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Gifted Kids or Pushy Parents?
Foreign Acquisitions and Plant Productivity in Indonesia
Jens Matthias ARNOLD* and Beata Smarzynska JAVORCIK**
October 11, 2005
Abstract
This paper uses micro data from the Indonesian Census of Manufacturing to analyze the
causal relationship between foreign ownership and plant productivity. To control for the
possible endogeneity of FDI decision, a difference-in-differences approach is combined
with propensity score matching. An advantage of this method, which has not been
previously applied in this context, is the ability to follow the timing of observed changes
in productivity and other aspects of plant performance. The results suggest that foreign
ownership leads to significant productivity improvements in the acquired plants. The
improvements become visible in the acquisition year and continue in subsequent periods.
After three years, the acquired plants outperform the control group in terms of
productivity by 34 percentage points. The data also suggest that the rise in productivity
is a result of restructuring, as acquired plants increase investment outlays, employment
and wages. Foreign ownership also appears to enhance the integration of plants into the
global economy through increased exports and imports.
Keywords: foreign direct investment, productivity
JEL classification: F23, O33, D24
* World Bank and Bocconi University, 1818 H Street, NW; MSN MC3-303; Washington, DC, 20433. Email:
jarnold1@worldbank.org.
** World Bank and CEPR, 1818 H Street, NW; MSN MC3-303; Washington, DC, 20433. Email:
bjavorcik@worldbank.org.
The authors would like to thank Mona Haddad, Sjamsu Rahardja and Kai Kaiser for making the data available and
John Romalis for information on domestic M&As in Indonesia. We are also indebted to Mary Amiti, Ana Fernandes,
Aart Kraay, Jan de Loecker, Philippe Martin, Gianmarco Ottaviano, Kathryn Russ, Farid Toubal and seminar
participants at the World Bank International Trade Seminar, the Inter-American Development Bank, the European
Research Workshop on International Trade, Katholieke Universiteit Leuven, Syracuse University, the Fourth
Workshop of the Regional Integration Network in Montevideo sponsored by LACEA, the World Bank-LSE Conference
on Industrialization and Development in London and the Empirical Investigations in International Economics
conference in Ljubljana for helpful suggestions. Moreover, we thank Edwin Leuven, Sascha Becker and Andrea Ichino
for valuable advice on the empirical implementation and Hans Shrader for his support and advice. The findings,
interpretations and conclusions expressed in this paper are entirely those of the authors. They do not necessarily
represent the view of the World Bank or its Executive Directors.
1
1. Introduction
The conventional wisdom suggests that multinational companies have an advantage
over local firms, which allows them to offset the extra cost of operating in distant
and unfamiliar markets. However, is the superior performance of foreign affiliates
due to the intrinsic advantage of a ‘pushy’ foreign parent company, or are foreign
investors simply good at picking the best performing local plants as acquisition
targets (the ‘gifted kids’ in our metaphor)? Recently, the application of sophisticated
econometric techniques to longitudinal micro data has cast some doubt on an
intuitive positive answer to these questions, often taken for granted by economists
and policymakers.1 As Harris and Robinson (2003) remark, if foreign ownership
per
se
is not associated with a productivity advantage, “then it is difficult to see how
FDI can have a positive impact on overall (..) productivity and thus growth” in the
host country.
This study analyzes the causal link between foreign ownership and plant
performance in Indonesia. While to the best of our knowledge this question has not
been examined in a developing country context,2 there are several reasons to expect
that the effect of foreign ownership will be particularly pronounced in the developing
world. First, the difference in technological sophistication between foreign investors
and plants they acquire is likely to be larger in developing countries than in
industrialized economies. Second, foreign direct investment (FDI) is widely
considered to be a key mechanism of cross-border technology transfer.3 The
plausibility of this mechanism is supported by theoretical arguments stressing the
importance of intangible assets, transfer of technology from headquarters to foreign
affiliates (e.g., Markusen 1995) and the fact that most of the world’s R&D effort is
undertaken by multinational companies. Additionally, recent theoretical work by
Helpman et al. (2004) on heterogenous firms suggests that multinationals come from
the upper part of the productivity distribution of firms in their country of origin.4
1 Harris and Robinson (2003) demonstrate that foreign investors acquire the best performing firms in
the UK, but subsequently the acquired firms do not reap any benefits from foreign ownership. Using
Italian data, Benfratello and Sembenelli (2002) provide evidence of a productivity advantage stemming
from foreign ownership, but
only
in the case of subsidiaries of US multinationals. Conyon et al. (2002),
however, find a 14 percent differential in labor productivity between foreign and domestically owned
firms in the UK, which can be attributed to differences in ownership
per se
. Surveying the empirical
literature, Barba Navaretti et al. (2004, Chapter 7.3) stress that much of the available empirical
evidence “supports a statistical association between foreign ownership and productivity, but not a
causal link.” They further report that in those studies where a more careful analysis of causality was
conducted “differences in productivity between the two groups of firms are smaller than in earlier
estimations and often insignificant.”
