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Project 985

Tsinghua Alumni Survey

Charles Eesley

on 4 April 2017

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Transcript of Project 985

On-going and Future Work
Most Work on Strategy and Entrepreneurship
Done in Developed Economies
Institutions linked with long-run economic growth (Acemoglu, 2002; 2005; Porta, 1998)
Transition economies - Rona-Tas (1994) Walder (2003) Johnson, McMillan & Woodruff (2002) Nee (1996)

Emerging Institutions-based view (Baumol, 1990)

How do institutions shape the proportion of innovating firms?
Institutional Environment and Competitive Advantage
Successful entrepreneur, developing economy?

Individual characteristics, context characteristics, and their interaction drive performance
Team and industry (Eisenhardt and Schoonhoven, 1990)

Institutions – political, social, legal (formal and informal) constraints on indiv. and orgs. (Scott, 2001; North, 1990)

As institutional environment changes, individual and context characteristics that drive performance change

Transition – government planning and control - market institutions
H1: In the beginning, … entrepreneurs with government ties will create larger firms compared to entrepreneurs without such ties. (father in government)
H2: …, entrepreneurs in locations where government control was initially relaxed will create larger firms. (coastal regions, privatized)
H3: … entrepreneurs who can access programs by non-gov. institutions (such as science parks) to create entrepreneurial behavior will create larger firms. (science parks)
H4: In institutional environments that have transitioned … to market-based institutions, greater competition will make entrepreneurs with exposure to the firm creation process and those who are innovating create larger firms. (entrep. index, serial, innovation)
2004 2007 2010
Deng Xiao Ping Southern Tour
1997 Private sector reform
1999 Constitutional Amendment Private firms
1998 Tsinghua Science Park
1998 CAS Knowledge Innovation Program
VC/PE Reforms
1998 Innovation Fund for Tech SMEs
2001 WTO
Growth of Incubators/Science Parks
2006 Adoption of medium and Long Term S&T Strategic Plan
Tsinghua alumni data ends in 2007

Interviews suggest some reversal in movement towards market-based institutions
Entrepreneurs indicate more govt. interference, stronger SOEs, higher taxes, intrusive labor and disability laws
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, Simons & Roberts, 2007; Baron et al. 1996; 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)
***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Low entry barriers + high growth barriers Low human/social capital entrepreneurs, low growth startups

Low entry barriers + low growth barriers High human/social capital entrepreneurs, high growth, innovative startups

Institutional environment
can shift competition and
possibly the types of
start-ups being created
Conclusions and Implications
Institutional Change
Increased Entrepreneurship
Low human/social capital
Low-growth, low-tech, self-employment, replicative entrepreneurship
Organizational level effect
High human/social capital individuals
high-growth, high-tech,
Greater economic impact, innovative entrepreneurship
Chuck Eesley
Management Science & Engineering
Stanford University

Jian Bai (Jamber) Li (NUS)
Delin Yang (Tsinghua Univ)
(with support of a Kauffman Foundation Dissertation Fellowship, the Tsinghua Univ. Alumni Association, and the MIT Entrepreneurship Center)
62.5% in engineering
11.9% in sciences
12.9% in humanities (architecture, medicine and law comprise the remainder) 25-30% women
19.2% doctorate degrees
53.4% graduate degrees

Tsinghua survey sample:
62.2% engineering
10.6% sciences
13.7% humanities
28% women
19.3% doctorate degrees
53.9% graduate degrees
Focus on Human Capital as a Driver of Selection

Talent and labor skills- Roy Model (1951) Selection on Potential EarningsIdentification requires exclusion restrictions, exogenous shocks, or instruments affecting returns or skill in one sector onlyJack-of-all tradesLiquidity Constraints – Evans and Jovanovic (1989)Assets are endogenous, static models, typically poorly measuredBuera (2008) 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

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

Differences-in-differences estimation

Innovation = F(α + Ai’θi + B’ θi*POST + C’POSTi + ’Xi + τt+ ηj + φa+ εi)

