Outsourced R&D and Its Impact

Anne Marie Knott

2015 Innovation and IP SummitApril 30, 2015

I gratefully acknowledge support under NSF Award 1246893: The Impact of R&D Practices on R&D Effectiveness, and NSF Award 0965147: Firm IQ: A Universal, Uniform and Reliable Measure of R&D Effectiveness DISCLAIMERS: Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. I have a financial interest in amkANALYTICS, a subscription database of firm RQs.

My life before academia

US innovation engine is broken Historically R&D viewed as

engine of economic growth Reflected in Obama’s

“America COMPETES Reauthorization Act of 2010” Restore US competitiveness

through investment in R&D Link broken

R&D labor increased 2.5x GDP growth declining 0.03%

per year To restore R&D role in GDP

growth, need to understand the disconnect

Romer (1990) provides first formal theory linking R&D to growth Finished goods production function:

Y=Ka(ALY)1-a

Imbedded in that is the knowledge production function: A’=dALA

Where: A = knowledge/technology d = research productivity LA= research labor

Combining: Y=Ka((A+dALA)*LY)1-a

Most important conclusions: Steady state (and balanced) growth: gA=gY=A’/A=(dALA)/A=dLA

Scale effects: doubling L doubles g

The puzzle

Scientific labor (LA) has increased 2.5x GDP growth declining 0.03% per year

Two explanations for the disconnectJones (1995) explanation R&D has gotten harder Eliminates scale effects

prediction, by adding Fishing out effect, q, -

probability of finding new idea is declining in the stock of ideas

Diminishing returns to scientific labor, l, -due to higher likelihood of duplicate efforts

Revision: Implication: growth

converges to 0

Alternative explanation Firms have become

worse at R&D Scientific labor productivity,

d, has declined Leave Romer intact Implication:

Steady state growth Scale effects

Paper tests the two explanations1. Measure R&D productivity (RQ)

2. Conduct critical test of the two hypotheses

3. Identify source of declining RQ

4. Demonstrate sufficient to account for decline

5. Rule out alternative explanations linking source to declining RQ

6. Speculate why it causes decline in RQ

7. Speculate why firms persist with it

1. RQTM is most intuitive measure you could construct for R&D productivity*

Derived from production function: Y = Aa Bb

Expands it to include intangibles:Output = Capitala *Laborb * R&Dg * spilloversd* Advertisinge

Makes all exponents firm-specific: RQ is the exponent, g, on R&D

Exact definition: “firm-specific output elasticity of R&D” % increase in output from 1%

increase in R&D

Important properties of RQTM

Universal: RQTM can be estimated for all firms doing R&Donly 50% of firms patent their R&D

Uniform: It’s essentially a ratio of inputs to outputs, so interpretation is same across firms Patents are apples and oranges:10% of patents comprise 85% of total value of all patents

Reliable: Firm behavior and outcomes consistent with endogenous growth theory (tested over 47 years): Increasing RQ increases optimal R&D investment Increasing RQ increases market value Increasing RQ increases firm growth

Patent intensity is negatively correlated with all three measures

RQTM gaining traction among important groups• Firms:

• Industrial Research Institute (IRI) • “New Hope for Measuring R&D Effectiveness”, Research-Technology Management (RTM) (2009)• Plenary talk at Annual Meeting (2014), “Demonstrating the Value of R&D

• Harvard Business Review “R&D: The Trillion Dollar Fix” (May 2012) “What Do These Two States Have in Common” (Dec 2014)

Investors• CNBC annual RQ50 platform www.cnbc.com/RQ50 online plus on-air• CFA Institute-2 hour Master Class at annual meeting (April 2015)

• Policymakers (National Science Foundation) Award 0965147: Firm IQ: A Universal, Uniform and Reliable Measure of R&D

Effectiveness Award 1246893: The Impact of R&D Practices on R&D Effectiveness

• Academics:• “Measuring Innovation” has 500 SSRN downloads in 2 weeks • Wharton Data Research Services (WRDS) providing RQ database as part of their

Compustat suite

2a. Test of firms getting worse at R&D

2b. Test of R&D getting harder

If R&D has gotten harder, maximum RQ should decline over time

Maximum RQ is actually increasing Though the increase

comes from new industries

Max RQ is declining within industries

3. Identify source of declining RQusing NSF Survey of Industrial R&D (SIRD) Annual survey of US firms

conducting R&D 1957 to 2007 Replaced by BRDIS in 2008

Sample intended to represent all for-profit R&D-performing companies

The data includes: domestic sales domestic employment number of scientists/engineers total domestic R&D expenditures by

source of funding (Federal R&D versus company R&D funds)

horizon (basic research, applied research, and development)

location (internal, outsourced within US, foreign entities).

