Discussion Paper No.372
Do public research funds for universities hinder theacquisition of external funds, or do they induce it?
—Department-level empirical analysis
using system GMM
Miki Miyaki
Faculty of Economics,
Chuo University
May 2022

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, Japan

Do public research funds for universities hinder the acquisition of external funds,or do they induce it?—Department-level empirical analysis using system GMM
Miki Miyaki†
Faculty of Economics, Chuo University

Abstract
In Japan’s developed economy, there are high expectations for university research activities asbearers of innovation. With the tightening of national and local finances and the increasing pressure
to reduce fundamental public funds allocated to universities, universities are expected to obtain
external funding to strengthen their financial resource base and stimulate their research activities.
However, in Japan, only a few studies have empirically analyzed how the enhancement of publicfunding affects the acquisition of external funds for university research. In this study, we examine theimpact of the allocation of public research funds on the acquisition of external funds by national andlocal public universities by applying a system GMM method. We distinguish five academic fields(economics, science, engineering, agriculture, and medical) and develop a department-level panel datafrom 2004 to 2016. The results reveal that in Japan, public research funding has a crowding-in effectthat induces rather than crowds out external funding. Specifically, we find that public funds induce theacceptance of external funds from firms in economics and engineering departments. In economics,
science, and medical departments, public funds induce the acceptance of external funds from nonprofit
organizations. In the medical departments, external funding acceptance from other universities is also
induced. Moreover, the past performance of receiving external funds and the allocation of humanresources in the department engaged in research activities also have an impact on the acquisition of
external funds.

†

miyaki.093@g.chuo-u.ac.jp

11. Introduction
For Japan's developed economy to achieve long-term economic growth, it is essential to create
innovations that raise the level of technology. In addition to playing a central role in promoting basic
research, universities are expected to play an even greater role in making research results available toindustrial society and producing added value.
Among the OECD countries, public spending on higher education institutions in Japan is at thelowest level, which was approximately 0.4% of GDP in 2018 1 , and it is difficult to significantly
increase public support under tight fiscal conditions. Regarding the financial situations of national andlocal public universities, which finance most of their operating expenses with public funds, national
universities have continued to see a downward trend in subsidies for their fundamental operating
expenses since their incorporation in 2004. The operating expenses of local public universities are
funded by the local allocation tax, but it has become difficult to stabilize and expand operating
financial resources because of the tightening of national and local finances.
Although the amount of funding for competitive funds is increasing, the pressure to reduce
fundamental expenses for universities is also increasing. In Japan and other developed countries,
performance-based funding is being expanded from input-oriented funding allocation to outputoriented funding measures. The shift from traditional public funding to encouraging market financing
is in progress (Steil, et al., 2002). Universities are expected to secure limited funds in an increasinglycompetitive environment and strengthen their financial resource base by acquiring external researchfunds to stimulate their research activities.
There are few studies in Japan that empirically analyze how the allocation of public funds affects
the acquisition of external funds for university research funding. This study discusses how thegovernment can effectively finance university research activities in an era of tight financial constraint.
This paper focuses on national and local public universities, where the majority of operating
expenses are publicly funded, and empirically analyzes the impact of the allocation of public researchfunds on the acquisition of external private funds by universities. Specifically, we examine whether
the allocation of public research funds hinders (crowding out) or induces (crowding in) the acquisitionof external funds.

2. Overview of external funding data
The data used for research funding at public universities are department level data from the

1

OECD (2021) Education at a Glance, FigureC2.2 Total expenditure on educational institutions as a percentage ofGDP, by source of funds (2018)2

"Universities” questionnaire in the "Survey of Science and Technology Research” of the Statistics
Bureau of the Ministry of Internal Affairs and Communications. The data period is from 2004 to 2016.
In this survey, the expenses incurred by universities for research-related work are recorded as “researchexpenses used internally.” The sources of these funds are broadly classified into “funds received fromoutside sources” (hereinafter referred to as “external funds”) and “own funds.” External funds include
commissioned funds, scientific research funds, subsidies, and grants; all other funds are treated as own
funds. In addition to own income, such as tuition and other student fees collected by universities fromstudents, subsidies for operation and facility development received from the government are also
included in the category of own funds.
As subsidy for operating expenses, which is a fundamental expense granted by the government, isalso a source of funds for university research activities, it is important to examine the impact it has on
the acquisition of external private funds. However, because it is not possible to extract only thesubsidies for operation expenses from own funds because of data limitation, this analysis focuses on
examining the influence of the sources of external funds.
Table 1 presents the classification of sources of external funding. The sources of external funds canbe broadly divided into (1) public institutions, (2) private sector, and (3) foreign sources. (1) The public
institutions are subdivided into the national government, local governments, national and public
universities, public research institutes, etc.; (2) private sectors are subdivided into firms, private
universities, and nonprofit organizations, and (3) foreign countries are subdivided into foreign firms,
universities, and others.

<Table1: The categories of the source of external research funding>
MIC "Report on the survey of research and development"
this paper

funding

2004‐2013source2014‐2016
central government

government

local governemt
national university/local public univeristy(1)from
public sector

publicresearch
institution①national/local government institution,②special public corporation, independentadministrative agency①national/local government institution,
independent administrative agency②public enterprise, public financial institution③public financial institution

