The Right Stuff

Who has ‘The Right Stuff’?
Human Capital, Entrepreneurship and
Institutional Change in China
Charles E. Eesley
cee@stanford.edu

Stanford University
Department of Management Science & Engineering

Washington University
Oct. 7th, 2009

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

Strategy / Economics of Innovation and Technological Change

High Tech
Entrepreneurship
(selection and
performance)

Current
Research

Labor Economics Institutional
(individual/micro- Variation
level) (macro-level)

Charles Eesley

Motivation
19892004 China 29% growth vs. US 1% (State
Statistics Bureau)
―High-impact or high-
growth entrepreneurs … in
particular, help drive growth
in productivity and living
standards and, thus, are of
special interest and
importance.‖
– Robert Litan, Vice
President, Research and
Policy Ewing Marion
Kauffman Foundation

(Chinese State Statistics Bureau)
• Critical for their role in creating new markets, technologies and products, … process
of ―creative destruction‖ (King and Levine, 1993a; 1993b; Djankov et al. 2002; Klapper et al., 2007)
• Acs, Z. J., and D. B. Audretsch, 1988; Christensen; Henderson, Utterback, etc.
• How might we encourage highly talent individuals to found
firms? Charles Eesley - The Right Stuff

Results
Proportion Becoming Entrepreneurs by Year
(Unobserved Human Capital)
0.10

0.09

0.08

0.07

0.06
Proportion

0.05
Low human capital
0.04
High human capital
0.03

0.02

0.01

0.00

Founding Year

Charles Eesley - The Right Stuff

Overview
1. Theory
2. Hypotheses
3. Two reforms
4. Empirical context – novel survey data
5. Results
6. Conclusion and Implications
7. Robustness checks (US data)

Institutional Level Theories
• Linked with long-run economic growth (Acemoglu, 2002; 2005;
Johnson, McMillan & Woodruff; Porta, 1998)
• Transition,idea common to all usages property „institution‟ is
“The only institutional reform, of the term rights, financial
liberalization, liquidity constraints
that of some sort of establishment of relative permanence of a
(Walder, 2003; Gans, Hsu &social sort.” (Hughes, 1936:Woodruff; Baumol, 1990;
distinctly Stern 2002; Johnson, McMillan & 180)
Thornton, 1999; Nee, 1996; Katila& Chen, 2009; Katila& Shane, 2005)
• Neo-institutionalism (Meyer & Rowan, 1977; DiMaggio & Powell, 1983)

Individual Level Theories
• Training and prior experience
(Hsu, 2007; Sorensen, 2007; Baumol, 2004; Groysberg, et al. 2007; Burton et al. 2002; Phillips, 2002;
Dobrev& Barnett, 2005, Gompers et al, 2005;Simons & Roberts, 2007; Baron et al. 1996; Klepper&
Sleeper, 2002; Eisenhardt&Schoonhoven, 1990)

• Networks(Stuart and Ding, 2006; Katila et al., 2008)
• Human capital, preferences
(Zucker, Darby & Brewer, 1998; Lazear 2004; Irigoyen 2002, Nanda 2008; Amit et al. 1995; Amit et al.
1990; Knight 1921; Lucas 1978; Jovanovic 1982; Holmes and Schmitz 1990; Jovanovic and Nyarko
1996; Sorensen 2004)

Institutional Level

Very little on ability/human capital (Banerjee and
Munshi, 2004)

Individual Level

Focus on Human Capital as a Driver of Selection
•Talent and labor skills- Roy Model (1951) Selection on Potential Earnings
•Identification requires exclusion restrictions, exogenous shocks, or instruments
affecting returns or skill in one sector only
•Jack-of-all trades

•Liquidity Constraints – Evans and Jovanovic (1989)
•Assets are endogenous, static models, typically poorly measured
•Buera (2008) Liquidity
Constraints

•Psychological Traits – Kihlstrom and Laffont (1979),
Dunn and Holtz-Eakin (1996), Stajkovic et al (2000)
•Evidence on risk-aversion or tastes for
independence is limited
Talent
Psychological
Traits and Labor Demographic
Factors

•Network factors (Stuart and Ding, 2006, Skills
Ruef, Aldrich, & Carter, 2003, Nicolaouand Birley, 2003)
•Demographic factors (McClelland, 1961, Blau& Duncan, 1967
Dunn & Holtz-Eakin, 2000; Roberts, 1991) Network
Factors


Model
Roy Model as extended by Borjas (1987)
Labor markets 0 and 1, respectively. Log earnings in the wage sector:
w0 = μ0 + ε0
ε0 ~ N (0, ς02)

The wage sector earnings would be the following:
w1 = μ1 + ε1
ε1 ~ N (0, ς12)

Assume that the cost of becoming an entrepreneur is C π = C/w0

The correlation between entrepreneur and wage worker earnings is
ς01 = cov(ς0, ς1). A worker will choose entrepreneurship if


Model
Define the indicator variable I, equal to 1 if this selection condition is satisfied, 0
otherwise.
Define ν = ε1 − ε0. The probability that a randomly chosen worker from the wage
sector chooses to entrepreneurship is equal to:

Φ (·) is the CDF of the standard normal and z = (μ0 − μ1 + π) /ςν
Calculate expectation of earnings in wage and entrep. sectors for those who
chose the other sector.
Selection conditions:
THREE CASES:
Define Q0 = E (ε0|I = 1), Q1 = E (ε1|I = 1)


Model
Case 1: Positive hierarchical sorting:
• Entrepreneurs are positively selected from the wage sector distribution and
are also above the mean of the entrepreneurship distribution: Q0> 0, Q1> 0.
This will be true iff

• First, ς1/ ς0> 1 implies that entrepreneurship has a higher ‘return to skill’
than the wage sector. Second, ρ > ς0 /ς1, implies that the correlation
between the skills valued in the wage sector and in entrepreneurship is
sufficiently high

Case 2: Negative hierarchical sorting
• In this case, entrepreneurs are negatively selected from the wage sector
distribution and are also below the average of the entrepreneur distribution:
Q0< 0, Q1< 0. This will be true iff


Model
Case 3: ‘Reverse’ sorting
Where Q0 < 0, Q1> 0, that is, entrepreneurs are selected from the lower tail of
the wage sector distribution but arrive in the upper tail of the
entrepreneurship distribution. This can only occur if

A fourth case?
Note that there is not a fourth case where Q0> 0, Q1< 0. This would only happen
if an individual from the top of the wage sector distribution joined the
bottom tail of the entrepreneurship distribution.


