Pacific Economic Review, 16: 4 (2011)
doi: 10.1111/j.1468-0106.2011.00557.x

pp. 421–465

VENTURE CAPITAL AND INNOVATION:
WHICH IS FIRST?
paer_557

421..465

Masayuki Hirukawa Setsunan University
Masako Ueda* Northwestern University
Abstract. Policy-makers typically interpret positive relations between venture capital (VC) investments and innovations as evidence that VC investments stimulate innovation (VC-first hypothesis).
This interpretation is, however, one-sided because there may be a reverse causality that innovations
induce VC investments (innovation-first hypothesis): an arrival of new technology increases demandfor VC. We analyze this causality issue of VC and innovation in the US manufacturing industry
using both total factor productivity growth and patent counts as measures of innovation. We findthat, consistent with the innovation-first hypothesis, total factor productivity growth is often positively and significantly related with future VC investment. We find little evidence that supports theVC-first hypothesis.
JEL Classifications: G24, D24, O31, O32.

1.

introduction

Policy-makers who aim to stimulate economic growth often attempt to create or
expand their local venture capital (VC) industries. These attempts include the
Yozma program in Israel, the Small Business Investment Company (SBIC)
program in the United States, and various initiatives to create stock markets
where listing requirements are less stringent than in traditional markets.1 There
are two common rationales for this attempt: one is that venture capitalists mitigate a problem of underinvestment in innovative activities by small and new firms(Hall, 2002) and the other is that venture capitalists can help new firms to growfast and become profitable (Sahlman, 1990). Thus, creating infrastructure for andsubsidizing venture capitalists are supposed to make more financial and managerial resources available for small and new firms than otherwise and thereby
encourage innovations (see e.g. European Commission, 1995, Venture EnterpriseCenter, Ministry of International Trade and Industry, 1991, for Japan).
There is indeed both ad hoc and academic evidence suggesting that firms growfast and can circumvent the issue of underinvestment in innovative activities if*Address for Correspondence: Kellogg School of Management, Northwestern University, 2001
Sheridan Road, Evanston, IL 60208-2009, USA. E-mail: m-ueda@kellogg.northwestern.edu. Previously, this paper was circulated under the title, ‘Does innovation spur venture capital?’ We thankBruno Cassiman, Marco Da Rin, Mariassunta Giannetti, Bronwyn Hall, Dietmar Harhoff, theManaging Editor, two anonymous referees, and the seminar participants at the CESifo Conference
on Venture Capital and Public Policy, the TMR Euroconference, the 1999 SED meetings, Oxford
University, and Stockholm School of Economics for helpful comments, and Giovanni Cespa forvaluable suggestions. We also thank Norm Morin at the Federal Reserve Board for providing the
manufacturing real net capital stock dataset. All remaining errors are ours.
1Black and Gilson (1998) argue that stock markets facilitate exits of venture capitalist supporting
creation of stock market segments for young firms.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

422

m. hirukawa and m. ueda

they are backed by venture capitalists. At firm level, Hellmann and Puri (2000)
and Engel (2002) find that VC-backed firms grow faster than their industry
counterparts. Venture-backed firms in Japan have also experienced rapid
growth (Suzuki, 1996). Regarding innovation, Kortum and Lerner (2001) findthat patents granted to VC-backed companies are cited more often than otherpatents, suggesting that VC-backed companies are engaged in important innovative activities. At industry level, Kortum and Lerner (2001) find that in the
United States, VC investments have a stronger impact on patent counts thanR&D expenditure. Using German data, Tykvova (2000) also finds a positive
relation between VC investment and patent application.2
A common interpretation of the results found in the literature cited above isthat VC spurs growth and innovation of new firms. Hereafter, we call this view
the VC-first hypothesis. This interpretation is one-sided, however, because there
may be an opposite causality: arrivals of significant innovation opportunities
stimulate new firm start-ups to exploit such opportunities and these start-ups
demand VC because venture capitalists are complements to such firms.
There are two reasons why new firms as opposed to established firms often
exploit significant innovation opportunities. First, an arrival of a significant
innovation may create business opportunities and trigger firm start-ups. For
instance, a drastic cost reduction in computer technology enlarged the scope ofcomputer users, not only professional users but also individual customers. Dueto this expansion of the market, a number of new computer manufacturers, suchas Apple in the 1980s and later Dell, emerged and entered the market that used
to be dominated by IBM. Second, it is argued in the rich literature on industrialorganization that entrant firms are more likely to innovate than establishedfirms when the scale of potential innovation is large. Thus, arrival of significant
innovation is supposed to be positively associated with new firm entries (e.g.
Reinganum, 1983; Gans and Stern, 2000).
The complementarity between new firms that exploit significant innovation
opportunities and VC might arise from various sources. First, a venture capitalist typically specializes in a narrow set of businesses and, therefore, may havean advantage in evaluating the businesses accurately. This accurate evaluation
may lessen the cost associated with asymmetric information (Leland and Pyle,
1977; Chan, 1983). Second, VC may have high flexibility in terms of financial
instruments because VC industries are relatively free from regulations. The
financial instrument most commonly used by venture capitalists is convertible
debts. Banks are not allowed to use such equity instruments. Cornelli and Yosha(2003) show how convertible debts can lessen the entrepreneur’s incentive toengage in ‘window dressing’ or short-termism. Third, besides financing portfolio
firms, VC often supplies firms with other resources essential to new firms. Such
resources include legal and marketing expertise, and are invaluable for new firms
whose assets typically consist of their blueprints of prospective projects alone.
2

There is other evidence that supports the role of venture-backed firms in driving innovation andgrowth. According to the National Venture Capital Association (1998), 80% of venture capital
investment is towards high-technology industries, such as computers, communications, medical andhealth, and biotechnology.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltdventure capital and innovation

423

New firms typically lack many types of resources that large firms internalize bytaking advantage of their scale economies and business history. For instance,
Lerner (1995) finds that venture-backed firms are more likely to file lawsuits
related to trade secrecy infringement and suggests that venture capitalists
actively help portfolio firms with such legal issues. Hellmann and Puri (2000)
find that venture-backed firms can bring their products to the market faster thanother non-venture-backed firms can, suggesting that venture capitalists can help
new firms to find marketing channels and customers.
Given that significant innovation opportunities stimulate new firm start-ups
and these start-ups demand VC, we hypothesize that innovations spur the VC
market, via stimulating new firm start-up. In contrast to the VC-first hypothesis,
we refer to this view as the innovation-first hypothesis.
This paper addresses the causality issues described above by studying
dynamic panel data for US manufacturing industries. We study two types of VC
investment measures (the first and the follow-on investments) and two types ofinnovation measures (total factor productivity (TFP) growth and patentcounts). Using a panel autoregressive (AR) model, we begin by testing for
Granger-type causality between innovation and VC investment. We also
examine AR models individually for each of the 5 VC-intensive industries.
We find weak evidence for the VC-first hypothesis when TFP growth is usedas the measure of innovation. In particular, the estimated panel AR models
under various specifications indicate that 2-year lagged first round VC investment is positively and significantly related with TFP growth. Nevertheless,lagged follow-on round VC investment is not significantly related with TFP
growth. We do not find any evidence for the VC-first hypothesis when patent isused as the measure of innovation.
Surprisingly, we find that 1-year lagged VC investment is negatively and often
significantly related with both TFP growth and patent counts. Several theories
explain the VC’s negative impact on innovation. First, significant amounts ofcapital that VC provide might promote entrepreneurs to make inefficient strategic decisions. For instance, abundant financial resources might reinforce
managerial optimism, resulting in inefficient strategic decisions (e.g. Kahneman
and Lovallo, 1993; deMeza and Southey, 1996). Abundant financial resources
might also allow escalation of commitment (defending and continuing a courseof action in spite of negative outcomes), as advocated by Ross and Staw (1993).
Consistent with these theories, George (2005), who analyses privately-held firms
in the United States, finds that profitability declines when firms have a large
amount of assets. Second, the negative relation between lagged VC investment
and TFP growth is consistent with the bubbles and crashes theory (e.g. Abreu
and Brunnermeier, 2003). This theory contends that economic booms will
trigger subsequent crashes. As VC investments increase during economic booms
and TFP growth slows down during crashes due to low capacity utilization, thebubbles and crashes theory predicts that a VC investment boom leads to a
slowdown in TFP growth. Third, the negative relation between lagged VC
investment and patent counts is consistent with the firm-level evidence of Engeland Keilbach (2007) and Caselli et al. (2009). Using German data and Italian© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

424

m. hirukawa and m. ueda

data, respectively, these two papers find that after receiving VC financing, thefirms experience high sales growth but patenting slows down. One explanation
behind these findings is that venture capitalists change the strategy of theirportfolio firms from innovating to cashing out from innovation.
We find some evidence for the innovation-first hypothesis when TFP growthis used as the measure of innovation. In particular, estimating the panel ARmodels and the AR model of Communication and Electronic industries, we findthat lagged TFP growth is positively related with the first round VC investment.
We do not find any evidence for the innovation-first hypothesis when patentcounts are used as the measure of innovation.
Besides the articles cited above, this paper is closely related to the literature on
financial development and growth. For instance, close to the spirit of this paper,
Robinson (1952) argues that financial development follows economic development. Greenwood and Jovanovic (1999) rigorously model how economic
growth and financial development are mutually dependent. Levine et al. (2000)
find that exogenous development of financial intermediary sectors enhances
economic growth. Compared to the literature on banking sectors and stock
markets, there exist few academic studies on the economic impact of VC. One
important exception is Zucker et al. (1998), who study the causes of biotechnology start-up firms. Interestingly, they find that controlling for the presence of
local star scientists, the size of the VC market negatively affects the rate ofbiotechnology start-up.
The rest of the paper is organized as follows. Section 2 describes the data usedin this paper, and details how we construct the data set for analysis. Section 3
presents the results of empirical analyses. Section 4 concludes. The Appendix
explains how we construct the proxy for VC commitment.
2.

data description

As measures of innovation, we use both TFP growth and patent counts. It isinteresting to study both of these innovation measures for the following reason.
An important difference between TFP growth and patent counts is that TFP
growth results from adopting new technology, whereas patents are based on
ideas about new technology that has not necessarily been adopted yet. Therefore, if VC investment is used for generating new technology ideas rather thanusing new technology, we expect the VC-first hypothesis to hold for patentcounts but not for TFP growth. If VC investment is used for adopting newtechnology instead of creating it, we expect the VC-first hypothesis to hold for
TFP growth but not for patent counts.
As measures of VC investments, we examine first round investments andfollow-on round investments, separately. First round investments are often
made to early-stage ventures and many of them eventually fail, whereas
follow-on round investments are often made to later-stage ventures, which are
more likely to have proven their viability than early-stage ventures. As a consequence, when we test for the VC-first hypothesis, we expect follow-on investments to have bigger impacts on innovation than first round investments. When© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltdventure capital and innovation425

we test for the innovation-first hypothesis, we expect it to hold better for firstround investments than for follow-on round investments, because first roundinvestment decisions are made mainly based on technological opportunities ofthe ventures, whereas follow-on round investment decisions are made based onother individual firm-specific factors, such as how well they performed to date.
We normalize VC investments using privately-funded industry R&D expenditure because the degree to which VC investment affects innovations may varyacross industries.
In what follows, we detail how we construct the data set for the results. There
are two major challenges in assembling this data set. The first challenge isconcordance between VC data and TFP data. The second challenge is extending
the TFP data series beyond the period over which the National Bureau of
Economic Research (NBER) originally constructed them.
2.1. Data sources
The data analyzed in the present paper come from four main data sources:
VentureXpert; Bartelsman, Becker and Gray’s NBER-CES Manufacturing
Industry Database (‘the NBER productivity database’); the NBER U.S. Patent
Citations Data File (‘the NBER patent database’); and Funds for Industrial
R&D Performance, by Industry and by Size of Company: 1953–98 from the
National Science Foundation (‘the R&D database’).
VentureXpert is a proprietary database of Venture Economics, which is adivision of Thomson Financial. Venture Economics receives quarterly reports
from VC organizations and from major institutional investors on their portfolio
holdings and, in exchange, provides summary data on investments and returns.
VentureXpert records Standard Industrial Classsification (SIC) codes of thecompanies that were financed from venture capitalists. However, this variable isvery often missing. Instead of SIC codes, VentureXpert uses its own proprietaryindustry classification system, the Venture Economics Industry Code (VEIC).
There is no missing record for this VEIC variable. As detailed in Ueda andHirukawa (2008), for some observations, we find SIC codes either by merging
with other data sources, such as CRSP, or by hand-collection. Then, using theobservations with SIC codes, both originally recorded and collected by us, wedevelop a bridge table between SIC codes and VEIC codes. According to thisbridge table, we distribute the investment amount of the observations withwhich SIC is not recorded.
The NBER productivity database draws the original data from the Census
Bureau and contains productivity related variables for all manufacturingindustries at the SIC four-digit level.3 The data are annual, start from 1958 andend in as early as 1996, which limits one from extending an analysis into recent
years. To study the impact of rapid increases in VC investment on TFP growthin the late 1990s, we extend the NBER productivity database up to 2001 using
the method described in the next section. The NBER productivity database3

Bartelsman and Gray (1996) provide a detailed description of this NBER productivity database.
© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

426

m. hirukawa and m. ueda

covers only manufacturing. Thus, we limit our scope to manufacturingindustries.
The NBER patent database and its extension contain information on utility
patents granted at the U.S. Patent and Trademark Office (USPTO) from 1963 to
2002.4,5 For our empirical analysis, we sort the patent data by year of application
instead of by year of grant. The NBER patent database and its extension do notcover all patents applied between 1963 and 2001 because it is customary to takemore than a year for a patent to be granted. Therefore, we also extract updated
data from the patent bibliographic raw files at USPTO.6
The R&D database contains annual spending on R&D sorted by industry andby source of funding. As in Kortum and Lerner (2001), we interpolate if numbers
are missing due to the NSF’s non-disclosure policy. The R&D database’s industry
classification scheme roughly corresponds to the SIC two-digit level. The name ofeach industry and corresponding SIC codes under the R&D database areincluded in Table 2. Hereafter, we refer to this industry classification system as the‘KL industry classification’, due to Kortum and Lerner (2001).
2.2. Extending the NBER productivity database and VC investment by KL
industry classification
We extend the NBER productivity database and VC investment data, tabulated
according to KL classification, up to 2001. The reason for the extension is toinclude the impact of unprecedented increases in VC investment in the late
1990s. In the subsection that follows, we describe how we extend the NBER
productivity database. The method of extending VC investment data is
described in Ueda and Hirukawa (2008).7
2.2.1. Extension and modification of NBER productivity database
We use the NBER’s five-factor (production labor hours, non-production
workers, capital, energy and non-energy material) productivity as our measureof TFP.8 The original NBER productivity database contains this TFP series up
to 1996. To include the later 1990s period in our study, when the US VC industry
experienced an explosive growth, we extend both TFP and capital expenditureseries up to 2001.
4