university

government

others(2)from
privatesector(3)fromabroadfirm

firm

private university

university

nonprofit organization
abroadfirm, university, others

Source: Author
3

nonprofit
organizationーGraph 1 presents the total amount of external funding and the share of burden sources for public
universities in 2004 and 2016. The departments analyzed in this study are university faculties, graduate
schools, university-affiliated research institutes, and inter-university research institute corporations.
The academic fields of the departments are classified as economics, science, engineering, agriculture,and medical (including dentistry and pharmacy). The amount in Graph 1 is the total of thesedepartments.
The total amount of external funds for national universities in 2004 was approximately 253 billion
yen. In descending order, the sources of funding were the government (55%), firms (22%), publicresearch institutions (14%), and nonprofit organizations (7%). In 2016, the total external funding
increased about 1.5 times to approximately 386.4 billion yen, comprising public research institutions(55%), the national government (20%), firms (15%), and nonprofit organizations (6%). Acceptancesfrom other national and local public universities and local governments also increased from 2004.
From 2004 to 2016, there has been a large decrease in acceptance from the national government anda large increase in acceptance from public research institutions, which can be attributed to changes inthe entities that implement grant programs. For example, some of the Ministry of Medical, Labour andWelfare’s Scientific Research Funds were transferred to the National Agency for Medical Researchand Development. Similarly, some of the Ministry of Education, Culture, Sports, Science andTechnology's Grants-in-Aid for Scientific Research were transferred to the Japan Society for the
Promotion of Science. Thus, in some cases, grants that were previously allocated directly by thegovernment are now allocated by public research institutions.
The total amount of external funding for local public universities in 2004 was approximately 13.8
billion yen, with the largest shares going to the government (41%), firms (38%), public research
institutions (10%), and nonprofit organizations (7%). In 2016, the total amount increased byapproximately 1.6 times to about 21.7 billion yen, with the total amount increasing significantly overthe past 12 years. In order of share, public research institutions received 47%, firms 25%, the national
government 13%, and nonprofit organizations 5%. The reasons mentioned above also account for thereversal in the order of the share of the national government and public research institutions. The
acceptance of external funds from firms is relatively larger in local public universities than in national
universities.

4

<Graph 1: The amount of external research funding and the share of its sources in national and local
public universities>

national university
55%

14%

22%

7%

2004
20%

53%

15%

6%

2016

0

100,000

200,000

300,000

400,000

national gov

local gov

public univ

public inst

public others

firm

private univ

nonprofit organization

abroad500,000

millionlocal public university
10%

41%

38%

7%

2004
13%

25%

47%

5%

2016

0

5,000

10,00015,00020,000national gov

local gov

public univ

public inst

public others

firm

private univ

nonprofit organization

abroad25,000

millionSource: Author
As presented in Table 1, the Survey of Science and Technology Research reveals the changes in thesurvey items during the 2004–2013 and 2014–2016 survey periods. To unify the data of these survey
items, this paper re-categorizes the sources of external funding into four—government, firms,
universities, and nonprofit organizations—and then compiles the data. By putting government and5public organizations into one category (government), we take into account the impact of changes inthe entities that implement grant programs. As depicted in Graph 1, the share of external funding byforeign countries is limited to 1% or less; thus, it is not included in this analysis.
Graph 2 depicts the total amount of external funds received by national universities in five academic
fields—economics, science, engineering, agriculture, and medical—as well as the share of funding
sources. The funding scale in the engineering and medical fields, which are the largest, experienced adecreasing trend after peaking at around 160 billion yen for engineering and 150 billion yen for
medical, although they had been on an increasing trend until around 2014. The funding scale in thefield of science continues to be large, but the total amount of funds has been increasing and decreasing
repeatedly, peaking at about 73 billion yen in 2012, and is on a downward trend. Compared with otherfields, the agriculture field has been relatively flat, peaking at over 30 billion yen in 2015, and has
been on a downward trend since then. In the field of economics, where the size of funds is the smallest,
the peak was 4 billion yen in 2008 and has continued to decline until recently.
Regarding the share of funding sources, the share of government has remained significant in all
fields. In particular, in the field of science, nearly 90% of external funding is received from thegovernment. The next largest source is from firms, with a share of nearly 20% in the medical field andmore than 15% in the economics and engineering fields. The acceptance from nonprofit organizations
is also more prominent in the medical and economics fields than in other fields. In all fields, the shareof funds received from other universities has been increasing in recent years.
Local public universities have experienced more rapid increases and decreases in external funding
than national universities. In national universities, there has been a declining trend in recent years after
peaking around 2014. The scale of funding is the largest in the medical fields and has been increasing
and remained flat among local public universities until recently.
As with national universities, the share of funding from the government is much larger, exceeding
80% in the fields of science and economics, whereas it is approximately 70% in engineering andapproximately 60% in the medical and agriculture fields. The share of acceptance from firms also
differs remarkably by field. In the medical field, it exceeds 30%, and in the engineering and agriculture
fields, the share remains above 20%, which is larger than that of national universities. However, in theeconomics and science fields, the share is only 2%–5%. In the agriculture field, compared withnational universities, local public universities accept more funds from nonprofit organizations.

6<Graph 2-1: The transition of the share of external research funding sources by academic fields(National universities)>

economics

450,000

millionmillion

total
400,000
350,000

4,500
4,000
3,500

300,000

3,000

250,000

2,500

200,000

2,000

150,000

1,500

100,000

1,000

50,000500

002004200520062007200820092010201120122013201420152016

government

firm

nonprifit organization

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

university

government

firm

100,000
90,00080,00070,00060,00050,00040,00030,00020,00010,0000

180,000
160,000
140,000
120,000
100,000
80,00060,00040,00020,00002004200520062007200820092010201120122013201420152016

government

firm

nonprifit organization

2004200520062007200820092010201120122013201420152016

university

government

firm

agriculture

nonprifit organization

university

medicine

35,000

millionmillionuniversity

engineering
millionmillionsciencenonprifit organization

30,00025,000160,000

140,000
120,000
100,000

20,00080,00015,00060,00010,00040,0005,000

20,000002004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

government

firm

nonprifit organization

2004200520062007200820092010201120122013201420152016

university

government

7

firm

nonprifit organization

university

<Graph 2-2: The transition of the share of external research funding sources by academic fields(Local public universities)>

economics

25,000

millionmilliontotal

20,00015,00010,0005,000
02004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

government

firm

nonprofit organization

500
450
400
350
300
250
200
150
100
50
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

university

government

firm

4,000

millionmillionuniversity

engineeringscience
3,500
3,000

8,000
7,000
6,000

2,500

5,000

2,000

4,000

1,500

3,000

1,000

2,000

500

1,000
0

0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 20162004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

