together, we can see how long, on net, we
tie up cash. The difference between the operating cycle and the
number of days of payables is the
net operating cycle:
Net operating cycle = Operating Cycle - Number of days of
purchases
or, substituting for the operating cycle,
purchases of
days ofNumber
sreceivable of
daysofNumber
inventory of
daysofNumber
cycle operatingNet −+=
The net
operating cycle
therefore tells
us how long it

takes for the
company to get
cash back from
its investment
in inventory
and accounts
receivable,
considering that
purchases may be made on credit. By not paying for purchases
immediately (that is, using trade
credit), the company reduces its liquidity needs. Therefore, the
longer the net operating cycle, the
greater the company’s need for liquidity.
Microsoft's Number of Days Receivables
2004:
Average day's receivables = $36,835 million / 365 = $100.9178
million
Number of days receivables = $5,890 million / $100.9178
million = 58.3643 days
Now try it for 2005 using the 2005 data from Microsoft’s

financial statements.
Answer: 65.9400 days
Source of data: Income Statement and Balance Sheet, Microsoft
Corporation Annual Report 2005
2 For example, depreciation is included in the Cost of Goods
Sold, yet it not a purchase. However, as a quite
proxy for purchases, we can use the accounting relationship:
beginning inventory + purchases = COGS + ending
inventory.
Measures of liquidity
Liquidity ratios provide a measure of a company’s ability to
generate cash to meet its immediate
needs. There are three commonly used liquidity ratios:
1. The current ratio is the ratio of current assets to current

liabilities; Indicates a company's
ability to satisfy its current liabilities with its current assets:
sliabilitieCurrent
assetsCurrent
ratioCurrent =
2. The quick ratio is the ratio of quick assets (generally current
assets less inventory) to
current liabilities; Indicates a company's ability to satisfy
current liabilities with its most
liquid assets
sliabilitieCurrent
Inventory - assetsCurrent
ratio Quick =
3. The net working capital to sales ratio is the ratio of net
working capital (current assets
minus current liabilities) to sales; Indicates a company's liquid
assets (after meeting
short−term obligations) relative to its need for liquidity
(represented by sales)

Sales
sliabilitieCurrent - assetsCurrent
ratio sales to capital workingNet =
Generally, the larger these liquidity ratios, the better the ability
of the company to satisfy its
immediate obligations. Is there a magic number that defines
good or bad? Not really.
Consider the current ratio. A large amount of current assets
relative to current liabilities provides
assurance that the company will be able to satisfy its immediate
obligations. However, if there are
more current assets than the company needs to provide this
assurance, the company may be
investing too heavily in these non- or low-earning assets and
therefore not putting the assets to the
most productive use.
Another consideration is the
operating cycle. A company
with a long operating cycle
may have more need to

liquid assets than a
company with a short
operating cycle. That’s
because a long operating
cycle indicate that money is
tied up in inventory (and then receivables) for a longer length of
time.
Microsoft Liquidity Ratios -- 2004
Current ratio = $70,566 million / $14,696 million = 4.8017
Quick ratio = ($70,566-421) / $14,696 = 4.7731
Net working capital-to-sales = ($70,566-14,969) / $36,835 =
1.5515
Source of data: Balance Sheet and Income Statement, Microsoft
Corporation Annual
Report 2005

3. Profitability ratios
Profitability ratios (also referred to as profit margin ratios)
compare components of income with sales.
They give us an idea of what makes up a company's income and
are usually expressed as a portion
of each dollar of sales. The profit margin ratios we discuss here
differ only by the numerator. It's in
the numerator that we reflect and thus evaluate performance for
different aspects of the business:
The gross profit margin is the ratio of gross income or profit to
sales. This ratio indicates how
much of every dollar of sales is left after costs of goods sold:
Gross income
Gross profit margin
Sales
=
The operating profit margin is the
ratio of operating profit (a.k.a. EBIT,
operating income, income before
interest and taxes) to sales. This is a

ratio that indicates how much of each
dollar of sales is left over after operating
expenses:
Microsoft's 1998 Profit Margins
Gross profit margin = ($14,484 - 1,197)/$14,484 = 91.736%
Operating profit margin = $6,414 / $14,484 = 44.283%
Net profit margin = $4,490 / $14,484 = 31%
Source of data: Microsoft Corporation Annual Report 1998
___
Microsoft's 2004 Profit Margins
Gross profit margin = ($36,835 – 6,716)/$36,835 = 81.767%
Operating profit margin = $9,034 / $36,835 = 24.526%
Net profit margin = $8,168 / $36,835 = 22.175%
Source of data: Income Statement, Microsoft Corporation

