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Offshore Outsourcing and Productivity: Evidencefrom Japanese Firm-level Data Disaggregatedby Tasksroie_965 555..567
Banri Ito, Eiichi Tomiura, and Ryuhei Wakasugi*
AbstractThis paper examines the relationship of offshoring with productivity, based on the original survey data ofJapanese firms. Productivity gains were found in the firms offshoring both manufacturing and service tasks,but not in the firms offshoring only either manufacturing or service tasks. This paper also finds that firmsoffshoring to various destinations tend to be more productive than non-offshoring firms.These results suggestthat the level of firms’ engagement in offshoring is more important for productivity than whether or not firmsengage in offshoring.
1. Introduction
It is noteworthy that multinational firms are beginning to offshore a wide range ofoperations. It is also remarkable that the offshoring of not only the production of parts,intermediate goods, and final assemblies, but also financial, legal, and customer supportservices have increased. As a result, theoretical studies on offshore sourcing havebecome more popular. Grossman and Rossi-Hansberg (2008) and Baldwin andRobert-Nicoud (2007) showed that offshore sourcing contributes to higher productionefficiency. Antràs (2003) and Antràs and Helpman (2004) indicated that on the basis ofproductivity and sectoral characteristics, firms decide whether to produce intermediateinputs or outsource them. A number of empirical studies have focused on the effect ofoffshoring on the labor market in source countries (Ekholm and Hakkala, 2006; Eggerand Egger, 2006; Feenstra and Hanson, 1996, 1999; Geishecker and Görg, 2005; Headand Ries, 2002; Helg and Tajoli, 2005; Hijzen, 2006; Hijzen et al., 2005). This paper aimsto provide empirical evidence on the relation of offshoring with production efficiencyand examines empirically whether the range of offshoring (i.e. coverage of task typesand destinations) is related with firm productivity.
Although previous studies that have explored this issue using industry-level datasuggest that offshoring positively affects productivity (Amiti and Wei, 2006; Egger andEgger, 2006), analyses using firm-level data have reported mixed results. Hijzen et al.(2010) estimated the impact of offshoring on firm productivity using Japanese firm-level data for the period 1994–2000 and found that a 1% increase in offshoring intensityraises productivity growth by 0.17%. Although they showed the positive impact of
* Ito (corresponding author): Senshu University, 2-1-1 Higashimita, Tama-ku, Kawasaki-shi Kanagawa, 214-8580, Japan. Tel: +81-44-900-7988, Fax: +81-44-900-7849, E-mail: [email protected]. Tomiura:Yokohama National University, 79-4 Tokiwa-dai, Hodogaya-ku, Yokohama City, 240-8501, Japan. Tel: +81-45-339-3563, Fax: +81-45-339-3574, E-mail:[email protected]. Wakasugi: Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan. Tel: +81-75-753-7135, Fax: +81-75-753-7138, E-mail:[email protected]. The authors appreciate helpful comments and suggestions of an anonymous referee. Theauthors also thank the statistics offices of the Ministry of Economy, Trade and Industry and the ResearchInstitute of Economy, Trade and Industry for granting permission to access firm-level data.
Review of International Economics, 19(3), 555–567, 2011DOI:10.1111/j.1467-9396.2011.00965.x
© 2011 Blackwell Publishing Ltd
offshoring, their offshoring measure was restricted to the manufacturing of goods andmaterials. Recent studies that divide offshoring into two types—materials andservices—suggest that the effects of offshoring on firm productivity are different acrosstasks. For example, Görg and Hanley (2005), who used Irish firm-level data in theelectronics industry over the period 1990–1995, found the impact of offshoring on thetotal factor productivity (TFP) to be positive, when estimating the effect of offshoringof materials and services combined. They discovered, however, that the effect of off-shoring of services no longer had a significant impact when distinguishing two tasks.1
This result implies that the benefit of offshoring is different across offshored tasks.Further, the impact of offshoring on firm productivity may be dependent on the
destination. It is becoming increasingly important for firms to look for offshore sup-pliers who can provide high quality inputs at lower costs than suppliers in the homecountry, in order to raise the competitiveness of firms. Market-specific factors such asinstitutions, level of development, and costs are expected to affect offshored operations.Therefore, it is interesting to examine how the impact of offshoring changes withdestinations. This issue has not been analyzed in previous studies owing to a lack ofdata, as it is essential to utilize detailed information on offshoring.
