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Center for Economic Institutions

Working Paper Series

Institute of Economic Research

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No. 2013-5

“Plant Productivity Dynamics and Private and Public R&D

Spillovers: Technological, Geographic and Relational

Proximity”

René Belderbos, Kenta Ikeuchi, Kyoji Fukao,

Young Gak Kim, and Hyeog Ug Kwon

October 2013

September 2013

1

Plant Productivity Dynamics and Private and Public R&D

Spillovers: Technological, Geographic and Relational

Proximity

René Belderbos

University of Leuven, UNU-MERIT, Maastricht University, and NISTEP

Kenta Ikeuchi

NISTEP

Kyoji Fukao

Hitotsubashi University, NISTEP, and RIETI

Young Gak Kim

Senshu University and NISTEP

Hyeog Ug Kwon

Nihon University, NISTEP, and RIETI

October 2013

Keywords: R&D, spillovers, plant productivity, distance JEL codes: D24, O32.

Acknowledgements

This paper is the result of a joint research project of the National Institute of Science and Technology

Policy (NISTEP), the Research Institute for Economy, Trade and Industry (RIETI), and Hitotsubashi

University, under the “Science for Science, Technology and Innovation Policy” program. The authors

are grateful to Masayuki Morikawa, Toshiyuki Matsuura, Chiara Criscuolo, Pierre Mohnen, Jacques

Mairesse, and participants at the NISTEP-RIETI Workshop on Intangible Investment, Innovation and

Productivity (Tokyo, January 2012), the 2012 CAED conference in Neurenberg (Germany), the 2013

CAED conference in Atlanta, the Workshop on Intangibles, Innovation Policy and Economic Growth

at Gakushuin University (December 2012, Tokyo) and the HIT-TDB-RIETI International Workshop

on the Economics of Inter-firm Networks (November 2012, Tokyo) for comments on earlier drafts.

Rene Belderbos gratefully acknowledges financial support from NISTEP and the Centre for

Economic Institutions at the Institute for Economic Research, Hitotsubashi University.

2

Plant Productivity Dynamics and Private and Public R&D Spillovers:

Technological, Geographic and Relational Proximity

ABSTRACT

We examine the effects of R&D spillovers on total factor productivity in a large panel of

Japanese manufacturing plants matched with R&D survey data (1987-2007). We

simultaneously examine the role of public (university and research institutions) and private

(firm) R&D spillovers, and examine the differential effects due to technological, geographic

and relational (buyer-supplier) proximity. Estimating dynamic long difference models and

allowing for gradual convergence in TFP and geographic decay in spillover effects, we find

positive effects of technologically proximate private R&D stocks, which decay in distance

and become negligible at around 500 kilometres. In addition to knowledge spillovers from

technologically proximate R&D stocks, ‘relational’ spillovers from buyer and supplier R&D

stocks exert positive effects on TFP growth that are similar in magnitude. The elasticity of

TFP is highest for public R&D (corrected for industrial relevance), in particular for plants

operated by R&D conducting firms. We do not find evidence of geographic decay in the

impact of public and relational spillovers. Over time, declining R&D spillovers appear to be

responsible for a substantial part of the decline in the rate of TFP growth. The exit of

proximate plants operated by R&D intensive firms plays a notable role in this process and is

an important phenomenon in major industrial agglomerations such as Tokyo, Osaka, and

Kanagawa.

3

1. Introduction

It is well established in the literature that the productivity effects of R&D spillovers are

enhanced by technological proximity and geographic proximity (Jaffe et al., 1993; Adams

and Jaffe, 1996; Aldieri and Cincera, 2009; Lychagin et al., 2010; Bloom et al., 2013;

Orlando, 2004; Griffith et al., 2009; Mairesse and Mulkay, 2008). Despite the increasing

number of large-scale firm-level studies on R&D spillovers,1 existing studies have a number

of limitations in scope and methodology. First, they typically relied on data on publicly listed

firms, aggregating over the various locations and technologies in which firms are active.2

Second, the focus has been on inter-firm private spillovers while abstracting from the role of

public research. A different research stream focusing on the role of knowledge spillovers

from public research conducted at universities and research institutes has however suggested

the importance of such spillovers, with an explicit role of proximity (e.g. Jaffe, 1989; Adams,

1990; Anselin et al., 1997; Furman et al., 2005). Third, R&D spillovers at the firm level have

in most cases been modelled as knowledge spillovers as a function of proximity between

technology portfolios of the firm, while the role of spillovers through supplier and customer

linkages has only received limited attention.3 A separate literature on the role of spillovers in

the context of foreign direct investments has strongly suggested that 'vertical' spillovers

through buyer-supplier relationships often is the key channel through with spillovers occur

(e.g. Haskel et al., 2007; Görg and Strobl, 2001; Javorcik, 2004; Kugler, 2006). While

knowledge and technology transfer in these relationships is often purposeful and embedded in

intermediates, their value tends not to be fully reflected in the price of such intermediates,

leading to ‘pecuniary spillovers (Hall et al., 2012; Crespi et al, 2007). Compared with

‘horizontal’ spillovers in technological proximity within narrowly defined industries, the

absence of market rivalry provides greater incentives for productivity and growth enhancing

knowledge exchange and spillovers (e.g. Bloom et al., 2013). Since suppliers and clients may

be active in a variety of industries, these 'relational' spillovers are yet a different dimension of

heterogeneity in spillover pools.

This paper addresses these limitations in prior work. We contribute an analysis of the

1 Early work examined R&D spillovers at the industry level (e.g. Mohnen and Lepine, 1991; Audretsch and

Feldman, 1996; Goto and Suzuki, 1989). 2 Adams and Jaffe (1996) do analyse plant level productivity but focuses on the effects of internal R&D. The

analysis of Griffith et al. (2009) for UK plants focuses on proximity effects but does not incorporate the role of

R&D. 3 An exception is Crespi et al. (2007), who examine data from UK Community Innovation Surveys for direct

(self assessed) evidence of incoming knowledge flows at the firm level. They find, among others, that supplier

information positively affects TFP growth, but do not examine geographic or technological proximity.

4

various sources of R&D spillovers, which until now have not been considered simultaneously,

and examine these relationships at the plant level. We analyse the effects of technologically,

geographically, and relationally proximate private R&D stocks, as well as of technologically

and geographically proximate public R&D stocks on TFP in an unbalanced panel of close to

20000 Japanese manufacturing plants, 1987-2007. The plant level data from the Census of

Manufacturers are matched with information on R&D expenditures from the comprehensive

Survey of R&D Activities in Japan covering virtually all R&D spending firms (and public

research institutions). The R&D survey data, which are decomposed by field or industry of

application, allow us to construct relevant R&D stocks weighted by technological proximity

(e.g. Bloom et al., 2013), while the information on plant locations allows us to explore the

role of geographic distance between firms and between firms and public research institutions

in much more detail than in previous studies. Relationally proximate R&D stocks are

calculated using input-output tables. Public R&D stocks are differentiated by science field,

which can be mapped into technologies and industries reflecting their varying relevance for

firms. We estimate long (five year) difference models of plant TFP growth to reduce the

influence of measurement errors and cyclical effects (e.g. Haskel et al., 2007; Branstetter,

2000). We allow for gradual convergence in TFP by estimating dynamic TFP growth models

(e.g. Klette, 1996; Klette and Johanson, 1998; Lokshin et al., 2008), and we identify distance

effects by estimating exponential decay parameters (e.g. Lychagin et al., 2010; Duranton and

Overman, 2005)). The simultaneous inclusion of multiple sources of spillovers, the detail on

location and field of R&D, the long panel, and the uniquely large set of plants should allow

more precise estimates of spillover effects and an assessment of their relative importance over

time. Our study contributes to the very limited literature on R&D and spillovers at the plant

level.

Our research is also motivated by the observation that Japan's total factor productivity

growth has been declining since the mid-1980s (e.g. Fukao and Kwon, 2011), while at the

same time R&D expenditures as a percentage of GDP have been steadily increasing to reach

3.8% in 2008, from 2.5% in 1980s. The discrepancy between the trends in R&D expenditures

and TFP suggests that the aggregate returns to R&D have been falling. One possible

explanation for this phenomenon may be a decline in R&D spillovers due to the exit (and

potential relocation abroad) of sophisticated manufacturing plants of R&D intensive firms

and the accompanied changing patterns of R&D agglomeration, which may have reduced the

size and effectiveness of the relevant pool of R&D spillovers across firms. Prior studies

suggest that exit rates of relatively productive plants operated by multi-plant (multinational)

5

firms have been typically higher than the exit rates of single establishments (e.g. Fukao and

Kwon, 2006; Kneller et al. 2012).

The remainder of the paper is organized as follows. The next section describes the model,

the particularities of the data and the empirical strategy followed. Section 3 presents the

empirical results and section 4 concludes and discusses avenues for future research.

2. Model Setup and Data

We conduct a plant-level panel analysis of total factor productivity, in which we relate

plant-level TFP to firms’ own R&D stock, private R&D stocks (the private spillover pool),

public R&D stocks, and a set of plant-, firm- and industry-level controls. We assume that firm

level R&D stocks are available to all the firms’ plants and that R&D spillovers occur between

plants due to the R&D stock the plants have access to. This allows us to investigate the

geographic dimension of R&D spillover in detail, taking into account the population of R&D

conducting firms and the spatial and industry configuration of their plants.

We adopt the standard knowledge stock augmented production function framework (e.g.

Hall et al, 2012). We define the production function at the plant-level generally as:

(1)

Where:

: Gross output of the plant

: Inputs of plant in year

: Firm-level R&D stock

: Private R&D stock

: Public R&D stock

: a vector of other observable factors (control variables) affecting plant

productivity

: plant-year specific unobserved efficiency.

Total factor productivity (TFP) is defined as:

(2)

6

R&D stocks are assumed to influence production with a one-year lag to reflect that the

application of new knowledge and insights due to R&D takes time. If we adopt a log-linear

specification for and allow , where is a plant specific

fixed effect and is a plant-year specific efficiency shock, we obtain:

(3)

and if we difference the equation between two periods:

(4)

where the plant-specific efficiency parameter drops out. We assume that the change in plant-

specific efficiency levels ) is a function of past productivity relative to the industry

mean, in order to allow for a gradual convergence in efficiency levels between firms (e.g.

Lokshin et al., 2008). Klette (1996) and Griffith et al. (2009) have shown that the empirically

observed persistent productivity differences between plants or firms require a model

specification that allows for gradual convergence.4 Specifically, we model:

(5)

where is the level of TFP of plant relative to the industry mean in the previous

period. We expect to fall within the interval [-1,0]. If is zero there is no gradual

convergence between leading firms and lagging firms; if is –1 complete convergence

materializes in one period. We assume that the error term can be decomposed into four

components, year-specific effects , industry-year specific technological opportunity or

efficiency shocks (with s denoting industry), regional shocks and measurement error

:

(6)

4 Kneller et al. (2012) show that productivity catch up is an important phenomenon among Japanese

manufacturing plants as well

7

Data sources and sample

We match plant level data from the Japanese Census of Manufacturers with information

on R&D expenditures from the yearly (comprehensive) Survey of R&D Activities in Japan,

1987-2007. The census has a comprehensive coverage of manufacturing plants with more

than 4 employees. From 2001 onwards, information on plant level fixed capital investment

has not been surveyed for plants with less than 30 employees, with the exception of the

benchmark surveys organized every 5 years. The number of plants for which panel data on

TFP can be calculated is roughly 40,000 yearly.

The Survey of R&D activities in Japan is a comprehensive and mandatory survey of

R&D performing firms and public research institutes and universities in Japan. It contains

information on R&D expenditures, differentiated by field, for roughly 9,000 firms yearly and

has a response rate greater than 90 percent. Large firms (with more than 1 billion Yen of

capital) are always included in the survey; smaller firms are included in higher sampling rates

if they are identified as R&D conducting firms in the previous survey. The information on

R&D by field (30 fields are distinguished) is easily mapped into industries, and allows us to

distinguish R&D expenditures relevant to 20 manufacturing industries. The response rate by

research institutes and universities is close to 100 percent.

The matching between the surveys posed a number of challenges. Firm names are only

recorded in the R&D survey from 2001 onwards and parent firm names are only provided on

the plant records in the census from 1994 onwards. Firm identifiers in the R&D survey are

not compatible between the years before and after 2001 because the identifiers for all firms

were revised in 2001; only the R&D survey in 2001 includes both the old and new versions

of firm identifiers. Because of the absence of common firm identifiers in the surveys,

matching had to be done semi-manually (by firm name, address and capitalization). From

2001 onwards, we could match more than 97.5 percent of reported R&D expenditures to

firms and plants included in the census (Figure 1). The situation is more complicated for the

years 1983-2000, for which we could only match R&D to plants 1) that could be linked to the

parent firm in 1994 or one of the later years, and 2) that belong to firms identified in the R&D

survey of 2001. This caused the coverage rate to decline from 98 percent in 2001 to 92.5

percent in 2000, declining progressively further to 73 percent in 1983.

Insert Figure 1

8

The matching issues cause several problems. First, there is a difficulty ascertaining

whether a plant belongs to a parent firm conducting R&D or not. Here we excluded all

unmatched firms from our sample to avoid measurement error in R&D stocks at the firm

level. Second, for some firms R&D series are incomplete. We proceeded to calculate R&D

stocks on the basis of the information available only if there was sufficient information to

derive an R&D growth rate for a specific period. Firms that are included in the R&D survey

multiple times reporting absence of R&D activities are included in the sample with zero R&D

stock. Third, we require reliable estimates of private R&D spillover pools. Here we obtained

estimates that are as accurate as possible by 1) using the weights provided in the R&D survey

to correct for non-response and arrive at an estimate of total R&D expenditures in Japan; 2)

allocating the R&D (stocks) to locations and fields/industries for R&D conducting firms that

could not be matched to the manufacturing census (and hence for which no geographic

information on plants is available) on the basis of the location of the firm, rather than on the

basis of the location of plants. The second correction may be a reasonable approximation as

most of the unmatched firms are smaller enterprises for which the plant and administrative

unit are collocated.

Using the above matching rules, we obtain an unbalanced panel of over 19000 plants,

observed for a maximum of 20 years and a minimum of 5 years, during 1987-2007. The five

year minimum observation period is due to the fact that we will estimate (five-year) long

difference models. About 57 percent of the plant observations, plants are owned by parent

firms for which we could confirm the absence of formal R&D. Zero R&D cases are not

compatible with the specification in natural logarithms in (4) but provide important variation

in the sample. We deal with this in two ways: 1) we include a dummy for continuous

engagement in, or absence of, R&D; 2) we add the value 1 to the R&D stock before taking

the logarithm, such that we treat the continuous absence of R&D as zero growth.

