Kyoto University,
Graduate School of Economics
Research Project Center Discussion Paper Series

Firm Productivity and Foreign Direct Investment in theServices Sector: A Firm-Level Analysis using Japanese Data

Ayumu Tanaka

Discussion Paper No. E-11-008

Research Project Center
Graduate School of Economics
Kyoto University
Yoshida-Hommachi, Sakyo-ku
Kyoto City, 606-8501, Japan

February 2012

Firm productivity and Foreign Direct Investment in the Services Sector:
A Firm-Level Analysis using Japanese Data

Ayumu Tanaka†February 7, 2012

Abstract
Using Japanese ﬁrm-level data, I examine whether multinational
enterprises (MNEs) are more productive than non-MNEs in the services sector. I employ the Kolmogorov-Smirnov (KS) test to compare
the overall distribution of productivity by multinational status. The
results indicate that MNEs tend to be more productive than non-MNEs
in the services sector and suggest that the standard ﬁrm heterogeneity
model can well explain foreign direct investment (FDI) by ﬁrms in theservices sector.
Keywords: Services sector; Firm heterogeneity; Multinationals; Foreign direct investment
JEL Classiﬁcation: F1, F23, L8†

Research Fellow, Research Project Center, Graduate School of Economics, Kyoto
University; Fellow, Research Institute of Economy, Trade and Industry (RIETI).
E-mail: tanaka-ayumu<at>rieti.go.jp

11

Introduction

Multinational services ﬁrms are establishing presence more aggressively in allover the world than ever before. However, little is known about MNEs in theservices sector, while those in the manufacturing sector are subjects of manystudies. Facing shrinking domestic market due to decreasing population,
Japanese services ﬁrms as well as policy makers have begun to explore foreign
markets. It is important to investigate the determinants of foreign direct
investment (FDI) by services ﬁrms.
Firm-heterogeneity model of trade and FDI by Helpman et al. (2004)
predict that MNEs are productive than non-MNEs. This prediction aresupported for manufacturing ﬁrms by many empirical studies. However,there are little evidence that support the prediction in the services sector.
The purpose of this study is to examine the relationship between ﬁrm
productivity and foreign engagement in the services sectors, using extensiveﬁrm-level data from Japan. The data is collected from a survey conducted by
the Japanese Ministry of Economy, Trade, and Industry (METI). I employ
the Kolmogorov-Smirnov (KS) test to compare productivity distribution of
MNEs with those of non-MNEs. I ﬁnd that MNEs tend to be more productive than non-MNEs even in the serivces sector. I also ﬁnd ﬁrst evidence ofthe so-called pecking order in the services sector that more productive ﬁrms
tend to invest in the wider range of foreign regions.
The remainder of this paper is divided into six sections. In Section 2, I
review the literature. In Section 3, I brieﬂy describe the data and variables
used in this paper and present descriptive statistics of the data. In Section
4, I introduce my empirical strategy. In Section 5, I present the results.
In Section 6, I report the result of robustness checks. The summary andconclusion are presented in the ﬁnal section.

2

Related literature

Many previous empirical studies conﬁrm the theoretical prediction of Helpman et al. (2004) that MNEs are more productive than non-MNEs. Amongothers, Girma et al. (2005a, b) conﬁrm the prediction for the Republic of
Ireland and the United Kingdom using a non-parametric approach. Headand Ries (2003) conﬁrm the prediction for listed ﬁrms in Japan. Thesestudies analyze ﬁrms or plants in the manufacturing sector only.
Recently, studies on trade and FDI in the services sector have beendeveloped. Francois and Hoekman (2010) provide a comprehensive overview

2

of internationalization in the services sector. Data on trade and FDI in theservices sector is limited but has been increasing recently. Francois et al.
(2009) have constructed a database on trade and FDI in the services sector.
Kimura and Lee (2006), Kolstad and Villanger (2008), and Ramasamy andYeung (2010) examine the determinants of exports and FDI in services,using aggregated data.
Recent studies conduct ﬁrm-level analysis of trade and FDI in the services sector. Using data from the United Kingdom, Breinlich and Criscuolo(2011) ﬁnd several stylized facts for ﬁrm-level exports and imports of services and conclude that existing heterogeneous ﬁrm models*1 for goods trading can be applied to services trading. Buch and Lipponer (2007) provide
evidence that MNEs are more productive than exporters in the Germanbanking industry. This evidence is consistent with the standard ﬁrm heterogeneity model of exports and FDI provided by Helpman et al. (2004).
This paper contributes the literature in several regards. First, I utilize
a non-parametric approach based on the principle of ﬁrst order stochastic
dominance and show that MNEs tend to be more productive than non-MNE
even in the services sector. Second, my data have a panel structure covering
the year 2001–2008 and they are not restricted to large ﬁrms. Third, I
provide ﬁrst evidence that more productive ﬁrms tend to invest in the larger
number of foreign regions in the services sector, using the non-parametricapproach.

