Global COE Hi-Stat Discussion Paper Series 076

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Producer Services and Manufacturing Productivity:
Evidence from Japan Industrial Productivity Database

Hi-Stat Discussion Paper

Kiyoyasu Tanaka

July 2009

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

Producer Services and Manufacturing Productivity:
Evidence from Japan Industrial Productivity Database
Kiyoyasu Tanaka*
Institute of Economic Research
Hitotsubashi University
Email: tanakak@ier.hit-u.ac.jp
July 2009
Abstract
As service sectors account for a growing share of economic activity in advanced economies,
economists claim that the quality and cost of producer services supplied by the service sectors
are crucial in supporting the competitiveness of manufacturing firms. This paper provides an
empirical assessment on this argument by exploring a link between service-sector performanceand manufacturing productivity in Japan for the period 1980-2005. Assuming that an
improvement in producer services is measured with errors by a price deflator growth of service
outputs, I propose an estimation framework in which an observed productivity depends partly
on the performance of service sectors weighted by service-input intensities. Robust to a wide
range of specifications and alternative indicators of services upgrading, I find little evidence thatthe service sectors contributed to productivity growth of the manufacturing sector. Thus, myfindings do not support the claim that the upgrading of producer services improves
manufacturing competitiveness.
Keywords: Producer Services, Service Sector, Productivity, Japan,
JEL classification: D24, L60, L80*

This paper is prepared for a research workshop on service-sector productivity at the ResearchInstitute of Economy, Trade, and Industry (RIETI). I am grateful to comments and suggestions from
Kyoji Fukao and other participants of the project. This research is supported by the Global COE
program, the Research Unit for Statistical and Empirical Analysis in Social Sciences (G-COE
Hi-Stat Program) at Hitotsubashi University.

1. Introduction
Service sectors account for a dominant and rising share of economic activity in Japan,
as the economic importance of service production in the Japanese economy has increased bothabsolutely and relatively to manufacturing production in recent decades. Based on the Japan
Industrial Productivity Database 2009 (JIP), Figure 1 shows that the value-added of the broadly
defined services as a share of aggregate production in Japan increased from 50% in middle
1970s to over 70% in middle 2000s. 1 In contrast, manufacturing sectors in the Japanese
economy have steadily declined in importance, as the value-added share of manufacturing
production has decreased from 30% in middle 1970s to below 20% in middle 2000s.
[Figure 1 around here]
Since Baumol (1967) argued that the growing share of a non-progressive sector would
eventually lead to a “stagnation” of aggregate economic growth, economists have widely
examined the implications of the expansion of service sectors in the economy. As Figure 1
indicates that the average growth rate of total factor productivity (TFP) adjusted by the Domar
weight in the service sectors has not prominently increased relative to that in manufacturingsectors for the past decades, the “Baumol’s disease” appears to be a legitimate concern at least1

JIP 2009 database is available at http://www.rieti.go.jp/jp/database/JIP2009/index.html. Refer tothe Appendix for the JIP sector code.
1

for the Japanese economy.2 These observed patterns are often taken as the basis for theargument that the liberalization of service markets in Japan is critical to improve theproductivity of the service sectors in order to sustain the overall economic growth (Jones andYoon, 2008).
Despite the seemingly depressing contribution of services to the economy, service
sectors such as finance, transportation, and telecommunications, play a fundamental role in thefunction of economic activities through the provision of producer services. Financial institutions,for instance, ameliorate transactions costs due to imperfect information in an uncertain businessenvironment, and facilitate the allocation of scarce financial resources over space and time.
Transportation sectors link manufacturers with suppliers through the delivery of parts andcomponents to point of final assembly, and with consumers via the transport of final products topoint of consumption.
Table 1 shows the average service inputs as a share of total non-energy input inmanufacturing industries for the periods 1973 through 2006. While the intensity of purchased
services varies by industries, the service inputs account, on average, for over 30% of aggregate
purchased inputs; the shares were 0.37 and 0.40 in chemicals and electrical machinery sectors
during the period 2000-2006, respectively. These patterns indicate that manufacturing firms

2

The Domar weight is defined as a ratio of sectoral gross output to aggregate value-added.
2

widely engage in contracting-out of business services, or “service outsourcing”, and the service
sectors are deeply integrated in the vertical production chains of manufacturing sectors.
[Table 1 around here]
The immediate implication is that the performance of service sectors can indirectly
contribute to economic growth through forward linkages with manufacturing production; an
improvement in the quality and variety of producer services would support manufacturingsectors (Outlton, 2001). Recently, it is often argued that producer services supplied from service
sectors are critical to the competitiveness of manufacturing firms in the globalizing economies(Francois, 1990; Arnold et al. 2007; Hoekman and Mattoo, 2008). The quality upgrading ofservice inputs plays a crucial role in facilitating manufacturing operations in developing
countries (Arnold et al, 2008, Fernandes and Paunov, 2008). Despite the increasingly important
issues, formal econometric work on these claims has been limited. In particular, there is little
economic analysis on the implications of the quality upgrading of producer services with respect
to the “inter-industry” linkages.
In this paper, I provide an empirical assessment on the role of producer services inmanufacturing sectors by addressing the following questions. How is an improvement inproducer services related to the growth of manufacturing productivity? Did the performance ofservice sectors improve manufacturing efficiency? What type of producer services is key to3

improving the efficiency of manufacturing production?
This paper proposes an estimation framework based on the work of Griliches andLichtenberg (1984) in order to explore a link between service and manufacturing sectors. I startfrom the observation that an accurate measurement of the quality and availability of producer
services is extremely difficult, which implies that a growth rate of service price deflators ismeasured with errors in official statistics for constructing input-output data. The errors ofmeasurement in the price deflators of service outputs translate into measurement of service
inputs of manufacturing operations. An observed growth of manufacturing productivity contains
the measurement errors of changes in service prices arising from unobservable improvements inthe quality of producer services.
The errors in service inputs are assumed to depend on the economic performance ofservice sectors measured by a variety of indicators, which is weighted by service inputs as ashare of total non-energy inputs for each manufacturing sector in order to take into accountcross-industry dependency on service sectors. The service link is related to manufacturing
productivity growth. Thus, the proposed empirical specification serves to study the hypothesisthat measures of the quality upgrading of producer services can have a stronger influence on
manufacturing sectors that are more intensive in service inputs.
My estimation approach contrasts with prior work by Siegel and Griliches (1992) and4

ten RAA and Wolff (2001). The former paper examines only a simple correlation between
manufacturing TFP growth and the average ratio of purchased service inputs to output acrossindustries. The latter work studies the effect of service outsourcing on U.S. manufacturing
productivity by decomposing the manufacturing TFP growth into a component of service inputs
and a component of material inputs; the consolidated TFP growth rate embodies an effect of
TFP growth in upstream service sectors. The difference in the standard and consolidated TFP
growth rates is attributed to the effects of service outsourcing. In this paper, I relax theassumption that the price deflator of service outputs is measured with precision to computemanufacturing TFP growth rates, thereby allowing for regression analysis on the linkage
between manufacturing productivity and the upgrading of service quality.
In the proposed estimation framework, this paper employs a comprehensive panel
dataset on Japanese manufacturing industries. Specifically, the sample taken from the JIP 2009
covers 52 manufacturing industries and the period 1980-2005. Robust to a wide range ofalternative specifications, I find little evidence that measures of the performance of aggregate
service sectors have contributed to the growth rate of manufacturing productivity throughforward linkages. This conclusion does not change when a variety of alternative measures suchas TFP growth, labor productivity, and service worker shares are used. To the extent that thesemeasures reflect partly unadjusted quality improvements of producer services, the evidence5

