ERIA-DP-2009-04

ERIA Discussion Paper Series

Learning-by-exportingin Korean Manufacturing:
A Plant-level Analysis
Chin Hee HAHN *Korea Development Institute, Korea

Chang-Gyun PARK †College of Business Administration, Chung-Ang University, Korea

March 2009

Abstract: The paper analyzes whether firms that start exporting become moreproductive utilizing recently developed sample matching procedures to control the
problems from self-selection into the export market. We use plant level panel data on
Korean manufacturing sector from 1990 to 1998. We find clear and robust empiricalevidence in favor of the learning-by-exporting effect; total factor productivity
differentials between exporters and their domestic counterparts arises and widens
during several years after export market entry. We also find that the effect is more
pronounced for firms that have higher skill-intensity, higher share of exports in
production, and are small in size. Overall, the evidence suggests that exporting is one
important channel through which domestic firms acquire accesses to advanced
knowledge and better technology. Also, the stronger learning-by-doing effect forfirms with higher skill-intensity seems to support the view that “absorptive capacity”
matters to receive knowledge spillovers from exporting activity.
Keywords:

Learning-by-exporting, Productivity, Propensity score matching.

JEL Classification: F14, O12, O19.
*

Chin Hee Hahn: Senior Research Fellow, Korea Development Institute; chhahn@kdi.re.kr.
Chang-Gyun Park: Assistant Professor, College of Business Administration, Chung-Ang
University; cp19@cau.ac.kr.
†

1. Introduction

One of the most frequently asked question in trade and growth literature is whether
and how international trade or openness of trading regime promotes productivity
growth of countries. Although numerous studies, both theoretical and empirical, havebeen conducted on this issue, there seems to be no clear consensus yet.

Recently, a

growing number of studies have started to utilize firm or plant level data and reexamined this issue, particularly focusing on exporting as a channel of international
technology diffusion or knowledge spillover. One empirical regularity emerging fromthese studies is that exporters are more productive than non-exporters.

The positive

correlation between exporting and productivity in cross-sectional context, however,provides little useful information on the direction of causality.

On one hand, this

could reflect self-selection into export market: only productive firms can expect torecoup the sunk entry cost of entering into the export market and join the export
market.

In this case, the causality runs from productivity to exporting.

On the other

hand, it is also plausible that the positive correlation between exporting andproductivity reflects learning-by-exporting effect: firms that become exporters could
gain new knowledge and expertise after entering export market and improve theirproductivity relative to average player in the same industry.

The self-selection

hypothesis is supported by most studies, but the evidence on learning-by-exporting
seems less clear-cut (Tybout 2000).
This paper examines the exporting-productivity nexus utilizing the plant level
panel data on Korean manufacturing sector (Survey of Mining and Manufacturing,
SMM henceforth) from 1990 to 1998. The main question to be addressed is whether

1exporting activity improves productivity performance of plants.

The emphasis onlearning-by-exporting in the paper stems from the recognition that it is the area where
existing literature presents mixed empirical results and, nevertheless, whether or not
the learning-by-exporting effect exists has an important implication on the formulationof appropriate policy stance toward “openness”.

As discussed by Bernard and Jensen(1999a), if the gains do accrue to firms once they become exporters, then theappropriate policy interventions would be those that reduce barriers to entering foreign
markets including macroeconomic trade policies to promote openness to trade andmicroeconomic policies to reduce entry costs, such as export assistance, information
programs, joint marketing efforts, and trade credits.

On the other hand, if there are no

post-entry rewards from exporting, these policies designed to increase the numbers ofexporters are more likely to end up wasting resources. 4
Furthermore, this paper attempts to clarify the conditions, if at all, under which thelearning-by-exporting may or may not take place, utilizing information on some plantor industry characteristics. As plant characteristics, we consider skill-intensity, export
propensity, plant size, and R&D intensity. Most existing studies utilized information
only on whether a plant exports or not and focused on the existence of learning-byexporting effect. However, it is plausible that the degree of learning-by-exporting
could be related to, for example, how important exporting activity is to the plant
involved, in as much as learning-by-exporting arises through interactions with foreign
buyers which requires costly resources. Thus, we examine whether plants with higher
export propensity enjoys more benefits of learning-by-exporting.

Meanwhile, if

knowledge spillovers from exporting activities require domestic “absorptive capacity”,

4

See Bernard and Jensen (1999a) for detailed discussion.

2

then we could expect that plants with higher absorptive capacity will exhibit stronger
learning-by-exporting.

We use the skill-intensity of plants as a proxy for the domestic

absorptive capacity.
We also examine whether the destination of exports matter in learning-byexporting a là Loecker (2007). He shows that the degree of learning-by-exporting
depends on destination of exports, using plant level information on the export
destination in Slovenian manufacturing.

The analysis is based on the presumptionthat learning-by-exporting effect will be stronger for plants that start exporting to moreadvanced countries.

In case of Korea, however, the plant level information about theexport destination is not available.

So, we examine instead whether plants inindustries with higher share of exports to advanced countries tend to exhibit stronger
learning-by-exporting.
Examining these issues in the Korean case is particularly important in several
respects. Above all, as well recognized, Korea is one of the few success countries
that has narrowed the income gap with advanced countries by adopting an outwardoriented trade strategy. 5

So, examining and clarifying the openness-productivity

nexus in the Korean case could provide valuable lessons on other developing countries
that hope to catch-up with advanced countries.

Furthermore, Korea is a country withlarge external exposure in trade that still needs to make a transition toward a fully
developed country.

Thus, in so far as learning-by-exporting, if it exists, reflects trade-

related uni-directional knowledge spillovers from advanced to less-advanced countries,
Korea is the appropriate place to examine these issues.
There are some empirical studies that scrutinize the causal relationship between

5

See Krueger (1997), for example.

3

exporting and productivity. Most studies report that exporters are more productive
than non-exporters before they start to export, suggesting that cross-sectionalcorrelation between exporting and productivity partly reflects a self-selection effect.
For example, Clerides, Lach and Tybout (1998) find very little evidence that previous
exposure to exporting activities improves performance, using the plant-level panel datafrom Colombia, Mexico, and Morocco. Similar results are reported by Aw, Chung,and Roberts (2000) and Aw, Chen, and Roberts (2001) for Taiwan, Bernard and Jensen(1999b) for U.S. By contrast, the evidence on a learning effect is mixed.

