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

The Causal Effects of Exporting on Domestic Workers:
A Firm-Level Analysis using Japanese Data

Ayumu Tanaka

Discussion Paper No. E-11-009

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

February 2012

The Causal Eﬀects of Exporting on Domestic Workers:
A Firm-Level Analysis using Japanese Data
Ayumu Tanaka†February 7, 2012

Abstract
Japan has experienced rapid growth of non-regular workers under
the globalization in 2000s. This study seeks to identify the causal
eﬀects of exporting on growth of labor and growth of the share of
non-regular workers in Japanese manufacturing and wholesale sectors,
using an extensive ﬁrm-level data. I employ propensity score matching
technique and investigate whether ﬁrms that start exporting experiencehigher growth of labor and higher growth of the share of non-regular
workers than non-exporters. I ﬁnd positive eﬀects of exporting on labor
growth in manufacturing but I ﬁnd little evidence for the eﬀects on theshare of non-regular workers in both sectors.
Keywords: exporting; non-regular workers; ﬁrm heterogeneity
JEL Classiﬁcation: F16, J31, L81†

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

Japan has experienced rapid growth of the share of non-regular workers in
2000s under the globalization*1 . The share of the non-regular workers hasgrown rapidly from 26.0% of 2000 to 33.7% of 2010*2 . Some argued that this
rapid growth of non-regular workers were partly caused by the globalization.
To examine the argument, this study seeks to identify the causal eﬀects ofexporting on the share of non-regular workers, using an extensive Japaneseﬁrm-level data. I employ propensity score matching technique and investigate whether ﬁrms that start exporting experience increase in the share of
non-regular workers, compared with non-exporters. Unlike previous studies, I examine the eﬀects of exporting in not only manufacturing but also
wholesale sector, where many ﬁrms conduct exporting*3 .
Against the above argument, I ﬁnd little evidence that export-starters
have increased share of non-regular workers than non-exporters in both sectors. Japanese ﬁrms that started exporting during the period, 2003–2005,
experienced higher growth of labor but not of the share of non-regular workers than non-exporters in manufacturing. In contrast to manufacturing, inwholesale they have not experienced higher growth of labor. They have increased share of non-regular workers than non-exporters but the eﬀects ofexporting disappeared three years after starting exporting.
The remainder of this paper is divided into six sections. In Section
2, I discuss possible link between exporting and the share of non-regular
workers under the current situation in Japanese labor market. In Section
3, I introduce my empirical strategy. In Section 4, I brieﬂy describe thedata and variables used in this paper and present descriptive statistics ofthe data. In Section 5, I present the estimation result of ﬁrms’ decision tostart exporting. In Section 6, I report the causal eﬀects of exporting. The
summary and conclusion are presented in the ﬁnal section.

*1

According to the World Bank’s World Development Indicators 2010, in Japan, theshare of exports in GDP has grown to 17.60% of 2007, from 10.99% of 2000.
*2
Labour Force Survey by the Japanese Statistics Bureau of the Ministry of Internal
Aﬀairs and Communications.
*3
Bernard et al. (2010) revealed that wholesalers accounted for 10% of the 2002 U.S.
exports.

2

2

Japanese labor market and the eﬀects of exporting

In Japan, ﬁrms can employ both regular and non-regular workers. Nonregular workers consists of part-time and dispatched workers. Firms can employ dispatched workers, even in manufacturing after deregulation in 2004*4 .
Hiring costs are relatively low for both types of non-regular workers, compared with regular workers. Firms can easily ﬁre non-regular workers since
their employment terms are short. Thus, labor market for the non-regular
workers can be regarded as less frictional, compared with one for regular
workers.
Dispatched workers are diﬀerent from part-time workers in several dimensions. Most important diﬀerence is that dispatched workers are employed indirectly by ﬁrms from intermediary agents by paying fees to theagents*5 , while part-time workers are employed directly by ﬁrms. Anotherdiﬀerence is that dispatched workers work for longer hours per day thanpart-time workers. In some ﬁrms, dispatched workers have the same tasks
as regular workers but they earn much lower wage.
The relationship between exporting and the share of non-regular workers is theoretically unexplored. Recently, several theoretical studies suchas Helpman et al. (2010) consider the relationship between exporting andworkers at home, assuming imperfect labor market*6 . However, those studies did not consider the relationship between exporting and the share of
non-regular workers. In addition, no studies examine the eﬀects of exporting in wholesale sector. This study tries to provide ﬁrst evidence on therelationship between exporting and the share of non-regular workers in bothmanufacturing and wholesale sectors.
The relationship between exporting and the share of non-regular workers
is unclear. Exporting may results in decrease in the ratio of non-regular
workers in total labor since it requires the skilled workers while non-regular
workers are less skilled than regular workers*7 .
*4Asano et al. (2011) provide more detailed explanation.
The agents pay wage to dispatched workers from the fee. The average ratio of thefee to the wage received by the workers is around 1.47 in 2008, which is calculated, basedon the Japanese Ministry of Health, Labor and Welfare’s General Survey on Dispatched
Workers.
*6
Research on the relationship between trade and wage has long tradition. Recent
development was surveyed by Harrison et al. (2010).
*7
Average wage of non-regular workers are much lower than those of regular workers,
as shown in Section 4.
*53