government

firm

nonprofit organization

government

university

firm

agriculture

nonprofit organization

university

medicine

2,500

millionmillionnonprofit organization

2,000
1,500

14,00012,00010,000
8,000

6,000

1,000

4,000

500

2,000
0

0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

government

firm

nonprofit organization

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

university

government

Source: Author

8

firm

nonprofit organization

university

3. Previous Studies
Traditional theory points to an alternative relationship in which public funding crowds out private
funding. It has been pointed out that universities may stop looking for funds once they obtaingovernment grants (Blume-Kohout, 2009). However, public funding performance can signal research
quality and academic reputation to attract private funding (Muscio et al,2013; Jensen et al.,2010;
Murray, 2004), or companies can free ride on publicly funded university capital investment and humanresources (Muscio et al,2013); the two may be complementary. Whereas Sav (2012) suggested analternative relationship between public and private funding, complementary relationships have beensuggested by Connolly (1997), Muscio et al. (2013), and Lanahan et al. (2016), but Payne (2001) andBlume-Kohout et al. (2015), and others suggested both relationships, revealing mixed results in
empirical research.
Empirical studies about the influence of public and private funding on university research funding
have been accumulating, especially in the United States (U.S.). Connolly (1997) applied a panel vector
autoregressive model to university level data from 1979 to 1990 to analyze the interdependence in theacquisition of internal and external funding. He found that there is no crowding out of both sources offunds, but they induce each other's acquisitions. It was also pointed out that causality can be confirmedin both directions for both financial sources, and past acquisitions have an impact on future
performance.
Diamond (1999) examined the impact of federal research funding on the acquisition of private
basic research funding by applying Granger causality tests using time series data from 1953 to 1995.
Here, the private sector refers to academia, nonprofit organizations, and industry, and the result
revealed that federal research funding crowds in basic research funding from these sectors.
Payne (2011) applied the fixed effects model and instrumental variable (IV) methods to examine
the impact of federal research spending on private endowment acquisition using panel data of 10,795
universities from 1972 to 1997. The results revealed that crowding in is observed among research
universities, whereas crowding out is observed among nonresearch universities. Sav (2012) also
applied the two-stage least squares method to university panel data to examine the impact of private
funding on government funding. He revealed that private funding crowds out government funding andpointed to a free ride in the allocation of university research funds by the government.
Using generalized momentum method-IV (GMM-IV) models, Blume–Kohout et al. (2015)
examined the impact of the U.S. National Institutes of Medical grants on the acquisition of biomedicalrelated nongovernmental funding by universities. They pointed out that during periods of substantial
grant increases, grants crowd in nongovernmental funding, especially in less research-oriented
universities. However, they revealed that since the end of the increase in grants, grants have beencrowding out nongovernmental funds, especially in universities with high research orientation.
9

A recent research trend has been the application of dynamic models that capture the effects ofdynamism in acquiring research funds. Using panel data on a 13,840 sample of science departmentsfrom 2010 to 2014, Lanhan et al. (2016) analyzed the impact of the U.S. federal government's
allocation of research funding on the acquisition of research funding from state governments, nonprofit
organizations, and industry by applying a dynamic IV model. They concluded that the federal
government’s allocation crowds in research funding from other sources in the fields of engineering,
environmental science, life science, mathematics and computer science, physical chemistry, as well associal science and psychology.
Muscio et al. (2013) used the dynamic Tobit and dynamic probit models to examine the impact ofpublic research funds on private research funds in 228 Italian university departments from 2005 to
2009. The results revealed that research funds allocated by various public institutions, including the
EU and Italian central and local governments, crowd in private research funds. Wang et al. (2020)
pointed out that there is limited research that examines the interrelationship between public and private
university research funding in developing countries and analyzed the factors that encourage
university–industry linkages in China. By applying the GMM method to university panel data from2009 to 2018, they analyzed the impact of total research funding on private research funding. The
results revealed an alternative relationship between the two, although the trends vary by type ofuniversity. They proposed that the results might be due to the following reasons: high dependence ofuniversities in China on government funding, relatively low-tech companies and low demand for theintellectual property of domestic universities, faculty members' reluctance to collaborate with theprivate sector due to an evaluation system that emphasizes academic performance, and the signaling
effect of public funding on private funding. They pointed out that the phenomenon of crowd in is acharacteristic of developed countries, such as Europe and the U.S.
Recent studies that used dynamic models have found that public funding relatively crowds in private
funding in university research funding, but the results vary by country and type of university. In
addition, recent studies have increasingly conducted empirical analyses at the department level tocontrol differences in the trends of the academic fields.
Based on the above review of previous studies, this paper applies a dynamic model to analyze theimpact of public research funds on private research funds for departments of Japanese public
universities (national and local public universities). As the actual situations of obtaining research funds
and the cost structure of research activities differ greatly depending on the academic discipline, adatabase that is based on academic fields is constructed. Using this database, we analyze where various
faculties and graduate schools are concentrated at the department level rather than at the university
level. In constructing the model, we also consider the impact of the human resource allocation in thedepartment, such as the number of faculty members, the working style of researchers, the number ofdoctoral course enrollments, and the number of research support staff, on the private sector research
10funding.
4. Empirical Analysis
4.1 The model
The models used in the analysis are as follows
𝑃𝑅𝐼𝑉𝑖,𝑡 = 𝜌𝑃𝑅𝐼𝑉𝑖,𝑡−1 + 𝛽𝐺𝑂𝑉𝑖,𝑡−1 + 𝛾𝑌𝐸𝐴𝑅𝑡 + 𝛼𝑖 + 𝑢𝑖,𝑡(1)

𝑃𝑅𝐼𝑉𝑖,𝑡 = 𝜌𝑃𝑅𝐼𝑉𝑖,𝑡−1 + 𝛽𝐺𝑂𝑉𝑖,𝑡−1 + 𝑋𝑖,𝑡 𝛅 + 𝛾𝑌𝐸𝐴𝑅𝑡 + 𝛼𝑖 + 𝑢𝑖,𝑡(2)