By collecting detailed firm-level data about tasks and destinations for offshoring, thispaper examines different impacts of offshoring across various offshored operations onthe productivity, as well as possibly different impacts of offshoring over destinations.With the collaboration of the Research Institute of Economy, Trade and Industry(RIETI), we conducted a survey on offshoring by Japanese firms. This survey coverednot only offshoring of production activities, but also offshoring of services, such asR&D, information services, customer support, and professional services. The produc-tion offshoring in the survey includes final assemblies, production of intermediates, andof jigs/dies. Further, the survey collected disaggregation data on geographical destina-tions. These data help us examine the different effects of offshoring on firmproductivity, in terms of detailed operations and destinations.2
We estimate the dynamic productivity model on a large sample of Japanese manu-facturing firms for 1999–2000 and 2004–2005, controlling for the possible endogeneityof the offshoring variable.The main estimation results are summarized as follows. First,offshoring a wide range of tasks has a positive impact on productivity. The effect ofoffshoring both manufacturing and service tasks on productivity is positive but offshor-ing either only manufacturing or service tasks does not appear to affect productivity.Second, we find that the firms offshoring to various destinations are more productivecompared with non-offshoring firms. These results suggest that the level of firms’engagement in offshoring is more important than whether or not firms engage inoffshoring per se.
2. A Model of Productivity Growth
The Productivity Gain from Offshoring
The theoretical intuition that offshoring is positively related with productivity is rela-tively straightforward. Offshoring of a task is defined as unbundling the task from theproduction process and replacing it with intermediate goods produced abroad. Firmswill choose to unbundle a task from the production process and offshore it to a foreigncountry where the cost is lower or the quality of input is more sophisticated than thatavailable domestically. As a result, increase in offshoring of unbundled tasks may
556 Banri Ito, Eiichi Tomiura and Ryuhei Wakasugi
© 2011 Blackwell Publishing Ltd
improve the productivity through the access to input at lower cost or high quality(Girma and Görg, 2004; Görg and Hanley, 2005).
The theoretical study of a positive effect of offshoring on productivity has recentlybeen formulated by Grossman and Rossi-Hansberg (2008) who identified three chan-nels in terms of the effect of imported input by offshoring on domestic factor prices.They demonstrated that there is a potential productivity gain from task trade when thecost of trade in a particular task declines under the situation of unchanged prices forfinal goods, whether the offshored task is performed by low-skilled or high-skilledlabor.3 The cheaper offshoring cost lowers the cost of a task offshored to foreignsuppliers, and then the profitability increases. This effect can be considered as a sector-biased technology (TFP) change as suggested by Feenstra and Hanson (1999) andKohler (2008).4 Based on the theoretical conjecture, it is expected that offshoring firmsgain the productivity effect from offshoring through the recent reduction of offshoringcosts as a result of development of transportation and ICT. As in Feenstra and Hanson(1999) and Hijzen et al. (2010) which estimated the effects of offshoring on the TFP, wealso investigate how a one-shot change in the production process by offshoring affectsthe TFP.
Specification and Empirical Strategy
On the basis of the framework of offshoring and productivity growth, we expect thatoffshoring is positively related with a firm’s TFP growth. We decompose the sources ofTFP growth into a factor of structural change by offshoring zit, technical change attrib-uted to the growth of knowledge stock Drit, and error term eit. Since it is difficult todirectly observe the growth of knowledge stock, we express it in an alternative manneras follows:5
γ γ γΔ Δ Δ Δr
RR
RR
YR
RY
Rit
it
it
it
it
it
it
it
it
it= +⎛⎝
⎞⎠ ≈ =
− −
−
−ln 1
1 1
1
1
∂∂ RR
RYit
it
it− −=
1 1
ρ Δ, (1)
where g is the knowledge-stock elasticity of output, r = ∂Y/∂R, and DRit is the R&Dinvestment expressed in flow (Iit–1). We estimate a parameter on the basis of thefollowing equation, wherein we bring the lagged TFP term to the right-hand side inorder to account for the persistence of TFP growth:
ln lnTFP TFP z I Y eit it it it it it= + + ( ) +− − −θ φ ρ1 1 1 . (2)
In this specification, an econometric issue is the direction of causality betweenproductivity and offshoring because only firms with high productivity may select toinvolve themselves in offshoring activity owing to the existence of fixed costs such asthe search cost in finding foreign suppliers. Therefore, we treat the offshoring variablez as endogenous and estimate a two-stage least squares (2SLS) regression model.Regarding additional instruments used in the first stage estimation, we use the ratio oftotal purchase to total sales, the ratio of import to total purchase and firm age. Theseadditional instruments are supposed not to be correlated with the error term in (2), asthese variables are outside the frame of the productivity decision. It is expected that thefirms procuring more have a better chance of receiving information on suppliers andthe firms importing more have superior information on the overseas market. We addthe procurement rate and import rate as the proxy variables of external information onsuppliers in foreign countries.A firm’s age is also included in the instruments to controlfor a possible vintage effect on offshoring decisions. The orthogonality test for
OFFSHORE OUTSOURCING AND PRODUCTIVITY 557
© 2011 Blackwell Publishing Ltd
additional instruments and endogeneity test for offshoring variable are carried out tocheck the validity of instruments and 2SLS regression, respectively. In the estimation,we add two-digit industry dummy variables for industry-specific factors.
