Armanios august aom


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  • NSBC – National Bureau of Statistics of ChinaCBRC – China Banking Regulatory CommissionMost of the scholarship focuses on the public sector side or how market reforms such as privitization drives venture performance rather than the venture side or how ventures actually acquire the investment/financing these reforms make available
  • Two Possible Pathways for Public Sector Investment Acquisition:Government officials: in this process, an entrepreneur personally seeks out government officials and those officials facilitate connections between the venture and public sector funding bodies. These ties largely depend upon on the entrepreneur’s social capitalMixed Institutions: Mixed institutions are hybrid organizations that receive resources from both the private and public sectors and that have features of both market and government institutional logics. In our case, the mixed institution of interest are science parks which often result from public-private partnerships in emerging economies. In this process, government mandates strengthen or establish mixed institutions. Because these government mandates legitimate these institutions, they can confer legitimacy on the ventures linked to them, and, in so doing, help such ventures acquire public sector investment From this, we ask three research questions: 1) how do private ventures in emerging economies acquire public sector investment, government officials or mixed institutions. 2) Are they pathways complements or are they alternatives? 3) What are the implications of these pathways on venture performance?
  • Because government officials hold positions inside the public sector, they can help ventures gain access to public sector financing. In this pathway, the entrepreneur personally seeks out government officials, and those officials facilitate connections between the entrepreneur and public sector funding bodies. The degree to which entrepreneurs can form government ties depends upon the entrepreneur’s social capital such as family, birthplace, university, common third-party contacts, prior interactions, or even military draft class. Those with more social capital have more likelihood to form new ties with a desired partner, and, therefore, those entrepreneurs without adequate social capital have a more difficult time forming such ties.
  • Mixed institutions, due to their public-private ownership, help resolve complex issues that require coordination between the two sectors. Science parks are particularly useful in this regard as they help resolve the complex issue of translating basic science into commercial high-tech products. Mixed institutions often gain legitimacy through government backing. In China, science parks were formally established through the Torch Program in 1988 and mandated to facilitate connections between the private and public sector so as to help incubate ventures that could translate basic science into commercially successful products. Science parks reaffirm that legitimacy through maintaining the public sector involved in their governance and operation as well as demonstrating they can incubate successful ventures such as Lenovo and Founder.With this legitimacy, mixed institutions can help ventures acquire public sector investment in two ways. First, similar to federal agencies in developed countries, they can certify a venture’s quality which helps attract public sector funding bodies to them. Second, because the public sector is still involved in the science park’s governance and operation, science park officials can leverage that public sector involvement to help facilitate connections between their ventures and public sector funding bodies.Unlike government official ties, science park membership does not depend upon an entrepreneur’s social capital. Recall, a science park’s reputation hinges upon continuing to successfully incubate ventures. Therefore, they seek ventures addressing and exploiting important S&T market opportunities, and demonstrating those abilities does not require social capital.
  • Recall, our two pathways. The first is through government official ties. In this pathway, entrepreneurs personally seek out government officials and then these officials facilitate connections between the entrepreneur and public sector funding bodies. This pathway depends on whether the entrepreneurs have the necessary social capital to personally form government official ties. The second is through science park membership. Science parks help ventures gain access to public sector investment in two ways. First, they certify a venture’s quality, which attracts public sector funding bodies to them. Second, science park officials can leverage their public sector stakeholders to facilitate connections between their ventures and public sector funding bodies. Because science parks maintain their reputation through the ventures they successfully incubate, they do not seek ventures based upon the entrepreneur’s social capital but the venture’s ability to address and exploit key S&T market opportunities.Therefore, both government officials and science parks both help ventures arrive at the same outcome but come at a cost. Establishing and maintain government ties entail a social cost such as the need to maintain reciprocity through gift-giving. Membership in a science park entails rental costs, service fees, and sometimes even an equity stake. Given both provide same desired outcome, with respect to gaining access to public sector investment, yet both come at a cost, having both science park membership and government ties should not provide added benefits.
  • Though the private sector is developing in many emerging economies, the public sector still houses significant financial resources and wield significant power and control. Beyond financing, public sector investment also provides additional benefits such as reducing the likelihood the venture will experience arbitrary government interference, unlawful competition, or illegal expropriation. Public sector investment also signals government support, which enhances the venture’s credibility.