2 Two notable exception are Djankov and Hoekman (2000) and Evenett and Voicu (2002). Both studies
consider only publicly listed companies in the Czech Republic.
3 There is a large literature focusing on knowledge spillovers from FDI. For a review of the literature on
intra-industry
spillovers see Görg and Strobl (2001) and Saggi (2002), for evidence on
inter-industry
spillovers see Javorcik (2004).
4 This prediction has found empirical support in the context of Germany (Arnold and Hussinger 2005a)
and Ireland (Girma, Görg and Strobl 2004).
2
Third, the evidence based on stock market data suggests that when firms from
developed countries acquire firms in emerging markets, the stock market anticipates
significant value creation and substantial gains for shareholders of both acquirer and
target firms (Chari et al. 2004).5
Disentangling correlation and causality is not straightforward. If high productivity
plants are chosen by foreign investors, the ownership status becomes endogenous
and a simple least-squares estimation invalid. This is why we use propensity score
matching to assess the causal effect of foreign ownership on plant productivity. The
matching technique creates the missing counterfactual of an acquired plant had it
remained under domestic ownership. It does so by pairing up each plant that will
receive FDI in the future with a domestic plant with very similar observable
characteristics operating in the same sector and year. Propensity score matching is
then combined with a difference-in-differences approach. The causal effect of foreign
ownership is hence inferred from the average divergence in the productivity growth
paths between each acquired plant and its matched control plant, starting from the
pre-acquisition year. This strategy allows us to control for observable and
unobservable but constant differences between the acquired and the control plants.
While this approach has been widely used in labor economics it has not been applied
to the estimation of host country effects of FDI.
Employing this strategy has several advantages. First, unlike studies using the
Heckman (1979) two-step procedure, we do not require a variable that influences the
probability of receiving FDI but not the subsequent plant performance. Finding a
suitable measure is frequently close to impossible. Second, in contrast to the GMM
approach, our strategy does not require multiple lags of variables of interest and
avoids questions about the appropriateness of lags as instruments. Besides, it is not
dependent on the lack of the second-order correlation in the data. Third, it allows us
to follow the performance trajectory of FDI recipients rather than just estimating
the average effect of receiving FDI. Finally, we are able to trace changes in other
aspects of plant operations, such as investment, employment and exporting without
having to model them explicitly.
Our analysis, based on the plant-level data from the Census of Indonesian
Manufacturing Plants covering the period 1983-96, shows that foreign ownership has
a significant positive effect on plant performance measured in terms of total factor
productivity (TFP). TFP is estimated at the level of 4-digit sectors using the
Levinsohn-Petrin (2003) procedure to correct for simultaneity between productivity
shocks and input choices. The estimated increase in plant productivity is quite large,
reaching about 34 percent in the third year of foreign ownership. About half of the
5 Gopinath and Romalis (2005) find that such an effect is mainly present during the times of financial
crises.
3
positive productivity effect is realized during the year foreign investment takes place
with the rest occurring during the following two years. While this effect is larger
than the 14 percent differential found in the UK by Conyon et al. (2002), it is
smaller than the 43 percent advantage obtained for the Czech Republic by Evenett
and Voicu (2003). As the productivity gap between domestic plants and
multinational companies is most likely considerably larger in a developing country
than in the UK, finding a bigger effect in a developing country context is not
surprising.
Several robustness checks are performed to assess the validity of the findings. First,
we show that the results are robust to extending the time horizon under
consideration to five years of foreign ownership. This exercise indicates that
receiving FDI leads not only to an immediate boost to productivity but that the
improvements continue to take place in subsequent periods. Second, to eliminate the
possibility that pre-acquistion trends in productivity may be influencing our
findings, we demonstrate that the results hold when matching takes into account the
rate of productivity change in the period prior to acquisition. Third, our results are
not affected when we relax the restriction of matching within the same sector and
year.
Additionally, we provide evidence indicating that productivity improvements take
place simultaneously with increases in investment outlays, employment, wages and
output, thus suggesting an on-going restructuring process. We also demonstrate that
plants receiving foreign investment become more integrated into the global economy
by exporting a larger share of their output and sourcing a larger share of their
inputs from abroad.
Our results, pointing to profound changes taking place in FDI recipients, are
consistent with anecdotal evidence. For instance, when the German company
Caatoosee AG acquired an Indonesian software firm, Sigma, the employment in the
acquired firm increased by 20 percent within just twelve months.6 Two years later,
AlphaBITS, the software developed by Sigma received Merit Award for the best
industrial application at the Asia Pacific ICT Award 2001. It was the first time ever
Indonesia participated in the event attended by competitors from 11 countries.7
Similarly, when H.J. Heinz purchased a majority stake in PT ABC Central Food
Industry, maker of Indonesia’s hot chili sauce and the world’s second largest
producer of soy sauce, it did so with an intention to transform the Indonesian plant
into a launch pad for an ethnic foods business worldwide.8 The steel industry
provides an example of technology transfer from abroad to an Indonesian subsidiary.