On the other hand, exporting ﬁrms may need higher ratio of non-regular
workers since they face high volatility of export sales. They may prefer
non-regular workers since they can ﬁre non-regular workers easily once theirexport sales drop*8 .
The eﬀects of exporting on the growth of labor is also unclear. Exporting may bring about increase in labor as a whole in manufacturing. This isjust because exporting ﬁrms need more labor to produce products for foreign markets. However, I cannot predict whether exporting increases laborin wholesale. Most wholesale ﬁrms may not need additional labor for exports because they do not produce products by themselves but instead theyprocure and export products produced by manufacturing ﬁrms.
In sum, Japanese labor market consists of diﬀerent kinds of workers andthe eﬀects of exporting on domestic workers are theoretically unclear. Thus,
the impacts of exporting on labor and the share of non-regular workers inJapan are empirical questions requiring the analysis of disaggregate ﬁrmlevel data.

3

Empirical strategy: propensity score matching

To evaluate the causal eﬀects of exporting on the growth of labor and growthof the share of non-regular workers, I use propensity score matching. Many
previous studies in trade literature have employed this technique, including
Wagner (2002) and Girma et al. (2004).
The causal eﬀects of ﬁrm i’s exporting on the outcome variables, ∆y,can be written as follows:
10∆yi,t+s− ∆yi,t+s(1)where y are log of labor and the share of non-regular workers in my analysis.
Superscript 0 refers to the case of non-treatment (non-exporting), and 1 totreatment (switching to exporting). t is the year of switching.
As pointed out in the previous studies, the fundamental problem of the0
is unobservable. I adopt the propensity scorecausal inference is that ∆yi,t+s
matching techniques to construct an appropriate counterfactual that can be0
used instead of ∆yi,t+s. Using this techniques, I examine the average eﬀectof treatment on the treated (ATT) as
10δ = E(∆yi,t+s− ∆yi,t+s|Dit = 1)(2)

10= E(∆yi,t+s|Dit = 1) − E(∆yi,t+s|Dit = 1)*8

Indeed, exporting ﬁrms have ﬁred many dispatched workers in Japan during the GreatRecession, 2008–2009. This became an object of public concern.

4where Dit is an indicator variable of whether ﬁrm i started exporting forthe ﬁrst time at year t. Using the propensity score matching techniques, I
0construct the counterfactual for the last term, E(∆yi,t+s|Dit = 1).
To construct the counterfactual, I, ﬁrst, estimate the propensity score to
start exporting:P (Dit = 1) = F (ln T F Pi,t−2 , ln KAP IN Ti,t−2 , RDIN Ti,t−2 ,(3)

ln AGEi,t−2 , F OREIGNi,t−2 , M N Ei,t−2 ,ln Li,t−2 , year, industry)where F is logistic cumulative distribution function. T F P , KAP IN T ,RDIN T , AGE, F OREIGN , M N E, and L are total factor productivity,
capital intensity (capital-labor ratio), R&D intensity (R&D-sales ratio), ﬁrmage, share of foreign owner in stock, indicator variable for multinational enterprise, and labor, respectively. year and industry are year and industry
ﬁxed eﬀects. The choice of explanatory variables follows the previous studies
such as Hijzen et al. (2007) and Ito (2007).
Firms are matched using several matching methods. In the case of
the nearest-neighbor (one-to-one) matching method with replacement, nonexporter, c(i), which has the closest propensity score to start exporting, isselected for each export starter i, as follows:c(i) =

minj∈{Djt =0}||Pˆit − Pˆjt ||.

(4)

After constructing the control group by this matching, the ATT will be
estimated.