The extension is downloadable from the Bronwyn Hall’s website (http://elsa.berkeley.edu/users/
bhhall/bhdata.html). This extension has the primary international classification, which is not present
in the original NBER patent database. We compile the patent data by SIC code using the concordance between the primary international classification and SIC developed by Brian Silverman.
(http://www.rotman.utoronto.ca/~silverman/ipcsic/documentation_IPC-SIC_concordance.htm)
5
See Hall et al. (2005) for the details of these patent databases.
6
See http://www.uspto.gov/web/menu/patdata.html
7
For a robustness check of our results, we also construct a proxy for VC commitment (funds
available for VC investment) as an additional explanatory variable for VC investment. The methodof constructing this data item is described in the Appendix.
8
The deflated value of shipment is defined as the real output, and the five factors are the real capitalstock, production worker hours, non-production workers, the deflated energy cost and the deflated
non-energy material cost. Factor shares are calculated as the corresponding expenditures divided by
the value of shipment, whereas the capital share as the residual so that the sum of shares is equal to one.
© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltdventure capital and innovation

427

Except for the calculation of labor costs, as detailed later, we follow the same
method as the NBER productivity database for extending the TFP series. (Bartelsman and Gray, 1996) We collect the dollar amount of shipment (output),energy expenditure, non-energy material cost, labor cost and capital expenditure, and production labor hours and non-production workers from the Annual
Survey of Manufacturers (ASM) by the the Census Bureau. The real capitalstock is provided by the Federal Reserve Board. Deflators for shipment, energy
expenditure and non-energy material cost are constructed using two types ofdata sources. The first type of data source is the input–output flow tables thatdetail the composition of inputs and outputs (shipments) for each industry. For
equipment and structure shipment, we use the 1997 version of the Capital Flow
Table from the Bureau of Economic Analysis. For other types of shipment andenergy and non-energy material, we use the 1997 Benchmark Input-Output
Accounts and use tables from the Bureau of Economic Analysis. The second
type of data sources is price information of each commodity that constitutes
inputs and outputs. We draw these price data from the producer price index andthe consumer price index from the Bureau of Labor Statistics. When we do notfind an exact match of a commodity with the price index, we use the index of aclosely related commodity.
The NBER productivity database is recorded on the four-digit SIC system,
whereas, for every dataset after 1996, the industry classification is based on thesix-digit North American Industry Classification System (NAICS). The KL
industry classification is, roughly speaking, a two-digit SIC system. As the KL
industry classification is coarser than the four-digit SIC, up to 1996, we generate
dollar figures based on the KL industry classification by aggregating dollar
figures from the NBER productivity database. Productivity growth for each KL
industry up to 1996 is computed as value-added weighted average of productivity growth using the NBER productivity database.
The KL industry classification is not always coarser than the six-digit NAICS;
that is, two establishments that share the same six-digit NAICS might belong todifferent KL industries. To convert the six-digit NAICS figures into those basedon the KL industry classification, we first use the bridge table between four-digit
SIC and six-digit NAICS as of 1997 published by the Census Bureau. Numberof employees and average number of production workers are converted on thebasis of the bridge table for ‘Paid Employees’. Annual payroll and production
worker wages are converted on the basis of the bridge table for ‘Annual Payroll’.
All others are converted on the basis of the bridge table for ‘Sales, Receipts,
or Shipments’. The four-digit SIC based figures constructed this way arefurther aggregated into the figures tabulated according to the KL industry
classification.
There are two challenges in extending the productivity data beyond 1996.
First, through the transition from SIC to NAICS in 1997, some industries
classified as manufacturing until 1996 are no longer classified as manufacturing.
Therefore, their data are no longer available in the ASM. They are entire
portions of SIC 2411 (Logging), 2711 (Newspapers), 2721 (Periodicals), 2731
(Book publishing), and 2741 (Miscellaneous publishing), and some portions of© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

428

m. hirukawa and m. ueda

2771 (Greeting cards) and 3732 (Boat building and repairing).9 These industries
all belong to ‘Others’ in the KL industry classification scheme and, as a consequence, we dropped ‘Others’ from our analysis. Therefore, the original KL
industry classification scheme contains 20 industries, whereas our analysis isfocused on 19 industries. Second, whereas new capital expenditures are directly
available in the NBER productivity database, the latest ASM does not distinguish new and used capital expenditures; instead, it provides the sum of thecapital expenditures for each industry. Then, we estimate the new capital expenditure of each four-digit SIC by using the share of the industry’s new capital
expenditure to the total in 1996.
We also modify the NBER productivity database by adding the employer’ssocial security contribution and fringe benefit to payroll. These two items makeup a significant portion of employers’ labor cost, and their importance hasgrown over the past two decades. For instance, they constituted 10.8% of total
pay in 1968 and grew to 21% in 2001. Therefore, if we were to ignore these twolabor cost items, we would significantly underestimate labor shares and, as aresult, would underestimate productivity growth, because labor input growth isslower than growth of other inputs. We obtain employers’ social security contribution and fringe benefit from the ASM.
2.3. Descriptive statistics
Table 1 shows that VC investments in the US manufacturing industry havedramatically grown during the past four decades, in terms of both dollar
amounts and ratios to privately-funded R&D expenditures. The amount ofinvestment in 1999–2001 is approximately 100 times as much as that in 1968–
1970. Notably, stimulated by a sequence of regulatory changes favorable to VC,
the investment amount significantly increased from the 1970s to the 1980s.
These changes involve clarification of the “prudent man rule” of the US Department of Labor Employment Retirement Income Security Act (ERISA), thereduction of the capital gains tax rate,10 and the introduction of the Bayh–Dole
Act, which facilitated technology transfer from universities to private sectors.11
The whole VC industry experienced a downturn in the early 1990s as a result ofpension funds’ asset quality problems. Pension funds were pulled out fromprivate equity investments to reduce the riskiness of their portfolios. Pension
funds are major financing sources for US venture capitalists, and this assets
reallocation of pension funds severely hit venture capitalists.
9
In addition to the change from SIC to NAICS in 1997, there was a sequence of redefinitions in SIC
in the years, 1972, 1977 and 1987. Data for some years are reallocated from one SIC four-digit
industry to another. We follow the method specified in section 3.1 of Bartelsman and Gray (1996)
and use the bridge tables that the ASM reports for each of the redefinition years.
10
See Gompers and Lerner (1998) for details.
11
Enactment of the Bayh–Dole Act (P.L. 96-517), the ‘Patent and Trademark Act Amendments of
1980’, on 12 December 1980 created a uniform patent policy among the many federal agencies thatfund research. Bayh–Dole enables small businesses and nonprofit organizations, including universities, to retain title materials and products that they invent under federal funding. Amendments to
the Act were also created to include licensing guidelines and expanded the law’s purview to include
all federally-funded contractors (P.L.98-620).

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

1.67
0.36
-1.69
2.16
2.89
2.03
-0.62
-2.39
2.79
1.57
1.16
1.01
-0.71
0.50
0.04
2.04
1.62
0.90
-0.03
3.43
0.94
-0.69
-0.40
-0.68
2.72
0.99
2.83
2.33
1.58
1.95
0.40
2.73
1.82
-1.01

Year

1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001

42 43643 45542 94942 63139 71340 00839 11339 26838 68937 98436 85136 30936 29434 47234 28732 28333 99035 33036 38939 62643 87246 89749 72750 41153 58656 56663 52776 36072 48185 44884 12486 63884 48373 072

Number of patentapplications
25
71
67
68
59
66
45
42
44
65
125
179
254
467
578
760
844
826
809
878
799
758
649
529
571
471
451
603
736
902
1 144
983
1 384
1 081

Number of firms
receiving VC funding
58
258
159
344
278
335
125
147
108
187
356
490
900
1 827
2 263
3 926
3 922
3 396
3 619
3 420
3 231
2 952
2 397
1 630
2 668
2 041
2 075
3 198
3 897
5 478
6 385
10 85322 666
12 312

Total
56
238
80
225
134
163
66
46
47
75
191
218
485
888
643
1 003
941
692
801
791
723
764
555
272
728
629
686
1 151
1 219
1 685
1 713
2 621
5 475
2 584

First round
2
20
79
119
144
172
59
101
61
112
165
272
416
938
1 621
2 922
2 981
2 704
2 818
2 629
2 507
2 189
1 842
1 358
1 940
1 413
1 389
2 047
2 678
3 793
4 672
8 232
17 191
9 727

Follow-on round

VC investment ($M)

0.14
0.58
0.36
0.77
0.59
0.67
0.25
0.30
0.21
0.34
0.62
0.81
1.44
2.78
3.23
5.26
4.82
3.95
4.10
3.93
3.66
3.29
2.73
1.85
3.00
2.41
2.39
3.42
3.86
5.04
5.91
10.89
21.10
11.92

Total (%)
0.13
0.53
0.18
0.50
0.29
0.33
0.13
0.09
0.09
0.14
0.33
0.36
0.77
1.35
0.92
1.34
1.15
0.80
0.91
0.91
0.82
0.85
0.63
0.31
0.82
0.74
0.79
1.23
1.21
1.55
1.58
2.63
5.10
2.50

First round (%)

0.00
0.05
0.18
0.27
0.31
0.34
0.12
0.20
0.12
0.21
0.29
0.45
0.66
1.43
2.32
3.92
3.66
3.14
3.19
3.02
2.84
2.44
2.10
1.54
2.18
1.67
1.60
2.19
2.65
3.49
4.32
8.26
16.00
9.41

Follow-on round (%)

VC investment/R&D

Venture capital (VC) investments refer to the 2001 constant million dollar amount that VC funds invested in US companies of each industry and in each year. ‘First round (VC)’ refers to VC investments
made in companies that have never received VC financing before. ‘Follow-on round (VC)’ refers to VC investments made in companies that have received VC financing before. ‘Total (VC)’ refers to
the sum of ‘first round (VC)’ and ‘follow-on round (VC)’. ‘R&D’ refers to privately-funded R&D expenditure. ‘TFP growth’ is the value-added weighted average of total factor productivity growthover 19 manufacturing industries. All others are sums of industry-level numbers.

TFP growth(%)

Table 1. Summary statistics by year

venture capital and innovation429© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

430

m. hirukawa and m. ueda

Compared to the data used in Kortum and Lerner (2001), our VC investment
figures are systematically large, even after taking into account the difference inconstant dollar expressions.12 This discrepancy probably occurs because
Venture Economics backfills the older part of their database. The backfilling
creates a survivorship bias such that a higher fraction of older data points isinvestment made by successful and surviving VC funds. As VC investment
significantly increased in the late 1990s, and the recent investment, in theabsence of backfilling, is likely to represent lower quality investment on averagethan in early periods, we might underestimate the effect of VC investment on
innovations.
Table 2 shows VC investment tabulated by industry. It is easy to see that VC
investments are clustered. In particular, Drugs (KL 6), Office and Computing
Machines (KL 13), Communication and Electronic (KL 15), and Professionaland Scientific Instruments (KL 19) account for 83% of the total VC investmentin the manufacturing industries to date.
Table 3 shows the descriptive statistics of three variables examined in thispaper. Panels A and B provide summary statistics of VC investment by industry
in terms of the constant dollar amount and the ratio to privately-funded R&D
expenditures. Comparing these two panels, one can see that VC investment in
Office and Computing Machines is not only large in absolute terms but also soin relative terms, representing 9.55% of the industry R&D expenditure. Notably,
VC investment in Textile and Apparel is the second largest in relative terms,
although it is small in absolute terms. In other industries, the relative presence of
VC investments is quite small and often it is less than 1% of industry R&D
expenditures, on average.
Panels C and D present summary statistics of two innovation measures;
namely, annual TFP growth and the number of patent applications. We can see
from Panel C that the average TFP growth in the Office and Computing
Machines industry has is as much as 11.3%, indicating a positive correlation
between innovation and VC investments. There is one caveat for interpreting
this high number. One of the biggest obstacles in measuring innovation by TFP
growth is the difficulty in measuring quality improvement. Unlike cost-reducing
innovation, to identify quality improvement requires detailed knowledge inassessing and measuring product quality. For this reason, TFP growth associated with quality improvement is infrequently incorporated in analysis. In the
1980s, with the help of IBM, the Census Bureau measured quality change in the
Office and Computing Machines industries. This is the only large-scale attempt
made by the Bureau to incorporate quality improvement in analysis. For thisreason, industries other than computer related industries might not exhibit
substantial quality improvement in their TFP growth figures and their productivity growth rates might be underestimated.

12
Kortum and Lerner (2001) and this paper express venture capital investments in 1992 and 2001
constant dollars, respectively.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

Food and Kindred
Textile and Apparel
Lumber and Furniture
Paper
Industrial Chemicals
Drugs
Other Chemicals
Petroleum Refining
and Extraction
Rubber Products
Stone, Clay and Glass
Products
Primary Metals
Fabricated Metal Products
Office and Computing
Machines
Other Non-electrical
MachineryCommunication andElectronic
Other Electrical Equipment
Transportation Equipment
Aircraft and Missiles
Professional and Scientific
Instruments

361–365,369
371,373–375,379
372,37638

366,367

351–356,358–359

33
34
357

30
32

20
22,23
24,25
26
281,282,286
283
284,285,287–289
13,29

SIC codes

317

13
1
0
17

80

75

0
6
77

0
0

0
1
3
4
22
4
0
13

1968–1969

1 240

47
11
0
91

392

20

13
15
501

21
32

21
20
0
1
2
26
14
11

1970–1974

1 287

103
11
1
206

222

14

9
20
402

34
1

8
18
11
1
4
125
8
91

1975–1979

12 838

198
39
10
1 343

3 084

436

38
30
6 184

41
71

35
28
11
4
174
540
10
562

1980–1984

16 618

470
186
79
3 163

3 889

499

45
58
5 611

78
148

339
100
116
22
308
1 231
113
164

1985–1989

10 811

543
244
64
2 708

1 820

497

85
82
1 646

128
41

246
303
77
105
218
1 878
69
59

1990–1994

Venture capital investments refer to the million dollar amount that venture capital funds invested in US companies of each industry and in each year.