We also implement panel-data analysis, defining the error term as eit = mi + lt + mit,where mi is unobserved individual effect at the firm-level, lt is time-specific effect, anduit is the idiosyncratic error term distributed as iid. We assume that the offshoringvariable is correlated with the idiosyncratic error term, and estimate (2) by applying thegeneralized 2SLS with random effects model (G2SLS) using the same set of additionalinstruments as in the pooling estimation.6
3. Empirical Results
Data and Descriptive Statistics
The data on basic firm characteristics were collected by the Basic Survey of JapaneseBusiness Structure and Activities7 for the period 1997–2005, conducted by the JapanMinistry of Economy, Trade and Industry (METI survey hereafter). For collectinginformation on offshoring, we used the Survey of Corporate Offshore Activities, whichis an academic survey conducted by the RIETI (RIETI survey hereafter) on 14,062manufacturing firms listed in the METI survey. The RIETI survey succeeded in col-lecting responses from 5528 firms. Considering that other previously available firm-level offshoring data include only a limited number of firms and were not designed tocover the entire manufacturing industry, this survey has a clear advantage in its cover-age. Although this survey is a one-shot survey, its data includes the status of offshoringfive years ago, as a retrospective question. Hence, we matched the METI survey and theRIETI survey in 2000 and 2005. The data set allowed us to create an unbalanced paneldata set. As a result, we could draw 8072 observations on 4872 firms. In the sample, theshare of offshoring firms increases from 16% to 22% for five years (2000–2005).8
Although quantitative data are not available on how much each firm offshored, thesurvey collected detailed information on what kind of tasks were offshored to whichregions.We classified offshored tasks into the following eight categories: final assembly,production of intermediates, production of jigs/dies, R&D, information services, cus-tomer support, professional services, and other tasks. As offshoring destinations, thesurvey identifies the following five regions: China (including Taiwan and Hong Kong),ASEAN countries, other Asian countries, United States or European countries, and therest of the world. In our sample, almost all offshoring is concentrated in the Asian area.China is the home to half of offshoring firms, and ASEAN and other Asian countriesaccount for a third of the total share, indicating that East Asia is the most preferredoffshoring destination for various production processes (Fukao et al., 2003; Wakasugi,2007). Further, two tasks most frequently offshored are the production of intermediatesand the final assembly, followed by the production of jigs/dies. Offshoring of service-related tasks, however, is considerably limited.9
The first key variable of our analysis is the offshoring dummy which takes value oneif the firm engages in offshoring and zero otherwise. We found 1493 observations foroffshoring firms and 6579 observations for non-offshoring firms over the two periods inthe sample used in estimation. In addition to this offshoring dummy, we also constructthe proxy variables for the level of engagement in offshoring activity which is definedas the number of tasks or destinations, using the disaggregated information on eighttasks and five destinations identified in the RIETI survey.Although we have no data on
558 Banri Ito, Eiichi Tomiura and Ryuhei Wakasugi
© 2011 Blackwell Publishing Ltd
how much each firm is offshoring, firms offshoring a wider range of tasks to widerranges of destinations are likely to be intensively offshoring.