4

Data

I use ﬁrm-level data from the Basic Survey of Japanese Business Structure
and Activities (BSJBSA) by the Japanese Ministry of Economy, Trade, andIndustry (METI). In this study, I refer to this 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 andmore than 30 million yen in capital. The survey, therefore, excludes small
ﬁrms. Nevertheless, it is the most comprehensive for my study among thesurveys 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).

5

Table 1: Firm types in Japan (2003–2005 cohorts)

Manufacturing
Wholesale
Total

Non-Exporters
16,3827623
24,005

Starters
318
80
398

Exporters
6940
2211
9,151

Others
15699
7548
23,247

Total
39,33917462
56,801

Notes: The number of ﬁrms are based on three years balanced panel of cohort, which isoriginally constructed from Japanese ﬁrms’ panel data for the period 2001–2008. Starters
are deﬁned by ﬁrms that started exporting during 2003–2005. Non-exporters are ﬁrms
that did not export during the all 6 years, [t − 2, t + 3], while exporters are ﬁrms thatexported during the all 6 years.

4.1

Panel of cohort

Following Hijzen et al. (2011), I construct a three years panel of cohort ofswitchers, i.e. ﬁrms that start exporting, and non-switchers from Japaneseﬁrms’ panel data for the period 2001–2008. Cohorts are deﬁned as 6-year
windows, [t − 2, t + 3], where t is the year in which domestic non-exporters
may start exporting. In my data, switch year t is in [2003, 2005]. I impose
the condition that within a 6-year window the panel is balanced.
Table 1 reports the total number of non-exporters, switchers, and exporters in my data. Switchers are ﬁrms that started exporting during theperiod 2003–2005. Non-exporters are ﬁrms that did not export during theall 6 years, [t − 2, t + 3], while exporters are ﬁrms that exported during theall 6 years.
Exporting and ﬁrst-time exporting, are prevalent in manufacturing andwholesale sectors. I, therefore, restrict my analysis on these two sectors. My
data set includes a total of 318 erxport-starters in manufacturing and 80 inwholesale.

4.2

Labor variables

As already mentioned, in Japan, ﬁrms can employ three kinds of workers: (i)regular workers, (ii) part-time workers, and (iii) dispatched workers. These
three kinds of workers’ wage and hours worked are substantially diﬀerent
from each other. Table 2 reports the country average wage and hours workedof the three kinds of workers. It shows that regular workers work for longer
hours and obtain more than twice higher hourly wage than part-time ordispatched workers. The diﬀerence between part-time and dispatched work-

6

Table 2: Country average of wage and hours worked in Japan (2008)

Regular worker
Part-time worker
Dispatched worker(A)
wage per hour
2,712.1
1,082.0
1,290.0(B)
hours worked per year
1,995.1
1,167.1
1,829.5(B) / 260 days
hours worked per day
7.7
4.5
7.0

Notes: The data on regular and part-time workers are from Monthly Labour Survey, while
the data on dispatched workers are from General Survey on Dispatched Workers.

ers is that dispatched workers work for much longer hours than part-timeworkers. Regular workers, in turn, work for longer hours than dispatchedworkers.
I use total hours worked by all kinds of workers in Japan as ﬁrm-levelmeasure of labor, L. Labor does not include hours worked by employees
in foreign aﬃliates. I use hours worked rather than the number of workers,
because hours worked substantially vary across the three kinds of workers.
I construct the ﬁrm-level total hours worked (L) as the number of eachtype of workers multiplied by its average yearly hours worked as follows:
L = Nr × H r + Np × H p + Nd × H d(5)where N and H are the number of workers and yearly total hours worked,
respectively. The subscripts r, p, and d indicate regular, part-time, anddispatched workers, respectively. The industry average yearly hours worked
for regular employees and part-time workers are provided by the Japanese
Ministry of Health, Labor and Welfare’s Monthly Labor Survey, while thecountry average hour for dispatched workers are calculated as yearly wage
divided by hourly wage, both of which are taken from the Ministry’s theGeneral Survey on Dispatched Workers.
Tables 3 and 4 present the descriptive statistics of wage, labor, andworkforce composition in manufacturing and wholesale for the year, 2005.
N ON REGR, DISP AT CHR, and P ART R are deﬁned as
N ON REGR =DISP AT CHR =P ART R =Np × Hp + Nd × Hd
× 100,
L
Nd × Hd
× 100, andL
Np × Hp
× 100,
L
7(6)

Table 3: Descriptive statistics of labor variables in manufacturing (2005)

