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SPOTLIGHT ON INNOVATION FOR THE 21ST CENTURY
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The
TrillionDollar
R&D Fix
Most big companies
should spend more
on R&D. But how
much more?
by Anne Marie Knott
H
ow does a company
know what kind of return it’s getting from
R&D? Is it better at R&D
than the competition?
How much should it
be spending, and what
can it do to improve
the effectiveness of its
investments?
Existing measures of R&D effectiveness—for instance, amount of spending or number of patents—
don’t answer those questions or reliably predict market value. Year after year Booz & Company publishes
“The Global Innovation 1000.” And year after year
the consulting giant points out that R&D spending
does not correlate with market value or growth. The
2010 report argues, “Spending more on R&D won’t
drive results. The most crucial factors are strategic
May 2012 Harvard Business Review 77
SPOTLIGHT ON INNOVATION FOR THE 21ST CENTURY
alignment and a culture that supports innovation.”
The trouble is that it’s also hard to measure strategic
alignment and culture, let alone link them to profitability or market value.
R&D is thus an easy target when firms face quarterly earnings pressure. Since it is expensed rather
than capitalized, cuts yield immediate increases in
profit, while the detrimental impact of those cuts
aren’t felt for a few years. In marginal trade-offs
between investments in, say, physical capital or
advertising, whose returns are more quantifiable,
R&D loses out. In response to the recent prolonged
recession, for instance, firms with revenues greater
than $100 million reduced their R&D intensity (R&D
spend divided by revenue) by 5.6%, on average,
whereas capital intensity at those firms fell only 4.8%
and advertising intensity actually increased 3.4%.
A new metric for R&D productivity—which I call
RQ, short for research quotient—can change all that.
RQ allows you to estimate the effectiveness of your
R&D investment relative to the competition and to
see how changes in your R&D expenditure affect the
bottom line and, most important, your company’s
market value. My research—which includes a comprehensive analysis of all publicly traded companies
in the U.S.—suggests that if the top 20 firms traded
on U.S. exchanges had optimized their 2010 R&D
spending using the RQ method, the collective increase in market cap would have been an astonishing
$1 trillion. The longer-term benefits are even greater,
as RQ also allows companies to link changes in R&D
strategy, practices, and processes more closely to
profitability and value.
This is a story we’ve seen played out before:
Thirty years ago, W. Edwards Deming’s quality metrics inspired the TQM movement, which revolutionized the way companies manufacture. I believe that
RQ can do the same for R&D—and that the payoff
will be even greater.
The Measure
Calculating RQ doesn’t involve fancy new math.
Economists have been calculating capital and labor
productivity for years—that is, determining the marginal value of increasing either one. R&D productivity can be determined using the same method, although few, if any, analysts or academics have done
so at the level of individual companies.
Essentially, the equation defines the relationship between a firm’s inputs (what it spends) and
its output (its revenues). The formula typically con78 Harvard Business Review May 2012
The Theory Behind RQ
DEFINITION The RQ method for measuring productivity of R&D investment
uses the well-known economic formula for measuring labor and capital productivity. This equation defines the relationship between a firm’s inputs (what
it spends) and output (its revenues). The formula typically looks like this:
Y=K L
𝛂 𝛃
OUTPUT
(REVENUES)
CAPITAL
THE EXPONENTS INDICATE HOW
PRODUCTIVE EACH INPUT IS IN
GENERATING OUTPUT.
SPECIFICALLY, THEY SHOW THE
PERCENTAGE INCREASE IN A
FIRM’S REVENUES RESULTING
FROM A 1% INCREASE IN CAPITAL
(ALPHA) OR LABOR (BETA).
LABOR
The RQ method expands the calculation
to include another input, R&D (R):
Y=K L R
OUTPUT
CAPITAL
LABOR
R&D
THE NEW EXPONENT,
GAMMA, TELLS
YOU HOW MUCH
OF A PERCENTAGE
INCREASE IN OUTPUT
YOU WOULD GET FROM
A 1% INCREASE IN
R&D SPENDING.