Conventional View
Cost to Start a
Business
Net Returns to
Entrepreneurship

Initial institutional
environment

Institutional
environment #2

Higher Human Capital

Increase is among those of relatively
lower ability (Nanda, 2008)

Much Less Focus on Returns
Entrepreneurial
Returns
(net of entry $)
Wage empl. income

Institutional environment
#2

Initial institutional
environment

Higher Human
Capital

Selection on Talent and Expected Earnings

Payoff in high Payoff in low
variance sector variance sector

Individuals form expectations on
talent / earnings in each sector (Roy, 1951)


2. Hypotheses
H1: An institutional change reducing barriers to growth will
increase entrepreneurship among individuals located
relatively higher in the talent distribution.

H2: Individuals who show evidence of higher talent in their
wage employment careers will experience higher returns to
talent (in entrepreneurship) after an institutional change
lowering barriers to growth.

wage employment careers will start firms that have higher
performance.


3. Ideal Experiment
Exogenous increase
(decrease) in σ1
Returns to talent in
entrepreneurship
relative to wage
Ideal data is on the t
employment
relative variance of wage
and entrepreneurial
income Roy Model
(Appendix A)

1988 – barriers to 1999 – barriers to
entry growth

t


Reform Easing Constraints on Growth

March 1999 An amendment of Article 11 of the
Constitution
- Officially ending discriminatory practices against
private firms
…places private businesses on an equal footing with the public sector by changing
the original clause "the private economy is a supplement to public ownership" to "the
non-public sector, including individual and private businesses, as an important
component of the socialist market economy" (China Daily, March 16, 1999).

Kellee S. Tsai, ―Capitalists without a Class: Political Diversity Among Private
Entrepreneurs in China, Comparative Political Studies, 38:9, 2005.
- Privateproperty
- R&D policy (expansion of tax incentives, incubators, science parks)
18

Reform
Yingyi Qian, current SEM Dean at Tsinghua University, former
economics faculty at Stanford and UC Berkeley

• …places private businesses on an equal footing with the
public sector
• The Jiangsu Provincial Government adopted a new policy to
give private enterprises equal treatment as state-owned and
collective enterprises… (People's Daily, April 9, 1999).

Qian, Yingyi. "The Process of China's Market Transition (1978-1998): The
Evolutionary, Historical, and Comparative Perspectives." Journal of Institutional and
Theoretical Economics, March 2000, 156(1), pp. 151-171.

Reform
Yingqiu Liu, a Senior Research Fellow and Professor of Economics at the
Chinese Academy of Social Sciences (CASS)
Since 1999:
• Zeng Peiyan, minister at the State Development Planning Commission:
―[We will] eliminate all restrictive and discriminatory regulations that are
not friendly towards private investment and private economic development
in taxes, land use, business start-ups, and import and export. In the area of
stock listings, private enterprise should enjoy equal opportunity which was
enjoyed by the state-owned enterprises.‖

• A large number of provincial governments that have issued documents that
support and promote private enterprises in order to help them develop and
grow rapidly.

Liu, Y. Development of Private Entrepreneurship in China: Process, Problems and
Countermeasures. Working paper.

Entrepreneur‘s Perspective
• One pair of founders had very high human capital with one being a lawyer
and an MBA and the other having a Ph.D. Founded in 2003 and said that:

I then spent 20 years in the Bay area in life sciences companies. In the mid-
1990’s I came back to China to survey biotech companies in China and
found that the environment was not ready yet.

…spent an entire year just looking for the right office space…each product
must be registered and approved by the government. It’s an expensive and
time consuming procedure. She eventually found space for the company’s
first store in a children’s museum which was perfect since they were selling
toy bears aimed at children. This also allowed them to “hide” from
government inspectors.


4. Context
Alumni survey Tsinghua University
• 30,000 mailed
• 3,000 surveys
• 10% r.r.
• Growing tradition: Stanford GSB, Chicago, HBS
• Disadvantages: Biased towards tech., other
response bias?
• Advantages: Defined‗at risk‘ set, first
abroad, detailed work historydevelopfounding
―Additionally, there is a strong need to
and data sets to study
data, less biased political factors affect entrepreneurship.‖ -
how economic and by govt. concerns
(Klapper, Amit, Guillén, & Quesada, 2008)

Hsu, D.H., Roberts, E.B., Eesley, Charles. 2007.
Entrepreneurs from Technology-Based Universities: 22
Evidence from MIT. Research Policy, 36: 768–788.

Survey Questions

8. How many companies have you founded (not
including State-Owned Enterprises (SOEs))?
____
9. How many companies have you privatized or
bought? ____
Please answer the remaining following questions about your first company
(Company A)
2. Will spending money on research and development be a major priority for this
new business? Yes No
3. Were the products and services to be provided by your new business available
on the market 3 years ago? No Yes, but only a few Yes, but not too
many Yes, almost everywhere


Coefficient on Year Fixed Effect
3

2.5

2

1.5

1
Coefficient

0.5 Dep. Var. = income from start-
up
0
Dep. Var.= employees
-0.5

-1

-1.5

-2


Reform
Indep. Vars. log(revenue) log(employees)
POST-1999 1.262** (0.744) 0.510** (0.309)
Master’s degree 0.370 (0.348) 0.083 (0.164)
Doctorate degree -0.342 (0.631) 0.200 (0.305)
Privatized 1.407** (0.690) 1.409*** (0.315)
Bought -1.212 (0.751) 0.087 (0.349)
Firm Age 0.460*** (0.085) 0.278*** (0.034)
Communist Party 0.128 (0.346) 0.039 (0.163)
Overseas 0.710 (0.449) 0.328* (0.198)
FamilyEconomic
Status -0.078 (0.179) -0.018 (0.083)
Constant 2.544** (1.208) 1.420** (0.518)
Obs. 195 267
***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.