Dependent variable: Importance of IP, Innovation, Performance

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)
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

1. Lowering barriers to entry increases entrepreneurship among lower human capital individuals.

2. Lowering barriers to growth increases entrepreneurship among higher human capital individuals.

3. High human capital individuals start firms that grow larger.

4. While as expected firms grow faster/bigger when barriers to growth are lower, firms founded by high human capital people grow particularly bigger.
Drivers of entrepreneurial entry and performance (different contexts)

Science Park Institutions?: How Ventures Acquire Resources to Innovate in Emerging Economies (with Daniel Armanios)

Bankruptcy reform, IPO reform (with Bob Eberhart)

Skill Transfer and Startup Chile (with Mike Leatherbee)

Institutional Flexibility and Entrepreneurship (with Delin Yang)
Does Institutional Change in Universities Influence High-Tech Entrepreneurship? Evidence from China’s Project 985
Chuck Eesley (Stanford), Jian Bai (Jamber) Li (NUS), Delin Yang (Tsinghua)
The Research Center of Intelligent Robotics (RCIR), approved by the Project 985, was established in 2000.

15 faculty members in this Center, gathered together from Lab of Robotics and Automation, Institute of Precise Engineering and Intelligent Micro-System, and The Research Institute of Micro/Naro Science and Technology in Shanghai Jiao Tong University.
A researcher conducts a practice of a man-to-computer communication in a scientific experiment at the Laboratory of the Tianjin Medical University in north China’s Tianjin municipality. The Tianjin Neural Engineering Research Centre was established as a result of cooperation between Tainjian Medical University and Tianjin University.
Shanghai Yingsi Software Technical Co. Ltd. has developed various kinds of robots that will demonstrate state-of-the-art Chinese robotics.
The Institute of Robotics at the Shanghai Jiao Tong University will demonstrate intelligent wheelchairs that will allow people to use speech, sign language and even brainwaves to command the robots

Project 985 (Chinese: 985工程; pinyin: 985 gōngchéng) is a project first announced by Chinese President Jiang Zemin at the 100th anniversary of Peking University on May 4, 1998 to promote the development and reputation of the Chinese higher education system (and codenamed after the date of the announcement, 5/98 or 98/5, according to the Chinese date format).[1]

The project involves both the national and local governments allocating large amounts of funding to certain universities[2] in order to build new research centers, improve facilities, hold international conferences, attract world-renowned faculty and visiting scholars, and help Chinese faculty attend conferences abroad.