CALCULATION To calculate your RQ, you need several years’ data on
revenues and annual expenditures on PP&E (property, plant, and equipment),
labor, and R&D. Those data are converted into logs, a standard transformation needed to run the regression analysis that produces RQ. The spreadsheet
looks something like this:
2007
2008
2009
2010
REVENUE
$4,847
$5,273
$5,450
$5,534
PP&E EMPLOYEE
$976
$8
$960
$8
$955
$8
$979
$8
R&D
$108
$111
$114
$119
LN(REV)
8.49
8.57
8.60
8.62
LN(PPE)
6.88
6.87
6.86
6.89
LN(EMP)
2.05
2.12
2.12
2.12
LN(R&D)
4.68
4.71
4.74
4.78
$ IN MILLIONS
When you run the regression analysis, you get productivity levels for PP&E,
employees, and R&D. An analysis of the full set of U.S. publicly traded companies yielded an average R&D exponent of 0.109—which means that increasing
R&D spending by 1% would increase revenue by 0.11%.
COMPARISON Finally, to compare research productivity across firms and to
help firms track changes in their R&D productivity, you rescale the exponent
number relative to the mean of all U.S. traded firms to create an index number. (An RQ of 100, therefore, denotes the average R&D exponent across all
firms.) Most companies (67%) have RQs between 85 and 115.
HBR.ORG To see the RQs of all U.S. publicly traded companies that do R&D (more than
1,500 firms), go to the online lookup tool at www.hbr.org/magazine/rq.
THE TRILLION-DOLLAR R&D FIX HBR.ORG
Idea in Brief
siders two costs, capital and labor. Of course, those
aren’t the only determinants of revenue, and most
economists would accept that the equation could be
expanded to include another central input: R&D. Using standard regression analysis, the calculation tells
us in a very precise way how productive each of the
inputs is in generating output. It tells us, for instance,
how much a 1% increase in R&D spending would increase a firm’s revenue.
A precise estimation of RQ examines thousands
of firms simultaneously using fairly sophisticated
software, but a coarse estimate of a single firm’s RQ
can be run on an ordinary spreadsheet using historical data easily obtainable at most large companies—
revenue figures and spending on PP&E (property,
plant, and equipment), employment, and research.
(See the sidebar “The Theory Behind RQ” for more
on the method.)
Once you know your RQ—how effective your
company is at R&D—you can determine the amount
of R&D spending that would produce the maximum
profits. That calculation involves a standard piece of
math, called a partial derivative, that can be easily
embedded in a spreadsheet. In essence, it’s an exercise in marginal returns—determining at what point
an additional dollar spent on R&D begins to reduce
revenues and profitability.
Why It Works
Good measures have three properties: universality,
uniformity, and reliability. Uniformity means the
measure is interpreted the same way in all contexts;
universality means it applies to all relevant entities
(in this case, firms); and reliability means that its
predictions confirm what theory says should happen. The easiest way to explain why these properties
are important is to show why another measure often
used to gauge R&D effectiveness—patent counts—
fails because it lacks them.
First, patent counts aren’t universal in that not
all firms doing R&D patent their innovations. In fact,
fewer than 50% of firms engaged in R&D file patents
in any given year. Moreover, even among patenting
firms, few of them patent all their innovations. It’s
often more effective to protect intellectual property
by keeping it a trade secret. Patents aren’t uniform,
either. Compare, for example, the economic value
of the patent for copying DNA with that of the 97%
of patents that are never commercialized. On average, 10% of patents account for up to 85% of the
value of all patents. Finally, higher patent counts
don’t reliably predict higher profits and market
value—the outcome companies expect from R&D
investments.
In contrast, RQ exhibits all three properties. RQ
is estimated entirely from standard financial data,
so it can be calculated for any firm doing R&D. And
because RQ is a ratio, its interpretation is uniform
across firms regardless of currency. Most important,
RQ is reliable. It confirms what you would expect it
to: (1) that firms with higher RQ—those that are better at R&D—spend more on R&D than firms with low
RQ; (2) that R&D spending beyond the optimal limit
identified by RQ reduces firm market value; and
(3) that firms with higher RQ have higher profits and
market value for a given set of inputs.
My colleagues Carl Vieregger and James Yen and I
have demonstrated all three effects rigorously across
all publicly traded U.S. firms from 1981 through 2006.
Our analysis of the data shows that a 10% increase
in RQ—that is, in R&D productivity—results in an increase in market value of 1.1%.
The Payoff
Using the RQ measure has immediate benefits.
Firms—not to mention the financial analysts who
track them—can now identify the marginal returns
to R&D and the level of R&D investment that generates the greatest market cap. As the exhibit “The
Trillion-Dollar Opportunity” illustrates, the gains
from bringing R&D closer in line with optimal levels
prescribed by RQ are enormous.