3

The data and descriptive statistics

3.1

Data

This section describes the data and provides some basic facts about Japanese
MNEs. I use ﬁrm-level data from the Basic Survey of Japanese Business
Structure and Activities (BSJBSA) by the METI. In this study, I refer tothis survey as “the METI survey.” The survey covers both manufacturing
and non-manufacturing industries. The targets of the METI survey are
ﬁrms with more than 50 employees and more than 30 million yen in capital.
The survey, therefore, excludes small ﬁrms. Nevertheless, it is the mostcomprehensive for my study among the surveys currently available in Japan,
and it has been used by many studies including Nishimura et al. (2005),
Kimura and Kiyota (2006), and Wakasugi et al.(2008). A more detailed*1

Melitz (2003) and Bernard et al. (2007b) are standard theoretical papers. Bernard etal. (2007a) provide a concise survey of recent studies.

3

Table 1: Distribution of ﬁrms (Japan, 2008)

Number of ﬁrms
share of each sector
fraction of ﬁrms
with domestic aﬃliateswith foreign aﬃliates
in North America
in Europein Asia
in other region

Agriculture andrelated industries
51
0.2%

Manufacturing

Services

Total

13,62446.4%

15,68053.4%

29,355100.0%

49.0%
9.8%
x
x
x
x

36.6%
23.7%
9.3%
5.3%
21.4%
2.5%

36.7%
10.5%
3.4%
1.8%
9.1%
1.0%

36.7%
16.6%
6.1%
3.4%
14.8%
1.7%

Note: Figures for less than four ﬁrms are replaced by “x.”explanation is provided in the Appendix 1.
Table 1 presents the distribution of Japanese ﬁrms in the data acrossthree sectors: (i) agriculture and related industries, (ii) manufacturing, and(iii) services. The number of ﬁrms in the whole sample is 29,355 for theyear 2008, the latest year in our data. The manufacturing sector accounts
for 46.4% of it, while the services sector accounts for 53.4%. The share of
agriculture and related industries accounts for only 0.2%.
Table 1 also reveals that in the sample, the fraction of MNEs in theservices sector, which is only 10.5%, is much lower than that in the manufacturing sector, 36.6%. The Establishment and Enterprise Census 2006 *2
also shows that the fraction of MNEs in the services sector is lower thanthat in the manufacturing sector.
In the sample, the fraction of MNEs in the services sector is lower thanthat in manufacturing sector in all four host regions: North America, Europe, Asia, and other region*3 . The most popular destination is Asia forboth manufacturing and services sectors with the fraction of MNEs investing in this region being 21.4% and 9.1%, respectively. The second mostpopular destination is North America again for both sectors, followed by
Europe. The fraction of MNEs investing in these two regions is less than
10% for both sectors, which indicates that Asia is clearly the most popular
destination.
*2

This census is conducted by the Japanese Ministry of Internal Aﬀairs and Communications.
*3
Middle East, Central and South America, Africa, and Oceania are classiﬁed as “otherregions” in the METI survey.

4

Table 2: List of services industries and the fraction of MNEs (Japan, 2008)
Industry
code
25
26
27
28
29
30
31
32
33
34
35

description
construction
electricity, gas and water supply
wholesale traderetail tradeﬁnance and insurance
real estatetransport
telecommunications
education, health, and researchbusiness services
personal service activities

N. of ﬁrms
376
123
5728
3522
86
56
133
53
119
2493
2991

fraction of
MNEs
0.072
0.114
0.165
0.043
0.058
0.089
0.098
0.113
0.092
0.087
0.085

Total

15680

0.105

I restrict my analysis to the services sector. Table 2 provides the listof services industries with the number of ﬁrms and the fractions of MNEs
in my data. The fraction of MNEs varies across industries within sector.
The wholesale trade industry has the largest fraction of MNEs among theservices industries.