Distribution of Observable Human Capital
Highest Degree Earned
60

50

40
Percent

30

20

10

0
Bachelor's Master's Doctorate

Histogram of GPA Rank
35

30

25

20

15

10

5

0
First (Highest) Second Quartile Third Quartile Fourth (Lowest)
Quartile Quartile

5. Methods
Differences-in-differences estimation
Cox Hazard Rate Model
(robust to logit)
Prob (founding a firm = 1) = F(α + 1’θi + 2’ θi*POST + 3’POSTi + ’Xi + τt+ ηj + φa+εi)

• Dependent variable: First start-up founded
• Subjects start being “at risk” of founding a firm at the time of their
graduation
• Θi = human capital measures
• POST = 1 if individual was at risk for founding between 2000 and 2007.
• 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, region and Bachelor’s academic dept. fixed
effects
• (Acemoglu& Finkelstein, JPE 2008)
28
Proportional Hazards Test

Coefficients on Year Fixed Effects Hazard Rate Coefficients for Year-by-Year
(controls for region, education, department) Interactions
1.7
1
0.9 1.5
0.8 1.3 Unobs.
0.7 Talent
0.6 1.1 (Income
0.5 Year fixed Residual)
0.4 effects 0.9
Years
0.3 0.7 Education
0.2
0.1 0.5
0

2000
1996
1997
1998
1999

2002
2003
2004
Coefficients on Educ. Interaction
with Year Fixed Effects
0.2
0.25
0.2 0.1
0.1
0.15
0.05
-0.05
-0.15 Master's Degree
-0.25 ** **
-0.35
-0.45 **


Reduced Barriers to Growth: Observable Measures
Dependent Variable = Start-up founded (subjects start being at risk upon graduation)
Note: reported coefficients are hazard ratios
Master’s degree 0.444***
(0.121)
Master’s x POST 1.771*
(0.581)
PhD degree 1.131
(0.630)
PhD x POST 0.889
(0.549)
Highest salary 0.771**
Student leader 0.718**
(pre-founding) (0.079)
(0.097)
Salary x POST 1.225*
Leader x POST 1.336*
(0.147)
(0.209)
Log(yrs. work exp) 0.832*** Parents’ educ. 0.240***
(0.022) (above median) (0.073)
Parent edu. x
Log(yr. work exp.) 0.999
POST 4.575***
x POST (0.027)
(1.588)
Promoted 0.216***
High GPA 0.350***
(0.112)
(0.111)
Promoted x POST 3.361**
GPA x POST 1.811*
(1.953)
(0.651)
Years 2000-07 0.000*** 0.033*** 0.060*** 0.012*** Years 2000-07 0.007*** 0.027*** 0.020***
(POST) (0.000) (0.001) (0.019) (0.007) (POST) (0.004) (0.007) (0.006)

N=1,821; 308 foundings; 44,248 total years at risk; ***, **, and * indicate statistical significance
at the 1%, 5%, and 10% levels, respectively.

Drawbacks to Observable Measures

• Could be correlated with family wealth

• Consistent with an opportunity costs story

• Subject to shifts in who gets a graduate degree, gets
promoted, etc.

• Difficult to test changes in the shape of the distribution

Ideally want some continuous underlying measure of
talent


Unobserved Talent: Income Regression
Specification for the ordered logit regression is as follows:

Yi = Φ(α + γXi +τt+ ηj + φa + εi)

Individual i ; 2:1 match on grad. yr. and salary year
Xit = controls including job type (academia, business, government),
tenure, Communist party, overseas, family wealth, and education
Dependent variable: salary (6 categories)
τt+ ηj + φa = year + region + Bachelor’s department

Use εi in the entrepreneurship regressions

Andersson, F., M. Freedman, J.C. Haltiwanger, J. Lane, K.L. Shaw. 2006. Reaching for the stars: Who
pays for talent in innovative industries? NBER Working Paper No. 12435

Gibbons, R. et al., 2005 JLE

5. Quantile Regression Model

• Many of the attractive properties of OLS or mean regression

• Advantage of allowing changes in the shape of the entire conditional
distribution to be examined (Koenker& Basset, 1982; Koenker,
Hallock 2001)

• Dependent variable: income regression residual

• Bootstrap method (with 100 repetitions) is used to generate standard
errors (Horowitz 2001, Rogers 1992)

33

Quantile Regression
Panel A Dependent variable = income residuals (N=595)
Percentiles 10 25 50 75 80
Founded in 1978-89 1.558*** 0.776*** 0.125** -0.494*** -0.680***
(0.506) (0.271) (0.065) (0.187) (0.246)
Founded in 1990-99 0.769*** 0.496*** 0.009 -0.210 -0.233
(0.196) (0.161) (0.072) (0.259) (0.381)
Founded in 2000-07 0.501*** 0.519*** 0.382*** 0.722*** 0.688***
(0.215) (0.174) (0.136) (0.194) (0.151)
Panel B Entrepreneurs only (N=132)
Percentiles 10 25 50 75 90
Ln(profit) 0.215 0.093 0.195 0.242** 0.246**
(0.295) (0.234) (0.155) (0.123) (0.126)
Observations 132 132 132 132 132
Pseudo R-squared 0.461 0.360 0.293 0.418 0.601

Controls for firm age, registered capital, privatized, bought, and salary.
Dependent variable is the residual from the income regression in Table 6. Bootstrapped standard
errors (100 repetitions); Standard errors are in parentheses. * p< 0.10, ** p< 0.05, *** p< 0.01
34

Individual Fixed Effects
Dependent Variable = Start-up founded (subjects start being at risk upon graduation) Note: reported
coefficients are hazard ratios

Indiv. F.E. 90th quartile 0.651
(0.203)
Indiv. F.E. 90th * POST 1.293*
(0.206)
Indiv. F.E. 10th quartile 0.976
(0.479)
Indiv. F.E. 10th * POST 1.130
(0.810)
Indiv. F.E. > median 0.624**
(0.144)
Indiv. F.E.
median*POST 7.329***
(2.165)

N=1800; Controls not shown; ***, **, and * indicate statistical significance at the 1%, 5%, and 10%
levels, respectively.


Hypotheses
H1: An institutional change reducing barriers to growth will
increase entrepreneurship among individuals located
relatively higher in the talent distribution.

wage employment careers will experience higher returns to
talent (in entrepreneurship) after an institutional change
lowering barriers to growth.

wage employment careers will start firms that have higher
performance.