Earlier

research such as Bernard and Jensen (1999b) find little evidence in favor of learning.
They report that new entrants into the export market experience some productivity
improvement at around the time of entry, they are skeptical about the existence ofstrong learning-by-exporting effect. However, several recent studies utilizing morerefined empirical technique to deal with self-selection problem such as matchedsampling techniques provide some empirical evidence in favor of learning-byexporting.

See Girma, Greenaway, and Kneller (2004) for UK, De Loecker (2007)

for Slovenia, and Albornoz and Ercolani (2007) for Argentina.
Related previous studies on Korea include Aw, Chung, and Roberts (2000) and
Hahn (2004).

Aw, Chung, and Roberts (2000), using plant-level panel data on Korean

manufacturing for three years spaced at five-year intervals, does not find evidence infavor of either self-selection or learning-by-exporting.

It differs from similar studies

on other countries in that even the self-selection hypothesis is not supported.

Aw,

Chung, and Roberts (2000) argue that Korean government’s investment subsidies tied
to exporting activity rendered plant productivity a less useful guide on the decision toexport.

By contrast, following the methodologies of Bernard and Jensen (1999a,4

1999b), Hahn (2004) finds some supporting evidence for both selection and learning in
Korean manufacturing sector, using annual plant-level panel data from 1990 to 1998.
However, Hahn (2004) suffers from the same technical difficulties as Bernard andJensen (1999a, 1999b) in that the uncontrolled self-selection problem in export market
participation may have contaminated the result.
In this paper, we re-examine the learning-by-exporting hypothesis in Korean
manufacturing sector controlling for the self-selection in export market participation
with a recently developed statistical tool: propensity score matching.
The organization of this paper is as follows. The following section explains thedata set and the calculation of plant total factor productivity. Section 3 briefly discusses
the estimation strategy to overcome the difficulties arising from self-selection indecision making for export market participation and to obtain a better estimate for theeffects of learning-by-exporting.

Section 4 discusses our main empirical results andthe final section concludes.

2. Data and Plant Total Factor Productivity

2.1. Data
This paper utilizes the unpublished plant-level census data underlying the Surveyof Mining and Manufacturing in Korea.

The data set covers all plants with five or

more employees in 580 manufacturing industries at KSIC (Korean Standard Industrial
Classification) five-digit level.

It is an unbalanced panel data with about 69,000 to

97,000 plants for each year from 1990 to 1998. For each year, the amount of exports5

as well as other variables related to production structure of plants, such as production,
shipments, the number of production and non-production workers and the tangible
fixed investments, are available. The exports in this data set include direct exports
and shipments to other exporters and wholesalers, but do not include shipments forfurther manufacture.

2.2. Plant Total Factor Productivity
Plant total factor productivity (TFP) is estimated following the chainedmultilateral index number approach as developed in Good (1985) and Good, Nadiri,and Sickles (1996).

This procedure uses a separate reference point for each cross-section of observations and then chain-links the reference points together over time.
The reference point for a given time period is constructed as a hypothetical firm with
input shares that equal the arithmetic mean input shares and input levels that equal thegeometric mean of the inputs over all cross-section observations.

Thus, output, inputs,

and productivity level of each firm in each year is measured relative to the hypotheticalfirm at the base time period. This approach allows us to make transitive comparisonsof productivity levels among observations in panel data set. 6
Specifically, the productivity index for firm i at time t in our study is measured in
the following way.

6

Good, Nadiri, and Sickles (1996) summarize the usefulness of chaining multilateral productivity
indices. While the chaining approach of Tornqvist-Theil index, the discrete Divisia, is useful intime series applications where input shares might change over time, it has severe limitations incross-section or panel data framework where there is no obvious way of sequencing theobservations. To the contrary, the hypothetical firm approach allows us to make transitive
comparisons among cross-section data, while it has an undesirable property of sample dependency.
The desirable properties of both chaining approach and the hypothetical firm approach can beincorporated into a single index by chained-multilateral index number approach.

6

(

)

t

(

)

ln TFPit = ln Yit − ln Yt + ∑ ln Yτ − ln Yτ −1 −
τ =2

t
N
1
N 1

nit
nt
nit
nt
+
−
+
(
)(ln
ln
)
( Snτ + Snτ − 1)(ln Xnτ − ln Xnτ − 1)
S
S
X
X
∑
∑
∑
τ = 2 n =1 2
 n =1 2
(1)

where Y , X , S , and TFP denote output, input, input share, TFP level, respectively,
and symbols with an upper bar are corresponding measures for the hypothetical firm.
The subscripts τ and n are indices for time and inputs, respectively. The year 1990
is chosen as the base year.
As a measure of output, we use the gross output (production) of each plant in theSurvey deflated by the producer price index at disaggregated level.

The capital stock

used in this paper is the average of the beginning and end of the year book value ofcapital stock in the Survey deflated by the capital goods deflator.

As for labor input,

we use the number of workers, which includes paid employees 7, working proprietors
and unpaid family workers.

We allowed for the quality differential between

production workers and all other types of workers.

The labor quality index of thelatter was calculated as the ratio of non-production workers’ and production workers’
average wage at each plant, averaged again over the entire plants in a given year.

The

sum of “major production cost” and “other production cost” reported in the Survey was
taken as the measure of intermediate input.

Major production cost covers costs

arising from materials, parts, fuel, electricity, water, manufactured goods outsourced
and maintenance. Other production cost covers expenditures on outsourced services
such as advertising, transportation, communication and insurance.
intermediate input was deflated by the intermediate input price index.

7

Paid employees is the sum of production and non-production workers.

7

The estimatedWe assumed constant returns to scale production technology so that the sum offactor elasticities equals to one.

Labor and intermediate input elasticities for eachplant are measured as average factor cost shares within the same plant-size class in thefive-digit industry in a given year. Here, plants are grouped into three size classes
according to the number of employees; 5-50, 51-300, and over 300.

Thus, the factor

elasticities of plants are allowed to vary across industries and plant size classes andover time.

2.3. Definition of Exporters
Following convention in the literature, we define an exporter in a given year as aplant reporting positive amount of exports. Accordingly, non-exporters in a given
year are those plants with zero exports.