6 http://www.hv-info.de/download/Caatoosee_02-03-31_GB.pdf
7 http://www.sigma.co.id/history.asp
8 Source: “U.S. Firms See Hope Amid Woe in Indonesia–A Hardy Few Brave Riots to Make
Acquisitions; Ford, Citygroup Wade In”
The Wall Street Journal,
June 1, 1999, A16.
4
The Maspion Stainless Steel Indonesia, a joint venture between Indonesia’s PT
Alumindo and Kanematsu Corp of Japan, is on the way to become the first stainless
steel cold rolling mill in Indonesia to produce stainless steel coil and sheet of grades
SUS 304 and 43 thanks to the technology provided by Sumitomo Metals of Japan
under the technical assistance agreement.9
While we have confidence in our results, we also address the plausibility of
alternative explanations that could be consistent with the observed pattern. First,
we eliminate the possibility that the observed improvements are purely driven by a
valuation effect by pointing out that the productivity improvement is not a level
effect but a gradual process taking place over a longer period of time. Moreover, as
there is no difference between the acquired plants and the control group in terms of
royalty payments, our productivity results cannot be attributed exclusively to an
introduction of new brand names. We also demonstrate that productivity
improvements are present in plants that are not engaged in exporting, importing
and do not make royalty payments, which suggests that our findings are not driven
by accounting differences related to international transactions motivated by transfer
pricing.
Second, to attenuate the concern that the benefit of foreign ownership might be
limited to easing credit constraints, our matching procedure includes a binary
variable indicating the use of bank loans. Furthermore, our conclusions do not
change when matching is performed using a Mahalanobis distance measure that
includes the value of investment undertaken
during
the year when foreign
investment is received, thus eliminating differences in contemporaneous investment
between the treatment and the control group.
Third, our findings are unlikely to be due solely to scale economies. The production
functions estimated at the sectoral level indicate that in 77 percent of sectors
constant returns to scale cannot be rejected. We also show that foreign ownership is
not associated with an increase in capacity utilization. Fourth, we demonstrate that
our results cannot be explained by improvements undertaken in preparation for
entering foreign markets, as they hold even for the subsample of non-exporting
plants.
Fifth, to support our conclusion that it is foreign ownership
per se
rather than
mergers and acquisitions in general that leads to an improved performance, we use
propensity score matching combined with a difference-in-differences approach to
compare productivity outcomes for privatizations into domestic and foreign hands.
We show that transfer of public ownership to foreign investors is associated with
greater productivity improvements than domestic privatizations. Additionally, we
9 Source: http://www.alumindo.com/subsidiary.html
5
utilize data on several domestic acquisitions from the Securities Data Corporation
Mergers and Acquisitions Database to show that in contrast to foreign acquisitions,
domestic M&As are not associated with an increase in cost efficiency.
Finally, by confirming our findings using the Generalized Method of Moments
(GMM) we eliminate the possibility that our choice of econometric strategy is
crucial to our findings.
To summarize, while there is some indication that better performers become FDI
recipients, foreign ownership
per se
is found to lead to an improved performance of
acquired plants. Thus we conclude that FDI has a positive direct effect on the
productivity of recipient plants in the host country. This finding confirms an
implicit assumption made in the literature on FDI spillovers and indicates that FDI
indeed presents a potential for knowledge transfer through spillover effects.
The remainder of the paper is structured as follows. The next section reviews the
existing literature. Section 3 outlines our empirical strategy for identifying the causal
relationship. Section 4 describes the Indonesian Census of Manufacturing. Section 5
provides evidence suggesting that plants receiving FDI exhibit superior performance
several years before the change in ownership takes place. Section 6 explains the
details of propensity score matching and the difference-in-differences technique used.
Section 7 presents the results of this analysis, while Section 8 focuses on robustness
checks. The last section concludes.
2. Existing Literature
Multinational companies compensate for disadvantages of operating in foreign and
unfamiliar markets through their large endowments of intangible assets, such as,
superior technologies, patents, trade secrets, know-how, brand names, management
techniques and marketing strategies (Dunning 1993). Indeed the existing empirical
literature has shown that firms undertaking FDI are characterized by high levels of
R&D relative to sales, a large share of professional and technical workers in total
employment, new and/or technically complex products and high levels of product
differentiation and advertising (Markusen 1995). It has also been demonstrated that
multinational companies tend to invest more in labor training than local firms in
host countries.10 A significant portion of outlays on employee training is associated
10 For instance, according to the survey described by Kertesi and Köllö (2001), foreign-owned firms in
Hungary spent 14.2 percent of their investment on training, as compared to 2.4 percent in the case of
domestic firms. Similarly, Filer et al. (1995) found that in foreign-owned firms in the Czech Republic
spent 4.6 times more than domestic firms on hiring and training. A recent study focusing on Malaysia
also showed that foreign-owned firms provide more training to their workers than domestic enterprises
(World Bank 1997).