Shift in Returns to Talent in Entrep.
Log(income from start-up) Log(profit margin)
Independent Variables
(7-5) (7-6) (7-7) (7-8) (7-1) (7-4)
Master’s degree -1.037 0.090 -0.449 -0.269
(0.905) (0.928) (0.276) (0.340)
POST x Master’s degree 1.783* 0.163 0.236 -0.111
(1.039) (1.054) (0.345) (0.412)
High GPA -1.191 -1.417 -0.523
(0.737) (0.903) (0.589)
POST x High GPA 2.348*** 2.376** 0.866
(0.887) (1.042) (0.595)
Income residual -3.239*** -- --
(0.901)
POST x income residual 2.624** -- --
(0.987)
Promoted -1.123 -0.900
(1.182) (0.611)
POST x Promoted 5.803*** 1.751**
(1.678) (0.768)
POST-1999 founding year -1.329 -0.139 1.027 -6.443*** -0.301 -2.200**
(0.977) (0.686) (0.819) (1.861) (0.455) (0.937)

Standard errors are robust. The results are Tobit models, but are robust to a Poisson specification (as
well as not taking the natural log). ***, **, and * indicate statistical significance at the 1%, 5%, and
10% levels, respectively. All models include controls for year and region fixed effects as well as
revenues, capital, employees, firm age, overseas returnee, and bach. graduation year. 37

Robustness checks
1. Returns to talent in wage employment
2. MIT Data – 1998-2000 dotcom boom
3. Increased legitimacy
4. Alternative talent measures
5. Universities and research institutes began to invest and had better
information on underlying ability levels
6. Demography of the funders/lead investor community itself might have
changed and preferences towards higher ability individuals or technically
skilled entrepreneurs (dotcom)
7. The nature of the economic opportunities or competition changed as China
liberalized and the available opportunities required those with higher ability
levels. Or payoff to skills, demand for innovation/R&D in the economy
increased
8. Labor market, Increased college student enrollment, flood of graduate
students with limited wage job opportunities
9. Specification

Dotcom Boom (MIT Alumni Data)
Dependent Variable = Start-up founded
(subjects start being at risk upon graduation)
Independent vars. Note: reported coefficients are hazard ratios
Software firms
Software firms
only, only EE&CS All Grads, All firms
only, all grads
grads
Master’s degree 0.281*** 0.767 1.226
(0.120) (0.204) (0.098)
Doctorate Degree 0.249** 0.733 1.261
(0.153) (0.333) (0.127)
Master’s x Years 98-00 7.466** 1.936* 0.936
(5.844) (0.741) (0.152)
Doctorate x Years 98-00 5.560* 1.446 0.905
(5.683) (0.849) (0.185)
Non-U.S. citizen 1.866* 1.172 0.825
(0.634) (0.292) (0.078)
Gender (male=1) 5.814* 3.305*** 1.495
(5.934) (1.302) (0.169)
Years 1998-2000 0.001*** 0.002*** 0.011
(0.001) (0.001) (0.002)
Obs. 3,266 18,896 19,188

Note: Grad. years 1980 and after; 52 failures; 44,525 total years at risk; ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively. All models include controls
for Bachelor‟s graduation year (age), Bachelor‟s Major (academic department).

6. Conclusion and Implications
Hypothesis: Supported?
Decr. barriers to growth = increases entrep. by
H1 YES
those higher in talent distribution
Talent = higher performance in
H2 YES
entrepreneurship

• Institutional Level
• Second margin at which the institutional environment affects entrepreneurship
• Different market failures affecting diff. individuals and start-ups, at different
stages in market development
Social Welfare Analysis
•
ΔSW IndividualΔPS
= ΔCS + Level
Δ CS •= Δp(Innovation)policies to encourage high ability entrepreneurs (need more
Suggestive of + Competition
analysis here)
Δ PS •= I(profith-profitl) –characteristics associated with performance/innovation
Human capital C(prev. organization)
Induce greater investments in certain types of human capital
• Strategy
• Large firm new ventures
Cofounder / Investor Choice Analysis
• Understanding the drivers of high growth entrepreneurship
Increased probability of survival or growth
• Institutional environment can shift competition and possibly the types of start-
Extra equity or salary to obtain cofounder (amt. required affected by inst.
ups being created
change)
40

Strategy / Economics of Innovation and Technological Change

High Tech
Entrepreneurship
(selection and technical skills and science and
•Interact
performance)
engineering / industry context
•Regional level policy experiments
•Comparison with US data
Current
Research

Labor Economics Institutional
(individual/micro- Variation
level) (macro-level)

Charles Eesley

Research Trajectory
•Eesley, Charles. 2009. Who has „The Right Stuff‟? Human Capital,
Entrepreneurship and Institutional Change in China. Winner SASE
Student Paper Award, June 2009.
• Eesley, Charles, Lenox, Michael. 2009. Secondary Stakeholder
Actions and the Selection of Firm Targets. Working paper.
•Eesley, Charles; Roberts, E.B. 2009. Cutting Your Teeth: Learning
from (One or More) Rare Experiences. Under review.
•Rockart, Scott & Eesley, Charles. 2009. Prestige and Collaboration
Patterns. Work in progress. Entrepreneurial Firms
•Roberts, E.B.; Eesley, Charles. Entrepreneurial Impact: The Role of
- Selection
MIT. Kauffman Foundation, Level 2009.
Institutional
Feb.
• Lenox, M. and Eesley, Charles. 2009. Private Environmental
(Stakeholders) - Strategy / Idea
Strategy Activism and the Selection and Response of Firm Targets. Journal
- Outcomes
•Hsu, D.H., Roberts, E.B., Eesley, Charles. 2009. Entrepreneurial
- Rate and Direction of Economics Management and Strategy, 18(1), Jan. issue.
of
Innovative Activity Ventures from Technology-Based Universities: Evidence from MIT.
Established Firms
Work in progress.
• Eesley, Charles; Lenox, Michael. 2006. Firm Responses to
- Selection
Individual Level
Secondary Stakeholder Action. Strategic Management Journal,
- Strategy / Idea
•Hsu, D.H., Roberts, E.B., Eesley, Charles. 2007. Entrepreneurs
27(8):765-781. - Outcomes
from Technology-Based Universities. Research Policy, 36: 768–
788.
• Sloan, Frank A.; Eesley, Charles. 2007. Implementing a Public
Subsidy for Vaccines. in Pharmaceutical Innovation: Incentives,
Competition, and Cost-Benefit Analysis in International Perspective.
edited by Frank A. Sloan and Chee-Ruey Hsieh. New York:
Cambridge University Press. Right Stuff
Charles Eesley - The

Directions for Future Work
Focus on research designs disentangling causal mechanisms in
commercializing innovation
Field Experiments – US or abroad
Natural Experiments
- Work well for institutional and policy questions
• Add advisors, Strategic choices
• Education/Training
Instrumental Variables
• Upgradeto find instruments in strategic contexts
- Difficult technology

Individual Level
• High Tech Entrepreneurship
• Joe-the-Plumber < Joe-the-bio/nano-tech-founder
• Commercializing Innovation

Different Institutional Environments
• MIT and Tsinghua Data
• India and other countries

Thank you!

