Foreign Presence Spillovers and Firms’ Export Response:
Evidence from the Indonesian Manufacturing *Dionisius Narjoko †Economic Research Institute for ASEAN and East Asia

September 2009

Abstract: This paper examines the existence of spillovers associated with the presence ofmultinationals (MNEs) on a firm’s decision to export, and on export intensity. It utilizes data of
the Indonesian manufacturing for the census year 1996 and 2006. Channels through which MNEs
can affect other firms’ export behavior are considered and tested. The econometric analysis
suggests that the contribution of MNEs in increasing technological knowledge raises thelikelihood that domestic firms will enter the export market, and improves export performance.
The analysis finds weak evidence to support for the hypothesis that competition, created by theoperation of MNEs, facilitates entry into export markets. Further analysis however shows that the
impact of competition depends on the level of productivity of the domestic firms. In particular, themore productive firms are suggested to have been able to benefit more than the less productive
ones. The overall analysis suggests that given the mixed evidence, policies to promote MNEs arestill worth pursuing. The most obvious justification comes from the positive impact of theincreased pooled of technological knowledge. Other than this, strengthening trade facilitation
seems to be a proposition, given the finding that many of new domestic exporters seem to haveconstrained in increasing their exports.

Keywords: Indonesia, multinationals, export participation

JEL Classification: F23, L16, L60*

The author thanks Fukunari Kimura for his suggestions on the earlier draft of the study.
Dionisius Narjoko. Economic Research Institute for ASEAN and East Asia (ERIA).
Email: dion.narjoko@eria.org†

1. Introduction
Proponents of globalization believe a positive impact of foreign direct investment (FDI) on
development. An underlying argument justifying this is the believe that the presence of FDI,
through operation of multinationals (MNEs) in host countries, helps to improve the productivityof domestic firms.
There is now growing literature that formalizes and collects evidence of the positive
externalities, or often termed as technology/productivity spillover. However, there is yet
convergence evidence about the existence or positive impact of the spillover. On the one hand,
Gorg and Greenaway (2004) for example reported negative productivity spillover that occurredin several European countries, while on the other, there exist some studies find the evidence of
positive spillover effect for some Asian countries, such as Takii (2006) and Kohpaiboon (2006)
for Indonesia and Thailand, respectively.
This study elaborates this subject, by examining the impact the presence of MNEs on export
performance of domestic firms. Specifically, it asks whether MNEs helps domestic firms toparticipate and perform well in export markets. This study takes the reference of the Indonesian
manufacturing as a case study, utilizing the rich plant-level census data of the sector for the year
1996 and 2006.
The Indonesian manufacturing provides a good case study in examining the issue, considering
the rapid FDI flow into the country since the early of 1990s, and even after the deep economiccrisis of 1997/98. The rapid flow was often cited as an impact of the bold trade and investment
liberalizations taken by the Indonesian government since late 1980s. During the same period, thecountry also experienced a rapid growth in its manufacturing exports. Given the domesticorientation of the trade and industrial policy before the liberalizations, it is only natural to argue
that the Indonesian experience serves as a natural experiment for answering the researchquestion.
The rest of this paper is organized as follows. Section 2 presents analytical framework andidentifies the testable hypotheses. Section 3 describes the methodology adopted by the study.

2Section 4 present the econometric results and the analysis. Section 5 summarizes and concludes
the study.

2. Analytical framework
Export spillovers, or broadly defined as the positive externalities arising from the presence of
MNEs, is an implication of the theory that links productivity and exporting behavior or
performance. The improvement in domestic firms’ export performance is the consequence or
result of export spillovers.
The accumulation of evidence from a greater availability of firm or plant level data indicates a
substantial difference in the productivity of exporters and non-exporters. For developed
countries, Bernard et al. (1995) and Bernard and Jensen (1999), for example, documented thatexporters in US manufacturing are larger, more productive, more capital intensive, pay higher
wages, and employ more skilled workers. A similar finding was observed by Aw and Hwang(1995) and Berry (1992) for developing countries. For Indonesian manufacturing, Sjoholm andTakii (2003) observed that exporting plants are larger and more productive. They found thatlabour productivity of these plants was about twice as high as non-exporting plants and thisdifference seems to have increased over time during the 1990s.
Two theories were put forward to explain this phenomenon. First, which is commonly referred toas the ‘self-selection’ hypothesis, argues that only the most productive firms are able to survive
in the highly competitive export markets. The hypothesis is based on the presumption that there
are additional costs involved in participating in export markets. These costs, which usually
involve high fixed costs, include transport costs and expenses related to establishing
distributional channels and production costs in adapting products for foreign tastes (Bernard andJensen 1999). The alternative explanation argues that there is a learning effect from participating
in exporting activities which will result in productivity improvement. One example is thatexporters are often argued to be able to gain access to technical expertise, including productdesign and method, from their foreign buyers (Aw et al. 2000, p.67). This explanation is often
termed as a ‘learning-by-exporting’ hypothesis.

3Each of these theories applies to different state of exporting status of a firm. The self-selection
hypothesis applies for a firm that is yet exporting but about to, and the learning-by-exporting
hypothesis applies when a firm has become an exporter. Thus, the theories explain that
productive firms self-select themselves to become exporters, and once there, these exporters
learn and become even more productive than before they enter export markets.
In respect to the self-selection hypothesis, Bernard and Jensen (1999) found that exporters in US
manufacturing are more efficient, larger and grow faster several years before they become
exporters. Meanwhile, as for the other hypothesis, Hallward-Driemeier et al. (2002, p.25)
observed a substantial productivity difference between domestic firms that were established asexporters and domestic firms that were not. This indicates that firms participating in export
markets make a conscious decision to operate differently from ones that focus on the domestic
market. Supporting this interpretation, they show that domestic exporters indeed bear aresemblance to foreign exporters. In particular, they are more capital intensive and use moreequipment of recent vintage than domestic non-exporters.
It is worth noting here an implication of the presumption of additional costs required to engage inexporting activities, which is persistency in export participation. Once a firm decides to service
export markets in a period of time, it tends to stay as exporter in the next period. While there has
not been much study on this topic, there is an indication that the extent of these costs is large andserves as an important source of exporting persistency. For example, Roberts and Tybout (1997)
found that exporting experience in the previous year had a strong and positive effect indetermining export participation in the current year for plants in Colombian manufacturing. 3
Export spillovers
Another implication of the sunk cost of exporting is that, if entering foreign markets is costly,
there might be localized spillovers associated with exporting by one firm that reduces the cost offoreign market access for nearby firms. This is the idea of export spillovers. Two arguments
support the idea (Aitken et al. 1997). First, geographic concentration of exporters may make it
feasible to construct facilitates that are able to support export activities, such as seaports,
3

Similar findings can also be observed in Campa (2004) and Bernard and Jensen (2004) for Spain and US
manufacturing plants, respectively.

