eesley comparing china us

S T A N F O R D U N I V E R S I T Y • Chuck Eesley • Management Science & Engineering

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What Should Drive an Innovation Strategy?

Chuck Eesley (Stanford), Edward B. Roberts (MIT), Delin Yang (Tsinghua Univ.)

Strategic Management SocietyOctober, 2009 (with support of a Kauffman Foundation Dissertation Fellowship, the Tsinghua Univ. Alumni Association, and the MIT Entrepreneurship Center)


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New Firms Undertake Search Activity

What enables (or constrains) the adoption of an innovation strategy?

Search (Simon, 1957; Nelson, Winter 1982, Cyert, March 1963)- Firm-Centric, Past (Levinthal & March, 1981; Katila & Ahuja, 2002)- External environment - Educ./Training – (Beckman and Burton, 2008, Burton, Sørensen, Beckman

2002, Burton and Beckman, 2007)

Liquidity Constraints (Arrow 1962, Nelson 1959)• Kortum & Lerner 2000; Hall 2005

Effects of Public R&D (Romer, 1990; Stern and Porter, 2004)

• Direct vs. indirect effects (Goolsbee, 1998; Henderson, Jaffe, Trachtenberg, ’98)

• Bush 1945, Aghion, Dewatripont, Stein 2008

• Grant-based vs. Contract-Based• Short run vs. Long run effects (Mansfield, 1977)

• David et al. 2000, Evenson, Kislev 1976, Adams 1990, Adams 1993, Kortum 1997


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New Firm (two searches)

• Product/market search

• Funding search

R&D investment decisions

Survival/Growth or

Productivity

Strategy

Founder characteristics

Input marketSupply of technical labor

Productivity of applied

R&D

External Funding

Search: Environment, Initial Conditions, & Strategy

Contract R&D

Grant R&D


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Search ModelPr(Iit=1) = (H

Ait Ait )/ C(Vit, i) (1)

A = total stock of knowledge/ideas, the ease of finding an

innovation opportunity (Romer, 1990)H

At is a firm-specific component - ease of search technology space scaled by

Search costs for funding C(V, )= 1/(V) (2)

p= (0,1) - firm-specific component ability to raise funding

Level of VC funding V = ρθAt

(3)

ρ Proportion technological opportunities that are radical (Kortum & Lerner, 2000)

θ = [0,1] financial frictions (information asymmetry/moral hazard)

Public R&D human capital, knowledge stock and the level of VC funding


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Two Solutions to More Innovation in Society

Pr(It=1) = (HAt At

)/ [1/(ρθAt)] (4)

(1) existing firms doing more innovation

(2) new firms are created, a higher percentage of these innovate

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Public R&D Influence on Firm Search

H1: Grant based public R&D expenditures will (via indirect effects) result in greater knowledge spillovers and greater use of an innovation strategy (with a lag) H2: Grant-based public R&D expenditures will result (via direct effects in higher prices for research inputs) in lower use of an innovation strategy (contemporaneous)

H3: Grant or contract-based public R&D expenditures will result in more scientists/engineers becoming entrepreneurs with a lag.

H4: Venture capital funding will result in greater use of an innovation strategy. (Counter-hypothesis to hypothesis 1)


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Proportion of firms adopting an innovation strategy

t

Exogenous shift in H

Ait , Ait or C(Vit, i)

t

No shift

Proportion of firms adopting an innovation strategy

The effectiveness of government incubators, seed funding, …and other such policies for funding R&D deserves further study, ideally in an experimental or quasi-experimental setting. In particular, studying the cross-country variation in the performance of such programs would be desirable, because the outcomes may depend to a great extent on institutional factors that are difficult to control for using data from within a single country.