With this definition of exporters, it is possible

to classify all plants into five sub-groups: Always, Never, Starters, Stoppers, andOther. 8“Always” is a group of plants that were exporters in the year that they first

appear in the data set and never changed their exporting status.

Similarly, “Never” isa group of plants that were non-exporters in the year that they first appear in the data
set and never switched to exporters. “Starters” includes all plants that were nonexporters in the year that they first appear, but switched to exporters in some later year
and remained as exporters thereafter. “Stoppers” consists of all plants that were
exporters in the year that they first appear, and then switched to non-exporters, neverswitching back to exporters thereafter.

All other plants that switched their exporting

status more than twice during the sample period are grouped as “Other”.
8

We eliminated plants that switch in and out of the dataset more than twice during the sample
period. Thus, we keep only those plants that do not have a split in time series observations. This
procedure eliminates about 10 percent of the sample in terms of number of plants.

82.4. A Preliminary Analysis: Performance of Exporters and Non-exporters
Table 1 shows the number of exporting plants and average exports as percentage ofshipments, or export intensity, for each year during the sample period.

Exportingplants accounted for between 11.0 and 15.3 percent of all manufacturing plants. The
share of exporting plants rose slightly between 1990 and 1992, but since then steadily
declined until 1996. However, with the outbreak of the financial crisis in 1997, theshare of exporting plants rose somewhat noticeably to reach 14.8 percent in 1998.
The rise in the share of exporting plants can be attributed mostly to the closure of nonexporting plants, rather than increase in the number of exporting plants. Note that theincreases in the number of exporters in 1997 and 1998 were modest, which are broadly
consistent with the severe contraction of domestic demand and huge depreciation of
Korean Won associated with the crisis.
Table 1. Number of Exporters and Export Intensity
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998

Total number ofplants(percent)

Nonexporters(percent)

Exporters(percent)

68,690(100)
72,213(100)
74,679(100)
88,864(100)
91,372(100)
96,202(100)
97,141(100)
92,138(100)
79,544(100)

58,392(85.0)
61,189(84.7)
63,241(84.7)
77,514(87.2)
80,319(87.9)
85,138(88.5)
86,502(89.0)
80,963(87.9)
67,767(85.2)

10,298(15.0)
11,024(15.3)
11,438(15.3)
11,350(12.8)
11,053(12.1)
11,064(11.5)
10,639(11.0)
11,175(12.1)
11,777(14.8)

Source: Hahn (2004).

9

Exports/shipments ratio(percent)
unweighted
54.8

weighted
37.3

54.3

37.3

51.7

36.3

49.9

36.0

47.2

35.9

44.8

37.2

43.6

35.3

44.2

38.0

44.7

48.7

Consistent with the high export propensity of the Korean economy, the share ofexports in shipments at plant level is quite high. During the sample period, theunweighted mean export intensity is between 43.6 and 54.8 percent, declining from1990 to 1996 but rising with the onset of the crisis in 1997. The average export
intensity weighted by shipment shows a similar pattern, with generally lower figures
than the unweighted average, suggesting that smaller exporting plants have a higher
export intensity.
It is a well-established fact that exporters are better than non-exporters by various
performance standards.

Table 2 compares various plant attributes between exporters

and non-exporters for three selected years.

First, exporters are on average muchlarger in the number of workers and shipments than non-exporters.

The differential in

shipments is more substantial than that in the number of workers. So, the averagelabor productivity of exporters measured by either production per worker or value
added per worker is higher than that of non-exporters.

Compared with the cases ofvalue added, the differential in production per worker between exporters and nonexporters is more pronounced.

This might reflect a more intermediate-intensive

production structure of exporters relative to non-exporters.

Although exporters show

both higher capital-labor ratio and a higher share of non-production workers in
employment than non-exporters, they do not fully account for the differences in laborproductivity.

As a consequence, total factor productivity levels of exporting plants

are, on average, higher than those plants that produce for the domestic market only.
Some differences in the total factor productivity may be attributed to the differences in
R&D intensity.

Note that, controlling for the size of shipments, exporters spent about

twice as much on R&D as non-exporters.

From a worker’s point of view, exporters10

had more desirable attributes than non-exporters.
exporters is higher than that of non-exporters.

That is, the average wage of

Although both a production worker’s

wage and a non-production worker’s wage are higher in exporters than in nonexporters, the differential in the non-production worker’s wage is more pronounced.

Table 2. Performance Characteristics of Exporters vs. Non-exporters
1990

1994

1998

exporters

nonexporters

exporters

nonexporters

exporters

nonexporters

Employment(person)

153.6

24.5

119.4

20.0

95.1

17.8

Shipments(million won)

11,505.5

957.0

17,637.1

1,260.3

25,896.8

1,773.8

production per worker(million won)

50.5

26.8

92.4

47.0

155.0

74.2

value-added per worker(million won)

16.5

11.3

31.0

20.4

51.3

29.6

TFP

0.005

-0.046

0.183

0.138

0.329

0.209

capital per worker(million won)

16.8

11.9

36.0

21.9

64.6

36.7

non-production worker/
total employment(percent)

24.9

17.1

27.5

17.5

29.6

19.2

average wage
(million won)

5.7

5.1

10.3

9.2

13.7

11.5

Average production wage
(million won)

5.5

5.1

10.0

9.2

13.1

11.4

average non-production
wage (million won)

6.8

5.3

11.6

9.4

15.6

12.4

R&D/shipments(percent)

-

-

1.2

0.6

1.4

0.6

Source: Hahn (2004).

11

3. Empirical Strategy: Propensity Score Matching

It is now well-recognized in the literature that the decision to become an exporter
is not a random event but a result of deliberate choice, requiring special efforts tocorrectly identify the true effect of becoming an exporter on its productivity (Loecker
2007, Albornoz and Ercolani 2007). The participation decision in the export market
is likely to be correlated with the stochastic disturbance terms in the data generatingprocess for a firm’s productivity, so that the traditional simple mean difference test on
productivity differences between exporters and non-exporters does not provide thecorrect answer.

The matching method has been gaining popularity among appliedresearchers since it is viewed as a promising analytical tool with which we can copewith statistical problems stemming from an endogenous participation decision.
The underlying motivation for the matching method is to reproduce the treatmentgroup (exporters) out of the non-treated (non-exporters), so that we can reproduce theexperiment conditions in a non-experimental setting.