suggests that aggregate producer services did not play a significant role in accelerating Japanesemanufacturing productivity in the past decades.
To address a concern that the characteristics of producer services are heterogeneous
across service sectors, I also estimate the effect of service linkages with manufacturing
productivity for a variety of specific service sectors: financial, insurance, road transportation,
telecommunications, and information services. The results show that TFP growth rates inmanufacturing sectors are not positively correlated with the service-link variable for the specificservice sectors; the quality upgrading of specific producer services as measured by a wide range
of indicators did not contribute to a change in manufacturing productivity growth. To thecontrary, economic performance in some of the service sectors discourages manufacturing TFP
growth. Possibly, the growing dependence of manufacturing sectors on specialized producer
services may generate additional transaction costs to coordinate in-house production withservice tasks produced outside the firm.
In the reminder of this paper, section 2 describes the related literature on producer
services. Section 3 explains theoretical motivation, estimation framework, and data descriptions.
Section 4 provides an estimation results. Section 5 concludes.

6

2. Related LiteratureThis section reviews two broad strands of the related literature to illustrate theimportance of producer services in manufacturing sectors; macroeconomic consequences of thegrowing service sector and the globalization of services. From a macroeconomic point of view,
there has been an argument that the expansion of service sectors characterized by low
productivity growth would eventually slow down an aggregate productivity growth in advanced
countries. Baumol (1967) is among the first to formally demonstrate that a shift of resources to astagnant industry could drive down the aggregate rate of productivity growth to the rate in thestagnant industry. However, this conclusion can be overturned if the stagnant industry supplies
only an intermediate input for other industries producing final goods and economic growth ismeasured only by the productivity of final good producers (Oulton, 2001). As long as
productivity in the producer-service sector grows at a positive rate, a rising share of
employment in producer services can sustain the productivity growth in downstream industries.
The presence of an inter-industry linkage prevents a slowdown in the overall productivity
growth. This possibility is supported by the work of Francois (1990) that provides a
microeconomic foundation for the productivity-accelerating role of producer services indownstream manufacturing production. Because producer services coordinate interdependent
tasks in complex production processes, the service sector promotes the degree of specialization

7

in manufacturing operations, thereby generating productivity gains in downstream industries.
These discussions suggest that an inter-industry linkage between service andmanufacturing sectors plays an important role in accounting for macroeconomic consequencesof the service economy. However, there has been limited empirical work on these issues. Siegel
and Griliches (1992) examine whether an increase in purchased services could explain arecovery in U.S. manufacturing productivity for the 1980s. They find little correlation between
manufacturing TFP growth rates and the intensity of service purchases. Ten RAA and Wolff(2001) explore the effect of service outsourcing on U.S. manufacturing productivity by isolating
a component of service inputs from a component of material inputs in the manufacturing TFP
growth. Because the consolidated TFP growth rate embodies an effect of TFP growth in
upstream service sectors, they argue that the difference in the standard and consolidated TFP
growth rates accounts for the effect of service outsourcing. However, the quantitative difference
between these TFP measures is fairly small, and their analytical approach does not allow for astatistical test on the effect of service outsourcing on manufacturing productivity.
In contrast to previous approaches, I propose an estimation framework to statisticallytest the linkage between manufacturing productivity and the upgrading of service quality. Under
the assumption that the price deflator growth of service outputs is imprecisely measured, thisapproach allows for isolating the effect of service outsourcing from the effects of other8determinants of manufacturing productivity. In addition, prior work focuses on the quantitative
importance of purchased services whereas my analysis sheds light on the qualitative
significance of producer services.
The strength of industry-level analysis lies in empirically assessing an aggregate
impact of the cross-sectoral link between service and manufacturing sectors, which offers an
insight on macroeconomic consequences of the rising service sector. On the other hand,firm-level analysis is likely to be limited in the sample coverage, but demonstrates a detailed
link between firm characteristics and outsourcing.3 Prior work, including Kimura (2002) forJapan, Girma and Görg (2004) for the U.K., and Görg and Hanley (2004) for Ireland, examinesa relationship between domestic outsourcing and firm performance, respectively.4 Although
their empirical strategies such as the definition of outsourcing are fairly different, a general
conclusion seems that contracting-out of service jobs such as accounting, consulting, anddistribution are not systematically associated with firm performance as measured byprofitability and productivity. Alternatively, purchased material inputs such as raw materials andcomponents from outside the firm are positively associated with the firm performance.
Another strand of the related literature explores the globalization of service activities
through trade and foreign direct investment (FDI) in services, and their impact on economic
3

See Abraham and Taylor (1996) for empirical evidence on the causes of outsourcing based on U.S.
establishment-level.
4
Refer to Olsen (2006) for a nice survey on productivity effects of outsourcing and offshoring.
9

growth. As trade and investment flows in service sectors affect the variety, quality, and cost of
producer services for final goods producer, a vertical linkage between manufacturing production
and foreign suppliers of producer services is a focal point in the channels through which theglobalization of services can promote economic growth (Hoekman and Mattoo, 2008). For
instance, a relationship between FDI inflows in service sectors and manufacturing productivity
is examined by Arnold et al. (2007) in the case of the Czech Republic for 1998-2003 andFernandes and Paunov (2008) for Chilean manufacturing plants during 1992-2004. Investigating
an effect of services liberalization on manufacturing sectors, they find a significantly positive
association between services FDI and manufacturing productivity. The finding is interpreted assuggesting that the higher productivity of multinationals over domestic firms in service sectors
improves service inputs of local manufacturing production. In contrast to these studies, thefocus of my paper is not limited to producer services supplied from foreign firms; instead, I
assess the broader impact of the producer services on manufacturing productivity in theadvanced country, Japan, for the long period.
In the context of developing countries, Arnold et al. (2008) examine the effect ofservice-input availability on firm productivity in Sub-Saharan African manufacturing sectors forthe early 2000s. They relate firm productivity to a survey measure of difficulty in access to
producer services such as electricity, financial services, and telecommunications. Exploiting a

10

regional variation in access to services, they find a positive link between service inputs and firm
efficiency. However, it is difficult to assess the regional pattern as identifying the direction of
causality; an improvement in business environments can account for productivity gains andaccess to service inputs. On the other hand, my analysis exploits panel data on 52 manufacturingindustries over two decades to control for a wide range of unobserved industry- andtime-specific effects.