4

airports, and other logistics infrastructure. Thus, the source of export spillovers based on thisargument is government or public initiatives. The other argument comes from the existence of
MNEs. It is based on the presumption that activities or some particular characteristics of MNEs
allow domestic firms to reduce their cost for exporting.
Export spillovers generated by MNEs are the focus of this study, and to facilitate the empiricalanalysis, it is important to explain the channels through which MNEs helps domestic firms in
improving their export performance.
As indicated by Aitken et al. (1997) and detailed by Greenaway et al. (2004), there are three
ways or channels that facilitate export spillovers from MNEs. The first is information about
foreign markets. Subsidiaries usually acquire detailed information about foreign markets, whichmostly comes from their parent companies. This channel is important for both domestic firms in
the preparation stage for exporting and those which have already selling in export markets. The
information classified by this channel includes, for example, information about regulations offoreign markets, taste and preference of foreign consumers, market competition situation inforeign markets, etc. This channel emphasizes the information that directly related to markets
abroad.
The other channel focuses on technology or information about the technology brought by MNEs.
Information classified by this channel is not directly related to information about foreign
markets. As explained by Greenaway et al. (2004), domestic firms can benefit by using or
adopting more advanced technology used by MNEs, which usually is materialized bydemonstration effect and/or imitation. In practice, this channel usually works via – but notlimited to – outsourcing practices and activities (e.g. the dispacthment of engineers from MNEs
to domestic firms to supervise the production of the outsourced products, etc.). Supporting this,Machikita et al. (2009) found that in many Southeast countries, upstream-downstream
transactions and personal communication are important factors that moderate the technology
transfer from MNEs to domestic firms.
Finally, the last channel comes through competition effect. Entry of MNEs leads to increased
competition at the first stage, but after that, it creates pressure for domestic firms to become more5

productive. Given that higher productivity is needed to survive in export market, competition
effect from MNEs thus facilities domestic firms to join and perform well in export markets.
Using plant-level data of Mexican manufacturing for the period 1986-1990, Aitken et al. (1997)
found a robust result supporting the existence of export spillover that comes from MNEs on
export performance of export performance of domestic plants in the sector. However, thisfinding is not robust to changes in sample size. Their results suggest the lack of robustness isrelated to large differences in specific industry characteristics.
The positive export spillovers effect from MNEs was confirmed by Kokko et al. (1997) andGreenaway et al. (2004). Using the case study of Uruguay manufacturing firms in 1988, Kokko
et al. found that foreign ownership at sectoral level increases the likelihood of exporting. They,
however, only found the positive impact for multinationals that were established after 1973,
which was the more outward oriented period of Uruguay economy. There was no evidence of theexport spillover for group of multinationals established before 1973, when the policy was more
inward oriented. Greenaway et al., meanwhile, found that multinationals not only increase thedecision of domestic firms to export, but also export intensity. They used a panel of firms in the
UK in finding this. Unlike other previous studies, they showed evidence of the positive impactthat runs through the three channels identified above.
2.1 Hyphotheses
Drawing from the discussion above, the following presents the testable hypotheses that relate thechannels of export spillovers from the presence of MNEs with export response of domestic
plants.
Technology channel
The larger the technology intensity of MNEs operation increases the chances of successful
imitation by domestic firms. Thus, technological capability brought by MNEs ( FTECH ) is
hypothesized to increase export participation and performance of the domestic plants.
Unlike the more traditional approach which underlines the link extent of ownership share withthe degree of control, this study defines MNEs as plants that has any positive share of foreign

6

ownership. This consideration is based on previous empirical studies which suggest the share offoreign ownership does not necessarily reflect the extent of control. 4Competition channel
This study defines the importance of MNEs in an industrial sector to reflect the extent ofcompetitive pressure created by MNEs ( FEMPSH ). The hypothesis concerning FEMPSH
however is ambiguous. On the one hand, a positive relationship is expected, for the reason of the
improved productivity of domestic firms as a result of competitive pressure from MNEs. On theother, however, a negative relationship could also occur, for the reason that the operation of
MNEs may crowd out the operation of the domestic plants. This likely occurs if the motivationof investing abroad by the MNEs is expanding markets (i.e., the market-seeking hypothesis). The
model built by Markusen and Venables (1999), where MNEs compete with domestic firms inindustries producing final goods, predicts that the increase in the output due to the operation of
MNEs decreases market price and lead to the exit of some domestic firms.
Information channel
Following Greenaway et al. (2004), this study defines the relative importance of foreign plants’export activities in an industry – scaled by the relative importance of foreign plants’ export
activities in the whole manufacturing sector, or ( RFEXPSH ), to represent the extent of
information about foreign markets embedded in the operation of MNEs. The notion of ‘export
activities’ is proxied by the extent of exported sales. Higher RFEXPSH allows domestic plants
to learn about export markets more easily, which in turn increase their likelihood to participate in
the markets. Thus, a positive relationship between RFEXPSH and the domestic plants’ export
performance is expected.
Dependency on plant heterogeneity
Notwithstanding the theoretical prediction and evidence as discussed earlier, there is a reason toargue that the positive impact of the export spillovers may different across firms.

4

Aswicahyono and Hill (1995) for example reported that many Indonesian case studies have demonstrated that local
partners often play relatively minor role even when they hold the majority of equity.