Furthermore, this paper also divides offshoring firms into three categories; firmsoffshoring both manufacturing and service tasks (189 observations), firms offshoringmanufacturing tasks only (1213 observations), and firms offshoring service tasks only(91 observations). In the estimation, non-offshoring firms are set to be the benchmarkfor comparison with offshoring firms. Regarding the destinations, though our data ondestinations is disaggregated into five categories, we re-aggregate them into the fol-lowing three regions—(1) the USA or European countries or rest of world (ROW), (2)China, and (3) Asia (ASEAN members or other Asian countries).To consider overlapscaused by firms offshoring to multiple regions, we constructed seven dummies if thefirm engaged in offshoring to: (1) all three regions (154 observations), (2) both Chinaand the USA or Europe (69 observations), (3) both China and Asia (308 observations),(4) both the USA or Europe and Asia (55 observations), (5) the USA, Europe or ROWonly (64 observations), (6) China only (581 observations), and (7) Asia only (262observations). Thus, the estimated coefficients identify the average differences in theproductivity of firms offshoring to each region, relative to non-offshoring firms. Table 1describes the summary statistics for each variable (8072 observations).
Table 1. Summary Statistics
Variable description Mean Std. dev. Min Max
Log TFP 1.554 0.446 -0.805 4.053Lagged Log TFP 1.521 0.455 -3.432 4.271Ratio of R&D expenditure over value added in
previous year0.035 0.073 0 0.966
2005 year dummy 0.515 0.500 0 1Dummy taking a one for offshoring firms 0.185 0.388 0 1The number of offshored tasks in which the firm
engages0.333 0.830 0 8
The number of destinations to which the firm engagesin offshoring
0.296 0.731 0 5
Offshored task dummy: both manufacturing andservice tasks
0.023 0.151 0 1
Offshored task dummy: purely manufacturing tasks 0.150 0.357 0 1Offshored task dummy: purely service tasks 0.011 0.106 0 1Destination dummy all three regions: China + USA,
Europe or ROW + Asia0.019 0.137 0 1
Destination dummy two regions: China + USA,Europe or ROW
0.009 0.092 0 1
Destination dummy two regions: China + Asia 0.038 0.192 0 1Destination dummy two regions: USA, Europe or
ROW + Asia0.007 0.082 0 1
Destination dummy one region: China 0.072 0.258 0 1Destination dummy one region: USA, Europe or
ROW0.008 0.089 0 1
Destination dummy one region: Asia 0.032 0.177 0 1Input over total sales in previous year 0.458 0.212 0.0004 1Imported input over total input in previous year 0.033 0.112 0 1Firm age 40.928 28.006 0 131
OFFSHORE OUTSOURCING AND PRODUCTIVITY 559
© 2011 Blackwell Publishing Ltd
Estimation Results
Before we report our main estimation results, it will be informative to overview off-shoring premia in various firm characteristics. Offshoring premia are estimated fromthe following regression form using data in 2000 and 2005, respectively:
lnY z IND ui i i= + + +α β θ0 , (3)
where Yi indicates various firm attributes, a0 is constant, zi is a dummy variable foroffshoring firms, and IND is a vector of two-digit industry dummy variables. Theestimated b represents offshoring premia. The results are presented in Table 2.
The numbers indicate the coefficients of zi estimated in (3) and the numbers inparentheses are standard errors. All the coefficients are positive and significant at the1% level for both years. The result indicates that the largest differences betweenoffshoring and non-offshoring firms are in total sales and employment, in line withprevious studies which show that internationalized firms are larger than domestic firms.We also found that offshoring firms pay higher wages and are more capital intensive.10
Offshoring may change the composition of skilled and unskilled labor. For example,when the offshoring of unskilled tasks is carried out, it is predicted that skilled intensityincreases.The skill intensity of offshoring firms—defined as the number of employee inheadquarters over total employee—is 2–2.8% higher than that of non-offshoring firms.The average difference in R&D intensity ranges from 2.7% to 3.4%.
As for the productivity, we estimate TFP for each firm over 1997–2005.11 To avoid theendogeneity problem, the production function is estimated by the Levinsohn andPetrin (2003) procedure.12 Value added is defined as the total sales minus the sum of
Table 2. Offshoring Premia of Firm Attributes
Offshoring premia
2000 2005
Total sales 0.949 0.904[0.062]** [0.052]**
Total employment 0.700 0.631[0.049]** [0.040]**
Average wage 0.074 0.110(Wage payment/labor) [0.014]** [0.015]**Capital/labor ratio 0.245 0.313(Capital stock/labor) [0.048]** [0.040]**Skill intensity 0.020 0.028(Non-production labor/total) [0.005]** [0.005]**R&D intensity 0.034 0.027(R&D expenditure/value added) [0.004]** [0.003]**Labor productivity 0.110 0.182(Value added/labor) [0.019]** [0.022]**Estimated TFP 0.055 0.119
[0.019]** [0.021]**
Notes: Numbers indicate the estimated coefficient in equation (3). Stan-dard errors in brackets. *,** indicate significance at the 5% and 1% levels,respectively.