Total

16
17
18
19

15

14

11
12
13

9
10

1
2
3
4
5
6
7
8

Industry

29 812

1 265
274
59
6 008

9 300

602

305
131
4 274

308
113

345
270
160
156
223
4 900
161
954

1995–1999

Table 2. Venture capital investments for US manufacturing industries, by industry (in millions of 2001 dollars)

4 923
958
271
18 96833 189

2 688

708
584
24 016

907
596

1 073
929
443
347
1 114
13 364
513
2 306

Total

34 978 107 900

2 285
193
59
5 432

14 403

544

212
242
5 319

297
190

80
187
65
54
164
4 661
138
452

2000–2001

venture capital and innovation
431© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

19.44
9.99
7.05
1.89
31.11
160.50
6.23
22.09
9.06
8.88
4.42
8.10
360.2677.32
400.71
64.49
13.98
0.41
461.15
1.30%
4.07%
2.25%
0.16%
0.53%
2.89%
0.30%
0.83%
0.87%
1.32%
0.34%
0.62%
4.56%
1.69%
4.28%
2.08%
0.13%
0.01%
5.00%

Panel A: Venture capital investment (in millions of 2001 dollars)
1 Food and Kindred
31.56
2 Textile and Apparel
27.32
3 Lumber and Furniture
13.04
4 Paper
10.21
5 Industrial Chemicals
32.77
6 Drugs393.07
7 Other Chemicals
15.10
8 Petroleum Refining and Extraction
67.84
9 Rubber Products
26.68
10 Stone, Clay and Glass Products
17.54
11 Primary Metals
20.82
12 Fabricated Metal Products
17.19
13 Office and Computing Machines
706.35
14 Other Non-electrical Machinery
79.05
15 Communication and Electronic976.15
16 Other Electrical Equipment
144.8017 Transportation Equipment
28.18
18 Aircraft and Missiles
7.98
19 Professional and Scientific Instruments
557.89Panel B: Venture capital investment/privately-funded R&D
1 Food and Kindred
2 Textile and Apparel3 Lumber and Furniture
4 Paper
5 Industrial Chemicals
6 Drugs
7 Other Chemicals
8 Petroleum Refining and Extraction
9 Rubber Products
10 Stone, Clay and Glass Products
11 Primary Metals
12 Fabricated Metal Products
13 Office and Computing Machines
14 Other Non-electrical Machinery
15 Communication and Electronic
16 Other Electrical Equipment
17 Transportation Equipment
18 Aircraft and Missiles
19 Professional and Scientific Instruments
1.81%
8.08%
4.51%
0.61%
0.60%
3.89%
0.58%
4.85%
1.73%
2.06%
3.03%
1.27%
9.55%
1.74%
5.72%
3.79%
0.21%
0.13%
6.37%

MedianMean

Industry

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.36%
0.00%
0.41%
0.01%
0.00%
0.00%
0.28%

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
12.26
0.00
22.20
0.49
0.00
0.00
5.52

Minimum

6.45%
43.99%
25.13%
4.23%
1.83%
21.97%
2.73%
70.59%
8.43%
12.30%
21.14%
12.22%
73.50%
5.42%
38.66%
40.76%
0.90%
0.98%
28.91%

120.31144.9466.88
61.73
100.61
2 890.29
86.66
461.47189.20106.91130.01205.03
3 902.99
275.079 530.68
1 550.87
107.2177.00
3 142.02

Maximum

1.82%
11.10%
5.84%
0.97%
0.55%
4.88%
0.67%
12.70%
1.98%
2.67%
5.36%
2.15%
14.92%
1.44%
7.56%
7.20%
0.23%
0.25%
7.04%

33.40
35.25
16.82
17.94
29.40
621.1119.52
101.08
39.29
23.89
32.42
35.51
818.3472.47
1 855.40
291.5234.20
15.62
703.93Standard deviation

Table 3. Dollar amount (Panel A) and the ratio to privately-funded R&D expenditures of venture capital investments (Panel B),
TFP growth (Panel C) and the number of patent applications (Panel D) for US manufacturing industries, by industry

432

m. hirukawa and m. ueda

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

0.56%
0.72%
0.11%
0.48%
0.72%
-0.23%
0.23%
0.52%
1.05%
0.69%
0.55%
0.24%
11.29%-0.09%
5.50%
0.88%
0.50%
0.22%
0.72%
433
530
799
575
2 803
1 650
2 048
295
3 186
739
586
3 580
3 681
11 278
4 558
4 083
1 484
226
7 151

Panel C: TFP growth
1 Food and Kindred
2 Textile and Apparel3 Lumber and Furniture
4 Paper
5 Industrial Chemicals
6 Drugs
7 Other Chemicals
8 Petroleum Refining and Extraction
9 Rubber Products
10 Stone, Clay and Glass Products
11 Primary Metals
12 Fabricated Metal Products
13 Office and Computing Machines
14 Other Non-electrical Machinery
15 Communication and Electronic
16 Other Electrical Equipment
17 Transportation Equipment
18 Aircraft and Missiles
19 Professional and Scientific Instruments

Panel D: Number of patent applications
1 Food and Kindred
2 Textile and Apparel3 Lumber and Furniture
4 Paper
5 Industrial Chemicals
6 Drugs
7 Other Chemicals
8 Petroleum Refining and Extraction
9 Rubber Products
10 Stone, Clay and Glass Products
11 Primary Metals
12 Fabricated Metal Products
13 Office and Computing Machines
14 Other Non-electrical Machinery
15 Communication and Electronic
16 Other Electrical Equipment
17 Transportation Equipment
18 Aircraft and Missiles
19 Professional and Scientific Instruments
419
467
720
517
2 827
1 052
1 816
293
2 920
682
565
3 466
1 542
11 292
2 952
3 523
1 494
222
4 910

0.58%
0.58%
0.16%
0.61%
0.93%
-0.19%
0.20%
0.79%
1.22%
0.56%
0.94%
0.17%
12.10%-0.05%
3.25%
1.35%
0.41%
-0.02%
0.47%

MedianTFP growth for each industry is the value-added weighted average of the SIC four-digit level TFP growth.

Mean

Industry

Table 3. Continued

305
343
466
375
2 166
500
1 526
219
2 269
518
407
2 547
1 250
8 135
2 403
2 638
883
146
4 295

-2.86%-3.80%-3.25%-4.36%-11.38%-5.03%-4.19%-9.77%-4.05%-2.57%-7.68%-3.32%-1.60%-5.22%-2.92%-4.19%-4.85%-5.63%-2.82%

Minimum

644
872
1 265
915
3 872
5 316
3 425
385
4 721
1 113
891
4 909
12 43914 337
11 274
7 304
2 203
308
14 163

3.73%
3.73%
3.62%
4.47%
7.08%
8.29%
8.42%
8.73%
4.51%
4.73%
5.30%
3.96%
24.72%
5.34%
28.29%
4.46%
6.53%
6.07%
4.73%

Maximum

91
161
244
167
353
1324
503
37
725
180
127
691
3644
1769
2928
1466
375
42
3473

1.36%
1.45%
1.75%
2.19%
3.86%
2.90%
2.61%
4.14%
2.13%
1.94%
2.46%
1.96%
7.08%
2.41%
7.12%
2.21%
2.48%
2.83%
1.81%

Standard deviation

venture capital and innovation
433© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

m. hirukawa and m. ueda

434

Panel D demonstrates that the distribution of patent counts across industries
is different from that of TFP growth. The Other Non-electrical Machineryindustry dominates in patent counts, and the Professional and Scientific Instruments industry follows.
3.

empirical methods and results

In this section, we present the methods and the results of our empirical analyses.
The underlying methods used here are the analysis of panel AR models, studying forecasting powers of VC investments and the two innovation measures. We
begin by examining the relation between VC investment and TFP growth andthen proceed to study the relation between VC investment and patent count.
Unless stated otherwise, our sample period is from 1968 to 2001.
3.1. Causality between VC investments and TFP growth
We now present the model to be estimated and describe the results of ouranalysis on VC investment and TFP growth.
3.1.1. Panel AR models
We borrow the idea of examining causality problems from Granger causality.
The Granger causality test examines whether X causes Y by regressing Y on thepast realizations of X and Y and seeing whether the series of X has any explanatory power. We apply this test to VC investment and TFP growth in panels. Let
Xi,t and Yi,t be TFP growth and the ratio of VC investment to privately-funded
R&D expenditures in industry i at time t, respectively. Our causality test consistof estimating the following equations:
L

L

l =1

l =1

X i ,t = α 0 + ∑ α lYi ,t −l + ∑ βl X i ,t −l + l ′Zi ,t + ηi + ε i ,t , and
L

L

l =1

l =1

Yi ,t = γ 0 + ∑ γ l X i ,t −l + ∑ δ lYi ,t −l + y ′Zi ,t + vi + ui ,t ,

(1)

(2)

i = 1, … , N , t = 1, … ,T ,where L is the maximum lag length, hi and vi are unobserved industry-specificheterogeneities, Zi,t is a set of control variables, and ei,t and ui,t are idiosyncraticerrors that may be contemporaneously correlated but are serially uncorrelated,conditional on Zi,t. In particular, we choose year dummies as the elements of Zi,t.
We also assume hi and vi to be fixed effects, because if the industry effectrepresents omitted variables, it is highly likely that these industry-specific characteristics are correlated with the other regressors.
The nulls of no causality in Granger’s sense from VC investment to TFP
growth and from TFP growth to VC investment are hypothesized as© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltdventure capital and innovation435

H0:(a1, . . . , aL) = 0 and H 0′: ( γ 1 , … , γ L ) = 0, respectively. We perform the
hypothesis testing for two different VC investment variables: the ratio of firstround investment and that of follow-on investment to privately-funded R&D
expenditure. Two different lag scenarios are assumed: a 2-year lag and a 4-year
lag. These time horizons are chosen based on the fact that firm start-ups have arealistic chance to grow typically 2–4 years after their initial VC investment.
We estimate the dynamic panel regression models (1) and (2) using two
versions of the linear generalized method of moments (GMM) estimation andordinary least squares (OLS) estimation. The first version of GMM is built on
taking first differences of these equations (‘difference GMM’ by Holtz-Eakinet al., 1988, and Arellano and Bond, 1991). To estimate equation (1) using thedifference GMM, we impose the moment conditions:

E ( X i ,t −s Δε i ,t ) = E (Yi ,t −s Δε i ,t ) = 0, t = 3, … ,T , s ≥ 2,

(3)

as well as the conditions in which control variables are used as instruments.
Replacing Dei,t with Dui,t yields moment conditions for estimating (2). The second
version of GMM imposes additional moment conditions as well as those impliedby first-difference transformations (‘system GMM’ by Arellano and Bover,
1995, and Blundell and Bond, 1998). To equation (1) using the system GMM, in
addition to equation (3), we make use of the moment conditions:

E ( ε i ,t ΔX i ,t −1 ) = E ( ε i ,t ΔYi ,t −1 ) = 0, t = 3, … , T .

(4)Finally, the OLS-based estimation is referred to as the fixed effects within groupor least squares dummy variable (LSDV) estimation, which transforms the data
in deviations from the industry-specific means and runs OLS. We estimate
equations (1) and (2) in three ways and check the robustness.13 Moreover,for both difference and system GMM, we compute one-step estimators for
inference.14
Table 4 and 5 present the estimation results for equations 1 and 2, respectively. While our main focus is to study two types of VC investment measures(the first and follow-on VC investments), we also provide results on the total VC
investment (the sum of two measures), for reference. All standard errors are
based on the heteroskedasticity-robust formula. Looking into specification
testing results from the two GMM estimators, we find that the Arellano–Bond
AR(1) test strongly rejects the null of no first-order serial correlation for13
We estimate the models in three ways because both the time (T = 34, annually from 1968 to 2001)
and industry dimensions (N = 19) of our panel data are small. To the best of our knowledge, there
is no estimation strategy currently available that is well suited to the data. Both difference andsystem GMM estimators are designed for ‘large N, small T’ panels, whereas LSDV for ‘large T,
small N’ panels. In addition, LSDV is known to yield biased estimates if the time dimension ismodest. Judson and Owen (1999) show that biases may be substantial even when the time dimension
is as large as 20–30.
14
One-step estimators are often found to be more reliable than two-step estimators for inference
purposes. See Arellano and Bond (1991), Blundell and Bond (1998) and Judson and Owen (1999),
for instance. We do not run the two-step GMM to apply Windmeijer (2005) correction to thecovariance matrix, solely because this is designed for ‘large N, small T’ panels.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd
9.58(0.01)

Granger causality test:
Wald statistic
p-value
6.84(0.03)

0.01
0.29
0.13

0.2467***(0.0887)-0.0639(0.0811)-0.0115(0.0361)0.1037**(0.0499)-0.0043(0.0325)
0.1095***(0.0419)0.2616***(0.0786)-0.0500(0.0726)

D-GMM

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-value

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

First round VC/R&D(-4)

First round VC/R&D(-3)

First round VC/R&D(-2)

First round VC/R&D(-1)

Independent variable

Dependent variable = TFP growth

Table 4. Testing for VC-first hypothesis on TFP growth

8.57(0.01)

0.01
0.02
0.21

0.4973***(0.0707)0.1716(0.1263)-0.0056(0.0258)
0.1053***(0.0367)

S-GMM

10.81
(0.03)-0.0095(0.0336)0.1225***(0.0387)-0.0639(0.0585)-0.0268(0.0606)
0.2321***(0.0796)-0.0967(0.0705)
0.0205(0.0732)
0.1213(0.0874)

LSDV

18.10
(0.00)-0.0187(0.0436)0.1150**(0.0500)-0.0733*(0.0402)-0.0373(0.0577)0.2303**(0.0900)-0.0972(0.0790)0.0195(0.0354)0.1210(0.0858)
0.01
0.50
0.34