6
with technology transfer from the parent company to its foreign subsidiaries. It is
not uncommon for staff from headquarters to conduct training in subsidiaries or for
subsidiary staff to be trained at headquarters.11 The combination of large
endowment of intangible assets and high investment in staff training suggests that a
change from domestic to foreign ownership is likely to lead to improvements in the
plant’s operations through better production technologies and management
techniques.
Performance comparisons between foreign and domestic plants face a number of
challenges. Firms acquired by foreign investors are unlikely to be a random sample
from the population. To the extent that the acquisition targets differ systematically
from other firms, a problem of simultaneity between ownership status and other
performance-relevant variables will arise and bias the estimate of the productivity
advantage. Addressing the simultaneity issue imposes strong requirements on the
data, as one needs to observe firms changing ownership both before and after the
ownership change. Typically, in a short plant-level panel only a handful of such
cases can be found.
Therefore, most of the existing literature has focused on documenting the
productivity advantage foreign affiliates enjoy over local firm in host countries
without
attempting to assess causality. This literature includes work by Haddad and
Harrison (1993) for Morocco, Aitken and Harrison (1999) for Venezuela, Griffith and
Simpson (2001) for the UK, Girma et al. (2004) for Ireland and Javorcik (2004) for
Lithuania, just to mention a few.
The few studies aiming to examine the
causal
relationship between foreign
ownership and firm performance have produced mixed conclusions. Harris and
Robinson (2003) and Benfratello and Sembenelli (2002), using data from the UK and
Italy, respectively, find that foreigners tend to acquire the best performing local
firms and that foreign ownership does not lead to an improved performance of the
acquired firm. Conyon (2001) concludes that acquisitions by US multinationals, but
not those undertaken by investors from other countries, have a positive effect on the
productivity of the acquisition targets in the UK. Girma and Görg (2003), who also
consider the UK data, detect a positive effect in the food industry but find no effect
in the electronics sector. Only Griffith (1999) who considers the British car industry
and Doms and Jensen (1998) focusing on the US find evidence consistent with
foreign ownership leading to better performance.
11 Ramachandaram (1993) shows that as a result of a licensing agreement for technology transfer to a
subsidiary, foreign parent companies sent on average 2.46 employees from the headquarters to their
fully-owned subsidiaries in India and 1.91 subsidiary employees visited the headquarters for training.
For partially-owned foreign projects, the corresponding figures were 0.65 and 0.61.
7
A possible explanation for the lack of consistent findings is that all of the above
mentioned studies focused on industrialized countries where the technological gap
between multinationals and their acquisition targets is unlikely to be large. One
would expect that the positive effect of foreign acquisitions, if it exists, is more likely
to manifest itself in developing economies. Yet, with the exception of two studies
(Djankov and Hoekman 2000, and Evenett and Voicu 2003) which focused on
publicly traded firms in the Czech Republic, this question has not been examined in
a developing country context.12
Our work aims to fill this gap by focusing on a developing country and applying a
methodology that has not been previously used to study this question. We consider
ownership changes taking place within a plant, and we aim to address the selection
issue by combining propensity score matching with a difference-in-differences
estimation. We focus on total factor productivity estimated using the correction for
the simultaneity between productivity shocks and input choices, as suggested by
Levinsohn and Petrin (2003). In contrast to the earlier studies, we also consider
other aspects of a firm’s operation and the timing of the changes.
3. Empirical Strategy
The first part of our strategy to address the endogeneity of ownership status is to
focus on changes from domestic to foreign ownership taking place
within the same
plant
. Naturally, this approach implies a substantial reduction of the number of
plants considered. However, a nice feature of our data is that the sample size is large
enough that we are still left with a sufficient number of observations to generalize
our results with confidence. The advantage of focusing on plants observed before and
after an ownership change is that through a difference-in-differences approach we
can control for all non-random elements of the acquisition decision that are constant
or strongly persistent over time.
Using a difference-in-differences technique allows us to compare the performance of
acquired plants with the performance of plants remaining in domestic hands. This
comparison, however, is still vulnerable to problems of non-random sample selection.
To address the selection issue, we combine a difference-in-differences approach with
propensity score matching.13 The matching procedure controls for the selection bias
12 Both studies use the Heckman two-step model to control for the selection of FDI recipients using
firm-specific information from the first year available in the sample, which is not necessarily the year
preceding FDI inflow. Both conclude that foreign ownership contributes to better performance.
