DP2010-08

Financ ial Risks and Research Contracts
in a Model of Endogenous Growth*Colin DAV IS
Laixun ZH AOMarch 10, 2010

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Financial Risks and Research Contracts
in a Model of Endogenous GrowthColin Davis

Laixun Zhao

Kobe University∗

Kobe University†

March, 2010
Preliminary Draft. Comments Welcome.
Abstract
This paper examines researchers’ choices between either collaborating with venture capitalists (Regime C) or going independently (Regime I), and how their interaction affects long-run endogenous growth, in an economy characterized by incomplete
contracts and financial market imperfections. Both research and production require
labor and physical capital. We find that an improvement in financial regulation leads
to a higher rate of innovation under Regime I. In contrast, an improvement in R&D
incentives for researchers in Regime C can coincide with either an increase or a decrease in the long-run rate of innovation, due to the holdup problem in post bargaining
over created value. We also rank the growth rates in the two regimes under different
contractual and financial environments. Finally, we find that conflicts can arise whenentrepreneurs choose one regime based on investment incentives but the other regime
provides a higher growth rate.
Key Words: Endogenous Growth, R&D, Incomplete Contracts, Financial Imperfections
JEL Classification: O16∗

Faculty of Economics, Kobe University, 2-1 Rokkodai Machi, Nada-ku, Kobe 657-8501, Japan,
davis@econ.kobe-u.ac.jp, Fax: 81-78-803-.
†
Research Institute for Economics & Business, Kobe University, Kobe 657-8501, Japan,
zhao@rieb.kobe-u.ac.jp, Fax: 81-78-803-7006.

1 Introduction
Starting a business requires many essential elements. To begin with, one needs “an idea”,
or a blueprint that is the result of an innovation. Capital, is necessary not only to makethe blueprint into a real product, but also to carry out the original innovation. While many
able researchers can borrow directly from banks (or close relatives and friends) to financetheir research projects, very often, entrepreneurs and venture capitalists seek out each other,
combine the former’s good ideas and the latter’s deep pockets, to create a blueprint, andfinally to implement the innovation. Forbes (Jan., 2008) reports that companies that venture
capitalists helped launch cashed in $34 billion from 86 public offerings and 304 acquisitions in 2007, amid the most severe financial crisis and recession in the post war era. The
top financiers included, Kleiner Perkins Caufield & Byers, Sequoia Capital, Sherpalo, Stanford University, Sun Microsystems, etc. Policy makers see the venture capital industry as apotential means of boosting economic growth, and the conventional wisdom says that there
is a positive correlation between the entrepreneurial culture and innovation.
This paper attempts to model the relationship between the entrepreneur’s financing decision and the innovation rate, in an environment characterized by financial risks and incomplete contracts. Armed with an idea, entrepreneurs must choose between collaboratingwith venture capitalists (Regime C) or going independently (Regime I) to carry out the innovation. Successful innovation brings long-term profits. Under Regime C, these profits
must be shared between the entrepreneur and the venture capitalist. Since negotiation occurs after the realization of profits, a holdup problem may arise in contracting if either party
has an incentive for expropriation. In contrast, under Regime I, while the entrepreneur canretain full profits, in the event that innovation fails, risks associated with the recovery ofmoney lent out by banks lead to a higher cost of financing capital. We analyze these interesting issues in an endogenous growth model, and examine how the contract environment
and financial regulations affect the entrepreneur’s choice of research regimes, subsequentinnovation investments and eventually the economy-wide growth rate.
1

We first derive the conditions for a unique long-run equilibrium with a positive rate ofinnovation for each regime. Then in Regime I, we analyze how improvements in financial regulations affect the long-run innovation rate, through a reduction in financial market
imperfections that allows entrepreneurs to finance physical-capital investment at a lower
cost. We demonstrate that this increases the rate of innovation, which is supported by theempirical literature. For instance, King and Levine (1993a; 1993b) find positive correlation
between a country’s level of financial development and its prospects for growth that may
stem from either the innovation activity of entrepreneurs or capital accumulation. However, we find that an improvement in financial imperfections has an ambiguous effect on
the wage-rental ratio, because the latter depends on the relative factor intensities of production and innovation as well as the current rate of innovation.
Next in Regime C, we find surprisingly that changes in the contract environment that
improve the position of entrepreneurs through an increase in their shares of created value,
can have either a positive or a negative influence on the rate of innovation, depending on thecurrent contract environment. In particular, the relationship between entrepreneurs’ shareof created value and growth has an inverted-U shape with a maximum occurring for median ownership shares. This surprising result is in line with the claim that too much legal
protection may hurt innovation, as argued by some legal experts. For example, Graves and
DiBouse (2006) state that non-competition covenants and trade secret laws inhibit innovation. Samila and Sorenson (2009) find evidence that venture capital has a greater impact
on innovation and startups in regions with less stringent labor laws. Moreover, although
Kortum and Lerner (2000) and Samila and Sorenson (2010) conclude that venture capitalis associated with higher rates of patenting, Zucker et al. (1998) find that local venture capital may have a negative effect on the number of startups in a region when the abilities ofscientists are controlled for. Hirukawa and Ueda (2008) also provide evidence that venture
capital can have a negative impact on patenting when venture capitalists shift from R&D tosales strategies after successful innovations.