  • In conducting our research, we need to find an empirical context where science park and non-science park firms were in close proximity. The reason is that in China, policy can change even as one changes districts. Therefore, sampling science park and non-science parks firms would confound our results with these district policy differences. Given that, we chose to conduct our study in Beijing’s Haidian district where there are science park and non-science park firms in close proximity, often in separate complexes located along the same street.Because we could not randomly select who is assigned into a science park, we elected to conduct a matched sample design. Matching is when firms are matched on the most important observable characteristics. In this way, we ensured the most similar control was matched with the treatment observation of interest (science parks). In doing this, we ensured less variance across subgroups and improved covariate balance between the science park and non-science park firms analyzed.To conduct our matching, we surveyed 81 science park and 65 non-science park firms, and after applying matching using coarsened exact matching, we arrived at a final matched sample of 94 science and non-science park firms.
  • The following variables were used:Our dependent variables for H1a,b, and c was Public Sector Investment, which was measured based upon a 1-5 Likert scale measure as to how significant a role ventures believe the government played in providing financial support for the firm. To ensure it was properly objective, we correlated it with more objective versions of the question, which was whether or not the venture received government financing and found the measure was positively correlated (r=0.33), ensuring the measure was robust.For H2, the two performance measures were patents and venture growth where we asked the difference the number of employees the venture projects in 2011 and the number of employees it had in 2010. Patents was the number of domestic and international patents the venture had which was robust to validity from an independent archival source of all Chinese patents. Both measures were highly orthogonal (r=0.09), indicating if both show the same results that we have a robust measure for performance.For independent variables, we used Science Park where if the firm was located in a science park, we gave it a 1. Government Official Ties was constructed from asking whether the founders or their parents ever previously worked in government, and if they did, we have the firm a value of 1. These were also used as control for regressions on performanceThe controls were age and industry, which we controlled for in our matching, and policy, which we controlled for in our choice of district. The additional control we used was the venture’s number of employees in 2010 which we normalized by taking the natural log.
  • A key issue in studying science parks is selection bias where science parks may select the best firms or the worst firms seek entry because they know science parks can provide them credibility critical to acquiring resources. To address this we conducted an instrumental variable analysis where our instrument was Science Park Entry Help which we measured as whether the entrepreneur knew anyone in the science park before they entered (1/0). The instrument was highly correlated to Science Parks and relatively uncorrelated to Public Sector Investment, Patents, and Venture Growth. The instrument is also strong as the F-score is greater than the recommended value of 10. However, most importantly knowing someone to get inside the park is markedly different than the certification and connections we argue you get once inside the park. Thus, this is an excellent instrument.In our instrument variable analysis, we then added the science park’s selection criteria, which was the venture’s market potential, the sales and finance experience of the founding team, and the quality of the top management team, which we measured as the diversity in the TMT’s experience.To ensure our results were robust, we also ran our analysis without instrumental variable analysis using Poisson-Quasi Maximum Likelihood Estimations, Negative Binomial, and OLS with our regressions on Public Sector Investment and Patents as they are positive count data. For our regressions on Venture Growth, we conducted the analysis with OLS as the variable was quasi-normal.
  • Recall H1a argued Government Official Ties make ventures more likely to acquire public sector investment. From this we would expect the Government Official Ties is positively related to Public Sector Investment. As you can see from our results, we find support for this hypothesis.Recall H1b argued Science Parks make ventures more likely to acquire public sector investment. From this we would expect the Science Parks is positively related to Public Sector Investment. As you can see from our results, we find support for this hypothesis.Recall H1c argued Science Parks particularly help those without government ties acquire public sector investment. From this we would expect the interaction between Government Official Ties and Science Parks is negatively related to Public Sector Investment. As you can see from our results, we find support for this hypothesis.
  • To understand this more visually, look at the relationships between science parks and government official ties with relation to public sector investment. Both have a positive relationship to Public Sector Investment.
  • When we look at the interaction between Science Park and Government Official Ties and its relationships to Public Sector Investment, notice the difference. When the venture is outside the Science Park (Science Park =0, left side of the plot), you can see Government Official Ties positively influence Public Sector Investment. However, when a venture is inside the Science Park (Science Park = 1, right side of the plot), you can see that the Government Official Ties have even a slightly negative relationship to venture performance, indicating once inside the science parks, ties to government officials may be redundant or even more costly without having added benefit as we have argued.