3. Empirical Methodology
3.1. Theoretical Motivation
This paper explores the hypothesis that the quality upgrading of producer services
contributes to productivity gains in manufacturing sectors. There are a number of alternative
channels through which improvements in the quality, variety, and cost of service products could
affect production efficiency of manufacturing firms that use the services. This paper focuses on
the general role of specialized service providers in the manufacturing production process. In
particular, the formal model by Francois (1990) offers the theoretical underpinning for a
relationship between producer services and production in user industries.
A one-sector model in the monopolistic competition framework is developed tocharacterize increasing returns to scale at the firm level. Production technology exhibits the11degree of specialization in labor, which serves as a single input of production. To introduce arole of producer services in the specialization process, services labor is employed to organize acomplex set of distinct production processes. Producer-service costs increase in the degree of
specialization. For a given amount of output, firms determine the degree of specialization tominimize costs of production and services labor. In this production structure with consumer
demands characterized by a love of variety, the upgrading of producer services can generate
productivity gains in production activities by promoting the division of labor in manufacturing
production.
More intuitively, the linkage between producer services and production is that thegrowing extent of markets for manufacturing products increases the complexity of the
production process. As the number of production workers rises in the operation of production,
there is an increase in demand for producer services that are used to coordinate the finely
fragmented stages of production. The expansion of production scale results in the greater degreeof specialization in the production process. This contributes to an improvement in
manufacturing labor productivity when specialized producer services for coordinatingcomplicated tasks in production are also employed to reduce coordination costs. As a result of
the market expansion, producer services can help manufacturing firms to improve the degree of
specialization in production.

12

Since the model does not clearly distinguish the boundaries of firm activities, producer
services do not have to be supplied exclusively by workers who are formally employed at firms;
instead, manufacturing firms can purchase producer services from specialized service providers.
The theoretical link between producer services and production efficiency is carried over to thecase where firms contract out service tasks. Once the outsourcing of services labor is allowed,
specialized service providers can exploit scale economies in supplying business services topotential customers in markets. The economies of scale allow the independent service suppliers
to reduce the cost of producing service inputs as compared to the services that are produced only
within the firm. An improvement in quality, availability, and costs of producer services
promotes the division of labor in downward manufacturing sectors. Thus, the performance ofservice suppliers could generate productivity gains in user manufacturing production via
forward linkages.5

3.2. Estimation Framework
The purpose of this paper is to estimate an economic contribution of service-sector
performance to a change in production efficiency in downward manufacturing sectors. The

5

The theoretical linkage between producer services and the division of labor in production provides
a basis for an empirical investigation on the relationship between service-sector performance andmanufacturing productivity. Nevertheless, transmission channels could plausibly vary by specificcharacteristics of producer services. In particular, I examine the role of financial, transportation, andinformation services in explaining manufacturing productivity in section 4.
13

theory suggests that an improvement in the quality and variety of intermediate inputs produced
in service sectors should encourage the degree of specialization in manufacturing operations in
response to the rising demand for manufactured products. This channel leads to an increase in
the productivity growth of manufacturing sectors. Furthermore, improved performance in theservice sectors should have a stronger positive influence on manufacturing sectors that are moreheavily dependent on intermediate inputs from the service sectors.
An operational framework for estimation is needed to explore forward linkages withproductivity changes in manufacturing industries. A main issue is to estimate the extent of thequality upgrading of producer services from service sectors. There are profound and difficult
problems in measuring the output and quality of producer services (Griliches, 1992). Since
measurement problems are beyond the scope of this paper, I postulate that a change in thequality and variety of business services are not accurately reflected in the price deflator growthof service outputs. It is less controversial to assume the existence of measurement errors inservice-output prices than to assume producer services as accurately reflecting quality changes.
In fact, measurement problems on service inputs allow for estimating the link between service
performance and manufacturing productivity; the real value of intermediate inputs in
input-output tables would account totally for a change in the quality of service inputs if thequality upgrading were perfectly reflected in a change of service price deflators.

14

Given the presence of measurement errors in the service price deflator, I draw on thework of Griliches and Lichtenberg (1984) to construct an estimation framework for the purposeof this paper. Assume that manufacturing production at the industry level follows a
Cobb-Douglas production function of non-service and service inputs for sector i and time t:Q,β

A , X α, S ,(1)where Q is output, A is a Hick-neutral technology efficiency, X is a vector of non-service inputs
such as labor, capital, and materials, and S is a composite of intermediate service inputs.6
Taking the natural logarithm and first differences with respect to time for each variable,
equation (1) is rewritten as:∆lnQ ,∆lnA ,

α∆lnX ,

β∆lnS ,

(2)

where △ indicates the first differencing.
Assume that there exist errors in measurement of the growth rate of service output
deflator for the failure to account for a change in the variety and quality of producer services.
From a point of view in downward manufacturers, the measurement errors translate into a
deviation in service input deflators. Given that transactions in producer services are measured
with precise value, accounting identity between service inputs, values, and deflators gives thefollowing:

6

A composite of service input, S, consists of a variety of producer services in service sectors.
15

∆lnS ,

∆lnVS ,

∆lnPS ,

(3)

where S* is measured quantity of service inputs, VS is nominal value of service inputs, and PS*
is a measured price deflator of service inputs. The difference between true and measured
quantities of service inputs is expressed as:∆lnS ,∆lnS ,∆lnPS ,

∆lnPS ,

E,

(4)

where E indicates the extent of deviation in a measured growth rate of service inputs from thetrue growth rate. To focus on the link between quality changes in service inputs and productivity,
I assume that the actual amount of output and non-service inputs are measured with correct
price deflators. After expressing measured and true TFPs with the corresponding service inputs,
the difference between measured and true TFPs is denoted as:∆lnA ,∆lnA ,

β ∆lnS ,∆lnS ,βE ,(5)Rearranging the terms in equation (5) allows me to express the measured growth rate ofproduction efficiency in sector i for time t as a function of the true total factor productivity andthe measurement errors in the service input deflator:∆lnA ,∆lnA ,βE ,(6)

To investigate the central hypothesis that improved performance in service sectors
contributes to productivity growth in purchasing sectors, I further assume that the errors inmeasurement of service price deflators arise from improvements in producer services that are16

unobservable for statisticians. Because manufacturing sectors that are more intensive in service
inputs may benefit more strongly from spillover effects of service upgrading, I presume that abenefit of improved producer services is distributed to downstream manufacturing sectors inproportion to input sales from service suppliers to total non-energy input purchases in the
manufacturing sectors. Specifically, I define the deviation in the measured growth of service
deflators for service inputs from service sector k as follows:
E,

γ∑

I

Service_Performance ,T(7)where the service performance variable captures a change in service outputs of service sector k.
An important issue in this formulation is to measure economic performance in service
sector k as a proxy of unobservable improvements in producer services. The estimation
framework based on Griliches and Lichtenberg (1984) is built on the assumption that service
price deflators are measured with errors, making possible to estimate the link between service
and manufacturing sectors. Nevertheless, I need alternative indicators of service performance ina wide array of service sectors. Because it is not possible to measure a variation in the quality of
producer services across sectors over time in a consistent way, I employ the following variables
on service sector characteristics as an approximate indicator of overall service performance: TFP,
value added per worker, labor quality index, technical worker share, services worker share, andIT capital stock share.

17

Arranging equations (6) and (7) gives the following empirical specification on the roleof service linkages in manufacturing productivity:∆lnA ,

δδ Service_Link ,δ Z′ ,

μ

μ

ε,

(8)

where service link is performance in service sectors weighted by the service input intensity insector i, Z is a vector of control variables that may influence productivity growth, μi is
fixed-industry effects, μt is fixed-year effects, and ε is an error term. 7 As previously discussed,
I predict that the service link should promote productivity growth in manufacturing sectors via
forward linkages. The expected sign for its coefficient should be positive.8 For the control
variables, I include a share of IT capital in aggregate capital stock, a share of skilled labor in
total employment, and an import penetration ratio in manufacturing industries.