Non-exporter

Mean
SD
N

Wr
(yen)
2804.41201.95412

L548552.3
1877998.0
5451

N ON REGR
(%)
13.1
16.3
5451

DISP AT CHR
(%)
5.2
9.6
5451

P ART R
(%)
8.0
13.7
5451

Export-starter

Mean
SD
N

3103.21299.1
95

621310.2
822760.9
95

11.8
12.2
95

6.3
10.4
95

5.6
8.1
95

Exporter

Mean
SD
N

3578.41364.82311

1991312.0
6713637.0
2364

11.1
11.6
2364

5.8
8.7
2364

5.3
8.6
2364

Total

Mean
SD
N

3036.81301.87818

980612.4
4042340.0
7910

12.5
15.0
7910

5.4
9.4
7910

7.1
12.4
7910

Table 4: Descriptive statistics of labor variables in wholesale (2005)

Non-exporter

Mean
SD
N

Wr
(yen)
2707.0
825.2
2512

L
422859.8870287.92516

N ON REGR
(%)
10.0
13.8
2516

DISP AT CHR
(%)
2.0
5.1
2516

P ART R
(%)
8.0
13.0
2516

Export-starter

Mean
SD
N

3276.21084.828735634.7
2390070.0
28

7.9
7.9
28

3.0
4.9
28

4.9
7.2
28

Exporter

Mean
SD
N

3365.2995.7
723859055.1
3679521.0
726

7.2
8.6
726

3.7
5.3
726

3.6
7.3
726

Total

Mean
SD
N

2857.7910.73263

522381.4
1914609.0
3270

9.4
12.9
3270

2.4
5.2
3270

7.0
12.1
3270

8

respectively. Assuming that both part-time and dispatched workers’ wage
are determined by labor market outside individual ﬁrm*9 , I construct theﬁrm-level hourly real wage of regular workers, Wr , as follows:Wr =W C − Np × Hp × Wp
Nr × Hr(7)

where W C is real wage cost of a ﬁrm from the METI survey and Wp isindustry average hourly real wage of part-time workers from Monthly Labor Survey. W C includes real wage cost of regular and part-time workers
only*10 .
In both sectors, wage of regular workers is on average highest in exporters, followed by export-starters. The wage is lowest in non-exporters.
Similarly, exporters are on average the largest in terms of labor, exportstarters are the second largest, and non-exporters are the smallest. Bothresults are consistent with the ﬁrm heterogeneity model of export but do
not imply the causal eﬀects of exporting on wage and labor.
As for the share of non-regular workers, standard deviation is too large
to judge any ordering but, on average, share of dispatched workers are lower
but share of part-time workers in labor are higher in non-exporters thanexport-starters and exporters in both sectors. These tendency results in thefact that the share of non-regular workers are on average higher in nonexporters than export-starters and exporters.

4.3

The measurement of ﬁrm productivity

Next, I explain the measure of total factor productivity (TFP) used later inthis study. I obtain Japanese parent ﬁrms’ TFP from an estimated two-digit
industry-speciﬁc production function, using Levinsohn and Petrin (2003)
techniques. I use transportation and package costs to proxy unobserved
productivity shocks*11 . For output, I use Japanese parent ﬁrms’ real value
added, which is deﬂated using the industry-level deﬂator. The value addedin my data reﬂects parent ﬁrms’ domestic and export sales but not foreign
aﬃliates’ sales in host countries. I employ Japanese parent ﬁrms’ hours
worked (L) and ﬁxed tangible assets (K), as inputs.
*9This assumption is plausible but it is well known that hourly wage of part-time workers
varies across regions in Japan. I, however, cannot control this region-eﬀect due to lack of
the data.
*10
Wages and wage cost are deﬂated by the industry deﬂator, which is taken from the
Cabinet Oﬃce’s System of National Accounts (SNA) Statistics.
*11
My data does not contain costs for electricity or materials or fuels.

9

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 use the TFP from various industries.

5

Decision to start exporting

In order to construct the control group, I, ﬁrst, estimate the propensity
score to start exporting, using a sample of non-exporters and export-starters.
Table 5 shows the estimation result of equation (3).
In both manufacturing and wholesale, R&D intensity and multinational
status has large impacts on the decision to start exporting. As for productivity, the positive coeﬃcients on TFP is statistically signiﬁcant in wholesale*12 , but not in manufacturing against the standard ﬁrm heterogeneity
model. Insigniﬁcant coeﬃcient on TFP in manufacturing is surprising but
it can be interpreted that R&D intensity and multinational status reﬂect
technological advantage required for exporting.
In wholesale, both capital-labor ratio and ﬁrm size, measured as labor,
have negative coeﬃcients. This suggests that smaller and labor-intensiveﬁrms are more likely to start exporting in wholesale.