𝑃𝑅𝐼𝑉𝑖,𝑡 = 𝜌𝑃𝑅𝐼𝑉𝑖,𝑡−1 + 𝛽𝐺𝑂𝑉𝑖,𝑡−1 + 𝑋𝑖,𝑡 𝛅 + 𝑍𝑖,𝑡−1 𝜽 + 𝛾𝑌𝐸𝐴𝑅𝑡 + 𝛼𝑖 + 𝑢𝑖,𝑡(3)Equation (1) is the base model, where 𝑃𝑅𝐼𝑉𝑖,𝑡 is the external research funds from private
institutions and other sources in department i in year t. In the analysis, we estimate three different
sources of external research funds (𝑃𝑅𝐼𝑉𝑖,𝑡 )—firms (𝑓𝑖𝑟𝑚), nonprofit organizations (𝑛𝑝𝑜), and otheruniversities (𝑢𝑛𝑖𝑣 ). 𝑃𝑅𝐼𝑉𝑖,𝑡−1 is a one-lagged explained variable. It is more likely for external
research funds to be accepted continuously over several years than in a single year only, and the
dynamic behavior of the department in obtaining funds should also be considered. 𝐺𝑂𝑉𝑖,𝑡−1 denotes
the research funds received from public institutions in the previous year, and 𝛽 is the estimatedcoefficient that we focus on most in this paper. 𝑌𝐸𝐴𝑅𝑡 is a dummy variable controlling the time series
impact of macro-economic shocks; 𝛼𝑖 is an unobservable departmental characteristic that does notchange through time, and 𝑢𝑖,𝑡 is a stochastic error term. All series are log-transformed, and funding
data are standardized by dividing them by the number of full-time faculty members in the department.
In Equation (2), the number of doctoral students enrolled per full-time faculty member (𝑝ℎ𝑑𝑠𝑡_𝑓𝑎𝑐),
the number of part-time researchers per full-time faculty member (𝑝𝑎𝑟𝑡_𝑓𝑎𝑐), and the number of
research assistants per full-time faculty member (𝑠𝑢𝑝_𝑓𝑎𝑐) are used as the control variables (𝑋𝑖,𝑡 ).
These variables indicate the allocation of personnel to research activities in a department, and all ofthem are ratios to full-time faculty members who are the focus of research activities.
Regarding 𝑝ℎ𝑑𝑠𝑡_𝑓𝑎𝑐, it is not uncommon for faculty members, especially those in natural science
departments, to conduct research activities with doctoral students at the laboratory, and joint researchwith graduate students may contribute to obtaining external funding. However, when faculty members'
research time is reduced due to the need to devote time to the education of doctoral students, theopportunity to obtain external funding may be limited as the number of doctoral students increases.
Therefore, the estimated coefficient of 𝑝ℎ𝑑𝑠𝑡_𝑓𝑎𝑐 is assumed to be both positive and negative.
Regarding both 𝑝𝑎𝑟𝑡_𝑓𝑎𝑐 and 𝑠𝑢𝑝_𝑓𝑎𝑐, it is assumed to have a positive impact on the acquisitionof external research funds. Part-time researchers who have their proper status at other academic
institutions and engage in joint research and research assistants who follow the instructions of the11

departments to assist research activities are expected to play a complementary role in the research
activities of their full-time faculty members.
In Equation (3), following Lanahan et al. (2016), in addition to the explained variable, we add
external research funding from private institutions and other sources in the previous year as an
explanatory variable (𝑍𝑖,𝑡−1 ). This allows us to examine the impact of obtaining research funds fromother sources other than public institutions on each explained variable. Other financial sources otherthan public institutions are as follows: when 𝑓𝑖𝑟𝑚𝑖𝑡−1 is the explained variable, 𝑍𝑖,𝑡−1 includes
𝑛𝑝𝑜𝑖𝑡−1 and 𝑢𝑛𝑖𝑣𝑖𝑡−1 ; when 𝑛𝑝𝑜𝑖𝑡−1 is the explained variable, 𝑍𝑖,𝑡−1 includes 𝑓𝑖𝑟𝑚𝑖𝑡−1 and𝑢𝑛𝑖𝑣𝑖𝑡−1, and when 𝑢𝑛𝑖𝑣𝑖𝑡−1 is the explained variable, 𝑍𝑖,𝑡−1 includes 𝑓𝑖𝑟𝑚𝑖𝑡−1 and 𝑛𝑝𝑜𝑖𝑡−1 .
In the analysis, the following first difference model is estimated. The individual effect 𝛼𝑖 iseliminated in the model. Using Equation (1) as an example, the first difference model is as follows:∆𝑃𝑅𝐼𝑉𝑖,𝑡 = 𝜌∆𝑃𝑅𝐼𝑉𝑖,𝑡−1 + 𝛽∆𝐺𝑂𝑉𝑖,𝑡−1 + ∆𝑋𝑖,𝑡 𝛅 + 𝛾𝑌𝐸𝐴𝑅𝑡 + ∆𝑢𝑖,𝑡 .