560 Banri Ito, Eiichi Tomiura and Ryuhei Wakasugi
© 2011 Blackwell Publishing Ltd
cost of goods sold and general and administrative costs plus wage payments, rental,depreciation, and tax costs. Table 2 demonstrates that labor productivity is 11–18%higher for offshoring firms, while TFP is 6–12% higher.
While the comparison of averages based on offshoring premium is straightforward,we need to control for relevant firm characteristics and possible endogeneity. In whatfollows, we report our principal estimation results on the relation between offshoringand productivity and how it varies according to task types (i.e. both manufacturing andservice, manufacturing only, and service only). The basic equation (2) is estimated byapplying the pooled ordinary least squares (OLS), two-stage least squares (2SLS),random effects model (RE), and the generalized 2SLS with random effects model(G2SLS) for the unbalanced panel of 1999–2000 and 2004–2005. The estimation resultsare presented in Table 3. The columns from (1) to (4) present the estimates from aspecification employing the dummy for offshoring, and the columns (5) and (8) presentthe results with the number of offshored tasks, and the columns (9) and (12) show theresults with the number of offshoring destinations, and the columns (13) and (16)examine how the effect differs across manufacturing and service tasks. All modelsinclude industry dummies and a year dummy.
First, we note the appropriateness of our estimation techniques. The estimatedresults of the pooled OLS estimator show that the coefficient of the offshoring variableis not significant owing to the possible endogeneity problem, except for the result of thenumber of destinations displayed in the column (9). Interestingly, the estimator ischanged drastically by 2SLS.13 Although the test results are omitted to conserve space,we checked the validity of employing 2SLS regression technique for the pooled data.For example, the Durbin–Wu–Hausman tests of exogeneity of the offshoring dummyvariable reject the null hypothesis of no endogeneity. The results of the underidentifi-cation test (Anderson canonical correlations Langrange multiplier (LM) statistic)reject the null hypothesis that the equation is underidentified. As for the weak identi-fication test (Cragg–Donald Wald F statistic), the result supports the substantialcorrelation between the excluded instruments and the offshoring variable except thecase of introducing three task dummy variables as endogenous variables. Although theunder-identification is rejected for the case of model introducing task dummy variables,the estimator may be biased given the weak identification problem that the instrumentsare only weakly correlated with endogenous variables. Regarding the panel analysis,the results of the Breusch–Pagan test demonstrates that the RE model is favorable incomparison with the pooled OLS estimator for all specifications. To cope with theendogeneity of offshoring variable, G2SLS is also estimated. The exogeneity of theoffshoring variable is still rejected for the panel data. The Hansen test for overidenti-fying restrictions indicates that the instruments are orthogonal as for the models incolumns (4), (8) and (12). In the case of introducing the task dummy variables, theHausman test for over-identification supports the validity of instruments.
As shown in column (2), the coefficient of the offshoring dummy estimated by 2SLSregression becomes significant and positive. The significance of the offshoring dummyis not altered when we apply G2SLS regression as presented in the column (4). Theresults from 2SLS regression imply that offshoring leads to a 8–9% increase in TFP.Similarly, the coefficient of the number of offshored tasks is also positive and significantfor 2SLS regression in the column (6). The similar result is obtained from G2SLSregression as displayed in the column (8). This result suggests that the firms widelyoffshoring two or more types of tasks enjoy the productivity gain from offshoring.As analternative measure for the level of engagement in offshoring, we also estimate themodel with the number of offshoring destinations. The coefficients of the number of
OFFSHORE OUTSOURCING AND PRODUCTIVITY 561
© 2011 Blackwell Publishing Ltd
Tabl
e3.
Est
imat
ion
Res
ults
Dep
ende
ntva
riab
le:
ln(T
FP
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)(1
1)(1
2)(1
3)(1
4)(1
5)(1
6)O
LS
2SL
SR
EG
2SL
SO
LS
2SL
SR
EG
2SL
SO
LS
2SL
SR
EG
2SL
SO
LS
2SL
SR
EG
2SL
S
Off
sori
ngdu
mm
y0.