13 Apart from its original applications in labor economics, the matching estimator has become
increasingly popular in causal analyses in other areas of economics. Girma et al. (2004) and Arnold and
Hussinger (2005b) apply this technique to examine the relationship between firm productivity and
exporting. Barba Navaretti and Castellani (2004) also use this technique to examine the impact of
outward FDI on home performance for a sample of Italian firms.
8
by restricting the comparison to differences within carefully selected pairs of plants.
Each pair consists of an acquired plant and a domestic plant with similar observable
characteristics in the year preceding the acquisition of the former.
The aim of the analysis is to estimate the causal effect of foreign ownership on total
factor productivity growth, defined as
()()
(
)
|YE- |YE |Y-YE 1FDI01FDI11FDI01 === = (1)
which is the difference between the performance paths of plants that changed
ownership (first term) and the analogous outcome of the same plants had they not
been acquired by foreign investors (second term).14 The latter outcome is, however,
an unobserved counterfactual. The matching technique is a way of constructing this
missing counterfactual by drawing comparisons conditional on a vector X of
observable plant characteristics. The underlying assumption for the validity of the
procedure is that conditional on the observable characteristics that are relevant for
the acquisition decision, the treated (FDI recipients) and non-treated plants (those
remaining in domestic hands) would exhibit a similar performance under the same
circumstances:
(
)
(
)
(
)
)|E(Y-)|E(Y-)|E(Y-) |E(Y |Y-YE X ,0FDI0X 1,FDI0X ,0FDI0X 1,FDI1X 1,FDI01 ===== = (2)
The second difference in equation 2 is the selection bias, which is assumed to be zero
conditional on X. It represents the difference between the outcome of the acquired
plants, under the hypothetical circumstances that they had they not been acquired,
and those plants that remained in domestic hands, in the same (and this time true)
situation of no ownership change. If the selection bias represented by the second
term is zero for given realizations of the vector X, then we are left with only the
causal effect. In other words, the performance difference between acquired plants
and the carefully selected group of control observations is a consistent estimate of
the causal effect under the matching assumption. Hence, if our matching process is
successful, we can give a causal interpretation to the average performance difference
between treatment and control plants.
Conditioning on a vector of variables is difficult, since it requires weighting
differences in one dimension against another. Rosenbaum and Rubin (1983) provide
a proof that conditioning on the propensity score is equivalent to conditioning on all
variables in the treatment model. The propensity score is the predicted probability
14 Our notation is to be read as follows: The outcome variable Y represents productivity growth across
the ownership change of the acquired (treatment) plants. Its subscript describes the (potentially
hypothetical) circumstances under which an outcome is observed, while FDI=1 indicates reference to
the group of firms that have been acquired in reality, i.e. our treatment group. Similarly, FDI=0 refers
to control observations.
9
of treatment, which in our case is the probability of a plant receiving FDI. Making
use of this result, we employ propensity score matching and compare the
performance of plants within the pairs of observations matched on the propensity
score. We also make sure that the matched control observations are assigned only
from the same year and the same sector as the acquired plant. This eliminates the
possibility that productivity differences across sector/year combinations exert
influence on our estimated effects and shifts the focus of attention to the position of
each plant with respect to the performance of others in the same sector and year.
The combination of matching and a difference-in-differences approach means that we
look for divergence in the paths of performance between the acquired plants and the
matched control plants that had similar characteristics in the pre-acquisition year.
The performance analysis begins in the pre-acquisition period and focuses on the
(cumulative) change in performance over the following year and then each of the
subsequent two periods. Blundell and Costa Dias (2000) emphasize the benefits of
combining matching and a difference-in-differences approach for controlling for
observable and unobservable but constant differences between treatment and control
units. While matching accounts for differences in observable characteristics, its
combination with difference-in-differences analysis provides “scope for an unobserved
determinant of participation as long as it can be represented by separable
individual- and/or time-specific components of the error term.” Examples of such
determinants include a particular plant being chosen as an acquisition target
because of the qualities of its manager or a foreign investor’s preference for a plant
possessing particular tangible assets (e.g., a distribution network) or intangible
assets (an established brand name).
As the performance measure, we employ total factor productivity, defined as the
residual of a Cobb-Douglas production function. We address the simultaneity
problem in input choices by applying a semi-parametric estimator proposed by
Levinsohn and Petrin (2003) with intermediate input use serving as a proxy for
productivity shocks. More specifically, we utilize information the amount of
electricity consumed by each plant. As electricity cannot be stored, its consumption
is likely to follow changes in production activity more closely than the use of
materials.
4. Data
Indonesia is a suitable choice for studying the effects of FDI. The country’s
industrial success is a relatively recent phenomenon, and there have been significant
inflows of foreign direct investment in the last two decades. In terms of GDP, the
importance of foreign direct investment inflows has been rising steadily and
significantly from the mid-1980s onwards, as can be seen from Figure A1. For the
10
period 1990-1996, the country was the fifth largest developing country recipient of
FDI (IFC 1997, p.17).
