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 ofmultinational enterprises (MNEs) on a firm’s decision to export, and on export intensity. It
utilizes data from Indonesian manufacturing for the census years 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 improving technologicalknowledge raises the likelihood that domestic firms will enter the export market, and improves
export performance. The analysis finds weak evidence to support the hypothesis that competition,
created by the operation of MNEs, facilitates entry into export markets. Further analysis howevershows that the impact of competition depends on the level of productivity of the domestic firms. In
particular, the more productive firms are suggested to have been able to benefit more than theless productive ones. The overall analysis suggests that given the mixed evidence, policies topromote MNEs are still worth pursuing. The most obvious justification comes from the positive
impact of the increased pool of technological knowledge. Other than this, strengthening tradefacilitation seems to be a positive proposition, given the finding that many of the new domestic
exporters seem to have been constrained 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 anticipate a positive impact from foreign direct investment (FDI) on
development. An underlying argument justifying this is that the presence of FDI, through theoperation of multinationals (MNEs) in host countries, helps to improve the productivity ofdomestic firms.
There is now growing literature that formalizes and collects evidence of the positive
externalities, often termed technology/productivity spillover. However, there is still conflicting
evidence about the existence or positive impact of the spillover. On the one hand, Gorg andGreenaway (2004) for example reported negative productivity spillover occurring in several
European countries, while on the other there exist studies which find evidence of a positive
spillover effect for some Asian countries, such as Takii (2006) and Kohpaiboon (2006) for
Indonesia and Thailand, respectively.
This study elaborates upon this subject, by examining the impact of the presence of MNEs on
export performance of domestic firms. Specifically, it asks whether MNEs help domestic firms
to participate and perform well in export markets. This study takes the reference of theIndonesian manufacturing sector as a case study, utilizing the rich plant-level census data of thesector for the years 1996 and 2006.
Indonesian manufacturing provides a good case study, considering the rapid FDI flow into thecountry since the early 1990s and even after the deep economic crisis of 1997/98. The rapid flow
was often cited as an impact of the bold trade and investment liberalizations taken by theIndonesian government since the late 1980s. During the same period, the country also
experienced rapid growth in its manufacturing exports. Given the domestic orientation of the
trade and industrial policy before the liberalizations, it is only natural to argue that theIndonesian experience serves as a natural experiment for answering the research question.
The rest of this paper is organized as follows. Section 2 presents an analytical framework andidentifies the testable hypotheses. Section 3 describes the methodology adopted by the study.
Section 4 presents the econometric results and the analysis. Section 5 summarizes and concludes
the study.
2

2. Analytical framework
Export spillover, 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.
According to this theory, the improvement in domestic firms‟ export performance is theconsequence or result of export spillovers.
The accumulation of evidence from a wide 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 reported 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 thatlabor 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. The first, which is commonlyreferred to as the „self-selection‟ hypothesis, argues that only the most productive firms are able
to survive in the highly competitive export markets. This hypothesis is based on the presumptionthat there are additional costs involved in participating in export markets. These costs, whichusually involve high fixed costs, include transport costs and expenses related to establishing
distributional channels, as well as production costs in adapting products for foreign tastes(Bernard and Jensen 1999). The alternative explanation argues that there is a learning effect fromparticipating in exporting activities which will result in productivity improvement. One example
is that exporters are often argued to be able to gain access to technical expertise, including
product designs and methods, from their foreign buyers (Aw et al. 2000, p.67). This explanation
is often termed the „learning-by-exporting‟ hypothesis.
Each of these theories applies to different states of the exporting status of a firm. The selfselection hypothesis applies for a firm that is not yet exporting but is about to, and the learning-3