015
0.07
70.
018
0.09
5[0
.008
][0
.035
]*[0
.008
]*[0
.039
]*L
evel
ofen
gage
men
tm
usur
edby
num
ber
ofta
sks
0.00
80.
034
0.01
0.04
2
[0.0
05]
[0.0
15]*
[0.0
04]*
[0.0
17]*
Lev
elof
enga
gem
ent
mus
ured
bynu
mbe
rof
dest
inat
ions
0.01
30.
039
0.01
60.
05
[0.0
05]*
*[0
.019
]*[0
.005
]**
[0.0
22]*
Bot
hm
anuf
actu
ring
and
serv
ice
task
s0.
035
0.96
20.
050.
797
[0.0
23]
[0.4
61]*
[0.0
21]*
[0.2
66]*
*O
nly
man
ufac
turi
ngta
sks
0.01
5-0
.073
0.01
8-0
.087
[0.0
09]
[0.0
67]
[0.0
09]*
[0.0
49]
Onl
yse
rvic
eta
sks
-0.0
32-0
.091
-0.0
370.
805
[0.0
33]
[0.8
60]
[0.0
29]
[0.4
80]
Lag
ged
ln(T
FP
)0.
760.
757
0.72
70.
733
0.76
0.75
70.
727
0.73
40.
759
0.75
60.
726
0.72
80.
760.
746
0.72
60.
746
[0.0
19]*
*[0
.007
]**
[0.0
07]*
*[0
.007
]**
[0.0
19]*
*[0
.007
]**
[0.0
07]*
*[0
.007
]**
[0.0
19]*
*[0
.007
]**
[0.0
07]*
*[0
.007
]**
[0.0
19]*
*[0
.009
]**
[0.0
07]*
*[0
.009
]**
Lag
ged
(R&
D/v
alue
adde
d)0.
405
0.34
10.
433
0.35
10.
40.
336
0.42
70.
346
0.38
70.
325
0.41
30.
331
0.40
20.
111
0.42
70.
091
[0.0
71]*
*[0
.055
]**
[0.0
45]*
*[0
.058
]**
[0.0
72]*
*[0
.055
]**
[0.0
45]*
*[0
.058
]**
[0.0
73]*
*[0
.062
]**
[0.0
46]*
*[0
.066
]**
[0.0
72]*
*[0
.121
][0
.045
]**
[0.0
95]
Yea
rdu
mm
y-0
.051
-0.0
54-0
.05
-0.0
54-0
.051
-0.0
54-0
.05
-0.0
53-0
.052
-0.0
53-0
.05
-0.0
53-0
.051
-0.0
54-0
.05
-0.0
56[0
.006
]**
[0.0
06]*
*[0
.005
]**
[0.0
05]*
*[0
.006
]**
[0.0
06]*
*[0
.005
]**
[0.0
05]*
*[0
.006
]**
[0.0
06]*
*[0
.005
]**
[0.0
05]*
*[0
.006
]**
[0.0
07]*
*[0
.005
]**
[0.0
07]*
*In
dust
rydu
mm
ies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Con
stan
t0.
487
0.48
0.54
10.
516
0.48
60.
481
0.54
10.
517
0.48
70.
486
0.54
20.
528
0.48
70.
507
0.54
20.
51[0
.031
]**
[0.0
14]*
*[0
.014
]**
[0.0
15]*
*[0
.031
]**
[0.0
14]*
*[0
.014
]**
[0.0
14]*
*[0
.031
]**
[0.0
13]*
*[0
.014
]**
[0.0
14]*
*[0
.031
]**
[0.0
19]*
*[0
.014
]**
[0.0
17]*
*
Obs
erva
tion
s80
7280
7280
7280
7280
7280
7280
7280
7280
7280
7280
7280
7280
7280
7280
7280
72G
roup
s48
7248
7248
7248
7248
7248
7248
7248
72R
2 :w
ithi
n0.
316
0.31
70.
316
0.31
70.
316
0.31
70.
317
0.27
7be
twee
n0.
721
0.71
60.
721
0.71
70.
722
0.71
80.
721
0.62
6ov
eral
l0.
681
0.68
10.
677
0.68
10.
681
0.67
70.
680.
682
0.67
80.
680.