Table 1 shows the distribution of plants over industries and compares this with the

distribution of the population of Japanese manufacturing plants over industries. Plants in

technology intensive industries such as drugs & medicine and chemicals are overrepresented

in our sample, but the difference with the distribution of all plants over industries is not

generally pronounced. The 19389 unique plants are operated by 13188 firms, implying that

on average there are 1.5 plant observations per firm in the sample. Parent firm R&D stocks

are highest in the home electronics and information and telecommunication sectors, and

lowest in pulp & paper and printing.

9

Insert Table 1

We note that creating a sample of plants for which parent firms’ R&D stocks can be

calculated leads to various sample selection issues, with a natural oversampling of R&D

conducting firms (although the majority of plants in our sample have no access to internal

R&D), larger plants (post-2001), surviving plants (1987-1994), and surviving firms (1987-

2001). We will conduct several sensitivity analyses to examine potential selection bias.

Variables and Measurement

We utilize plant level TFP data from the Japan Industrial Productivity Database (JIP)

2010 (Fukao et al., 2008). TFP is measured using the index number method, following Good

et al (1997):

(1)(7)

where Qfsi,t is the gross output of plant i of firm f in industry s in year t, sX

,fsi,t is the cost share

of input X, and Xfsi,t is the amount inputs of the plant. Three inputs, labour (L), capital (C),

and intermediate input (M), are taken into account. Variables with upper bars denote the

arithmetic mean of each variable over all plants in that industry s in year t. The JIP database

provides index linked TFP estimates distinguishing 58 industries. The TFP indices express

the plants’ TFP as an index of the TFP level of a hypothetical representative plant in the

industry (with an index of 1). One of the main advantages of the index number method is that

it allows for heterogeneity in the production technology of individual firms, while other

methods controlling for the endogeneity of inputs (e.g. Olley and Pakes, 1996; Levinsohn and

Petrin, 2003) assume an identical production technology among firms within an industry (Van

Biesebroeck, 2007; Aw et al., 2001).

Drawing on the JIP database, we calculate the five-year growth rate in TPF for the

matched sample. We drop the observations with the largest (top 1 percent) and lowest

(bottom 1 percent) TFP growth to avoid a potentially strong influence of outliers. Figure 2

shows the 5-year moving average of the gross output weighted average TFP growth rate for

the sample. The figure confirms that the rate of TFP growth has been decreasing over time,

while there is a modest recovery in growth rates after 1999. The pattern of TFP growth in the

sample closely follows the pattern of TFP growth in the population of Japanese plants.

10

Insert Figure 2

R&D stocks by industry and location

R&D stocks measured at the parent firm level can be separated by industry/field of

application to arrive at R&D stocks of the firm per industry. We utilize a question in the R&D

survey asking firms to allocate R&D expenditures by field, which easily maps into 20

industries. R&D stock of firm in industry/field is defined by:

(8)

where is R&D investment of firm for activities in industry in year and is a

depreciation rate of the R&D stock. We use industry-specific depreciation rates to reflect

differences in the speed of obsolescence and technology life cycles. Industry specific

depreciation rates are based on Japanese official surveys of “life-span” of technology

conducted in 1986 and 2009 among R&D conducting firms5 and vary between 8 (food

industry) and 25 percent (precision instruments). To calculate initial R&D stocks (Hall and

Oriani, 2006), we similarly use industry-specific growth rates, which we calculate from the

R&D survey as average R&D growth rates per field in the 1980s. R&D investments are

deflated using a deflator for private R&D from the JIP database, calculated from the price

indices of the input factors for R&D expenditures for each industry; the deflator for public

R&D is obtained from the White Paper on Science and Technology.

Matching the field of firms’ R&D with the industry of the firms’ plants, we can calculate

R&D stocks across industries and space, where we assume that the R&D stock in a

field/industry is available to each same-industry plant of the firm. We map R&D stocks in

geographic space by using the information on the location of the plant, where we distinguish

more than 1800 cities, wards, towns, and villages.

Plant R&D stocks

We calculate plant R&D stocks as the R&D stock of the parent and assume that all parent

R&D provides relevant productivity improving inputs to the plants. Given that R&D at the

firm level is often organized to benefit from scope economies (e.g. Henderson and Cockburn,

1996; Argyres and Silverman, 2004) and involves active knowledge transfer to business units

5 See “White paper on Science and Technology” (1986, Science and Technology Agency) and “Survey on

Research Activities of Private Corporations” (2009, National Institute of Science and Technology Policy).

11

and plants, this may be a suitable assumption.6

Private R&D stocks (spillover pools)

Private R&D stocks (spillover pools) are derived from the calculated parent firms’ R&D

stocks, while we allow for geographic decay in the effectiveness of spillovers.

Technologically proximate R&D stocks are calculated based on the technological proximity

between the R&D field/industry of the plant and the industry of other plants. We define the

technologically relevant private R&D stock (spillover pool) as the sum total of other firms’

R&D assigned to their (nearest) plants in an industry, weighted by the technological

relatedness between the industry of the plants and the industry of the focal plant:

(10)

where:

: Minimum geographic distance between plant and the plant of firm in the

field in year ;

: the technological proximity weight;

: Weight for geographic proximity of plant to R&D stock firm for field

;

: a decay parameter, with .

If firms operate multiple plants, the R&D stock is only counted once using the plant with

the minimum distance to the focal plant, which avoids double counting of R&D. 7

We model

an exponential decay function in the effectiveness of spillovers with parameter to be

estimated, in line with recent studies (e.g. Lychagin et al. 2010). Distance d is the distance

between a pair of locations and is measured as the geo-distance between the centre of cities,

wards, towns, and villages. In order to correct for differences in the geographic areas covered

by the regions, distance is the radius of the region if plants are located in the same region.

6 We also calculated a technological proximity weighted parent R&D stock, applying the weighting scheme for

industries/fields outside the industry of the plant based on the technological proximity matrix used for R&D

spillovers, but obtained weaker effects. As the co-occurrence of different technologies in the R&D portfolios of

firms is often taken as an indicator of the potential for scope economies (Bloom et al. 2013; Breschi et al. 2003)

this is perhaps not surprising. 7 This would follow from the notion of redundancy in the type of R&D spillovers. On other hand, one may

argue that having multiple plants in the vicinity increases the likelihood of knowledge spillovers.

12

Our technological relatedness measure is derived from patent data and based on Leten et al.

(2007). The relatedness between technologies will be reflected in the intensity with which

technologies in a field build on prior art in a different field. Patent citation data are available

at the 4-digit IPC level. The IPC codes can subsequently be mapped onto industries using the

industry-technology concordance table developed by Schmoch et al. (2003) in which each

technology field is uniquely linked to its corresponding NACE two-digit industry. Appendix

A shows the resulting technological relatedness coefficients (weights) between industries

used in our analyses, with weights for the own industry normalized at 1.

We measure relationally proximate R&D stocks by the R&D stocks of supplier and

customer industries, identifying the importance of supplier and customer transactions from

Input-Output tables (yearly between 1987 and 2007) for 52 JIP industries. The calculation of

R&D stocks follows (10) but with substituted by supplier industry proximity weights

and customer proximity weights , with:

(12)

(13)

where denotes domestic sales of industry to industry and denotes exports of

industry . In equation (12), is the estimated output of industry sold to industry s.

Since domestic sales in the input-output tables include domestic sales of imported goods, we

estimate by applying the following correction to the domestic sales data:

, with imports of industry s. Hence we assume that the

imported goods of the industry are sold to other industries in proportion to total sales to these

industries. We note that industries s include services and other industries’ sales to industry s’,

such that the sum of input shares for industry s’ does not add up to 1. Weights for customer

R&D stocks for industry s are the shares of sales by industry s to industry in total sales,

with the latter including sales to non-manufacturing industries and exports. We use-yearly

input output tables provided by the JIP database, such that weights are varying by year.

Appendix B and C show the average the input and output share weights for the industries in

the analysis for the year 1990.

Public R&D stocks

13

Public R&D spillover pools derived from the R&D surveys have few measurement

issues, as response rates are virtually 100 percent. We differentiate public R&D by location

based on the region (city, ward, town, village) of the research institute or university, and by

industry/R&D field utilizing information on science fields with varying relevance for specific

industries. We define the R&D stock of public research institution in science field as:

(14)

where is research expenditure of public research institution in science field in year

and is a depreciation rate of public R&D stock, which we set at 15 percent per year.

Although the surveys do not include research expenditures by science field, they do contain

information on the number of researchers by science field for each institution for each year.

We estimate the public R&D expenditure by mutliplying total R&D expenditures with

the share of the number of scientists in the field in the total number of scientists for each

institution and year.

Second, we estimate a ‘relevant’ public R&D stock per industry/R&D field using weights

derived from a concordance matrix between science fields and industries. The weights are

based on a study by Van Looy et al. (2004) examining citation frequencies on patent

documents classified in different technology fields to Web of Science publications in each of

the science fields. The concordance attaches to each scientific discipline probabilities that it is

of relevance to each technology field (4-digit IPC fields). Applying this concordance to the

public R&D expenditures per science field, we subsequently apply the concordance matrix

between IPC classes and industries due to Schmoch et al. (2003) to arrive at public R&D

stocks per industry. Appendix D shows the compound weights used to relate R&D stocks per

science field to industries.

Using the above procedure, the technologically and geographically proximate public R&D

stock is defined as:

(15)

where:

: R&D stock of public institutes in location for academic field in year ;

: The compound proximity weights between industry/R&D field and science

14

field ;

: geographic distance between plant and location ;

: the geographic decay parameter, .

Figure 3 shows the 5-year moving average growth rates in the levels of public and private

R&D stocks. The growth in both public and private R&D shows a declining trend, as the

increase in overall R&D investments (Figure 1) has slowed over time and had just exceeded

deprecation rates in the most recent years.

Insert Figure 3

Control variables

The vector of time varying plant-specific characteristics includes plant size (number of

employees) and a dummy variable indicating whether the plant is active in multiple industries

(at the 4 digit level).8 In addition, we control for parent firm size (number of employees) and

the number of plants of the parent firm. On the one hand, increases in the number of a firm’s

plants may correlate with unmeasured firm-specific advantages. On the other hand a larger

numbers of plants drawing on the same R&D pool may lead to reduced effective knowledge

transfer (Adams and Jaffe, 1996). We include a set of year dummies and region

(prefecture) dummies . We model as a set of industry dummies in addition to the

average TFP growth rate for all plants in the industry, , which controls for industry-

specific technological opportunity and demand shocks over time affecting TFP growth.

Specification

We estimate equation (4) in its long difference form. The long difference models, while

sacrificing degrees of freedom, is a conservative estimation method to reduce the influence of

measurement error and cyclical effects (e.g. Haskel et al, 2007; Branstetter, 2000). To strike a

balance between degrees of freedom and reduction in measurement error, we take 5-year

differences starting from 1987, which leaves a maximum of exactly 4 non-overlapping long

differenced observations (for plants observed over the entire period): 1987-1992, 1993-1997,

1998-2002 and 2003-2007. To facilitate interpretation of the descriptives, we divide the long

8 Note that age effects are of no interest in differenced models, since the difference in age would be identical for

all plants.

15

difference by 5 to arrive at annual average growth rates of TFP and R&D stocks during the 5-

year periods. Since the geographic decay specification introduces nonlinearity in the TFP

equation, we estimate equation (4) with nonlinear least squares. The distance decay

parameters are estimated using a Taylor approximation.9 Error terms are cluster-robust at the

plant level.

Table 2 shows descriptive statistics of the variables and Table 3 contains the correlation

matrix. The correlations between the (growth in) relationally proximate R&D stocks (buyers

and suppliers) and the technologically proximate R&D stock are rather high at 0.66-0.78.

This is mainly stemming from the correlation in same-industry R&D stocks, while

correlations between stocks in other industries range between -0.04 and 0.12. Hence, the

different measures of proximity do suggest rather different weightings for R&D stocks and

the resulting spillovers potential.

Insert Tables 2 and 3

3. Empirical results

Table 4 reports the estimation results. Model 1 only includes the technologically

proximate R&D stock and the parent firm R&D stock. The coefficient on parent R&D

suggests an elasticity of TFP with respect to R&D of 0.033 percent, which is within, but at

the lower end, of the range estimated in Adams and Jaffe (1996) for plant level R&D

effects.10

The elasticity of the private R&D stock is higher – a common finding in R&D

spillover studies- at 0.058, while spillover effects decay in distance, as the significant

distance parameter suggests. The estimates on the past TFP level suggest that plants that are 1

percent more productive than the average TFP level in the industry have a 0.08 percent point

9 Without approximation we would need to sum up over all R&D conducting firm-pairs and

industries for each plant to arrive at an update of the distance parameter , which is

computationally infeasible. We therefore approximate the distance function by taking a H-order

Taylor ’s expansion:

, such that the expression for the plant level

technologically proximate R&D stock becomes:

The summation over f ’ and s’ no longer depends on the distance decay parameter , and

summation over H suffices. We set H conservatively at 50 and at 1500 km (the midpoint of the

smallest and largest possible distance). 10 We note that their specification was cross sectional, and one may expect smaller effects in a differenced

model.

16

smaller TFP growth rate, indicating that there is a modest gradual convergence in

productivity. TFP growth of the plants is strongly influenced by opportunities and shocks

captured by the average TFP growth in the industry, with an estimated elasticity of 0.89. Of

the plant and firm control variables, only (growth in) the number of plants operated by the

parent firm has a marginally significant positive effect on TFP.

Insert Table 4

In model 2 we add the dummy variable indicating continuous positive R&D. Both the

dummy variable indicating positive R&D and the R&D stock are significant. The dummy

variable suggests that R&D performing firms generate on average 0.5 percent points higher

TFP growth (independent of variation in their R&D stocks). At the same time, the coefficient

of the parent R&D stock declines to about 0.01. Model 3 adds the technologically proximate

public R&D stock. The coefficient on public R&D, at 0.077 is larger than the coefficient on

technologically proximate private R&D, demonstrating the importance of knowledge

spillovers from public R&D. The estimates however do not suggest a significant geographic

decay effect of public R&D spillovers. The addition of public R&D in model 3 does not

materially affect the estimated coefficient on private R&D, which may indicate little overlap

in the type of knowledge from technologically proximate private and public R&D.

In model 4 the relationally proximate R&D stocks of customers and suppliers are added.