For most companies, RQ will call for significant
increases to R&D budgets. A few firms, of course,
will find the opposite. To reach its optimal level,
Pfizer would have to cut its R&D spending by $3 billion a year, money that would be freed up for investment in other, more productive things. Once companies adopt RQ as a standard metric and align their
spending accordingly, market caps should rise very
quickly since increases to market value typically materialize as soon as beliefs about future performance
change.
Traditional measures
of R&D productivity are
only loosely linked to
profits or market value.
That makes it difficult
for executives to know
whether they’re spending as much (or as little)
as they should, let alone
make improvements in
the way they spend.
A new measure, RQ
or research quotient,
derived from classic
regression analysis, allows managers to make
these judgment calls. It
also enables managers
to estimate the optimal
amount they should
spend on R&D.
RQ is a very accurate
predictor of profits and
share price performance. If companies
were to adjust their R&D
budgets in line with the
levels appropriate to
their RQ, the positive
impact on corporate value would be
enormous.
For most companies, RQ will
call for significant increases to
R&D budgets. A few firms will
find the opposite.
May 2012 Harvard Business Review 79
SPOTLIGHT ON INNOVATION FOR THE 21ST CENTURY
HBR.ORG
Over the long term, RQ will improve the quality
and effectiveness of R&D initiatives. Managers will
be able to determine, for instance, whether a given
change in R&D strategy translates over time into a
higher or lower RQ. And as managers and analysts
get better at measuring the success of initiatives,
they will be able to make better judgments about the
quality of firms’ management decisions and start to
understand which R&D practices create the most
value in various contexts.
Since the measure is new and not widely used, I
can’t make conclusive statements about which practices and processes improve RQ. However, a National
Science Foundation study I conducted with Bruno
Cassiman, of IESE Business School, suggests three
preliminary insights:
RQ rises with the breadth of a firm’s activity. RQ is positively correlated with the number of
markets a company sells to outside its home region
(export breadth), the number of locations for R&D
The Trillion-Dollar Opportunity
My research suggests that if the top 20 firms traded on U.S. exchanges had
optimized their R&D spending in 2010 using the RQ method, the collective
increase in market cap would have been an astonishing $1 trillion.
First, I calculated
RQ for the 20
largest U.S. firms
(by 2010 revenue).
I then looked at
the amount each
firm actually
invested in R&D...
...and calculated
the optimal R&D
spend for the firms
based on their RQs.
I then estimated the
change in profits resulting from either increasing
spending by 10% (for underspenders) or reducing
it (for overspenders).
Finally, I applied the firms’
current price-to-earnings
ratio to the notional extra
profits to calculate the
corresponding increase
in market value.
INCREASE
R&D SPEND
10% OR
CUT EXCESS
$ IN MILLIONS
EXXON MOBIL
CHEVRON
CONOCOPHILLIPS
GENERAL ELECTRIC
GENERAL MOTORS
FORD
HEWLETT-PACKARD
MCKESSON
IBM
PROCTER & GAMBLE
PFIZER
APPLE
BOEING
MICROSOFT
ARCHER-DANIELS-MIDLAND
JOHNSON & JOHNSON
DELL
UNITED TECHNOLOGIES CORP.
DOW CHEMICAL
KRAFT FOODS
REVENUE
$341,578
$189,607
$175,752
$149,060
$135,592
$128,954
$126,033
$112,084
$99,871
$78,938
$67,791
$65,225
$64,306
$62,484
$61,682
$61,587
$61,494
$54,326
$53,674
$49,207
RQ
108
106
112
102
105
105
114
117
100
101
104
105
104
107
111
101
108
103
107
103
2010
R&D SPEND
$1,012
$526
$230
$3,939
$6,962
$5,000
$2,959
$407
$5,720
$1,950
$9,538
$1,782
$4,121
$8,714
$56
$6,844
$661
$1,746
$1,660
$583
OPTIMAL
R&D SPEND
$136,486
$56,690
$100,350
$19,947
$15,570
$14,405
$43,907
$111,598
$10,359
$7,816
$6,304
$9,468
$8,142
$9,210
$29,947
$5,371
$16,218
$6,192
$9,356
$7,254
UNDER
SPEND
$135,474
$56,163
$100,119
$16,008
$8,608
$9,405
$40,948
$111,190
$4,639
$5,866
$-3,234
$7,686
$4,021
$496
$29,891
$-1,472
$15,557
$4,446
$7,695
$6,671
EXPECTED
PROFIT
INCREASE
$18,190
$7,151
$10,112
$4,515
$6,264
$5,904
$13,891
$10,231
$2,124
$1,946
$3,235
$2,573
$2,441
$3,198
$2,386
$1,473
$2,887
$1,654
$2,459
$1,310
PRICE-TOEARNINGS
RATIO
(JAN ’12)
10.3
8.1
8.7
15.3
4.4
6.5
8.8
7.8
14.5
16.9
15.0
14.6
13.9
9.4
14.0
16.0
7.5
15.2
16.4
20.4
EXPECTED
MARKET
VALUE
INCREASE
$188,089
$58,208
$87,471
$68,857
$27,749
$38,084
$121,548
$79,393
$30,782
$32,959
$48,614
$37,637
$33,878
$30,192
$33,336
$23,562
$21,740
$25,113
$40,425
$26,695
The result:
a whopping
$1 trillion
$1,054,335,000,000
80 Harvard Business Review May 2012
SPOTLIGHT ON INNOVATION FOR THE 21ST CENTURY
HBR.ORG
however, Trimble’s RQ fell 40%. Three years later,
market cap plunged; the following year, profitability
In-house research trumps outsourced R&D. took a hit.