Matched samples enable us toconstruct a group of pseudo-observations containing the missing information on thetreated outcomes had they not been treated by paring each participant with members of
the non-treated group.

The crucial assumption is that, conditional on someobservable characteristics of the participants, the potential outcome in the absence ofthe treatment is independent of the participation status.
yi0 ⊥ d i X i(2)0where yi is the potential outcome in the absence of the treatment, d i is the dummy

to indicate participation, and X i is the vector of conditioning variables. The basic
12

idea of matching is to construct a sample analog of a counter factual control group by
identifying the members of a non-participating group that possess conditioning
variables as close to those of treatment group as possible.

In practice, it is very

difficult to construct a control group that satisfies the condition in (2), especially when
the dimension of the conditioning vector X i is high.
Rosenbaum and Rubin (1983) propose a clever way to overcome the curse ofdimensionality in the traditional matching method.

Suppose that the conditional

probability of firm i’s becoming an exporter can be specified as a function ofobservable characteristics of the firm before the participation;p( X i ) = Pr[d i = 1 X i ] = E (d i X i )(3)Rosenbaum and Rubin (1983) call the probability function in (3) propensity scoreand show that if the conditional independence assumption in (2) is satisfied it is also
valid for p( X i ) thatyi0 ⊥ d i p( X i )(4)

We have replaced the multi-dimensional vector with a one-dimensional variable
containing the same information contents so that the highly complicated matchingproblem in (2) is reduced to a simple single dimensional one in (4).
One can define the average treatment effect on the treated (ATT) as;

[
] [[
]]
= E [E [y d = 1, p( X )] − E [y d = 1, p( X )]]ATT = E y i1 − y i0 d i = 1 = E E y i1 − y i0 d i = 1, p( X i )
1
i

i

0
i

i

i

(5)

i

0
where yi is the potential outcome that would have been observable had participating13

1
firm i decided not to participate in an export market and yi is the observableoutcome for participating firm i . Note that ATT is not the measure for the effect ofexporting on all firms but on firms that start to export.
0
Since yi is not observable, the definition (5) is not operational.

Given that theunconfoundedness condition under propensity score (4) is satisfied and the propensity
score (3) is known, the following definition is equivalent to (5).

[

] [[

] [

]]

ATT = E y i1 − y i0 d i = 1 = E E y i1 d i = 1, p( X i ) − E y i0 d i = 0, p( X i )

(6)

0
1
Since both yi and yi are observable in (6), one can construct an estimator for

ATT by constructing its sample analog.
As the first step, we estimate the probability function in (3) with the following
probit specification.

p( X i : β , σ ) = 1 − ∫

β 'Xi

−∞

 z2 
exp − dz
2π
 2 
1

(7)

Log of total factor productivity, log of the number of workers employed, log of capitalper worker, 9 yearly dummies, and 10 industry dummies are included in theconditioning vector X i .

As for conditioning variables, we use the values from one

year before the firm starts to export in order to account for the time difference between
decision to participate and actual participation.
Based on estimated version of (7), one can calculate propensity score for allobservations, participants and non-participants.

Let T be the set of treated (exporting)units and C the set of control (non-exporting) units, respectively, and denote by C (i )14the set of control units matched to the treated unit i with an estimated value of
propensity score of pi .

Then, we pick the set of nearest-neighbor matching as;
C (i ) = min pi − p jj(8)Denote the number of controls matched with a treated unit i ∈ T by N i

Cthe weight wij =1
if j ∈ C (i ) and wij = 0 otherwise.
N iC

and define

Then, the propensity

score matching estimator for the average treatment effect on the treated at time t isgiven by;

ATTt * =


 1
1
0


y
w
y
−
∑
∑
,
,
i
t
ij
j
t

N T i∈T j∈C (i )


(9)

1
0
where yi ,t is the observed value on firm i in the treatment group at time t and y j ,tthe observed value on firm j in the matched control group for firm i at time t.
Moreover, one can easily show that the variance of the estimator in (9) is given by;()

Var ATTt* =

( )

( )

1
1
(wij )2Var y 0j ,tVar y i1,t +
T
2 ∑ ∑
T
N
i∈T j∈C (i )
N

( )

(10)

One can estimate an asymptotically consistent estimator for (10) by replacing two
variance terms for the treatment and control groups with corresponding sample analogs.
We use two different versions of the propensity score matching procedure written
in STATA language; attn.ado explained in Becker and Ichino (2002) (BI, hereafter)and psmatch2.ado provided by Leuven and Sianesi (2003) (LS, hereafter).

The two

procedures follow an identical approach in estimating propensity score andconstructing the control group, except for the fact that the former tries to verify the15unconfoundedness condition in the sample by dividing the entire region of estimatedpropensity scores into several blocks and construct the matched control group within
the block to which the treated observation belongs.
In order to allow for the possibility that the effect of learning by exporting works at
different intensities depending on a firm’s characteristics and industry, we divide theentire sample into several categories according to plant or industry characteristics, suchas the export intensity of plants, skill intensity of plants, plant size measured by the
number of workers, R&D intensity of plants, and export destination of industries. We
measure the average treatment effect of the treated for each sub-sample.

4. Empirical Results: Learning-by-exporting Effects

4.1. Starter vs Non-exporter
Table 3 reports the estimated productivity gain from participating in an export
market when heterogeneity in treatment effect is not taken into account.

Theestimated coefficients indicate percentage productivity differentials between plants thatstart exporting and their domestic counter-parts s years after entering the export market.
We report results from the two different versions of propensity score matchingprocedure, BI and LS.

16Table 3. Average Productivity Gain of Exporters
Matching
Method

s=0

s=1

s=2

s=3

ATT

0.041***(0.008)0.065***(0.010)0.077***(0.011)

0.064***(0.014)

No. Treated

5696

5696

5696

5696

No. Controls

3725

2206

1401

854

ATT

0.030***(0.008)0.051***(0.011)

0.056***(0.014)0.058***(0.019)

No. Treated

5650

2492

1354

743

No. Controls

76576

54362

38237

27244

BI

LS

First and foremost, all estimated coefficients are positive and highly significant,suggesting the existence of a learning-by-exporting effect.