3.3. Description of Japan Industrial Productivity Database 20097

Since true total factor productivity in equation (6) is not observable to econometricians, I assume

that it is absorbed in the error term. It seems reasonable to consider that true productivity growth
excluded from the model is sufficiently uncorrelated with the service link variable so that it would
not cause a serious bias in an estimated coefficient of the service link.
8

Equation (8) can be combined with the original production function to obtain an alternative

empirical specification with the dependent variable of output:∆lnQ ,

δδ Service_Link ,α∆lnX ,

β∆lnS ,

δ Z′,

μ

μ

ε,

This specification is an alternative way to test the hypothesis that manufacturing sectors benefit froman enhancement of business services to increase the growth rate of their output. However, my main
specification is based on equation (8) because results are not significantly sensitive to thisspecification. Estimation results on equation (8) are not reported, but available upon request.
18

This paper employs the 2009 version of Japan Industrial Productivity Database (JIP).
This database is prepared by the Research Institute of Economy, Trade, and Industry (RIETI) in
cooperation with Hitotsubashi University. The primary objective of the JIP 2009 is to provide acomprehensive database for investigating the structural change of Japanese industries andeconomic growth for the long term. The JIP 2009 includes a wide variety of indicators on
industry characteristics for 108 sectors between 1970 to 2006: capital service input indices andcapital costs, labor service input indices and labor costs, nominal and real values of inputs andoutputs, and TFP. Additional datasets on trade, FDI, and market reforms are also provided.
While details of the estimation methodology are found in Fuako and Miyagawa (2008), thissection briefly describes the JIP 2009, which is a single data source of my analysis.
A dependent variable is the measured growth rate of manufacturing productivity. In
the JIP 2009, TFP growth rates at the sector level are calculated in the following equation:∆ ln T∆ ln QωX, ∆ ln M

ωL, ∆ ln L

ωK, ∆ln K(9)where △ denotes first differencing of a variable, T is an indicator of technology efficiency, Q
is output, M is intermediate input, L is labor, and K is capital. A cost share of total expenditures
expressed by ω for each input is the simple average of cost shares at time t and t-1, with thefirst subscript indicating a type of input. Specifically, intermediate input costs are an aggregate
volume of intermediate inputs in nominal terms from input-output tables of the JIP; labor costs

19are the sum of the product of nominal wage rates and labor inputs disaggregated by laborcharacteristics and sector; capital costs are the sum of the product of nominal capital prices andcapital stocks disaggregated by capital goods and sector.
The service link variable is created from service input intensities and performanceindicators of service sectors. Following Ahn et al. (2008), service activity is defined to include
sectors 66 through 107 as classified by the JIP industry code. For example, the broad service
sector consists of retail, finance, insurance, transportation, and telecommunications. A detailed
list of service sectors is shown in the Appendix.9 Using a real volume of intermediate goods in
input-output tables of the JIP 2009, I create a matrix of intermediate inputs from a particularservice sector in manufacturing sectors. Then, service inputs are divided by the aggregate
volume of intermediate inputs excluding those from the energy sectors: mining, petroleum
products, coal products, electricity, gas and heat supply, waterworks, water supply for industrialuse. Finally, the same matrix of service input intensities across manufacturing industries isconstructed between 1980 and 2005.
An improvement in producer services supplied by each service sector is measured by avariety of indicators on service-sector characteristics. Specifically, I use TFP, value added per
worker, labor quality index, information technology (IT) capital stock share, technical worker

9

Manufacturing industries include sectors 8 through 59 without 30 and 31 in the JIP code.
20

share, and service worker share. The value added per worker is created by dividing aggregate
total value added with the total number of workers in each sector. Labor quality index is madefrom labor input index and man-hour index; a difference between the growth rate of labor input
and man-hour index is interpreted as a change in the quality of labor input. IT capital stock isestimated by a perpetual inventory method for an aggregate value of software and hardware
investment for each industry. A share of IT capital stock is constructed by dividing it with total
capital stock. Technical and service worker shares are the number of technical and service
workers as a share of total employment in each industry, respectively. Lastly, I combine data on
service input intensities and service performance to create the service-link variable as specified
in equation (7).
Control variables include a share of IT capital stock, an employment share of skilledlabor measured by non-production workers, and an import-penetration intensity in
manufacturing sectors. The import penetration is constructed by dividing the volume of imports
with the sum of domestic production and imports minus exports. Summary statistics of thesamples are provided in the Appendix.

21

4. Estimation Results
4.1. Aggregate Service Sectors
This section starts to show the basic result on equation (8) estimated with an ordinary
least square (OLS) method. Since errors in each cluster of manufacturing sectors could becorrelated, standard errors clustered at the industry level are reported with the estimatedcoefficients. The specification is estimated with sector and year dummy variables to control forindustry- and time-fixed effects. The dependent variable is a growth rate of TFP in
manufacturing sectors. The coefficients of the service-link variables indicate a relationshipbetween service-sector characteristics and manufacturing productivity.
Column (1) of Table 2 shows that the estimated coefficient of the service link via TFP
in service sectors is not statistically different from zero. In columns (2) to (6), I employ theservice-link variable with other indicators of service sectors in each specification. Consistent
with the result in column (1), any of the coefficients of the service link is not statisticallysignificant. The results suggest that an improvement in producer services measured by a wide
range of variables did not have a significant impact on the growth rate of productivity across
manufacturing sectors through forward linkages. While it is often claimed that thecompetitiveness of manufacturing firms is supported by reliable producer services in local
markets, the basic results do not lend support to the presence of a linkage between service and22manufacturing sectors.
[Table 2 around here]
How are these results related to the past findings on the inter-industry linkages
between service and manufacturing sectors? While my focus is on service performance, ten
RAA and Wolff (2001) argue that the outsourcing of services tasks – a low productivity part ofproduction – improves the observed growth of manufacturing productivity. While the TFP
growth rate in U.S. manufacturing sectors was 0.87 percentage points for the period 1977-1987,
they estimate that services outsourcing contributed to about 0.06 percentage points. This result
is taken as suggesting the productivity-accelerating effects of the contracting-out of service
tasks. However, the relatively small contribution of purchased services to manufacturing
efficiency accords with my findings that the linkage between service and manufacturing sectors
is weak in Japan. In contrast, Arnold et al. (2008) find that performance in specific service
sectors has a significantly positive association with firm productivity in 10 Sub-Saharan Africancountries between 2001 and 2005. Collectively, these studies may imply that producer services
have a larger positive impact on manufacturing firms in developing countries than those indeveloped countries.
There are three control variables in each specification. Among these variables, thewithin-industry share of skilled labor in total employment has a significantly positive impact on

23manufacturing productivity growth across the specifications. In column (1), the magnitude of
the coefficient indicates that a 10% point increase in the skilled-labor share is predicted toincrease manufacturing TFP by a 1.3% point. While the estimated coefficient of skilled laborneeds to be carefully interpreted for possible endogeneity bias, it is reasonable to conclude thatskill upgrading of workers within manufacturing industries played an important role inproductivity improvements in Japan for the past decades. On the other hand, the share of IT
capital stock has the insignificant coefficients across the specifications in Table 2. Motohashi(2005) employs a sample of Japanese firms for 1991-2000 to study an impact of IT use on firm
performance. He estimates that the share of IT capital stock at the firm-level accounted forabout 12% of value added of manufacturing firms. As there may be substantial heterogeneity in
IT investment and firm performance, my estimates at the industry level suggest that a variation
in benefits of IT investment to productivity gains can be much larger across firms than acrossindustries.
Import-penetration intensities across manufacturing sectors have an insignificant
influence on manufacturing productivity. Contrary to a common concern that importcompetition is detrimental to domestic industries, I do not find evidence that the importpenetration decreases manufacturing productivity at the industry-level. However, this result may
also be influenced by possible aggregation bias to some extent. For example, Ito and Kawakami