by-exporting hypothesis applies when a firm has become an exporter. Thus, the theories explainthat productive firms self-select themselves to become exporters, and once there, these exporters
learn and become even more productive than before they entered export markets.
In respect of 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, Hallward-Driemeier et al. (2002, p.25) observed a substantial productivity
difference between domestic firms that were established as exporters and domestic firms thatwere not. This indicates that firms participating in export markets make a conscious decision tooperate differently from ones that focus on the domestic market. Supporting this interpretation,
they show that domestic exporters indeed bear a resemblance to foreign exporters. In particular,
they are more capital intensive and use more equipment of recent vintage than domestic nonexporters.
It is worth noting here an implication of the presumed additional costs required for a firm toengage in exporting 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 an exporter in the next period.
While there has not been much study of this topic, there is an indication that the extent of thesecosts is large and serves 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 positiveeffect in determining export participation in the current year for plants in Colombian
manufacturing.1
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 facilities that are able to support export activities, such as seaports, airports,
and other logistics infrastructure. Thus, the source of export spillovers based on this argument is1

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

4

governmental or public initiatives. The other argument comes from the existence of MNEs. It isbased on the presumption that activities or some particular characteristics of MNEs allow
domestic firms to reduce their cost of 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 help 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 started selling in export
markets. The information classified by this channel includes, for example, information about
regulations in foreign markets, taste and preference of foreign consumers, the market
competition situation in foreign markets, etc. This channel emphasizes the information that
directly relates to markets abroad.
A second 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 the more advanced technology used by MNEs, which is usually brought about by thedemonstration effect and/or imitation. In practice, this channel usually works via – but notlimited to – outsourcing practices and activities (e.g. the allocation of engineers from MNEs todomestic firms to supervise the production of the outsourced products, etc.). Supporting this,Machikita et al. (2009) found that in many Southeast Asian 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 the competition effect. Entry of MNEs leads to increased
competition initially, but after that, it creates pressure for domestic firms to become moreproductive. Given that higher productivity is needed to survive in export markets, the5competition effect from MNEs thus encourages domestic firms to join and perform well inexport 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 coming from MNEs on theexport performance of domestic plants in the sector. However, this finding is not robust tochanges in sample size. Their results suggest the lack of robustness is related to large differencesin 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 manufacturing firms in Uruguay in 1988,
Kokko et al. found that foreign ownership at sectoral level increases the likelihood of exporting.
They, however, only found a positive impact for multinationals that were established after 1973,
which was a more outward oriented period for the Uruguayan economy. There was no evidenceof export spillover from the group of multinationals established before 1973, when the policy
was more inward oriented. Greenaway et al., meanwhile, found that multinationals not only
increase the desire of domestic firms to export, but also export intensity. They used a panel offirms in the UK in finding this. Unlike other previous studies, they showed evidence of thepositive impact that runs through the three channels identified above.
2.1 Hypotheses
Drawing from the discussion above, the following section presents the testable hypotheses that
relate the channels of export spillover resulting from the presence of MNEs with the export
response of domestic plants.
Technology channel
The larger the technology intensity of MNEs‟ operations, the higher the chances of successful
imitation by domestic firms. Thus, technological capability brought by MNEs ( FTECH ) is
hypothesized to increase the export participation and performance of domestic plants.
Unlike the more traditional approach which underlines the link extent of ownership share with
degree of control, this study defines MNEs as plants that have 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.2
Competition 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, stemming from theimproved 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 is likely to occur if the
motivation for 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 withdomestic firms in industries producing final goods, predicts that the increase in output due to theoperation of MNEs decreases market price and leads 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. Thus, 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 ofparticipating in the markets. Thus, a positive relationship between RFEXPSH and the domestic
plants‟ export performance is expected.
Dependency on plant heterogeneity
Notwithstanding the theoretical predictions, and the evidence as discussed earlier, there is reason
to argue that the positive impact of the export spillovers may differ across firms.