6

Causal eﬀects of exporting

6.1

Growth of labor

Constructing the counterfactual based on estimated propensity score, I examine causal eﬀects of exporting. I present the results from one and threenearest neighbor matching and kernel matching *13 . First, I investigate theeﬀects on the growth of labor. Tables 6 and 7 report the results in manufacturing and wholesale, respectively. The results make a striking contrast
between the two sectors. In manufacturing, I ﬁnd positive eﬀects of exporting on labor growth in following three years after starting exporting. On
the other hand, in wholesale, I ﬁnd no signiﬁcant eﬀects on labor growth.

*12
*13

This result is consistent with Tanaka (2010).
The balancing property is satisﬁed for all matching.

10Table 5: Decision to start exporting(1)
Manufacturing
0.045[0.151](1)
Wholesales
0.952***[0.266]0.036[0.069]-0.220***[0.082]

RDINT (t-2)

12.060***[2.441]42.737***[14.259]

ln AGE (t-2)

0.135[0.113]0.058[0.301]

FOREIGN (t-2)

0.081[0.493]0.611[0.645]

MNE (t-2)

1.508***[0.183]

1.151**[0.466]ln L (t-2)

0.083[0.173]-1.015***[0.336]

Year FE
Industry FE

Yes
Yes

Yes
Yes

15876
0.108

7589
0.077

ln TFP (t-2)

ln KAPINT (t-2)

Observations
Pseudo-R-squared

Notes: Standard errors are shown in brackets. Constants are suppressed. ***, **, *indicate signiﬁcance at the 1%, 5%, and 10% levels, respectively.

11

12

t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3

0.080
0.117
0.113
0.080
0.117
0.113
0.080
0.117
0.113
0.080
0.117
0.113

0.034
0.056
0.050
0.037
0.066
0.058
0.034
0.056
0.050
0.034
0.056
0.050

(2)
Controls
0.046
0.062
0.063
0.042
0.051
0.055
0.046
0.062
0.063
0.046
0.062
0.063

(3)
ATT
3.14
3.57
2.93
3.51
3.62
3.18
3.14
3.57
2.93
3.14
3.57
2.93

(4)
t-value
**
**
**
**
**
**
**
**
**
**
**
**

(5)
Bootstrapped
t-value
2.58
2.82
2.51
3.16
3.07
2.86
2.58
2.82
2.51
2.58
2.82
2.51

**
**
**
**
**
**
**
**
**
**
**
**

(6)
Balancingproperty
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

replications. Kernel matching uses the Epanechnikov kernel. ** and * indicate signiﬁcance at the 5% and 10% levels, respectively.

common support condition is imposed. ATT is the average treatment eﬀect on the treated; bootstrapped t-values are based on 100

Notes: The ﬁgures in columns (1) and (2) are the change from t-1 in the log of variables. The number of treated ﬁrms are 315. The

Kernel matching (bwith = 0.03)

Kernel matching (bwith = 0.01)

Three nearest neighbours

Matching
One nearest neighbour(1)TreatedTable 6: The causal eﬀect of exporting on growth of labor in manufacturing

13

t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3

0.050
0.061
0.061
0.050
0.061
0.061
0.050
0.061
0.061
0.050
0.061
0.061

0.016
0.060
0.058
0.046
0.077
0.089
0.016
0.060
0.058
0.016
0.060
0.058

(2)
Controls
0.034
0.001
0.003
0.004
-0.017-0.028
0.034
0.001
0.003
0.034
0.001
0.003

(3)
ATT
1.34
0.03
0.08
0.20
-0.64
-1.03
1.34
0.03
0.08
1.34
0.03
0.08

(4)
t-value(5)
Bootstrapped
t-value
1.14
0.02
0.07
0.17
-0.51
-0.70
1.14
0.02
0.07
1.14
0.02
0.07

(6)
Balancingproperty
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

replications. Kernel matching uses the Epanechnikov kernel. ** and * indicate signiﬁcance at the 5% and 10% levels, respectively.

common support condition is imposed. ATT is the average treatment eﬀect on the treated; bootstrapped t-values are based on 100

Notes: The ﬁgures in columns (1) and (2) are the change from t-1 in the log of variables. The number of treated ﬁrms are 80. The

Kernel matching (bwith = 0.03)

Kernel matching (bwith = 0.01)