24

(2008) find that small and medium firms in industries with intensive import competition arelikely to experience a lower growth of employment and sales for 1995-2003 in Japan. Theirresults imply that a clear distinction on the firm size can play an important role in identifying
the relationship between import competition and productivity.
The previous regressions have controlled for unobservable industry-specific effects
that were constant for the period of interest. But there were possible changes in specific industry
characteristics such as technological innovation, market demand, or government regulation.
Unobserved changes in the manufacturing industry could be correlated with service linkages ina way to downplay a role of service links. Thus, I take first differencing of equation (8) toremove fixed-effects across industry and time. Furthermore, sector and year dummy variables
are included in the first-differenced specification to control for the unobservable factors thatmay drive constant changes in the characteristics of manufacturing industries. Table 3 shows an
estimation result of the first-differenced model of equation (8).10 Consistent with the previous
results, all of the specifications in columns (1) to (6) do not show the significant coefficients ofthe service link created with a variety of indicators on service-sector characteristics. A change inthe service-link variable has little impact on a change in the growth rate of TFP in
manufacturing sectors. Thus, the conclusion that service sectors do not influence manufacturing
10

I also employ GMM estimation with instrument variables to directly address potential
endogeneity of service link variables. As the GMM results are similar to those from OLS estimationof the first-differenced specification, they are provided in the Appendix.
25

productivity through forward linkages is robust to the different specification.
[Table 3 around here]

4.2. Specific Service Sectors
A major concern about the estimation results up to this point is that each service sector
can play a heterogeneous role in providing producer services to manufacturing firms (Hoekman
and Mattoo, 2008). Since a type of intermediate service inputs is not necessarily identical acrossservice industries, an effect of improved producer services on manufacturing productivity may
depend on individual service sectors. It implies that estimated coefficients of the service link
may reflect aggregation bias across service sectors. To address this problem, I constructservice-link variables for specific service industries: finance, insurance, road transportation,
telecommunications, and information services. Data on a cross-industry intensity of service
inputs and economic performance in a particular service sector are obtained from the JIP 2009.
Using the specific service links, I estimate the first-differenced model of equation (8) withsector and year dummy variables to control for industry-specific effects in the growth rate of
manufacturing productivity.
I start to examine a role of financial and insurance sectors in manufacturing

26

productivity. As Levine (1997) illustrates a variety of functions of financial development ineconomic growth, the provision of financial services is vital to business activities inmanufacturing sectors. The emergence of financial systems ameliorates transactions costs due toimperfect information in an uncertain business environment, and facilitates the allocation of
scarce financial resources over space and time. An improvement in the quality of financial
services reduces transaction costs associated with the degree of specialization in production
processes. Consequently, a decline in coordination costs of the complex production enhances
the division of labor in manufacturing operations, which is likely to lead to productivity
improvements. While it is difficult to establish a clear direction of causality between financial
developments and specialization in manufacturing production, the hypothesis is that the service
link should be associated positively with manufacturing productivity.
Columns (1) and (2) of Table 4 show a list of the coefficients of service links forfinancial and insurance services sectors, respectively.11 Contrary to the theoretical intuition, anyof the coefficients of the service link are not significantly positive across the various definitionsof the service links. In the case of the finance sector, some of the service-link variables have thesignificantly negative coefficients, which indicate that improved performance in financial
services discourages the growth rate of TFP in manufacturing sectors. These results are in

11

Estimation results are available on request.
27

contrast with previous empirical findings. For example, Levine (1997) summarizes theempirical findings that aggregate productivity growth is correlated positively with a variety offinancial development indicators such as the size of financial intermediaries, the importance ofprivate banks relative to a central bank, and the level of commercial bank credit across 80
countries over the period 1960-1989. Furthermore, Rajan and Zingales (1998) show thatindustrial sectors in the greater need of external financing tend to grow faster in countries thatare more financially developed, which indicates the importance of financial markets forindustrial development. As compared with these studies, my analysis does not support a positive
link between financial services and efficiency in manufacturing production. The implication isthat financial services could play a significant role in the expansion of industrial sectors, but
may not be crucial in enhancing the productivity of industrial productions.
Transportation sectors provide important producer services for manufacturing firms.
Transportation is a key element of manufacturing operations at least in two aspects to connect
manufacturers with suppliers and consumers; the delivery of parts and components to point of
final assembly and the transport of final products to point of consumption. The efficient andtimely distribution of parts and final products in the domestic market comprises a critical part ofthe manufacturing production (Limão and Venables, 2001; Djankov et al., 2006). As production
becomes complex, transport tasks gain in importance. An increase in the number and variety of28

parts and final goods implies that transportation services must be supplied in an appropriate
mode to coordinate a complicated set of tasks in operations. As transportation must take into
account a wide range of characteristics of intermediate and final goods such as volume, form,
and timeliness, suppliers of transport services can specialize in the provision of specifictransport services to exploit the gains of specialization. Consequently, an improvement in thequality and availability of transport services will reduce coordination costs associated withtransport tasks in manufacturing production, which is likely to enhance productivity.
Column (3) of Table 4 shows the estimation result of the effect of service links with aroad-transportation sector on manufacturing productivity. The road transportation is analyzed asa proxy for general transport services. While service performance in the transport sector ismeasured by a variety of indicators, the coefficients of the service-link variables are
insignificant. I find little evidence that the measures of the quality upgrading of transport
services improve manufacturing productivity. Fernald (1999) studies the role of public
investment in road infrastructure in the productivity growth of U.S. industries. He finds that U.S.
industries that are intensive in vehicle use tend to benefit disproportionately from road growth,
but marginal benefits of infrastructure investments tend to decline over. In contrast, my paper
directly analyzes the effects of transportation services on industrial productivity in the case ofJapan, but the evidence does not point to the productivity-accelerating role of transport services.

29

Lastly, I analyze the linkage between information services and productivity. An
exchange of information constitutes a central stage of production processes. A headquarters of amanufacturing firm undertakes the processing of business information in consumer markets,
industries, and macroeconomy. The management decisions are strategically made on the basis ofcorporate resources and business environments. Within the firm, its headquarters provides awide range of informational services such as product development, process innovations, andmanagerial tasks for the operation of its production plant. Within the plant, a final assembly ofcomponents and parts involves coordination between production workers to operate alarge-scale production processes simultaneously, which requires an intense exchange of
information. To improve the flow of information, firms can link distinct segments of corporate
organizations with IT networks, which are established by investment in information andcommunication technology. While purchased computers can directly contribute to a reduction of
information costs, outsourcing of information services plays a large role in an introduction andmaintenance of comprehensive IT systems. A specialized knowledge of IT networks fromservice producers is critical to an efficient and operational form of IT systems in production
processes.
Columns (4) and (5) of Table 4 show the estimate of the service link variables with
telecommunications and information sectors, respectively. To the extent that the quality

30

upgrading of information services is captured at least partly by a variety of indictors, theestimated coefficients should represent the role of informational services in explaining
productivity growth of manufacturing industries. The results indicate that the coefficients of theservice link are not significantly positive across specifications. To the contrary, the service links
of value-added per worker and labor quality index in the information services have thesignificantly negative impact on manufacturing productivity. An improved labor productivity in
information sectors as a proxy for the quality upgrading of information services depresses the
TFP growth of manufacturing sectors that are linked with the information sector via forward
linkages.