2

Aswicahyono and Hill (1995) for example reported that many Indonesian case studies have demonstrated that local
partners often play relatively minor roles, 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.3 Melitz (2003) built a theoretical model thattakes into account the importance of heterogeneity in a setting of imperfect competition.
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 their productivity. Yet, at the same time, the reduction of cost forexporting brought about by trade liberalization lowers productivity level required by a firm toexport. Trade liberalization thus creates higher industry productivity because only the moreproductive firms survive entry into the industry, and output is reallocated towards these moreproductive 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 levels prior to the trade liberalization results in an outcome that
only the more productive-firms upgrade their technology. As a final prediction, tradeliberalization only causes firms with an intermediate level of productivity to upgrade theirtechnology so as better to compete in export market. Less productive firms, meanwhile, stay toserve the domestic market because they do not upgrade their technology. The model takes into
account technology upgrading, and gives a prediction that only a fraction of firms within anindustry are able to substantially 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
expect a positive relationship for the following interaction variables:

FTECH * LP ,FEMPSH * LP , and RFEXPSH * LP .
3This was born from growing evidence on the variation of firms that exports cannot be derived from a random
sample, because not all firms within an industry export. Eaton et al. (2004), for example, highlights this fact for
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 to the rich Indonesian large and medium plant manufacturing data
for the census years 1996 and 2006. The models utilize the panel-data feature of the data,
although they use only two data series. All of these decisions are explained below whenever they
are relevant.
This study adopts the general approach of model specification from the literature on firm‟sexport 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 firms that switch from exporting to non-exporting. The second behavior is where
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  0  1EPi , j ,t 1   2 EPi , j ,t 2  3 ' X i , j ,t  4 'Yj ,t  5 ' Z j ,t   i , j ,t

(3.1)

EXPi , j ,t  0  1 ' X i , j ,t  2 'Yj ,t  3 ' Z j ,t  i , j ,t

(3.2)

where (3.1) and (3.2) are export participation and export intensity equation, respectively.

9

i represent 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 wasexporting 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 intends to enter foreign markets (i.e. Roberts and Tybout 1997; Campa
2004).
Equations (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.
It should be noted that estimations are made only for the data of 1996 and 2006. The motivation
for this is twofold. First, these years are census years. Therefore, the number of observations issubstantially higher for these years compared to that of non-census, or “survey” years,
particularly for those of the post 2000 survey years. In general, for this period, the total numberof plants included in the census is about 30 percent more than of the number included in thesurvey. Secondly, key information needed to construct a variable that is commonly used to proxy
the pool of technology and knowledge, namely the expenditure for R and D activities, licenses
and royalties, and training, is not available in the data of survey 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 environments affecting the data in the two census years,
particularly related to the situations before and after the 1997/98 economic crisis.
Having argued for the use of only the census years‟ 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 used into the sample. As explained, this creates a reduction in thenumber of observations. However, as also explained, it is still worth going in this direction,

10

given that the key information to reflect the technological capability of MNEs is not available in
the non-census year data.
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.
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.4
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 the 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.5 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. In the second step, equation(3.2) is regressed with the estimated inverse Mills as an additional regressor. A test for a4

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‟s purpose is to obtain the direction of the effect of
explanatory variables.
5
See Johnston and Dinardo (1997) for more detailed exposition about the Heckman two-step approach.

11selectivity problem can be done by evaluating the statistical significance of the estimatedcoefficient of the inverse Mills ratio.
3.2 Data
The data for the empirical analysis in this study are drawn from the census of medium- andlarge-scale manufacturing establishments (Statistik Industry, or SI) for the years 1996 and 2006.
The establishments are defined as those with 20 or more employees. The surveys were
undertaken by the Indonesian Central Board of Statistics (Badan Pusat Statistik or BPS).6
As noted in many studies, SI data are considered to be among the best, by the standards ofdeveloping countries. The data cover a wide range of information on the establishments,
including some basic information (ISIC classification, year of starting production, location),ownership (share of foreign, domestic and government), production (gross output, stocks,
capacity utilization, share of output exported), material costs and various types of expenses, labor(head-count and salary and wages), capital stock and investment, and sources of investment
funds.
The data, however, have several limitations. Among others, they do not include information
which can identify whether an establishment is a single-unit or is part of a multi-plant firm. As aresult, establishments owned by an enterprise cannot be linked up, and hence the number ofenterprises is over counted: some plants may have been counted as firms whereas in practicethey are not.