3.2

The measurement of ﬁrm productivity

This section explains the measure of total factor productivity (TFP) used inthis study. I obtain Japanese parent ﬁrms’ TFP from an estimated two-digit
industry-speciﬁc production function, using Levinsohn and Petrin (2003)
techniques*4 . For output, I use Japanese parent ﬁrms’ real value added,
which is deﬂated using an industry-level deﬂator. The value added in mydata reﬂects parent ﬁrms’ domestic and export sales but not foreign aﬃliates’
sales in host countries. I employ Japanese parent ﬁrms’ hours worked (L)*5and ﬁxed tangible assets (K), as inputs.
Following Arnold and Hussinger (2010), I use the relative TFP obtainedby dividing the TFP estimates by the average TFP in the respective industry
and year, since I compare the TFP for various industries.
*4

I use transportation and package costs to proxy unobserved productivity shocks since
my data does not contain costs for electricity or materials or fuels*5
Unlike previous studies, I use hours worked as labor rather than the number of workers.
Appendix 1 provides more detailed explanation.

5

0

1

relative TFP

2

3

Figure 1 presents the average productivity of non-MNEs and MNEs inthe services sector. Figure 1 shows that on average, MNEs are more productive than non-MNEs in the services sector. This fact suggests that productivity is important for ﬁrms when considering investing abroad even in
the services sector, and that the standard ﬁrm heterogeneity model can well
explain FDI in the services sector. Next section will further examine the relationship between productivity and FDI by comparing overall productivity
distribution of non-MNEs and MNEs in the services sector.

Non−MNE

MNE

Figure 1: Mean of relative TFP
Note: The data are for Japanese ﬁrms in 2008. The graph displays the mean level ofrelative total factor productivity for MNEs and non-MNEs.
Data Source: The Ministry of Economy, Trade, and Industry (METI), the Basic Surveyof Japanese Business Structure and Activities.

4

Empirical strategy: Kolmogorov-Smirnov test

This study adopts the nonparametric one-sided and two-sided KS tests to
examine the relationship between ﬁrm productivity and foreign engagement,
following previous studies such as Girma et al. (2004) and Arnold andHussinger (2010). These tests allow to compare and rank the distributions ofmeasures of ﬁrm performance, based on the concept of ﬁrst order stochastic
dominance. Following Delgado et al. (2001), many studies in trade literature
have employed KS tests. The KS test is a stricter test of productivity
diﬀerences than just comparing mean levels of productivity, since it considers
all moments of the distribution.

6

Let F1 (ϕ) and F2 (ϕ) denote two cumulative distribution functions (CDF)
for two comparison groups. The ﬁrst-order stochastic dominance of F1 (ϕ)
relative to F2 (ϕ) is deﬁned as F1 (ϕ) − F2 (ϕ) ≤ 0 uniformly in ϕ ∈ R, withstrict inequality for some ϕ. Graphically, this implies that F1 (ϕ) lies entirely
to the right (higher-productivity side) of F2 (ϕ).
First, by the two-sided KS statistic, I test the hypothesis that F1 (ϕ) andF2 (ϕ) are identical. The null and alternative hypotheses can be expressedas
H0 : F1 (ϕ) − F2 (ϕ) = 0
vs.

for all ϕ ∈ R

H1 : F1 (ϕ) − F2 (ϕ) ̸= 0

for some ϕ ∈ R.

(1)

Second, the one-sided KS test examines the following hypotheses:
H0 : F1 (ϕ) − F2 (ϕ) ≤ 0
vs.

for all ϕ ∈ R

H1 : F1 (ϕ) − F2 (ϕ) > 0

for some ϕ ∈ R.

(2)

If I can reject the null hypothesis for the two-sided test, but not for the
one-sided test, I can conclude that F1 (ϕ) stochastically dominates F2 (ϕ).
The KS test statistics for the two-sided test is given by
√
nm
KS2 =
max |F1,n (ϕi ) − F2,m (ϕi )|,(3)
N 1≤i≤N
where n and m are the sample sizes from the empirical distributions of
F1 (ϕ) and F2 (ϕ), respectively, and N = n + m. The KS test statistics forthe one-sided test is√nm
max {F1,n (ϕi ) − F2,m (ϕi )} .
(4)
KS1 =
N 1≤i≤N
The limiting distributions of both test statistics are known under the assumption of independently drawn samples as described in Darling (1957)*6 .
*6Smirnov (1939) proposed these statistics. Kolmogorov (1933) and Smirnov (1939)
showed that under the assumption that all the observations are independent, the limiting
distribution of KS2 is given bylim P (KS2 > υ) = −2

n→∞

∞
X
(−1)k exp(−2k2 v 2 )k=1

and that of KS1 is given bylim P (KS1 > υ) = exp(−2v 2 )n→∞under H0 .