5. Discussion and Conclusion
The growing importance of service sectors characterizes the Japanese economy as it
applies to almost all of advanced economies. A stagnant productivity growth of the service
sector in Japan raises a concern of the “Baumol’s disease” that an expansion of low-productivity
sectors could eventually drive down aggregate economic growth to the rate in the stagnant
sector. However, the service sector plays a fundamental role in providing producer services for
manufacturing production. As manufacturing firms extensively engage in “service outsourcing”,
the service sectors are deeply linked with vertical chains of manufacturing production. This fact31

often leads to the claim that the quality upgrading of producer services can improve
manufacturing productivity (Francois, 1990, Oulton, 2001). As a result, the service sector is
expected to contribute indirectly to economic growth.
This paper attempts to offer an empirical assessment on the role of producer services
in manufacturing production. Specifically, I propose an empirical framework to study the effectof quality improvements of services on manufacturing productivity. The estimation strategy
starts from the observation that improvements in the quality, variety, and availability of services
may not accurately be reflected in the growth rate of service price deflators. Measurement errors
in the price deflators of service outputs enter the observed productivity growth rate of
manufacturing sectors through the purchased of service inputs.
I assume that the errors of measurement in service inputs resulting from unobserved
quality changes is related to the performance of service sectors such as labor productivity; good
performance proxies quality upgrading that is not explained in the price deflator growth ofservice outputs. Manufacturing productivity is related to the performance of the service sectors
weighted by the cross-sectoral intensity of service inputs. To implement the estimation, I exploit
comprehensive panel data on industry characteristics from the Japan Industrial Productivity(JIP) database 2009; the sample covers 52 manufacturing and 42 service sectors for the period
1980-2005 in Japan.

32

Contrary to the claim that producer services are critical to the competitiveness ofmanufacturing industries, I find little evidence that a variety of measures of the economic
performance in the service sectors improve total factor productivity in Japanese manufacturingsectors. The findings are robust to a wide range of alternative measures of service performance,
empirical specifications, and estimation methods. In addition, I address aggregation bias ofheterogeneous producer services by exploring five service sectors individually. Consistent withthe results of the aggregate service sector, the service linkage plays little role in acceleratingproductivity growth rates in manufacturing sectors. To the contrary, some measures of theservice linkage have even a significantly negative impact on manufacturing efficiency, possibly
implying that the growing dependence on specialized service providers may create additionaltransaction costs to coordinate in-house production with service tasks produced outside the firm.
In assessing a role of service sectors in economic growth, the evidence in this paper
points to the importance of a direct contribution of service-sector productivity. Contracting-out
of a low productivity component of service tasks in production may indeed prevent a slowdown
in manufacturing productivity growth from occurring. On the other hand, improved quality of
producer services may not necessarily promote the competitiveness of manufacturing industries.
However, these implications must be subject to further econometric evidence as there arelimitations in this paper. One of key issues lies in measurement of observed and unobserved

33changes in the quality of producer services. The presence of measurement errors in a pricedeflator growth of service output is necessary for estimating the service linkage, but measures of
economic characteristics on the service sectors are assumed to reflect a component of qualitychanges that is not accounted for by the official price deflator. These assumptions raises thequestion of whether the insignificant effects of service links result either from the absence ofmeasurement errors or from imperfection of the measures of unobserved quality upgrading.

34

35

Table 1. Service Input as a Share of Total Non-energy Input by Manufacturing Industry
Period1973-1979

1980-1989

1990-1999

2000-2006

Food and Beverages

0.248

0.249

0.201

0.191

Chemicals

0.363

0.344

0.357

0.374

Non-metalic Minerals

0.525

0.483

0.487

0.497

Basic Metals

0.334

0.340

0.331

0.316

Fabricated Metal Product

0.310

0.296

0.289

0.272

Machinery and Equipment

0.282

0.275

0.299

0.280

Electrical Machinery

0.266

0.305

0.351

0.398

Transport Equipment

0.165

0.166

0.180

0.203

Other Manufacturing

0.323

0.305

0.284

0.275

0.313

0.307

0.309

0.312

Industry

Simple Average

Source: Japan Industrial Productivity Database 2009
Note: Service input includes inputs from the JIP sector codes 66 through 107; total inputs exclude
energy inputs from the JIP sector codes 7, 30, 31, 62, 63, 64, and 65.

36

Table 2. OLS Regression of Service Link for 1980-2005
Dependent Variable: TFP Growth Rate in Manufacturing Sectors
Variable
Service Link via TFP

(1)

(2)

(3)

(4)

(5)

(6)

-0.224(0.580)Service Link via Value

0.002

Added per Worker(0.003)Service Link via Labor-0.032

Quality Index(0.039)Service Link via Technical-0.334

Worker Share(0.263)Service Link via Service-0.302Worker Share(0.290)Service Link via IT Capital-0.328Stock Share(0.400)IT Capital Stock Share-0.068-0.067-0.061-0.074-0.074-0.071(0.066)(0.063)(0.064)(0.063)(0.061)(0.065)

*

*

*

*

*

0.131

0.127

0.138

0.128

0.126

0.131*(0.061)(0.059)(0.059)(0.060)(0.059)(0.062)-0.014-0.014-0.012-0.013-0.013-0.015(0.022)(0.022)(0.021)(0.022)(0.022)(0.023)

Year Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Sector Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Observations

1300

1300

1300

1300

1300

1300

R-square

0.135

0.135

0.135

0.136

0.135

0.135

Skilled Worker Share
Import Penetration

Note: Robust standard errors clustered at the industry are in parentheses, with ***, **, and *indicating the 1, 5, and 10 percent significance levels, respectively; a constant term is not reported.

37

Table 3. OLS Regression of Service Link for 1980-2005
Dependent Variable: First Difference of TFP Growth Rate in Manufacturing Sectors
First-Differenced Variable
Service Link via TFP

(1)

(2)

(3)

(4)

(5)

(6)

-0.708(1.084)Service Link via Value-0.002

Added per Worker(0.016)

Service Link via Labor-0.082

Quality Index(0.146)

Service Link via Technical-0.311

Worker Share(0.675)Service Link via Service-0.215Worker Share(0.699)Service Link via IT Capital-2.760Stock Share(2.283)

IT Capital Stock Share

0.038

0.048

0.047

0.042

0.050

0.070

(0.469)(0.473)(0.480)(0.477)(0.477)(0.489)

0.143

0.142

0.142

0.148

0.145

0.138

(0.208)(0.206)(0.207)(0.208)(0.207)(0.208)-0.147-0.149-0.151-0.148-0.150-0.155(0.191)(0.192)(0.192)(0.193)(0.194)(0.193)

Year Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Sector Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Observations

1250

1250

1250

1250

1250

1250

R-square

0.029

0.028

0.029

0.029

0.028

0.030

Skilled Worker Share
Import Penetration

Note: Robust standard errors clustered at the industry are in parentheses, with ***, **, and *indicating the 1, 5, and 10 percent significance levels, respectively; a constant term is not reported.