This is quite a surprisingfinding considering the fact that most previous studies were skeptical about theexistence of the learning-by-exporting effect.

Second, productivity gain for starters

begins to materialize immediately after entering the export market, and the
productivity gap between the starters and non-exporters 9 widens further as time passes,
although at a decelerating pace. Third, it seems that the choice of procedures inconstructing the control group does not yield any material differences in the final result,not only qualitatively but also qualitatively.

The estimated coefficients from BIprocedure indicate that starters become about 4.1 percent more productive in the yearof entry. Over the following years, productivity gain for starters fluctuates between
6.4 and 7.7 percentage points. Thus, it is suggested that entering the export market
has a permanent effect on productivity level, especially during the first several years
after entry.

In other words, export market entry has a temporary effect on

productivity growth especially during the first few years after entry.
9Non-exporters correspond to the “never” group in our earlier definition.

17

4.2. Sub-group Estimation: Plant Characteristics
In order to allow for a differential treatment effect depending on plantcharacteristics, we divided our sample into three sub-groups according to various
features such as an exports-production ratio, the skill intensity, plant size measured by
the number of workers, and R&D-production ratio.

Then we apply the matching

estimators discussed in Section 3 and estimate the learning-by-exporting effectseparately for each sub-group.

Based on BI procedure 10, we report the estimatedproductivity gains for starters in each sub-group in Table 4.
First, the estimated coefficients are generally larger and more significant for plants
with higher exports-production ratio.

For example, in the group of low export

intensity with exports-production ratio of less than 10%, starters become moreproductive, between 2.5 and 4.1 percent during the three years after the participation.
By contrast, in the group of high export intensity with an exports-production ratio
greater than 50%, productivity gains for starters are between 9.5 and 11.4 percent forthe same time span.

In the earlier section, we argued that if the estimated effect oflearning-by-exporting indeed captures the beneficial consequences of learning
activities associated with exporting, then the effect is likely to be stronger for plants
with higher exports-output ratios; if learning-by-exporting arises from contact withforeign buyers and foreign markets, which require costly resources, then firms forwhom exporting is their major activity are likely to be more heavily exposed to foreigncontact and experience productivity gain. The results for sub-groups with different
export intensities are very consistent with this hypothesis.

10

Estimation results based on LS procedure are reported in the appendix.

18

Table 4. Average Productivity Gain of Starters by Firm Characteristics: BI Procedure
Firm
Characteri
stics
ATT
Low

ExportRatio

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

High

No. Treated
No. Controls
ATT

Low

Skill
Intensity

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

High

No. Treated
No. Controls
ATT

Small
Plant Size
(NumberOf
Workers)

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

Large

No. Treated
No. Controls
ATT

None

No. Treated
No. Controls
ATT

Low
R&D
Intensity

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

High

No. Treated
No. Controls

s=0

s=1

s=2

s=3

0.043***(0.013)
2141
1457
0.014
(0.013)1840
1338
0.060***(0.014)
1696
1230
0.009
(0.020)1100
552
0.026***(0.009)3329
2737
0.049***(0.017)1267
964
0.078***(0.015)1456
811
0.028***(0.010)
3183
2667
0.003
(0.020)1057
675
0.051***(0.009)4723
3130-0.009(0.035)352
216-0.016(0.031)
446
270
0.030
(0.048)175
113

0.041***(0.015)2141
834
0.066***(0.015)1840
755
0.112***(0.016)
1696
744
0.021
(0.027)1100
314
0.054***(0.010)3329
1590
0.065***(0.022)1267
511
0.124***(0.020)1456
381
0.055***(0.011)3183
1523-0.056***(0.023)1057
508
0.065***(0.010)
4723
1866
0.037
(0.036)
352
132
0.016
(0.038)446
157-0.034(0.061)
175
62

0.025
(0.018)2141
546
0.081***(0.017)1840
474
0.114***(0.019)
1696
481
0.015
(0.033)1100
185
0.065***(0.012)3329
1031
0.068***(0.024)1267
316
0.207***(0.027)1456
201
0.058***(0.013)3183
997-0.009(0.027)1057
361
0.080***(0.012)
4723
1225
0.065
(0.042)
352
87
0.022
(0.046)446
91-0.033(0.077)175
430.040**(0.020)2141
352
0.071***(0.021)
1840
288
0.095***(0.021)
1696
325
0.026
(0.046)1100
100
0.033**(0.014)3329
652
0.072***(0.027)1267
205
0.177***(0.033)1456
106
0.049***(0.016)
3183
607
0.033
(0.028)1057
248
0.069***(0.014)
4723
797
0.070
(0.044)
352
56
0.041
(0.041)
446
61
0.070
(0.073)175
27

19

Second, the learning-by-doing effect seems to be more pronounced for plants withhigher skill intensity11.

For the group of plants with a skill intensity of less than 10%,starters became more productive, between 1.5 and 2.6 percentage points during thethree years after beginning to export.

For the group of plants with a skill intensity

greater than 40%, starters became and remained between 6.5 and 7.2 percentage points
more productive during the same period.

These results suggest that domestic“absorptive capacity” matters for exporting plants to take advantage of the benefits ofinternational knowledge spillovers. Specifically, the result on the correlation between
skill intensity and productivity gain from starting to export in Table 4 is consistent withthe previous empirical literature that emphasizes the role of human capital in
facilitating technology adoption (Welch 1975, Bartel and Lichtenberg 1987, Foster andRosenzweig 1995, Benhabib and Spiegel 1994) 12.
Third, we also examine whether the degree of learning-by-exporting is related toplant size, dividing the entire sample into three groups: a group of small plants with thenumber of workers less than 10, a group of medium-sized plants with the number ofworkers between 11 and 49, and a group of large plants with 50 or more workers.
Table 4 suggests that effect of learning-by-exporting is generally larger and moresignificant for smaller plants. As argued by Albornoz and Ercolani (2007), there
seems to be no a priori reason to expect larger learning-by-exporting effects for small
exporters. 13

While one can argue that large firms are generally more structured andbetter suited to facilitate absorption and use new knowledge obtained through11

Skill intensity is measured by the share of non-production workers out of the total of
production and non-production workers.
12
These studies are empirical investigations of Nelson-Phelps hypothesis which suggests that therate at which the gap between the technology frontier and the current level of productivity is closed
depends on the level of human capital. See Benhabib and Spiegel (2005) for detailed explanation.
13
They also find that small firms learn more from exporting activities using firm-level panel dataon Argentinian manufacturing.