2

We then use the model to rank the growth rates in the two regimes under different contracting and financing environments. Specifically, if the financial market is well developed
and the lending rate is low, then Regime I gives the higher growth rate. On the contrary,
if the financial market is poor, Regime C provides the higher growth rate. These results
suggest that countries with poor financial systems but relatively better contractual environments with less holdup problems should encourage research collaboration. Furthermore, insome developing countries where the overall financial system is poor, lending is often done
among family members and close relatives and the lending rate is very low. Our model
predicts that business startups in such environments more often occur in Regime I.
In addition, we examine the alignment issue of the entrepreneur’s incentives for innovation with economic policy, and find cases with conflicts in which entrepreneurs will choose
Regime C over Regime I even though the latter provides a greater rate of innovation, given
the current contract environment and financial regulations.
Our paper contributes to the endogenous growth literature that examines the role of financial intermediaries. King and Levine (1993b) develop a theoretical framework in which
financial intermediaries spur economic growth indirectly through the provision of screening, monitoring and risk diversification services that improve the probability of successful
innovation activity. Michalopoulos et al. (2009) on the other hand show that growth is generated directly by the financial sector through innovations made in the screening technology
of financial intermediaries. In an international context Matsuyama (2004) and Aghion etal. (2005) investigate how the structure of the financial market influences convergence inthe wealth of rich and poor countries.
Our paper is also closely related to the literature that investigates the implications ofincomplete contract theory for economic growth and productivity. Antras (2005) introduces
a product cycle model where contractual frictions govern the intra-firm production shifts
from North to South that eventually result in the fragmentation of the production process.
Acemoglu and Antras (2007) examine the relationship between contractual incompleteness3and technology adoption, and show that improvements in the contracting institutions cantheoretically lead to large increases in productivity.
Finally, the corporate finance literature has extensively explored the creation of contracts between startups and venture capitalists.1 For instance, Landier (2001) and Ueda
(2004) investigate the financing choice of entrepreneurs and find that venture capital tends
to be associated with a high degree of risk, low collateral, and high profitability. Thesepapers employ static models, however, and are thus not concerned with implications for
economic growth.
While the present model is closely related to the aforementioned theoretical analyses,
our setup and focus are different. First, our simple setup enables us to examine financial
imperfections and contractual problems in a unified framework with endogenous growth.
We compare the entrepreneur’s financing choices and their impacts on the subsequent R&D
investments. Furthermore, we can clearly see how these affect the growth rate and can rank
the growth rates according to different financial and legal environments, which should haveappealing policy implications.
The rest of the paper proceeds as follows. Section 2 develops our basic model ofinnovation-based endogenous growth. In Section 3, we characterize the long-run equilibrium for Regime I, and investigate the effect of improvements in financial regulations. Section 4 describes the long-run equilibrium for Regime C and examines the impact of changes
in the contracting environment for entrepreneurs. Section 5 compares growth rates and analyzes the alignment issue between the optimal innovation regimes for long-run growth andthe R&D incentives of entrepreneurs. Section 6 provides concluding remarks.
1
Hall and Woodward (2007) examine the monetary incentives for entrepreneurs that use venture capital
to finance their startups.

4

2 The Model
This section presents an endogenous growth model in which R&D associated with product development is characterized by imperfect financial markets and incomplete contracts.
We consider a simple economy with two sectors, manufacturing and innovation. In the
manufacturing sector monopolistically competitive firms produce horizontally differentiated product varieties. In the innovation sector new product varieties are designed. Entry
into manufacturing is the end result of a product development process that takes place in
the innovation sector. There are two factors of production, labor (L) and physical capital(K), which are employed in both sectors.

2.1 Households
The demand side of the economy consists of a dynastic representative household that maximizes lifetime utility over an infinite horizon. The household’s preferences are describedby the following intertemporal utility function:

U=

Z ∞

e−ρt ln Y (t)dt,

0where ln Y (t) is instantaneous utility derived from consumption of a manufacturing composite of differentiated product varieties, and ρ is the subjective rate of time preference.
Intertemporal utility maximization under a standard flow budget constraint requires thatthe household choose an expenditure-savings path that follows the Ramsey saving rule, as
indicated by the following Euler equation:Ė(t)= r(t) − ρ,
E(t)

(1)

where E(t) is household expenditure, r(t) is the risk free interest rate, and a dot over avariable indicates differentiation with respect to time. We follow Grossman and Helpman5

(1991) and set expenditure as the model numeraire, E = 1, and the risk-free interest rateequal to the subjective discount rate at all moments in time, r = ρ. For the remainder ofthe paper we suppress time notation where doing so does not cause confusion.
Following Dixit-Stigiltz (1977), the composite manufacturing good takes the form ofa CES-type quantity index over the total number of product varieties n that have been
introduced to date:
Y =

Z n
0

 θ1
x(i) di ,θ

0 < θ < 1,(2)

where x(i) is the demand for variety i, θ = 1 − 1/σ is the degree of product differentiation,and σ is the constant elasticity of substitution between any two given product varieties. The
household allocates income budgeted for expenditure across product varieties to maximize
instantaneous utility. As such, under (2) the well-known instantaneous demand function
for a given product i isx(i) = p(i)−σ PYσ−1 ,(3)where p(i) is price, and the price index over available product varieties is

PY =

Z n
0

1
 1−σ
,
p(i)1−σ diwhich is decreasing in the number of product varieties.

2.2 Manufacturing
The manufacturing sector is characterized by a mass of n symmetric firms each producing
a unique differentiated variety and competing according to monopolistic competition. As
is standard in many love-of-variety models, these incumbent firms face no risk of failure
and therefore remain in the market indefinitely.
All firms employ capital and labor in production using the following constant returns to6scale technology:x(i) = alX (i)α kX (i)1−α ,(4)where lX (i) and kX (i) are firm-level employments of labor and capital, 0 < α < 1 is theintensity of labor in production, and a = α−α (1 − α)−(1−α) set to simplify algebra.
1−αThe unit cost function associated with the production function (4) is wLα wK, wherewL and wK are respectively the wage rate and the capital rental rate. Taking these factor
prices as given, the operating profit of a representative manufacturing firm is
1−απ(i) = p(i) − wLα wKx(i).
We assume that the mass of manufacturing firms is sufficiently large to eliminate strategicinteraction between firms, and thus each firm sets its price equal to a constant mark-up over1−αunit cost, p = wLα wK/θ. Using this pricing rule in the operating profit function yields

optimal profits equal to
π=

1
,
σn

(5)

where we have used the demand function (3) and we have dropped the firm index to indicatethat all firms earn the same operating profit.
The labor and capital demands for the manufacturing sector can be obtained using Shephard’s Lemma on the unit cost function and aggregating across firms:
 1−α1
LX = αnx
,
ω

 −α
1
KX = (1 − α)nx
,
ω

(6)

where ω ≡ wL /wK is the relative factor price (i.e., wage rental ratio).