The relationally proximate R&D stock due to supplier linkages has a significant effect on

TFP growth with an elasticity of 0.031. The significant elasticity of customer R&D stocks is

slightly smaller at 0.026. Meanwhile, the coefficient on the technologically proximate R&D

stock reduces with the inclusion of the supplier and customer R&D stock variables, and at

0.035 is similar in magnitude as the elasticity of the supplier R&D stock. The estimated

distance decay for private R&D spillovers becomes smaller overall, suggesting weaker

proximity influences for relationally proximate R&D. Model 5 confirms this pattern: when

we allow separate decay parameters for the three private R&D stocks, the decay parameter

for technologically proximate R&D increases in strength whereas the model does not identify

a distance decay effects for R&D spillovers from buyers and suppliers. For technologically

proximate R&D spillovers, the decay function on the basis of model 5 is depicted in Figure 4.

Spillover effects decline and become negligible at about 500 kilometers. This pattern is

similar to the estimates reported in Lychagin et al. (2010) for US listed manufacturing firms

based on inventor locations.

17

Model 6 presents the results of an alternative model with one parameter estimated for the

(unweighted) sum of the three types of private R&D. The estimated coefficient for this

combined private R&D stock is close to 0.08 and larger than the estimated coefficient for

technologically proximate R&D in models 1-3. This underscores that failure to take into

account relational proximity may lead to an underestimation of R&D spillover effects. The

estimate of the distance parameter for the combined private R&D stock is close to the

parameter estimated in model 4.

Prior studies have suggested that firms need to invest in internal R&D in order to benefit

from academic research (e.g. Cassiman and Veugelers, 2006; Anselin et al., 1997; Belderbos

et al., 2009), as firms need the absorptive capacity to screen, understand, and utilize the fruits

of relevant scientific research (Cohen and Levinthal, 1990). In model 7, we separate the

effect of public R&D into an effect for firms without formal R&D expenditures and an effect

for firms with positive R&D. The results confirm that the presence of internal R&D increases

the magnitude of public R&D spillovers: the elasticity increases to 0.12, while the coefficient

for firms without internal R&D is only marginally significant (at 0.068). The difference

between the two coefficients is statistically significant.

Insert Figure 4

Sensitivity analysis

We further explored the role of distance for public spillovers and the assumption that

(private) R&D spillovers as a function of distance play out at the plant level. In an alternative

specification, we examine distance between the firms’ R&D laboratories and between R&D

laboratories and the location of public R&D institutions. In particular for public spillovers,

linkages may occur at the laboratory level and not necessarily at the plant level, while the

R&D laboratories may not necessarily be located close to the firms’ plants. We derived the

location of R&D laboratories from published directories of R&D establishments in Japan. For

R&D performing firms lacking laboratory location information, we assigned R&D to the

location of headquarters – the safest option for these -mostly smaller- firms (e.g. Adams and

Jaffe, 1996; Orlando, 2004). Our results, however, did not show geographic decay effects in

this specification either.

We conducted a number of additional sensitivity analyses, estimating model 6 on

different samples. First, we estimated productivity models for the entire population of

Japanese manufacturing plants (plants with TFP information; more than 230000 observations)

18

to examine the robustness of our estimates. Here we treated the unmatched plants as zero

R&D plants while including a separate dummy variable indicating that the plants lack R&D

information. Second we estimated the model without smaller plants (leaving about 36000

observations) and on a balanced sample (limited to about 16000 observations), to explore the

implications of potential sample selection bias. All these models produced broadly similar

results, with some exceptions. The distance effect for technologically proximate R&D proved

difficult to identify in some of the models, while in two specifications only two of the

individual effects of supplier, customer, and technologically proximate R&D were

simultaneously estimated as significant. We aim to further explore the robustness of our

empirical model in future work.

Decomposition analysis

Given the time dimension in our data and the changes over time in R&D investments and

agglomeration, we can decompose long term TFP growth effects into several factors: firms’

internal R&D effects, private R&D spillover effects, and public R&D spillover effects. The

results of the decomposition analysis based on model 7 are presented in Figures 5-8. The

decomposition analysis is conducted for a balanced sample of close to 4200 plants. Keeping

the sample of spillover receiving plants stable ensures that the decomposition is not

influenced by period-on-period changes in the sample but highlights effects of the changing

‘supply’ of spillovers. The decomposition uses plants’ gross output as weights. Figure 5

shows that declining R&D spillovers, in particular private R&D spillovers, play an important

role in the decline in TFP growth over the years. The contribution of private R&D spillovers

to TFP growth for the plants in the balanced sample reduced from 0.896 percent points in

1987-1992 to 0.182 percent points in 2002-2007. The contribution of public R&D spillovers

also declined, but less so in relative and absolute terms. This is related to the more modest

decline in the growth in public R&D and a changing composition of public R&D

expenditures in the direction of life sciences with greater relevance for the private sector. The

role of internal R&D remained relatively stable, although this is to an important extent due to

the fact that R&D active firms record generally higher TFP growth than firms that are not

engaged in R&D.

We can further decompose the changing role of private R&D spillovers into the three

types of spillovers: spillovers due to technological proximity, buyer effects, and supplier

effects. Figure 6 shows that the technological proximity based spillovers and customer

spillovers have declined most, while the decline in supplier spillovers has been more modest.

19

These divergent effects arise because the share of procurement from (R&D intensive) local

manufacturing industries has not decreased that much over time, while an increasing role of

exports has reduced relational proximity to Japanese customer industries.

Figure 7 decomposes private spillovers into effects due to the exit of R&D active plants,

the entry of such plants, and the changing R&D stocks of surviving plants. The exit of R&D

active plants reduces the R&D stock available to other plants and has a negative effect on

TPF growth. However, if the parent firm operates multiple plants, the exit of one of its plants

implies that another plant of the firm takes its place as ‘minimum distance’ plant providing

R&D spillovers, such that there is a compensating ‘plant substitution effect’. In such cases,

net spillovers decline only to the extent that the exit increases average distance between

plants. Similarly, if a firm opens up a new plant, this may increase the R&D stock available

to plants in its proximity, but at the same time it displaces the R&D stock of the firm’s plant

that was previously located at minimum distance to these receiving plants. Hence, in case of

entry there is a partially compensating negative substitution effect. This decomposition

exercise shows that while the largest part of the decline in spillovers is due to a slowing down

of R&D stock growth in surviving plants, increasing exit effects and reduced entry effects

over time also play an important role. Figure 8 shows that most of the exits have taken place

in the major industrial agglomerations in Japan around Tokyo and Kanagawa, Osaka, and

Aichi (home of a large automobile cluster) during 1997-2007

4. Conclusions

This paper examined the effects of R&D spillovers on total factor productivity in a large

panel of Japanese manufacturing plants matched with R&D survey data. We simultaneously

analyse the role of public (universities and research institutes) and private R&D spillovers,

while examining effects due to ‘relational’ (supplier-customer) proximity as well as

technological and geographic proximity. Our analysis confirms the importance of positive

spillover effects from R&D by firms with plants in technologically related industries. The

latter spillover effects are attenuated by distance and our estimates suggest that most spillover

effects disappear beyond 500 kilometres. We also observe positive effects of public R&D

spillovers, with the effects substantially larger for plants with access to internal R&D. We do

not find evidence that public R&D spillover effects are attenuated by distance. In addition to

knowledge spillovers from technologically proximate plants, we find evidence that ‘relational

proximity’ due to buyer and supplier linkages generates additional ‘pecuniary’ R&D

spillovers of similar magnitude as the knowledge spillovers due to technological proximity.

20

We could not identify the role of geographic distance in these buyer and supplier spillovers.

We conclude that public as well as private R&D spillovers matter for TFP growth, while

relational proximity as well as technological proximity needs to be taken into account to

arrive at representative estimates of the social effects of private R&D. Decomposition

analysis shows that the contribution of private R&D spillovers to TFP growth has declined

since the late 1990s. This is due to a declining growth in R&D stocks while another important

factor is the exit of proximate plants operated by R&D intensive firms. A mildly declining

contribution of public R&D spillovers is primarily due to a reduction in the growth of R&D

by public research organization since the late 1990s. If we explore effects at the regional level,

we observe that strong adverse exit effects occurred in particular in Japan’s major industrial

agglomerations such as Tokyo and Osaka.

Our results help to explain the twin stylized facts of Japanese productivity growth: the

exit of relatively productive plants and the declining TFP growth or surviving plants (Fukao

and Kwon, 2006; Kneller et al., 2012). They suggest that these two trends may be causally

related. The exit of plants by R&D intensive firms reduces the available R&D spillovers and

hampers TPF growth of the surviving plants.

In future work, we aim to get a better understanding of the (absence of) distance effects

in R&D spillovers. One reason for the lack of estimated distance effects for public R&D may

be that public R&D spillovers occur most often through active collaboration across larger

distances (Okamuro and Nishimura, 2013; Gittelman, 2007). We can explore these

explanations by incorporating information available on research relationships between firms

and universities. Second, we aim to investigate the role of proximity effects in buyer-supplier

relationships in more detail by utilizing data on the most important buyers and suppliers of

individual Japanese firms. Third, we are planning to match the data with the Basic Surveys on

Business Activities in Japan, which contain information on corporate relationships and

foreign activities. Matching with the Basic Surveys allows bringing in controls on overseas

R&D conducted/outsourced by the firms and the potentially resulting international transfers

and knowledge spillovers (e.g. Branstetter, 2001; Griffith et al., 2008). It also allows analysis

of potentially greater R&D spillovers for firms operating within business groups (Suzuki,

1993; Branstetter, 2000). Collectively, the remaining challenges for exploration of R&D

spillover effects present a rich research agenda.

21

References

Adams, J. D. & Jaffe, A. B., 1996. Bounding the Effects of R&D: An Investigation Using

Matched Establishment-firm Data. Rand Journal of Economics, 27, pp.700-721.

Adams, J., 1990. Fundamental Stocks of Knowledge and Productivity Growth. Journal of

Political Economy, 98, pp.673-702.

Aldieri, L. & Cincera, M., 2009. Geographic and Technological R&D Spillovers within the

triad: micro evidence from US patents. Journal of Technology Transfer, 34(2), pp.196-

211.

Anselin, L., Varga, A. & Acs, Z., 1997. Local Geographic Spillovers between University

Research and High Technology Innovations. Journal of Urban Economics, 42, pp.422-

448.

Argyres, N. & Silverman, B., 2004. R&D, Organization Structure, and the Development of

Corporate Technological Knowledge. Strategic Management Journal, 25, pp.929-958.

Audretsch, D. & Feldman, P., 1996. R&D Spillovers and the Geography of Innovation and

Production. American Economic Review, 86, pp.630-640.

Aw, B. Y., Chen, X. & Roberts, M. J., 2001. Firm-level Evidence on Productivity

Differentials and Turnover in Taiwanese Manufacturing. Journal of Development

Economics, 66, pp.51-86.

Belderbos, R., Leten, B. & Suzuki, S., 2009. Does Academic Excellence Attract Foreign

R&D? Global COE Hi-Stat Discussion Paper Series, Institute for Economic Research,

Hitotsubashi University.

Bloom, N., Schankerman, M. & Van Reenen, J., 2013. Identifying Technology Spillovers and

Product Market Rivalry. Econometrica, 81(4), pp.1347-1393.

Branstetter, L., 2000. Vertical Keiretsu and Knowledge Spillovers in Japanese

Manufacturing: An Empirical Assessment. Journal of the Japanese and International

Economies, 14(2), pp.73-104.

Branstetter, L., 2001. Are Knowledge Spillovers International or Intra-national in Scope?

Microeconometric evidence from Japan and the United States. Journal of International

Economics, 53, pp.53-79.

Breschi, S., Lissoni, F. & Malerba, F., 2003. Knowledge-relatedness in Firm Technological

Diversification. Research Policy, 32, pp.69-87.

Cassiman, B. & Veugelers, R., 2006. In Search of Complementarity in Innovation Strategy:

Internal R&D and External Knowledge Acquisition. Management Science, 52(1),

pp.68-82.

Cohen, W. M. & Levinthal, D. A., 1990. Absorptive Capacity: A New Perspective on

Learning and Innovation. Administrative Science Quarterly, 35(1), Special Issue:

Technology, Organizations, and Innovation, pp.128-152.

Crespi, G., Criscuolo, C., Haskel, J. & Slaughter, M., 2007. Productivity Growth, Knowledge

Flows and Spillovers. CEP Discussion Papers.

Duranton, G. & Overman, H. G., 2005. Testing for Localization Using Micro-Geographic

Analysis. Review of Economic Studies, 72(4), pp.1077-1106.

22

Fukao, K. & Kwon, H. U., 2011. The Key Drivers of Future Growth in Japan, presentation

prepared for the CCJ Growth Strategy Task Force White Paper, June 10, 2011.

Fukao, K, Kim, Y. G. & Kwon. H. U., 2008. Plant Turnover and TFP Dynamics in Japanese

Manufacturing. In A. Heshmati & J.-D. Lee (eds.), Micro-Evidence for Dynamics of

Industrial Evolution, Nova Science Publishers, Inc., Ch. 3, pp.23-59.

Fukao, K. & Kwon, H. U., 2006. Why Did Japan’s TFP Growth Slowdown in the Lost

Decade? An Empirical Analysis Based on Firm-level Data of Manufacturing Firms.

Japanese Economic Review, 57(2), pp.195-228.

Furman, J., Kyle, M., Cockburn, I. & Henderson, R., 2005. Public & Private Spillovers,

Location and the Productivity of Pharmaceutical Research. Annales d’Economie et de

Statistique, 79-80, pp.165-188.

Gittelman, M., 2007. Does Geography Matter for Science-Based Firms? Epistemic

Communities and the Geography of Research and Patenting. Organization Science, 18

(4), pp.724-741.

Good, D. H., Nadiri, M. I. & Sickles, R. C., 1997. Index Number and Factor Demand

Approaches to the Estimation of Productivity. In M. H. Pesaran & P. Schmidt (eds.),

Handbook of Applied Econometrics: Vol.2. Microeconometrics, Basil Blackwell, pp.14-

80.

Görg, H. & Strobl, E., 2001. Multinational Companies and Productivity Spillovers: A Meta-

Analysis. The Economic Journal, 111(475), pp.F723-F739.

Goto, A. & Suzuki, K., 1989. R&D Capital, Rate of Return on R&D Investment and

Spillover of R&D in Japanese Manufacturing Industries. Review of Economics and

Statistics, 71, pp.555-564.

Griffith, R., Redding, S. & Simpson, H., 2009. Technological Catch-up and Geographic

Proximity. Journal of Regional Science, 49, pp.689–720.