20

exporting activities, it is also possible to argue that knowledge might be easier todisseminate in a small firm due to its flexibility and simplicity of organizational
structure and its decision making process.

Our findings in Table 4 seem to suggestthat the latter effect dominates.
Finally, we examine whether plants with higher R&D investment exhibit a larger
learning-by-exporting effect. To do so, we classify plants into four sub-groups: agroup with no R&D investment, a low R&D group with a ratio of R&D expenditure
to production less than 2 percent, a medium R&D group with a ratio from 2 to 10
percent and a high R&D group with a ratio higher than 10 percent.

Somewhatsurprisingly, the learning-by-exporting effect is statistically significant only in the no
R&D group.

Although we cannot come up with a clear explanation for the results,

we can conjecture that R&D intensity reflects industry specific characteristics rather
than the innovativeness of firms. 14

4.3. Sub-group Estimation: Export Destinations as an Industry Characteristic
As far as we are aware of, little is known about industry characteristics that affectthe degree of learning-by-exporting.

In this subsection, we examine whether theexport destination of industry as an industry characteristic affects the strength oflearning-by-exporting of the plants.

If the learning-by-exporting effect found in thispaper captures international knowledge spillovers from advanced to less advanced
countries which arise through the contact with foreign buyers in more advanced
countries, then we could expect to find that the learning-by-exporting effect is stronger
in industries that have larger share of their exports directed to more advanced countries.
14

It is a well known fact that R&D intensity varies a lot across industries

21However, we cannot expect that learning-by-exporting will be stronger
unambiguously in industries with a larger share of exports directed to more advanced
countries for many reasons, including the following.

First of all, international

knowledge spillovers might arise not only through direct contact with foreign buyers inadvanced countries but also through indirect contact with foreign competitors in themarkets of less advanced countries.

For example, Korea’s car exporters could learn

from the business practices of German car exporters in the Chinese market. Secondly,
generally more intense competition in export markets can exert pressure on firms thatstart to export to improve their productive efficiency.

Then the degree of competitionin an export market could be an important factor in determining the degree of“learning-by-exporting” effect. Thirdly, there should be an industry-level technology
gap between the exporting country and the frontier country in order for the learningby-exporting effect to take place.

That is, there should be some “advanced

knowledge” out there to learn from in the first place.

If this is the case, then the

direction of exports would be immaterial for an industry that is at or close to the world
frontier. 15
Fourthly, if exporting is associated with fragmentation of production bymultinational firms, then efficiency improvement coming from the fragmentation of
production which, in some cases, involves exporting to lower income countries within
the production network might be captured as learning-by-exporting effect.

Kimura,Hayakawa, and Matsuura (2009) provide a theoretical explanation related to this story.
They show that in the case of vertical FDI, the larger the gap in capital-labor ratios
between a Northern fragment and a Southern fragment, the greater the total cost
15

This might be one reason that learning-by-exporting effect is occasionally reported in studies ofdeveloping countries but not in developed countries, such as the U.S.

22

reduction in international fragmentation.

In this case, exporting to lower income

countries within a production network might be associated with a greater learning-byexporting effect.
Although exploring all these possibilities is out of the scope of this paper, we thinkthat examining whether the direction of exports matters for the strength of learning-bydoing is the first step toward understanding the exact nature of the learning-byexporting effect captured in this paper.
As a preliminary step, we first examine whether there are cross-industry
differences in productivity gains from becoming exporters. To do so, we divided oursample into 10 sub-industries 16 and repeated the matching procedure for each industry.
Table 5 shows that productivity gains from learning-by-exporting are visible in the
textile and apparel, chemical, metal, and transport equipment industries. However,
we cannot find significant productivity gains in the food, wood and pulp, generalmachinery, precision instrument, and electronics industries. Roughly speaking, theformer group of industries largely coincides with the area for which Korea is believed
to have a comparative advantage.

Therefore, the result can be interpreted asproviding a piece of evidence supporting the hypothesis that involvement in exporting
activities results in productivity gains. However, it is somewhat surprising that we
can find no significant evidence for the existence of a learning-by-exporting effect inthe electronics industry.

Although we could conjecture that this reflects that many

Korean producers in the electronics industry are the “frontier” producers, a moredefinitive assessment cannot be made until a more in-depth analysis is carried out.
Nevertheless, Table 5 seems to show that there are some industry characteristics
16

They are food, textile and apparel, wood and pulp, chemical, metal, general machinery,electronics, precision instrument, transport equipment, and others.

23that affect the strengths of the learning-by-exporting effect.

Table 5. Average Productivity Gain of Starters by Industry: BI Procedure
Industrys=0
0.048(0.038)s=1
0.010(0.042)s=2-0.028(0.052)s=3-0.006(0.058)278
194
0.099***(0.018)278
100
0.117***(0.019)278
660.129***(0.021)278
51
0.097***(0.025)1331
894-0.015(0.033)1331
552-0.016(0.039)1331
355-0.043(0.042)1331
223-0.138***(0.054)

243
177
0.026
(0.021)

243
115
0.041
(0.028)243
77
0.063*(0.033)243
520.158***(0.035)696
444
0.09***(0.029)696
255
0.090**(0.038)

696
163
0.067
(0.044)

696
109
0.013
(0.045)

319
215
0.019
(0.015)

319
128
0.005
(0.019)319
74-0.013(0.024)319
49-0.002(0.026)1436
936-0.003(0.026)1436
528-0.016(0.031)1436
332-0.045(0.033)1436
193-0.024(0.033)618
401-0.016(0.048)618
235-0.022(0.056)

618
157
0.004
(0.054)618
109-0.001(0.074)

207
122
0.018
(0.040)

207
76
0.039
(0.045)207
440.111**(0.052)207
27
0.15***(0.051)

ATT

246
176
0.043
(0.029)246
114
0.071*(0.040)246
770.100**(0.050)246
520.183***(0.055)

No. Treated
No. Controls

322
212

322
112

322
70

322
44

ATT
Food

Textile andApparelWood andPulp

No. Treated
No. Controls
ATT
No. Treated
No. Controls
ATT
No. Treated
No. Controls
ATT

ChemicalNo. Treated
No. Controls
ATT

Metal

GeneralMachinery

No. Treated
No. Controls
ATT
No. Treated
No. Controls
ATTElectronics

Precision
Instrument

Transport
Equipment

Other

No. Treated
No. Controls
ATT
No. Treated
No. Controls
ATT
No. Treated
No. Controls24

We next turn to the export destinations of industries as one possible factor
explaining differential strengths of the learning-by-exporting effect estimated at the
sub-group level of industries. As explained above and also in Loecker (2007), thishypothesis is based on the presumption that a learning-by-exporting effect will bestronger for plants that start exporting to more advanced countries, where theopportunities for learning new knowledge and technology are relatively abundant.
Although Loecker (2007) examined this issue using plant-level information on thedestination of exports, we do not have such information available for Korea.