2.3 Innovation
Entry into the manufacturing sector requires the development of a new product design
through a research project in the innovation sector. Each research project is directed by a7

research team and requires investments of labor and physical capital. The physical capitalcan exhibit in the form of labs, research equipment and other materials. The research teamhas two options when financing such physical capital investment: one is to borrow directlyfrom banks (Regime I), and the other is to enter into a contractual arrangement with aventure capitalist (Regime C). As will be explained in detail below, the borrowing rates andrepayment methods differ under the two options, and the research team may choose either
type of financing depending on the financial and contractual environments.
The value of the new product design is equal to the present value of the future streamof operating profits (5) that can be earned with entry into the manufacturing sector:

v(t) =

Z ∞e−(τ −t)ρ π(τ )dτ ,(7)twhere we have used r = ρ. This value is earned upon completion of the research projectand is used to cover loans taken out to finance the costs of product development and to pay
for the use of venture capital if that financing option has been chosen.
The successful development of a new product design ensures market entry and accessto the same flow of operating profits (7). A representative research project develops a single
product design according to
1 = bnlβ k 1−β ,(8)where n is the number of existing product varieties, l and k are respectively labor andcapital employment in innovation, 0 < β < 1 is the factor intensity of labor in innovation,and b = β β (1 − β)−(1−β) . As in Romer (1990), the creation of new product designs issubject to intertemporal externality, where a sector-wide learning curve results from theaccumulation of knowledge capital as a by-product of product creation, thereby increasing
the productivity of future research projects. Here the number of existing product varieties
n is used as a proxy for knowledge capital. While the research team supplies labor required
for the research project, physical capital is rented, and the cost of renting k is covered8through either a bank loan or investment by a venture capitalist.
The following subsections look into the specific features associated with each methodof financing in innovation.

2.3.1 Financial Market Imperfections
Researchers can choose to undertake innovation independently (Regime I), when they mustseek financing for capital-equipment investment directly from financial institutions, andface financial market imperfections. Following Galor and Zeira (1993), we assume thatthese imperfections arise from monitoring costs incurred by lenders attempting to prevent
possible debt evasion by borrowers.
Specifically, while normal financial institutions (e.g., banks) can obtain funds at therisk-free interest rate ρ, they incur a monitoring cost of z after lending funds to individual
research projects. A loan of value kwK , where wk is the rental rate on capital as before,
has an interest rate γ that is set higher than the risk-free interest rate ρ (= r) in order
to cover monitoring costs, that is γkwK = ρkwK + z.2 If a default occurred, the lenderwould not get back its loan. To prevent this from happening, authorities impose financial
regulations that satisfy an incentive-compatibility constraint kwK (1 + γ) = µz, where
we interpret µ > 1 as the toughness or completeness of the financial regulations on loan
default. Equivalently, lenders can set the level of monitoring high enough to ensure thatborrowers have no incentive to default, and therefore always pay off their loans. These
conditions lead to a lending rate,γ(µ) =

1 + µρ
> ρ,
µ−1

(9)

that is strictly greater than the risk-free interest rate ρ, and is decreasing in µ. When µ is
close to 1, financial regulations are relatively lax and lenders charge a large mark-up over ρ
to cover monitoring costs. Alternatively, for large values of µ the financial regulations are2

Note that we are assuming a competitive banking industry where lenders earn zero economic profits.

9relatively strict and lenders reduce the gap between γ(µ) and ρ.
Venture capitalists are a special type of financial institution; that is, they are able tofinance physical-capital investment at the risk-free interest, ρ, which is lower than the rate
provided by banks to independent researchers, γ. However, under this type of financing,
the research team must share the generated profits with the venture capitalist. This createsa tension in the research project’s choice between independent financing through banks(Regime I) and a research collaboration with the venture capitalist (Regime C).

2.3.2 Independent Research Projects
As discussed above, in the case of independent research projects (Regime I), entrepreneurs
must obtain loans directly from the financial market at a higher interest rate to cover thecost of renting physical capital, but the good side is, the research team can keep all its
profits. We assume there exist either zero or many such research teams.
From (7) and (8), a given research project is capable of creating value equal to v eachperiod. Taking the cost of borrowing funds as given, the research team chooses the optimal effective labor and physical capital inputs with the objective of maximizing net value
creation:max
hI ,kI

vbnlIβ kI1−β − hI wL − (1 + γ)kI wK ,(10)

where lI and kI are the labor and physical capital inputs for an independent research project(subscript I denotes variables associated with independent research projects).
The first order conditions for net value maximization determine the optimal effective
labor and physical capital investments respectively as wL lI = βv and (1 + γ)wK kI =(1 − β)v. Free entry into innovation and substituting the above into (8) yields:

vI =

1−β
(1 + γ)1−β wLβ wK
,
n

(11)

where the right-hand side is the unit cost of product development and is increasing in the10

wage, rental, and interest rates, and decreasing in the stock of knowledge n.
The total factor demands for innovation in Regime I can be obtained using Shephard’s
Lemma on the unit product development cost and aggregation:

LI = gβ



1+γ
ω

1−β

,

KI = g(1 − β)



1+γ
ω

−β

,

(12)

where g = ṅ/n is the rate of innovation. Once again, the factor demands are determinedby the wage rental ratio ω ≡ wL/wK .
2.3.3 Research Contracts
The other option is Regime C, which involves a research collaboration between a researchteam and a venture capitalist (e.g., a research institute). The research team leads the research project and invests effective labor while the venture capitalist provides low-costfinancing for product development and invests physical capital. Again we assume there
exists either zero or many research teams and venture capitalists.
The initiation of a new R&D collaboration requires the creation of a contractual relationship stipulating the mission of the research project and ownership rights over created value. We model this process following the literature that examines the influence of
threats of expropriation on negotiations in the incomplete contract environment developed
by Grossman and Hart (1986) and Hart and Moore (1990). The ex ante contract does notcover rights over all aspects of value creation as the full characteristics of the final productdesign are not known until the completion of the project. The collaboration thus faces arisk of lost value from either party leaving the project before the final product design iscomplete. The research team may be able to part with the venture capitalist without reporting key research results. This enables the research team to threaten the venture capitalist
into renegotiating the contract once a major research breakthrough has been made. Alternatively, if the venture capitalist has specialized knowledge of the industry or maintains a11

concern in a competing research project, it may be able to complete the research project
without the entrepreneur (Kaplan and Stromberg; 2003, 2004). Both the research team andthe venture capitalist balance their investments of labor and capital against the knowledge
that ex-post rights over created value will be renegotiated before the research project has
been completed.
Specifically, we adopt Nash bargaining to determine the researchers’ share of the created value, δ ∈ (0, 1). Setting the negotiation powers of the two parties to 1/2, Nashbargaining yields a value for δ that maximizes G ≡ [δv − oL v]1/2 [(1 − δ)v − oK v]1/2 . We
rule out the corners when δ = 0 and δ = 1. In the event that these negotiations break down,
the values retained by the research team and the venture capitalist are respectively oL v andoK v, which represent their outside options during negotiations. Parameters oL , oK ∈ [0, 1]
can be interpreted as the inverse of market thickness of researchers and venture capitalists.
For instance, a higher oL implies lower competition among researchers, yielding higher
outside options for them. Alternatively, these parameters may also represent the balanceof protection provided to each party by the legal regime. Graves and DiBoise (2006), forexample, argue that non-compete clauses and trade secret laws may work to restrict innovation. Moreover, in the sample of venture capital investments examined by Kaplan andStromberg (2003) approximately 70% include some form of non-competition clause, suggesting that venture capitalists perceive expropriation by entrepreneurs as a potential risk.
If, on the other hand, negotiations are successful, the research team derives a value ofδv and the venture capitalist a value of (1 − δ)v. Given the above, we obtainδ=