7

This proposition is motivated by the finding about the importance of firm heterogeneity inshaping firms productivity within an industry. 5

Melitz (2003), in response to this, built atheoretical model that takes into account the importance of the heterogeneity in imperfectcompetition setting. Predictions from Melitz’s model are derived from an interaction between
productivity difference across firms and the existence of some level of fixed cost for exporting.
As summarized in Helpman (2006), in predicting the impact of trade liberalization, or any policy
for export orientation, the dynamic version of Melitz’s model results in pressures for firms
within an industry to increase its productivity. Yet, at the same time, the reduction of cost forexporting borne by trade liberalization lowers productivity level required by a firm to export.
Trade liberalization thus creates higher industry productivity because only the more-productive
firms survive the entry into the industry, and output is reallocated towards these more-productive
firms.
Other models adopt Melitz’s model to include technology adoption and innovation to reflect
technology upgrading by firms (e.g. Bustos 2005; Yeaple 2005; Ekholm and Midelfart 2005). In
Bustos’ model, some firms adopt more-advanced technology to increase their productivity inresponding to trade liberalization, or a fall in cost for exporting. However, the coexistence offirms with different productivity level prior to the trade liberalization results in an outcome that
only more productive-firms upgrade their technology. As a final prediction, trade liberalization
only causes firms with intermediate level of productivity to upgrade technology for an intention
to better compete in export market. Less productive firms, meanwhile, stays to serve domestic
market because they do not upgrade their technology. The model takes into account technology
upgrading gives a prediction that only part of firms within an industry that are able tosubstantially increase their productivity after trade liberalization.
Guided by these theories, this study predicts that the impact of the export spillovers, through thechannels, varies across the domestic plants depending on the plants’ productivity. Thus, we
expects a positive relationship for the following interaction variables:

FTECH * LP ,FEMPSH * LP , and RFEXPSH * LP .
5This was born from growing evidence the variation of firms that exports cannot be derived from a random sample,
or not all firms within an industry export. Eaton et al. (2004), for example, highlights this fact for the French
manufacturing, and Helpman et al. (2004) also did so for the case of US manufacturing.

8

3. Methodology
3.1 Statistical framework
Considering the analytical framework discussed in the previous section, empirical models togauge the impact of the presence of MNES on domestic firms’ export performance are estimated.
This study applies the model on the rich Indonesian large and medium plant manufacturing data
for the census year 1996 and 2006. The models utilize the panel-data feature of the data,
although they uses only two data series. All of these decisions are explained below wheneverthey are relevant.
This study adopts the general approach of model specification from the literature on firm’s
export supply response. In particular, two dependent variables are considered to represent the
response: (1) export participation, and (2) export intensity. The adoption of this approach is
motivated by empirical literature on the subject, where export supply response is often examinedby evaluating the change in some measures of export performance between two points of time.
Calculating these measures is straightforward at the aggregate level, but not at the firm level.
This is because aggregate change in export is a result from two different, but related, firm
behaviors. First, existing exporters can increase or decrease their exported output. They may
increase by redirecting output to foreign markets or by expanding exports. Included in thismechanism are exporters that switch from exporting to non-exporting. The second behavior iswhere non-exporters that have been domestically oriented switch to participate in foreign
markets. The second mechanism can also be achieved by new firms entering the industry.
The empirical models are given as the following:
EPi , j ,t =+α 0 α1 EPi , j ,t −1 + α 2 EPi , j ,t − 2 + α 3 ' X i , j ,t + α 4 ' Y j ,t + α 5 ' Z j ,t + ε i , j ,t

(3.1)

EXPi , j ,t =+β 0 β1 ' X i , j ,t + β 2 ' Y j ,t + β3 ' Z j ,t + µi , j ,t

(3.2)

where (3.1) and (3.2) are export participation and export intensity equation, respectively. irepresent plant i , j represent industry j , defined at four-digit ISIC level, and t represents time(i.e. t=1996, 2006). EPi , j ,t is a binary variable which takes the value of 1 if the plant was

9exporting in time t . EXPit is a plant’s export intensity and is defined as the ratio of exports tototal output. Industry and regional dummies are included in both equations, to control fordifferences across industries and region, respectively. EPi , j ,t −1 and EPi , j ,t − 2 are defined asexporting history variables. Their inclusion in the export participation equation is motivated by
the persistency in exporting behavior. As explained, there are additional and large costs that afirm needs to pay if it had an intention to serve foreign markets (i.e. Roberts and Tybout 1997;
Campa 2004).
Equation (3.1) and (3.2) are estimated using the domestic plants only. This is natural given that
this study examines the impact on domestic firms.
Meanwhile, estimations are conducted only for the data of 1996 and 2006. The motivation forthis is twofold. First, the years are census year. Therefore, the number of observation issubstantially higher for these years compared to that of the other census year, particularly for thesurvey post-2000. For this period, the total number of plants included in the census is about 30
percent more than that of included in the survey. Second, key information to construct a variable
that commonly used to proxy the pool of technology and knowledge, which is the expenditure
for R and D activities, licenses and royalties, and trainings, are not available for the data ofsurvey years. This information is only available in the census data.
This study pooled the data for the estimations. A year time-dummy variable is included tocontrol for different business environment affecting the data in the two census year, particularly
related to the situation of before and after the 1997/98 economic crisis.
Having argued for the use only the census year data, it is unavoidable however that theestimation has to draw information on the domestic plants’ exporting status from the previous
two years. Thus, for the estimation of the sample of 1996, for example, the plants’ exporting
status in 1994 and 1995 are added up into the sample. As explained, this creates a lost in thenumber of observations. However, as also explained, it is still worth pursuing in this direction,
given that the key information to reflect technological capability of MNEs are not available in
the non-census year data.