  • Recall, we argued in Hypothesis 2 that public sector investment improves venture performance. From this, we would expect a positive relationship between Public Sector Investment and Patents and between Public Sector Investment and Venture Growth. As you can see, we find support for that assertion.
  • And here is the results confirms for Venture Growth
  • The previous literature on networks emphasize that forming new ties largely depends upon the individual’s social capital and upon network actions such as symbolic behaviors. In contrast, we show mixed institutions, such as science parks are another approach to forming ties and can broker between the entrepreneur and public sector bodies in emerging economies.The previous institutions literature has theorized but has not usually tested that mixed institutions help entrepreneurs to navigate situations where trust-based and market-based forms of economic exchange coexist. The literature currently shows mixed institutions or “collective hybrids” perform better but only theoretically shown nor does it analyze how those affiliated with a collective hybrid can also experience performance benefits. We present early empirical evidence on how mixed institutions improve venture performance through the public sector investment they help ventures acquire.Finally, the emerging economies literature argues either ties with government officials or formal market institutions improve venture performance. However, these two findings present a theoretical dilemma in this literature in that, on one hand, it demonstrates social still matter in emerging economies, yet, on the other, emphasizes formal institutions is where emerging economies should transition to maintain a well-functioning economy. However, as a result, what is not well acknowledged is the realties that such a transition from a trust-based economy dominated by social networks to a free market economy dominated by formal market institutions is not immediate nor may ever entirely reach completion. In finding mixed institutions are important for venture investment acquisition and performance, we show how entrepreneurs can manage this unclear transition while still gaining the public sector investment they need to achieve better performance. Morevoer, in showing mixed institutions are a substitute to government officials, we show how entrepreneurs with the social capital of political networks or entrepreneurs with the human capital of talent can both be successful in the midst of a such a transition.
  • We ran a series of counterarguments that we tested against. First, one could argue if gov’t ties help improve performance, science parks select ventures with government ties. Our t-tests indicate firms with government ties are actually significantly more locations OUTSIDE of science parks.The second counterargument is that government ties actually be a latent signal of venture quality. From t-tests between non-science park and science park firms as well as ventures without and with government ties we found no significant differences in their growth or patents.Finally, one could argue the actually path is through private NOT public sector resources. To address this we ran placebo regressions and included regressions on performance with both public and private sector resources and found public sector resources were still more important to venture performance than private sector resources and, as expected, science parks and ties to government officials do not help ventures acquire private sector investment
  • Because Science Park Entry Help is a nonlinear instrument, our IV approach is nonlinear. How to econometrically handle such a nonlinear IV analysis is an area of much debate. Some indicate a first stage probit with a second stage OLS model (probit-OLS) is appropriate.However, others caution against such an approach because it assumes you have modeled exactly the first-stage nonlinear model and that you have accurately measured local average treatment effects (LATE), both of which are unlikely when unobservable variables may exist.These scholars advocate running both stages as OLS models (OLS-OLS). When the instrument is nonlinear and the dependent variable is linear, some scholars agree a probit-OLS approach is indeed inappropriate, yet these same scholars argue probit-OLS may be appropriate when both the instrument and the dependent variable are nonlinear.To address these concerns, we conducted a probit-OLS IV approach for Public Sector Investment and Patents, which were positive count variables and nonlinear. However, to reflect the concerns of using a probit-OLS IV approach for linear dependent variables, we conducted an OLS-OLS IV approach for Venture Growth, which was linear.
  • Armanios august aom

    1. Government Officials or MixedInstitutions: How Do VenturesAcquire Public Sector Investment inEmerging Economies?Daniel E. Armanios, Stanford UniversityChuck E. Eesley, Stanford UniversityLi Jizhen, Tsinghua UniversityKathleen M. Eisenhardt, Stanford UniversityAcademy of Management Conference,Boston, MA, August 6, 2012
    2. Motivation• How ventures acquire financing in emerging economies remains unclear (Batjargal & Liu 2004; Vissa 2011)• Public sector an important financing source in emerging economies • Average assets of state-owned banks nearly 2x privately owned banks in emerging economies (Dinç 2005) • In China, 46% domestic financing sources in public sector(figures from NBSC 2011; CBRC, 2010)• Scholarship focuses on public sector perspective (Cuervo-Cazurra & Dau, 2009; Mahmood & Rufin, 2005, Spicer et al, 2005) and not venture perspective
    3. Introduction• Two Possible Pathways for Public Sector Investment Acquisition • Government Official Ties (Park & Luo, 2001; Xin and Pearce, 1996) • Mixed Institutions (Science Parks) (McDermott et al, 2009; Nee 1992, Peng & Heath, 1996)• Research Questions:1) How do private ventures in emerging economies acquire public sector investment: government officials or mixed institutions?2) Are these pathways complements or substitutes?3) What are the implications of these pathways for venture performance?