1 + oL − oK
.
2

(13)

δ is therefore increasing in the researcher’s outside option and decreasing in that of theventure capitalist.
One of the advantages of collaboration with a venture capitalist is the lower cost of fi-

12nancing physical capital investment. Given (13) and the researcher’s labor investment, theventure capitalist sets its capital investment to maximize its residual profit, (1−δ)vbnlCβ kC1−β −(1 + ρ)wK kC , where lC and kC are the investments of labor and physical capital. Maximization with respect to kC gives an optimal capital investment ofkC =(1 − δ)(1 − β)v.
(1 + ρ)wK(14)

A comparison of this first order condition with that for an independent research projectshows that, for the research project as a whole, the efficient investment of capital is only
made when the venture capitalist has full ownership rights over created value, or δ = 0. As
the ownership share of the research team increases, inefficiency in capital investment arises
as the venture capitalist only has an incentive to makes suboptimal capital investments. The
inefficiency generated in capital investment by renegotiation over ownership rights reaches
its highest level when δ = 1 and capital investment is reduced to zero.
The research team, taking the venture capitalist’s investment of physical-capital as
given, maximizes its residual profit δvbnlCβ kC1−β − wL lC , with respect to labor investment,
yielding
lC =

δβv
.
wL

(15)

Once again, comparison of this first order condition with that for an independent research
project, we can see that suboptimal labor investments are made when the research team does
not retain full ownership rights over created value. The inefficiency caused by suboptimallabor investment is greatest when δ = 0 and labour investment is zero.
Substituting the optimal labor and capital investments into (8) provides the unit productdevelopment cost in Regime C:
1−β
ζwLβ (1 + ρ)1−β wK
,
vC =
n

13

(16)

where ζ ≡ 1/δβ (1 − δ)1−β . Similar to Regime I, this unit cost is increasing in the wage,
rental, and interest rates, and decreasing in the stock of knowledge. The combined effectof the inefficiencies generated in capital and labor investment by the holdup problem associated with bargaining are described by ζ. When δ = 1, there is no incentive for capital
investment, and product development costs become prohibitively large as ζ = ∞. Similarly, when δ = 0, there is no labor investment, ζ = ∞, and once again high costs inhibit
product development. ζ has a minimum at δ = β, where the overall inefficiency associatedwith the holdup problem, and hence the cost of product development, is minimized.
The total factor demands for innovation under Regime C can then be obtained using
Shephard’s Lemma on the unit cost function and aggregating across investment projects:

LC = gβζ



1+ρ
ω

1−β

,

KC = gβζ



1+ρ
ω

−β

.

(17)

2.3.4 Free Entry
Regardless of which innovation regime arises, a positive rate of innovation requires that theappropriate free-entry condition bind, i.e., either (11) or (16) hold. Denoting the value ofa product design generated as vi , where i = I, C, the time derivatives of (11) or (16) yieldan asset condition that equates the rate of return to a new product design with the risk-freeinterest rate:
ρ=

π
v̇i
+ ,
vi vi

(18)

where the first and second terms on the RHS are respectively the dividend rate and the rateof capital gains.

14

2.4 Factor Markets
The model can be closed by deriving a relative factor price ω ≡ wL /wK that clears themarkets for labor and physical capital, which leads to

L = LX + Li ,K = K X + Ki ,(19)

where i = I, C. In the long-run equilibrium these conditions can be used to solve for therate of innovation g as a function of the relative factor price ω. The specific form for these
conditions depends, however, on whether Regime I or Regime C arises.

3 Independent Research Projects
We examine a steady-state equilibrium with a constant allocation of labor and capital across
manufacturing and innovation activities, which requires constant factor prices, and wetherefore have w˙L = w˙K = 0. In the long-run equilibrium of Regime I, all innovationis undertaken by independent research projects.
Beginning with the factor market clearing conditions, substitution of the factor demands(6) and (12) into (19) gives:

L = αω α−1 X + β(1 + γ)1−β ω β−1 g,

K = (1 − α)ω α X + (1 − β)(1 + γ)−β ω β g, (20)where X ≡ nx is aggregate production. These conditions can be solved for the long-run
values of X and g as functions of ω:

XI
gI



β(1 + γ)K − (1 − β)ωL1,=
ω α (1 − α)β(1 + γ) − α(1 − β)β
(1 − α)ωL − αK
1+γ=.
(1 − α)β(1 + γ) − α(1 − β)
ω

(21)
(22)

They define a range of the relative factor price over which both the manufacturing and inno15

K−
vations sectors are active. In particular, the numerator of (21) can be written as β(1+γ)w(1−β)wLL, where the first term is the ratio of the marginal products for labor and capital in innoK

vation, and thus represents the slope of the output expansion path for the innovation sector.
αwLL
Similarly, the numerator of (22) can be organized as K
− (1−α)w, where the second termKis the slope of the output expansion path for the manufacturing sector.
It is therefore clear that the denominators of (21) and (22) indicate the relative factor
intensities of production and innovation, or the ranking of expansion path slopes. Wheninnovation is relatively labor intensive, the denominator is positive and ω adjusts to ensurethat the output expansion paths for innovation and production respectively lie above andbelow the labor-capital ratio in the economy.3 Note that, given values for α and β, changes
in the lending rate γ may induce a reversal of the factor intensity ranking for production
and innovation.
Next, using the product market clearing condition pnx = 1, the pricing rule for manu1−αfacturing firms p = wLα wK/θ, and the operating profit (5), we can rewrite the no-arbitrage

condition (18) as
X = (ρ + g)(σ − 1)(1 + γ)1−β ω β−α ,where we have used the time derivative of (11) and the fact that factor prices are constantin the long-run equilibrium. This condition can be used to cancel X from the factor market
clearing conditions thereby reducing the steady-state system to two equations:ω 1−β (1 + γ)β−1 L − α(σ − 1)ρ
,α(σ − 1) + β
ω −β (1 + γ)β−1 K − (1 − α)(σ − 1)ρ
,=(1 − α)(σ − 1) + (1 − β)(1 + γ)−1

gL =

(23)

gK

(24)

where gL and gK respectively denote the rates of innovation that clear the markets for laborand physical capital for a given ω.
3

Alternatively, when production is relatively labor intensive, ω ensures that the output expansion path for
production lies above the factor endowment ratio and the expansion path for innovation lies below it.