10

X i , j ,t and Y j ,t are sets of explanatory variables capturing the plant i and industry jcharacteristics at time t , respectively. Y j ,t is designed to include variables that determine theentry of MNEs into a country. As noted in Greenaway et al. (2004), failure to address these
determinants likely results in biased estimates because of possible endogeneity between theexporting decision and performance of domestic firms and the factors of MNEs presence.
Meanwhile, Z j ,t is set of variables representing the channels of export spillover from MNEs. As
described below, this is to proxy the channels of the spillovers as discussed in the previous
section.
Equation (3.1) was estimated within the framework of a binary choice model (i.e. probit or logit),
instead of a linear probability model (LPM). This is because the predicted probability derived
from LPM may lie outside the 0-1 region, which is clearly not reasonable in practice. 6
An important statistical issue regarding the estimation is sample censoring. That is, thedependent variable of equation (3.2), or EXPi , j ,t , can only be calculated for the plants that switch
to become exporters. Given that the process that determines a firm’s export participation is a
non-random process, estimating equation (3.2) without taking into account the truncated sample
suffers from omitted-variable problem, and this would produce biased estimates. In thetheoretical econometric literature, the omitted variable is often called the inverse Mills ratio.
To solve this problem, the Heckman (1976) two-step estimation approach was employed. 7 The
approach that Heckman proposed is to include the inverse Mills ratio as another explanatory
variable in equation (3.2). This is done in two steps. In the first, a probit model to estimate
equation (3.1) is regressed and the inverse Mills ratio is estimated/constructed. In the second
step, equation (3.2) is regressed with the estimated inverse Mills as an additional regressor. A
test for a selectivity problem can be done by evaluating the statistical significance of theestimated coefficient of inverse Mills ratio.
6

Despite this, a binary response model also has a number of shortcomings. An important one is that the potential forbias arising from neglected heterogeneity (i.e. omitted variables) is larger in a binary choice model than in a linear
model. Nevertheless, Wooldridge (2002) points out that estimating a binary response model by a binary choicemodel still gives reliable estimates, particularly if the estimation purpose is to obtain the direction of the effect of
explanatory variables.
7
See Johnston and Dinardo (1997) for more detailed exposition about the Heckman two-step approach.

11

3.2 Data
The data for the empirical analysis in this study are drawn from the census of manufacturing
medium- and large-scale establishments (Statistik Industri, or SI) for the year 1996 and 2006.
The establishments are defined as those with 20 or more employees. The surveys are undertakenby the Indonesian Central Board of Statistics (Badan Pusat Statistik or BPS). 8
As noted in many studies, SI data are considered one of the best by the standard of developing
countries. The data cover a wide range of information on the establishments, including somebasic information (ISIC classification, year of starting production, location), ownership (share offoreign, domestic and government), production (gross output, stocks, capacity utilisation, shareof output exported), material costs and various type of expenses, labour (head-count and salaryand wages), capital stock and investment, and sources of investment funds.
The data, however, have several limitations. Among other, they do not include information
which can identify whether an establishment is a single-unit or a part of a multi-plant firm. As aresult, establishments owned by an enterprise cannot be linked up, and hence the number ofenterprises is over-numerated: some plants may have been counted as firms whereas in practicethey are not.

8

BPS provided the authors with the raw data of these surveys in electronic form.

123.2 Measurement of variables
This subsection lists and details how this study measures the variables used in the estimation.
Export spillover variables
Adopting the variables employed by Greenaway et al. (2004), three export spillovers variables
are included, each of which represents the channel of the spillovers: foreign technologicalcapability ( FTECH j ), foreign employment share( FEMPLSH j ), and foreign technological capability ( RFEXPSH j ). All these are defined atindustry level, i.e., at four-digit ISIC level, to capture together the concentration effect of MNEs
presence.
As commonly adopted in the literature, FTECH j is proxied by technology-related expenditureof foreign plants as a percentage of sales. The technology-related expenditure includes theexpenditure for R-and-D, training activities, and license fees. 9 For industry j , the formula is∑ (R-and-D cost + training cost + license and royalties fees)
FTECH =∑ (output)

f

f

j

i

i

where f and i denote foreign plant f and general plant i, respectively.
FEMPSH is proxied by the share of foreign plants’ employment in an industry. Thus, forindustry j ,∑ (total number of employees)
FEMPSH =∑ (output)

f

f

j

i

i

9

The inclusion of license fees to large extent is motivated by the general understanding that the major mode oftechnological transfer occurring in Indonesia has been through technical licensing agreements (Thee 2006).

13

RFEXPSH is the relative importance of foreign plants’ export activities in an industry, scaledby the relative importance of foreign plants’ export activities in the whole manufacturing sector.
For industry j, the formula to compute it is the following, ∑ (total exports) f  f∑i (output)i j
RFEXPSH j = ∑ (total exports) f  f∑i (output)i 

Plant level variables (control variables)
Size ( SIZEi ) is proxied by number of employees. The other common alternatives, such as output
or profits, are not used as they tend to be more sensitive to changes in the business cycle.
This study employs real value added per labour as a proxy for labour productivity ( LPi ).10
Wholesale price indices at three-digit ISIC level are used to compute the real value added.
Government ownership ( GOVi ) is proxied by the share of central and regional government in aplant’s capital structure.
Import dependence ( IMDEPi ) is proxied by the intensity of imported input in total input. For
plant i, it is defined as
IMDEPi =(value of imported input )i(value of imported + domestic input )i10

Value added is chosen to proxy output, instead of gross output, because it avoids the double-counting problem andis less sensitive to the substitution between intermediate and labor inputs.

14

Industry level variables (control variables)
As explained, this study includes a set of industry-level variables that account for thedeterminants of MNEs operation in host country (i.e., the matrix Y j ,t ). The following lists anddetails these variables which are also defined at four-digit ISIC.
Minimum efficient scale, or MES j , is included to account for the size of an industry. It isdefined as the average plant size accounting for 50 percent of industry output (Caves et al. 1975).
Plant size is measured by total number of workers.
Capital intensity ( ICI j ) is included to capture the likelihood of MNEs investing in industries
with above-average capital requirement and high capital intensity. As explained in Aswicahyono
and Hill (1995), MNEs are usually accustomed to large-based operation in their home countries
and brings advanced technology. The advanced technology is presumably could be adopted byfirms that have sufficient capital resources.
Following Globerman et al. (2004), for ICI in industry j , this is

ICI j =

(energy costs) j(total numbers of production employee) j=(fuel costs) j + (electricity cost) j(total numbers of production employee) j

Export intensity ( IEXPj ) is included to capture the interest of MNEs to invest in export oriented
sectors. For industry j, it is defined as as the ratio of export to total output,

IEXPj =

EX j
Output j

where IEXPj is exported output of an industry.