- Bronwyn Hall 2005


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Merged MIT and Tsinghua Dataset

Similar educational background, academic talent (engineering)

Similar industries (electronics & software) 2,067 + 330 firm observations Innovation measures

– Patents (foreign and domestic)– Product/service available in the market 3 years ago

(China)– Importance of innovation, speed to market, low cost,

other factors Detailed fundraising data US and China data on public R&D expenditures,

publications and venture capital Sources: OECD Science and Technology Indicators, 2008;

Ministry of Science and Technology, China; China Statistical Yearbooks; SDC Venture Economics Database; Asian Venture Capital Journal; Dow Jones VentureOne; Thomson ISI

Inflation and Purchasing Power Parity conversion process


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Merged MIT and Tsinghua Dataset


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Industry Breakdown


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Capital and Lack of Ideas

Panel B – Factors for Not Becoming an Entrepreneur

Rank (1 – 8)1 %

2 %

3 %

4 %

5 %

Difficult to raise capital

141 31

101 23

80 21

44 14 20 7

Difficult to find partners

47 10

114 26

95 25

56 17

31 10

Lack of good ideas171 37

67 15

43 12 26 8 28 9

Concept easily copied 6 1 30 747 13

60 18

76 25

Risk too great55 12

73 17

61 16

74 23

57 19

Family against entrepreneurship 8 2 16 4 12 3 25 8

41 13

Cannot leave current job 22 5 19 4 19 5 27 8

31 10

Gov. discouraged entrepreneurship at the time 9 2 17 4 20 5 12 4

20 7

Only 27% had never considered entrepreneurship.


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Strategy? MIT Tsinghua

IP author? Freq. percentage Freq. percentage

Yes 578 46.24 105 62.87No 672 53.76 62 37.13Total 1250 100 167 100 MIT Tsinghua

IP owner? Freq. Percentage Freq. Percentage

Yes 434 53.19 107 59.44No 382 46.81 73 40.56Total 816 100 180 100 MIT Tsinghua

IP important? Freq. Percentage Freq. percentage

Yes 481 33.83 123 37.85No 941 66.17 202 62.15Total 1422 100 325 100R&D/Revenue Ratio MIT Tsinghua Mean 0.17 0.2325%ile 0 0.05Median 0.1 0.175%ile 0.2 0.3


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Methods

Differences-in-differences estimation

Probit Model

Prob (innovation= 1) = Prob(Yt=1) = α + β1(funding)t + β2(science and technology funding)t + β3(human capital) + β4(business environment)t + β5(funding)t*(China) + β6(China location) + β7(science and technology)t*(China) + yeart + sector + η + φ + εt

Xi = Set of controls academic dept., region, education, work history, job type, Communist party, overseas educ. or work, family economic status.

Include (τ + η + φ) grad. year, sector and Bachelor’s academic dept. fixed effects 13

Proportional Hazards Test


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National Level

Independent Variables

Dependent Variable = IP Critical

(2-1) (2-2) (2-3)Log(Gross domestic product) (t-1) -0.215 (1.014) -0.046 (1.020) -0.046 (1.020)Log(stock exchange market cap) (t-1) 0.056 (0.418) 0.021 (0.421) 0.025 (0.421)Log(VC disbursements) (t-1) -0.059 (0.136) -0.076 (0.137) -0.080 (0.137)Log(VC disbursements x China) (t-1) 0.242 (1.343) 0.092 (1.352) 0.102 (1.352)Log(public R&D expenditure) (t-6) -0.060 (0.093) -0.127 (0.096) -0.132 (0.097)Log(public R&D exp.) x China (t-6) 0.468*** (0.154)Log(total SE pubs) (t-6) 0.116 (0.087) 1.289*** (0.392) 1.525*** (0.466)Log(total SE pubs) x China (t-6) 0.409*** (0.134)ControlsVenture capital funded 0.594*** (0.144) 0.605*** (0.143) 0.605*** (0.143)Angel investor funded 0.566*** (0.155) 0.600*** (0.156) 0.599*** (0.156)Master’s degree 0.043 (0.106) 0.032 (0.106) 0.031 (0.106)Ph.D. degree 0.673*** (0.144) 0.689*** (0.145) 0.688*** (0.145)China 0.413 (1.124) 0.395 (1.145) 0.388 (1.148)Constant -0.432 (7.751) -15.459* (9.153) -18.400* (9.683)Year fixed effects YES YES YESYear x China fixed effects YES YES YESIndustry fixed effects YES YES YESLog likelihood -442.348 -437.952 -437.966Number of observations 803 803 803Pseudo R-squared 0.159 0.167 0.167