6

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
Three export spillover variables are included, each of which represents the channel of thespillovers, These are foreign technological capability ( FTECH j ), foreign employment share( FEMPLSH j ), and foreign exporting activities ( RFEXPSH j ). All these are defined at industry
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.7 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

7

The inclusion of license fees is, to a large extent 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 labor as a proxy for labor productivity ( LPi ).8
Wholesale price indices at the 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 )i8

Value-added is chosen to proxy output, instead of gross output, because it avoids the double-counting problem andis less sensitive to 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‟ operations in their host country (i.e., the matrix Y j ,t ). The following
lists and details these variables which are also defined at the four-digit ISIC level.
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 requirements and high capital intensity. As explained in
Aswicahyono and Hill (1995), MNEs are usually accustomed to large-scale operations in theirhome countries and thus to the use of advanced technology. The advanced technology
presumably could be adopted by any firms 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 in investing in exportoriented sectors. For industry j, it is defined as the ratio of exports 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 industries with highlevels of 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 in entering
industries with either less competitive activity or with high import protection. This is particularly
true for the market-seeking MNEs. Two variables are included to proxy the extent ofcompetition, namely the Herfindahl Index ( HHI ) and the nominal tariff (TARIFF). The latter is
included to capture the extent of import protection which is likely to affect 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 the three-digit ISIC level, drawn fromthe WTO database through the service of the WITS database.

Other control variables
In addition to the control variables above, the estimations include dummy variables forprovinces, to control for regional differences in plant operations 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 periods 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 export markets. To do so, we define the export 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

andENX 2 j ,t 

ENXVAj ,t
TXVAj ,t 1where: ENXPj ,t = Total number of plants in industry j that become exporters in time t
TXPj ,t 1= Total number of exporting plants in industry j at time t  1

ENXVAj ,t = Exported value added of plants that become exporters in industry j at time t
TXVAj ,t 1 = Exported value added of all exporting plants in industry j at time t  1

Figure 4.1 Exporting entry rates of domestic and foreign plants in Indonesian
manufacturing, average 1996 and 2006.

Figure 4.1 presents the average 1996 and 2006 exporting entry rates for the group of foreign anddomestic plants in Indonesian manufacturing. The figure reveals that entry into exporting issubstantially higher for the group of domestic plants. In terms of number of plants, the rate isabout 30 percent, while in terms of value added, the rate is about 15 percent. This is in contrast
with the entry rate for the group of foreign plants, which is about 5 percent for both in terms of17

number of plants and value added. This is a favorable observation from the policy perspective,
because it indicates that domestic plants were actively seeking to sell into export markets.
However, looking at the figure more carefully, there is indeed an issue regarding the favorable
performance. Comparing the two of entry rates (i.e., ENX 1 j ,t and ENX 2 j ,t ) across the domestic
and foreign plants groups, it appears that many of the new domestic exporters are „small‟, interms of their exported output. This is in contrast to the picture of the new foreign exporters,
which seem to be much „larger‟ in terms of their exported output. The comparison suggests thata new foreign exporter exports twice as much as a new domestic exporter. Obviously there could
be many reasons to explain this, but it seems that many new domestic exporters are moreconstrained than their foreign counterparts.
Table 4.1 reports the probit regression results of the export participation equation (i.e., equation(3.1)), which comprises all domestic plants operating in 1996 and 2006. The regressions are thefirst step in the Heckman selection model. Some specifications were tested and the table reports
the most favorable ones in terms of model fit and estimated coefficients. The industry dummy
variables are included at the two-digit ISIC level.9 The table reports robust standard errors for thereason of heteroscedastic variance. The Wald test for overall significance in all reported
specifications passes at the 1 percent level. The examination for the presence of outliers wasdone in the experimental stage, and the presented results have been controlled for the outliers(i.e., by introducing a dummy variable which identifies the outliers).
The results provide a strong support for the importance of the technology channel in facilitating
export spillovers. The estimated coefficients of FTECH j ,t , which represent the channel, arepositive, large, and statistically very significant in the results of specification (4.1) and (4.2).
They support the hypothesis of the existence of the demonstration/imitation effect from thetechnology brought by MNEs. The magnitude of the coefficients suggests the demonstration
effect is substantially important in determining whether or not a domestic plant participates inexporting activities in time t.
9At the experimental stage, initially industry dummy variables at the four-digit ISIC were estimated. However,many industry-level variables were dropped for the reason of perfect collinearity. For this reason, the estimations
were tested at the three- and two-digit ISIC level. Finally, the estimations with the two-digit ISIC dummy variables
were chosen because they gave better results compared to the other estimations.