15

MNEs usually possess brand names, and therefore, they usually invest in industry with high levelof advertising activities. Advertising intensity ( ADV j ) is included to capture the extent ofdifferentiated product. For industry j, it is

ADV j =

(advertising expenditure) j(output) j

The other variables aim at capturing the importance of competition in an industry. Inclusion ofthese variables is motivated by the proposition that MNEs may be interested to enter industry
with either less competitive industry or an industry high import protection. This is particularly
true for the market-seeking MNEs. Two variables are included to proxy the extent ofcompetition, namely Herfindahl Index ( HHI ) and nominal tariff (TARIFF). The latter is
included to capture the extent of import protection which likely affects domestic competition.
For industry j, the formula for HHI is VAi HHI j = ∑ i  ∑ VAi 

2

where VAi is the value added of plant i in industry j .
As for TARIFF, this study uses the nominal tariff data at three-digit ISIC level, drawn from
WTO database through the service of WITS database.

Other control variables
In addition to the control variables above, the estimations include dummy variables forprovinces, to control for regional differences in the plant operation in Indonesia. A year dummy
variable for 2006 is included to control for differences across time. As noted, this variable shouldcapture the different business environment for the period of before and after the crisis. Finally,
industry dummy variables are also included to capture other cross-industry differences which are
not captured by the other variables.

164. Econometric results and analysis
Before presenting and discussing the econometric results, it is useful to describe the general
picture of the entry of domestic plants into exporting markets. To do so, we define exporting
entry rate, in terms of number of plants ( ENX 1 j ,t ) and value added ( ENX 2 j ,t ) as the following:

ENX 1 j ,t =ENXPj ,t
TXPj ,t −1

where: ENXPj ,t
TXPj ,t −1

and

ENX 2 j ,t =ENXVAj ,t
TXVAj ,t −1= Total number of plants in industry j that switch to be exporter in time t= Total number of exporting plants in industry j at time t − 1

ENXVAj ,t = Exported value added of plants that switch to be exporter in industry j attime tTXVAj ,t −1 = Exported value added of all exporting plants in industry j at time t − 1

Figure 4.1 Exporting entry rate of domestic and foreign plants in the Indonesian
manufacturing, average 1996 and 2006.
30%
25%
20%
15%
10%
5%
0%
Domestic exporters
In terms of number of plants (ENX1j,t)Foreign exporters
In terms of value added (ENX2j,t)

Source: Statistik Industri, 1996 and 2006.

Figure 4.1 presents the average 1996 and 2006 exporting entry rate for the group of foreign anddomestic plants in the Indonesian manufacturing. The figure reveals the entry into exporting issubstantially high for the group of domestic plants. In terms of number of plants, the rate is about
30 percent, while in terms of value added, the rate is about 15 percent. This is in contrast with the17entry rate for the group of foreign plants, which is about 5 percent for both in terms of number ofplants and value added. This is a favorable observation from policy perspective, which indicatesan active effort of domestic plants selling to export markets.
However, looking at the figure more carefully, there is indeed an issue regarding the favorable
performance. Comparing the two type of entry rates (i.e., ENX 1 j ,t and ENX 2 j ,t ) acrossdomestic and foreign plants group, it is suggested that many of the new domestic exporters are‘small’ exporters, in terms of their exported output. This is in contrast with the picture of the newforeign exporters, which is indicated to much ‘larger’ in terms of their exported output. The
comparison suggests that a new foreign exporter exports two times larger than a new domestic
exporter. Obviously there could be many reasons to explain this, but it seems that many new
domestic exporters are more constrained than their foreign counterparts.
Table 4.1 reports the probit regression results of the export participation equation (i.e., equation(3.1)), which consists all domestic plants operating in 1996 and 2006. The regressions are thefirst step in the Heckman selection model. Some specifications were experimented and the table
reports the most favorable ones in terms of model fit and estimated coefficients. The industry
dummy variables are included at two-digit ISIC level. 11 The table reports robust standard errors
for the reason of heteroscedastic variance. The Wald test for overall significance in all reported
specifications passes at 1 percent level. The examination for the presence of outliers was done in
the experimental stage, and the presented results have been controlled for the outliers (i.e., byintroducing a dummy variable which identifies the outliers).
The results provide a strong support for the importance of technology channel in facilitating theexport spillovers. The estimated coefficients of FTECH j ,t , which represent the channel, arepositive, large, and statistically very significant in the result of specification (4.1) and (4.2). They
support the hypothesis for the existence of demonstration/imitation effect from the technology
brought by MNEs. The magnitude of the coefficients suggests the demonstration effect is

11

At the experimental stage, initially industry dummy variables at four-digit ISIC were estimated. However, manyindustry-level variables were dropped for the reason of perfect collinearity. For this reason, the estimations wereexperimented at three- and two-digit ISIC level. Finally, the estimations with the two-digit ISIC dummy variables
were chosen for the reason of a better results compared to the other estimations.