681
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562 Banri Ito, Eiichi Tomiura and Ryuhei Wakasugi
© 2011 Blackwell Publishing Ltd
destinations are significant and positive even if it is treated as an endogenous variableand controlling for the unobserved individual effects. This result is supportive of theview that offshoring covering a wide range of regions has a positive impact on firmproductivity in the home country.
One would expect that a part of these productivity gains from offshoring may be dueto the possible increase in return to R&D investment since offshoring of tasks allowsfirms to specialize in high-skill intensive production stages such as R&D activity. Todistinguish the direct effect of offshoring on productivity growth from the indirecteffect through R&D, the specification excluding the lagged R&D intensity is alsoestimated.14 The estimation results without R&D are omitted for the sake of brevity butavailable upon request. The coefficients of the offshoring variable turn out to be largerthan that of the full model presented in Table 3. The difference in the two offshoringestimates with/without R&D suggests that the indirect effect through R&D accountsfor approximately 30–40% of the overall effect on productivity.
The results of estimation introducing task dummy variables in the columns (13)–(16)indicate that the effect of offshoring on firm productivity concentrates on the firmoffshoring both manufacturing and service tasks.Although the magnitude of coefficientestimates on task dummy variables are slightly changed when we apply 2SLS regres-sion, the sign of the dummy variable for offshoring both the manufacturing and servicetask is still strongly positive and significant. As for the dummy for offshoring of themanufacturing task or service task only, we do not find positive effects that are signific-ant and stable across different models. We therefore do not have robust evidencesupporting the different impacts on productivity according to the type of task off-shored, but we found across various models that offshoring both manufacturing andservice tasks at the same time is positively related with productivity.
Differences across Destinations
Table 4 displays the results for the model introducing dummy variables disaggregatedinto seven groups of geographical destinations as described in section 3. The columns(1) and (2) show the result of pooled OLS and that of the random effects model,respectively.The 2SLS regression is not applied here because it is practically difficult tofind additional instruments for separate regions. The result of the pooled OLS in thecolumn (1) indicates that the coefficients of the two dummies are statistically significantand positive. The coefficients of the dummy for offshoring to all three regions (USA–Europe–ROW, China and Asia) show the significant productivity difference relative tonon-offshoring firms. The dummy for firms offshoring to both China and Asia is alsosignificantly positive at the 1% level. In addition to this, the dummy for offshoring tothe USA, Europe or ROW only is also significant at the 5% level when we apply therandom effects model.15 A joint Wald test for the equality of coefficients across alldestination dummy variables yields a c2 statistic of 21.3 with a P-value of 0.0016, andrejects the equality. From the order in the magnitude of the coefficients among statist-ically significant dummies (all regions, China and Asia, and USA–EU–ROW only), itseems that the firms that are offshoring exclusively to the USA, Europe or ROW arelikely to have high productivity, compared with the firms offshoring to other regions.The hypothesis that the equality of coefficients across three destination dummies is notrejected (the Wald test yields a c2 statistic of 0.36 with a P-value of 0.8335). However,we found a statistically significant difference between the coefficients of “China only”or “Asia only” and that of “USA–EU–ROW only”. These results suggest that there isa difference in the productivity according to the destinations.
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From the above series of results, it can be judged that offshoring firms with widerange of tasks or destinations gain productivity improvements from offshoring. Theresults are in line with the theoretical conjecture presented by Grossman and Rossi-Hansberg (2008). They indicated that the magnitude of the effect on productivitydepends on the level of engagement in offshoring prior to decrease in the costs ofoffshoring for task trade. Our findings are consistent with this if we interpret thecoverage of offshored tasks or destinations as the level of engagement in offshoring.The productivity effect of offshoring seems to work through the extensive task-marginrather than the extensive firm-margin.
4. Conclusions
This paper has examined the relation between offshoring and productivity growthusing Japanese firm-level data in manufacturing industries over the periods 1999–2000
Table 4. Differences in Impact across Destinations
Dependent variable: ln(TFP)(1) (2)
OLS RE
All regions 0.052 0.065[0.026]* [0.023]**
Both China and USA–EU–ROW -0.029 -0.002[0.036] [0.033]
Both China and Asia 0.053 0.056[0.016]** [0.016]**
Both USA–EU–ROW and Asia 0.037 0.037[0.036] [0.037]
Only China -0.017 -0.013[0.012] [0.012]
Only USA–EU–ROW 0.064 0.077[0.037] [0.034]*
Only Asia 0.021 0.02[0.016] [0.017]
Lagged ln(TFP) 0.754 0.72[0.020]** [0.007]**
Lagged (R&D/value added) 0.391 0.413[0.072]** [0.046]**
Year dummy -0.05 -0.048[0.006]** [0.005]**
Industry dummies Yes YesConstant 0.496 0.552
[0.031]** [0.014]**
Observations 8072 8072Groups 4872R2: within
betweenoverall
0.3160.723
0.68 0.681
Notes: Standard errors in brackets; *,** indicate significance at the 5% and 1% levels, respectively. “Asia”includes ASEAN and other Asian countries.