    4. Hypotheses (1)• Government officials can help ventures gain access to public sector financing (Park & Luo 2001)• Depends upon entrepreneur’s social capital (Chen & Chen, 2004, Fei 1947/1992, Hermann-Pillath 2010)• Hypothesis 1a: Ventures whose entrepreneurs have preexisting ties to government officials are more likely to acquire public sector investment.
    5. Hypotheses (2)• Science Parks gain legitimacy through government backing(McDermott et al, 2009; Tan 1999, 2006)• With this legitimacy, science parks can: • certify a venture’s quality (Sine et al, 2007) • leverage public stakeholders to facilitate connections between venture and public sector• Does not depend upon social capital but upon entrepreneur’s human capital• Hypothesis 1b: Ventures who are members of mixed institutions such as science parks are more likely to acquire public sector investment.
    6. Hypotheses (3)• Government officials and science parks produce same outcome yet both incur costs • Government officials: reciprocity costs (Chen & Chen 2004, Hwang 1987) • Science Parks: rental costs, fees, and/or equity stake(Grimaldi & Grandi, 2005)• Hypothesis 1c: Mixed institutions such as science parks help those ventures without government official ties more likely acquire public sector investment.
    7. Hypotheses (4)• Public sector houses significant financial resources and power over allocation(Acquaah 2007; Li & Atuahene-Gima 2001, Nee 1992; Peng & Luo 2000; Walder 1995)• Public sector investment provides added benefits over private sector investment • Heightened credibility(Gu 1996; Tan 1999, 2006) • Protection from political uncertainty(Acquaah 2007; Peng & Luo 2000; Li & Zhang 2007)• Hypothesis 2: Acquiring public sector investment increases venture performance.
    8. Research Design• Haidian District: science parks/non-science park firms within same district (controls for district-level policy differences)• Matched Sample (O’Connor et al., 2006; Obloj & Capron, 2011) • Ensures treatment (science parks) matched w/most similar control (non-science park) • Surveyed 81 science park, 65 non-science park firms • N=94 matched science park and non-science park firms (matching controls: industry and age)
    9. Measures• Dependent Variables • Public Sector Investment (H1a, H1b, H1c) • Patents (H2) • Venture Growth (H2)• Independent Variables • Government Official Ties • Science Park• Additional Controls (industry, policy, age controlled in matching and district selection) • VentureSize2010
    10. Statistical Methods• Instrumental Variable Analysis • Instrument: Science Park Entry Help • F=18.8: strong instrument • r=0.69 w/Science Park, r=0.16, -0.07, -0.13 w/Public Sector Investment, Patents, and Venture Growth, respectively: indication (but not definitive proof) of instrument’s exogeneity • Science Park’s Selection Criteria: Market Potential, TMT quality, Sales and Finance Experience• Public Investment & Patents: P-QMLE, Negative Binomial, Venture Growth: OLS
    11. Results (1) Control Model 1 Model 2 Model 3 Model 4a Model 4b Model 4c Model 4d P-QMLE P-QMLE P-QMLE P-QMLE P-QMLE Neg Bin OLS IV N=94 (Intercept) 0.687* 0.639* 0.158 0.033 -0.095 -0.095 0.563 0.419 (0.319) (0.328) (0.302) (0.291) (0.303) (0.303) (0.599) (0.807) Venture Size2010 0.035 0.026 0.147+ 0.144* 0.115+ 0.115+ 0.247+ 0.216 (0.114) (0.107) (0.094) (0.082) (0.076) (0.076) (0.187) (0.236)Gov’t Official Ties 0.167 0.258* 0.621** 0.621** 1.106** 1.143* H1a  (0.165) (0.155) (0.213) (0.213) (0.401) (0.492) Science Park 0.605*** 0.660*** 0.963*** 0.963*** 1.950*** 2.184*** H1b  (0.167) (0.174) (0.212) (0.212) (0.425) (0.610)Gov’t Official Ties -0.762** -0.762** -1.473* -1.660* * Science Park H1c  (0.325) (0.325) (0.683) (0.890)Science Park Type Included Included Included Included Included Included Included Included Fixed Effects pseudo-R2 0.064 0.079 0.210 0.247 0.319 0.319 0.296 0.194***p<0.001,**p<0.01,*p<0.05,+p<0.10 (one-tailed)