18

substantially important in determining whether or not a domestic plant participate in exporting
activities in time t.
Table 4.1 The determinants of export participation in 1996 and 2006: regression results
EPi,tDependent variable
Specification(4.1)
21.689(2.19)*FTECHj,t

(4.2)

-0.19
(1.05)FEMPSHj,tEPi,t-2log(SIZE)i,tLPi,tGOVi,tIMDEPi,t
MESj,tICIj,tPDj,tIEXPj,tHIj,tTARIFFj,tDummy year 2006
Dummy variables for provinces
Dummy variables for industries
Constant
Observations
Wald chi2
Pseudo R-squareNotes:

1)

2)

(4.4)
27.024(2.64)**
0.175(0.84)-0.172(5.26)**
1.332(30.10)**0.841(18.97)**0.401(35.98)**a2.08(2.31)*-0.085(1.33)
0.315(5.66)**-0.00003(4.94)**
a5.32(0.70)
0.268(0.19)
1.001(12.25)**0.374(2.11)*-0.003(1.72)+
0.006(0.18)
Included
Included-4.089(16.40)**
25658

1.33
(30.10)**0.847(19.13)**0.4(35.98)**2.16a(2.41)*-0.072(1.14)
0.308(5.54)**-0.00003(4.38)**
a2.14(0.28)
0.379(0.27)
1.038(13.19)**0.325(1.89)+-0.004(2.00)*
0.023(0.71)
Included
Included-4.206(17.02)**
25801

1.332
(30.15)**0.847(19.13)**0.4(35.99)**2.19a(2.44)*-0.08(1.26)
0.322(5.79)**-0.00003(4.32)**
a6.42(0.85)
0.044(0.03)
1.05(13.04)**0.305(1.77)+-0.004(2.13)*
0.005(0.17)
Included
Included-4.165(16.87)**25801-0.148(5.15)**
1.334(30.15)**0.842(19.00)**0.401(35.97)**2.1a(2.34)*-0.092(1.43)
0.329(5.94)**-0.00003(4.81)**
a9.5(1.27)-0.151(0.11)
1.012(12.85)**0.354(2.02)*-0.004(1.86)+-0.014(0.47)
Included
Included-4.06(16.34)**25658

13562.23

13558.62

13528.36

13537.34

0.535

0.53490.535

0.5354

RFEXPSHj,tEPi,t-1(4.3)

Robust Z statistics in parentheses

Significance level: ** significant at 1%; * significant at 5%; + significant at 10%a)

The coefficient was multiplied by 10-07 to improve presentation

19

The results, however, provide completely different finding in respect to the other export spillover
channels, namely the competition and information channel. Consider first, the results forcompetition channel, represented by FEMPSH j ,t . The estimated coefficients of FEMPSH j ,t arehighly insignificant in the result of specification (4.2) and (4.4). Thus, the extent of competition
arising from the operation of MNEs does not seem to give any impact on export participation ofdomestic firms. The positive estimated coefficient in specification (4.4), however, indicates thatthe forces to improve the productivity of domestic firms created by this channel might exist,
although this may be so small.
Turning to the results for information channel, the estimated coefficients of RFEXPSH j ,t , which
represents the effect of this channel, are negative and highly significant (see the results ofspecification (4.3) and (4.4)). Therefore, the concentration of export activities of MNEs in anindustry is suggested to reduce the likelihood of domestic firms to participate in export markets.
This does not accord the hypothesis for the impact of this channel.
While it is rather difficult to reconcile, one possible explanation for the results may be because
RFEXPSH j ,t is not able to fully capture the extent of the information spillovers. As detailed in

section 3.3, this variable utilizes the information about the extent of exported output of all
foreign plants in an industry – in capturing the extent of potential information spillovers. Whileuseful, this variable may at the same time capture the extent domination of the foreign plants – asa representation of MNEs – in the export of the industry. Therefore, unlike in the Greenaway etal. (2004), this variable reflects more on competition effect rather than the contribution of
information spillover.
Another point to argue is that, much of the information spillovers from MNEs about export
markets could in fact be transferred by activities which are very hard to be measured, and someof this may even be very difficult to be linked up with presence of MNEs. Personal contacts, forexample, provides an avenue for information spillover. However, this is very difficult to measure
based on the available information in the dataset.
Notwithstanding the potential weakness of the variable, the results concerning RFEXPSH j ,t may
actually reflect a generally presumed behavior of MNEs which tend to protect the know-how and20

other important information they posses. In this respect, the results are in line with the findings in
the literature about weak observed impact of productivity spillover from the presence of MNEs
(e.g. Hanson 2001; Gorg and Greenaway 2004). As indicated by these studies, the weak spillover
effect may be due to the fact that MNEs protects their firm-specific assets – presumably
including the precious information about foreign markets – very effectively (Greenaway et al.
2004, p. 1029).
Table 4.2 reports the OLS regression results of the export intensity equation (i.e., eguation 3.2)
for all domestic plants which were exporting in 1996 and 2006. This is the second step of the
Heckman estimation model for sample selection. The coefficient of inverse Mills ratio isstatistically significant in all specifications at 1 percent level, implying the disturbance in theexport participation and export intensity equation is correlated. As explained, the used of
Heckman method correct the potential bias estimates from this correlation. The F-test for overallsignificance passes at 1 percent level and the While’s robust t-statistics were used to correct forheteroscedasticity.
Looking at the estimated coefficients of all variables representing the export spillovers, a similarfinding emerges. That is, technology channel is positively related to the export intensity of thedomestic plants that become exporters, and competition effect from the foreign plants does notseem to encourage domestic plants to improve their export intensity once these plants become
exporters.
However, according to the result in specification (4.6), the negative coefficient of FEMPSH j ,t isnow very statistically significant at 1 percent level. Thus, exports of domestic exporters tend tobe lower with a strong presence of MNEs. Hence, MNEs seems to crowd out domestic exporters.
This finding, while does not accord the hypothesis on the positive impact of export spillovers,
seems to capture the strategic motive of market-seeking hypothesis by MNEs. The competitionin domestic final-goods market between MNEs and domestic firms could decrease market pricewhich in turn could lead to the exit of some domestic producers (Markusen and Venables 1999).
It is worth commenting here that the crowding out may indicate a slow process of thecompetition effect in creating more productive firms that ready to export. As underlined by thetheory that recognizes plant heterogeneity (e.g. Melitz 2003), the impact of trade liberalization
21

takes time through the dynamics of competition in improving the productivity level of bothindustry and firms that populate it.
Table 4.2 The determinants of export intensity in 1996 and 2006: regression results
EXPi,tDependent variable
Specification