Historically, the Indonesian manufacturing sector (excluding oil-related activities)
has been of almost negligible importance until the 1970s, accounting for less than 10
percent of GDP in 1974-76. Only in the 1980s did the country begin to emerge as a
significant industrial power. The attitude towards foreign direct investment has been
generally welcoming since the late 1960s. However, as economic policy began to
reduce trade barriers and deregulate industry in the early 1980s, Indonesia received
a new surge in FDI inflows that tended to be geared towards efficient and
internationally competitive activities, mainly in the manufacturing sector (Hill, 2000,
p.76). This coincides with the beginning of our data window.
The data used in this paper come from the “Survei Manufaktur,” the Indonesian
Census of Manufacturing, which has been conducted by the National Statistical
Office (BPS) on annual basis since 1975. The census surveys all registered
manufacturing plants with more than 20 employees.15 It contains detailed
information on a large number of variables pertaining to input and output flows.
There is some variation on the availability of variables from year to year, and the
information of interest to us is available from as early as 1983. As the last year of
our sample, we include 1996 in order to avoid capturing the effects of the Asian
financial crisis, which strongly affected Indonesia beginning in 1997. In particular,
we are concerned about a decline in the data quality due to the crisis and about a
change in the motivation for foreign acquisitions in times when many Indonesian
plants found themselves in financial distress. Our sample, covering the period 1983 -
1996, contains more than 210 000 plant observations, of which about 5 percent
belong to foreign-owned plants. The average spell a plant remains in our sample is
between 8 and 9 years.16
In order to estimate the production function, we make use of the information on
output (net of energy costs) and four factors of production: the number of
production and non-production workers, materials and capital. The capital stock
variable has been newly constructed using the perpetual inventory method, making
use of detailed data on investment in land, buildings, machinery, vehicles and other
15 Since regional statistical offices in Indonesia have financial incentives to obtain the relevant
information from all active firms, we can be reasonably confident that the entire manufacturing sector
above the 20 employee threshold is included in our sample. The survey questionnaires can be accessed
online at http://www.rand.org/labor/bps.data/webdocs/statistik_industri/si_main.htm.
16 The data have been cleaned conservatively for obvious keypunch errors. Particularly for the share of
foreign ownership, we replaced outlier values with adjacent values whenever there was a drop to zero
followed by a return to the previous value (e.g. 58, 58, 0, 58), or a different position of the decimal
point followed by a return to the previous value (as in 60, 6, 60, 60) .
11
fixed assets.17 To each investment data series (land, buildings, etc.) we applied
estimated depreciation rates from Harris et al. (1994).18
Since the data contain no information on physical quantities of inputs used or
output produced by plants, we are forced to start with nominal values instead.
These are deflated using a set of 192 wholesale price indices for manufactured
commodities, published by the Indonesian Statistical Office. The commodity indices
are mapped to the 5-digit ISIC classification using a concordance table provided by
the Statistical Office. These detailed 5-digit ISIC level deflators are applied to plant
output and material inputs. Figures on investment and capital are deflated as
follows. For buildings, we use a wholesale price index for residential and commercial
buildings (WPI) published in the Statistical Yearbook of Indonesia, and for
machinery and vehicles the average of the WPIs for 5-digit sectors producing
machinery and vehicles, respectively. For other assets, we employ the economy-wide
WPI. Unfortunately, the Indonesian Statistical Office does not publish a wholesale
price index for energy, so we were constrained to use a CPI specific to energy
instead.
The production function is assumed to be Cobb-Douglas and is estimated using the
semiparametric procedure suggested by Levinsohn and Petrin (2003).19 As a proxy
for unobserved productivity shocks that may influence the input decision of the
plant, we employ the amount of electricity consumed by each plant. The data
contain information on electricity consumption net of own production and sales to
other plants, expressed in physical quantities (kWh) which rules out measurement
errors related to deflation. Our productivity measure is the residual of the
production function in logarithmic form. We allow the coefficient estimates to differ
over 62 manufacturing sectors, which is equivalent to the 4-digit ISIC level.20 Given
a substantial number of missing values in our data set, we are able to estimate TFP
17 We used the earliest available information on self-reported replacement values of each capital
category as an anchor for the perpetual inventory method. Where a plant did not report the
replacement values of its assets, we used the self-reported book values instead. Plants that never report
capital stocks were dropped from our sample. Since the investment question was not asked in 1996, for
that year we used linear extrapolation on the basis of real investment figures reported for the earlier
years
18 The assumed annual depreciation rate for buildings is 3.3 percent, for machinery 10 percent, and for
vehicles and other fixed assets 20 percent. For land, we assumed no depreciation. These rates are very
similar to estimates presented in Goeltom (1995).
19 The estimation was implemented in Stata 8 using the program described in Levinsohn et al. (2003).
In 29 out of 62 industries this procedure moved the coefficient on capital in the expected upward
direction when compared to a fixed effects estimation of the production function. This makes us feel
confident that the correction is performing sufficiently well.