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and 2004–2005. The empirical result from the 2SLS regression technique indicates thatoffshoring has a positive impact on firm productivity, and that it is consistent with thefindings of related previous studies that used Japanese firm-level data retrieved fromother sources (Hijzen et al., 2010; Tomiura, 2007).
However, it is found that the productivity gain from offshoring concentrates on thefirms that engage in offshoring with the wide coverage of tasks and destinations. Thisresult implies that the level of engagement of firms in offshoring activities is moreimportant than whether or not firms engage in offshoring at all, and is in line with thetheoretical result by Grossman and Rossi-Hansberg (2008). One would expect that theextensive task-margin is more pronounced than the extensive firm-margin. On thispoint, whether the intensive margin is substantial or not will be a remaining importantissue, while the data on how much each firm is offshoring is, of course, not easilyavailable. Regarding the distinction between manufacturing and service tasks in off-shoring, we have found that offshoring both manufacturing and service tasks at thesame time is positively related with the offshoring firm’s productivity.
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Notes
1. They also report that the positive effect from offshored materials holds only for plants withlow export intensities. Görg et al. (2008), which extended data coverage to 1990–1998 and allmanufacturing industries and took into account the status of trade activity and ownership, reportsimilar results on labor productivity.2. Ito et al. (2007) provided a comprehensive description of this survey.3. They also show that whether both low-skilled and high-skilled workers can share in the gainsfrom improved opportunities for offshoring depends on the changes in the relative price betweenlow-skilled and high-skilled workers and on the changes in labor supply for each type of worker.4. Offshoring also enhances the aggregate productivity through entry and exit of firms (Antràset al., 2005).5. For the derivation of knowledge capital flow, we drew on Griffith et al. (2003), Jones (2002),and Fors (1996).6. We adopted the G2SLS estimator by Balestra and Varadharajan-Krishnakumar (1987). Thefixed effect model could not be applied here because of the unbalanced panel data over the twoperiods. Baltagi and Chang (1994) showed that estimating only balanced data extracted from
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unbalanced data leads to a complete loss of validity.We also estimate the equation by taking firstdifference to remove the individual effect using balanced data; however, the results were almostthe same.7. This annual national survey is mandatory for all firms with 50 or more employees and whosepaid-up capital is over 30 million yen in mining, manufacturing, wholesale, retail, and food andbeverage industries.8. See Ito et al. (2007) for detailed information of the RIETI survey.9. Less than 5% of the firms are offshoring service tasks. See Ito et al. (2007) for detail.10. The capital stock is calculated by the perpetual inventory method, using the book value offixed tangible assets and investment data from the METI surveys.11. The deflators are taken from the Japan Industry Productivity (JIP) Database 2008, whichprovides comprehensive data at the three-digit industry-level for Japan for the period 1970–2005.The depreciation rate is also derived from the JIP database.12. The purchase of input is used as a proxy variable of productivity shock. Labor share andcapital share are set at 0.76 and 0.23, respectively.We also have used investment as an alternativeproxy, as proposed by Olley and Pakes (1996); however, the results were almost the same. Tocover firms with zero investment, we choose the estimator by the Levinsohn–Petrin procedure.13. Since the instrumental-variables regression assumes that the endogenous variables are con-tinuous, we also apply a two-step procedure described by Wooldridge (2002, pp. 623–24). First, weestimate probit model using the set of regressors in the equation for productivity growth andadditional instruments, and obtain predicted probabilities. In the second step, we estimate 2SLSregression using the fitted probabilities as instruments. The estimated coefficients are moreefficient but the main results were found to be almost the same.14. The estimation excluding R&D to isolate indirect effects of offshoring is suggested by ananonymous referee.15. The result of the Breusch–Pagan test rejects the pooling estimation and supports the randomeffects model.
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