Griffith, R., Harrison, R. & Van Reenen, J., 2008. How Special is the Special Relationship?

Using the Impact of US R&D Spillovers on UK Firms as a Test of Technology

Sourcing. American Economic Review, 96(5), pp.1859-1875.

Griliches, Z., 1992. The Search for R&D Spillovers. Scandinavian Journal of Economics, 94,

pp.29-47.

Hall, B. H., Mairesse, J. & Mohnen, P., 2012. Measuring the Returns to R&D. In B. Hall & N.

Rosenberg (eds.), Handbooks in Economics: Economics of Innovation Volume 2. North-

Holland, pp.1034-1074.

Hall, B. H. & Oriani R., 2006. Does the Market Value R&D Investment by European Firms?

Evidence from a Panel of Manufacturing Firms in France, Germany, and Italy.

International Journal of Industrial Organization, 5, pp.971-993.

Haskel, J., Pereira, S. & Slaughter, M., 2007. Does Inward Foreign Investment Boost the

Productivity of Domestic Firms? The Review of Economics and Statistics, 89(3),

pp.482-496.

Henderson, R. & Cockburn, I., 1996. Scale, Scope, and Spillovers: The Determinants of

Research Productivity in Drug Discovery. Rand Journal of Economics, 27, pp.31-59.

23

Jaffe, A. B., 1989. Real Effects of Academic Research. American Economic Review, 79,

pp.957-970.

Jaffe, A. B., Trajtenberg, M. & Henderson, R., 1993. Geographic Localization of Knowledge

Spillovers as Evidenced by Patent Citations. Quarterly Journal of Economics, 108,

pp.577-598.

Javorcik, B. S., 2004. Does Foreign Direct Investment Increase the Productivity of Domestic

Firms? In Search of Spillovers through Backward Linkages. American Economic

Review, 94(3), pp.605-627.

Klette, T. J., 1996. R&D, Scope Economics, and Plant Performance. Rand Journal of

Economics, 27, pp.502-522.

Klette, T. J. & Johansen, F., 1998. Accumulation of R&D Capital and Dynamic Firm

Performance: A Not-so-fixed Effect Model. Annals of Economics and Statistics, 49/50,

pp.389-419.

Kneller. R., McGowan, D., Inui, T. & Matsuura, T., 2012. Globalisation, Multinationals and

Productivity in Japan’s Lost Decade. Journal of the Japanese and International

Economies, 26, pp.110-112.

Kugler, M., 2006. Spillovers from Foreign Direct Investment: Within or Between Industries?

Journal of Development Economics, 80, pp.444-477.

Leten, B., Belderbos, R. & Van Looy, B., 2007. Technological Diversification, Coherence,

and Performance of Firms. Journal of Product Innovation Management, 24(6), pp.567–

579.

Levinsohn, J. & Petrin, A., 2003. Estimating Production Functions using Inputs to Control for

Unobservables. Review of Economic Studies, 70, pp.317-341.

Lokshin, B., Belderbos, R. & Carree, M., 2008. The Productivity Effects of Internal and

External R&D: Evidence from a Dynamic Panel Data Model. Oxford Bulletin of

Economics and Statistics, 70(3), pp.399-413.

Lychagin, S., Pinkse, J., Slade, M. E. & Van Reenen, J., 2010. Spillovers in Space: Does

Geography Matter? NBER Working Paper, National Bureau of Economic Research.

Mohnen, P. & Lepine, N., 1991. R&D, R&D Spillovers and Payments for Technology:

Canadian Evidence. Structural Change and Economic Dynamics, 2(1), pp.213-28.

Mairesse, J. & Mulkay, B., 2008. An Exploration of Local R&D Spillovers in France. NBER

Working Paper, National Bureau of Economic Research.

Okamuro, H. & Nishimura, J., 2013. Impact of University Intellectual Property Policy on the

Performance of University-industry Research Collaboration. Journal of Technology

Transfer, 38(3), pp.273-301.

Olley, S. & Pakes, A., 1996. The Dynamics of Productivity in the Telecommunications

Equipment Industry. Econometrica, 64, pp.1263-1297.

Orlando, M., 2004. Measuring Spillovers from Industrial R&D: On the Importance of

Geographic and Technology Proximity. Rand Journal of Economics, 35, pp.777-786.

Schmoch, U., Laville, F., Patel, P. & Frietsch, R., 2003. Linking Technology Areas to

Industrial Sectors: Final Report to the European Commission, DG Research.

24

Suzuki, K., 1993. R&D Spillovers and Technology Transfer among and within Vertical

Keiretsu Groups: Evidence from the Japanese Electrical Machinery Industry.

International Journal of Industrial Organization, 11(4), pp.573-591.

Van Biesebroeck, J., 2007. Robustness of Productivity Estimates. Journal of Industrial

Economics, 55(3), pp.529-569.

Van Looy, B., Tijssen, R. J. W., Callaert, J, Van Leeuwen, T. & Debackere, K., 2004.

European Science in Industrial Relevant Research Areas: Development of an Indicator-

based Bibliometric Methodology for Performance Analyses of Countries and Research

Organizations: Report for the European Commission (DG Research).

25

Figure 1: R&D expenditures and matching rate with census of manufacturers

Note: Nominal values are reported as R&D expenditures.

5.9 6.4

6.9

7.9

8.8 9.4 9.2

8.6 8.5 9.0

9.4

10.7 10.2 10.4

11.3 12.0 12.1

12.7 13.4 13.6

14.2

2.6 2.7 2.9 2.9 3.1 3.3 3.5 3.7 4.0 4.0 4.4 4.3 4.4 4.6 4.7 4.7 4.7 4.8 4.7 4.8 4.8

73 73 74 74 74 75 79 79 81 79

81 84 82

87

92

98 99 98 98 98 97

0

10

20

30

40

50

60

70

80

90

100

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

86' 87' 88' 89' 90' 91' 92' 93' 94' 95' 96' 97' 98' 99' 00' 01' 02' 03' 04' 05' 06'

(1 trillion yen) Private R&D: Manufacturing industries

Public R&D

Coverage by the matched firms (%; right axis)

26

Table 1: Sample characteristics

# of obs.

# of (unique)

plants in

sample

# of

(unique)

plants in

Japan (%)

# of (unique)

parent firms

Avg. # of

plants per

firm

Avg. parent

R&D stock

per plant

(billion yen)

% of plants

with positive

parent R&D Industries (R&D fields) # (%) # (%)

Food products 5,048 (10.8)

1,961 (10.1) (12.7) 1,032 1.9 7.3 42.8

Textile mill products 1,741 (3.7)

641 (3.3) (10.5) 432 1.5 7.3 37.4

Pulp and paper products 1,838 (3.9)

660 (3.4) (3.2) 365 1.8 2.6 32.6

Printing 1,270 (2.7)

489 (2.5) (5.6) 332 1.5 4.1 15.7

Chemical fertilizers and industrial chemicals 2,049 (4.4)

786 (4.1) (0.8) 519 1.5 17.6 61.0

Drugs and medicine 1,154 (2.5)

490 (2.5) (0.5) 398 1.2 22.2 47.6

Miscellaneous chemicals 2,135 (4.6)

913 (4.7) (1.1) 655 1.4 11.9 53.3

Petroleum and coal products 511 (1.1)

225 (1.2) (0.3) 113 2.0 7.6 58.5

Rubber products 1,072 (2.3)

426 (2.2) (1.4) 295 1.4 13.4 37.2

Ceramic, stone and clay products 2,969 (6.3)

1,187 (6.1) (5.5) 669 1.8 5.7 41.4

Iron and steel 1,744 (3.7)

642 (3.3) (2.6) 425 1.5 16.6 37.7

Non-ferrous metals and products 1,331 (2.8)

513 (2.6) (1.7) 371 1.4 11.2 39.5

Fabricated metal products 4,196 (8.9)

1,818 (9.4) (14.0) 1,271 1.4 3.8 31.3

General-purpose machinery 6,925 (14.8)

2,951 (15.2) (14.1) 2,284 1.3 15.8 33.1

Home electronics 444 (0.9)

225 (1.2) (1.9) 185 1.2 83.1 32.9

Electrical machinery 3,455 (7.4)

1,508 (7.8) (6.8) 1,101 1.4 26.3 36.6

Info.&com. electronics 3,585 (7.6)

1,714 (8.8) (7.7) 1,247 1.4 56.9 31.5

Motor vehicles, parts and accessories 3,285 (7.0)

1,304 (6.7) (5.1) 756 1.7 58.4 43.1

Other transportation equipment 724 (1.5)

289 (1.5) (1.7) 235 1.2 36.5 39.5

Precision instruments and machinery 1,447 (3.1)

647 (3.3) (2.7) 503 1.3 6.0 28.3

Total 46,923 (100.0) 19,389 (100.0) (100.0) 13,188 1.5 19.4 38.2

27

Figure 2: Trends in TFP growth: sample plants and population of Japanese plants

Figure 3: Growth rate in R&D stocks (5 year moving average)

0.000

0.005

0.010

0.015

0.020

0.025

0.030

Population

Sample

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Private R&D stock - total

Public R&D stock - total

28

Table 2: Descriptive statistics

Mean SD Min Median Max

TFP 0.007 0.079 -1.409 0.006 1.025

PARENT R&D 0.023 0.055 -0.563 0.000 1.604

Tech-proximate PRIVATE R&D 0.040 0.038 -0.155 0.035 0.421

Supplier PRIVATE R&D 0.043 0.043 -0.168 0.036 0.237

Customer PRIVATE R&D 0.040 0.041 -0.751 0.033 0.420

PUBLIC R&D 0.030 0.008 0.002 0.030 0.072

Number of other plants of the parent firm 0.004 0.058 -1.099 0.000 1.099

Number of firm employees -0.003 0.095 -2.290 -0.002 3.306

Number of plant employees) -0.005 0.082 -2.297 -0.004 1.285

Multi-products (4 digit) plant dummy -0.001 0.093 -1.000 0.000 1.000

Parent R&D stock > 0 (dummy) 0.435 0.485 0.000 0.000 1.000

Industry average TFP growth rate 0.006 0.019 -0.124 0.003 0.184

Prior TFP level relative to industry average 0.054 0.269 -1.529 0.036 1.383

Note: all variables are expressed as average 5-year differences, except for prior TFP

29

Table 3: Correlation coefficients

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

[1] TFP 1.000

[2] PARENT R&D 0.020 1.000

[3] Tech-proximate PRIVATE R&D 0.071 0.086 1.000

[4] Supplier PRIVATE R&D 0.076 0.103 0.612 1.000

[5] Customer PRIVATE R&D 0.091 0.108 0.656 0.746 1.000

[6] PUBLIC R&D 0.026 -0.021 0.065 0.213 0.100 1.000

[7] Number of other plants of the parent firm 0.012 0.041 0.059 0.082 0.075 0.021 1.000

[8] Number of firm employees 0.018 0.046 0.061 0.086 0.082 -0.057 0.297 1.000

[9] Number of plant employees) 0.014 0.030 0.051 0.073 0.072 -0.070 -0.012 0.562 1.000

[10] Multi-products (4 digit) plant dummy -0.004 0.004 -0.001 0.001 0.005 0.000 -0.013 0.001 0.025 1.000

[11] Parent R&D stock > 0 (dummy) -0.017 0.451 -0.101 -0.099 -0.095 -0.077 -0.038 -0.059 -0.039 0.001 1.000

[12] Industry average TFP growth rate 0.212 0.074 0.345 0.380 0.432 0.006 0.011 0.052 0.057 0.001 -0.045 1.000

[13] Prior TFP level relative to industry average -0.271 0.064 0.049 0.038 0.021 -0.004 0.000 -0.005 0.009 -0.010 0.128 -0.018 1.000

Note: all variables are expressed as 5-year differences, except for prior TFP

30

Table 4: Long Difference Analysis of Plant-level TFP (1987-2007) [1] [2] [3] [4] [5] [6] [7]

Distance parameters:

Tech-proximate PRIVATE R&D -0.0040 -0.0038 -0.0040

-0.0057

-0.0058

[0.0012]*** [0.0011]*** [0.0012]***

[0.0027]**

[0.0027]**

all PRIVATE R&D

-0.0018

-0.0017

[0.0008]**

[0.0010]*

Supplier PRIVATE R&D

0.0000

0.0000

[0.0027]

[0.0027]

Customer PRIVATE R&D

0.0000

0.0000

[0.0037]

[0.0037]

PUBLIC R&D

0.0000 0.0000 0.0000 0.0000

[0.0024] [0.0025] [0.0024] [0.0025]

PUBLIC R&D (parent R&D>0)

0.0000

[0.0020]

PUBLIC R&D (parent R&D=0)

-0.0060

[0.0059]

R&D parameters:

Parent R&D 0.0331 0.0097 0.0097 0.0096 0.0096 0.0096 0.0096

[0.0036]*** [0.0043]** [0.0043]** [0.0043]** [0.0043]** [0.0043]** [0.0043]**

Parent R&D stock > 0 (dummy)

0.0050 0.0050 0.0050 0.0050 0.0050 0.0034

[0.0004]*** [0.0004]*** [0.0004]*** [0.0004]*** [0.0004]*** [0.0012]***

Tech-proximate PRIVATE R&D 0.0583 0.0600 0.0582 0.0392 0.0346

0.0347

[0.0167]*** [0.0168]*** [0.0167]*** [0.0194]** [0.0167]**

[0.0167]**

Supplier PRIVATE R&D

0.0311 0.0360

0.0364

[0.0141]** [0.0140]**

[0.0140]***

Customer PRIVATE R&D

0.0260 0.0260

0.0259

[0.0131]** [0.0131]**

[0.0130]**

all PRIVATE R&D

0.0775

[0.0180]***

PUBLIC R&D

0.0766 0.0766 0.0832 0.0746

[0.0364]** [0.0373]** [0.0378]** [0.0363]**

PUBLIC R&D (parent R&D>0)

0.1211

[0.0416]***

PUBLIC R&D (parent R&D=0)

0.0678

[0.0356]*

Other parameters:

Plant's relative prior TFP -0.0792 -0.0802 -0.0802 -0.0803 -0.0803 -0.0802 -0.0803

[0.0007]*** [0.0007]*** [0.0007]*** [0.0007]*** [0.0007]*** [0.0007]*** [0.0007]***

Industry average TFP growth 0.8917 0.8919 0.8971 0.8962 0.8966 0.8977 0.8970

[0.0193]*** [0.0193]*** [0.0197]*** [0.0197]*** [0.0198]*** [0.0196]*** [0.0196]***

Number of other plants 0.0077 0.0087 0.0087 0.0087 0.0087 0.0087 0.0086

[0.0053] [0.0053]* [0.0053] [0.0053] [0.0053] [0.0053] [0.0053]

Number of firm employees -0.0008 0.0011 0.0012 0.0010 0.0010 0.0011 0.0010

[0.0047] [0.0047] [0.0047] [0.0047] [0.0047] [0.0047] [0.0047]

Number of plant employees -0.0040 -0.0032 -0.0031 -0.0033 -0.0033 -0.0032 -0.0032

[0.0051] [0.0051] [0.0051] [0.0051] [0.0051] [0.0051] [0.0051]

Multi-products (4digit) plant (dummy) -0.0033 -0.0033 -0.0034 -0.0033 -0.0033 -0.0033 -0.0033

[0.0029] [0.0029] [0.0029] [0.0029] [0.0029] [0.0029] [0.0029]

Constant -0.0040 -0.0035 -0.0057 -0.0092 -0.0086 -0.0072 -0.0084

[0.0073] [0.0073] [0.0073] [0.0074] [0.0074] [0.0073] [0.0073]

Industry dummies (JIP industry level) Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes Prefecture dummies Yes Yes Yes Yes Yes Yes Yes

# observations 46,923 46,923 46,923 46,923 46,923 46,923 46,923 R-squared 0.1685 0.1696 0.1696 0.1697 0.1697 0.1696 0.1698 F statistic 9486.43*** 9555.59*** 9556.97*** 9563.57*** 9566.77*** 9556.55*** 9568.20***

* p < 0.1, ** p < 0.05, *** p < 0.01.