20 The industry breakdown was adjusted to eliminate inconsistencies caused by the fact that BPS had
removed several sectors and introduced a few others into the classification during the period of interest.
In such cases, plants were regrouped into the corresponding ISIC Rev. 2 industries. Two petroleum
sectors (ISIC 3530 and 3540) were dropped from the sample because of a very small number of
observations. ISIC sectors 3901-3909 (Manufacturing industries not elsewhere classified) were also
dropped due to concerns about plant heterogeneity within these sectors.
12
for about 120,000 plant observations. To avoid capturing effects caused by a change
in principal activity of the plant, our matching analysis focuses only on plants that
do not switch their sector of operation.21
We perform our analysis on 185 plants that switched from domestic to foreign
ownership and remain in the data sufficiently long to be observed in the year before
the acquisition, the acquisition period and two subsequent years.22 This is a
considerable number, considering that Conyon et al. (2000) find only 129 cases of
foreign acquisitions with enough non-missing data to make them suitable for their
analysis. Their study analyzes a large developed country (the UK) and covers almost
the same time period (1987-96). In a UK data set covering the period from 1980 to
1994, Girma and Görg (2003) are able to identify only 266 foreign acquisitions.
Figures A3 and A4 show the distribution of acquired plants in our data across years
and sectors, respectively. Ownership changes occur in each 2-digit sector and in each
year during the 1984-94 period.23
5. Evidence of the Selection Bias
Our empirical strategy is driven by our concern about the selection bias that may
result from better performing plants being acquired by foreign investors. To examine
whether this concern is justified, we regress total factor productivity on a dummy
for plants with foreign ownership in year t and a dummy for future acquisition
targets during the three years prior to the ownership change.24 The model also
includes industry, region and year fixed effects. We exclude plants with foreign
ownership throughout the period.
The estimation results, presented in Table 1, demonstrate that future acquisition
targets of foreign investors outperform other Indonesian plants during the three
years preceding the ownership change. Not surprisingly, we also find that plants
with foreign ownership exhibit a higher productivity than domestic plants. The
magnitude of the effect is equal to 19.5 percent for future acquisition targets and 39
percent for plants with foreign ownership.
21 Recall the we assign matches within the same sector and year to assure comparability. Considering
plants that switch from one sector to another would make it impossible to maintain this matching
restriction.
22 We consider all plants with a foreign capital share above 20 percent as foreign owned. In practice,
however, the exact value of this threshold does not matter because in more than 95 percent of
acquisition cases the foreign capital share increased from 0 to 25 or more percent. Figure A2 depicts the
distribution of foreign ownership share in the year following the entry of a foreign investor.
23 Note that we do not consider changes in ownership taking place after 1994 as we want to observe
each plant for at least two years after such a change has taken place.
24 For example, in the case of a firm that receives FDI in 1993, the dummy would take on the value of
one for 1990, 1991 and 1992 and zero for all other years.
13
We interpret this finding as indicating that foreign investors acquire domestic plants
with an above average performance, a pattern sometimes called “cherry picking” in
the literature. The evidence is strong enough to make a strategy of simply ignoring
the issue imprudent. Therefore, in our analysis of the causal effect of foreign
ownership on the plant performance, we will control for the selection bias. At the
same time, the productivity premium exhibited by plants under foreign ownership is
twice as large as the premium exhibited before receiving FDI, suggesting that foreign
ownership may also have a positive effect on plant performance. In the next section,
we analyze this relationship in more detail.
Table 1. Evidence of the Selection Bias
Dependent Variable is Log TFP Premium
Plant will receive FDI within next 3 years 0.178 ***
(0.022)
Foreign Ownership 0.331***
(0.009)
No. of observations 111,707
The regression includes industry, year and region fixed effects.
Plants under foreign ownership throughout the period are excluded from the sample.
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
6. Controlling for the Sample Selection using the Matching Technique
In order to make a meaningful comparison between the performance of Indonesian
plants acquired by foreign investors and those remaining in domestic hands, we need
to create a missing counterfactual capturing the performance of the acquired plants
had they not received FDI. We do so by applying propensity score matching to
identify a suitable plant under continued domestic ownership to which we can
compare each acquired plant. The requirement for a suitable control observation is
sufficient similarity to the future acquisition target with respect to key determinants
of the acquisition decision, so as to make these two plants
a priori
equally probable
targets of a potential foreign acquisition.
For obvious reasons, the control group is created on basis of observable
plant
characteristics. We believe that this is a good starting point as potential foreign
investors rely heavily on basic observable characteristics of plants, such as their age,
size, employment composition, machinery and equipment available, productivity,
etc. to narrow down the number of potential acquisition targets. They may also
judge suitability of plants based on their reliance on imported inputs which may
indicate the sophistication level of the technology used. Finally, the fact that an
establishment has received a bank loan may also contain information on financial
institutions’ perceptions about trustworthiness and future prospects of an
14
establishment. All of these factors are taken into account when constructing the
control group.