7

Following previous studies such as Delgado et al. (2002), I test the hypothesis separately for each year from 2001 to 2008, since the independence
assumption is likely to be violated if I use pooled observations from several
years for the KS test.

5

Results

Using the KS tests, this section examines whether MNEs are more productive than non-MNEs in the services sector. Figure 2 presents the TFP
distributions for the year 2008 for both MNEs and non-MNEs. The graphsupports the theoretical prediction on productivity ranking. The CDF of
MNEs lies entirely to the right of the one corresponding to non-MNEs.
1.0

probability

0.8

0.6

0.4

0.2

0.0
−10

−5
0
ln relative TFP (LP method)
domestic

5

MNEs

Figure 2: Internationalized status and CDF of productivity in the services
sectorNote: The data are for Japanese ﬁrms in 2008.
Data Source: The Ministry of Economy, Trade, and Industry (METI), the Basic Surveyof Japanese Business Structure and Activities.

Table 3 conﬁrms the theoretical prediction more formally. Column 3 ofTable 3 presents the results of the two-sided KS tests, which test the null
hypothesis for the equality of distributions between non-MNEs and MNEs.
The null hypothesis is rejected at 1% signiﬁcance level for all years. From
the result in column 4 of Table 3, I cannot reject the null hypothesis thatthe productivity distribution of MNEs stochastically dominates that of nonMNEs. These results from both tests indicate that the distributions for
MNEs dominate that of Non-MNEs. I, therefore, can conclude that MNEs
are likely to be more productive than non-MNEs even in the services sector.
8

Table 3: KS tests statistics for services

year
2001
2002
2003
2004
2005
2006
2007
2008

Non-MNEs vs. MNEs
N. of ﬁrms
StatisticTwo-sidedNI
H0 : equality
13334 1275
0.403
(91.3) (08.7)[0.000]12998 1324
0.388
(90.8) (09.2)[0.000]12569 1346
0.396
(90.3) (09.7)[0.000]13296 1522
0.380
(89.7) (10.3)[0.000]12928 1488
0.358
(89.7) (10.3)[0.000]13388 1503
0.360
(89.9) (10.1)[0.000]13862 1596
0.355
(89.7) (10.3)[0.000]14035 1645
0.354
(89.5) (10.5)[0.000]One-sided
H0 : N < I
0.000[1.000]0.000[1.000]0.000[1.000]0.000[1.000]0.000[1.000]0.000[1.000]0.000[1.000]-0.003[0.978]Notes: KS tests for non-MNEs (N) vs. MNEs (I). Asymptotic P-values are shown inbrackets. The share of each ﬁrm type in all types is shown in parenthesis.

96

Number of FDI destinations

This section examines the relationship between the number of FDI destinations and ﬁrm productivity. As shown in Yeaple (2009), the ﬁrm heterogeneity model based on Helpman et al. (2004) predicts a “pecking order”
such that ﬁrms with higher productivity have their aﬃliates in a larger number of countries, while less productive ﬁrms invest in a smaller number ofcountries. In other words, ﬁrms with higher productivity can enter evenless attractive countries because their productivity will exceed the cut-oﬀ
productivity for a larger number of countries, while less productive ﬁrms
can enter more attractive countries only.
The METI survey asks a ﬁrm whether it has a subsidiary in the following four foreign regions: Asia, North America, Europe, and other regions.
Therefore, the number of FDI destinations vary across ﬁrms from zero tofour in our data. The majority of ﬁrms do not have their foreign subsidiaries.
For these non-MNEs, the number of FDI destinations is zero. Among MNEs,
one-region MNEs, i.e., MNEs with subsidiaries in one foreign region, are themajority. Four-region MNEs, i.e., MNEs with subsidiaries in four foreign
regions, are a minority.
1.0

probability

0.8
0.6
0.4
0.2
0.0
−10

−5
0
ln relative TFP (LP method)
0
2
4

5

1
3

Figure 3: The number of FDI Destinations and CDF of productivity in theservices sector
Note: The data are for Japanese ﬁrms in 2008.
Data Source: The Ministry of Economy, Trade, and Industry (METI), the Basic Surveyof Japanese Business Structure and Activities.