(4.5)
6.672
(1.89)+FTECHj,t(4.6)-0.203(3.16)**FEMPSHj,t
RFEXPSHj,tlog(SIZE)i,tLPi,tGOVi,tIMDEPi,t
MESj,tICIj,tPDj,tIEXPj,tHIj,tTARIFFj,tDummy year 2006
Mills ratio
Dummy variables for provinces
Dummy variables for industries
Constant
Observations
R-square(4.7)-0.014(3.54)**-4.27a(1.53)-0.01(0.49)
0.009(0.51)
b-2.6(0.98)-1.2b(4.55)**-2.773(5.26)**
0.416(15.39)**-0.14(2.13)*
0.002(3.42)**
0.073(6.67)**-0.087(11.53)**
Included
Included
0.599(7.42)**
4992
0.3-0.014(3.69)**-4.18a(1.49)-0.014(0.71)
0.012(0.66)
b-3.02(1.14)-1.1b(4.18)**-2.676(5.11)**
0.435(15.80)**-0.15(2.28)*
0.002(3.20)**
0.085(8.09)**-0.088(11.64)**
Included
Included
0.612(7.57)**
4992
0.3-0.075(6.86)**-0.014(3.55)**-4.64a(1.66)+-0.017(0.84)
0.013(0.75)
b-3.35(1.27)-1.05b
(4.02)**-2.684(5.14)**
0.414(15.39)**-0.148(2.26)*
0.003(3.60)**
0.072(6.91)**-0.088(11.63)**
Included
Included
0.646(8.03)**
4992
0.3

(4.8)
12.314(1.70)+
0.039(0.52)-0.077(6.11)**-0.013(3.52)**-4.68a(1.68)+-0.017(0.82)
0.014(0.79)-3.18b
(1.20)-1.04b
(3.92)**-2.735(5.21)**
0.409(14.76)**-0.146(2.23)*
0.003(3.60)**
0.067(5.83)**-0.088(11.59)**Included
Included
0.647(8.03)**
4992
0.3

Notes: 1) Robust F statistics in parentheses
2)

Significance level: ** significant at 1%; * significant at 5%; + significant at 10%a)-08
The coefficient was multiplied by 10 to improve presentationb)

The coefficient was multiplied by 10-06 to improve presentation

22

Another point worth elaborating is about the estimated coefficient of FTECH j ,t , which is muchless significant than that drawn from the export participation equation. It suggests a much lessimportant effect of the pooled technology once the domestic plants become exporters. This isconsistent with the ‘self-selection’ hypothesis. As explained, the hypothesis implies that firms
prepare to become much more productive only before selling to export markets, and not when
they are in. Therefore, it is natural to see the higher importance of FTECH j ,t as a determinant ofexport participation, rather than as a determinant of export intensity.
The fact that the estimated coefficient FTECH j ,t is still significant, albeit only at 10 percent
level, is also consistent with the hypothesis of learning by exporting, however. Again, as
explained earlier in Section 2, this hypothesis argues that exporters continuously learn toimprove their productivity even if they have succeeded to operate in export markets. The results
suggest that the domestic plants continues to learn from the pooled of technology brought by
MNEs. It is in fact accord the impression given by Figure 4.1, which indicates that many, or
perhaps most, of the domestic new exporters are still constrained, compared to the new foreignexporters.
All in all, the results presented in Table 4.1 and 4.2 suggest a rather mixed finding about the roleof export spillovers channels on export participation of domestic plants. As discussed, there is apossibility that the impact of export spillovers – through their channels – varies across firms withdifferent level of productivity. The following two tables present the results of testing thishypothesis on the equations, by including the interaction variables of FTECH j ,t * LPi ,t ,FEMPSH j ,t * LPi.t , and RFEXPSH j ,t * LPi ,t .

Table 4.3 presents the estimation results of the export participation equation. To reduce thepotential multicolinearity, each channel variable and its interaction with labor productivity was
included separately in the estimation.

23

Table 4.3 The determinants of export participation in 1996 and 2006: regression results,
with the export spillover interactive effects
Dependent variable
Specification
FTECHj,t(4.9)
21.027(2.11)*

EPi,t(4.10)-0.197(1.09)FEMPSHj,t-0.152(5.26)**

RFEXPSHj,tLPi,t * FTECHj,t(4.11)

0.00002(0.67)
a4.74(1.85)+

LPi,t * FEMPSHj,t

a

1.331
(30.11)**
0.846(19.11)**0.401(36.10)**-0.069(1.08)
0.312(5.62)**-0.00003(4.36)**
4a(0.53)
0.456(0.32)
1.035(13.17)**0.317(1.84)+-0.004(2.03)*
0.031(0.95)
Included
Included-4.201(17.00)**
25801

1.333
(30.15)**0.847(19.13)**0.401(36.07)**-0.078(1.23)
0.323(5.81)**-0.00002(4.29)**
7.01a(0.92)
0.082(0.06)
1.049(13.03)**0.304(1.77)+-0.004(2.11)*
0.011(0.36)
Included
Included-4.161(16.86)**
25801

1.22
(1.95)+
1.334(30.16)**0.842(19.00)**0.401(36.04)**-0.09(1.41)
0.331(5.98)**-0.00003(4.78)**
b
1.01(1.34)-0.12(0.09)
1.011(12.83)**0.357(2.03)*-0.004(1.86)+-0.009(0.31)
Included
Included-4.055(16.33)**25658

13557.15

13555.13

13525.07

53.48

53.48

53.49

LPi,t * RFEXPSHj,tEPi,t-1
EPi,t-2log(SIZE)i,t
GOVi,tIMDEPi,t
MESj,tICIj,tPDj,tIEXPj,tHIj,tTARIFFj,tDummy year 2006
Dummy variables for provinces
Dummy variables for industries
Constant
Observations
Wald chi2
Pseudo R-squareNotes:

1)

2)

Robust Z statistics in parentheses

Significance level: ** significant at 1%; * significant at 5%; + significant at 10%a)

The coefficient was multiplied by 10

-07

b)

The coefficient was multiplied by 10

to improve presentation-06

to improve presentation

24

The results do not suggest any variation across plants regarding the demonstration/imitation
effect from technology adopted by MNEs. The coefficient of