20.1x rise in R&D outsourcingversus 2.4x for R&D labor

4a. Demonstrate outsourcing can account for RQ decline Treat three sources of R&D (internal, US

outsource in US, foreign) as separate inputs Estimate contributions of each to firm production

function (using random coefficients):

4b. Results: Outsourced R&D unproductive for funding firm Elasticity of

outsourced R&D 0.001 Versus 0.13 for

internal R&D robust to exclusion of

spillovers No evidence R&D

has gotten harder: Elasticities for each

form of R&D are statistically equivalent at the beginning and the end of the sample

5a. Rule out selection effects

Do lower quality firms outsource? Treatment regression

Do firms outsource their less productive projects? If so, internal RQ should increase following

outsourcing Test what happens to internal RQ pre/post

outsource

5b. First step: interviews regarding outsource decisions One end of the spectrum (continuously but on limited basis)

firm outsource only to universities and government labs. They do this to gain access to basic research as well as to identify potential

employees.

In the middle of the spectrum (under special circumstances). as a flexible substitute for internal hiring when future demand is

uncertain, When lack capability and don’t intend to develop it internally

because they would operate below efficient scale outsource testing (particularly in the case of pharmaceutical trials).

At the extreme end, firms outsource all non-core R&D activities.

5c. Data indicates bulk of outsourcing conforms to middle of spectrum

Outsourcing not mandated by industry Outsourcing not merely stage of R&D

5d. Test of firm quality suggests productivity problem lies elsewhere Two stage treatment model:

First stage models p(outsource): neverout

Second stage models treatment effects on R&D productivity

If firm quality is driving outsourcing: Neverout should increase

internal RQ

Results: No treatment effect on internal

RQ Positive effect on aggregate

RQ (significant at 10%)

5e. Test of project selection suggests productivity problem lies elsewhere If firms outsource their less

productive projects should see internal RQ increase

post-outsourcing Characterize internal RQ in

window of first outsource Create 7 year moving estimates of

firm’s internal RQ Create dummies for years around

first outsource RQit=ai +Sbt(dummies)+eit

Results: No significant effect Mean coefficient:

0.0006 pre-outsource 0.0000 post-outsource

6. Speculate why outsourcing causes decline in RQ Internal spillovers

Substantial evidence that “external” spillovers of rivals’ R&D benefits firms

Similar effect may occur between projects within firms Redeploying knowledge from terminated projects

Hughes ion beam propulsion

Firms who outsource IT integration less able to develop new applications (Weigelt 2009)

Lower ability to exploit research outcomes Key technical resources lie outside the firm

Exploitation less likely and/or more costly Similar effect to that found in auto development

Product development takes longer and costs more when design and manufacturing separated (Clark and Fujimoto 1991)

7. Speculate why firms persist given outsourcing is unproductive Firms don’t know their R&D productivity

Need for better R&D measures top concern for IRI members (Schwartz, Miller, Plummer, Fusfeld 2011)

Evidence Fewer than 5% of firms R&D investment within +10% of optimum level (Knott 2012) Firm RQ changes randomly over 10 years 70% of surveyed CIOs and CEOs believe outsourcing innovation improves financial

performance (Osteria and Kotlarsky 2011)

Susceptible to information cascades (Bikhchandani et al 1992) Information cascade occurs when it is optimal for actors to ignore private

information in favor of following prior actors Relevant cascade is open innovation trend

Michael Armstrong, Hughes CEO, had goal of increasing outsourcing to “bring in more outside technology”

Note outsourcing is only subset of open innovation Other forms: alliances, joint ventures and external technology sourcing External technology sourcing seems to benefit firms (Cassiman & Veugulers 2006;

Arora, Cohen, Walsh 2014)

Recap

Proposed and tested an alternative to Jones for the broken link between R&D and growth No evidence R&D getting harder Rather firms have become worse at it--their R&D productivity, RQ,

has declined. Fortunately the decline in RQ appears to stem from

increased use of outsourced R&D No decline in firms’ internal RQ.

Implications Preserves model with growth in steady state (and scale effects)

vs Jones expectation of convergence toward 0 growth Since outsourcing is fairly easily reversed, may restore firm RQs

as well as US GDP growth.

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