(4)

As we take the log differences, the above equation captures the movement in the rate of change. In
Equation (4), Δ𝑃𝑅𝐼𝑉𝑖,𝑡−1 and Δ𝑢𝑖,𝑡 are correlated, so the usual fixed effects model or generalized
least squares model cannot satisfy the consistency of the estimators. To deal with this problem, thispaper uses a dynamic panel analysis with the Blundell-Bond (1998) system GMM (hereafter B-B
model).
The IVs to deal with the endogeneity of ∆𝑃𝑅𝐼𝑉𝑖,𝑡−1 at time t should be uncorrelated with Δ𝑢𝑖,𝑡but correlated with ∆𝑃𝑅𝐼𝑉𝑖,𝑡 . As variables for satisfying these conditions,𝑦𝑖,𝑡−2 and Δ𝑦𝑖,𝑡−2 areused as IVs, and the time series is considered by the trend term. The B-B model is a method that uses
the GMM to obtain matching estimators. Employing the following moment conditions to define the
GMM objective function, the B-B model combines the first-order difference regression equation withthe level regression equation to estimate the unknown parameters using an IV that satisfies 𝑦𝑖𝑠 , 𝑠 ≤𝑡 − 2.
𝐸[𝑦𝑖,𝑠 ∆𝑢𝑖,𝑡 ] = 0, 𝑡 = 2, … , 𝑇,𝑠 = 0, … , 𝑡 − 2(5)𝐸[∆𝑥𝑖,𝑠 ∆𝑢𝑖,𝑡 ] = 0, 𝑡 = 2, … , 𝑇,𝑠 = 1, … , 𝑇(6)

In addition to the B-B model, the two-stage least squares method of Anderson and Hisao (1982)(hereafter A-H model) and the first-order difference GMM of Arellano–Bond (1991) (hereafter A-B
model) are well known. According to Takahashi (2013), GMM estimation with the A-B and B-B
models is a more valid estimator than the two-stage least squares method of the A-H model for datawith a small number of time series than a sufficiently large number of cross-sections. Moreover, unlike
the A-B model, the B-B model does not suffer from the weak operating variable problem even when12

ρ→1. Therefore, the B-B model is employed in this paper.
For the lags of the IVs used in GMM estimation, increasing the number of lag periods may increase
the effectiveness, but as Hisao et al. (2002) and others have demonstrated, imposing excessive
orthogonality conditions may increase downward bias. Therefore, in this paper, we use the two-period
lag of the endogenous variable as the IV (s = 2) and consider higher lag period numbers if theorthogonality condition of the IV is not satisfied. In both models, the absence of serial correlation in
the error terms is an important assumption for obtaining a consistent estimator in GMM estimation. In
this paper, we also test for serial correlation for the first-order difference error term, and if the firstorder autocorrelation is significant and the second-order autocorrelation is not significant, we conclude
that the original error term is uncorrelated.
The descriptive statistics of the data are presented in Table 2, which totals 5,503 samples for all fiveacademic fields of study, of which engineering and agricultural departments have the largest (1,767)
and smallest (777) number of samples, respectively. Table 2 presents the average amount of external
research funds obtained in each academic field. Regarding research funds from public organizations(gov), science departments received the largest amount of 6.61 million yen per full-time faculty
member, followed by medicine (6.08 million yen) and engineering (6.05 million yen). The lowest is
1.15 million yen for economics departments. Research funds from firms (firm) amounted to 1.2–1.3
million yen in the medical and engineering fields, whereas those from the economics and science fields
were in the range of 180,000–350,000 yen. Research funds from nonprofit organizations (npo) are also
relatively more common in the medical and engineering fields, whereas those from other universities(univ) are more common in the science and agriculture fields.

13

<Table2: Descriptive statistics>
TOTAL
Variable
gov
firm
npo
univ
phdst_fac
pres_facsupres_fac

Obs.
5,503
5,503
5,503
5,503
5,503
5,503
5,503

Mean
528
89
36
18
3
2
0

S.D.
1,492
133
63
60
20
16
0

Min

SCI
Variable
gov
firm
npo
univ
phdst_fac
pres_facsupres_fac

Obs.
927
927
927
927
927
927
927

Mean
661
35
22
24
1
2
0

S.D.
780
55
37
64
9
24
0

Min

AGR
Variable
gov
firm
npo
univ
phdst_fac
pres_facsupres_fac

Obs.
777
777
777
777
777
777
777

Mean
488
70
30
23
18
8
0

S.D.
1,255
217
51
42
49
31
0

Min

(unit:10K)Max
077,639
0
3,992
0
897
0
1,556
0
357
0
533
0
7

ECON
Variable
gov
firm
npo
univ
phdst_fac
pres_facsupres_fac

Obs.
785
785
785
785
785
785
785

Mean
115
18
12
3
1
0
0

S.D.
188
55
47
7
1
0
0

Min

ENG
Variable
gov
firm
npo
univ
phdst_fac
pres_facsupres_fac

Obs.
1,767
1,767
1,767
1,767
1,767
1,767
1,767

Mean
605
125
45
20
1
0
0

S.D.
2,047
119
74
77
9
0
0

Min

0
0
0
0
0
0
0

Max
9,535
579
379
973
211
533
7

MED
Variable
gov
firm
npo
univ
phdst_fac
pres_facsupres_fac

Obs.
1,247
1,247
1,247
1,247
1,247
1,247
1,247

Mean
608
133
53
17
1
0
0

S.D.
1,518
123
70
56
1
1
0

Min

0
0
0
0
0
0
0

Max
20,280
3,992
583
379
297
207
2

0
0
0
0
0
0
0

Max
1,402
659
872
69
5
2
1

0
0
0
0
0
0
0

Max
77,639
1,125
897
1,556
357
6
5

0
0
0
0
0
0
0

Max
33,328
1,146
897
869
27
5
5

Source: Author
4.1 The estimation results
The estimation results for Equations (1) through (3) are presented in Table 3. First, let us examine
the results for the economics departments. For Models 1–3, the exogeneity of the IVs satisfies the
Hansen J statistic (values in the table are p-values), and there is no serial correlation in the error terms.
In every model, the impact of one-lagged public research funding on external funding from firms andnonprofit organizations is positively significant at the 5% significance level, whereas the impact on
external funding from other universities is not significant. This result demonstrates that public researchfunding, which induces external funding from firms and nonprofit organizations, has a crowding in
effect on research funding in economics departments.
The results of the estimated coefficients of the other explanatory variables reveal that the one-lagged
variable for each of the explained variables is significant at the 1% level, indicating that past funding
performance has a positive impact on the current scale of external funding. The estimation results inModel 3 also reveal that receiving external funding from firms is affected not only by the past
performance of firms but also by funding from nonprofit organizations. The same can be said about
receiving external funding from nonprofit organizations, suggesting that the past receipt of external
14