D-GMM

Lag = 422.20
(0.00)-0.0252(0.0354)0.1100***(0.0379)-0.0421(0.0550)
0.0118(0.0528)
0.4033***(0.0626)
0.0278(0.1158)
0.1486***(0.0359)
0.2635***(0.0642)
0.01
0.37
0.31

S-GMM

436

m. hirukawa and m. ueda

3.05
(0.22)

Granger causality test:
Wald statistic
p-value

0.2575***(0.0844)-0.0479(0.0931)-0.0351(0.0254)-0.0077(0.0528)

D-GMM

0.01
0.22
0.06
0.91
(0.63)

0.2650***(0.0785)-0.0415(0.0733)-0.0348(0.0408)-0.0031(0.0594)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-value

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

Follow-on round VC/R&D(-4)

Follow-on round VC/R&D(-3)

Follow-on round VC/R&D(-2)

Follow-on round VC/R&D(-1)

Independent variable

Dependent variable = TFP growth

Table 4. Continued

4.03(0.13)

0.01
0.02
0.10

0.4928***(0.0708)
0.1630(0.1271)-0.0143(0.0518)
0.1068(0.0828)

S-GMM

3.61
(0.46)-0.0510(0.0425)0.0011(0.0564)-0.0266(0.0382)-0.0467(0.0604)
0.2348***(0.0791)-0.0879(0.0704)
0.0178(0.0727)
0.1280(0.0869)

LSDV

4.68
(0.32)-0.0508*(0.0288)-0.0026(0.0577)-0.0282(0.0281)-0.0476(0.0676)0.2329***(0.0876)-0.0886(0.0828)0.0167(0.0322)0.1268(0.0837)
0.01
0.37
0.23

D-GMM

Lag = 43.22
(0.52)-0.0512(0.0430)
0.0449(0.0602)0.0506*(0.0291)
0.0436(0.0646)0.4015***(0.0614)0.0296(0.1188)
0.1460***(0.0341)0.2614***(0.0655)
0.01
0.33
0.17

S-GMM

venture capital and innovation
437© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd
6.05(0.05)

0.01
0.02
0.17

0.4935***(0.0715)0.1643(0.1263)-0.0117(0.0205)
0.0785**(0.0336)

S-GMM

4.35
(0.36)-0.0345(0.0290)0.0617(0.0448)-0.0351(0.0288)-0.0449(0.0407)0.2346***(0.0792)-0.0937(0.0709)
0.0187(0.0729)0.1266(0.0866)

LSDV

8.49
(0.08)-0.0370(0.0250)
0.0574(0.0485)-0.0389**(0.0154)-0.0480(0.0449)0.2309***(0.0887)-0.0955(0.0816)0.0158(0.0336)
0.1246(0.0838)
0.01
0.48
0.35

D-GMM

Lag = 49.41
(0.05)-0.0385(0.0280)
0.0640*(0.0356)
0.0150(0.0260)
0.0138(0.0384)
0.4024***(0.0624)0.0276(0.1176)
0.1460***(0.0347)
0.2629***(0.0651)
0.01
0.40
0.26

S-GMM

Does venture capital investment cause innovation? Dependent variables are TFP growth. Independent variables are lagged terms of various measures of VC investments and lagged TFP growth. The
sample period is 1970–2001 (‘Lags = 2’) or 1972–2001 (‘Lags = 4’). Results from least squares dummy variable (‘LSDV’), one-step difference generalized method of moments (GMM) (‘D-GMM’) andone-step system GMM (‘S-GMM’) estimations are presented. Heteroskedasticity robust standard errors are in parentheses. Estimates with ***, ** and * are significant at the 1, 5 and 10% levels,
respectively. Coefficients on time dummies are not reported. The null hypothesis for the Granger causality test is that all coefficients on VC investments are zero.

4.31(0.12)

Granger causality test:
Wald statistic
p-value

0.2516***(0.0854)-0.0582(0.0888)-0.0303*(0.0181)0.0436(0.0424)

D-GMM

0.01
0.26
0.12
1.49
(0.47)

0.2650***(0.0784)-0.0459(0.0732)-0.0281(0.0281)0.0474(0.0438)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-value

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

Total VC/R&D(-4)

Total VC/R&D(-3)

Total VC/R&D(-2)

Total VC/R&D(-1)

Independent variable

Dependent variable = TFP growth

Table 4. Continued

438

m. hirukawa and m. ueda

8.00
(0.02)

Granger causality test:
Wald statistic
p-value

0.0345(0.0452)
0.0072(0.0177)0.0874**(0.0342)0.0480(0.0916)

D-GMM

0.17
0.14
0.19
3.75
(0.15)

0.1465*(0.0882)
0.0915(0.0597)

0.0415(0.0294)
0.0250(0.0294)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-valueFirst round VC/R&D(-4)

First round VC/R&D(-3)

First round VC/R&D(-2)

First round VC/R&D(-1)

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

Independent variable

Dependent variable = First round VC/R&D

Table 5. Testing for innovation-first hypothesis on TFP growth

15.18(0.00)

0.16
0.27
0.19

0.1998***(0.0512)0.1654***(0.0508)

0.0912***(0.0252)
0.0189(0.0566)

S-GMM

10.69
(0.03)

0.0463(0.0310)0.0030(0.0272)
0.0778(0.0622)0.0040(0.0686)
0.1410(0.0911)0.0902(0.0617)-0.0266(0.0877)-0.0329(0.1070)

LSDV

43.23
(0.00)

0.1516***(0.0479)-0.0207(0.0879)0.2205(0.1784)0.0018(0.1841)0.0366(0.0457)
0.0115(0.0228)-0.1301***(0.0467)-0.1309(0.1713)
0.17
0.13
0.10

D-GMM

Lag = 470.55
(0.00)

0.1131**(0.0575)-0.0842(0.0757)
0.2281(0.1801)-0.1021(0.2097)0.1783***(0.0299)
0.1240***(0.0459)
0.1596***(0.0417)0.1516(0.1559)
0.15
0.21
0.15

S-GMM

venture capital and innovation
439© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd
6.63(0.04)

Granger causality test:
Wald statistic
p-value

0.5144***(0.1630)-0.0119(0.1243)0.2258(0.2130)-0.1082(0.1003)

D-GMM

0.04
0.77
0.09
2.17
(0.34)

0.6124***(0.1931)0.0805(0.1517)0.0941(0.0680)-0.0303(0.0446)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-value

Follow-on round VC/R&D(-4)

Follow-on round VC/R&D(-3)

Follow-on round VC/R&D(-2)

Follow-on round VC/R&D(-1)

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

Independent variable

Dependent variable = Follow-on round VC/R&D

Table 5. Continued

4.17(0.12)

0.05
0.01
0.05

0.6115***(0.0982)0.2207(0.1403)

0.2962*(0.1714)-0.1102*(0.0642)

S-GMM

8.62
(0.07)

0.0887(0.0622)-0.0569(0.0450)
0.0623(0.0584)
0.1412**(0.0708)
0.5965***(0.1895)
0.0610(0.1471)
0.0219(0.0673)
0.0364(0.0655)

LSDV

25.89
(0.00)

0.3047*(0.1712)-0.1228(0.0786)
0.1946**(0.0866)
0.3812**(0.1877)
0.4753***(0.1301)
0.0172(0.1063)-0.0989(0.0691)-0.0857(0.1010)
0.03
0.73
0.16

D-GMM

Lag = 444.84
(0.00)

0.2105*(0.1152)-0.2534***(0.0848)0.1855***(0.0680)0.2303*(0.1263)
0.5698***(0.0956)
0.1480(0.1304)
0.1365**(0.0578)
0.1446**(0.0690)
0.03
0.83
0.06

S-GMM

440

m. hirukawa and m. ueda

6.96
(0.03)

0.04
0.93
0.14

0.4529***(0.0966)
0.3308***(0.0690)0.4060**(0.1698)-0.0938(0.1067)

S-GMM

10.34
(0.04)

0.1306(0.0893)-0.0620(0.0618)0.1372(0.0992)
0.1314(0.1216)0.4148***(0.1516)0.2586**(0.1295)
0.0321(0.1286)-0.1103(0.1002)

LSDV

66.11
(0.00)

0.4551***(0.1708)-0.1816(0.1507)0.4088**(0.2003)0.3503(0.3530)
0.3179***(0.1084)
0.2011***(0.0305)-0.1131(0.1190)-0.2335*(0.1305)
0.03
0.54
0.20

D-GMM

Lag = 474.64
(0.00)

0.3426***(0.1018)-0.3435***(0.1032)
0.4141**(0.2103)0.1333(0.3056)
0.4111***(0.0841)0.2927***(0.0674)
0.1762*(0.1034)
0.0315(0.1122)
0.04
0.37
0.11

S-GMM

Does innovation cause VC investment? Dependent variables are various measures of VC investments. Independent variables are lagged TFP growth and lagged terms of various measures of VC
investments. The sample period is 1970–2001 (‘Lags = 2’) or 1972–2001 (‘Lags = 4’). Results from least squares dummy variable (‘LSDV’), one-step difference generalized method of moments (GMM)(‘D-GMM’) and one-step system GMM (‘S-GMM’) estimations are presented. Heteroskedasticity robust standard errors are in parentheses. Estimates with ***, ** and * are significant at the 1, 5 and
10% levels, respectively. Coefficients on time dummies are not reported. The null hypothesis for the Granger causality test is that all coefficients on TFP growth are zero.

6.09(0.05)

Granger causality test:
Wald statistic
p-value

0.3326**(0.1393)0.1735***(0.0239)0.3184(0.2335)-0.0898(0.1899)

D-GMM

0.03
0.38
0.15
1.94
(0.38)

0.4292***(0.1477)0.2418**(0.1179)0.1343(0.0964)-0.0101(0.0624)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-value

Total VC/R&D(-4)

Total VC/R&D(-3)

Total VC/R&D(-2)

Total VC/R&D(-1)

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

Independent variable

Dependent variable = Total VC/R&D

Table 5. Continued

venture capital and innovation
441© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

442

m. hirukawa and m. ueda

equation (1), regardless of the VC investment measure or the lag length. The
same test also rejects the null at the 5% level for equation (2) using the follow-on
round and total VC investments. These results are not surprising. Because theArellano–Bond test is applied to the residuals in differences, negative first-orderserial correlation is expected due to the first-difference transformation before
implementing GMM. Therefore, it is meaningful to check the presence of firstorder serial correlation in levels using the results of the Arellano–Bond AR(2)test. Overall, the AR(2) test does not reject the null of no second-order serial
correlation at the 5% level. The results from the two lag specification of equation (1) using the system GMM indicate a presence of second-order serial correlation, but this issue is resolved when the lag length is increased to four.
Furthermore, most of the Sargan statistics provide little evidence of violating
exogeneity in instruments.
Next, we examine the estimated coefficients. We can see from both tables thatfor each lag length and each measure of VC investments, estimated coefficientsare qualitatively similar across three estimation methods. Both tables also showthat coefficients on first-order AR terms are, in general, significantly positive,
not surprisingly suggesting the presence of positive autocorrelation for bothvariables. An interesting observation is that in Table 5, estimated coefficients ofthe first-order AR terms when the first round VC investment is used are considerably smaller than those when the follow-on VC investment is used. Indeed,
there are some industries that exhibit weak serial dependence in the first round
VC investment and strong dependence in the follow-on investment.15 It seems
that these industries are a main reason for the discrepancy in the size ofthe coefficients on first-order autocorrelation terms between two investment
measures.
We then look into the testing results of Granger causality. We start byexamining the results of testing the null of no Granger causality from VC
investment to TFP growth in Table 4. This table shows that for each lag lengthand each estimation method, the Granger test rejects the null at the 5% level forthe case of the first round VC investment, whereas it does not for the case of thefollow-on investment. This result is surprising, given that the follow-on roundinvestment is more likely to have immediate positive impacts on innovation than
the first round VC investment.
The significance of the Granger test for the first round VC investment appears
to be mainly due to significantly positive coefficients of 2-year lagged VC investment. We can also see that coefficients on 1-year lagged VC investment tend tobe negative (although insignificant at the 5% level). This suggests that VC
investment is associated with a slowdown of TFP growth in the year following.
We now turn to Table 5, where we examine the results of testing the null of no
Granger causality from TFP growth to VC investment. For the case of the firstround VC investment, all Wald statistics other than the one from LSDV in the15
For example, first-order sample autocorrelations of the first and follow-on round VC investments(relative to privately-funded R&D expenditures) are substantially different in the following 5 industries: KL 3 (-0.00 for the first round VC investment, 0.60 for the follow-on round VC investment);
KL 7 (0.18, 0.62); KL 8 (0.22, 0.71); KL 10 (0.06, 0.61); and KL 14 (0.21, 0.71).

