eesley comparing china us
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Strategic Management Society Presentation Oct. 2009, Washington DCTRANSCRIPT
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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1
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)
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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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
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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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
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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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
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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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
5
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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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)
S T A N F O R D U N I V E R S I T Y • Chuck Eesley • Management Science & Engineering
Wha
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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
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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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
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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Merged MIT and Tsinghua Dataset
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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Industry Breakdown
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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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.
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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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
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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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
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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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
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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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.
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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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).
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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Progress Update
National levelIndividual levelRegional levelFurther robustness checksAlternative definitions of innovationAlternative measures of R&D/funding environment
Other shifts – law/IP
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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Conclusion and Implications
18
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
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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Thank you!
Chuck EesleyStanford University
Management Science & Engineering (MS&E)[email protected]
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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Response Bias Innovation MeasuresSource of IdeasFounding RatesMIT and Tsinghua Firm CharacteristicsTheory
20
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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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
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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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
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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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
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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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
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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MIT and Tsinghua alumni firms
Purchasing Power Parity converted to constant 2005 U.S. dollars
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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