Instead,we examine whether plants in industries with a higher share of exports to advanced
countries exhibit higher productivity gains. 17
To do so, we first matched the direction of exports dataset at SITC 5 digit levelcomplied from UNComtrade (Rev. 3) with the Mining and Manufacturing Survey
dataset at KSIC 18 three-digit level.

Then, we classified Korea’s export destination

countries into two groups: “lower-income” and “higher-income” countries.

Here,

higher-income countries are those with an average per capita GDP for the period from1990 to 1998 larger than that of Korea. The remaining countries are lower-income
countries. Next, for each of the 58 three-digit manufacturing industries, we calculated
their shares of exports to lower-income and higher-income countries averaged over thesame period.

Then, we classified each industry into “higher-income” or “lower-

income” group if its share of exports to higher-income countries is greater or smaller
than lower-income countries, respectively.
The estimated productivity gain for starters is reported in Table 6 for each sub-17

In some respect, direction of exports is more likely to be an industry characteristic rather thanplant characteristic.
18
Korean Standard Industrial Classification.

25

group.

At first glance, the results are not supportive of the hypothesis that thelearning-by-exporting effect is more pronounced in industries with more of theirexports directed to more advanced countries.

In fact, the result is the other way

around: Learning-by-exporting effect in the lower-income group is stronger than thatof the higher-income group, although both are highly significant.

We conjecture thatthe result is driven by the fact that the gain from participating in export markets
depends on many factors conveniently branded as the benefits of openness.

Webelieve that those factors must be interlinked in a very complicated fashion and asimple approach like ours cannot give the definite answer to this important question.

Table 6. Average Productivity Gain of Starters by Export Destinations: BI procedure

Higher-income

t=0

t=1

t=2

t=3

ATT

0.068***(0.011)

0.068***(0.013)0.057***(0.016)

0.036*(0.020)No. Treated
No. Controls

3108
2002

3108
1144

3108
707

3108
455

ATT

0.059***(0.012)0.079***(0.013)0.081***(0.016)0.074***(0.020)No. Treated
No. Controls

2559
1629

2559
975

2559
631

2559
376

Lower-income

Given the inadequate control of various factors that might be relevant for
determining the degree of learning-by-exporting effect, the above results should not betaken as a definitive piece of evidence against the hypothesis that the learning-byexporting effect is larger in industries with more of their exports directed to higherincome countries. We think that various industry as well as plant characteristics
might also play a role here.

Further analysis seems to be warranted to shed light on

this issue.

26

5. Conclusion

This paper examined the presence of a learning-by-exporting effect utilizing aunique plant level panel data covering all manufacturing sectors in Korea.

Koreanexperiences offer a good window of opportunity to analyze this issue in the sense that
Korea is one of the best known success stories having achieved fast economic growth
driven by “outward-oriented” development strategies.
We find clear and robust evidences for a learning-by-export effect.

The total

factor productivity gap between exporters and their domestic counterparts is significant
and shows the tendency to widen during three years after entry into the export market.
We also find that the beneficial effect of productivity gain is more pronounced forplants with a higher skill-intensity or higher share of exports in production.
Although this paper examined the learning-by-exporting effect, it should be bornin mind that learning-by-exporting is just one of many channels through which thebenefits of openness are realized.

That is, the results of this paper does not at all

exclude the possibility that the beneficial effects of openness are realized throughvarious other channels, such as increases in consumer surpluses and improvements ofallocation efficiency, knowledge spillovers and market-disciplining effects fromimports, and improvement of scale efficiency, among others.
One interesting policy implication which arises from this paper might be thatneoclassical orthodoxy of prescribing unconditional openness policy 19 might not be
entirely warranted.

If domestic absorptive capacity is complementary to the openness

policy, as suggested by the evidence of larger a learning-by-exporting effect in skill-19

See Sachs and Warner (1995), for example.

27

intensive plants, then upgrading the quality of human capital might be necessary tomore fully utilize the benefits from openness.

28

Table A.1. Average Productivity Gain of Starters by Firm Characteristics: LS Procedure
Plant
Characteristics
ATT
Low

ExportRatio

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

High

No. Treated
No. Controls
ATT

Low

Skill
Intensity

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

High

No. Treated
No. Controls
ATT

Low

NumberOf Workers

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

High

No. Treated
No. Controls
ATT

None

No. Treated
No. Controls
ATT

Low
R&D

No. Treated
No. Controls
ATT

Medium

No. Treated
No. Controls
ATT

High

No. Treated
No. Controls

s=0

s=1

s=2

s=3

0.036***(0.011)
2129
76576
0.019
(0.012)
1835
76576
0.054***(0.013)1686
76576-0.014(0.016)1086
30592
0.026***(0.009)3306
37772
0.062***(0.017)1258
8212
0.056***(0.015)1443
39564
0.057***(0.010)
3161
33433
0.031
(0.019)1046
3579
0.033***(0.008)
4678
73923
0.005
(0.035)351
825-0.007(0.030)
446
1201
0.049
(0.047)
175
627

0.001
(0.016)972
54362
0.071***(0.018)769
54362
0.109***(0.019)
747
54362
0.004
(0.026)406
20469
0.046***(0.013)1517
27997
0.057**(0.025)569
5896
0.074***(0.026)423
25645
0.059***(0.014)1407
25722-0.023(0.024)662
2995
0.041***(0.012)2040
52426-0.008(0.041)
188
605
0.031
(0.038)199881-0.014(0.062)
65
424