31

Figure 4: Decay in the effect of technologically proximate R&D spillovers as a function

of distance

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500 600 700 800 900 1000

exp

(τ *

Dis

tan

ce)

Distance

32

Figure 5: TFP Growth Composition: Intra-firm R&D vs. Private and Public Spillovers

Note: based on a balanced sample, 1987-2007

Figure 6: TFP Growth Composition: Effects of types of Private R&D spillovers

Note: based on a balanced sample, 1987-2007

0.389 0.346 0.335 0.333

0.896

0.393 0.254 0.182

0.345

0.429

0.353 0.217

-0.227

0.521

0.102 0.267

-0.3

0.2

0.7

1.2

1.7

1987-1992 1992-1997 1997-2002 2002-2007

Other factors

Public R&D spillovers

Inter-firm private R&D spillovers

Intra-firm R&D effects (incl. R&D>0 dummy)

TFP growth rate

0.327

0.149 0.074 0.064

0.305

0.140

0.105 0.076

0.264

0.104

0.075 0.042

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1987-1992 1992-1997 1997-2002 2002-2007

Customer industry R&D spillovers

Supplier industry R&D spillovers

Tech-related industry R&D spillovers

33

Figure 7: TFP Growth Composition: Effects of R&D Active Firms’ Plant Entry and Exit

Note: based on a balanced sample, 1987-2007

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1987-1992 1992-1997 1997-2002 2002-2007

Entries of plants with R&D

Substitution

Surviving plants R&D effects

Exits of plants with R&D

Total

34

Figure 8: TFP Growth Composition: Effects Plant Entry and Exit by Prefecture

a. 1987-1997

Note: based on a balanced sample, 1987-1997

b. 1997-2007

Note: based on a balanced sample, 1997-2007

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Aic

hi

Kan

agaw

a

Tokyo

Shiz

uo

ka

Osa

ka

Sai

tam

a

Hyo

go

Mie

Ch

iba

Ibar

aki

Toch

igi

Fukuo

ka

Gum

ma

Sh

iga

Hir

osh

ima

Yam

anas

hi

Nag

ano

Miy

agi

Yam

agu

chi

Okay

ama

Kyo

to

Hokkai

do

Fukush

ima

Gif

u

Toyam

a

Nii

gat

a

Ehim

e

Sag

a

Ishik

awa

Oit

a

Yam

agat

a

Entries of plants with R&D

Substitution

Surviving plants R&D effects

Exits of plants with R&D

Total

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Aic

hi

Kan

agaw

a

To

kyo

Sh

izu

oka

Osa

ka

Sai

tam

a

Hyo

go

Mie

Ch

iba

Ibar

aki

To

chig

i

Fu

ku

oka

Gu

mm

a

Sh

iga

Hir

osh

ima

Yam

anas

hi

Nag

ano

Miy

agi

Yam

agu

chi

Okay

ama

Kyo

to

Ho

kkai

do

Fukush

ima

Gif

u

To

yam

a

Nii

gat

a

Eh

ime

Sag

a

Ish

ikaw

a

Oit

a

Yam

agat

a

Entries of plants with R&D

Substitution

Surviving plants R&D effects

Exits of plants with R&D

Total

35

Appendix A. Technological proximity between industries Spillovers sources (cited)

Focal industries (citing) [04] [05] [06] [07] [08] [09] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]

[04] Food products 1.00 .003 .006 .000 .125 .359 .041 .001 .000 .004 .001 .001 .001 .094 .021 .001 .003 .002 .000 .026 .026

[05] Textile mill products .007 1.00 .045 .024 .631 .065 .104 .001 .002 .172 .007 .006 .023 .243 .026 .013 .033 .019 .005 .148 .114

[06] Pulp and paper products .022 .073 1.00 .126 .415 .049 .089 .002 .000 .100 .003 .003 .043 .301 .009 .008 .190 .004 .001 .123 .083

[07] Printing .000 .011 .042 1.00 .270 .021 .095 .000 .000 .028 .008 .011 .020 .085 .003 .003 .181 .002 .000 .087 .017

[08] Chemical fertilizers and industrial chemicals .009 .020 .008 .015 1.00 .147 .050 .012 .004 .039 .007 .007 .005 .070 .005 .010 .032 .006 .001 .041 .027

[09] Drugs and medicine .026 .002 .001 .001 .147 1.00 .013 .000 .000 .002 .000 .000 .000 .010 .001 .000 .005 .000 .000 .076 .001

[10] Miscellaneous chemicals .031 .032 .012 .035 .488 .128 1.00 .020 .000 .038 .008 .007 .010 .093 .010 .006 .057 .014 .003 .055 .036

[11] Petroleum and coal products .004 .004 .002 .001 .763 .031 .143 1.00 .000 .008 .006 .005 .014 .209 .003 .036 .074 .030 .004 .130 .014

[12] Rubber products .000 .008 .001 .001 .400 .002 .006 .000 1.00 .008 .014 .011 .004 .030 .001 .005 .028 .064 .002 .050 .116

[13] Ceramic, stone and clay products .003 .064 .026 .021 .439 .015 .047 .001 .001 1.00 .030 .027 .073 .225 .020 .022 .108 .032 .008 .112 .197

[14] Iron and steel .001 .006 .002 .013 .248 .011 .028 .004 .007 .120 1.00 .580 .069 .410 .030 .059 .152 .036 .008 .065 .048

[15] Non-ferrous metals and products .001 .009 .003 .030 .392 .020 .042 .004 .010 .187 1.00 1.00 .108 .486 .034 .111 .233 .052 .009 .097 .075

[16] Fabricated metal products .001 .009 .012 .015 .066 .006 .016 .004 .000 .104 .025 .024 1.00 .259 .027 .050 .082 .081 .025 .070 .102

[17] General-purpose machinery .010 .012 .008 .007 .114 .019 .018 .005 .001 .040 .019 .013 .033 1.00 .018 .020 .059 .078 .014 .082 .058

[18] Household appliances .022 .015 .003 .004 .091 .012 .022 .001 .000 .039 .014 .010 .039 .188 1.00 .057 .121 .056 .004 .079 .106

[19] Electrical machinery .000 .003 .001 .001 .080 .003 .004 .003 .000 .019 .013 .015 .026 .084 .022 1.00 .244 .082 .009 .127 .031

[20] Info.&com. electronics .000 .001 .003 .008 .024 .003 .005 .001 .000 .008 .003 .003 .005 .027 .005 .026 1.00 .010 .001 .068 .009

[21] Motor vehicles, parts and accessories .000 .003 .001 .001 .028 .001 .008 .002 .003 .017 .004 .004 .029 .183 .012 .046 .055 1.00 .022 .076 .041

[22] Other transportation equipment .000 .004 .001 .001 .032 .002 .012 .003 .000 .031 .006 .005 .064 .260 .008 .043 .041 .197 1.00 .060 .064

[23] Precision instruments and machinery .003 .009 .004 .007 .070 .129 .011 .003 .001 .019 .003 .003 .009 .078 .007 .030 .151 .030 .003 1.00 .035

[24] Miscellaneous manufacturing .011 .019 .009 .007 .180 .007 .024 .001 .008 .106 .007 .006 .042 .184 .034 .023 .076 .048 .009 .117 1.00

Source: calculations based on Leten et al. (2008)

36

Appendix B. Applied weights for relationally proximate (Supplier) R&D stocks

Spillover sources (supplier)

Focal industries (buyer) [04] [05] [06] [07] [08] [09] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] Total

[04] Food products .120 .001 .015 .007 .006 .000 .002 .004 .000 .005 .000 .001 .018 .000 .000 .000 .000 .000 .000 .000 .181

[05] Textile mill products .003 .223 .009 .008 .034 .000 .009 .006 .003 .000 .000 .000 .002 .000 .000 .000 .001 .000 .000 .000 .298

[06] Pulp and paper products .003 .006 .275 .014 .018 .000 .012 .012 .001 .001 .000 .000 .001 .000 .000 .000 .001 .000 .000 .000 .344

[07] Printing .002 .001 .111 .081 .001 .000 .029 .002 .001 .000 .000 .002 .000 .000 .000 .000 .002 .000 .000 .000 .233

[08] Chemical fertilizers and industrial chemicals .003 .001 .005 .002 .339 .000 .007 .084 .001 .003 .000 .003 .005 .000 .000 .000 .001 .000 .000 .000 .454

[09] Drugs and medicine .012 .002 .033 .008 .071 .048 .013 .003 .002 .013 .000 .001 .013 .001 .000 .000 .002 .000 .000 .000 .222

[10] Miscellaneous chemicals .005 .001 .034 .012 .177 .001 .083 .005 .001 .006 .000 .004 .016 .001 .000 .000 .001 .000 .000 .000 .346

[11] Petroleum and coal products .001 .001 .000 .000 .002 .000 .003 .050 .000 .001 .000 .000 .002 .000 .000 .000 .000 .000 .000 .000 .060

[12] Rubber products .001 .017 .008 .002 .185 .000 .007 .005 .041 .001 .003 .001 .025 .000 .000 .000 .001 .000 .000 .000 .296

[13] Ceramic, stone and clay products .002 .003 .017 .003 .016 .000 .007 .022 .002 .090 .010 .003 .009 .003 .000 .000 .001 .000 .000 .000 .187

[14] Iron and steel .001 .001 .001 .001 .005 .000 .001 .029 .001 .007 .453 .006 .001 .001 .000 .000 .000 .000 .000 .000 .508

[15] Non-ferrous metals and products .001 .002 .004 .002 .013 .000 .003 .007 .000 .007 .002 .245 .002 .001 .000 .000 .001 .000 .000 .000 .289

[16] Fabricated metal products .002 .002 .004 .004 .002 .000 .008 .005 .002 .004 .192 .046 .062 .002 .000 .001 .004 .000 .000 .000 .342

[17] General-purpose machinery .001 .001 .003 .004 .001 .000 .005 .002 .011 .005 .073 .014 .034 .189 .000 .020 .022 .000 .000 .004 .391

[18] Home electronics .002 .003 .012 .014 .012 .000 .004 .002 .006 .003 .023 .022 .027 .021 .099 .033 .132 .000 .000 .002 .417

[19] Electrical machinery .002 .002 .011 .004 .007 .000 .005 .003 .006 .009 .039 .052 .025 .016 .000 .123 .028 .000 .000 .001 .334

[20] Info.&com. electronics .003 .003 .012 .009 .008 .000 .005 .003 .004 .015 .004 .018 .016 .005 .001 .034 .256 .000 .000 .000 .396

[21] Motor vehicles, parts and accessories .001 .002 .003 .002 .002 .000 .007 .002 .015 .006 .030 .012 .007 .009 .005 .031 .005 .445 .000 .000 .583

[22] Other transportation equipment .001 .003 .002 .004 .002 .000 .013 .003 .014 .006 .092 .013 .028 .036 .003 .020 .008 .030 .189 .001 .470

[23] Precision instruments and machinery .001 .002 .010 .005 .004 .000 .003 .003 .005 .018 .011 .017 .016 .011 .000 .014 .066 .000 .000 .095 .284

Source: JIP database. Data are for 1990.

37

Appendix C. Applied weights for relationally proximate Buyer R&D stocks

Spillover sources (buyer)

Focal industries (supplier) [04] [05] [06] [07] [08] [09] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] Total

[04] Food products .120 .001 .001 .000 .001 .002 .001 .000 .000 .000 .000 .000 .001 .001 .000 .000 .001 .001 .000 .000 .132

[05] Textile mill products .006 .223 .005 .001 .001 .001 .001 .001 .005 .003 .001 .001 .002 .004 .002 .003 .007 .006 .001 .001 .275

[06] Pulp and paper products .067 .011 .275 .088 .007 .022 .026 .001 .003 .017 .002 .003 .006 .010 .009 .014 .029 .011 .001 .005 .607

[07] Printing .039 .012 .018 .081 .003 .007 .011 .001 .001 .004 .002 .002 .008 .017 .014 .005 .027 .010 .003 .003 .269

[08] Chemical fertilizers and industrial chemicals .020 .030 .014 .001 .339 .034 .100 .002 .049 .012 .008 .006 .003 .003 .006 .007 .015 .005 .001 .001 .655

[09] Drugs and medicine .002 .000 .000 .000 .000 .048 .002 .000 .000 .000 .000 .000 .000 .001 .000 .000 .001 .000 .000 .000 .056

[10] Miscellaneous chemicals .009 .013 .015 .030 .013 .011 .083 .006 .003 .009 .004 .002 .016 .020 .004 .007 .018 .038 .010 .002 .313

[11] Petroleum and coal products .011 .004 .008 .001 .073 .001 .002 .050 .001 .015 .041 .003 .005 .005 .001 .003 .004 .004 .001 .001 .236

[12] Rubber products .002 .008 .002 .002 .003 .003 .001 .000 .041 .004 .008 .000 .009 .098 .013 .021 .029 .179 .023 .006 .453

[13] Ceramic, stone and clay products .023 .001 .001 .000 .004 .009 .005 .001 .000 .090 .016 .004 .006 .017 .003 .011 .043 .027 .004 .008 .273

[14] Iron and steel .000 .000 .000 .000 .000 .000 .000 .000 .001 .004 .453 .001 .139 .107 .008 .022 .005 .059 .025 .002 .827

[15] Non-ferrous metals and products .005 .000 .000 .002 .006 .001 .004 .000 .001 .004 .018 .245 .107 .069 .026 .096 .067 .078 .011 .012 .751

[16] Fabricated metal products .051 .002 .001 .000 .004 .006 .008 .002 .006 .006 .001 .001 .062 .070 .014 .020 .025 .019 .011 .005 .312

[17] General-purpose machinery .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .000 .000 .001 .189 .005 .006 .004 .012 .007 .002 .227

[18] Home electronics .001 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .001 .001 .099 .000 .002 .026 .002 .000 .134

[19] Electrical machinery .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .002 .053 .021 .123 .067 .102 .009 .005 .381

[20] Info.&com. electronics .001 .000 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .002 .023 .039 .012 .256 .007 .002 .008 .352

[21] Motor vehicles, parts and accessories .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .445 .004 .000 .449

[22] Other transportation equipment .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .189 .000 .189

[23] Precision instruments and machinery .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .029 .004 .004 .002 .004 .002 .095 .140

Source: JIP database. Data are for 1990.