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltdventure capital and innovation

443

two-lag specification reject the null at the 5% level. In contrast, for the case of thefollow-on investment, when the lag length is two, all Wald statistics other thanthe one from the difference GMM are so small as not to reject the null at the 5%
level; even the one from the difference GMM fails to reject the null at the 1%
level. It may be the case that it is hard to detect the causality from TFP growth
to VC investment over the time horizon of a maximum 2 years. Once the lag
length is increased to four, however, two GMM results strongly reject the null.
These results may indicate that causality tests are sensitive to choices of lag
length, as is often reported. For both the first and the follow-on VC investment,
the coefficients on 1-year lagged TFP growth are positive. In addition, as we
expect, a strong positive causality from TFP to VC investments seems to exist,
especially for the first round case (but not strong for the follow-on case), in thatthe coefficients are often significant for the first round.
To summarize, we find some evidence to support the innovation-first hypothesis: past TFP growth is positively related with both the first and the follow-on
round VC investment. This evidence is stronger for the first round VC investment, as predicted. We also find that the first round VC investment is positively
and significantly related with TFP growth in 2 years, supporting the VC-first
hypothesis. Nevertheless, both the first and the follow-on VC investment are
negatively related with TFP growth in 1 year.
3.1.2. Industry analysis
So far we have examined the relationship between TFP growth and VC investment using the panel AR analysis. The analysis gives us a general idea about
how TFP growth and VC investment are related in the manufacturing industry
as a whole. The problem is, however, that the regression coefficients pick up
both cross-sectional and time-series effects, and we cannot separate these twofor the purpose of interpreting the coefficients. Because the degree to which VC
investment affects TFP growth is likely to differ across industries, we now
examine the relation between VC investment and TFP growth for each industry
individually. We focus on the following top 5 industries in terms of dollar
amounts of VC investment: Drugs (KL 6); Office and Computing Machines (KL13); Communication and Electronic (KL 15); Other Electrical Equipment (KL16); and Professional and Scientific Instruments (KL 19). These industries are ofparticular interest in terms of assessing the interactions between VC investments
and innovations, because they account for 88% of the total VC investment in
manufacturing industries to date.
To conduct the industry analysis, we introduce two new variables. First, we
control for the industry capacity utilization when TFP growth is the dependent
variable.16 The construction of the TFP series assumes that capital is fully16
The data source for capacity utilization is the Federal Reserve Bank, Board of Governors. The
capacity utilization data is classified by NAICS. We have matched with the KL code as follows (notethat the numbers in the parentheses are associated with NAICS): KL 1 (311, 312), KL 2 (313, 314,
and 315); KL 3 (321, 337), KL 4 (322), KL 5-7 (325), KL 8 (211, 213, 324); KL 9 (326), KL 10 (327),
KL 11 (331), KL 12 (332), KL 13 (3341), KL 14 (333), KL 15 (3342), KL 16 (335), KL 17 (G3361T3),
KL 18 (G3364T9), KL 19 (334), and KL 20 (316, 323, 339). The correlation coefficient between TFP
growth and capacity utilization is 0.11, with a p-value of 0.01.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

444

m. hirukawa and m. ueda

utilized. Nevertheless, this assumption is not satisfied when the industry is inrecession. As a result, TFP tends to be underestimated during recessions. By
controlling for capacity utilization, we attempt to lessen the mismeasurement
problem of TFP. Second, for equation (2), we control for the policy changes thattook place in 1979 and presumably stimulated the US VC industry. One of thechanges concerns the supply side of VC investments. Before 1979, most pension
funds had refrained from investing in VC so as not to violate the ERISA prudent
man rule. In 1978, the Department of Labor clarified VC as a possible investment target for pension funds and, in 1979, this clarification was implemented.
This clarification is considered to have made it substantially easier for VC toraise funds because each VC organization is typically small and does not have its
own means of raising a large amount of funds directly from original investors.
Another change is associated with the demand side of VC investments. In 1979,
the highest marginal capital gains tax rate was reduced from 33.8 to 28%.
Entrepreneurs backed by VC investments typically cash in on the firms that they
have created by selling their stakes to third parties. These incomes are subject tocapital gains tax. Thus, the reduction of the capital gains tax rate presumably
encourages entrepreneurship and enhances the demand for VC investments. To
control for the impact of this policy change on VC investment, we construct adummy variable, the ERISA dummy, which takes a value of zero until 1979 andtakes a value of one otherwise.17
Table 6 and 7 present the estimation results of the first and follow-on round
VC investments as either dependent or independent variables, respectively. All
regressions include 4-year symmetric lags of two variables and a quadratic timetrend. Because the regressions are based on pure time-series data for individual
industries, they are estimated using OLS. For the VC investment regression,
results with and without the ERISA dummy are reported. In addition, for the
TFP regression, results with and without the industry capacity utilization arereported. Note that coefficients on AR terms are not reported. All standard
errors are based on the heteroskedasticity-robust formula.
We look into the results industry-by-industry.
In the Drugs industry, TFP growth exhibits a convex and decreasing trend nomatter whether the first or the follow-on round VC investment is used. The firstround VC investment also has a convex trend, which is not significant in thefollow-on investment. The Granger test using the follow-on round investment is
consistent with the VC-first hypothesis, but none of the coefficients on lagged
VC investments in this regression are significant. This result does not come as asurprise. In the Drugs industry, most technological innovations are improvements in the quality of drugs. Nevertheless, such improvement is difficult tomeasure and, therefore, it is unlikely that TFP growth is a good measure ofinnovation. Furthermore, drug development is a long process and, therefore,
VC investments might have a positive impact on TFP growth after 4 years.
17

Correlation coefficients (p-values) between ERISA and two VC investment measures are 0.21
(0.00) for the first round VC investment relative to private R&D expenditures, and 0.27 (0.00) for thefollow-on round VC investment relative to private R&D expenditures, respectively.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

Granger causality test:
Wald statistic
p-value

CapUtil

Trend2

Trend

First VC/R&D(-4)

First VC/R&D(-3)

First VC/R&D(-2)

First VC/R&D(-1)

Independent variable

6.31(0.18)

3.77(0.44)

1.6607(1.0728)0.4245(1.2297)-0.5727(1.0154)0.6763(1.1308)-1.2284***(0.4010)0.0207**(0.0085)-0.0139(0.1164)

Drugs

1.7088*(0.8734)
0.4553(1.1008)-0.5559(0.9679)
0.6989(1.0737)-1.2305***(0.3830)0.0205**(0.0080)

Dependent variable = TFP growth

5.38(0.25)-1.2897(0.8611)
0.9568(1.7009)-0.6018(1.6016)0.3671(1.5501)-1.3008(0.9970)0.0339(0.0249)

1.34(0.86)-0.2544(0.7290)0.5649(1.6123)-1.3572(1.5633)
1.6479(1.5158)
0.0461(0.8415)-0.0033(0.0210)0.7900***(0.1848)

Office andComputing Machines

20.55(0.00)-2.5342**(1.1012)0.0055(1.7279)
0.5448(1.8275)-3.2737(2.1122)-1.1952**(0.4883)0.0496***(0.0164)20.17(0.00)-2.5672**(1.1533)-0.1404(1.8964)0.4702(1.9389)-2.7254(2.8698)-1.3854*(0.7178)
0.0546***(0.0209)
0.1095(0.2530)

Communicationand Electronic

Table 6. Testing for VC-first hypothesis on TFP growth in selected industries

24.00(0.00)-0.7350***(0.1828)0.1821(0.4873)-0.3743(0.4162)0.2648(0.3208)-0.2045(0.2610)0.0057(0.0061)

5.82(0.21)-0.2101(0.1856)-0.0046(0.3360)-0.3446(0.2582)-0.0618(0.2720)
0.1667(0.1526)-0.0051(0.0040)
0.3359***(0.0580)

Other Electrical
Equipment

4.54(0.34)-0.1065(0.5757)-0.8805(0.6161)0.6445(0.6823)
0.2621(0.5983)-0.4515**(0.2036)
0.0078(0.0053)

3.92(0.42)

0.0853(0.4475)-0.6088(0.5478)0.3074(0.6251)0.6477(0.6933)-0.5038***(0.1767)
0.0084*(0.0045)0.1938**(0.0813)

Professional andScientific Instruments

venture capital and innovation445© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

14.42(0.01)

0.2137(0.3010)0.7907(0.5331)-0.1511(0.8167)
0.7686(0.6308)-1.4460***(0.3107)0.0189**(0.0081)12.33(0.02)

0.1375(0.3037)0.8340(0.5226)-0.1400(0.8320)0.8365(0.6598)-1.4476***(0.3400)
0.0191**(0.0086)-0.0727(0.0845)

Drugs

8.01
(0.09)-0.3791*(0.2120)0.3577(0.3868)-0.1032(0.4325)-0.2621(0.7275)-0.8658(1.1364)0.0241(0.0280)

0.79(0.94)-0.1445(0.2408)0.3034(0.4097)-0.2007(0.5908)
0.1647(0.7911)-0.1004(1.0082)0.0008(0.0257)
0.7330***(0.2334)

Office andComputing Machines

27.10(0.00)-1.6971**(0.7363)0.7491(1.3189)-0.4883(1.4030)-1.7090(1.6605)-0.6657(0.4429)0.0587***(0.0187)31.20(0.00)-1.8121**(0.7680)
1.0818(1.4884)-0.7854(1.5861)-1.3677(1.8995)-0.9791*(0.5340)0.0651***(0.0181)0.1685(0.1858)

Communicationand Electronic

51.46(0.00)-0.1019(0.1785)-0.3327(0.8208)-0.2768(0.6034)
0.6095(0.7728)-0.2642(0.2433)
0.0064(0.0052)

2.46(0.65)

0.0659(0.1402)-0.2917(0.6073)-0.4746(0.3753)0.1842(0.5649)0.2758(0.1744)-0.0078*(0.0041)0.3570***(0.0669)

Other Electrical
Equipment

3.98(0.41)-0.1481(0.1583)-0.0204(0.3018)0.3445(0.2697)
0.0020(0.2337)-0.5504***(0.1750)
0.0090*(0.0048)

5.86(0.21)

0.0258(0.1579)
0.0076(0.2737)
0.1995(0.2395)
0.1498(0.1800)-0.5628***(0.1767)
0.0073*(0.0043)0.2206**(0.0876)

Professional andScientific Instruments

The sample period is 1972–2001. For each industry, the first and second columns display OLS results of the regression with a quadratic time trend and the regression with trends and a control variable‘CapUtil’, respectively, as well as lagged VC investments and TFP growth. Heteroskedasticity robust standard errors are in parentheses. Estimates with ***, ** and * are significant at the 1, 5 and 10%
levels, respectively. Constant terms and coefficients on autoregressive terms are not reported. The null hypothesis for the Granger causality test is that all coefficients on VC investments are zero.

Granger causality test:
Wald statistic
p-value

CapUtil

Trend2

Trend

Follow-on VC/R&D(-4)

Follow-on VC/R&D(-3)

Follow-on VC/R&D(-2)

Follow-on VC/R&D(-1)

Independent variable

Dependent variable = TFP growth

Table 6. Continued446

m. hirukawa and m. ueda

Granger causality test:
Wald statistic
p-value

ERISA

Trend2Trend

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

Independent variable

0.47(0.98)

0.0062(0.0419)0.0160(0.0346)
0.0059(0.0457)-0.0198(0.0545)-0.0823(0.0673)
0.0037**(0.0018)

3.70(0.45)

0.0257(0.0479)
0.0642*(0.0356)
0.0329(0.0500)
0.0075(0.0460)-0.2448*(0.1266)
0.0082**(0.0034)
1.1826(0.7890)

Drugs

Dependent variable = First round VC/R&D

7.46(0.11)

0.0301(0.0449)
0.0276(0.0390)0.0537(0.0512)0.0633(0.0449)-0.4544*(0.2502)0.0137*(0.0071)

8.74(0.07)

0.0343(0.0457)
0.0335(0.0343)0.0443(0.0401)
0.0615(0.0424)-0.9163***(0.3358)
0.0234***(0.0084)
2.6334**(1.2852)

Office andComputing Machines

23.50(0.00)

0.1017*(0.0608)-0.1057(0.0730)-0.0348(0.0464)0.1910***(0.0609)
0.0423(0.1053)-0.0007(0.0035)19.51(0.00)

0.0833(0.0585)-0.0876(0.0705)-0.0769*(0.0415)0.1865***(0.0589)-0.3887(0.2441)0.0094(0.0062)2.0027**(0.9144)

Communication andElectronic

Table 7. Testing for innovation-first hypothesis on TFP growth in selected industries

1.86(0.76)-0.0927(0.1624)0.0475(0.1106)-0.0281(0.0828)-0.1635(0.1972)-0.0848(0.2215)0.0050(0.0067)

1.63(0.80)-0.0994(0.1713)0.0428(0.1128)-0.0228(0.0881)-0.1716(0.2031)-0.0297(0.2904)
0.0039(0.0077)-0.3320(1.0297)

Other Electrical
Equipment

1.07(0.90)

0.0268(0.1135)-0.0635(0.0767)-0.0203(0.0790)-0.0246(0.0792)-0.0361(0.1075)
0.0021(0.0026)

1.16(0.89)

0.0348(0.1143)-0.0632(0.0800)-0.0200(0.0780)-0.0243(0.0800)-0.0747(0.2075)
0.0029(0.0047)0.2497(0.8029)

Professional andScientific Instruments

venture capital and innovation447© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

2.21(0.70)

0.0532(0.1436)
0.0229(0.1247)0.1806(0.1749)-0.1343(0.1260)-0.3022(0.2471)
0.0098*(0.0052)

Drugs

2.40
(0.66)

0.1306(0.1834)
0.1616(0.1693)0.2727(0.2067)-0.0224(0.1399)-0.6190(0.3795)
0.0195*(0.0106)
2.7497(2.3223)
3.52(0.47)

0.0903(0.1857)-0.0757(0.1515)0.0776(0.1957)
0.3417(0.2267)-0.9695(1.0936)0.0309(0.0307)3.82(0.43)

0.1072(0.1927)-0.0638(0.1482)
0.0467(0.1917)0.3377(0.2065)-2.1114(1.4193)0.0548(0.0367)
6.1451(4.5897)

Office andComputing Machines

24.75(0.00)-0.0757(0.0994)-0.0099(0.1343)-0.1708(0.1225)
0.4526***(0.1372)0.2243(0.1738)-0.0031(0.0066)32.18(0.00)-0.0985(0.1053)-0.0017(0.1381)-0.2240*(0.1194)
0.4492***(0.1289)-0.3126(0.3550)
0.0094(0.0100)
2.7059(1.7798)

Communicationand Electronic

1.75(0.78)-0.6629(0.5189)-0.2497(0.3178)-0.2243(0.2803)-0.6836(0.5586)-0.8449(0.6657)
0.0315(0.0209)1.64(0.80)-0.6510(0.5344)-0.2390(0.3379)-0.2415(0.2831)-0.6635(0.5793)-0.9932(0.7891)
0.0343(0.0223)0.8235(2.6258)

Other Electrical
Equipment

4.45(0.35)

0.0446(0.2115)-0.1281(0.1513)-0.2233(0.1648)0.1185(0.1553)-0.1684(0.1997)0.0104*(0.0060)

4.27(0.37)

0.0843(0.2264)-0.1312(0.1585)-0.2201(0.1699)
0.1175(0.1594)-0.3581(0.3518)0.0142*(0.0083)
1.0863(1.0429)

Professional andScientific Instruments

The sample period is 1972–2001. For each industry, the first and second columns display OLS results of the regression with a quadratic time trend and the regression with trends and a control variable‘ERISA’, respectively, as well as lagged VC investments and TFP growth. Heteroskedasticity robust standard errors are in parentheses. Estimates with ***, ** and * are significant at the 1, 5 and 10%
levels, respectively. Constant terms and coefficients on autoregressive terms are not reported. The null hypothesis for the Granger causality test is that all coefficients on TFP growth are zero.