Standard errors are robust. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed). PPP GDP is used for China. Log indicates that a log transformation was done to address the skewed distribution. In parentheses, (t-1) and (t-6) indicate that the variables were lagged one year and six years, respectively


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Individual Level

Standard errors are robust. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed).

Independent Variables China only US onlyMaster's degree 0.034 (0.180) 0.125 (0.096)Doctorate degree 0.681** (0.303) 0.373*** (0.128)Work in R&D 0.442** (0.200)Work as Tech Manager -0.024 (0.211)Ever job in academia 0.118 (0.221)Family Economic Status -0.445* (0.252)Overseas Experience -0.291 (0.233)Prior acquisition 0.205** (0.104)Prior IPO 0.102 (0.173)ControlsVC funded 0.353 (0.400) 0.720*** (0.122)Angel investor funded 0.127 (0.376) 0.537*** (0.128)Beijing -0.051 (0.189)Shanghai -0.691** (0.320)Chongqing -0.845 (0.693)Shenzhen -0.020 (0.371)Mass. 0.211** (0.100)California 0.079 (0.112)N 271 1167

Controls: Non-US citizen, Communist party, Gender, graduation year, founding year, Bach. Dept.


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Regional Level (2004-2007)

Dep. Var=IP critical

National contribution to R&D

0.020(0.01

8)

0.339***

(0.020)

0.348***

(0.015)

Local contribution to R&D

0.061(0.07

1)

-0.117

(0.142)

-0.148(0.150)

Local ratio R&D to fiscal exp.

0.476(1.07

7)

9.996***

(1.123)

Growth rate of National R&D exp.

-0.026(0.20

9)

-1.655*

**(0.069)

Master's degree-0.345(0.501)

Doctorate Degree0.717

(0.717)

Used VC0.884

(0.721)N 101 46 46 80 46 46 43R2 0.029 0.043 0.036 0.024 0.0915 0.092 0.165

Expenditures are in billions of yuan, ratios and growth rates are percentages, all lagged 1 year. Controls for region and founding year are included.

Standard errors are robust. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed).


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Progress Update

National levelIndividual levelRegional levelFurther robustness checksAlternative definitions of innovationAlternative measures of R&D/funding environment

Other shifts – law/IP


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Conclusion and Implications

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Hypothesis: Supported?

H1

Increases in grant based public R&D expenditures will (via indirect effects) result in greater knowledge spillovers and greater use of an innovation strategy with a lag.

Some support

H2

Increases in grant-based public R&D expenditures lower use of an innovation strategy contemporaneous to the increase in funding.

-

H3

Increases in grant or contract-based public R&D expenditures will result in more scientists/engineers becoming entrepreneurs with a lag.

-

H4Increases in prior year venture capital funding will result in greater use of an innovation strategy. (Counter-hypothesis to hypothesis 1)

No

Institutional Level• Types of institutional support needed for innovative, high

growth firms• If evolutionary theory correct, larger impacts may be on new

firms

Individual Level• Suggestive of who to look for as cofounders

Strategy• Better understanding of environmental influences on search• Where to spend more time for early-stage, high tech founders• Active view on identification of valuable resources, difficult to

imitate


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Thank you!