Three nearest neighbours

Matching
One nearest neighbour(1)TreatedTable 7: The causal eﬀect of exporting on growth of labor in wholesales

This sharp contrast between the two sectors in the eﬀects of exportingon labor growth can be explained by the fundamental diﬀerence between
manufacturing ﬁrms and wholesale ones. Basically, pure wholesale ﬁrms export goods purchased from manufacturing ﬁrms, while pure manufacturing
ones export goods produced by themselves. Thus, manufacturing ﬁrms needadditional labor to produce goods for exports, while wholesale ﬁrms do notneed such additional labor for production. This story is consistent with theresults that the signiﬁcantly positive eﬀects of exporting on labor growth isfound only in manufacturing.

6.2

Share of non-regular workers

Next, I examine causal eﬀects of exporting on the share of non-regular workers. First, Tables 8 and 9 report the eﬀects on the share of dispatched workers in manufacturing and wholesale, respectively. In manufacturing, I do notﬁnd any eﬀects of exporting on the share of dispatched workers. In wholesale, the eﬀects of exporting on the share of dispatched workers are found to
be positive in some cases two years after starting exporting. However, thestatistical signiﬁcance level is low and the eﬀects disappeared next year.

14

15

t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3

1.563
1.942
1.685
1.563
1.942
1.685
1.563
1.942
1.685
1.563
1.942
1.685

1.255
1.999
2.268
1.237
1.922
2.251
1.255
1.999
2.268
1.255
1.999
2.268

(2)
Controls
0.308-0.057-0.5820.327
0.020-0.5650.308-0.057-0.5820.308-0.057-0.582

(3)
ATT
0.47
-0.08
-0.77
0.61
0.03
-0.91
0.47
-0.08
-0.77
0.47
-0.08
-0.77

(4)
t-value(5)
Bootstrapped
t-value
0.40
-0.07
-0.59
0.51
0.03
-0.70
0.40
-0.07
-0.59
0.40
-0.07
-0.59

(6)
Balancingproperty
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

100 replications. Kernel matching uses the Epanechnikov kernel. ** and * indicate signiﬁcance at the 5% and 10% levels, respectively.

The common support condition is imposed. ATT is the average treatment eﬀect on the treated; bootstrapped t-values are based on

Notes: The ﬁgures in columns (1) and (2) are the change from t-1 in the variables (percentage). The number of treated ﬁrms are 315.

Kernel matching (bwith = 0.03)

Kernel matching (bwith = 0.01)

Three nearest neighbours

Matching
One nearest neighbour(1)TreatedTable 8: The causal eﬀect of exporting on growth of share of dispatched workers in manufacturing

16

t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3

0.367
0.903
0.709
0.367
0.903
0.709
0.367
0.903
0.709
0.367
0.903
0.709

-0.106-0.151
0.064
0.437
0.641
0.666
-0.106-0.151
0.064-0.106-0.151
0.064(2)
Controls
0.474
1.054
0.644
-0.070
0.262
0.043
0.474
1.054
0.644
0.474
1.054
0.644

(3)
ATT
0.97
1.95
0.89
-0.16
0.54
0.06
0.97
1.95
0.89
0.97
1.95
0.89

(4)
t-value

*

*

*

(5)
Bootstrapped
t-value
0.73
1.34
0.74
-0.13
0.40
0.06
0.73
1.34
0.74
0.73
1.34
0.74

(6)
Balancingproperty
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Notes: The ﬁgures in columns (1) and (2) are the change from t-1 in the variables (percentage). The number of treated ﬁrms are 80.
The common support condition is imposed. ATT is the average treatment eﬀect on the treated; bootstrapped t-values are based on
100 replications. Kernel matching uses the Epanechnikov kernel. ** and * indicate signiﬁcance at the 5% and 10% levels, respectively.

Kernel matching (bwith = 0.03)

Kernel matching (bwith = 0.01)

Three nearest neighbours

Matching
One nearest neighbour(1)TreatedTable 9: The causal eﬀect of exporting on growth of share of dispatched workers in wholesale

Second, Tables 10 and 11 report the eﬀects of exporting on the share ofpart-time workers. Again, I do not ﬁnd any eﬀects of exporting on the shareof part-time workers in both sectors. Share of part-time workers in totallabor in export starters did not show statistically signiﬁcant relative increase
after the export-starting year, compared with those in non-exporting control
group.
To summarize, there are little evidence that exporting causes the increasein the share of non-regular workers in both manufacturing and wholesale
sectors. This result suggests that export-starters need high skilled workers
even under the high volatility of export sales, therefore, they do not increase
the share of non-regular workers.