Granger causality test:
Wald statistic
p-value

ERISA

Trend2Trend

TFP growth(-4)

TFP growth(-3)

TFP growth(-2)

TFP growth(-1)

Independent variable

Dependent variable = Follow-on round VC/R&D

Table 7. Continued

448

m. hirukawa and m. ueda

venture capital and innovation

449

In the Office and Computing Machines industry, the estimated coefficients on
the capacity utilization and the ERISA dummy are positive in both tables, aspredicted, and they are often significant. The results of the Granger test provide
no strong evidence of the causality between TFP growth and VC investments.
The estimated coefficients on 1-year lagged TFP growth in both VC regressions
are positive, consistent with the innovation-first hypothesis, whereas those on
1-year lagged VC investment in both TFP regressions are negative. One interpretation is that a rapid increase in VC investment is associated with a subsequent stock market crash and, therefore, recession (Abreu and Brunnermeier,2003). TFP is positively related to macroeconomic conditions because manufacturing plants are more efficiently utilized in economic booms than during
recessions and, therefore, if TFP growth slows down, recessions occur. Consistent with this explanation, the negative impact of VC on TFP growth no longer
exists once capacity utilization is controlled for.
In the Communication and Electronic industry, TFP growth exhibits a negative and convex trend. The signs of the coefficients on the capacity utilizationand the ERISA dummy are both positive, as expected, but they are mostly
insignificant. The Granger test strongly suggests both ways of causality, nomatter whether the first or the follow-on round VC investment is used. The
coefficients on 1-year lagged VC investments in the TFP regressions are significantly negative. Although the Granger test strongly supports the causality from
VC to innovation in this industry, VC investment appears to slow down TFP
growth in the year following, similar to the results of the panel regression. In
contrast, the positive and significant coefficients on 4-year lagged TFP growth in
VC regressions are consistent with the innovation-first hypothesis. A puzzlingfinding is that the estimated coefficients on 1–3-year lagged TFP growth in thefollow-on VC regression are all negative.
This puzzling finding may reflect a life-cycle of innovation and industry. A
typical life-cycle of an innovation is: generation of idea, creation of prototype,development of cost-effective manufacturing of the prototype (commercialization) and then adoption and learning by buyers of the innovation. VC investment is typically made during the commercialization stage of innovation. TFP
growth occurs during the stage of adoption and learning. As in Jovanovic
and Rob (1990), this life-cycle should be repeated due to marginally diminishing returns to each innovation. Therefore, if it takes 4 years from adoption
and learning of one innovation to commercialization of the next innovation,
we should observe the pattern that the 4-year lagged TFP growth has asignificantly positive effect on VC investment but not other lagged TFP
growth.
In the Other Electrical Equipment industry, we merely find that coefficients on
the capacity utilization are significantly positive. There is little evidence of
causality in either direction. However, similar to the results for the Office andComputing Machines industry, 1-year lagged VC investment in TFP regressions
are negative and significant, and this significance disappears once capacity utilization is controlled for. These results are consistent with Abreu and Brunnermeier (2003).
© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

450

m. hirukawa and m. ueda

In the Professional and Scientific Instruments industry, TFP growth seems tofollow a negative trend. The coefficients on the capacity utilization are again
significantly positive as expected. However, the Granger test does not suggest
causality in either direction.
Combining the results of all manufacturing industries in previous sections, we
can point out that the results from the Communication and Electronic industry
confirm the validity of the innovation-first hypothesis at the entire manufacturing industry level. In particular, the significantly positive coefficients on 4-year
lagged TFP growth in the follow-on VC equation appear to come from thisindustry’s results. However, the same Communication and Electronic industry
results provide supporting evidence that VC investment predicts a TFP growth
slowdown in 1 year at the entire manufacturing industry level for the first round
VC case.

3.2. Causality between VC investments and patents
We are now going to use the number of patent applications that were eventually
granted as the measure of innovation, and redo the same analyses as in theprevious two subsections.
3.2.1. Panel AR models
We reestimate the panel AR models (1) and (2) by replacing TFP growth withthe logarithm of patent counts, and perform the Granger causality test. The
results in Table 8 and 9 correspond to those in Table 4 and 5, respectively.
Again, the results on the total VC investment are provided for reference. All
standard errors are based on the heteroskedasticity-robust formula. Overall, theestimated coefficients are robust across three different estimation methods. The
coefficients on first-order AR terms are significantly positive. The results fromthe Arellano–Bond AR(2) test are acceptable in almost all cases. Moreover,most of the Sargan statistics do not indicate possible misspecification of themodels.
We now look into the testing results of Granger causality. A major difference
from the results using TFP growth is that when the first round VC investment isused, neither the VC-first nor the innovation-first hypothesis is supported. Notethat all coefficients on 1-year lagged VC investment and patent counts are
positive but insignificant. However, for the case of the follow-on VC investment,
the VC-first hypothesis is strongly supported over the 4-year time span. Nevertheless, the 1-year lagged follow-on VC investment is significantly and negatively related to patents, suggesting that follow-on VC investments slow down
patenting activities in the subsequent year. Interestingly, the negative impact of
the follow-on investment on innovation in the following year is common,
whether innovation is measured by TFP growth or patent counts. The Granger
test from the follow-on VC regression also indicates the possibility of theinnovation-first hypothesis, but none of the coefficients on lagged patent counts
are significant at the 5% level (although they are positive in general).
© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd0.28(0.87)

Granger causality test:
Wald statistic
p-value

0.8126****(0.1129)0.0683(0.1034)-0.0005(0.0009)-0.0002(0.0010)

D-GMM

0.01
0.11
0.06
0.16
(0.93)

0.9068****(0.0900)
0.0687(0.0825)0.0002(0.0011)-0.0005(0.0013)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-valueln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

First round VC/R&D(-4)

First round VC/R&D(-3)

First round VC/R&D(-2)

First round VC/R&D(-1)

Independent variable

Dependent variable = ln(Patent)

Table 8. Testing for VC-first hypothesis on patent

0.41(0.81)

0.02
0.08
0.15

0.9437****(0.1816)0.0595(0.1835)-0.0004(0.0008)
0.0001(0.0011)

S-GMM

1.01
(0.91)

0.0001(0.0012)-0.0004(0.0014)-0.0004(0.0015)-0.0015(0.0016)0.9126****(0.0983)0.1452***(0.0734)-0.0492(0.0754)-0.0481(0.0519)

LSDV

1.86
(0.76)-0.0007(0.0010)0.0001(0.0010)-0.0011(0.0013)-0.0020(0.0025)0.8278****(0.1070)0.2066****(0.0428)-0.1321**(0.0694)-0.0401(0.0671)
0.02
0.92
0.06

D-GMM

Lag = 45.18
(0.27)-0.0009(0.0009)
0.0005(0.0010)0.0014(0.0020)-0.0034***(0.0016)0.9388****(0.1604)
0.1958****(0.0747)-0.0650(0.1083)-0.0712**(0.0365)
0.04
0.27
0.10

S-GMM

venture capital and innovation
451© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd
5.38(0.07)

Granger causality test:
Wald statistic
p-value
9.02(0.01)

0.01
0.09
0.08

0.7672****(0.0746)0.1451****(0.0562)-0.0043***(0.0020)-0.0003(0.0010)-0.0066****(0.0024)0.0052****(0.0020)0.9684****(0.0910)0.0445(0.0865)

D-GMM

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-valueln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

Follow-on round VC/R&D(-4)

Follow-on round VC/R&D(-3)

Follow-on round VC/R&D(-2)

Follow-on round VC/R&D(-1)

Independent variable

Dependent variable = ln(Patent)

Table 8. Continued

8.88(0.01)

0.02
0.06
0.22

0.9137****(0.1597)
0.0951(0.1588)-0.0059****(0.0020)0.0058***(0.0023)

S-GMM

12.83
(0.01)-0.0041***(0.0017)0.0006(0.0015)0.0002(0.0011)0.0032****(0.0012)0.8814****(0.0950)0.1671***(0.0692)-0.0281(0.0735)-0.0393(0.0466)

LSDV

26.35
(0.00)-0.0039***(0.0019)0.0000(0.0008)-0.0014(0.0013)0.0009(0.0020)
0.7913****(0.0788)
0.2164****(0.0408)-0.1020**(0.0561)-0.0057(0.0409)
0.02
0.50
0.07

D-GMM

Lag = 425.95
(0.00)-0.0060****(0.0019)0.0038(0.0026)0.0011(0.0013)0.0029***(0.0012)0.8910****(0.1344)
0.2223****(0.0595)-0.0210(0.0774)-0.0873(0.0552)
0.04
0.30
0.14

S-GMM

452

m. hirukawa and m. ueda

5.48
(0.06)

0.02
0.06
0.19

0.9205****(0.1682)
0.0845(0.1685)-0.0017**(0.0009)
0.0007(0.0005)

S-GMM

3.57
(0.47)-0.0016(0.0010)-0.0003(0.0009)
0.0005(0.0009)
0.0014(0.0011)
0.8948****(0.0962)0.1615***(0.0710)-0.0449(0.0739)-0.0436(0.0503)

LSDV

9.66
(0.05)-0.0017**(0.0010)0.0000(0.0004)-0.0006(0.0010)-0.0001(0.0018)0.8016****(0.0869)0.2247****(0.0403)-0.1226***(0.0573)-0.0275(0.0513)
0.02
0.79
0.07

D-GMM

Lag = 412.99
(0.01)-0.0022***(0.0010)0.0003(0.0004)
0.0015(0.0014)0.0008(0.0011)0.8972****(0.1398)
0.2314****(0.0580)-0.0465(0.0889)-0.0810(0.0548)
0.05
0.29
0.12

S-GMM

Does VC investment cause innovation? Dependent variables are ‘ln(Patent)’, which is the logarithm of annual patent applications that were eventually granted. Independent variables are lagged termsof various measures of VC investments and lagged (logarithms of) patent counts. The sample period is 1970–2001 (‘Lags = 2’) or 1972–2001 (‘Lags = 4’). Results from least squares dummy variable(‘LSDV’), one-step difference generalized method of moments (GMM) (‘D-GMM’) and one-step system GMM (‘S-GMM’) estimations are presented. Heteroskedasticity robust standard errors arein parentheses. Estimates with ***, ** and * are significant at the 1, 5 and 10% levels, respectively. Coefficients on time dummies are not reported. The null hypothesis for the Granger causality testis that all coefficients on VC investments are zero.

5.18(0.07)

Granger causality test:
Wald statistic
p-value

0.7800****(0.0871)
0.1135**(0.0682)-0.0019(0.0012)-0.0003(0.0007)

D-GMM

0.01
0.06
0.07
2.19
(0.33)

0.8892****(0.0875)
0.0946(0.0764)-0.0015(0.0010)0.0001(0.0008)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-valueln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

Total VC/R&D(-4)

Total VC/R&D(-3)

Total VC/R&D(-2)

Total VC/R&D(-1)

Independent variable

Dependent variable = ln(Patent)

Table 8. Continued

venture capital and innovation
453© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd
2.34(0.31)

Granger causality test:
Wald statistic
p-value

0.1021*(0.0522)0.0466*(0.0239)

1.6864(2.5690)-1.4596(4.7755)

D-GMM

0.13
0.19
0.13
2.77
(0.25)

0.1535*(0.0880)
0.0941*(0.0561)

1.4184(1.6753)-1.1110(2.7281)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-valueFirst round VC/R&D(-4)

First round VC/R&D(-3)

First round VC/R&D(-2)

First round VC/R&D(-1)

ln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

Independent variable

Dependent variable = First round VC/R&D

Table 9. Testing for innovation-first hypothesis on patent

1.35(0.51)

0.11
0.33
0.15

0.2347***(0.0674)
0.1634***(0.0507)2.5298(2.2141)-2.6897(2.4049)

S-GMM

3.39
(0.49)

1.4261(1.8734)-0.1537(1.5240)-1.3392(2.1991)
0.3483(1.9480)0.1487*(0.0880)
0.0968*(0.0575)-0.0285(0.0877)-0.0326(0.1051)

LSDV

5.94
(0.20)

1.5717(2.9836)0.3248(1.7443)-2.3749(2.8384)
0.6336(2.9090)0.1016*(0.0587)
0.0687***(0.0218)-0.1336***(0.0454)-0.1453(0.1330)
0.14
0.38
0.08

D-GMM

Lag = 45.64
(0.23)

2.7052(2.4597)-0.6696(2.4237)-2.9670(2.7447)0.7553(2.2267)
0.2118***(0.0558)
0.1381***(0.0443)
0.1021***(0.0371)
0.1099(0.1292)
0.11
0.21
0.13

S-GMM

454

m. hirukawa and m. ueda

16.88(0.00)

Granger causality test:
Wald statistic
p-value

0.4987***(0.1263)
0.0368(0.1384)

1.2198(2.3608)
3.6830(4.0550)

D-GMM

0.03
0.43
0.03
7.53
(0.02)

0.5438***(0.1792)
0.0636(0.1436)

1.7858(1.6217)1.7671(2.0985)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-value

Follow-on round VC/R&D(-4)

Follow-on round VC/R&D(-3)

Follow-on round VC/R&D(-2)

Follow-on round VC/R&D(-1)

ln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

Independent variable

Dependent variable = Follow-on round VC/R&D

Table 9. Continued

3.03(0.22)

0.04
0.04
0.07

0.6528***(0.1213)0.1951(0.1555)

3.7010(2.4883)-3.3726(2.4506)

S-GMM

8.81
(0.07)

1.6728(1.7325)
1.7092(2.2427)-0.3670(2.4596)
0.8117(2.2774)
0.5383***(0.1792)
0.0522(0.1451)0.0107(0.0652)0.0017(0.0586)

LSDV

13.75
(0.01)

1.0043(2.9844)1.9526(2.1606)-0.0924(3.8092)
2.4435(2.5457)
0.4928***(0.1232)
0.0343(0.1475)-0.0132(0.0612)-0.0183(0.0414)
0.03
0.45
0.02

D-GMM

Lag = 415.48
(0.00)

1.9276(1.7810)4.6326(3.5658)-1.5467(3.6475)-4.8865(3.9357)
0.6228***(0.1286)
0.1313(0.1638)
0.1154**(0.0557)
0.1066(0.0720)
0.04
0.59
0.05

S-GMM

venture capital and innovation455© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd
3.76(0.15)

0.14
0.45
0.03

0.7320***(0.1080)
0.0434(0.1584)

2.7926(17.0800)-0.6932(18.7135)

S-GMM

10.02
(0.04)

3.3219(3.1160)
1.7549(2.8686)-2.1481(4.3696)
1.9858(3.8984)
0.3895***(0.1342)0.2762***(0.1050)
0.0114(0.1298)-0.1459(0.0924)

LSDV

22.92
(0.00)

2.8175(3.8147)
1.5295(3.3141)-2.2345(4.9252)
3.5791(3.5582)
0.3627***(0.0753)0.2648***(0.0558)-0.0234(0.1205)-0.1984***(0.0571)
0.04
0.83
0.06

D-GMM

Lag = 43.86
(0.43)

2.7145(11.4254)-3.6150(13.6874)-0.4913(14.7628)
2.2229(7.8296)
0.3965***(0.1026)
0.6144**(0.2473)
0.8464**(0.4068)-0.2482(0.4295)
0.21
0.73
0.01

S-GMM

Does innovation cause VC investment? Dependent variables are various measures of VC investments. Independent variables are lagged (logarithms of) patent applications that were eventually granted
and lagged terms of various measures of VC investments. The sample period is 1970–2001 (‘Lags = 2’) or 1972–2001 (‘Lags = 4’). Results from least squares dummy variable (‘LSDV’), one-step
difference generalized method of moments (GMM) (‘D-GMM’) and one-step system GMM (‘S-GMM’) estimations are presented. Heteroskedasticity robust standard errors are in parentheses.
Estimates with ***, ** and * are significant at the 1, 5 and 10% levels, respectively. Coefficients on time dummies are not reported. The null hypothesis for the Granger causality test is that all coefficientson (logarithms of) patent counts are zero.