Chuck EesleyStanford University

Management Science & Engineering (MS&E)[email protected]

mailto:[email protected]


Wha

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Response Bias Innovation MeasuresSource of IdeasFounding RatesMIT and Tsinghua Firm CharacteristicsTheory

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Variable Responded to 2001 survey(N=43,668)

Did not respond to 2001 survey (N=62,260)

t-stat for equal means

Male 0.83 0.86 10.11Engineering major 0.48 0.47 -4.49Management major 0.16 0.15 -5.75Science major 0.23 0.23 0.37Social sciences major 0.05 0.06 4.07Architecture major 0.06 0.08 11.82Non-US citizen 0.81 0.82 3.77North American (not US) citizen 0.13 0.11 -4.14Latin American citizen 0.13 0.12 -1.44Asian citizen 0.33 0.34 1.45European citizen 0.30 0.26 -5.08Middle Eastern citizen 0.05 0.08 6.32African citizen 0.03 0.05 6.25

Variable Responded to 2003 survey(N=2,111)

Did not respond to 2003 survey(N=6,131)

t-stat for equal means

Male 0.92 0.92 0.12Engineering major 0.52 0.47 -3.63Management major 0.17 0.21 4.17Science major 0.17 0.18 1.09Social sciences major 0.06 0.05 1.18Architecture major 0.09 0.09 1.06Non-US citizen 0.82 0.81 -1.36North American (not US) citizen 0.17 0.14 -1.34Latin American citizen 0.19 0.19 0.13Asian citizen 0.22 0.24 0.73European citizen 0.31 0.32 0.38Middle Eastern citizen 0.08 0.07 -0.59African citizen 0.04 0.04 0.17


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Strategy?Panel A: Tsinghua Alumni (entrepreneurs and non-entrepreneurs)

Foreign Patents Domestic Patents

Number of Patents per Individual

Freq. percent Freq. percent

0 2924 98.58 2565 86.481 18 0.61 163 5.502 14 0.47 90 3.033 3 0.10 56 1.894 3 0.10 24 0.815 0 0.00 27 0.91

6 or more 4 0.13 41 1.38Total 2966 100 2966 100

Panel BFirms MIT Tsinghua MIT Tsinghua

Number of Patents per Firm Freq. percent Freq. percentFirm Age </=15

yrs Freq.percent

Firm Age</= 15 yrs Freq.

percent

0 1263 74.91 66 20.12 755 78.00 20 7.721 112 6.64 33 10.06 73 7.54 31 11.972 64 3.80 58 17.68 37 3.82 49 18.923 40 2.37 52 15.85 25 2.58 50 19.314 20 1.19 56 17.07 11 1.14 52 20.085 16 0.95 53 16.16 6 0.62 47 18.15

6 or more 171 10.14 10 3.05 61 6.30 10 3.86Total 1686 100 328 100 968 100 259 100


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Founding Rates

MIT Tsinghua

Dept. Freq. Percent

freq. founder

% becoming founders

Freq. Percent

freq. founders

% founders

Engineering

21714 51.28 3483 16.04 1771 69.72 456 25.75

Sciences 9086 21.46 1984 21.84 406 15.98 79 19.46Management 6365 15.03 1634 25.67 100 3.94 31 31.00

Social Sciences 2838 6.70 265 9.34 163 6.42 27 16.56

Architecture 2339 5.52 487 20.82 100 3.94 27 27.00


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come from?Idea sources MIT Data Tsinghua data

percentage percentage

In school-doing outside-funded research 2.40 1.66

In school- graduate thesis 4.64 3.96 In school- in class 1.98 5.88

In school-informal discussion with students 3.41 11.00

In school-other research 2.28 1.92

In school-professional literature 1.73 4.48

In school- visiting scientists, engineers etc 1.77 4.86

In school-working with outside company 3.20 4.86

Other sources-discussions in social/professional conferences 21.54 17.65

Other sources-research conference 2.66 4.48

Other sources-working in the industry 41.44 24.81

Other sources- working in the military (government experience) 4.01 2.94

Other sources- doing outside-funded research 2.07 0.77

Total 100 100 Number of observations 1284 110


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MIT and Tsinghua alumni firms

Purchasing Power Parity converted to constant 2005 U.S. dollars


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