16

Figure 1: Long-run Equilibrium
Figure 1 illustrates the long-run equilibrium associated with Regime I. The steady-statei1/(1−β)hα(σ−1)ρ
condition for the labor market (23) has a horizontal intercept at (1 + γ)
andLa strictly positive but diminishing slope. The condition for the physical capital market (24),on the other hand, has a strictly negative but increasing slope and a horizontal intercept at
i1/βhK. The intersection of these two curves determines the long-run rate(1−α)(σ−1)ρ(1+γ)1−β
of innovation g ∗ and the relative factor price ω ∗ . The following Lemma summarizes theparameter requirements for the existence of a unique long-run equilibrium, with a positive
rate of innovation and an active manufacturing sector.
Lemma 1 The existence of a unique long-run equilibrium with independent research projects
 β 
1−βK
requires Lα> (σ − 1)ρ, and either(1−α)(1+γ)

α
αK
< 1−α
, β(1+γ)K< ω < (1−α)Lor(i) for (1+γ)β1−β(1−β)LααK(ii) for (1+γ)β> 1−α, β(1+γ)K> ω > (1−α)L.
1−β(1−β)L

Therefore, in Regime I, sufficiently large endowments of labor and capital are the main
requirements for a unique long-run equilibrium with a positive rate of innovation, and therelative factor intensity rankings for production and innovation determine the feasible range
for ω, as discussed above.
Conditions (23) and (24) can be used to investigate the effects of changes in the financial
regulations associated with loan default, µ. First on the long-run rate of innovation, we
17

obtain:
Proposition 1 (Financial regulations and growth): An increase in µ raises the innovation
rate g ∗ through a decrease in γ.
Proof: See Appendix A.
That is, an improvement in financial regulations (i.e., making them tougher and more
complete) that raises the cost of debt evasion, by an increase in µ, unambiguously increases
the rate of innovation. As shown in condition (9), an increase in µ decreases the lending
rate γ and reduces the cost of financing physical-capital investment. This leads to an improvement in efficiency as financial market imperfections are corrected, and thus the rateof innovation rises.
Next, on the relative factor price ω, we obtain:
Proposition 2 (Financial regulations and relative factor price): The relationship between+(α−β)(σ−1)ρ
.
µ and ω ∗ has an inverted-U shape with a maximum at γ(µ) = (α(σ−1)+β)g(1−α)β(σ−1)ρ
∗Proof: See Appendix A.
An increase in µ affects ω through two channels, as shown in Appendix A. The first isa substitution effect whereby a lower cost of financing physical-capital investment induces
researchers to substitute capital for labor, putting downward pressure on ω:g (α(σ − 1) + β) .

The first term in paretheses is the value of an additional unit of labor employed in manufacturing, and the second term is the value of an additional unit of labor employed ininnovation. The substitution effect is illustrated in panel (a) of Figure 2 by the arced arrow,that indicates the decrease in the relative labor intensity of innovation which coincides witha rotation of the output expansion path as the lending rate decreases from γ 1 to γ 2 .

18

Figure 2: Improvements in the Financial Regulations
The second channel is an output expansion effect whereby a higher level of innovation activity leads to a shift in factor employment from production to innovation that puts
upward pressure on ω if product development is relatively labor intensive, and downward
pressure if product development is relatively capital intensive:(σ − 1)ρ [α(1 − β) − (1 − α)β(1 + γ)] .

The expansion effect is indicated in panel (a) of Figure 2 by the arrow running up the
expansion path associated with a lending rate of γ 1 .
The relationship between ω and γ is determined by the relative strengths of the substitution and expansion effects. Referring to panel (b) of Figure 2, suppose that initiallythe lending rate is high such that γ 1 > γ(µ), and product development is relatively labor
intensive. In this case, a negative expansion effect dominates the substitution effect, andhence an improvement in financial regulations raises ω. Next, consider a lending rate beand γ(µ). The expansion effect is still negative but now dominatedtween γ(µ) = α(1−β)β(1−α)
by a positive substitution effect, and an increase in µ lowers ω (at γ(µ) the slopes of the
expansion paths for innovation and production are the same and the expansion effect iszero). Lastly, for a lending rate below γ(µ), the substitution and expansion effects work inthe same direction and an increase in µ lowers ω. Note that the lending rate γ converges to19α< β(1+ρ), then dωmay be negative for allρ for a high level of µ, and consequently if 1−α1−βdγvalues of µ.

4 Research Contracts
Now we characterize the long-run equilibrium for Regime C, where all innovation is undertaken by collaborations between researchers and venture capitalists. Again we examine
the steady-state equilibrium where w˙L = w˙K = 0.
Combining the factor demands given in (6) and (17), the factor market clearing conditions (19) can be written as

L = αω α−1 X + ζβ(1 + ρ)1−β ω β−1 g,

K = (1 − α)ω α X + ζ(1 − β)(1 + ρ)−β ω β g. (25)These conditions can be solved for aggregate production and the innovation rate as

XC
gC



β(1 + ρ)K − (1 − β)ωL1,=
ω α (1 − α)β(1 + ρ) − α(1 − β)


β
1
(1 − α)ωL − αK
1+ρ=.
ζ (1 − α)β(1 + ρ) − α(1 − β)
ω

(26)
(27)

With the exception of the term ζ ≡ 1/{δβ (1 − δ)1−β } in (27), which measures the inefficiency created by the holding problem in bargaining, these conditions are the same as thosederived for Regime I in (21) and (22), with a lending rate of ρ rather than γ > ρ. Thus, therelative factor intensity ranking across sectors once again determines the feasible range forω over which both the production and innovation sectors are active.
The no-arbitrage condition for Regime C can be obtained using (18) with the production
1−αmarket clearing condition pnx = 1 and the price p = wLα wK/θ:

X = ζ(ρ + g)(σ − 1)(1 + ρ)1−β ω β−α .