22.66(0.00)

Granger causality test:
Wald statistic
p-value

0.3628***(0.0827)0.2202***(0.0501)

2.9570(2.9367)2.4108(5.5469)

D-GMM

0.04
0.62
0.08
6.55
(0.04)

0.3948***(0.1288)0.2446***(0.0944)3.2467(2.6776)
1.1718(4.0949)

LSDV

Lag = 2

Arellano–Bond AR(1) test: p-value
Arellano–Bond AR(2) test: p-value
Sargan test: p-value

Total VC/R&D(-4)

Total VC/R&D(-3)

Total VC/R&D(-2)

Total VC/R&D(-1)

ln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

Independent variable

Dependent variable = Total VC/R&D

Table 9. Continued

456

m. hirukawa and m. ueda

venture capital and innovation457

To summarize, for the first round VC investment, we do not find any support
for the causality between VC investment and patent counts. For the follow-on
round VC investment, our results support the VC-first hypothesis over the
4-year horizon. However, the negative impact of the follow-on round VC
investment on patent count casts doubt on the validity of the VC-first
hypothesis.
3.2.2. Industry analysis
For the same 5 industries as used in Section 3.1.2., we run VC and patent
regressions. As before, all the regressions are estimated using OLS. The ERISA
dummy is still used as a control variable for the VC regression. For the patent
regression, we introduce a new control variable. There are two changes to patent
policies in the early 1980s; namely, the introduction of the Bayh–Dole Act in1980 and the establishment of the Federal Circuit Patent Court in 1982. The
former change allowed to patent innovations funded by federal grants. The
latter change made it easier to enforce patent rights. Therefore, after thesechanges, we expect that patent propensity went up. To see if controlling theimpact of these policy changes increases the explanatory power of patent counts,
we construct a dummy variable, D1981, which takes a value of zero until 1980
and takes a value of one otherwise.18
Table 10 and 11 present the estimation results of the first and follow-on round
VC investments as either dependent or independent variables, respectively.
Again, all regressions include 4-year symmetric lags of two variables and aquadratic time trend. The same specifications as before are considered
for the VC regression, whereas results with and without the dummy variable
D1981 are reported for the patent regression. Coefficients on AR terms are notreported. All standard errors are based on the heteroskedasticity-robust
formula.
Overall, the results from the patent regressions support that VC investments
predict future patenting, no matter whether the first or the follow-on round
VC investment is used. Exceptions are the Drug industry for the first roundcase and the Other Electrical Equipment industry for the follow-on case; infact, in the latter case, none of coefficients of lagged VC investments issignificant. Interestingly, whenever the causality from VC to patent is significant, negative coefficients on 1-year lagged VC investment contribute to
the significance of the Granger test; again, it appears that VC investment
slows down innovation in the year following. In addition, whenever thecoefficient of D1981 is significant, it is negative, as opposed to our expectation; see Other Electrical Equipment in both tables and Professional and Scientific Instruments in Table 10. In contrast, the results do not provide
evidence of the causality from innovation to VC investment in general. The
only exception is significantly negative coefficients on 1-year lagged patentcounts for the follow-on VC case in the Professional and Scientific Instruments industry.
18

The correlation coefficient between patent counts and D1981 is 0.13, with a p-value of 0.00.
© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

Granger causality test:
Wald statistic
p-valueD1981

Trend2

Trend

First VC/R&D(-4)

First VC/R&D(-3)

First VC/R&D(-2)

First VC/R&D(-1)

Independent variable

3.89(0.42)

3.52(0.47)-0.0477(0.0559)-0.0283(0.0677)-0.0125(0.0868)-0.1945*(0.1139)0.0118(0.0214)0.0013(0.0012)0.0172(0.1354)

Drugs-0.0461(0.0527)-0.0264(0.0623)-0.0098(0.0803)-0.1927*(0.1107)0.0146(0.0163)0.0012(0.0012)

Dependent variable = ln(Patent)

21.18(0.00)-0.0301***(0.0109)
0.0067(0.0162)-0.0140(0.0177)
0.0070(0.0174)-0.0221(0.0258)
0.0013(0.0011)18.07(0.00)-0.0308**(0.0123)
0.0069(0.0167)-0.0148(0.0184)0.0067(0.0184)-0.0234(0.0289)0.0013(0.0012)0.0134(0.0780)

Office andComputing Machines

29.26(0.00)-0.0463***(0.0113)-0.0046(0.0166)
0.0073(0.0154)-0.0216(0.0142)-0.1194***(0.0317)0.0048***(0.0012)34.96(0.00)-0.0479***(0.0104)-0.0082(0.0189)0.0055(0.0155)-0.0201(0.0144)-0.1231***(0.0317)0.0049***(0.0012)0.0319(0.0448)

Communication andElectronic

Table 10. Testing for VC-first hypothesis on patent in selected industries

95.17(0.00)-0.0286***(0.0032)
0.0086(0.0079)-0.0143**(0.0071)0.0146***(0.0048)-0.0305*(0.0174)0.0011**(0.0005)

101.73(0.00)-0.0295***(0.0032)
0.0063(0.0073)-0.0142**(0.0065)
0.0112**(0.0054)-0.0329**(0.0138)0.0014***(0.0004)-0.0851***(0.0315)

Other Electrical
Equipment

13.07(0.01)-0.0468**(0.0229)0.0110(0.0257)-0.0418**(0.0162)-0.0160(0.0266)-0.0266(0.0208)0.0017*(0.0009)

11.01(0.03)-0.0462*(0.0240)0.0102(0.0274)-0.0396**(0.0166)-0.0167(0.0263)-0.0257(0.0213)0.0017*(0.0009)-0.0164(0.0476)

Professional andScientific Instruments

458

m. hirukawa and m. ueda

34.18(0.00)-0.0730***(0.0158)
0.0404*(0.0236)-0.0138(0.0291)-0.1450***(0.0558)-0.0190(0.0151)
0.0054***(0.0013)31.44(0.00)-0.0739***(0.0163)
0.0412*(0.0235)-0.0121(0.0299)-0.1421**(0.0565)-0.0089(0.0180)
0.0053***(0.0013)-0.0755(0.0946)

Drugs

27.80
(0.00)-0.0095***(0.0033)-0.0033(0.0053)0.0059(0.0050)-0.0079(0.0072)-0.0700**(0.0310)0.0035**(0.0014)24.79(0.00)-0.0094**(0.0037)-0.0033(0.0055)
0.0061(0.0059)-0.0080(0.0074)-0.0687*(0.0354)0.0035**(0.0015)-0.0078(0.0675)

Office andComputing Machines

67.35(0.00)-0.0156***(0.0032)-0.0117*(0.0062)0.0253**(0.0121)-0.0193(0.0120)-0.1102***(0.0346)0.0047***(0.0012)66.01(0.00)-0.0153***(0.0032)-0.0117*(0.0064)0.0254**(0.0122)-0.0198(0.0125)-0.1115***(0.0355)
0.0048***(0.0013)-0.0131(0.0366)

Communication andElectronic

38.92(0.00)-0.0043(0.0053)-0.0217(0.0208)0.0104(0.0172)
0.0121(0.0190)-0.0328*(0.0175)0.0012**(0.0005)

49.20(0.00)-0.0065(0.0051)-0.0171(0.0204)
0.0108(0.0164)0.0064(0.0167)-0.0370**(0.0145)0.0016***(0.0005)-0.0964**(0.0377)

Other Electrical
Equipment

14.44(0.01)-0.0221***(0.0076)
0.0056(0.0155)
0.0020(0.0160)
0.0054(0.0115)-0.0269(0.0216)0.0015*(0.0009)18.43(0.00)-0.0242***(0.0072)
0.0045(0.0146)0.0011(0.0142)0.0057(0.0114)-0.0329(0.0208)
0.0020**(0.0009)-0.0862**(0.0362)

Professional andScientific Instruments

The sample period is 1972–2001. For each industry, the first and second columns display OLS results of the regression with a quadratic time trend and the regression with trends and a control variable‘D1981’, respectively, as well as lagged VC investments and lagged (logarithms of) patent counts. Heteroskedasticity robust standard errors are in parentheses. Estimates with ***, ** and * aresignificant at the 1, 5 and 10% levels, respectively. Constant terms and coefficients on autoregressive terms are not reported. The null hypothesis for the Granger causality test is that all coefficientson VC investments are zero.

Granger causality test:
Wald Statistic
p-valueD1981

Trend2

Trend

Follow-on VC/R&D(-4)

Follow-on VC/R&D(-3)

Follow-on VC/R&D(-2)

Follow-on VC/R&D(-1)

Independent variable

Dependent variable = ln(Patent)

Table 10. Continued

venture capital and innovation459© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

Granger causality test:
Wald statistic
p-value

ERISA

Trend2Trendln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

Independent variable

6.11(0.19)-1.2679*(0.7126)
0.0345(0.4871)-0.8294(0.7723)-0.3379(0.7877)-0.1317*(0.0701)0.0100**(0.0043)

6.64(0.16)-1.2238*(0.6704)
0.0557(0.4987)-0.8600(0.7508)-0.5090(0.7855)-0.2698*(0.1475)
0.0135**(0.0054)0.7533(0.7414)

Drugs

Dependent variable = First round VC/R&D

9.44(0.05)

1.1573(5.2803)-0.2414(8.9768)
13.4245(9.4009)-0.7059(4.8305)
1.7186**(0.6933)-0.0664**(0.0275)

11.66(0.02)

0.9166(5.0493)-0.4979(8.4587)
11.5135(9.6610)1.4448(4.7773)
1.2290*(0.7066)-0.0546**(0.0262)2.4415(1.6600)

Office andComputing Machines

6.44(0.17)-5.1298(8.9284)8.0505(7.2538)
17.3130(13.6285)-4.2130(6.5011)
1.5874(1.0063)-0.0563(0.0385)17.89(0.00)-7.5302(8.3459)
7.2705(5.9559)
16.2333(12.5084)-1.0507(5.5893)0.8825(0.9158)-0.0379(0.0342)
3.1386**(1.5063)

Communication andElectronic

Table 11. Testing for innovation-first hypothesis on patent in selected industries

4.33(0.36)-13.1245(10.3067)
12.8759(8.6352)
1.9953(14.0747)-10.1563(10.2661)-0.6495(0.4678)0.0249(0.0166)

4.27(0.37)-13.5998(10.8085)
13.3490(9.1377)
1.8962(14.3841)-10.0571(10.5107)-0.5941(0.4938)
0.0239(0.0170)-0.2961(0.5995)

Other Electrical
Equipment

6.45(0.17)-5.4855*(3.2876)-2.5881(1.9301)
4.3827(3.5673)-3.2873(2.5342)-0.3932(0.2654)0.0206*(0.0116)

6.11(0.19)-5.7242(3.6471)-2.5143(2.0389)
4.6211(3.8565)-3.4261(2.6345)-0.3526(0.3008)0.0198(0.0121)-0.2454(0.8026)

Professional andScientific Instruments

460

m. hirukawa and m. ueda

1.23
(0.87)-2.2681(2.6882)
0.4537(2.4918)0.8053(3.6144)-0.9592(3.9233)-0.4232(0.3161)
0.0166(0.0196)

Drugs

1.22
(0.88)-2.1595(2.5644)0.5860(2.4461)
0.9263(3.6216)-1.1723(4.0554)-0.7104(0.5332)
0.0223(0.0227)
1.6771(1.5931)
4.74(0.31)

0.6912(21.3191)-2.3284(30.1300)
42.1723(31.3159)
0.7347(20.1682)
5.2618*(2.9145)-0.2005*(0.1165)

4.97(0.29)-0.2671(20.8175)-3.2573(28.2122)
37.5431(32.2300)
7.3479(20.5800)
3.8846(2.9803)-0.1689(0.1162)7.0527(5.4211)

Office andComputing Machines

3.78(0.44)

1.5272(13.1803)
32.0341(24.8393)
1.1472(17.1614)-1.3126(16.6848)
3.3954(2.3167)-0.1267(0.0907)4.68(0.32)

2.0936(13.7480)
33.7924(24.0092)
2.6317(16.2465)
2.4149(18.1095)
3.4326(2.1108)-0.1394(0.0883)
3.5492(2.8914)

Communication andElectronic

2.46(0.65)-58.6360(38.9337)
41.1320(36.5794)
6.9828(26.5569)-4.3964(24.6770)-1.7424(1.4344)0.0708(0.0507)2.31(0.68)-58.3796(40.2748)
40.8590(38.1685)
7.0416(27.2955)-4.4338(25.3062)-1.7794(1.6270)0.0715(0.0543)0.2006(2.2360)

Other Electrical
Equipment

8.97(0.06)-19.5063**(7.9847)
0.8176(8.7198)
5.4727(12.7769)-9.4657(9.1161)-1.6562**(0.7851)
0.0763**(0.0325)

8.52(0.07)-19.9613**(8.4396)
0.7787(9.0246)5.6430(13.2425)-9.6232(9.4364)-1.6047**(0.7881)0.0761**(0.0330)-0.4466(1.0312)

Professional andScientific Instruments

The sample period is 1972–2001. For each industry, the first and second columns display OLS results of the regression with a quadratic time trend and the regression with trends and a control variable‘ERISA’, respectively, as well as lagged VC investments and lagged (logarithms of) patent counts. Heteroskedasticity robust standard errors are in parentheses. Estimates with ***, ** and * aresignificant at the 1, 5 and 10% levels, respectively. Constant terms and coefficients on autoregressive terms are not reported. The null hypothesis for the Granger causality test is that all coefficientson (logarithms of) patent counts are zero.