When first announced in 1998, the project funding was made available to an elite group of 10 universities.[3] By the end of the first phase of the project, 34 universities were sponsored. In the second phase of the project, five more universities were added to the project, bringing the total number to 39. It was announced in September 2007 that the project will not admit other universities.
Energy R&D
Although there are many reasons to be skeptical of a simplified “linear model” of research, “basic” science can be thought of as opening up new “search distributions” for applied researchers, raising the productivity and level of applied research effort in the short run (Bush 1945, Aghion, Dewatripont, Stein 2008). Applied research is a search process that exhausts the technological opportunities within a particular field. The rate of progress of basic knowledge by this model, determines the rate of technological change (Evenson, Kislev 1976, Adams 1990, Adams 1993, Kortum 1997). Several scholars have developed models of R&D investment as a driver of firm growth (Klette and Griliches, 2000; Klette and Jakob, 2004; Aghion and Howitt, 1992; Grossman and Helpman, 1991; Lentz and Mortensen, 2008; Klepper and Thompson, 2006). However, these models do not deal well with important aspects of imitation or the large fraction of presumably imitating firms reporting no R&D activity. David and Hall (2000) reviewed the literature on public-private R&D interactions and offer a useful framework for the existing theoretical literature. They note that there has been underinvestment in sorting out the channels and mechanisms of influence involved.
There has been an extensive empirical literature on the indirect effects known by the term “knowledge spillovers” and since these studies have been reviewed elsewhere, we only briefly mention the results and empirical difficulties (Jaffe 1996, Griliches 1992). The stream of research on knowledge spillovers from academic research seems consistent in linking at a local geographic level, university R&D and publications with increased patenting by firms (Jaffe 1989, Jaffe, Fogarty, Banks 1998, Jaffe 1996, Henderson, Jaffe, Trajtenberg 1998; Barnes et al. 1998, Klette and Griliches, 2000; Mowery et al. 1998, Kim et al. 2005).
Several empirical articles find evidence of a positive relationship between public R&D expenditures and firm innovation (Balasubramanian and Sivadasan, 2008; Branstetter and Ogura, 2005; Lentz and Mortensen, 2008; Rothaermel and Hess, 2007). Notably, Furman and coauthors (2002) are one of the few papers to examine the correlation between aggregate country-level patenting and measures of public R&D expenditures and access to venture capital funding. In a follow-on paper, Furman and Hayes (2004) find that similar measures of investment in R&D and in science and technology personnel are associated with higher levels of investment in those countries that experienced substantial increases in innovation over the time period. As David et al. (2000) discuss; this analysis does not break out the mechanisms of the type of public R&D expenditures or the possible lags through which different mechanisms are working. Yet, the analysis is suggestive that public R&D investments are associated with greater country level patenting at the technology frontier. On the other hand, Da Rin and coauthors (2006) find no evidence of a correlation with total country-level public R&D (where this measure includes all business and government, contract and grant-based funding, and university R&D) contemporaneous with the measured VC investment. Other work has also found evidence that increased government funding or subsidies lead to more private R&D in some firms but not all (Mansfield and Switzer, 1984; Lach, 2002; Cassiman and Veugelers, 2006). In contrast to the literature above, our emphasis is on adoption of an innovation strategy rather than on the relative levels of innovation taking place.

Theoretical motivation:

Further develop cognitive and normative pillars of institutional

Do these institutional changes work? How do they work?

“Rules”: institutional boundaries for organizational activities (North, 1990)
Prohibition - soft drink firms (Hiatt et al., 2009)
Barriers to growth - (Eesley, 2016)

Taken-for-granted understandings, and values guide how entrepreneurs process info, make decisions

Influencing entrepreneurs by altering their beliefs (Powell and DiMaggio, 1991)
Belgium: Action Plan for Education 2011-2014 - create positive attitudes towards entrepreneurship (EACEA, 2012)
Background: Policy and Entrepreneurship

Taken-for-granted understandings, and values guide how entrepreneurs process info, make decisions

Influencing entrepreneurs by altering their beliefs
Belgium: Action Plan for Education 2011-2014 - create positive attitudes towards entrepreneurship (EACEA, 2012)
Startup Chile
Background: Influence of Beliefs

Rule changes affect entrepreneurship
Entrepreneurs’ beliefs influence strategic decisions

RQ: How do policy initiatives that change educational institutions affect entrepreneurs’ beliefs, behaviors, & firm performance?
Research Question
Gov't Policy:
Launched May 4th, 1998
Aim: increase the national innovative and technological capability of China

Increase beliefs about the importance of intellectual property and innovation
Provided funding to universities for R&D (10-20% increase per year for 5 years, $276M to Tsinghua 2016$)
At discretion of the univ.
Equipment, labs, international conferences, attract overseas researchers, new curricula
Project 985
Project 985’s influence on alumni entrepreneurs:
Altered values and beliefs

Changes in beliefs regarding IP
Concept of IP

H1: Entrepreneurs who graduated from 985 universities after Project 985 was implemented are more likely to hold the belief that IP is important.
Project 985 and strategic behavior

Beliefs act as guides for how to interpret info & make decisions (Rindova and Kotha, 2001; Tripsas, 2009)
View innovation and IP as important
Role model effect (Prideaux et. al., 2000; Jordan et. al., 2003)

H2: Entrepreneurs who hold the belief that IP is important are more likely to engage in technologically intensive activities.