18

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)**2.08a(2.31)*-0.085(1.33)
0.315(5.66)**-0.00003(4.94)**
5.32a(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)**
2.14a(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)**
6.42a(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)**
9.5a(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

The results, however, provide completely different findings in respect to the other export
spillover channels, namely the competition and information channel. Consider first the results for19the competition channel, represented by FEMPSH j ,t . The estimated coefficients of FEMPSH j ,tare highly insignificant in the result of specification (4.2) and (4.4). Thus, the extent ofcompetition arising from the operation of MNEs does not seem to have any impact on the export
participation of domestic firms. The positive estimated coefficient in specification (4.4),
however, indicates that the forces tending to improve the productivity of domestic firms createdby this channel might exist, although they may be very small.
Turning to the results for the 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 would seem to reduce the likelihood of domestic firms participating in export markets.
This does not accord with the hypothesis for the impact of this channel.
While they are 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 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 of domination of the foreign plants
in the exports of the industry. Therefore, unlike in Greenaway et al. (2004), this variable reflects
more the competition effect rather than the contribution of information spillover.
Another point for discussion is that much of the information spillover from MNEs relating toexport markets could in fact be transferred by activities which are very hard to measure, andsome of this may even be very difficult to be linked to the presence of MNEs. Personal contacts,
for example, provide an avenue for information spillover. However, this is very difficult tomeasure 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 andother important information they posses. In this respect, the results are in line with the findings in
the literature concerning the 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 weak20

spillover effect may be due to the fact that MNEs protect their firm-specific assets very
effectively, presumably including their precious information about foreign markets (Greenaway
et al. 2004, p. 1029).
Table 4.2 reports the OLS regression results of the export intensity equation (i.e., equation (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 the 1 percent level, implying that the disturbance inthe export participation and export intensity equation is correlated. As explained, the use of the
Heckman method corrects the potentially biased estimates from this correlation. The F-test for
overall significance passes at the 1 percent level and While‟s robust t-statistics were used tocorrect for heteroscedasticity.
Looking at the estimated coefficients of all variables representing the export spillovers, a similarfinding emerges. That is, the technology channel is positively related to the export intensity ofthe domestic plants that become exporters, and the competition effect from foreign plants does
not seem 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 the 1 percent level. Thus, exports of domestic exporters tend
to be lower when there is a strong presence of MNEs. Hence, MNEs seem to crowd out domestic
exporters. This finding, while it does not accord with the hypothesis of the positive impact ofexport spillovers, seems to capture the strategic motive of market-seeking hypothesis by MNEs.
Competition in the domestic final-goods market between MNEs and domestic firms could
decrease market prices, which 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 become ready to export. As underlinedby the theory that recognizes plant heterogeneity (e.g. Melitz 2003), the impact of tradeliberalization takes time to work, through the dynamics of competition, in improving the
productivity level of both industry and the firms that populate it.
21

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)**
a-4.27(1.53)-0.01(0.49)
0.009(0.51)-2.6b(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)**
a-4.18(1.49)-0.014(0.71)
0.012(0.66)-3.02b
(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)**
a-4.64(1.66)+-0.017(0.84)
0.013(0.75)-3.35b
(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)**
a-4.68(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)
The coefficient was multiplied by 10-08 to improve presentationb)