Chuck Eesley
eesley@mit.edu

Performance
Log(profit Pr(IPR
Independent margin) Log(employees) important)
Variables (5-1) (5-3) (5-5)

Promoted -0.118 (0.228) 0.380* (0.211) 0.042 (0.966)

Log(work exp.) 0.986 (1.230) -2.447** (1.094) 0.668 (4.723)

Years of Education 0.004 (0.086) 0.152* (0.082) 0.850** (0.407)
Talent (income
residual) 0.410** (0.180) -0.12 (0.159) 1.535* (0.837)

Prior salary -0.442** (0.170) 0.173 (0.150) -0.231 (0.727)

Overseas 0.731** (0.359) 0.128 (0.305) 2.120 (1.672)

High GPA 0.308 (0.232) -0.321 (0.213) -0.848 (0.914)

Worked in R&D - - 1.639* (0.925)

N=150; Standard errors are in parentheses. * p< 0.10, ** p< 0.05, *** p< 0.01


Index of Backup Slides
• Future Work • Quotes
• Identification • Types of Talent
• Theory contribution • Web survey
• Constructs/Measures • Four Levels of Social Analysis
• Response Bias • Robustness checks
• Boundary Conditions – Returns in wage emp.
• Opportunity Costs – U.S. data
• Descriptive statistics – Placebo regression
– # jobs, industries – Legitimacy
• Implications – Parallel Trends
• China policy reforms – Logit
• Tsinghua history – Macroeconomic data
• Roy Model – Proportional Hazards

Theoretical contribution
• Economics of technological innovation and entrepreneurship
• Rational expectations, profit max. – not necessarily
• Institutional theory
• Talent theory of bubbles or technology progress
• Theory of the direction of innovation (non-SCOT)
– artifact – larger scale story
• Large vs. small firms tech waves of creative destruction
• Industry differences
– more room for strategically influencing perceptions of payoffs
– VC herding behavior
• Strategic influencing directions (e.g. Eyeonics, YouTube, vaccines
- less competition)


Reform Easing Constraints on Entry
1988
Reform officially recognizing private businesses with more than 8
employees (Xu and Zhao, 2008)

Lower barriers to entry  Lower quality entrepreneurs
• Nanda, 2008
• Xu and Zhao, 2008

In the years following the 1988 reduction in barriers to entry individuals located
relatively lower in the talent distribution were more likely to found firms.


Methods
Cox Hazard Rate Model
(robust to logit)
Prob (founding a firm = 1) = F(α + 1’θi + ’Xi + τt+ ηj + φa+εi)

• Dependent variable: First start-up founded
• Subjects start being “at risk” of founding a firm at the time of their
graduation
• Θi = human capital measures
• Xi = Set of controls academic dept., region, education, work history, job
type, comm. party, overseas educ. or work, family economic status.

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


Reduced Barriers to Entry: 1988 - 1999
Dependent Variable = Start-up founded (subjects start being at risk
upon graduation)
Note: reported coefficients are hazard ratios; coefficients below 1.0 represent a
Independent Variables decreased likelihood of entrepreneurship (N=1,540)
Master’s degree 0.675* 0.562**
(0.158) (0.152)
Doctorate degree 0.344** 0.641
(0.166) (0.323)
Low work exper. (0-10 yrs.) 1.333 1.500
(0.351) (0.442)
High work exper. (>30 yrs.) 0.060*** -
(0.039) -
Promoted 0.689 0.686
(0.251) (0.270)
High GPA (above median) 0.685* 1.288
(0.150) (0.325)
Last Salary (Pre-founding) 0.667*** 0.694***
(0.065) (0.071)
Communist Party member 0.760 0.744 0.807 0.77 0.774 0.776
(0.167) (0.166) (0.176) (0.169) (0.173) (0.185)
Controls for academic dept., region, education, work history, job type, comm. party, overseas educ. or
work, family economic status, graduation year, major, and region fixed effects.

Note: 102 foundings; 30,716 total years at risk; ***, **, and * indicate statistical significance at the 1%, 5%, and
10% levels, respectively. All models include controls for Bachelor‟s graduation year (age), Bachelor‟s Major 51
(academic department), and region fixed effects.

Income Regression
Ordered Logit Dependent variable = salary
Independent Variables category (1-6)
Master’s degree 0.466***
(0.051)
Doctorate degree 0.905***
(0.051)
Work exper. (0-10 yrs.) 0.938***
(0.054)
Work exper. (10-30 yrs.) 1.065***
(0.047)
Work exper. (>30 yrs.) 0.500***
(0.055)
Gender (male=1) 0.498***
(0.058)
GPA quartile (1st = top) -0.199***
(0.046)
GPA quartile (3rd) 0.514***
(0.046)
GPA quartile (4th = bottom) 0.047
(0.045)
Overseas exper. 0.407***
(0.043)
Academia -0.943*** Bach. Dept. and Year Effects
(0.051) included.
Business 0.335*** Standard errors are in
(0.052) parentheses. * p< 0.10, ** p<
Government -0.326*** 0.05, *** p< 0.01
(0.044)
Pseudo R2 0.148
Number of observations 561 52

First Foundings by Year

35

30
Number of Start-ups

25

20

15

10

5

0

First Founding Year

Industry Breakdown for First Firms
NUMBER OF
INDUSTRY FIRMS %
AEROSPACE 3 0.90
ARCHITECTURE 13 3.88
BIOTECH AND DRUGS 7 1.09
CHEMICALS 8 2.39
CONSUMER
PRODUCTS 17 5.07
ELECTRIC 12 3.58
ELECTRONICS 69 20.60
ENERGY 14 4.18
FINANCE 10 2.99
INTERNET 33 9.85
LAW, ACCOUNTING 22 6.57
MACHINERY 19 5.67
MANAGEMENT 21 6.27
MATERIALS 13 3.88
MED DEVICES 4 1.19
OTHER MFG 16 4.78
PUBLISHING 11 3.28
SOFTWARE 34 10.15
TELECOM 9 2.69
TOTAL 335 100

Income Regression Residuals

Founders only

Matched sample

Acknowledgements
• Funding - Ewing Marion Kauffman Foundation
Dissertation Fellowship, Charles Zhang, founder and CEO
of Sohu.com, and the MIT Entrepreneurship Center
• MIT
– Professors Diane Burton, Fiona Murray, Edward B. Roberts
– Yanbo Wang (PhD student)
– Profs. Yasheng Huang and Elena Obukhova
– Undergraduate Research Assistants
• Stephanie Yaung, Jennifer Sim, Celia Chen
• Tsinghua University
– Prof. Delin Yang
– Li Zhihua (Tsinghua Alumni Association)


Percentage of "At-Risk" Individuals Becoming
Entrepreneurs (By Education Level)
4