Granger causality test:
Wald statistic
p-value

ERISA

Trend2Trendln(Patent)(-4)

ln(Patent)(-3)

ln(Patent)(-2)

ln(Patent)(-1)

Independent variable

Dependent variable = Follow-on round VC/R&D

Table 11. Continued

venture capital and innovation
461© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

462

m. hirukawa and m. ueda

To summarize, when patent counts are chosen as a measure of innovation, thecausality from VC investment to innovation is largely supported at individual
industry levels. However, as opposed to the VC-first hypothesis, VC investment
appears to slow down the innovation measure 1 year later.
3.3. Robustness checkWe run two types of robustness checks. One is to normalize VC investment bycapital expenditure instead of R&D and the other is to include ‘funds available
for VC investment’ as an explanatory variable for VC investment; see the
Appendix for an explanation of how to construct the variable ‘funds available
for VC investment’. Testing results are qualitatively similar to those reported inthis section.
4.

concluding remarks

This paper has examined both directions of the causality between innovation
and VC investment using a framework similar to the Granger causality test. For
this purpose, we studied a panel of US manufacturing industries. The panel AR
analyses have shown that the results on causality vary across our choices ofmeasures of VC investment and innovation.
We find some evidence of the innovation-first hypothesis if TFP growth isused as the measure of innovation. The causality from TFP growth runs to boththe first and the follow-on round VC investment. We find weak support for the
VC-first hypothesis if TFP growth is used as the measure of innovation. Twoyear lagged first round VC investment is positively related with TFP growth.
Nevertheless, 1-year lagged first round VC investment is negatively related with
TFP growth, suggesting the presence of a bubble–crash cycle.
When patent counts are used as the measure of innovation, we find little
evidence for both the innovation-first and the VC-first hypotheses. Nevertheless,we find that both the first and the follow-on round VC investment often predict
lower patent counts 1 year later at individual industry levels. This result is
consistent with Engel and Keilbach (2007) and Caselli et al. (2009), who findsignificant slowdown of patenting activities once patenting firms obtain VC
funding.
These results suggest that the time-series relation between VC investment andinnovation is not as simple as we thought. Consistent with the innovation-first
hypothesis, lagged TFP growth is positively related with VC investment but notwith patent count. The VC-first hypothesis has only weak support. We often
find that VC investment leads to a slowdown in TFP growth and patenting
activity.
references
Abreu, D. and M. K. Brunnermeier (2003) ‘Bubbles and Crashes’, Econometrica 71, 173–204.
Arellano, M. and S. Bond (1991) ‘Some Tests of Specification for Panel Data: Monte Carlo Evidence
and An Application to Employment Equations’, Review of Economic Studies 58, 277–97.
© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltdventure capital and innovation

463

Arellano, M. and O. Bover (1995) ‘Another Look at the Instrumental Variable Estimation ofError-Component Models’, Journal of Econometrics 68, 29–51.
Bartelsman, E. J. and W. Gray (1996) ‘The NBER Manufacturing Productivity Database’, Technical Working Paper No. 205, National Bureau of Economic Research.
Black, B. S. and R. J. Gilson (1998) ‘Venture Capital and the Structure of Capital Markets: Banks
Versus Stock Markets’, Journal of Financial Economics 47, 243–77.
Blundell, R. and S. Bond (1998) ‘Initial Conditions and Moment Restrictions in Dynamic Panel
Data Models’, Journal of Econometrics 87, 115–43.
Caselli, S., S. Gatti and F. Perrini (2009) ‘Are Venture Capitalists A Catalyst for Innovation?’,European Financial Management 15, 92–111.
Chan, Y. (1983) ‘On the Positive Role of Financial Intermediation in Allocations of Venture Capitalin a Market with Imperfect Information’, Journal of Finance 38, 1543–68.
Cornelli, F. and O. Yosha (2003) ‘Stage Financing and the Role of Convertible Debt’, Review of
Economic Studies 70, 1–32.
deMeza, D. and C. Southey (1996) ‘The Borrower’s Curse: Optimism, Finance and Entrepreneurship’, Economic Journal 106, 375–86.
Engel, D. (2002) ‘The Impact of Venture Capital on Firm Growth: An Empirical Investigation’,Discussion Paper No. 02-02, Centre for European Economic Research.
Engel, D. and M. Keilbach (2007) ‘Firm Level Implications of Early Stage Venture Capital Investment – An Empirical Investigation’, Journal of Empirical Finance 14, 150–67.
European Commission (1995) Green Paper on Innovation. Available from URL: http://europa.eu/
documentation/official-docs/green-papers/index_en.htm.
Gans, J. S. and S. Stern (2000) ‘Incumbency and R&D Incentives: Licensing the Gale of Creative
Destruction’, Journal of Economics & Management Strategy 9, 485–511.
George, G. (2005) ‘Slack Resources and the Performance of Privately Held Firms’, Academy ofManagement Journal 48, 661–76.
Gompers, P. and J. Lerner (1998) ‘What Drives Venture Capital Fundraising?’, in C. Winston, M.
N. Baily and P. C. Reiss (eds.), Brookings Papers on Economic Activity, Microeconomics: 1998,
Washington, DC: Brookings Institution Press.
Greenwood, J. and B. Jovanovic (1999) ‘The Information-Technology Revolution and the Stock
Market’, American Economic Review, Papers and Proceedings 89, 116–22.
Hall, B. H. (2002) ‘The Financing of Research and Development’, Oxford Review of Economic Policy
18, 35–51.
Hall, B. H., A. B. Jaffe and M. Trajtenberg (2005) ‘Market Value and Patent Citations’, Rand
Journal of Economics 36, 16–38.
Hellmann, T. and M. Puri (2000) ‘The Interaction between Product Market and Financing Strategy:
The Role of Venture Capital’, Review of Financial Studies 13, 959–84.
Holtz-Eakin, D., W. Newey and H. S. Rosen (1988) ‘Estimating Vector Autoregressions with Panel
Data’, Econometrica 56, 1371–95.
Jovanovic, B. and R. Rob (1990) ‘Long Waves and Short Waves: Growth Through Intensive andExtensive Search’, Econometrica 58, 1391–409.
Judson, R. A. and A. L. Owen (1999) ‘Estimating Dynamic Panel Data Models: A Practical Guidefor Macroeconomists’, Economics Letters 65, 9–15.
Kahneman, D. and D. Lovallo (1993) ‘Timid Choices and Bold Forecasts’, Management Science 39,
17–31.
Kortum, S. and J. Lerner (2001) ‘Does Venture Capital Spur Innovation?’ in G. D. Libecap (ed.),Advances in the Study of Entrepreneurship, Innovation and Economic Growth, Volume 13:
Entrepreneurial Inputs and Outcomes: New Studies of Entrepreneurship in the United States,
Amsterdam: Elsevier.
Leland, H. and D. Pyle (1977) ‘Informational Asymmetries, Financial Structure, and Financial
Intermediation’, Journal of Finance 32, 371–87.
Lerner, J. (1995) ‘The Importance of Trade Secrecy: Evidence from Civil Litigation’, Working Paper
95-043, Harvard Business School.
Levine, R., N. Loayza and T. Beck (2000) ‘Financial Intermediation and Growth: Causality andCauses’, Journal of Monetary Economics 46, 31–77.
National Venture Capital Association (1998) NVCA Yearbook, New York: Thomson Reuters.
Reinganum, J. (1983) ‘Uncertain Innovation and the Persistence of Monopoly’, Bell Journal of
Economics 12, 618–24.
Robinson, J. (1952) ‘The Generalization of the General Theory’, in J. Robinson (ed.), The Rate ofInterest and Other Essays, London: MacMillan.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd

464

m. hirukawa and m. ueda

Ross, J. and B. Staw (1993) ‘Organizational Escalation and Exit: Lessons from the Shoreham
Nuclear Plant’, Academy of Management Journal 36, 701–32.
Sahlman, W. A. (1990) ‘The Structure and Governance of Venture-Capital Organizations’, Journalof Financial Economics 27, 473–521.
Suzuki, K. (1996) ‘Nihon Ni Okeru Venture Management No Jittai (Facts on Venture Managements in Japan)’, in K. Yanagi and T. Yamamoto (eds.), Venture Management No Henkaku,
Tokyo: Nihon Keizai Shimbumsha.
Tykvova, T. (2000) ‘Venture Capital in Germany and Its Impact on Innovation’, Available from
URL: http://papers.ssrn.com/paper.taf?abstract_id=235512.
Ueda, M. and M. Hirukawa (2008) ‘Venture Capital and Industrial “Innovation” ’, Discussion
Paper No. 7089, Centre for Economic Policy Research.
Venture Enterprise Center, Ministry of International Trade and Industry (1991) Promotion of
Venture Businesses and the Venture Capital Industry, Venture Enterprise Center, Tokyo.
Windmeijer, F. (2005) ‘A Finite Sample Correction for the Variance of Linear Efficient Two-Step
GMM Estimators’, Journal of Econometrics 126, 25–51.
Zucker, L. G., M. R. Darby and M. B. Brewer (1998) ‘Intellectual Human Capital and the Birth of
U.S. Biotechnology Enterprises’, American Economic Review 88, 290–306.

appendix: construction of ‘funds available’ for venture
capital investment
We employ the amount of funds available for venture capital investment as anadditional explanatory variable for venture capital investments. Due to a hightransaction cost, venture capital firms raise funds infrequently: typically, every
2–5 years. Therefore, the amount of funds available for venture capitalists toinvest is restricted by the difference in the amount of funds raised and theamount of funds disbursed, at least for the short run. We call this difference‘funds available’. Here, we describe how we constructed this dataset.
VentureXpert provides the amount of funds raised most of the time.
However, the amount of funds that were actually disbursed is not well recorded.
If it is recorded, the total syndicated amount of disbursement is recorded but theindividual contribution amount is not available. Thus, we estimate how thefunds were disbursed in the following manner. We take four steps in constructing the commitment for each VC fund: (i) estimating years of disbursement; (ii)estimating the annual amount of disbursement over years of disbursement; (iii)allocating the annual amount of disbursement according to ‘industry preferences’ of each fund; and (iv) reclassifying the amount assigned to each industry
each year from VEIC to KL classification.
In the first step, we primarily define as ‘the disbursement life’ for each fund theperiod from the earlier of establishment year or the year of first investment, toliquidation year, as far as the variable liquidation year is recorded. We do not
define each fund’s establishment year as the beginning of the life, because
VentureXpert sometimes records the funds that ‘started’ their first investments
earlier than their establishments and those without establishment year but withinvestment history. For funds without liquidation year, we define the greater ofthe length of disbursement, which is the duration between the first disbursement
episode and the last, and 5 years as the disbursement life. Some funds haveestablishment years but no investment history recorded. For such funds, we
simply set their disbursement life equal to 5, and thus we ‘assign’ 5 years after
establishment as the end of the life.
© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltdventure capital and innovation

465

In the second step, we estimate the annual amount of disbursement for eachyear of the disbursement life by using annual total VC disbursements as weights.
For funds that, we estimate, continue disbursement beyond 2002, we also
prepare point forecasts of the disbursements by fitting a simple time-seriesmodel (specifically, fitting ARIMA(2,1,2) to the logarithm of VC disbursements). We call the difference between fund size and accumulated disbursementthat we estimate, the fund’s ‘available capital’ in the year.
In the third step, we assume that each fund’s portfolio companies represent
the fund’s preferences on industries and that their preferences remain unchanged
over the disbursement life. Although VentureXpert records ‘Firm Industry Preference’ data as well, it is often hard to find an exact match of the data to VEIC:
how can we find an exact match to VEIC if a fund’s industry preference isexpressed as ‘Diversified’ or ‘High Tech’? Alternatively, VentureXpert records
VEIC for each portfolio company and, therefore, we do not encounter suchdifficulty. Then, for each fund we allocate available capital in each year to allindustries preferred by using the corresponding actual disbursements as weights.
Some funds have no investment history, and, therefore, no portfolio companies.
For these funds, we assume that they have no particular industrial preferences.
Then, for each such fund we allocate available capital in each year over allindustries disbursed in the year by using actual disbursements as weights. Thismethod is also used for the cases in which a fund has portfolio companies but noactual disbursements in these industries are recorded in some years during the
disbursement life.
To construct the funds available in the way described above, we examine all
VC funds that were established from 1960 to 2002 and focus on investing in
the US companies. We drop those without fund size data from our sample. The
funds eliminated in this screening are typically those established before themid-1980s, and include 3i Capital and ABS Ventures, for example. Nonetheless,the number of funds remaining in our sample is 4787, which accounts for nearly
two-thirds of all such funds. We also eliminate the funds that have a fund size
but neither an establishment year nor an investment history. Then, in the final
step, we obtain the KL-classified funds available data in constant dollars by
aggregating all available capital over each KL classification in each year anddeflating it.

© 2011 The Authors
Pacific Economic Review © 2011 Blackwell Publishing Asia Pty Ltd