funding from firms and nonprofit organizations is closely related to the external funding scheme ineconomics departments. Other estimates from Models 2 and 3 indicate that the number of doctoral
students per full-time faculty member has a significantly positive impact on external funding fromfirms. In economics departments, collaborative research with graduate students may also contribute toobtaining external funds. The number of research assistants per full-time faculty member has also asignificantly positive impact on external funding from nonprofit organizations.
Next, we look at the estimation results for the science departments. In all models, the exogeneity of
the IV is satisfied. However, when external funds from firms are used as the explained variable, thepossibility of serial correlation in the error term cannot be denied. The effect of the public researchfunds that we focus on is not significant in this case. Focusing on the estimation results for when otherexternal funds are the explained variable, the impact of public research funds on external funds from
nonprofit organizations is positively significant at the 10% level. This suggests that public researchfunds have a crowding in effect on research funding in science departments, which induces externalfunding from nonprofit organizations.
In all models, the past performance of receiving external funds also affects the current scale of therelevant funds, but unlike the economics departments, there is no cross-over effect when theacceptance performance of different sources also affects the acceptance of the relevant funds.
Regarding the effects of other variables related to human resources, it appears that a higher number of
research assistants per full-time faculty member increases the acceptance of funds from otheruniversities.
The estimation results for the engineering departments indicate that the exogeneity of the operating
variable is satisfied in all models, but the serial correlation of the error term is suspect when externalfunding from other universities is used as the explained variable. The effect of public research funding
on external funding from other universities is not significant. Regarding the other explained variable,
in both models, public research funds have a significantly positive effect on the acceptance of externalfunding from firms at the 1% level. In Model 1, there is also a positive effect on external funding from
nonprofit organizations at the 10% significance level. This suggests that public research funds have a
crowding in effect on research funding in engineering departments, which induces external funding
from firms and may also induce external funding from nonprofit organizations.
Similar to previous departments, past external funding performance has a positive impact on thecurrent funding status of relevant funds, and the status of acceptance from firms is also influenced bypast acceptance from nonprofit organizations. Other findings suggest that the acceptance of externalfunding from firms and nonprofit organizations increases with the number of part-time researchers
and research assistants who are expected to play a complementary role in the research activities of the
full-time faculty member.
Next, we examine the estimation results for agricultural departments. Although the exogenous
15

nature of the IV is satisfied in all models, the serial correlation of the error term for agricultural
departments is questionable, except for when the external funds from firms are used as the explained
variable. When external funds from firms are used as the explained variable, the estimated coefficientof public research funds, which has been the focus of attention, is not significant. This result reveals
that the level of public research funds does not have a statistically significant effect on any of theexternal funding of research in agricultural departments. However, as in the case of other academic
fields, past receipt of external funding is associated with the current funding status.
Due to the characteristic of agricultural departments, the number of doctoral students per full-time
faculty member has a negative and significant impact on external funding from firms (and externalfunding from other universities, although the robustness of the estimation results is questionable dueto the existence of serial correlation).
Finally, we look at the estimation results for the medical departments. In all models, the exogeneityof the IVs is satisfied, and there is no serial correlation in the error terms. First, in all models, theestimated coefficients of public research funds, which are the focus of interest, are positive andsignificant at the 1% and 5% levels for the receipt of external funds from nonprofit organizations andother universities, respectively. In addition, Model 1 is statistically significant at the 10% level forreceipt of funds from firms. This indicates that public research funds induce crowding in of externalfunds from nonprofit organizations and other universities and may also induce external funds fromfirms for research funding in medical departments.
In all models, the past external funding performance has a positive impact on the current funding
status of the relevant funds. However, like most other natural science departments, there is no crosseffect, where the acceptance performance of different sources also affects the acceptance status of therelevant funds. Regarding the variables related to human resources, both positive and negative effectsof the number of doctoral students per full-time faculty member are confirmed for medical
departments, which contributes to obtaining external funds from companies, but limits obtaining funds
from nonprofit organizations and other universities. The relationship with graduate students is diverse,
as faculty members engage in education and research activities within their limited time, and the
direction of the impact on the results of external funding acquisition is complex.
The number of adjunct faculty members who are expected to play a complementary role in the
research activities of regular faculty members has a positive impact on the receipt of external funds
from companies. However, it has a negative impact on the receipt of funds from other universities,
suggesting that for medical departments, other than regular faculty members, having many staff
members who are engaged in research activities does not necessarily lead to the acquisition of externalfunds. This suggests that for medical departments, having a large number of staff engaged in researchother than the core faculty members does not necessarily lead to the acquisition of external funds. The
number of part-time faculty members who are expected to play a complementary role in the research
16

activities of full-time faculty members has a positive impact on external funds from firms. However,it has a negative impact on funds from other universities, suggesting that for medical departments, alarge number of staff engaged in research other than the main faculty members does not necessarily
lead to the acquisition of external funds.
<Table3: Estimation results>(ECN)
VARIABLES
L.lngov_int_fac
L.lnfirm_int_fac

firm0.181**(0.0904)
0.680***(0.198)L.lnnpo_int_fac

Model1: eq.(1)
npo

univ

firm

0.142**(0.0566)

0.0174(0.0236)0.148**(0.0674)
0.605***(0.171)0.477***(0.137)

L.lnuniv_int_fac

Model2: eq.(2)
npo

univ

firm

Model3: eq.(3)
npo

univ

0.0875**(0.0403)