20Substituting this into the factor market clearing conditions yields:
ω 1−β (1 + ρ)β−1 ζ −1 L − α(σ − 1)ρ
,α(σ − 1) + β
ω −β (1 + ρ)β−1 ζ −1 K − (1 − α)(σ − 1)ρ
.
=(1 − α)(σ − 1) + (1 − β)(1 + ρ)−1

gL =

(28)

gK

(29)

These conditions provide combinations of the innovation rate and ω that clear the labor andphysical capital markets.
The long-run equilibrium in Regime C can be similarly depicted as in Figure 1, with g ∗and ω ∗ now determined by the intersection of (28) and (29). The horizontal intercepts of thei1/(1−β)i1/βhh
ζα(σ−1)ρ(1+ρ)1−βKand ζ(1−α)(σ−1)ρ(1+ρ)1−β.
gL and gK curves are respectively
L
Once again the respective slopes of the gL and gK curves are positive and negative, andthus the following Lemma summarizes the parameter values necessary for the existence ofa unique long-run equilibrium.
Lemma 2 The existence of a unique long-run equilibrium with research contracts requires1−β L β K> ζ(σ − 1)ρ, and eitherα(1−α)(1+ρ)αKα(i) β(1+ρ)K< ω < (1−α)Lfor (1+ρ)β
< 1−α
, or
(1−β)L1−βαKα(ii) β(1+ρ)K> ω > (1−α)Lfor (1+ρ)β> 1−α.
(1−β)L
1−β

The key requirement is again sufficiently large labor and capital endowments, although
the factor requirement may be smaller or larger than that for Regime I depending on thelevel of inefficiency generated by contract negotiations and the difference in the lending
rates. The relative factor intensity ranking continues to determine the relevant range for ω.
Next we use conditions (28) and (29) to examine the effects of changes in the contracting environment on g ∗ and ω ∗ . A quick examination of the outcome of Nash bargaining (13)indicates that the research team’s share of created value δ(oL, oK ) is an increasing functionof the researchers’ outside option oL and a decreasing function of the venture capitalists’outside option oK . We can then establish
21

Figure 3: Improvements in the Contract Environment
Proposition 3 (Outside option and growth): The relationship between oL and g ∗ has aninverted-U shape with a maximum at β = δ.
Proof: See Appendix B.
Figure 3 provides an illustration of Proposition 3, where the rate of innovation is measured on the vertical axis and the research team’s share of created value on the horizontal
axis. The outside option for the research team, oL , determines the contract environment
through (13), as shown by the bold line δ(oL , oK ), holding the outside option of the venture
capitalist fixed. The contract environment, in turn, determines the long-run rate of innovation, as depicted by the curve labelled g ∗. The highest point on this curve occurs whereδ(oL , oK ) = β.
The economic intuition behind this result is made clear through an examination of (16).
The holdup problem, and its impact on the efficiency of the innovation process, arises directly from the influence of the contractual environment on the investment incentives ofentrepreneurs and venture capitalists. As each party foresees the inevitability of renegotiation of ownership rights over created value before a new product design can be brought tomarket, neither party has an incentive to make the optimal investment in any given contract
environment. The inefficiency thus generated leads to an increase in the cost of productdevelopment and hinders the innovation rate. As shown in Figure 3, however, this ineffi-

22

Figure 4: Improvements in the Contract Environment
ciency can be mitigated through adjustments in the outside options, and is minimized whenδ(oL , oK ) = β. 4 This suggests that a contract environment which provides entrepreneurs
with a either a smaller larger share of created value than the contribution of labor to innovation will inhibit economic growth.
Proposition 4 (Outside option and relative factor price): The relationship between oL andα< 1−α; and (ii) has an invertedω ∗ , (i) has an U shape with a minimum at β = δ for β(1+ρ)
1−β
α
> 1−α
.
U shape with a maximum at β = δ for β(1+ρ)
1−βProof: See Appendix B.
The two possible cases are depicted in Figure 4. In either case, for δ < β an improvement in the contractual environment reduces inefficiency and creates incentives for further
investment in innovation that will attract more labor and capital to this sector, increase theinnovation rate (gC ), and decrease manufacturing output (XC ). In contrast, for δ > β an
improvement in the contractual environment raises inefficiency, causing a contraction of
the innovation sector and an expansion of the manufacturing sector.
The effect of shifts in factor employment on ω depends on the factor intensity ranking.
Panel (a) illustrates the case where innovation is labor intensive compared to the manufacturing sector. An expansion of the innovation sector requires more labor than capital andβ
It is straightforward to show that dζ 2 /d2 δ > 0 and setting dζ/dδ = ζ( 1−β1−δ − δ ) = 0 gives δ = β, which
minimizes the inefficiency.
423

therefore drives up ω. Alternatively, panel (b) describes the case where innovation is capital intensive. Then an expansion of the innovation sector increases the demand for capital
relative to labor, and drives down ω.

5 Optimal Innovation Regimes
In this section we make a comparison of the long-run growth rates associated with each innovation regime, and investigate the R&D incentives of entrepreneurs. For a given createdvalue, entrepreneurs choose the regime that requires the lowest product development cost,
and the government prefers the regime that provides the greatest rate of growth in welfare,as measured in instantaneous utility (2). In the steady-state equilibrium a constant factor
allocation requires Ẋ = 0, and the long-run growth rate is therefore Ẏ /Y = g/(σ − 1).
Accordingly, we focus on the innovation rate when comparing the growth rates associatedwith each regime.
In deciding between whether to undertake independent innovation, or to conduct research collaboration with a venture capitalist, entrepreneurs make a simple comparison of
product development costs and choose the best option, given factor prices and the costof financing physical-capital investment. Referring to the free entry conditions for eachinnovation regime, i.e., (11) and (16), an entrepreneur will be indifferent when1

γ v = (1 + ρ)ζ 1−β − 1,(30)

where ζ ≡ 1/{δβ (1 − δ)1−β } continues to represent the inefficiency of the holdup problem
in Regime C. This condition clearly shows the tension between the higher financing costsof Regime I, and the lower retention rate of created value in Regime C. It is convex in
the contract environment δ with a minimum at β = δ, as depicted by the vI = vC locus
in Figure 5. For combinations of γ and δ above (respectively below) this locus, venture
capital provides a lower (higher) development cost, and accordingly entrepreneurs choose24Figure 5: Research Incentives
Regime C (I).
Our comparison of the long-run growth rates associated with each regime, allows factor
prices to adjust with changes in financial regulations and the contract environment. In
particular, we consider a gI = gC locus to describe the preference of the government. The dgC
I, and thus, evoking the results obtained in Propositions 1 and 3,
slope of this locus is dgdγdδ

the locus is convex in the contract environment with a minimum at β = δ, as illustrated by
the gI = gC curve in Figure 5. Setting the innovation rates derived in (23) and (28) equal,

the gI = gC locus can be expressed as a function of the relative factor prices associatedwith each regime.
γg =