    12. Results (2)
    13. Results (3)
    14. Results (4) Control Model 1a Model 1b N=94 OLS OLS IV (Intercept) -83.372+ -90.357+ -104.658* (55.687) (55.331) (61.151) Venture Size2010 36.622* 33.959* 36.800* (20.332) (17.422) (18.018) Gov’t Official Ties 7.042 2.081 2.553 (11.931) (10.345) (14.148) Science Park 10.746 -2.886 2.482 (12.150) (10.251) (18.129) Public Sector Investment 9.437+ 12.246+ H2 (6.494) (7.780) Science Park Type Fixed Effects Included Included Included pseudo-R2 0.382 0.423 0.456 ***p<0.001,**p<0.01,*p<0.05,+p<0.10 (one-tailed)
    15. Results (5) Control Model 1a Model 1b Model 1c Model 1d P-QMLE P-QMLE Neg Bin OLS IV N=94 (Intercept) -0.785 -1.551* -1.805* -2.027+ -1.871 (0.762) (0.915) (0.784) (1.576) (1.711) Venture Size2010 0.502* 0.565* 0.682*** 0.996* 0.749 (0.219) (0.264) (0.217) (0.601) (0.589) Gov’t Official Ties 0.381 -0.124 0.056 0.230 1.085 (0.368) (0.380) (0.410) (0.815) (1.041) Science Park -0.370 -1.047* -0.591 -1.802+ -0.536 (0.548) (0.573) (0.634) (1.176) (1.853) Public Sector Investment 0.443*** 0.350* 0.985* 0.740+ H2 (0.099) (0.164) (0.429) (0.543) Science Park Type Fixed Included Included Included Included Included Effects Included pseudo-R2 0.115 0.233 0.172 0.188 0.134 One-tailed hypothesis testing: ***p<0.001,**p<0.01,*p<0.05,+p<0.10
    16. Summary• Two pathways to acquire public sector investment • Personal Ties to Gov’t Officials • Mixed Institutions (Science Parks)• These pathways are substitutes• Acquiring public sector investment increases performance
    17. Discussion & Contributions• Networks • Social capital or network actions->tie formation (Hallen & Eisenhardt 2012, Powell et al 1996) • Here: mixed institutions->tie formation (particularly those lacking social capital)• Institutions • Mixed institutions->performance (theory, Nee 1992) • Here: Mixed institutions->performance (test)• Emerging Economies • Gov’t Ties->Performance(Li & Zhang 2007, Peng & Luo 2000) • Market Institutions->Performance(Cuervo-Cazurra & Dau 2009) • Here: Mixed Institutions->Performance
    18. Acknowledgements • US NSF EAPSI program • Li Sha • NSF Graduate Research Fellowship • Klaus Meyer • Stanford Graduate Research Fellowship • Riitta Katila • Stanford Technology Ventures Program • Robert Eberhart • Qin Lan • Marc Schneiberg • Li Xiaoqi • Henning Piezunka • Wang Yiran • Walter Powell • Kou Yixin • Daisy Chung • Mao Liqing • Dan Wang • Lin Xin • Curtiss Cobb • Qian Jin Jian • Yishen Wang • Luo Jing • Hong Yun • Li “Frank” Heng • Zheng Feng • Mengying Zhang • Liu Tong • Xuan Ping Lim • Mo Yingchuan • Chen Rui
    19. Thank You!• Questions or comments? Email:
    20. Appendix (1): RobustnessChecks• Counterargument #1: Science parks select those w/government ties (t-tests indicate firms with government ties outside science park)• Counterargument #2: Government ties signal of venture quality (t-tests between science/non-science park and with/without gov’t ties show no performance differences)• Counterargument #3: Private Sector Investment not Public Sector Investment matters more (with Private Sector Investment included, Public Sector Investment still matters more)
    21. Appendix (2): Backup Materialfor Questions • Handling nonlinearity in IV Analysis