38

Table 4. OLS Estimates of Coefficients of Service Link in Individual Service Sectors
Dependent Variable: First Difference of TFP Growth Rate in Manufacturing Sectors
Service Sector

(1)

(2)

(3)

(4)

(5)

Financial

Insurance

Road

Tele-

Information

Services

Services

Transportation

communication

Services

Services

Services

First-Differenced Variable
Service Link via TFP-1.3935.069-3.056-16.23-5.750(1.796)(6.779)(1.695)(16.13)(7.774)Service Link via Value Added-0.084***-0.541-0.121-0.034-0.756**

per Worker(0.021)(0.554)(0.062)(0.052)(0.234)Service Link via Labor Quality-0.485-4.473-0.660-0.388-6.505*Index(0.354)(4.984)(0.455)(2.874)(2.773)Service Link via Technical-46.19*-621.5-462.1-29.30-11.80Worker Share(17.629)(347.427)(332.4)(48.47)(7.063)Service Link via Service-93.68*-1814.6-41.46-865.7-162.3

Worker Share(44.91)(1157.735)(28.82)(1801.9)(86.90)Service Link via IT Capital-8.998-73.92-18.71-19.62

5.228

Stock Share(4.579)(62.173)(16.86)(17.38)(33.31)

Note: Robust standard errors clustered at the industry are in parentheses, with ***, **, and *indicating the 1, 5, and 10 percent significance levels, respectively; each specification includes IT
capital stock share, skilled worker share, import penetration, and year and sector dummies.

39Appendix
Table A1. List of Industries in JIP 2009 Database
Category
JIP Code
JIP Industry Classification
Primary1
Rice, wheat production
Primary2
Miscellaneous crop farming
Primary3
Livestock and sericulture farming
Primary4
Agricultural services
Primary
5
ForestryPrimary
6
Fisheries
Energy-related
7
MiningManufacturing
8
Livestock products
Manufacturing
9
Seafood products
Manufacturing
10
Flour and grain mill products
Manufacturing
11
Miscellaneous foods and related products
Manufacturing
12
Prepared animal foods and organic fertilizers
Manufacturing
13
Beverages
Manufacturing
14
TobaccoManufacturing
15
Textile products
Manufacturing
16
Lumber and wood products
Manufacturing
17
Furniture and fixtures
Manufacturing
18
Pulp, paper, and coated and glazed paper
Manufacturing
19
Paper products
Manufacturing
20
Printing, plate making for printing and bookbinding
Manufacturing
21
Leather and leather products
Manufacturing
22
Rubber products
Manufacturing
23
Chemical fertilizers
Manufacturing
24
Basic inorganic chemicals
Manufacturing
25
Basic organic chemicals
Manufacturing
26
Organic chemicals
Manufacturing
27
Chemical fibers
Manufacturing
28
Miscellaneous chemical products
Manufacturing
29
Pharmaceutical products
Energy-related
30
Petroleum products
Energy-related
31
Coal products
Manufacturing
32
Glass and its products
Manufacturing
33
Cement and its products
Manufacturing
34
Pottery
Manufacturing
35
Miscellaneous ceramic, stone and clay products
Manufacturing
36
Pig iron and crude steel
Manufacturing
37
Miscellaneous iron and steel
Manufacturing
38
Smelting and refining of non-ferrous metals
Manufacturing39
Non-ferrous metal products
Manufacturing
40
Fabricated constructional and architectural metal products
Manufacturing
41
Miscellaneous fabricated metal products
Manufacturing
42
General industry machinery
Manufacturing
43
Special industry machinery
Manufacturing
44
Miscellaneous machinery
Manufacturing
45
Office and service industry machines
Manufacturing
46
Electrical generating, transmission, distribution and industrial
Manufacturing
47
Household electric appliances
Electronic data processing machines, digital and analog computer
Manufacturing
48
equipment and accessories
Manufacturing
49
Communication equipment
Manufacturing
50
Electronic equipment and electric measuring instruments
Manufacturing
51
Semiconductor devices and integrated circuits
Manufacturing
52
Electronic parts
Manufacturing
53
Miscellaneous electrical machinery equipment
Manufacturing
54
Motor vehicles
Manufacturing
55
Motor vehicle parts and accessories
Manufacturing
56
Other transportation equipment
Manufacturing
57
Precision machinery and equipment
Manufacturing
58
Plastic products
Manufacturing
59
Miscellaneous manufacturing industries

40

(continued)
Energy-related
Energy-related
Energy-related
Energy-related
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
S i
Services
Services
Services
Services

60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92

Services

93

Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services
-

94
95
96
97
98
99
100
101
102
103
104
105
106
107
108

Construction
Civil engineering
Electricity
Gas, heat supply
Waterworks
Water supply for industrial use
Waste disposal
Wholesale
RetailFinanceInsurance
Real estate
Housing
RailwayRoad transportation
Water transportation
Air transportation
Other transportation and packing
Telegraph and telephone
Mail
Education (private and non-profit)
Research (private)
Medical (private)
Hygiene (private and non-profit)
Other public services
Advertising
Rental of office equipment and goods
Automobile maintenance services
Other services for businessesEiEntertainment
Broadcasting
Information services and internet-based services
Publishing
Video picture, sound information, character information production
and distribution
Eating and drinking places
Accommodation
Laundry, beauty and bath services
Other services for individuals
Education (public)
Research (public)
Medical (public)Hygiene (public)
Social insurance and social welfare (public)
Public administration
Medical (non-profit)
Social insurance and social welfare (non-profit)
Research (non-profit)
Other (non-profit)
Activities not elsewhere classified

41

Table A2. Summary Statistics
Variable

No. ofMean

Observations

Std.

Min

Max

Dev.

Total Factor Productivity Growth

1300

0.01

0.056

-0.674

0.608

Service Link via TFP

1300

0.002

0.004

-0.0150.032

Service Link via Value Added per Worker

1300

2.245

0.953

0.278

6.601

Service Link via Labor Quality Index

1300

0.287

0.098

0.044

0.588

Service Link via Technical Worker Share

1300

0.017

0.016

0

0.119

Service Link via Service Worker Share

1300

0.021

0.016

0

0.074

Service Link via IT Capital Stock Share

1300

0.019

0.011

0.002

0.068

IT Capital Stock Share

1300

0.081

0.063

0.005

0.366

Skilled Worker Share

1300

0.331

0.085

0.149

0.613

Import Share

1300

0.082

0.103

0.00006

0.689

First Difference Specification
Total Factor Productivity Growth1250-0.00082

0.079-1.2820.763

Service Link via TFP

1250-0.0002

0.006-0.0280.022

Service Link via Value Added per Worker

1250

-0.05

0.156

-1.501

2.117

Service Link via Labor Quality Index

1250-0.0020.016-0.1590.223

Service Link via Technical Worker Share1250-0.001

0.006-0.0980.011

Service Link via Service Worker Share1250-0.001

0.006

-0.06

0.012

Service Link via IT Capital Stock Share1250-0.0010.001-0.0110.018

IT Capital Stock Share

1250

0.004

0.008

-0.0570.054

Skilled Worker Share

1250

0.002

0.007

-0.025

0.039

Import Share

1250

0.005

0.017

-0.1270.131

42

Table A3. GMM Estimation of Service Link for 1980-2005
Dependent variable: TFP Growth Rate in Manufacturing
Sectors
Variable
Service Link via TFP