1
ωI
(1 + ρ)ζ 1−β − 1.
ωC

(31)

Above (below) this locus Regime C (I) has the greater innovation rate. A quick comparisonof (30) and (31) reveals that the position of the gI = gC locus depends on the ratio ofrelative factor prices for each regime. In Appendix C we show that wI /wC > 1 for gI = gC
and obtain the following:
Lemma 3 The gI = gC locus always lies above the vI = vC locus.
Proof: See Appendix C.
The gI = gC locus provides a convenient means of ranking the growth rates of eachregimes at different lending rates. The result is summarized in the following Proposition.
25

Proposition 5 (Growth comparison): (i). Regime I has the higher growth rate for ρ < γ <γ g ; (ii). Regime C has the higher growth rate for γ g < γ.
Proof.

Reorganizing ρ < γ v yields the inequality 1 < ζ, which holds for all feasible

values of δ. Thus, ρ lies below the vI = vC locus and, from Lemma 3, the gI = gC locus
as well.
Consider first the case where financial regulation is well developed, or µ is high, andthe lending rate is the same for both regimes (γ = ρ). In this case, the difference in productdevelopment costs stems solely from the holdup problem of Regime C and the growth rateof Regime I is higher (gI > gC ). Next, a deterioration in financial regulation, or a decreasein µ, raises the lending rate and increases the product development cost of Regime I. If thelending rate rises above the threshold rate γ g indicated by (31), the product development
cost of Regime I will become more expensive than that of Regime C, and accordingly thegrowth rate of Regime C will be higher (gC > gI ).
Lemma 3 yields an interesting comparison concerning the alignment of R&D incentives
and economic policy. In the region between the gC = gI and vC = vI curves, although
Regime I provides a greater long-run rate of innovation, Regime C has the lower productdevelopments cost. Thus, while the optimal innovation regime is I, the R&D incentivesof investors (including entrepreneurs and venture capitalists) pull the economy towards
Regime C. We summarize this result in the following Proposition.
Proposition 6 (Regime conflicts): (i). Entrepreneurs choose the regime with the lower
growth rate for γ g > γ > γ v ; (ii). For other values of γ, they choose the regime with thehigher growth rate.
These results suggest that countries with poor financial systems but relatively better
contractual environments should encourage research collaboration. Furthermore, in somedeveloping countries where the overall financial system is poor, lending is often done26among close relatives and the lending rate is very low. Our model predicts that businessstartups in such environments more often occur in Regime I.

6 Conclusion
This paper has examined incentives for R&D in a model featuring financial market imperfections and incomplete contracts in the product development process, and how these
affect the rate of innovation and endogenous growth. In particular, new products can be
developed by either independent entrepreneurs or through research collaborations between
entrepreneurs and venture capitalists. While independent entrepreneurs retain full ownership over created value, they face financial market imperfections that make physical-capital
investments costly. In contrast, entrepreneurs that enter into research contracts with venture
capitalists can avoid costs associated with market imperfections, but only retain a reduced
share of equity in their research projects.
Investigating long-run equilibria for both regimes, we obtain the following results: (i)An increase in the R&D incentives for entrepreneurs always raises the long-run innovation
rate in Regime I, but may raise or lower it in Regime C. (ii) Improvements in the contract
environment may cause an increase or a decrease in the relative factor price for labor andphysical capital depending on the contract environment and the factor intensities of production and innovation. (iii) Regime I provides the higher growth rate when financial markets
are well functioning, and it is Regime C if legal environments for contracting are relatively
more developed and the inefficiency stemming from the holdup problem is kept low.
Our model has implications for the alignment of economic policy and R&D incentives for entrepreneurs. In some cases, although an innovation regime with independent
entrepreneurs developing new products provides the greatest long-run rate of innovation,short-run incentives for entrepreneurs lead to an innovation regime with venture capitalists
and a lower innovation rate.

27

Appendix A: Propositions 1 and 2
The total differentials of (23) and (24) with respect to g ∗ , ω ∗ , and γ can be written in matrix
form as 11−β (1+γ)β−1 L− (1−β)ω
α(σ−1)+β

1−β (1+γ)β−2 L− (1−β)ωα(σ−1)+β  dg  hi  dγ,
=

∗
−β
β
−(1+β)β−1
g −ω (1+γ) K
βω
(1+γ)
K
1−β
dω ∗
(1+γ)2 (1−α)(σ−1)+(1−β)(1+γ)−1(1−α)(σ−1)+(1−β)(1+γ)−1∗Using Cramer’s rule we obtaindω ∗(1 − β) [g (α(σ − 1) + β) + (σ − 1)ρ (α(1 − β) − (1 − α)β(1 + γ))],=dγ
(1 + γ)2 [α(σ − 1) + β] [(1 − α)(σ − 1) + (1 − β)(1 + γ)−1 ] ΩI
(1 − α)(1 − β)(σ − 1)(ρ + g)(1 + γ)β−2 ω −β L
dg ∗
< 0.
= −
dγ
[α(σ − 1) + β] [(1 − α)(σ − 1) + (1 − β)(1 + γ)−1 ]2 ΩI
where

ΩI =

(1 − β)ω −β (1 + γ)β−1 L
βω −(1+β) (1 + γ)β−1 K+> 0,α(σ − 1) + β(1 − α)(σ − 1) + (1 − β)(1 + γ)−1

and we have used ( (23) and (24). While the comparative static dg ∗/dγ > 0 proves Proposition 2, the sign of dω ∗ /dγ depends on the termg (α(σ − 1) + β) + (σ − 1)ρ (α(1 − β) − (1 − α)β(1 + γ)) ,(A1)which is a decreasing function of γ. The relative factor price is therefore concave in γ witha maximum occurring where (A1) equals zero.