Mixed predictions on performance effects of innovation:

Startups need patent protection and ability to contract for complementary assets to profit from innovation (Teece, 1986; 2006)
Inconsistent with the broader institutional environment
Inconsistent with traditionally effective strategies (political ties)

H3: Firms that engage in technologically intensive activities will exhibit lower performance
Tsinghua alumni survey: 723 entrepreneurs
570 - highest degree from 985 university
153 - highest degree from non-985 university
Slight increase in entrepreneurship post-985, no differences in human/social capital levels

Difference in where individuals received highest degree enables us to test our hypotheses
Dependent Variables
IP Importance: measure of alumni’s beliefs regarding the importance of IP protection

ln(R&D intensity): measure of entrepreneurs’ activities regarding technology innovation

ln(Revenues): measure of performance (most recent year)

Independent Variables

Human Capital (Overseas, Masters, PhD, serial)
Social Capital (govindex, student leader, Communist Party)
University (Highest University Rank)
Firm-level controls (Firm Size, firm age)
Industry fixed effects
Macroeconomic conditions (GDP)
Independent Variables

Human Capital (Highest University Rank, Overseas, Masters, Phd)
Social Capital (govindex, student leader, Communist Party)
Prior entrepreneurial experience (serial)
Firm-level controls (Firm Size)
Industry fixed effects
Macroeconomic conditions (Year Founded or Privatized)
Treatment group: Tsinghua alumni who received highest degree from 985 university
Control group: Tsinghua alumni who received highest degree from non-985 university
Treatment and control universities are matched along key attributes

Pre - post difference based on graduation year
Research Design
Prior literature:

Successful cases of bottom up institutionalization
Institutional contradictions as a source of innovation and institutional change (positive view)

Our contribution:
Offer boundaries on institutionalization of beliefs (top down case)
Institutional consistency: degree to which institutions at the field level are supported by national level
Isolated belief-changing institutional changes do not necessarily produce high-performing organizations
Institutional Theory
Prior work focuses on rules and on founding rates
In contrast, we focus on beliefs, innovation/performance, consistency

Academic Entrepreneurship
Prior work focuses on TLO policy, faculty spin-offs, education level
In contrast, we focus on alumni, R&D funding, content

Policy Implications
Increased university R&D can influence tech entrepreneurship

Caution against partially copying institutions and policies of Western nations

Especially if inconsistent with existing local institutions and culture
(Dutton and Dukerich, 1991; Kogut and Zander, 1996; Markides, 2000)
Robustness checks

Alternative measures
Factor created out of ip_importance, product newness, and development time
entrepreneurial performance by using ln(Firm_size)

Selection effects - two-step Heckman model

University or Graduate School Effects
remove Tsinghua/Beida, non-grad students

Alternative Explanations
increased the likelihood for individuals with lower human or social capital to start new ventures
Project 985 may have led to the founding of a few very high-performing firms - Quantile regression
(Epstein, 1989; Bishop, 1992; Oddo, 1997)

(Li, 2008; Jiang, 2009)
“Yes Project 985…had a pretty big effect on how I view innovation and IP. Before I had no idea what IP even was…I mean everyone in China downloaded stuff off of the internet and bought [pirated] CD’s, and nobody really cared [about IP]. Now I’m starting to see that, if I want to make money off of my innovations, then IP is pretty important.”
We refined our measures through in-depth interviews with 42 entrepreneurs, investors, and government officials.

These interviews enable us to better understand the context of our study and improve the appropriateness and precision of our survey questions.

We also conducted follow-up phone calls with some of our respondents after the surveys were collected to gain better understanding of their answers.
“I know that a lot of [Chinese] people are hyped about technology entrepreneurship, but I think that the kind of technology entrepreneurship that happens in Silicon Valley won’t work [in China]. People forget that China is still a command economy, and Silicon-Valley style innovation doesn’t work so well in a command economy. So even if you innovate, you still have to play by the command economy rules…and that’s hard.”
University reforms to foster innovative entrepreneurship
Hong Kong: collaboration - universities / industry (Mok, 2005)

China: top 100 universities must take entrepreneurship (Ministry of Education, 2012)
Full transcript