Annual Report
2005
Operating income
Operating profit margin =
Sales
The net profit margin is the ratio of
net income (a.k.a. net profit) to sales,
and indicates how much of each dollar
of sales is left over after all expenses:
Net income
Net profit margin
Sales
= .
4. Activity ratios
Activity ratios are measures of how well assets are used.
Activity ratios -- which are, for the most
part, turnover ratios -- can be used to evaluate the benefits
produced by specific assets, such as
inventory or accounts receivable. Or they can be use to evaluate
the benefits produced by all a

company's assets collectively.
These measures help us gauge how effectively the company is at
putting its investment to work. A
company will invest in assets – e.g., inventory or plant and
equipment – and then use these assets to
generate revenues. The greater the turnover, the more
effectively the company is at producing a
benefit from its investment in assets.
The most common turnover ratios are the following:
1. Inventory turnover is the ratio of cost of goods sold to
inventory. This ratio indicates how
many times inventory is created and sold during the period:
Inventory
sold goods ofCost
turnover Inventory =
2. Accounts receivable turnover is the ratio of net credit sales to
accounts receivable. This
ratio indicates how many times in the period credit sales have
been created and collected on:

receivable Accounts
credit on Sales
turnover receivable Accounts =
3. Total asset turnover is the ratio of sales to total assets. This
ratio indicates the extent that
the investment in total assets results in sales.
assets Total
Sales
turnover asset Total =
4. Fixed asset turnover is the ratio of sales to fixed assets. This
ratio indicates the ability of
the company’s management to put the fixed assets to work to
generate sales:
assets Fixed

Sales
turnover asset Fixed =
Microsoft’s Activity Ratios – 2004
Accounts receivable turnover = $36,835 / $5,890 = 6.2538 times
Total asset turnover = $36,835 / $92,389 = 0.3987 times
Source of data: Income Statement and Balance Sheet, Microsoft
Corporation Annual
Report 2005
Turnovers and numbers of days
You may have noticed that there is a relation between the
measures of the operating cycle and
activity ratios. This is because they use the same information
and look at this information from
different angles. Consider the number of days inventory and the
inventory turnover:
=
Inventory

Number of days inventory
Average day's cost of goods sold
Inventory
sold goods ofCost
turnover Inventory =
The number of days inventory is how long the inventory stays
with the company, whereas the
inventory turnover is the number of times that the inventory
comes and leaves – the complete cycle
– within a period. So if the number of days inventory is 30 days,
this means that the turnover within
the year is 365 / 30 = 12.167 times. In other words,
= =
365 365 Cost of goods sold
Inventory turnover =
InventoryNumber of days inventory Inventory

Cost of goods sold / 365
Try it!
Wal-Mart Stores, Inc., had cost of revenue of $219,793 million
for the fiscal year ended January 31, 2005. It
had an inventory balance of $29,447 million at the end of this
fiscal year.
Source: Wal-Mart Stores 10-K
Wal-Mart’s number of days inventory for fiscal year 2004
(ending January 31, 2005) is
= =
$29,447 $29, 447
$219,793/365 $602.173

Wal-Mart’s inventory turnover is:
=
$219,793
Inventory turnover = 7.464 times
$29,447
And the number of days and turnover are related as follows:
Inventory turnover = 365 / 48.9 = 7.464 times
Number of days inventory = 365 / 7.464 = 48.9 days
5. Financial leverage ratios
A company can finance its assets either with equity or debt.
Financing through debt involves risk
because debt legally obligates the company to pay interest and
to repay the principal as promised.
Equity financing does not obligate the company to pay anything
-- dividends are paid at the
discretion of the board of directors. There is always some risk,