17

18

t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3

0.393
0.327
0.847
0.393
0.327
0.847
0.393
0.327
0.847
0.393
0.327
0.847

-0.294
0.002
0.220
0.231
0.430
0.565
-0.2940.002
0.220-0.294
0.002
0.220

(2)
Controls
0.687
0.325
0.627
0.162
-0.102
0.281
0.687
0.325
0.627
0.687
0.325
0.627

(3)
ATT
1.41
0.58
1.01
0.40
-0.21
0.61
1.41
0.58
1.01
1.41
0.58
1.01

(4)
t-value(5)
Bootstrapped
t-value
1.20
0.44
0.84
0.31
-0.17
0.46
1.20
0.44
0.84
1.20
0.44
0.84

(6)
Balancingproperty
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

100 replications. Kernel matching uses the Epanechnikov kernel. ** and * indicate signiﬁcance at the 5% and 10% levels, respectively.

The common support condition is imposed. ATT is the average treatment eﬀect on the treated; bootstrapped t-values are based on

Notes: The ﬁgures in columns (1) and (2) are the change from t-1 in the variables (percentage). The number of treated ﬁrms are 315.

Kernel matching (bwith = 0.03)

Kernel matching (bwith = 0.01)

Three nearest neighbours

Matching
One nearest neighbour(1)TreatedTable 10: The causal eﬀect of exporting on growth of share of part-time workers in manufacturing

19

t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3
t+1
t+2
t+3

0.088
0.214
0.779
0.088
0.214
0.779
0.088
0.214
0.779
0.088
0.214
0.779

0.640
0.921
0.996
0.142
0.645
0.786
0.640
0.921
0.996
0.640
0.921
0.996

(2)
Controls-0.552-0.707-0.217-0.053-0.431-0.007-0.552-0.707-0.217-0.552-0.707-0.217

(3)
ATT
-0.72
-0.87
-0.28
-0.08
-0.62
-0.01
-0.72
-0.87
-0.28
-0.72
-0.87
-0.28

(4)
t-value(5)
Bootstrapped
t-value
-0.51
-0.62
-0.17
-0.06
-0.46
-0.01
-0.51
-0.62
-0.17
-0.51
-0.62
-0.17

(6)
Balancingproperty
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

100 replications. Kernel matching uses the Epanechnikov kernel. ** and * indicate signiﬁcance at the 5% and 10% levels, respectively.

The common support condition is imposed. ATT is the average treatment eﬀect on the treated; bootstrapped t-values are based on

Notes: The ﬁgures in columns (1) and (2) are the change from t-1 in the variables (percentage). The number of treated ﬁrms are 80.

Kernel matching (bwith = 0.03)

Kernel matching (bwith = 0.01)

Three nearest neighbours

Matching
One nearest neighbour(1)TreatedTable 11: The causal eﬀect of exporting on growth of share of part-time workers in wholesale

7Conclusion

This study investigates whether exporting raises the growth of labor and theshare of non-regular workers in Japan. I employ propensity score matching
technique and investigate whether ﬁrms that started exporting experienced
higher growth of labor and higher growth of the share of non-regular workers
than non-exporters, using an extensive Japanese ﬁrm-level data. First, I ﬁnd
positive eﬀects on the growth of labor in manufacturing but not in wholesale.
Second, against public fears, I ﬁnd little evidence that exporting results in
the increase in the share of non-regular workers in both manufacturing andwholesale.

Acknowledgment
I thank Toshiyuki Matsuura for his guidance on Japanese labor market, andYasuyuki Todo and Joachim Wagner for providing me with the information on the propensity score matching. I also thank Taiji Furusawa, Naoto
Jinji, James R. Markusen, Kamal Saggi, Ryuhei Wakasugi, and other participants in the 2011 annual conference of the Japan Society of International
Economics at Keio University, fall 2011 Midwest International Economics
Conference at Vanderbilt University, and seminars at RIETI for helpful comments. I gratefully acknowledge the ﬁnancial support from the Japan Society for the Promotion of Science’s Grants-in-Aid for Scientiﬁc Research (No.
22830134).