3.5

3

2.5
Percentage (%)

2
Graduate Degree
Bachelor's Only
1.5

1

0.5

0


Returns to Talent in Wage Empl.
Ordered Logit Dep. Var = salary (6 bands)
Master’s degree 0.693*** Non-entrepreneurs only
(0.137)
Robust to high salary and last salary
Master’s x POST -0.228 as well as negative binomial
(0.157) specifications.
Doctorate degree 1.211***
Controls for bachelor‟s graduation
(0.237) year, age, gender, overseas, comm.
Ph.D. x POST -0.782*** Party, and family economic status.
(0.243)
High GPA 0.384**
(0.170)
High GPA x POST -0.292
(0.193)
Ln(yrs. tenure) -0.028*** -0.033***
(0.005) (0.008)
Business 0.179 0.24
(0.149) (0.238)
N=3,276 job spells; Robust
Government -0.883*** -1.054***
standard errors (clustered
(0.160) (0.244)
at individual level) are in
Academia -0.646*** -0.659***
(0.165) (0.248)
parentheses. * p< 0.10, **
Years 2000-07 (POST) 1.760*** 1.348*** p< 0.05, *** p< 0.01
(0.157) (0.177)

Robustness checks
1. Returns to Talent in wage employment
2. MIT Data – 1998-2000 dotcom boom
3. Increased legitimacy
4. Alternative talent measures
5. Universities and research institutes began to invest and had better
information on underlying ability levels
6. Demography of the funders/lead investor community itself might have
changed and preferences towards higher ability individuals or technically
skilled entrepreneurs (dotcom)
7. The nature of the economic opportunities or competition changed as China
liberalized and the available opportunities required those with higher ability
levels. Or payoff to skills, demand for innovation/R&D in the economy
increased
8. Labor market, Increased college student enrollment, flood of graduate
students with limited wage job opportunities
9. Specification

Robustness – legitimacy
high status = Ever job gov., Comm. Party, Ph.D.

Independent Variables Dependent Variable = Year start-up founded
(subjects start being at risk upon graduation) Note:
reported coefficients are hazard ratios (N = 1,910)

(1) (2)
Years Education - 0.537*** (0.072)
Education x Post-1999 - 1.737*** (0.259)
High Status (Gov., Ph.D.
and Comm. Party) 1.442 (0.401) 1.269 (0.341)
Status x Post-1999 0.590* (0.187) 0.725 (0.224)
Post-1999 dummy 0.055*** (0.014) 0.000*** (0.000)
1991-1999 dummy 0.071*** (0.011) 0.064*** (0.010)

pair-wise correlations High Status
Years of Education 0.101
High GPA 0.080
Promoted 0.040
Ln(work exp.) 0.066
Parents’ Education 0.046
Back
N= 626; Standard errors are in parentheses. * p< 0.10, ** p< 0.05, *** p< 0.01

Placebo Regression
Dependent Variable = Start-up founded (subjects start being at risk upon graduation)
Note: reported coefficients are hazard ratios

Years of Education 0.630*** (0.086)
Educ. x POST 1.082 (0.205)
GPA High 0.726 (0.231)
GPA x POST 0.716 (0.317)
Promoted 0.218** (0.131)
Promoted x POST 7.714** (7.210)
Ln(workexp) 0.454*** (0.116)
Work exp. x POST 0.367*** (0.119)
Income residual 0.717 (0.179)
Residual x POST 1.333 (0.450)
Years 1997-1999
(POST) 0.160 (0.521) 0.690 (0.256) 0.083*** (0.079) 4.362* (3.346) 0.887 (0.349)
Years 1990-1996 0.206*** (0.065) 0.307*** (0.098) 0.216*** (0.070) 0.258*** (0.097) 0.479* (0.182)

Controls for academic dept., region, education, work history, job type, comm. party, overseas
educ. or work, family economic status, graduation year, major, and region fixed effects.

N=1,821; Note: 119 failures; 20,541 total years at risk; ***, **, and * indicate statistical
significance at the 1%, 5%, and 10% levels, respectively.

Identification
Concerns: Tests:
• 1999 reform lowered entry • MIT Data
barriers • Placebo regression
• Reform had broader effects • Direct test of returns to
• More continuous changes talent in entrep.
• Reform did not increase • Incr. in foundings controlling
returns to entrepreneurship for macro-economic vars.
• Simultaneous changes with • Returns in wage empl.
similar results on growth • Qualitative data
barriers


Boundary Conditions
1) Types of skills and talent necessary to overcome entry barriers are
not largely orthogonal to those useful for firm growth

2) Feedback loops are weak: that is, increases in the number of
entrepreneurial firms do not strongly increase competition or
create significantly better wage employment opportunities

3) Initial relationship between talent and returns to entrepreneurship
is not one where primarily those at the top of the talent distribution
become entrepreneurs

4) Sufficient variation in the distribution of talent (by measures
relevant for wage and entrepreneurial payoffs) in the sample at
risk for entrepreneurship

Back

Institutional Initial institutional
Human Capital environment #2 environment
Market Ability /

Higher Non-Market Ability
(Gov. connections,
bureaucracy nav., wealth)
The increase is among those of relatively higher
ability.

Back

Kaplan-Meier curves


Kaplan-Meier curves

BackBackup

Tests and graphs based on the Schoenfeld residuals


Histogram of Number of Jobs
60

50

40
Percent

30

20

10

0
0 1 2 3 4 5

Number of Jobs Reported


Proportion of Foundings with a Direct Match to the Industry and Founder's
Bachelor's Degree

0.45

0.4

0.35

0.3
Proportion

0.25

0.2

0.15

0.1

0.05

0
Before 1999 After 1999

* Differences are statistically significant (p<0.05; t = 1.759)


Human Capital and Opp. Costs
wages = a+ *schooling
high-HC people can either have high a or high

wages=a+ 1*HC+( 2-c)*entrep+ 3*HC*entrep


Human Capital and Opportunity Costs
(incr. opportunity costs, decreased entrepreneurship)

$

Wage empl. income

Entrepreneurial
Returns

Higher Human
Capital

(Incr. opportunity costs, decreased entrepreneurship)
Entrepreneurial
$ Returns

Wage empl. income

Higher Human
Capital


(incr. entrep. returns to talent)
$

Wage empl. income

Entrepreneurial
Returns

Higher Human
Capital

Robustness: Logit
Dep. Var = 1 if founded a firm
Indep. variables Graduation Years < 2002
Talent (income resid.) -0.400 -0.643
(0.460) (0.521)
Talent x POST 1.099** 1.454***
(0.475) (0.544)
Years of Education -0.402 -0.974
(0.311) (0.723)
Educ. x POST 0.320 0.424
(0.319) (0.739)
Promoted -0.398 0.678
(0.900) (1.718)
Promoted x POST 0.294 -0.873
(0.957) (1.796)
POST -4.722*** -9.964* -4.742*** -11.388
(0.943) (5.544) (0.915) (13.029)
Observations 451 1277 1277 451
Controls for academic dept., region, education, work history, job type, comm. party, overseas educ. or work, family
economic status, graduation year, major, and region fixed effects.