0.021
(0.022)526
38237
0.052**(0.024)424
38237
0.105***(0.025)402
38237
0.086**(0.037)191
13645
0.043***(0.017)844
20343
0.063**(0.033)319
4249
0.108***(0.042)153
16386
0.069***(0.018)764
19349-0.036(0.030)437
2502
0.055***(0.015)
1080
36829
0.000
(0.049)122455-0.024(0.056)114
637-0.029(0.086)38
298-0.005(0.026)
304
27244
0.054
(0.033)222
27244
0.074**(0.035)216
27244
0.099**(0.050)
90
8953
0.025
(0.025)472
14916
0.104***(0.041)
181
3120
0.082
(0.060)68
10862
0.084***(0.024)
411
14321
0.035
(0.040)264
2061
0.039*(0.022)
598
26816
0.066
(0.066)
66
302
0.055
(0.068)
61
453
0.089
(0.132)18
180

29

Table A.2. Productivity Gain of Starters by Industry: LS Procedure
s=0

s=1

s=2

s=3

ATT

0.074**(0.036)0.077(0.052)0.031(0.063)0.100(0.064)

No. Treated
No. Controls

273
4868

132
3837

90
2939

58
2224

ATT

0.118***(0.016)0.128***(0.024)0.145***(0.030)0.113***(0.042)

No. Treated
No. Controls

1316
17415

561
11983

293
8374

150
5743

ATT

0.033
(0.036)0.029(0.051)

0.009(0.059)0.003(0.097)

No. Treated
No. Controls

240
8888

102
6466

56
4726

22
3557

ATT

0.038**(0.019)0.031(0.030)0.086**(0.037)

0.091*(0.047)

No. Treated
No. Controls

695
6188

332
4462

181
3198

102
2329

ATT

0.052*(0.027)0.046(0.040)0.123**(0.054)0.044(0.064)

No. Treated
No. Controls

313
5707

138
4346

73
3287

42
2554

ATT

0.015
(0.014)0.016(0.022)-0.020(0.034)-0.017(0.043)

No. Treated
No. Controls

1427
18280

604
12732

325
8572

170
5895

ATT

0.002
(0.023)0.020(0.033)0.010(0.042)-0.026(0.048)

No. Treated
No. Controls

615
5541

268
3837

148
2639

89
1815

ATT

0.028
(0.043)0.009(0.062)0.087(0.078)0.139(0.091)

No. Treated
No. Controls

207
1225

93
820

50
560

32
368

ATT

-0.019(0.038)0.010(0.048)0.016(0.075)0.124*(0.075)

No. Treated
No. Controls

245
3473

120
2465

68
1705

37
1251

ATT

0.043
(0.028)0.043(0.040)0.108**(0.051)0.101(0.087)

No. Treated

319

142

70

41

No. Controls

4991

3414

2237

1508

Industry

Food

Textile andApparelWood and
Pulp

Chemical

Metal

GeneralMachinery

Electronics

Precision
Instrument

Transport
Equipment

Other

30

Table A.3. Average Productivity Gain of Starters by Export Destinations:
LS Procedure
t=0

t=1

t=2

t=3

0.021**(0.010)0.025(0.015)0.019(0.022)0.016(0.027)

ATT

3080
46758
0.034***(0.011)1327
33239
0.034**(0.016)713
23549
0.078***(0.021)406
16247
0.091***(0.030)No. Treated
No. Controls

2541
29267

1155
20713

636
14378

335
9652

ATT

0.057***(0.015)0.068***(0.022)0.104***(0.026)

0.091***(0.038)

ATT

1354
15804
0.025*(0.015)615
11297
0.062***(0.021)

346
7990
0.011
(0.032)

178
5228
0.029
(0.042)

No. Treated
No. Controls

1187
13463

540
9416

290
6388

157
4424

ATT
Higher-income

Lower-income

Other lowerincome

East Asia

No. Treated
No. Controls

No. Treated
No. Controls

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34

ERIA Discussion Paper Series
No.

Title

Year

Akie IRIYAMAInternational Production Networks and Export/Import
Responsiveness to Exchange Rates:
The Case of Japanese Manufacturing Firms

Mar
2009

2009-08

Archanun
KOHPAIBOON

Vertical and Horizontal FDI Technology Spillovers:
Evidence from Thai Manufacturing

Mar
2009

2009-07

Kazunobu HAYAKAWA, Gains from Fragmentation at the Firm Level:
Fukunari KIMURA, andEvidence from Japanese Multinationals in East Asia
Toshiyuki MATSUURA

Mar
2009

2009-09

Author(s)Mitsuyo ANDO and

Plant Entry in a More Liberalised Industrialisation
Process: An Experience of IndonesianManufacturing during the 1990s

2009-06

Dionisius A. NARJOKO

2009-05

Kazunobu HAYAKAWA,
Fukunari KIMURA, andFirm-level Analysis of Globalization:
Tomohiro MACHIKITA

2009-04

Chin Hee HAHN and
Chang-Gyun PARK2009-03

A Survey

Learning-by-exporting in Korean Manufacturing:

Mar
2009
Mar
2009

A Plant-level Analysis

Mar
2009

Ayako OBASHI

Stability of Production Networks in East Asia:
Duration and Survival of Trade

Mar
2009

2009-02

Fukunari KIMURAThe Spatial Structure of Production/Distribution
Networks and Its Implication for Technology
Transfers and Spillovers

Mar
2009

2009-01

Fukunari KIMURA and
Ayako OBASHI

International Production Networks:
Comparison between China and ASEAN

Jan
2009

2008-03

Kazunobu HAYAKAWA
and Fukunari KIMURAThe Effect of Exchange Rate Volatility on
International Trade in East Asia

Dec
2008

2008-02

Satoru KUMAGAI,
Toshitaka GOKAN,
Ikumo ISONO, andSouknilanh KEOLA

Predicting Long-Term Effects of Infrastructure
Development Projects in Continental South East
Asia: IDE Geographical Simulation Model

Dec
2008

2008-01

Kazunobu HAYAKAWA,
Fukunari KIMURA, andFirm-level Analysis of Globalization:
Tomohiro MACHIKITAA SurveyDec
2008