The coefficient was multiplied by 10-06 to improve presentation

22

Another point worth elaborating concerns the estimated coefficient of

FTECH j ,t , which ismuch less significant than that drawn from the export participation equation. It suggests a muchless important effect for 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 the 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 find ways toimprove their productivity even once they have successfully entered export markets. The results
suggest that the domestic plants continue to learn from the pool of technology brought by MNEs.
This accords the impression given by Figure 4.1, which indicates that many, or perhaps most, ofthe new domestic exporters are still constrained, compared to the new foreign exporters.
All in all, the results presented in Table 4.1 and 4.2 suggest a rather mixed finding about the roleof export spillover channels on the export participation of domestic plants. As discussed, there isa possibility that the impact of export spillovers – through their channels – varies across firms
with different 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.
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 highly insignificant (see the result of specification (4.9)).

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,t1.331(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

1.22a
(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)**
1.01b(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 to improve presentationb)

The coefficient was multiplied by 10-06 to improve presentation

24

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 the 10 percent level, and is very small. Nonetheless, thisindicates that the extent of competitive pressure on domestic firms to improve their productivity– for a 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 of trade liberalization on technology upgrading depends on firms‟ productivity levels.
Firms with intermediate productivity levels are predicted to upgrade their technology – andhence improve their productivity and export performance – while firms with low levels of
productivity continue using traditional technology and do not seek entry 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 the 10
per cent level (see the result of specification (4.11)). This, of course, presumes 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
with that of the previous one (i.e., the interaction between competition effect and laborproductivity), and together the findings suggest that only the more productive firms are able toutilize the positive effect of export spillovers from the presence of MNEs.
Table 4.4 shows the OLS estimation results that test the effect of the interactive variables onexport intensity. The results are similar to those of the export participation equation except inthat the estimated coefficients 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, there is in general 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 the productivity level 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.

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)-0.202(3.13)**FEMPSHj,t-0.073(6.65)**

RFEXPSHj,tLPi,t * FTECHj,t(4.14)

3.03b(0.33)-5.26a(0.45)

LPi,t * FEMPSHj,taLPi,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)-2.65b
(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)-3.11b
(1.18)-1.13b
(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

-5.37
(1.44)-0.014(3.59)**-0.017(0.83)
0.013(0.73)-3.48b
(1.32)-1.03b(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%a)-08
The coefficient was multiplied by 10 to improve presentationb)

The coefficient was multiplied by 10-06 to improve presentation

26

5. Summary and conclusion
This study examines the positive externalities from the presence of MNEs 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 Indonesian manufacturing as acase study, utilizing the rich data of the national manufacturing census.
In its empirical analysis, the study attempts to answer the question stated above by examining thechannels through which the positive export spillover effect can be transmitted to domestic firms.
In particular, it examines whether or not the pool of technology, and information about foreign
markets, brought by MNEs, as well as the competition effect from the MNE operations, are able
to increase the likelihood of domestic firms participating in export markets, 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 behavior according to themarket-seeking hypothesis by MNEs in Indonesian manufacturing. The study also found anegative export spillover impact from the channel of information about foreign markets. Thisfinding, however, may be due to weakness in the proxy used by the estimations. Further analysis
gives some evidence that the positive impact of export spillovers in Indonesian manufacturing
depends on the level of productivity of domestic firms. Specifically, the impact of thecompetition effect in export participation is higher for the more productive domestic plants.
Notwithstanding the mixed findings, this study still supports the importance of policies that
invite MNEs into the domestic economy. In terms of the export spillover effect, the most obvious
justification can be drawn from the finding regarding the demonstration/imitation effect fromtechnology 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 moreproductive exporters. In addition, strengthening trade facilitation seems to be a good policy
proposition. As the analyses show, many of the new domestic exporters in the country‟s

27manufacturing sector are somehow still constrained. Policies that improve trade facilitation,
therefore, should be able to also „unlock‟ these constrained exporters.

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