Constructs and Measures
• Human Capital/Talent – education, parents’ education, GPA,
work experience, income, income residuals, student leader

• Variance of returns – categories of wage income, income from
start-up, profit

• Barriers to Growth

• Status/Legitimacy – PhD, government


Appendix A
Comparison of Key Demographic Characteristics by Survey Wave
Variable Responded before Responded during/after Aug. t-stat for equal
Aug. 2007 2007 means
(N=2,667) (N=299)
Age 49.3 54.1 -4.216**
Age (founders only) 38.4 37.4 0.602
Bachelor’s Graduation Yr 1980.9 1977.4 3.777**
Bach. Grad yr (founders only) 1991.6 1993.2 0.941
Years of Education 17.2 17.0 2.381**
Entrepreneur parents 0.09 0.12 -0.713
Entrepreneur 0.29 0.40 -2.168**
Privatized 0.10 0.05 1.392
First start-up founded 2000.3 2001.1 -0.661
Tech only 0.28 0.29 0.757
Business only 0.10 0.09 0.235
Gender 0.88 0.90 0.901
Family economic status 3.75 3.85 -1.871*
High Salary 3.21 2.93 3.351**
Avg. Tenure 6.94 8.01 -2.045*
Overseas work exp. 0.26 0.26 -0.126
Number of positions 2.39 2.26 -2.012*
High government 0.03 0.03 -0.239
Low government 0.18 0.17 0.617
Last job academia 0.19 0.19 -0.051
Ever job academia 0.32 0.27 2.323**
Last job business 0.62 0.61 0.348
Student Leader 0.61 0.57 0.874
GPA Rank 2.28 2.58 -2.661**
Bach. Grad Yr. 10th percentile 1954 1953 --
Bach. Grad Yr. 90th percentile 2001 2001 -- 77
Back

Benchmarking Tsinghua Data
Categories Tsinghua CHNS NBS HH survey NBS HH survey

Sample Urban Rural and Urban Urban – self- Urban –
employed non.Entrep.
Male 0.89 0.53 0.56 0.50
Age 50.13 41.45 36.2 37.2
Married 0.88 0.98 83.4 84.2
Years of Education 17.1 9.1 9.2 9.4
Household Size 3.40 3.9 -- --
Self-employed 0.26 0.14 (4% in 1999) --
(0.8% in 1999)
Experienced a 0.13 -- 0.26 0.19
layoff
Father’s Educ. 4.11 -- 5.4 5.2
Mother’s Educ. 4.89 -- 6.0 5.9
Parent Self-Empl. 0.08 -- 0.06 0.05
Comm. Party 0.62 -- 0.05 0.18

Work History Attributes
Non-Founders Founders
t-stat
(n=2152) (n=670)
Variable Mean
Age 52.8 42.9 12.388***
Entrepreneur Parents 0.036 0.031 0.614
Privatized 0 0.260 -
Male 0.879 0.933 -3.931***
Family Econ. Status (category) 3.834 3.639 4.348***
Recent Salary (category) 2.317 2.045 3.686***
Avg. Tenure 8.074 4.813 7.554***
Overseas 0.126 0.212 -5.525***
Number of Positions 2.115 3.109 -18.605
Ever Job High Government 0.036 0.042 -0.713
Ever Job Low Government 0.242 0.176 3.574***
Last Job Academia 0.166 0.081 5.475***
Ever Job Academia 0.233 0.207 1.402*
Last Job Business 0.399 0.687 -13.451***
Master’s 0.402 0.550 7.187***
Ph.D. 0.101 0.115 1.066
Student leader 0.674 0.903 -5.314***

Percentage of "At-Risk" Alumni Body Becoming
First-Time Entrepreneurs By Year
1.4

1.2

1
Percentage (%)

0.8

0.6

0.4

0.2

0


Four Levels of Social Analysis
Williamson (2000)
• Social Embeddedness
– Culture, Religion (Economic
Historians, Anthropologists, Sociologists)

• Institutional Environment
– “Rules of the Game”, Constitutions, judiciary, politics (Poli.
Sci., Inst. Economics)

• Governance
– Contracts, Theory of the firm (Contract/Info Econ, TCE)

• Resource Allocation
– Incentive aligment, Quantities and Prices (Neoclassical
Economists)

Negative Binomial Regressions on Macroeconomic Data

Dependent variable = number of firm
Independent Variables
foundings (1959-2007)
R&D to GDP ratio (t-1) -0.143
GDP per capita (in RMB, t-1) (0.723)
Shanghai Stock Exchange Market Cap (t-1) 3.43E-04*** 3.30E-04***
Domestic Patents Issued (t-1) (8.59E-05) (8.63E-05)
Post-1999 dummy -2.52E-05** -2.36e-05**
Post-1988 dummy (1.16E-05) (1.20E-05)
Constant 5.01E-06 -1.01e-05** -9.48E-06*
Log likelihood (3.60E-06) (4.82E-06) (5.04E-06)
Num. obs. 4.326*** 3.856*** 2.901***
Pseudo R2 (0.652) (0.530) (0.590)

Data merged from the State Statistics Bureau, Chinese Statistical Yearbooks
***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 83

Strategic and Competitive Implications of Different Institutional Shifts

Lower Barriers to Entry Lower Barriers to Growth
Market/Commercializat Increase in high and low ability Unclear predictions on whether
ion Talent is Orthogonal entrepreneurs increase is among high or low ability
to ‘Bureaucratic’ Talent No prediction on the type of Easier to overcome the opportunity
firms founded costs for entrepreneurship
Possibly greatest increase in Increase in high growth firms
marginal firms which were not More high-growth entrepreneurial
profitable with the previously opportunities supports venture
high cost of entry capital
Increased competition (possibly
lower profit margins?)
Market/Commercializat Relative increase in low ability Increase in high ability entrepreneurs
ion Talent is NOT entrepreneurs Easier to overcome the opportunity
Orthogonal to Those who cannot maintain costs for entrepreneurship
wage employment can overcome Easier to recruit talented co-
‘Bureaucratic’ Talent the barriers to entry founders
(a large component is Relative increase in low growth More high-growth entrepreneurial
common between the two) firms opportunities supports venture
Increased competition, smaller, capital
less profitable firms Increase in high-growth firms
Increase in innovative firms (higher
returns can support higher risk of an
innovation strategy)