FTECH j ,t * LPi ,t , although

positive, is very small and highly insignificant (see the result of specification (4.9)).
The results, however, suggest that the impact of competition pressure from MNEs is different
across firms. In the result of specification (4.10), the estimated coefficient of FEMPSH j ,t * LPi.t
is positive, although significant only at 10 percent level and very small. Nonetheless, thisindicates the extent of competitive pressure on domestic firms to improve their productivity – fora higher chance of participating in export markets – is higher for the more productive domestic
firms. This supports the theoretical model of Bustos (2005) which predicts that the impact oftrade liberalization on technology upgrading depends on firms’ productivity level. Firms withintermediate productivity level is predicted to upgrade their technology – and hence improvetheir productivity and export performance – while firms with low level of productivity stays
using traditional technology and do not supply to export markets.
There is weak evidence that the effect of information about export markets that can be spilledover to domestic firms depends on whether the domestic firms are more or less productive. The
estimated coefficient of RFEXPSH j ,t * LPi ,t is positive albeit very small and significant at 10 per
cent level (see the result of specification (4.11)). This, of course, presuming that RFEXPSH j ,tcaptures the extent of the available information provided by MNEs (see the earlier discussion on
the potential weakness of the variable in capturing the information). This finding is consistent tothat of the previous one (i.e., the interactive between competition effect and labor productivity),
and together, the findings suggest that only more productive firms are able to utilize positiveeffect of export spillovers from the presence of MNEs.

25

Table 4.4 The determinants of export intensity in 1996 and 2006: regression results, withthe export spillover interactive effects
Dependent variable
Specification
FTECHj,t(4.12)
6.728(1.90)+

EXPi,t(4.13)(4.14)-0.202(3.13)**FEMPSHj,t-0.073(6.65)**

RFEXPSHj,tbLPi,t * FTECHj,t3.03(0.33)
a-5.26(0.45)

LPi,t * FEMPSHj,tLPi,t * RFEXPSHj,tlog(SIZE)i,t
GOVi,tIMDEPi,t
MESj,tICIj,tPDj,tIEXPj,tHIj,tTARIFFj,tDummy year 2006
Mills ratio
Dummy variables for provinces
Dummy variables for industries
Constant
Observations
R-square-0.014(3.55)**-0.01(0.52)
0.008(0.43)
b-2.65(1.00)-1.25b
(4.75)**-2.734(5.15)**
0.416(15.36)**-0.145(2.21)*
0.002(3.38)**
0.07(6.51)**-0.087(11.53)**
Included
Included
0.599(7.42)**
4992
0.3-0.014(3.70)**-0.015(0.74)
0.011(0.62)
b-3.11(1.18)
b-1.13(4.24)**-2.67(5.09)**
0.434(15.77)**-0.154(2.34)*
0.002(3.18)**
0.084(7.96)**-0.088(11.65)**
Included
Included
0.612(7.57)**
4992
0.3-5.37a(1.44)-0.014(3.59)**-0.017(0.83)
0.013(0.73)
b-3.48(1.32)
b-1.03(3.88)**-2.696(5.16)**
0.412(15.31)**-0.161(2.45)*
0.002(3.55)**
0.071(6.86)**-0.088(11.64)**
Included
Included
0.647(8.04)**
4992
0.3

Notes: 1) Robust F statistics in parentheses
2)

Significance level: ** significant at 1%; * significant at 5%; + significant at 10%-08a)
The coefficient was multiplied by 10 to improve presentationb)-06

The coefficient was multiplied by 10

to improve presentation

26

Table 4.4 shows the OLS estimation results that test the effect of the interactive variables onexport intensity. The results are similar to that of the export participation equation except inthese, the estimated coefficient of FEMPSH j ,t * LPi.t and RFEXPSH j ,t * LPi ,t are now negative(i.e., changing sign). However, these coefficients are insignificant, particularly for the former
where it is highly insignificant. Therefore, all in all, there is no evidence that the contribution ofexport spillover depends on the productivity level of exporters. To some extent, this is consistent
with the ‘self-selection’ hypothesis, for the reasons that productivity level of within the group ofexporters should not be much different – setting aside the importance of other factors that are
unable to be captured by these regressions.

5. Summary and conclusion
This study examines the positive externalities from the presence of MNEs in affecting the export
performance of domestic firms. It asks whether the existence of the MNEs helps domestic firms
to participate and perform well in export markets. The study takes the case study of theIndonesian manufacturing as a case study, utilizing the rich data of the country manufacturing
census.
In its empirical analysis, the study attempts to answer the question by examining the channels
through which the positive export spillovers effect can be transmitted to domestic firms. In
particular, it examines whether or not the pooled of technology and information about foreign
markets brought by MNEs, as well as competition effect from the MNE operation, are able toincrease the likelihood of domestic firms to participate in export market and to increase theextent of the domestic firms exports.
The empirical results provide rather mixed findings. While the extent of pooled technology
brought by foreign plants was found to increase the participation and exporting performance ofdomestic plants, the competition arising from the operation of the foreign plants seems to crowdout domestic exporting plants. The crowding out effect suggests the behavior of market-seeking
hypothesis of MNEs in the Indonesian manufacturing. The study also found a negative export
spillover impact from the channel of information about foreign markets. This finding, however,27

may be due to the weakness in the proxy used by the estimations. Further analysis give someevidence that the positive impact of export spillovers in the Indonesian manufacturing depends
on the level of productivity of domestic firms. Specifically, the impact of competition effect inexport participation is higher for the more productive domestic plants.
Notwithstanding the mixed findings, this study still suggests the importance of policies that
invite MNEs. In terms of export spillover effect, the most obvious justification can be drawn
from the finding regarding the demonstration/imitation effect from technology brought by
MNEs. Meanwhile, the competition effect from the presence of MNEs should, in the longer run
and through the dynamics of competition, produce a population of more productive exporters. In
addition, strengthening trade facilitations seems to be a proposition. As the analysis show, muchof the exports the new domestic exporters in the country’s manufacturing sector are somehow
still constrained. Policies that improve trade facilitations, therefore, should be able to also
‘unlock’ these constrained exporters.

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