Figure 3 presents the TFP distribution by the number of FDI destinations in the services sector, for the year 2008. The ﬁgure shows that the10more destinations ﬁrms invest in, the higher-productivity ranges they are
distributed over. The TFP distribution of non-MNEs is located on the left
side of that of MNEs. The distribution of four-region MNEs are located on
the right side of those of the other types of MNEs. These results are consistent with the theoretical prediction that the most productive ﬁrms can entereven the least attractive foreign regions, while the less productive ﬁrms canenter more attractive regions only. Table 4 presents the KS tests statistics.
The results also conﬁrm the theoretical prediction. We can conclude that
more productive ﬁrms tend to invest in a larger number of foreign regionsin the services sector.
Table 4: Kolmogorov-Smirnov tests statistic: the number of FDI destination(2008)
N. of ﬁrms
N
14035
-89.5

I1
1165
(07.4)

StatisticTwo-sided
H0 : equality
0.322[0.000]

I1
1165
-7.4

I2
273
(01.7)

H0 : equality
0.239[0.000]H0 : I1 < I2
-0.009[0.963]

I2
273
-1.7

I3
135
(00.9)

H0 : equality
0.383[0.000]H0 : I2 < I3
-0.001[1.000]

I3
135
-0.9

I4
72
(00.5)

H0 : equality
0.254[0.000]H0 : I3 < I4
-0.007[0.972]One-sided
H0 : N < I1
-0.003[0.973]

Notes: The data are for Japanese ﬁrms in 2008. The Table shows the Kolmogorov-Smirnov
tests statistics for non-MNEs (N ) vs. MNEs with subsidiaries in one region (I1 ), MNEs
with subsidiaries in one region (I1 ) vs. MNEs with subsidiaries in two regions (I2 ), MNEs
with subsidiaries in two regions (I2 ) vs. MNEs with subsidiaries in three regions (I3 ),MNEs with subsidiaries in three regions (I3 ) vs. MNEs with subsidiaries in four regions(I4 ). Asymptotic P-values are shown in brackets. The share of each ﬁrm type in all types
is shown in parenthesis.

11

7

Robustness check

This section conducts a number of robustness checks. First, this section
focuses on a more narrowly deﬁned services sector, while the above analysis employs a broader deﬁnition. In the above analysis, the services sector
includes not only pure services industries but also wholesale and retail industries as shown in Table 2. I focus on data on the personal services activities
industry, since ﬁrms in this industry is pure services ﬁrms in the sense thatthey are assumed to provide services directly to foreign consumers*7 . Figure
4 presents the CDF of productivity by MNE status in the personal services
industry and supports the model’s prediction that MNEs are more productive than non-MNEs.
1.0

probability

0.8

0.6

0.4

0.2

0.0
−6

−4

−2
0
ln relative TFP (LP method)
Non−MNEs

2

4

MNEs

Figure 4: Internationalized status and CDF of productivity in the personal
services industry
Note: The data are for Japanese ﬁrms in 2008.
Data Source: The Ministry of Economy, Trade, and Industry (METI), the Basic Surveyof Japanese Business Structure and Activities.

I also conduct the KS tests to examine whether MNEs are more productive than non-MNEs in the personal services industry. The results areshown in Table 5. I can reject the null hypothesis of the two-sided tests
but cannot reject that of the one-sided tests at conventional levels for all
years. These results are consistent with the ﬁrm heterogeneity model andthe previous results.
*7

Tanaka (2011) provides the results from the other services industries. Almost all
results are consistent with the theoretical prediction.

12

Table 5: KS tests statistics for personal services

year
2001
2002
2003
2004
2005
2006
2007
2008

Non-MNEs vs. MNEs
N. of ﬁrms
StatisticTwo-sidedNI
H0 : equality
2334
148
0.468
(94.0) (06.0)[0.000]
2308
170
0.413
(93.1) (06.9)[0.000]
2234
172
0.399
(92.9) (07.1)[0.000]
2360
206
0.418
(92.0) (08.0)[0.000]
2273
201
0.376
(91.9) (08.1)[0.000]
2416
219
0.390
(91.7) (08.3)[0.000]
2637
247
0.337
(91.4) (08.6)[0.000]
2737
254
0.354
(91.5) (08.5)[0.000]One-sided
H0 : N < I-0.002[0.998]-0.004[0.996]-0.009[0.977]-0.003[0.997]-0.002[0.998]0.000[1.000]0.000[1.000]-0.003[0.995]

Notes: KS tests for non-MNEs (N) vs. MNEs (I). Asymptotic P-values are shown inbrackets. The share of each ﬁrm type in all types is shown in parenthesis.