References[1] Arnold, Jens M. and Katrin Hussinger. (2010) “Exports versus FDI in
German Manufacturing: Firm Performance and Participation in International Markets,” Review of International Economics, 18(4): 595–606.
[2] Asano, Hirokatsu, Takahiro Ito, and Daiji Kawaguchi (2011) “Why Has
the Fraction of Contingent Workers Increased? A Case Study of Japan,”
RIETI Discussion Paper, No.11-E-021, March 2011.
[3] Bernard, Andrew B., Stephen J. Redding, and Peter K. Schott. (2010)“Wholesalers and Retailers in US Trade,” American Economic Review:
Papers & Proceedings, 100(2): 408–413.

20

[4] Girma, Sourafel, David Greenaway, and Richard Kneller. (2004) “Does
Exporting Increase Productivity? A Microeconometric Analysis of
Matched Firms,” Review of International Economics, 12(5): 855–866.
[5] Harrison, Ann, John McLaren, and Margaret S. McMillan. (2010)“Recent Findings on Trade and Inequality,” NBER Working Paper,
No.16425.
[6] Helpman, Elhanan, Oleg Itskhoki and Stephen Redding. (2010) “Inequality and Unemployment in a Global Economy,” Econometrica,
78(4): 1239–1283.
[7] Hijzen, Alexander, Tomohiko Inui, and Yasuyuki Todo (2007). “The Effects of Multinational Production on Domestic Performance: Evidence
from Japanese ﬁrms,” RIETI Discussion Paper, No. 07-E-006.
[8] Hijzen, Alexander, Sébastien Jean, and Thierry Mayer. (2011) “The Effects at Home of Initiating Production Abroad: Evidence from Matched
French Firms,” Review of World Economics, 147:457–483.
[9] Ito, Yukiko. (2007) “Choice for FDI and Post-FDI Productivity,” RIETI
Discussion Paper, No.07-E-049.
[10] Kimura, Fukunari and Kozo Kiyota. (2006) “Exports, FDI, and Productivity of Firm: Dynamic Evidence from Japanese Firms.” Review ofWorld Economics, 142(4): 695-719.
[11] Melitz, Marc J. (2003) “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” Econometrica, 71(6):1695–1725.][12] Nishimura, Kiyohiko G., Takanobu Nakajima and Kozo Kiyota. (2005)“Does the Natural Selection Mechanism still Work in Severe Recessions?: Examination of the Japanese Economy in the 1990s,” Journalof Economic Behavior & Organization, 58(1): 53–78.
[13] Tanaka, Ayumu. (2011) “Multinationals in the Services and Manufacturing Sectors: A Firm-level Analysis using Japanese Data,” RIETI
Discussion Paper, No.11-E-059, July 2011.
[14] Wagner, Joachim. (2002) “The Causal Eﬀects of Exports on Firm Sizeand Labor Productivity: First Evidence from a Matching Approach,”Economics Letters, 77:287–292.
21

[15] Wagner, Joachim. (2011) “Oﬀshoring and Firm Performance: Selfselection, Eﬀects on Performance, or Both?,” Review of World Economics, 147:217–247.
[16] Wakasugi, Ryuhei, Yasuyuki Todo, Hitoshi Sato, Shuichiro Nishioka,
Toshiyuki Matsuura, Banri Ito, and Ayumu Tanaka. (2008) “Internationalization of Japanese Firms: New Findings Based on Firm-Level
Data,” RIETI Discussion Paper, 08-E-036.

22

Appendix 1: descriptive statistics
Table 12: descriptive statistics for logit estimation in manufacturing (20032005)
variableln TFP (t-2)
ln KAPINT (t-2)
RDINT (t-2)
ln AGE (t-2)
FOREIGN (t-2)
MNE (t-2)
ln L (t-2)

mean-1.274-5.726
0.004
3.531
0.011
0.057
12.626

sd
1.070
1.138
0.014
0.602
0.103
0.233
0.799

N
16460
16460
16460
16460
16460
16460
16460

min
-7.315-14.780
0.000
0.000
0.000
0.000
11.316max
4.950-0.669
0.593
4.625
1.000
1.000
18.373

Table 13: descriptive statistics for logit estimation in wholesale (2003-2005)

variableln TFP (t-2)
ln KAPINT (t-2)
RDINT (t-2)
ln AGE (t-2)
FOREIGN (t-2)
MNE (t-2)
ln L (t-2)

mean-0.894-5.904
0.001
3.576
0.013
0.036
12.512

sd
0.905
1.380
0.003
0.589
0.115
0.187
0.753

23

N
7633
7633
7633
7633
7633
7633
7633

min
-6.590-12.886
0.000
0.000
0.000
0.000
11.306max
4.106-1.145
0.057
4.654
1.000
1.000
17.186