38

Appendix D: Applied weights in the science field - industry concordance

Spillover sources (cited science fields)

Focal industries (citing industries) Agri

cult

ure

Bio

logy

Med

icin

e

Nurs

ing

Den

tist

ry

Chem

istr

y

Appli

ed-

Chem

istr

y

Physi

cs

Geo

logy

Engin

eeri

ng

Ele

ctro

nic

s

Ener

gy

Mat

eria

l

Sci

ence

Mat

hem

atic

s

Educa

tion

Art

-Lit

erat

ure

-

Soci

ety

Eco

nom

ics-

Busi

nes

s-

Man

agem

ent

His

tory

-

Poli

tics

-Law

Phil

oso

phy

[04] Food products 1.5 0.5 0.1 0.2 0.0 0.1 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[05] Textile mill products 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[06] Pulp and paper products 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0

[07] Printing 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[08] Chemical fertilizers and industrial chemicals 1.8 3.9 1.2 0.4 0.7 4.5 3.2 0.3 0.1 0.2 0.1 0.5 1.3 0.0 0.0 0.0 0.0 0.0 0.0

[09] Drugs and medicine 3.4 15.6 5.8 2.3 2.1 7.0 3.2 0.3 0.1 0.2 0.3 0.4 0.3 0.0 0.1 0.2 0.0 0.0 0.0

[10] Miscellaneous chemicals 0.2 0.1 0.0 0.0 0.0 0.2 0.5 0.1 0.0 0.0 0.1 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0

[11] Petroleum and coal products 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[12] Rubber products 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.0 0.0 0.1 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0

[13] Ceramic, stone and clay products 0.1 0.1 0.0 0.0 0.0 0.3 0.4 0.2 0.0 0.1 0.1 0.1 1.0 0.0 0.0 0.0 0.0 0.0 0.0

[14] Iron and steel 0.0 0.0 0.0 0.0 0.0 0.2 0.2 0.2 0.0 0.1 0.2 0.1 0.9 0.0 0.0 0.0 0.0 0.0 0.0

[15] Non-ferrous metals and products 0.0 0.0 0.0 0.0 0.0 0.2 0.2 0.2 0.0 0.1 0.2 0.1 0.9 0.0 0.0 0.0 0.0 0.0 0.0

[16] Fabricated metal products 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0

[17] General-purpose machinery 1.5 1.4 0.4 0.2 0.1 1.1 1.8 0.5 0.1 0.5 0.4 0.5 1.7 0.0 0.0 0.0 0.0 0.0 0.0

[18] Home electronics 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0

[19] Electrical machinery 0.0 0.0 0.0 0.0 0.0 0.3 0.1 0.6 0.0 0.3 1.0 0.4 0.7 0.0 0.1 0.0 0.0 0.0 0.0

[20] Info.&com. electronics 0.1 0.4 0.2 0.1 0.1 0.9 0.4 2.5 0.2 1.2 12.5 0.8 2.0 0.3 2.2 0.1 0.3 0.0 0.0

[21] Motor vehicles, parts and accessories 0.0 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.0 0.1 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[22] Other transportation equipment 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[23] Precision instruments and machinery 0.7 3.7 2.4 0.9 1.7 2.9 1.2 1.5 0.3 0.6 1.9 0.7 0.7 0.0 0.1 0.1 0.0 0.0 0.0

[24] Miscellaneous manufacturing 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

[25] Electricity and gas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Source: Calculations based on Van Looy et al. (2004) and Schmoch et al. (2004)

wp-1

Center for Economic Institutions Working Paper Series

2000-1 Jean Tirole, “Corporate Governance” , January 2000.

2000-2 Kenneth A. Kim and S. Ghon Rhee, “A Note on Shareholder Oversight and the Regulatory Environment: The Japanese Banking Experience”, January 2000.

2000-3 S. Ghon Rhee, “Further Reforms after the “BIG BANG”: The JapaneseGovernment Bond

Market”, June 2000.

2000-4 Stijn Claessens, Simeon Djankov , Joseph Fan , and Larry Lang, “Expropriation of Minority

Shareholders in East Asia”, July 2000.

2000-5 Stijn Claessens, Simeon Djankov, Joseph Fan , and Larry Lang, “The Costs of Group

Affiliation: Evidence from East Asia”, July 2000.

2001-1 Masaharu Hanazaki and Akie Takeuchi, “An International Comparison of Corporate

Investment Behavior -Some Implications for the Governance Structure in Japan-”, February

2001.

2001-2 Katsuyuki Kubo, “The Determinants of Executive Compensation in Japan and the UK:

Agency Hypothesis or Joint Determination Hypothesis?”, February 2001.

2001-3 Katsuyuki Kubo, “Changes in Directors’ Incentive Plans and the Performance of Firms in

the UK”, March 2001.

2001-4 Yupana Wiwattanakantang, “Controlling Shareholders and Corporate Value: Evidence from

Thailand”, March 2001.

2001-5 Katsuyuki Kubo, “The Effect of Managerial Ownership on Firm Performance: Case in

Japan”, March 2001.

2001-6 Didier Guillot and James R. Lincoln, “The Permeability of Network Boundaries: Strategic

Alliances in the Japanese Electronics Industry in the 1990s”, March 2001.

2001-7 Naohito Abe, “Ageing and its Macroeconomic Implications-A Case in Japan-”, May 2001.

2001-8 Yupana Wiwattanakantang, “The Equity Ownership Structure of Thai Firms”, July 2001.

2001-9 Megumi Suto, “Capital Structure and Investment Behaviour of Malaysian Firms in the

1990s--A study of Corporate Governance before the Crisis--”, August 2001.

2001-10 Naohito Abe, Noel Gaston, and Katsuyuki Kubo, “Executive Pay in Japan : The Role of

Bank-Appointed Monitors and the Main Bank Relationship”, September 2001.

2001-11 Colin Mayer, “The Financing and Governance of New Technologies”, September 2001.

2001-12 Masaharu Hanazaki and Akiyoshi Horiuchi, “Can the Financial Restraint Hypothesis

Explain Japan’s Postwar Experience?”, September 2001.

2001-13 Shin-ichi Fukuda, “The Role of Long-term Loans for Economic Development: Empirical

Evidence in Japan, Korea, and Taiwan”, September 2001.

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2001-14 S. Ghon Rhee, “Further Reforms of the JGB Market for the Promotion of Regional Bond

Markets”, September 2001.

2001-15 Stijn Claessens, Simeon Djankov, Joseph P. H. Fan, and Larry H. P. Lang, ”The Benefits

and Costs of Internal Markets: Evidence from Asia’s Financial Crisis”, September 2001.

2001-16 Kenneth A. Kim and John R. Nofsinger, “Institutional Herding, Business Groups, and

Economic Regimes: Evidence from Japan”, September 2001.

2001-17 Mitsuhiro Fukao, “Financial Deregulations, Weakness of Market Discipline, and Market

Development: Japan’s Experience and Lessons for Developing Countries”, September 2001.

2001-18 Akio Kuroda and Koichi Hamada, “Towards an Incentive Compatible Financial System:

Accounting and Managing the Non-Performing Loans”, September 2001.

2001-19 Randall Morck and Bernard Yeung, “Japanese Economic Success and the Curious

Characteristics of Japanese Stock Prices”, September 2001.

2001-20 Miguel A. García-Cestona, “Ownership Structure, Banks and the Role of Stakeholders: The

Spanish Case”, September 2001.

2001-21 Joseph P. H. Fan and T. J. Wong, “Corporate Ownership Structure and the Informativeness

of Accounting Earnings in East Asia”, September 2001.

2001-22 Heather Montgomery, “The Effect of the Basel Accord on Bank Lending in Japan”,

September 2001.

2001-23 Naoyuki Yoshino, Sahoko Kaji, and Ayako Suzuki, “The Basket-peg, Dollar-peg and

Floating---A Comparative Analysis of Exchange Rate Regimes”, September 2001.

2001-24 Colin Mayer, Koen Schoors, and Yishay Yafeh, “Sources of Funds and Investment

Strategies of Venture Capital Funds: Evidence from Germany, Israel, Japan and the UK”,

September 2001.

2001-25 Yukinobu Kitamura, Megumi Suto, and Juro Teranishi, “Towards a New Architecture for

the Japanese Financial System: Participation Costs, Intermediated Ownership and Wealth

Distribution”, September 2001.

2002-1 Evgeni Peev, “The Political Economy of Corporate Governance Change in Bulgaria:

Washington Consensus, Primitive Accumulation of Capital, and Catching-Up in the 1990” ,

March 2002.

2002-2 Naohito Abe, “Saving, Capital Flows, and the Symmetric International Spillover of

Industrial Policies” , June 2002.

2002-3 Masaharu Hanazaki and Akiyoshi Horiuchi, “A Review of Japan’s Bank Crisis from the

Governance Perspective” , July 2002.

2002-4 Chutathong Charumirind, Raja Kali and Yupana Wiwattanakantang, “Crony Lending:

Thailand before the Financial Crisis” , September 2002.

2002-5 Maitreesh Ghatak and Raja Kali, “Financially Interlinked Business Groups” , September

2002.

2002-6 Tarun Khanna, Joe Kogan, and Krishna Palepu, “Globalization and Similarities in Corporate

Governance: A Cross-Country Analysis” , September 2002.

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2002-7 Chongwoo Choe, “Delegated Contracting and Corporate Hierarchies” , September 2002.

2002-8 Tarun Khanna and Yishay Yafeh, “Business Groups and Risk Sharing around the World”,

September 2002.

2002-9 Yitae Kim, Kwangwoo Park, Ronald A. Ratti, and Hyun-Han Shin, “Do Main Banks Extract

Rents from their Client Firms? Evidence from Korean Chaebol” , September 2002.

2002-10 Armen Hovakimian, Edward J. Kane and Luc Laeven, “How Country and Safety-Net

Characteristics Affect Bank Risk-Shifting” , September 2002.

2002-11 Vidhan K. Goyal and Takeshi Yamada, “Asset Price Shocks, Financial Constraint, and

Investment: Evidence from Japan” , September 2002.

2002-12 Clive S. Lennox, “Opinion Shopping and Audit Committees” , September 2002.

2002-13 Seki Obata, “Pyramid Business Groups in East Asia: Insurance or Tunneling?” , September

2002.

2002-14 Ishtiaq Pasha Mahmood and Will Mitchell, “Two Faces: Effects of Business Groups on

Innovation in Emerging Economies” , September 2002.

2002-15 Kwangwoo Park, “Foreign Ownership and Firm Value in Japan” , September 2002.

2002-16 Adrian van Rixtel, Yupana Wiwattanakantang, Toshiyuki Souma, and Kazunori Suzuki,

“ Banking in Japan: Will “To Big To Fail” Prevail?”, December 2002.

2002-17 Stijn Claessens and Leora F. Klapper, “Bankruptcy around the World: Explanations of its

Relative Use” , December 2002.

2003-1 Anya Khanthavit, Piruna Polsiri, and Yupana Wiwattanakantang, “Did Families Lose or

Gain Control after the East Asian Financial Crisis?”, February 2003.

2003-2 Hidenobu Okuda, Hidetoshi Hashimoto, and Michiko Murakami, “The Estimation of

Stochastic Cost Functions of Malaysian Commercial Banks and Its Policy Implications to

Bank Restructuring”, February 2003.

2003-3 Masaharu Hanazaki and Liuqun, “Asian Crisis and Corporate Governance, (in Japanese)”,

March 2003.

2003-4 Fukuju Yamazaki and Hiroyuki Seshita, “Economic Analysis of Bankruptcy law in Japan,

(in Japanese) ”, February 2003.

2003-5 Hirofumi Uchida and Hiroshi Osano, “Bank Monitoring and Corporate Governance in Japan,

(in Japanese)”, March 2003.

2003-6 Fukunari Kimura and Kozo Kiyota, “Foreign Ownership and Corporate Performance:

Evidence from Japanese Micro Data, (in Japanese) ”, March 2003.

2003-7 Yukinobu Kitamura, “Corporate Profit and Debt- Panel Data Analysis of The Japanese

Firms in the 1990s, (in Japanese) ”, March 2003.

2003-8 Chaiyasit Aunchitworawong, Toshiyuki Soma, and Yupana Wiwattanakantang, “Do

Families Control Banks Prevail after the East Asia Financial Crisis? Evidence from

Thailand”, March 2003.

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2003-9 Junko Maru, Yasuhiro Yonezawa and Yuki Matsumoto, “Corporate Governance by Foreign

Investors in East Asia Corporations (in Japanese) ”, March 2003.

2003-10 Sui Qing-yuan, “Declining Firm's Dependence upon Bank Borrowing and Corporate

Performance (in Japanese) ”, March 2003.

2003-11 Katsumi Matsuura, “Changes in Ownership Structures and Their Impacts upon Corporate

Performance in Japan (in Japanese) ”, March 2003.

2003-12 Kathy S. He, Randall Morck and Bernard Yeung, “Corporate Stability and Economic

Growth”, May 2003.

2003-13 Robert Dekle and Heajin Ryoo, “Exchange Rate Fluctuations, Financing Constraints,

Hedging, and Exports: Evidence from Firm Level Data”, June 2003.