which we refer to as business risk,
inherent in any operating segment of a business. But how a
company chooses to finance its
operations -- the particular mix of debt and equity -- may add
financial risk on top of business risk
Financial risk is the extent that debt financing is used relative
to equity.
Financial leverage ratios are used to assess how much financial
risk the company has taken on. There
are two types of financial leverage ratios: component
percentages and coverage ratios. Component
percentages compare a company's debt with either its total
capital (debt plus equity) or its equity
capital. Coverage ratios reflect a company's ability to satisfy
fixed obligations, such as interest,
principal repayment, or lease payments.
Component-percentage financial leverage ratios
The component-percentage financial leverage ratios convey how
reliant a company is on debt
financing. These ratios compare the amount of debt to either the
total capital of the company or to
the equity capital.

1. The total debt to assets ratio indicates the proportion of
assets that are financed with
debt (both short−term and long−term debt):
assets Total
debt Total
ratio assets todebt Total =
Remember from your study of accounting that total assets are
equal to the sum of total debt
and equity. This is the familiar accounting identity: assets =
liabilities + equity.
2. The long−term debt to assets ratio indicates the proportion of
the company's assets that
are financed with long−term debt.
assets Total
debt term-Long
ratio assets todebt term-Long =

3. The debt to equity ratio (a.k.a. debt-equity ratio) indicates
the relative uses of debt and
equity as sources of capital to finance the company's assets,
evaluated using book values of
the capital sources:
equity rs'shareholde Total
debt Total
ratioequity todebt Total =
One problem (as we shall see)
with looking at risk through a
financial ratio that uses the book
value of equity (the stock) is that
most often there is little relation
between the book value and its
market value. The book value of
equity consists of:
• the proceeds to the

company of all the stock
issued since it was first
incorporated, less any
treasury stock (stock
repurchased by the
company); and
• the accumulation of all
the earnings of the
company, less any
dividends, since it was
first incorporated.
Let's look at an example of the
book value vs. market value of
equity. IBM was incorporated in
1911. So its book value of equity
represents the sum of all its stock
issued and all its earnings, less all dividends paid since 1911.
As of the end of 2003, IBM's book value
of equity was approximately $28 billion and its market value of
equity was approximately $162 billion.
The book value understates its market value by over $130
billion. The book value generally does not
give a true picture of the investment of shareholders in the

company because:
Note that the debt-equity ratio is related to the debt-to-total
assets
ratio because they are both measures of the company’s capital
structure. The capital structure is the mix of debt and equity
that
the company uses to finance its assets.
Let’s use short-hand notation to demonstrate this relationship.
Let D
represent total debt and E represent equity. Therefore, total
assets
are equal to D+E.
If a company has a debt-equity ratio of 0.25, this means that is
debt-
to-asset ratio is 0.2. We calculate it by using the ratio
relationships
and Algebra:
D/E = 0.25
D = 0.25 E

Substituting 0.25 E for D in the debt-to-assets ratio D/(D+E):
D/(D+E) = 0.25 E / (0.25 E + E) = 0.25 E / 1.25 E = 0.2
In other words, a debt-equity ratio of 0.25 is equivalent to a
debt-to-
assets ratio of 0.2
This is a handy device: if you are given a debt-equity ratio and
need
the debt-assets ratio, simply:
D/(D+E) = (D/E) / (1 + D/E)
Why do we bother to show this? Because many financial
analysts
discuss or report a company’s debt-equity ratio and you are left
on
your own to determine what this means in terms of the
proportion of
debt in the company’s capital structure.
• earnings are recorded according to accounting principles,
which may not reflect the true
economics of transactions, and

• due to inflation, the dollars from earnings and proceeds from
stock issued in the past do not
reflect today's values.
The market value, on the other hand, is the value of equity as
perceived by investors. It is what
investors are willing to pay, its worth. So why bother with the
book value of equity? For two reasons:
first, it is easier to obtain the book value than the market value
of a company's securities, and
second, many financial services report ratios using the book
value, rather than the market value.
We may use the market value of equity in the denominator,
replacing the book value of equity. To do
this, we need to know the current number of shares outstanding
and the current market price per
share of stock and multiply to get the market value of equity.
Financial ratios, a reading prepared by Pamela Peterson Drake
10

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