0.0121(0.0310)0.130**(0.0603)0.601***(0.182)
0.0944**(0.0431)-0.000113(0.0610)0.208*(0.113)0.0871(0.521)0.0590(0.286)-0.404***(0.156)0.0799**(0.0405)
0.144***(0.0364)0.482***(0.0946)
0.0442(0.0486)
0.0374(0.0724)0.00519(0.229)0.583*(0.315)0.0509(0.133)0.00655(0.0334)
0.0476(0.0348)-0.0317(0.0334)
0.918***(0.0379)-0.0263(0.0369)-0.0300(0.127)0.240(0.423)-0.000745(0.137)0.594***(0.0723)
0.918***(0.0365)

phdst_fac

0.101(0.0658)
0.152(0.263)0.609**(0.290)0.00524(0.140)0.915***(0.0365)-0.00740(0.0349)-0.0409(0.131)0.252(0.400)-0.0121(0.148)-0.477***(0.173)0.0945(0.132)-0.0167(0.145)0.236**(0.115)0.236(0.575)0.111(0.297)-0.385**(0.170)Observations
714
Number of scicode70
Hansen J[0.450]AR(1)[0.004]AR(2)[0.887]
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.171470[0.842][0.000][0.536]714
70[0.408][0.000][0.163]714
70[0.542][0.003][0.805]714
70[0.676][0.000][0.617]714
70[0.388][0.000][0.160]714
70[0.491][0.005][0.792]714
70[0.603][0.000][0.635]714
70[0.422][0.000][0.136]

Model1: eq.(1)
npo

univ

firm

Model2: eq.(2)
npo

univ

firm

Model3: eq.(3)
npo

univ

0.126*(0.0681)0.0252(0.0830)

0.0597(0.0492)0.720***(0.145)0.115*(0.0602)0.0170(0.0712)0.0511(0.0852)
0.699***(0.129)0.149(0.124)-0.0733(0.121)-0.0126(0.0437)
0.00251(0.0166)-0.0337(0.0655)
0.381(0.284)0.0903*(0.0541)0.0702(0.0430)
0.590***(0.151)0.00534(0.0386)
0.0469(0.0538)-0.0199(0.0200)-0.105(0.0740)0.314(0.278)-0.00176(0.0680)
0.0581(0.0477)-0.0579(0.0514)0.885***(0.101)0.00487(0.0351)-0.00273(0.0128)
0.332***(0.120)0.291(0.298)834
85[0.281][0.001][0.024]834
85[0.450][0.000][0.226]834
85[0.460][0.000][0.305]pres_facsupres_fac
Constant(SCI)
VARIABLES
L.lngov_int_fac
L.lnfirm_int_fac

firm
0.0579(0.0477)
0.728***(0.134)L.lnnpo_int_fac

0.525***(0.171)

L.lnuniv_int_fac

0.524***(0.175)0.867***(0.116)

phdst_fac

0.486*(0.250)0.519*(0.302)0.335(0.402)0.0118(0.0339)-0.00722(0.0128)-0.0361(0.0612)0.390(0.282)

Observations
834
Number of scicode85
Hansen J[0.200]AR(1)[0.001]AR(2)[0.018]
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.183485[0.179][0.000][0.300]834
85[0.350][0.000][0.348]834
85[0.184][0.001][0.020]pres_facsupres_fac
Constant

17

0.0798(0.0625)-0.0330(0.0232)-0.0987(0.0861)
0.453(0.324)0.852***(0.106)0.00173(0.0354)-0.00175(0.0129)
0.338***(0.128)0.316(0.310)834
85[0.190][0.001][0.307]834
85[0.394][0.000][0.332]

(ENG)
VARIABLES
L.lngov_int_fac
L.lnfirm_int_fac

firm0.231***(0.0746)0.430***(0.134)L.lnnpo_int_fac

Model1: eq.(1)
npo

univ

firm

0.114**(0.0573)

0.0365(0.0421)0.188***(0.0679)
0.459***(0.125)0.601***(0.0804)

L.lnuniv_int_fac

Model2: eq.(2)
npo

univ

firm

Model3: eq.(3)
npo

univ

0.0840(0.0675)

0.0347(0.0434)0.178***(0.0571)0.444***(0.120)0.0535**(0.0267)-0.00372(0.0130)
0.00286(0.00453)
0.130**(0.0628)0.309**(0.141)1.184***(0.310)0.0548(0.0564)0.0692(0.0759)0.596***(0.0810)0.0115(0.0281)-0.00647(0.0348)0.0575(0.0846)0.440*(0.263)0.579(0.390)0.0520(0.0517)-0.0513(0.0517)0.0183(0.0321)0.913***(0.0915)-0.00319(0.00381)-0.0367(0.0695)
0.0844(0.147)0.214(0.200)0.601***(0.0820)0.901***(0.0925)

phdst_fac-0.00255(0.0350)
0.0684(0.0868)
0.423*(0.256)0.708**(0.338)0.907***(0.0930)-0.00447(0.00364)-0.0349(0.0739)0.0747(0.137)0.164(0.204)

1.146***(0.321)0.593*(0.308)0.165(0.203)0.000695(0.00444)
0.124**(0.0620)0.295**(0.140)1.209***(0.329)Observations
1,608
Number of scicode154
Hansen J[0.401]AR(1)[0.000]AR(2)[0.861]
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

1,608
154
[0.113][0.000][0.850]1,608
154[0.480][0.000][0.007]1,608
154[0.423][0.000][0.807]1,608
154[0.119][0.000][0.868]1,608
154[0.490][0.000][0.007]1,608
154[0.464][0.000][0.837]1,608
154[0.122][0.000][0.863]

1,608
154[0.485][0.000][0.007]

Model1: eq.(1)
npo

univ

firm

Model2: eq.(2)
npo

univ

firm

Model3: eq.(3)
npo

univ

-0.000143(0.0311)0.00258(0.0428)