The drop in RQ can be linked to changes in TrimRQ is negatively correlated with cooperative R&D,
ble’s strategy. Throughout the 1990s Trimble develand the correlation between RQ and R&D is higher
oped and patented many technologies, reaching a
for internal R&D than external R&D.
RQ varies for different types of innovation. peak of 94 patents in 1997. In addition, Trimble was
RQ is positively correlated with product (versus pro- rapidly expanding the product markets in which
this technology was deployed, according to the
cess) innovation. Also, RQ is higher for companies
that do incremental innovation (those that are new “Company History” on the firm’s website. In 2000,
activity (technical breadth), and the number of product lines (product breadth).
Firms can identify the marginal returns to R&D
and the level of investment that generates the
greatest market cap.
to the firm) rather than radical innovation (those
that are new to the world). And RQ is positively correlated with organizational innovations that complement product innovations.
These insights, while useful, don’t reveal why or
how they act to improve R&D productivity. Nonetheless, managers—and analysts—who use RQ will be
able to tell quite a lot about what’s likely to happen
to any company’s share price in response to changes
in strategy and management practice. The case of
Trimble Navigation provides an interesting example.
The company was founded in 1978 to develop positioning and navigation products utilizing LORAN
(and subsequently GPS) technology. While Trimble’s initial markets were in military applications for
which the technology was originally developed, it
quickly applied the technology to commercial markets, such as surveying and mariner navigation.
Trimble’s net income grew fairly rapidly from
2000 to 2007 but then suffered a steep decline in
2009, largely owing to the recession. Trimble’s profit
collapse of 54% was on par with the 62% average
decline for U.S. publicly traded firms. But Trimble
failed to bounce back. The average net income for
U.S. firms is now 8.5% higher than the pre-recession
peak, whereas Trimble’s net income remains 27%
below its peak.
Trimble’s RQ history reveals what may account
for this prolonged slump. Trimble’s RQ steadily
increased though 2004—the peak in RQ preceded
the peak in market cap by about three years and increases in net income by about four years. In 2004,
82 Harvard Business Review May 2012
however, the company appears to have changed its
strategy from one of in-house development to acquisition. This switch is documented in the “Company History” (up until 2000, each year’s summary
described a technological development; after 2000
there is no mention of developments, only of acquisitions) and by the decline in patents obtained per
year, which dropped to almost zero. A shrewd analyst might pick up on either or both of these patterns,
but without RQ it would be difficult to tell whether
the change in strategy was value-enhancing or
value-destroying. Using RQ, a manager or analyst
could easily see that the shift was value-destroying.
The Promise
At first, RQ might be viewed with suspicion, even
hostility, at many corporations, appearing to be a
device the R&D community could use to line its coffers. But concern over providing yet another metric
for company managers to abuse should not outweigh the substantial long-term benefits that RQ
can deliver.
Improving manufacturing operations creates
value, but R&D is a basic engine of economic and social growth. If enough firms adopt RQ and align their
R&D spending and strategies accordingly, we should
see a systematic improvement in overall corporate
R&D effectiveness. The benefits for us all would be
remarkable.
HBR Reprint R1205D
Anne Marie Knott is a professor of strategy at Washington University’s Olin Business School in St Louis and
a director of the Berkeley Research Group in Los Angeles.
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