13

As a second robustness check, I conduct the KS tests using labor productivity instead of TFP. The results are similar to the previous results andsupport theoretical predictions. Third, the results excluding ﬁrms with employees in the manufacturing or mining sections from the sample yield thesame results as the previous ones. Finally, I have replicated the results using
only MNEs whose foreign subsidiaries have the same industry code as theJapanese parent ﬁrm.

8Concluding remarks

This study is the ﬁrst attempt in examining the relationship between productivity distribution and multinational status in the services sectors. Little
is known about the determinants of FDI by ﬁrms in the services sector, while
many previous studies have focused on FDI by ﬁrms in the manufacturingsector. This study reveals that MNEs in the services sector tend to be moreproductive than non-MNEs. This result suggests that ﬁrms in the services
sector must incur huge costs for foreign engagement as those in the manufacturing sector do, and that only a minority of productive ﬁrms in the services
sector can incur these costs and supply foreign consumers with their services.

14

Acknowledgment
I would like to thank participants in the seminars held at Kyoto and Keio
Universities and RIETI and the Spring 2011 conference of the Japan Societyof International Economics for their valuable comments and suggestions. I
also thank the RIETI Data Division and METI for providing me with accessto conﬁdential ﬁrm-level data, Haruka Yasunaga for excellent research assistance, and other RIETI staﬀ for supporting this research project. I gratefully
acknowledge the ﬁnancial support from the Japan Society for the Promotion of Science’s Grants-in-Aid for Scientiﬁc Research (No. 22830134). I am
solely responsible for any remaining errors.

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Appendix 1: Data
This appendix describes the data sources.
The ﬁrm-level data are from the Basic Survey of Japanese Business
Structure and Activities (BSJBSA), which is an annual survey conductedby the Ministry of Economy, Trade, and Industry (METI). METI requires
all ﬁrms in the selected industries with more than 50 employees and morethan 30 million yen in capital to respond to the survey. While the numberof target enterprises is 38,042, the number of enterprises that responded in2009 is 32,265*8 —the survey aimed to obtain data on the previous ﬁnancial
year, 2008. The response rate is therefore 84.8%. The response rate in our
sample period, 2001–2008, is almost stable.
The variables used in this study are as follows.
1. Labor (L): the number of total working hours of all kinds of workers
in Japan by ﬁrm. Labor does not include number of hours worked by
employees in foreign aﬃliates. I use hours rather than the number ofworkers, because working hours substantially vary across three kindsof workers which the survey contains: regular employees, part-timeworkers, and dispatched workers. Moreover, ﬁrms in the services sector employ more part-time workers than those in the manufacturingsector. I constructed the total working hours as the number of eachtype of workers multiplied by its average working hours. The industry
average hours for regular employees and part-time workers are provided by the Ministry of Health, Labor and Welfare’s Monthly Labor
Survey, while the country average hours for dispatched workers arecalculated as yearly wage divided by hourly wage, both of which aretaken from the General Survey on Dispatched Workers.
2. Real sales: Sales divided by deﬂator. The industry deﬂator is takenfrom the Cabinet Oﬃce’s System of National Accounts (SNA) Statistics as shown in Morikawa (2010). Sales includes both domestic andexport sales, while they do not include local sales by foreign aﬃliates.
3. Labor productivity: real value added per hour worked. Value added
are calculated as the sum of operating proﬁt, depreciation cost, total
wage, welfare costs, rents, and taxes. Operating proﬁt is deﬁned as
sales minus operating cost, where the operating cost is the sum of costof sales and SGA (Selling and General Administrative expenses).
*8

http://www.meti.go.jp/statistics/tyo/kikatu/result-2/h21kakuho/pdf/
riyochu.pdf

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4. TFP: total factor productivity. I estimate TFP as the residual of CobbDouglas production function with K and L inputs. I use real value
added as the output. Production function coeﬃcients are estimatedseparately for two-digit industries, using Levinsohn and Petrin (2003)
method. I use transportation and package costs to proxy unobserved
productivity shocks.

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