2003-14 Tsun-Siou Lee, Yin-Hua Yeh and Rong-Tze Liu, ”Can Corporate Governance Variables

Enhance the Prediction Power of Accounting-Based Financial Distress Prediction Models?”,

June 2003.

2003-15 Hideaki Miyajima and Yishay Yafeh, “Japan’s Banking Crisis: Who has the Most to Lose?”,

June 2003.

2003-16 Guifen Pei, “Asset Management Companies in China”, June 2003.

2003-17 Takeshi Nagase, “The Governance Structure of IPO Firm in Japan”, July 2003.

2003-18 Masaharu Hanazaki and Qun Liu, “The Asian Crisis and Corporate Governance ―

Ownership Structure, Debt Financing, and Corporate Diversification ―”, July 2003.

2003-19 Chutatong Charumilind, Raja Kali and Yupana Wiwattanakantang, “Connected Lending:

Thailand before the Financial Crisis”, July 2003.

2003-20 Gilles Hilary and Tomoki Oshika, “Shareholder activism in Japan: social pressure, private

cost and organized crime”, August 2003.

2003-21 Sanghoon Ahn, “Technology Upgrading with Learning Cost”, September 2003.

2003-22 Masaharu Hanazaki and Akiyoshi Horiuchi, “Have Banks Contributed to Efficient

Management in Japan’s Manufacturing?”, November 2003.

2003-23 Chongwoo Choe and In-Uck Park, “Delegated Contracting and Corporate Hierarchies”,

November 2003.

2003-24 Bruno Dallago, ”Comparative Economic Systems and the New Comparative Economics:

Foes, Competitors, or Complementary?”, November 2003.

2003-25 Adrian van Rixtel, Ioana Alexopoulou and Kimie Harada, “The New Basel Capital Accord

and Its Impact on Japanese Banking: A Qualitative Analysis”, November 2003.

2004-1 Masaharu Hanazaki, Toshiyuki Souma and Yupana Wiwattanakantang, “Silent Large

Shareholders and Entrenched Bank Management: Evidence from Banking Crisis in Japan” ,

January 2004.

2004-2 Ming Ming Chiu and Sung Wook Joh, “Bank Loans to Distressed Firms: Cronyism, bank

governance and economic crisis” , January 2004.

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2004-3 Keun Lee, Keunkwan Ryu and Jungmo Yoon, “Corporate Governance and Long Term

Performance of the Business Groups: The Case of Chaebols in Korea” , January 2004.

2004-4 Randall Morck and Masao Nakamura, “Been There, Done That –The History of Corporate

Ownership in Japan” , March 2004.

2004-5 Dong-Hua Chen, Joseph P. H. Fan and T. J. Wong, ”Politically-connected CEOs, Corporate

Governance and Post-IPO Performance of China’s Partially Privatized Firms” , March 2004.

2004-6 Jae-Seung Baek, Jun-Koo Kang and Inmoo Lee, “Business Groups and Tunneling: Evidence

from Private Securities Offerings by Korean Chaebols” , March 2004.

2004-7 E. Han Kim, “To Steal or Not to Steal: Firm Attributes, Legal Environment, and Valuation” ,

March 2004.

2004-8 Yin-Hua Yeh and Tracie Woidtke, “Commitment or Entrenchment?: Controlling

Shareholders and Board Composition” , June 2004.

2004-9 Hugh Patrick, “Thoughts on Evolving Corporate Governance in Japan” , June 2004.

2004-10 Utpal Bhattacharya and Hazem Daouk, “When No Law is Better than a Good Law”, June

2004.

2004-11 Sanghoon Ahn, Utpal Bhattacharya, Taehun Jung and Giseok Nam, “Do Japanese CEOs

Matter?”, June 2004.

2004-12 Megumi Suto and Masashi Toshino, “Behavioural Biases of Japanese Institutional

Investors; Fund management and Corporate Governance”, July 2004.

2004-13 Piruna Polsiri and Yupana Wiwattanakantang, “Business Groups in Thailand: Before and

after the East Asian Financial Crisis”, August 2004.

2004-14 Fumiharu Mieno, “Fund Mobilization and Investment Behavior in Thai Manufacturing

Firms in the Early 1990s”, August 2004.

2004-15 Chaiyasit Anuchitworawong, “Deposit Insurance, Corporate Governance and Discretionary

Behavior: Evidence from Thai Financial Institutions”, September 2004.

2004-16 Chaiyasit Anuchitworawong, “Financial fragility under implicit insurance scheme: Evidence

from the collapse of Thai financial institutions”, September 2004.

2004-17 Chaiyasit Anuchitworawong, “Ownership-based Incentives, Internal Corporate Risk and

Firm Performance”, September 2004.

2004-18 Jack Ochs and In-Uck Park, “Overcoming the Coordination Problem: Dynamic Formation

of Networks”, September 2004.

2004-19 Hidenobu Okuda and Suvadee Rungsomboon, “Comparative Cost Study of Foreign and

Thai Domestic Banks 1990–2002: Estimating Cost Functions of the Thai Banking

Industry” , February 2005.

2004-20 Hidenobu Okuda and Suvadee Rungsomboon, ”The Effects of Foreign Bank Entry on the

Thai Banking Market: Empirical Analysis from 1990 to 2002 “, March 2005.

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2004-21 Juro Teranishi, “Investor Right in Historical Perspective: Globalization and the Future of the

Japanese Firm and Financial System” , March 2005.

2004-22 Kentaro Iwatsubo, “Which Accounts for Real Exchange Rate Fluctuations, Deviations from

the Law of One Price or Relative Price of Nontraded Goods?”, March 2005.

2004-23 Kentaro Iwatsubo and Tomoyuki Ohta, ”Causes and effects of exchange rate regimes (in

Japanese)” , March 2005.

2004-24 Kentaro Iwatsubo, “Bank Capital Shocks and Portfolio Risk: Evidence from Japan”, March

2005.

2004-25 Kentaro Iwatsubo, “On the Bank-led Rescues Financially Distressed Firms in Japan” ,

March 2005.

2005-1 Yishay P. Yafeh and Tarun Khanna, “Business Groups in Emerging Markets: Paragons or

Parasities?” , September 2005.

2005-2 Renee B. Adams and Daniel Ferreira, “Do Directors Perform for Pay?," September 2005.

2005-3 Qun Liu, Shin-ichi Fukuda and Juro Teranishi, “What are Characteristics of Financial

Systems in East Asia as a Region?”, September 2005.

2005-4 Juro Teranishi, “Is the Financial System of Postwar Japan Bank-dominated or Market

Based?” , September 2005.

2005-5 Hasung Jang, Hyung-cheol Kang and Kyung Suh Park, “Determinants of Family

Ownership: The Choice between Control and Performance” , October 2005.

2005-6 Hasung Jang, Hyung-cheol Kang and Kyung Suh Park, “The Choice of Group Structure:

Divide and Rule” , October 2005.

2005-7 Sangwoo Lee, Kwangwoo Park and Hyun-Han Shin, “The Very Dark Side of International

Capital Markets: Evidence from Diversified Business Groups in Korea” , October 2005.

2005-8 Allen N. Berger, Richard J. Rosen and Gregory F. Udell, “Does Market Size Structure

Affect Competition? The Case of Small Business Lending” , November 2005.

2005-9 Aditya Kaul and Stephen Sapp, “Trading Activity and Foreign Exchange Market Quality” ,

November 2005.

2005-10 Xin Chang, Sudipto Dasgupta and Gilles Hilary, “The Effect of Auditor Choice on

Financing Decisions” , December 2005.

2005-11 Kentaro Iwatsubo, “Adjustment Speeds of Nominal Exchange Rates and Prices toward

Purchasing Power Parity” , January 2006.

2005-12 Giovanni Barone-Adesi, Robert Engle and Loriano Mancini, “GARCH Options in

Incomplete Markets”, March 2006.

2005-13 Aditya Kaul, Vikas Mehrotra and Blake Phillips, “Ownership, Foreign Listings, and Market

Valuation”, March 2006.

2005-14 Ricard Gil, “Renegotiation, Learning and Relational Contracting”, March 2006.

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2005-15 Randall Morck, “How to Eliminate Pyramidal Business Groups -The Double Taxation of

Inter-corporate Dividends and other Incisive Uses of Tax Policy-”, March 2006.

2005-16 Joseph P.H. Fan, T.J. Wong and Tianyu Zhang, “The Emergence of Corporate Pyramids in

China”, March 2006.

2005-17 Yan Du, Qianqiu Liu and S. Ghon Rhee, “An Anatomy of the Magnet Effect: Evidence

from the Korea Stock Exchange High-Frequency Data”, March 2006.

2005-18 Kentaro Iwatsubo and Junko Shimizu, “Signaling Effects of Foreign Exchange Interventions

and Expectation Heterogeneity among Traders”, March 2006.

2005-19 Kentaro Iwatsubo, “Current Account Adjustment and Exchange Rate Pass-Through(in

Japanese)”, March 2006.

2005-20 Piruna Polsiri and Yupana Wiwattanakantang, “Corporate Governance of Banks in

Thailand”, March 2006.

2006-1 Hiroyuki Okamuro and Jian Xiong Zhang, “Ownership Structure and R&D Investment of

Japanese Start-up Firms,” June 2006.

2006-2 Hiroyuki Okamuro, “Determinants of R&D Activities by Start-up Firms: Evidence from

Japan,” June 2006.

2006-3 Joseph P.H. Fan, T.J. Wong and Tianyu Zhang, “The Emergence of Corporate Pyramids in

China,” August 2006.

2006-4 Pramuan Bunkanwanicha, Jyoti Gupta and Yupana Wiwattanakantang, “Pyramiding of

Family-owned Banks in Emerging Markets,” September 2006.

2006-5 Bernardo Bortolotti and Mara Faccio, “Reluctant privatization,” September 2006.

2006-6 Jörn Kleinert and Farid Toubal, “Distance costs and Multinationals’ foreign activities”,

October 2006.

2006-7 Jörn Kleinert and Farid Toubal, “Dissecting FDI”, October 2006.

2006-8 Shin-ichi Fukuda and Satoshi Koibuchi, “The Impacts of “Shock Therapy” on Large and

Small Clients: Experiences from Two Large Bank Failures in Japan”, October 2006.

2006-9 Shin-ichi Fukuda, Munehisa Kasuya and Kentaro Akashi, “The Role of Trade Credit for

Small Firms: An Implication from Japan’s Banking Crisis”, October 2006.

2006-10 Pramuan Bunkanwanicha and Yupana Wiwattanakantang, “Big Business Owners and

Politics: Investigating the Economic Incentives of Holding Top Office”, October 2006.

2006-11 Sang Whi Lee, Seung-Woog(Austin) Kwang, Donald J. Mullineaux and Kwangwoo Park,

“Agency Conflicts, Financial Distress, and Syndicate Structure: Evidence from Japanese

Borrowers”, October 2006.

2006-12 Masaharu Hanazaki and Qun Liu, “Corporate Governance and Investment in East Asian

Firms -Empirical Analysis of Family-Controlled Firms”, October 2006.

2006-13 Kentaro Iwatsubo and Konomi Tonogi, “Foreign Ownership and Firm Value: Identification

through Heteroskedasticity (in Japanese)”, December 2006.

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2006-14 Kentaro Iwatsubo and Kazuyuki Inagaki, “Measuring Financial Market Contagion Using

Dually-Traded Stocks of Asian Firms”, December 2006.

2006-15 Hun-Chang Lee, “When and how did Japan catch up with Korea? –A comparative study of

the pre-industrial economies of Korea and Japan”, February 2007.

2006-16 Kyoji Fukao, Keiko Ito, Shigesaburo Kabe, Deqiang Liu and Fumihide Takeuchi, “Are

Japanese Firms Failing to Catch up in Localization? An Empirical Analysis Based on

Affiliate-level Data of Japanese Firms and a Case Study of the Automobile Industry in

China”, February 2007.

2006-17 Kyoji Fukao, Young Gak Kim and Hyeog Ug Kwon, “Plant Turnover and TFP Dynamics in

Japanese Manufacturing”, February 2007.

2006-18 Kyoji Fukao, Keiko Ito, Hyeg Ug Kwon and Miho Takizawa, “Cross-Border Acquisitons

and Target Firms' Performance: Evidence from Japanese Firm-Level Data”, February 2007.

2006-19 Jordan Siegel and Felix Oberholzer-Gee, “Expropriators or Turnaround Artists? The Role of

Controlling Families in South Korea (1985-2003)”, March 2007.

2006-20 Francis Kramarz and David Thesmar, “Social Networks in The Boardroom”, March 2007.

2006-21 Morten Bennedsen, Francisco Pérez-González and Daniel Wolfenzon, “Do CEOs matter?”,

March 2007.

2007-1 Ichiro Iwasaki, “Endogenous board formation and its determinants in a transition economy:

evidence from Russia*”, April 2007, Revised on October 2007.

2007-2 Joji Tokui, Tomohiko Inui, and Katsuaki Ochiai, “The Impact of Vintage Capital and R&D

on Japanese Firms’ Productivity”, April 2007.

2007-3 Yasuo Nakanishi and Tomohiko Inui, “Deregulation and Productivity in Japanese

Industries”, April 2007.

2007-4 Kyoji Fukao, “The Performance of Foreign Firms and the Macroeconomic Impact of FDI”,

May 2007.

2007-5 Taku Suzuki, “The Role of the State in Economic Growth of Post-Communist Transitional

Countries”, June 2007.

2007-6 Michiel van Leuvensteijn, Jacob A. Bikker, Adrian A.R.J.M. van Rixtel and Christoffer

Kok-Sørensen*, “A new approach to measuring competition in the loan markets of the euro

area”, June 2007.

2007-7 Sea Jin Chang, Jaiho Chung, and Dean Xu, “FDI and Technology Spillovers in China”, July

2007.

2007-8 Fukunari Kimura, “The mechanics of production networks in Southeast Asia: the

fragmentation theory approach”, July 2007.

2007-9 Kyoji Fukao, Tsutomu Miyagawa, Miho Takizawa, “Productivity Growth and Resource

Reallocation in Japan”, November 2007.

2007-10 YoungGak Kim, “A Survey on Intangible Capital”, December 2007.

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2007-11 Sea-Jing Chang and Jay Hyuk Rhee, “Rapid International Expansion Strategy of Emerging

Market Enterprises: The Interplay between Speed and Competitive Risks on International

performance”, November 2007.

2007-12 Ishtiaq Mahmood, Will Mitchell, and Chi-Nien Chung, “The Structure of Intra-Group Ties:

Innovation in Taiwanese Business”, January 2008.