0.0963(0.0874)
0.457**(0.225)0.00321(0.0289)0.00918(0.0427)0.0891(0.0799)0.457**(0.218)0.0165(0.0454)0.0654***(0.0237)-0.00551**(0.00256)-0.00599(0.00526)-0.0565(0.354)
1.104**(0.475)0.000937(0.0326)
0.00811(0.0588)0.861***(0.116)0.0213(0.0325)-0.00262(0.00326)
0.00105(0.00318)-0.252(0.576)0.254(0.283)-0.00643(0.0413)0.0734*(0.0399)-0.0199(0.0524)0.867***(0.0647)-0.00260***(0.000967)
0.000268(0.00109)-0.0811(0.144)0.516**(0.259)709
67[0.486][0.039][0.144]709
67[0.239][0.000][0.035]709
67[0.459][0.000][0.047]

pres_facsupres_fac
Constant(AGR)
VARIABLES
L.lngov_int_fac
L.lnfirm_int_fac

firm
0.124
(0.0826)
0.464**(0.232)

L.lnnpo_int_fac

0.903***(0.0995)

L.lnuniv_int_fac

0.864***(0.142)0.902***(0.0577)

phdst_fac

1.234**(0.480)0.159(0.305)0.544**(0.259)-0.00663**(0.00269)-0.00573(0.00553)-0.0843(0.367)1.273***(0.476)

Observations
709
Number of scicode67
Hansen J[0.510]AR(1)[0.044]AR(2)[0.147]
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

709
67[0.241][0.000][0.028]709
67[0.483][0.000][0.044]709
67[0.481][0.039][0.121]

pres_facsupres_fac
Constant

18-0.00292(0.00440)
0.000931(0.00320)-0.221(0.665)0.307(0.427)0.876***(0.0661)-0.00260**(0.00112)-0.000504(0.000926)-0.0576(0.138)0.606**(0.264)709
67[0.256][0.000][0.036]709
67[0.464][0.000][0.043]

(MED)
VARIABLES
L.lngov_int_fac
L.lnfirm_int_fac

firm0.102*(0.0574)
0.613***(0.112)

L.lnnpo_int_fac

Model1: eq.(1)
npo

univ

firm

0.175**(0.0730)

0.109**(0.0451)

0.0723(0.0754)0.614***(0.156)0.645***(0.197)

L.lnuniv_int_fac

univ

firm

Model3: eq.(3)
npo

univ

0.192**(0.0782)

0.122**(0.0491)0.0798(0.0627)
0.577***(0.151)0.0393(0.0364)-0.0251(0.0260)
0.124***(0.0279)
0.0783**(0.0323)
0.0329(0.221)1.269***(0.424)0.181***(0.0647)0.132(0.0899)
0.488**(0.213)0.0190(0.0547)-0.0944***(0.0299)-0.0886(0.0706)-0.0852(0.213)0.203(0.261)0.125**(0.0502)-0.0206(0.0309)
0.0251(0.0287)
0.775***(0.0580)-0.0510***(0.0181)-0.0527*(0.0310)-0.00183(0.0796)-0.570**(0.254)1,140
101[0.220][0.004][0.375]1,140
101[0.609][0.001][0.666]1,140
101[0.200][0.000][0.150]0.630***(0.189)0.800***(0.0538)

phdst_fac

1.142***(0.356)0.261(0.394)-0.576**(0.239)0.125***(0.0286)
0.0767***(0.0285)0.0420(0.254)1.227***(0.464)

Observations
1,140
Number of scicode101
Hansen J[0.335]AR(1)[0.002]AR(2)[0.298]
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

1,140
101
[0.425][0.000][0.395]1,140101[0.131][0.000][0.150]1,140101[0.180][0.004][0.322]

pres_facsupres_fac
Constant

Model2: eq.(2)npo-0.0965***(0.0332)-0.0584(0.0563)
0.0292(0.187)0.310(0.370)0.782***(0.0571)-0.0504***(0.0187)-0.0545*(0.0304)-0.0172(0.0809)-0.584**(0.255)1,140101[0.531][0.000][0.433]1,140101[0.199][0.000][0.155]

Source: Author

5. Concluding remarks
In this paper, using external funding data for Japanese public universities from 2004 to 2016, weconduct an empirical analysis at the department level on the impact of the allocation of public researchfunds on external funds in five academic fields (economics, science, engineering, agriculture, andmedicine). A dynamic panel analysis of B-B’s system GMM is used to account for the impact of pastexternal funding receipt performance in the model. The main conclusions are as follows.
First, regardless of the academic field, we could not identify any crowding out effect of publicresearch funding allocations that prevent a department from acquiring external funding. This suggeststhat even if the government implements additional public research funding measures in the future, it
is unlikely that they will reduce the acquisition of research funds from the private sector. Conversely,
this result also reveals that if the allocation of public research funds by the government decreases, thelack of crowding out effect suggests that the university could not expect to obtain enough externalfunding from the private sector to compensate for the decrease, and the university might find it moredifficult to raise funds for research.
Second, we find that public research funding does not crowd out external funding but rather leads
to a crowding in effect, which further induces it. Specifically, the fact that public research funds induce
19external funds from firms is confirmed in the economics and engineering departments, and their ability
to induce external funds from nonprofit organizations is confirmed in the economics, science, andmedical departments. In the medical department, public research funds also induce external funds fromother universities, which suggests that public funding may promote research collaboration among
universities. Regarding agriculture departments, the impact of public research funds on the acquisitionof external funds is not confirmed. The crowding in effects suggest that in many cases, increasing
public research funding may strengthen the research funding base of universities.
Third, it is demonstrated that past external funding performance has a positive impact on the current
funding status of the relevant funds. It can be suggested that it is important to build long-term
relationships with external funding sources. In particular, in the economics and engineering
departments, the performance of receiving funds from different sources also affects the status ofreceiving funds from relevant sources. This suggests that establishing comprehensive relationships
with counterparts and forming individual relationships with the sources of external funds may lead tothe acquisition of funds in the future.
The remaining issue is to conduct an analysis that considers the qualitative aspects of research
activities in university departments. For instance, future research can examine whether theenhancement of public or private research funding leads to desirable research outcomes. Although it
is difficult to quantify research outcomes, it is necessary to develop evaluable indicators of researchoutcomes from a longer-term perspective.

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Journal of public Economics, Vol. 64(3), pp.389-406.
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