28

Appendix B: Propositions 3 and 4
The total differentials of (28) and (29) with respect to g ∗ , ω ∗ , and δ can be written in matrix
form as


 1

1

−β
β−1 δ β (1−δ)1−β L− (1−β)ω (1+ρ)α(σ−1)+ββω −(1+β) (1+ρ)β−1 δ β (1−δ)1−β K(1−α)(σ−1)+(1−β)(1+ρ)−1







ω 1−β (β−δ)(1+ρ)β−1 L
δ1−β (1−δ)β [α(σ−1)+β]  dg  

=

 dδ.
−β
β−1
ω (β−δ)(1+ρ)
Kdω ∗δ1−β (1−δ)β [(1−α)(σ−1)+(1−β)(1+ρ)−1 ]∗Using Cramer’s rule we obtain


dω ∗
K
(β − δ)ω −β (1 + ρ)β−1
ωL
,
=
−
dδ
(1 − α)(σ − 1) + (1 − β)(1 + ρ)−1 α(σ − 1) + β
δ 1−β (1 − δ)β ΩC(β − δ)(1 + ρ)2(β−1) δ 2β−1 (1 − δ)1−2β ω −2β LK
dg ∗
=
,
dδ
[(1 − α)(σ − 1) + (1 − β)(1 + ρ)−1 ] [α(σ − 1) + β] ΩC
where

ΩC =

(1 − β)ω −β (1 + ρ)β−1 δ β (1 − δ)1−β L
βω −(1+β) (1 + ρ)β−1 δ β (1 − δ)1−β K+> 0.
(1 − α)(σ − 1) + (1 − β)(1 + ρ)−1α(σ − 1) + β

The comparative static for the relative wage depends on the sign of[α(σ − 1) + β] K − (1 − α)(σ − 1) + (1 − β)(1 + ρ)−1 ωL,(B1)which is increasing in (1 + ρ) and, using Lemma 2, equals zero when (1 − α)β(1 + ρ) =α(1 − β). Thus, (B1) is greater than zero for (1 − α)β(1 + ρ) > α(1 − β) and less than zerofor (1 − α)β(1 + ρ) < α(1 − β) . This result in combination with the term β − δ determines
the effects of an increase in δ on the relative factor price as summarized in Proposition 3.
The comparative static for the rate of innovation depends solely on the sign of β − δ asstated in Proposition 4.

29

Appendix C: Lemma 3
First, we use (23) and (24) to solve for the relative factor price of Regime I asKgI [α(σ − 1) + β] + α(σ − 1)ρ
.
ωI =gI [(1 − α)(σ − 1) + (1 − β)(1 + ρ)−1 ] + (1 − α)(σ − 1)ρ L
Second, (28) and (29) are combined to obtain the relative factor price of Regime C:
gC [α(σ − 1) + β] + α(σ − 1)ρ
K
ωC =.
gC [(1 − α)(σ − 1) + (1 − β)(1 + γ)−1 ] + (1 − α)(σ − 1)ρ L
A comparison of these relative factor prices indicates that γ > ρ, which is true by definition,
is a sufficient condition for ω I /ω C > 1 when gI = gC .

References[1] Aghion, P., Howitt, P., Mayer-Foulkes, D., 2005. The Effect of Financial Development
on Convergence: Theory and Evidence. Quarterly Journal of Economics 120, 173222.
[2] Antrás, P., 2005, Incomplete Contracts and the Product Cycle. The American Economic Review 95 (4), 1054-1073.
[3] Acemoglu, D., Antrás, P., Helpman, E., 2007. Contracts and Technology Adoption.
American Economic Review 97 (3), 916-943.
[4] Dixit, A., Stiglitz, J. E., 1977. Monopolistic Competition and Optimum Product Diversity. American Economic Review 67, 297-308.
[5] Galor, O., Zeira, J., 1993. Income Distribution and Macroeconomics. Review of Economic Studies 60, 35-52.

30

[6] Graves, C., DiBoise, J., 2006. Do Strict Trade Secret and Non-Competition Laws
Obstruct Innovation? Entrepreneurial Business Law Journal 1(2), 323-344.
[7] Grossman, G., Helpman, E., 1991. Innovation and Growth in the Global Economy.
Cambridge: MIT Press.
[8] Grossman, S., Hart O., 1986, The Costs and Benefits of Ownership: A Theory ofVertical and Lateral Integration. Journal of Political Economy 94, 691-719.
[9] Hall, R., Woodward, S., 2007. The Incentives to Start New Companies: Evidence
from Venture Capital. NBER Working Paper Series No. W13056.
[10] Hart, O., Moore, J., 1990. Property Rights and the Nature of the Firm. Journal of
Political Economy 98, 1119-1158.
[11] Hirukawa, M., Ueda, M., 2008. Venture Capital and Innovation: Which is First?
Working Paper, Center For Economic Policy Research, DP7090.
[12] Kaplan, S., Stromberg, P. 2003. Financial Contracting Theory Meets the Real World:
An Empirical Analysis of Venture Capital Contracts. The Review of Economics Studies 70, 281-315.
[13] Kaplan, S., Stromberg, P. 2004. Characteristics,Contracts, and Actions: Evidence
from Venture Capitalist Analyses. The Journal of Finance 59(5), 2177-2210.
[14] Kortum, S., Lerner, J. 2000. Assessing the Contribution of Venture Capital to Innovation. The Rand Journal of Economics 31(4), 674-692.
[15] King, R., Levine R., 1993a. Finance, Entrepreneurship, and Growth. Journal of Monetary Economics 32, 513-542.
[16] King, R., Levine R., 1993b. Finance and Growth: Schumpeter Might be Right. The
Quarterly Journal of Economics 108(3), 717-737.
31

[17] Landier, A., 2001. Start-up Financing: Banks vs. Venture Capital. Working Paper
MIT.
[18] Matsuyama, K., 2004, Financial Market Globalization, Symmetry-Breaking and Endogenous Inequality of Nations. Econometrica 72(3), 853-84.
[19] Michalopoulos, S., Laeven, L., Levine, R., 2009. Financial Innovation and Endogenous Growth. NBER Working Paper Series, No. W15356.
[20] Romer, P., 1990. Endogenous Technical Change. Journal of Political Economy 98,
S71-S102.
[21] Samila, S., Sorenson, O., 2009. Non-compete Convents:
novate or Impediments to Growth. Working Paper,

Incentives to In-

Available at SSRN:

http://ssrn.com/abstract=1411172.
[22] Samila, S., Sorenson, O., 2010. Venture Capital, Entrepreneurship, and Economic
growth. Review of Economics and Statistics 92, forthcoming.
[23] Ueda, M., 2004. Banks versus Venture Capital: Project Evaluation, Screening, andExpropriation. The Journal of Finance 59(2), 601-621.
[24] Zucker, L., Darby, M., Brewer, M., 1998. Intellectual Human Capital and the Birth of
U.S. Biotechnology Enterprises. The American Economic Review 88(1), 290-306.

32