2007-13 Kyoji Fukao, Tomohiko Inui, Shigesaburo Kabe and Deqiang Liu, “ An International

Comparison of the TFP Levels of Japanese, Korean and Chinese Listed Firms“, March

2008.

2007-14 Pramuan Bunkanwanicha and Yupana Wiwattanakantang, “Allocating Risk Across

Pyramidal Tiers: Evidence from Thai Business Groups”, March 2008.

2008-1 Rüdiger Fahlenbrach and René M. Stulz, "Managerial Ownership Dynamics and Firm

Value", April 2008.

2008-2 Morten Bennedsen, Kasper Meisner Nielsen, and, Thomas Vester Nielsen, “Private

Contracting and Corporate Governance: Evidence from the Provision of Tag-Along Rights

in an Emerging Market”, April 2008.

2008-3 Joseph P.H. Fan, Jun Huang, Felix Oberholzer-Gee, and Mengxin Zhao, “Corporate

Diversification in China: Causes and Consequences”, April 2008.

2008-4 Daniel Ferreira, Miguel A. Ferreira, Clara C. Raposo, “Board Structure and Price

Informativeness”, April 2008.

2008-5 Nicola Gennaioli and Stefano Rossi, “Judicial Discretion in Corporate Bankruptcy”, April

2008.

2008-6 Nicola Gennaioli and Stefano Rossi, “Optimal Resolutions of Financial Distress by

Contract”, April 2008.

2008-7 Renée B. Adams and Daniel Ferreira, “Women in the Boardroom and Their Impact on

Governance and Performance”, April 2008.

2008-8 Worawat Margsiri, Antonio S. Melloy, and Martin E. Ruckesz, “A Dynamic Analysis of

Growth via Acquisition”, April 2008.

2008-9 Pantisa Pavabutra and Sukanya Prangwattananon, “Tick Size Change on the Stock

Exchange of Thailand”, April 2008.

2008-10 Maria Boutchkova, Hitesh Doshi, Art Durnev, and Alexander Molchanov, “Politics and

Volatility”, April 2008.

2008-11 Yan-Leung Cheung, P. Raghavendra Rau, and Aris Stouraitis, “The Helping Hand, the Lazy

Hand, or the Grabbing Hand? Central vs. Local Government Shareholders in Publicly Listed

Firms in China”, April 2008.

2008-12 Art Durnev and Larry Fauver, “Stealing from Thieves: Firm Governance and Performance

when States are Predatory”, April 2008.

2008-13 Kenneth Lehn, Sukesh Patro, and Mengxin Zhao, “Determinants of the Size and Structure of

Corporate Boards: 1935-2000”, April 2008.

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2008-14 Ishtiaq P. Mahmood, Hong-Jin Zhu and Edward J. Zajac, “Where Can Capabilities Come

From? How the Content of Network Ties Affects Capability Acquisition”, April 2008.

2008-15 Vladimir I. Ivanov and Ronald W. Masulis, “Corporate Venture Capital, Strategic Alliances,

and the Governance of Newly Public Firms”, May 2008.

2008-16 Dick Beason, Ken Gordon, Vikas Mehrotra and Akiko Watanabe, “Does Restructuring Pay

in Japan? Evidence Following the Lost Decade”, July 2008 (revision uploaded on Oct.

2009).

2009-1 Vikas Mehrotra, Dimitri van Schaik, Jaap Spronk, and Onno Steenbeek, “Creditor-Focused

Corporate Governance: Evidence from Mergers and Acquisitions in Japan,” August, 2009.

2009-2 Debin Ma, “Law and Economic Change in Traditional China: A Comparative Perspective,”

September, 2009.

2009-3 Robert C. Allen, Jean-Pascal Bassino, Debin Ma, Christine Moll-Murata, and Jan Luiten van

Zanden, “Wages, Prices, and Living Standards in China, 1738-1925: in Comparison with

Europe, Japan, and India,” June 2009.

2009-4 Jung-Wook Shim, “The Existence of Nepotism: Evidence from Japanese Family Firms,”

October 2009.

2009-5 Morten Bennedsen and Kasper Meisner Nielsen, “Incentive and Entrenchment Effects in

European Ownership,” March 2009.

2009-6 Joseph P.H. Fan, TJ Wong, Tianyu Zhang, “Founder Succession and Accounting Properties,”

April 2009.

2009-7 Hiroyuki Okamuro, Masatoshi Kato, and Yuji Honjo, “Determinants of R&D Cooperation in

Japanese High-tech Start-ups,” November 2009.

2009-8 Bill Francis, Iftekhar Hasan, Michael Koetter, and Qiang Wu, “The Effectiveness of

Corporate Boards: Evidence from Bank Loan Contracting,” November 2009.

2009-9 Allen N. Berger, Iftekhar Hasan and Mingming Zhou, “The Effects of Focus Versus

Diversification on Bank Performance: Evidence from Chinese Banks,” November 2009.

2009-10 Leonardo Becchetti, Andrea Carpentieri and Iftekhar Hasan, ”The Determinants of Option

Adjusted Delta Credit Spreads: A Comparative Analysis on US, UK and the Eurozone,”

November 2009.

2009-11 Luciano I. de Castro and Harry J. Paarsch, “Testing Affiliation in Private-values Models of

First-price Auctions Using Grid Distributions,” December 2009.

2009-12 Chulwoo Baek, YoungGak Kim and Heog Ug Kwon, “Market Competition and Productivity

after the Asian Financial Crisis: Evidence from Korean Firm Level Data,” December 2009.

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2009-13 Jee-Hyeong Park, Stephen J. Spurr, and Sheng-Kai Chang, “A Model of Hierarchical

Professionals: Cooperation and Conflict between Anesthesiologists and CRNAs,” October

2009.

2009-14 Jee-Hyeong Park, “Enforcing International Trade Agreements with Imperfect Private

Monitoring: Private Trigger Strategies and the Possible Role of the WTO,” December 2009.

2009-15 Yuji Honjo, Masatoshi Kato and Hiroyuki Okamuro, “R&D financing of start-up firms:

How much does founders’ human capital matter?”, March 2010.

2010-1 Sergei V. Ryazantsev, “Migrant Workers from Central Asian Russian Federation”, June

2010.

2010-2 Tue Gørgens, Xin Meng, and Rhema Vaithianathan, “Stunting and Selection Effects of

Famine: A Case Study of the Great Chinese Famine,” October 2010.

2010-3 Masatoshi Kato and Yuji Honjo, “Heterogeneous Exits: Evidence from New Firms,”

November 2010.

2010-4 Sung-Jin Cho, Harry J. Paarsch, and John Rust, “Is the ’Linkage Principle’ Valid?:

Evidence from the Field,” November 2010.

2010-5 Jean-Pascal Bassino and Noriko Kato, “Rich and slim, but relatively short Explaining the

halt in the secular trend in Japan,” November 2010.

2010-6 Robert G Gregory, Dark Corners in a Bright Economy; The Lack of Jobs for Unskilled

Men,” December 2010.

2010-7 Masatoshi Kato and Hiroyuki Odagiri, “Development of University Life-Science Programs

and University-Industry Joint Research in Japan,” December 2010.

2010-8 Han Hong, Harry J. Paarsch and Pai Xu, “On the Asymptotic Distribution of the Transaction

Price in a Clock Model of a Multi-Unit, Oral, Ascending-Price Auction within the

Common-Value Paradigm,” January 2011.

2010-9 Tue Gørgens and Allan W¨urtz, “Testing a Parametric Function Against a Nonparametric

Alternative in IV and GMM Settings,” January 2011.

2010-10 Timothy P. Hubbard, Tong Li and Harry J. Paarsch, “Semiparametric Estimation in Models

of First-Price, Sealed-Bid Auctions with Affiliation,” January 2011.

2010-11 Yutaka Arimoto, Kentaro Nakajima, and Tetsuji Okazaki, “Agglomeration or Selection? The

Case of the Japanese Silk-Reeling Clusters, 1908–1915,” March 2011.

2010-12 Yukiko Abe, “Regional Variations in Labor Force Behavior of Women in Japan,” March

2011.

2010-13 Takashi Kurosaki and Hidayat Ullah Khan, “Vulnerability of Microfinance to Strategic

Default and Covariate Shocks: Evidence from Pakistan” , March 2011.

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2010-14 Fumiharu Mieno, “Foreign Ownership, Listed Status and the Financial System in East Asia:

Evidence from Thailand and Malaysia”, March 2011.

2010-15 Hidenobu Okuda and Lai Thi Phuong Nhung, "Fundraising Behaviors of Listed Companies

in Vietnam: An Estimation of the Influence of Government Ownership", March 2011.

2011-1 Hiroyuki Okamuro and Junichi Nishimura, “Impact of University Intellectual Property

Policy on the Performance of University-Industry Research Collaboration”, May 2011.

2011-2 Yutaka Arimoto, “Participatory Rural Development in 1930s Japan: The Economic

Rehabilitation Movement”, July 2011.

2011-3 Yutaka Arimoto, “The Impact of Farmland Readjustment and Consolidation on Structural

Adjustment: The Case of Niigata, Japan”, July 2011.

2011-4 Hidayat Ullah Khan, Takashi Kurosaki, and Ken Miura, “The Effectiveness of

Community-Based Development in Poverty Reduction: A Descriptive Analysis of a

Women-Managed NGO in Rural Pakistan”, September 2011.

2011-5 Jane Harrigan, “Food Security in the Middle East and North Africa (MENA) and

sub-Saharan Africa: A Comparative Analysis”, September 2011.

2011-6 Machiko Nissanke, “International and Institutional Traps in Sub-Saharan Africa under

Globalisation: A Comparative Perspective”, September 2011.

2011-7 Hiroyuki Okamuro and Junichi Nishimura, “Management of Cluster Policies: Case Studies

of Japanese, German, and French Bio-clusters”, October 2011.

2011-8 Anne Booth, “Growing Public? Explaining the Changing Economic Role of the State in

Asia over the 20th Century”, December 2011.

2011-9 Jarko FidrmucI, Iikka KorhonenII, and Ivana BátorováIII, “China in the World Economy:

Dynamic Correlation Analysis of Business Cycles”, December 2011.

2011-10 Yutaka Arimoto, Kentaro Nakajima, and Tetsuji Okazaki, “Productivity Improvement in the

Specialized Industrial Clusters: The Case of the Japanese Silk-Reeling Industry”, December

2011.

2011-11 Masatoshi Kato, Hiroyuki Okamuro, and Yuji Honjo, “Does Founders’ Human Capital

Matter for Innovation? Evidence from Japanese Start-ups”, December 2011.

2011-12 Yoshihisa Godo, “A New Database on Education Stock in Taiwan”, February 2012.

2011-13 Yutaka Arimoto, Narumi Hori, Seiro Ito, Yuya Kudo, and Kazunari Tsukada, “Impacts of an

HIV Counselling and Testing Initiative: Results from an Experimental Intervention in South

Africa”, March 2012.

2011-14 Fumiharu Mieno and Hisako Kai, “Do Subsidies Enhance or Erode the Cost Efficiency of

Microfinance? Evidence from MFI Worldwide Micro Data”, April 2012.

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2012-1 Youngho Kang and Byung-Yeon Kim, “Immigration and Economic Growth: Do Origin and

Destination Matter?”, July 2012.

2012-2 Hee-Dong Yang, Christoph Karon, Sora Kang, “To Convert or not to Convert to the

Upgraded Version of de-facto Standard Software?”, August 2012.

2012-3 Yutaka Arimoto, Takeshi Fujie, and Tetsuji Senda, “Farmers' Debt in 1930's Japan”, October

2012.

2012-4 Kyoji Fukao and Tangjun Yuan, “China’s Economic Growth, Structural Change and the

Lewisian Turning Point”, November 2012.

2012-5 Jonathan Morduch, Shamika Ravi, and Jonathan Bauchet, “Failure vs. Displacement: Why

an Innovative Anti-Poverty Program Showed No Net Impact”, December 2012.

2012-6 Yutaka Arimoto, Seiro Ito, Yuya Kudo, and Kazunari Tsukada, “Stigma, Social Relationship

and HIV Testing in the Workplace: Evidence from South Africa”, February 2013.

2012-7 Yutaka Arimoto, Shinsaku Nakajima, and Kohji Tomita, “Farmland Consolidation by Plot

Exchange: A Simulation-based Approach”, March 2013.

2012-8 Takashi Kurosaki, “Household-level Recovery after Floods in a Developing Country:

Evidence from Pakistan”, November 2012.

2012-9 Yuko Mori and Takashi Kurosaki, “Does Political Reservation Affect Voting Behavior?

Empirical Evidence from India”, January 2013.

2012-10 Takashi Kurosaki, “Vulnerability of Household Consumption to Floods and Droughts in

Developing Countries: Evidence from Pakistan”, March 2013.

2012-11 Takashi Kurosaki and Hidayat Ullah Khan, “Household Vulnerability to Wild Animal

Attacks in Developing Countries: Experimental Evidence from Rural Pakistan”, March

2013.

2012-12 Ann M. Carlos, Erin Fletcher, and Larry Neal, “Share Portfolios and Risk Management in

the Early Years of Financial Capitalism: London 1690-1730”, September 2012.

2012-13 Katsuo Kogure, “Impacts of Institutional Changes in Cambodia under the Pol Pot Regime”,

March 2013.

2012-14 Jun-ichi Nakamura and Shin-ichi Fukuda, “What Happened to ‘Zombie’ Firms in Japan?:

Reexamination for the Lost Two Decades”, March 2013.

2012-15 Vikas Rawal, “Cost of Cultivation and Farm Business Incomes in India”, March 2013.

2013-1 Ryo Kambayashi and Takao Kato, “Good Jobs, Bad Jobs, and the Great Recession: Lessons

from Japan’s Lost Decade”, June, 2013.

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2013-2 Jonathan Morduch, Shamika Ravi, and Jonathan Bauchet, “Substitution Bias and External

Validity: Why an Innovative Anti-poverty Program Showed no Net Impact”, July 2013.

2013-3 Robert Cull, Asli Demirgüç-Kunt, and Jonathan Morduch, “Banks and Microbanks”,

February 7, 2013.

2013-4 William H. Greene, Max Gillman, Mark N. Harris, and Christopher Spencer, “The

Tempered Ordered Probit (TOP) Model with an Application to Monetary Policy”,

September 2013.

2013-5 René Belderbos, Kenta Ikeuchi, Kyoji Fukao, Young Gak Kim, and Hyeog Ug Kwon, “Plant

Productivity Dynamics and Private and Public R&D Spillovers: Technological, Geographic

and Relational Proximity”, October 2013.