(1)

(2)

(3)

(4)

(5)

(6)

0.843
(1.118)Service Link via Value Added per

0.003

Worker

(0.002)Service Link via Labor Quality Index

0.017(0.017)

Service Link via Technical Worker

0.193*

Share(0.095)Service Link via Service Worker Share

0.173(0.135)Service Link via IT Capital Stock Share

0.362(0.211)IT Capital Stock Share

0.102

0.118

0.125

0.119

0.111

0.118*(0.065)(0.067)(0.067)(0.067)(0.063)(0.055)

0.021

0.010

0.012

0.010

0.018

0.012

(0.015)(0.015)(0.015)(0.015)(0.015)(0.014)-0.002

0.011

0.000

0.004

0.016

0.011

(0.027)(0.029)(0.029)(0.026)(0.028)(0.031)

Year Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Sector Dummy

No

No

No

No

No

No

Observations

1300

1300

1300

1300

1300

1300

R-square

0.047

0.055

0.053

0.055

0.055

0.055

Hansen J Statistics (p-value)

0.590

0.327

0.256

0.804

0.853

0.532

Skilled Worker Share

Import Penetration Share

Note: Robust standard errors clustered at the industry are in parentheses, with ***, **, and * indicating the 1,
5, and 10 percent significance levels, respectively; a constant term is not reported; instruments for service linkvariables are first- and second-lags of the service link as well as service linkage variables constructed withregulation index and foreign investment in the corresponding service sectors, taken from the JIP 2009.

43

References
Abraham, Katharine G. and Susan K. Taylor. 1996. "Firms' Use of Outside Contractors: Theory andEvidence." Journal of Labor Economics, 14:3, pp. 394-424.
Ahn, Sanghoon, Kyoji Fukao, and Keiko Ito. 2008. "Outsourcing in East Asia and its impact on theJapanese and Korean Labour Markets." OECD Trade Directorate, OECD Trade Policy
Working Papers: 65.
Arnold, Jens, Beata S. Javorcik, and Aaditya Mattoo. 2007. "Does services liberalization benefit
manufacturing firms ? Evidence from the Czech Republic." The World Bank, Policy ResearchWorking Paper Series: 4109.
Arnold, Jens Matthias, Aaditya Mattoo, and Gaia Narciso. 2008. "Services Inputs and Firm
Productivity in Sub-Suharan Africa: Evidence from Firm-Level Data." Journal of African
Economies, 17:4, pp. 578-99.
Baumol, William J. 1967. "Macroeconomics of Unbalanced Growth: The Anatomy of Urban Crisis."
American Economic Review, 57:3, pp. 415-26.
Djankov, Simeon, Caroline Freund, and Cong S. Pham. 2006. "Trading on time." The World Bank,
Policy Research Working Paper Series: 3909.
Fernald, John G. 1999. "Roads to Prosperity? Assessing the Link between Public Capital and
Productivity." American Economic Review, 89:3, pp. 619-38.
Fernandes, Ana M. and Caroline Paunov. 2008. "Foreign direct investment in services andmanufacturing productivity growth: evidence for Chile." The World Bank, Policy ResearchWorking Paper Series: 4730.
Francois, Joseph F. 1990. "Producer Services, Scale, and the Division of Labor." Oxford Economic
Papers, N. S., 42:4, pp. 715-29.
Fukao, Kyoji and Tsutomu Miyagawa. 2008. Productivity and Economic Growth in Japan: Firmand Industry-level Empirical Analysis Based on JIP Database (in Japanese). Tokyo:
University of Tokyo Press.
Girma, Sourafel and Holger Gorg. 2004. "Outsourcing, Foreign Ownership, and Productivity:
Evidence from UK Establishment-Level Data." Review of International Economics, 12:5, pp.
817-32.
Gorg, Holger and Aoife Hanley. 2004. "Does Outsourcing Increase Profitability?" Economic andSocial Review, 35:3, pp. 267-87.
Griliches, Zvi, ed. 1992. Output measurement in the service sectors: With the assistance of Ernst R.
Berndt, Timothy F. Bresnahan, and Marilyn E. Manser. National Bureau of Economic
Research Studies in Income and Wealth, vol. 56. Chicago: University of Chicago Press.

44

Griliches, Zvi and Frank Lichtenberg. 1984. "Interindustry Technology Flows and Productivity
Growth: A Re-examination." Review of Economics and Statistics, 66:2, pp. 324-29.
Hoekman, Bernard and Aaditya Mattoo. 2008. "Services trade and growth." The World Bank, Policy
Research Working Paper Series: 4461.
Ito, Keiko and Atsushi Kawakami. 2008. "Structural changes of trade and production and a gap
between firms (in Japanese)," in Productivity and Economic Growth in Japan: Firm- andIndustry-level Empirical Analysis Based on JIP Database. Kyoji Fukao and Tsutomu
Miyagawa eds: University of Tokyo Press.
Jones, Randall S. and Taesik Yoon. 2008. "Enhancing the Productivity of the Service Sector inJapan." OECD, Economics Department, OECD Economics Department Working Papers: 651.
Kimura, Fukunari. 2002. "Subcontracting and the Performance of Small and Medium Firms inJapan." Small Business Economics, 18:1-3, pp. 163-75.
Levine, Ross. 1997. "Financial Development and Economic Growth: Views and Agenda." Journal of
Economic Literature, 35:2, pp. 688-726.
Limao, Nuno and Anthony J. Venables. 2001. "Infrastructure, Geographical Disadvantage, Transport
Costs, and Trade." World Bank Economic Review, 15:3, pp. 451-79.
Motohashi, Kazuyuki. 2007. "Firm-Level Analysis of Information Network Use and Productivity inJapan." Journal of the Japanese and International Economies, 21:1, pp. 121-37.
Olsen, Karsten B. 2006. "Productivity Impacts of Offshoring and Outsourcing: A Review." STI
Working Paper 2006/1, OECD Directorate for Science, Technology and Industry (STI).
Oulton, Nicholas. 2001. "Must the Growth Rate Decline? Baumol's Unbalanced Growth Revisited."
Oxford Economic Papers, 53:4, pp. 605-27.
Rajan, Raghuram G. and Luigi Zingales. 1998. "Financial Dependence and Growth." American
Economic Review, 88:3, pp. 559-86.
Siegel, Donald and Zvi Griliches. 1992. "Purchased Services, Outsourcing, Computers, andProductivity in Manufacturing," in Output measurement in the service sectors. Zvi Griliches
ed: With the assistance of Ernst R. Berndt, Timothy F. Bresnahan, and Marilyn E. Manser.
National Bureau of Economic Research Studies in Income and Wealth, vol. 56. Chicago:
University of Chicago Press, pp. 429-58.
ten Raa, Thijs and Edward N. Wolff. 2001. "Outsourcing of Services and the Productivity Recovery
in U.S. Manufacturing in the 1980s and 1990s." Journal of Productivity Analysis, 16:2, pp.
149-65.

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