China’s S&T Policy Reform
1978-85 1986-97 1998-2006

•Deng Xiao •1992 Tour
Ping reform •Private ownership •Adoption of
•Bankruptcy law
report (1975) (1999) medium and Long
for SOEs (1986)
•Open door Term S&T
•National Natural •Promotion of VC/PE Strategic Plan
policy (1978)
Science Fndtn. (1998) (2006)
•National Key (1986)
Tech. R&D •CAS Knowledge
•>7 emp. Innov. Prog. (1998)
Program
permitted
(1984) •Innov. Fund for Tech.
Torch Program
•Univ. reform (1988) SMEs (1999)
(1985)
•Stock Exchange
•SE Zones (1990) •Join WTO (2001)
created (1980)
•1997

Tsinghua Univ.

•Established in Beijing in 1911
•1952 reorganized Soviet style
•1966-1976 Battlefield during Cultural Revolution
•1978 restored departments in sciences, economics and
management, and humanities
•1984 – First Graduate school in China created at Tsinghua
•1998 – Tsinghua Science Park established

86

Model
Roy Model as extended by Borjas (1987)
Labor markets 0 and 1, respectively. Log earnings in the wage sector:
w0 = μ0 + ε0
ε0 ~ N (0, ς02)
The wage sector earnings would be the following if everyone from the wage
sector were to migrate to entrepreneurship (ignoring general equilibrium
effects):
w1 = μ1 + ε1
ε1 ~ N (0, ς12) .
Assume that the cost of becoming an entrepreneur is C π = C/w0
π is constant, meaning that C is directly proportional to w0
The correlation between entrepreneur and wage worker earnings is
ς01 = cov(ς0, ς1). A worker will choose entrepreneurship if


Entrepreneur‟s Perspective on Pre-period (4/5)
• Environment was costly, not prohibitive for entrepreneurship. One
entrepreneur reported that she:

…spent an entire year just looking for the right office space…each product
must be registered and approved by the government. It’s an expensive and
time consuming procedure. She eventually found space for the company’s
first store in a children’s museum which was perfect since they were selling
toy bears aimed at children. This also allowed them to “hide” from
government inspectors.

• Individuals managed to find ways to raise funding

At that time there was not much VC investment in China (1997) so they
raised the first round of money among their family and divided up the
shares according to how much each of them could raise from family. – JX


Interview Quotes

In the 1990s … government was giving them
less support. Many universities created
“university –run” enterprises and were
basically selling off the periphery of
campus.Even high schools had school—run
enterprises that were considered acceptable.
There is a Tsinghua name to many of them. -
GC – 2nd generation investor

90

Interview Quotes, cont.

Strategy-making is different in China than in the US. In China
you pick up a person who has resources, then you choose the
appropriate field to enter then you come up with the strategic
goals, then the vision. In the US he feels that it is the
opposite. - WW – Sea Corp.


Percentage of Businessmen/Businesswomen Becoming Entrepreneurs

3.00%
2.50%
Percentage

2.00% Last Job w as in
Business
1.50%
Ever had a job in
1.00% Business

0.50%
0.00%
90

91

92

93

94

95

96

97

98

99

00

01

02

03

04

05

06
19

19

19

19

19

19

19

19

19

19

20

20

20

20

20

20

20
Founding Year


Percentage of Government Officials Becoming Entrepreneurs

12.00%
10.00%
Percentage

8.00%
High level
6.00% government
official position
4.00% Low level
government
2.00% official

0.00%
90

91

92

93

94

95

96

97

98

99

00

01

02

03

04

05

06
19

19

19

19

19

19

19

19

19

19

20

20

20

20

20

20

20
Founding Year


Why China?
• Tech-based entrepreneurship in developing countries
rarely appears in academic literature (Lu 1997, 2000;
Puga and Trefler, 2005)
• Vernon‘s (1966) product-cycle model

• 19892004 China 29% vs. US 1% (State Statistics
Bureau)
19782004 # employed in private business up 300X

• Policies and institutions changing rapidly (Cull &
Xu, 2006; Nee, 1998; 1992; 1996; Peng &
Heath, 1996; Steinfeld, 2007)

Context


Entrepreneurship and Institutions

(Gollin, JPE 2002)

Research Trajectory

Science and Tech.-based Entrepreneurship
Hsu, D.H., Roberts, E.B., Eesley, Charles. 2007. Entrepreneurs from
Technology-Based Universities. Research Policy
Roberts, Edward B., Hsu, David , Eesley, Charles. Entrepreneurial
Ventures from Technology-Based Universities: An Empirical First
Look.
Eesley, Charles. Roberts, Edward B. The Second Time Around?: Serial
Entrepreneurs from MIT.
Eesley, Charles; Roberts, Edward. Cutting Your Teeth: Learning from
(One or More) Rare Experiences.
Hsu, D.H., Roberts, E.B., Eesley, Charles. 2007. Experienced
Founders and Early Financing. (in progress)
Eesley, Charles. Who Has the Right Stuff? (in progress)


Future Work
•Eesley, Charles. 2008. Who has „The Right Stuff‟? Human
Capital, Entrepreneurship and Institutional Change in China. Winner
SASE Student Paper Award, June 2008.
Individuals
•Eesley, Charles; Roberts, E.B.Firms Cutting Your Teeth: Learning
Entrepreneurial 2008.
from (One or More) Rare Experiences. Under review.
- Selection
•Hsu, D.H., Roberts,- E.B., Eesley, Charles. 2007. Entrepreneurs
Outcomes
from Technology-Based Universities. Research Policy, 36: 768–
Institutional Level
Strategy 788.

- Rate and DirectionEesley, Charles, Lenox, Michael. 2008. Secondary Stakeholder
• of
Innovative Activity
Actions and the Selection of Firm Targets. Working paper.
Stakeholders
•Lenox, M. and Eesley, Charles. 2009. Private Environmental
Established Firms
Activism and the Selection and Response of Firm Targets. Journal
- Selection
of Economics Management and Strategy, 18(1), Jan. issue.
- Outcomes
•Eesley, Charles; Lenox, Michael. 2006. Firm Responses to Level
Institutional
Secondary Stakeholder Action. Strategic Management
Journal, 27(8):765-781. - The Right Stuff
Charles Eesley

The Right Stuff

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