We use one-to-one nearest neighbor matching on the propensity score, which
expresses the estimated probability of a plant becoming acquired by a foreign
investor.25 As mentioned in Section 3, this solves the dimensionality problem when
considering differences on more than one observable characteristic. Moreover, we
impose the additional requirement that the matched plant observations come from
the same sector and year.26 Therefore, in a first step, we use a probit regression to
model the binary outcome of a plant becoming acquired by foreigners on the basis of
plant-specific characteristics. To avoid endogeneity, all explanatory variables (except
for age) are lagged one year. 27
The results from the probit regression, presented in Table 2, indicate that plants
acquired by foreign investors differ systematically from other domestic plants. The
model suggests that younger and larger (in terms of employment) plants are more
likely to become acquired. The model allows for nonlinear effects of these two
variables which indeed appear to be statistically significant. Further, the data show
that plants with higher capital-labor ratio, plants engaged in sourcing inputs from
abroad and plants with a higher fraction of white-collar employees tend to be more
attractive to foreign investors. As the goal of the study is to examine improvements
in productivity due to the change in ownership, the model includes controls for the
TFP level (normalized by the average TFP observed in the same industry and year)
in the period prior to receiving FDI. This variable does not appear to be statistically
significant, which is most likely due to a high correlation with other controls. Recall,
however, that the results presented in the previous section suggest that the acquired
plants exhibit superior performance already three years before the acquisition.
To eliminate the possibility that improvements observed after the ownership change
may be due to investments undertaken by plants prior to or in preparation for a
foreign acquisition, the matching procedure controls for investment outlays lagged
one period. This variable, however, does not appear to be statistically significant. To
attenuate the possibility that the effect of FDI works purely through easing access to
credit, the probit model also includes a dummy for plants having a bank loan but
again the coefficient does not reach conventional significance levels. Finally, the
25 We also tried other matching methods, such as kernel matching and caliper matching, and the results
were qualitatively similar.
26 Our matching procedure is implemented in Stata 8 using a modified version of the procedure
described in Leuven and Sianesi (2001). The modifications were necessary to make sure that matched
pairs come from the same year and sector.
27 In order to increase the precision of our model, we dropped all combinations of sectors, years and
regions where no foreign acquisitions occurred. Not making this adjustment would increase the number
of observations in Table 2 to 57,607 but would not change the conclusions of the paper.
15
model includes a dummy for plants with public ownership and a time trend, neither
of which are statistically significant.
Table 2. Probit results
Dependent Variable: Foreign acquisition
ln Employment 0.813***
(0.246)
ln Employment2 -0.069***
(0.023)
Age -0.051***
(0.008)
Age2 0.0006***
(0.0001)
ln Capital intensity 0.084***
(0.201)
Share of imported inputs 0.650***
(0.102)
Ratio of non-production workers 1.170***
(0.243)
ln Relative TFP 0.059
(0.076)
ln Investment -0.003
(0.010)
Bank loan dummy 0.0003
(0.0003)
Public ownership dummy 0.110
(0.157)
Time trend 0.026
(0.016)
Intercept -4.042***
(0.645)
No. of obs. 2,355
Chi2 186.01
Prob > Chi2 0.00
Pseudo R2 0.11
All explanatory variables with the exception of age and age2 are
lagged one year.
*, **, *** indicate statistical significance at the 10, 5 and 1%
level, respectively
To assess how well the propensity score matching performs in our case, we calculate
the difference between the treated and the control group in terms of each of the
above variables and run simple t-tests on the differences within 8 bands of the
propensity score. This test is called the balancing hypothesis, and it can be
performed using the procedure suggested by Becker and Ichino (2002). All of the
differences are found to be small and statistically insignificant. This gives us
confidence that our approach is capable of grouping together relatively homogeneous
plants.28
The predicted probabilities are used to assign to each future acquisition target a
domestic plant that has the closest propensity score within the same year and
sector. Thanks to a large number of available control observations in our data, the
28 In our matching procedure we also exclude observations outside the common support. The common
support is bound by the lowest propensity score of a treatment observation and the highest propensity
score of a control observation.
16
matching procedure produces an average distance in propensity scores within
matched pairs of less than 4 percent, with a standard variation of approximately 5
percent. This convinces us that our matching procedure has managed to find
appropriate comparison observations for each acquired plant.
7. Results from the Difference-in-Differences Analysis on the Matched Sample
(a) Baseline results
The primary result of interest is the average difference in TFP in the matched pairs,
net of the average initial difference before the acquisition. As can be seen in Table 3,
between the year prior to the acquisition, in which the matches are assigned, and
the acquisition year, the treatment and control observations diverge significantly in
terms of productivity. A foreign acquisition leads to an additional 15-percent
productivity boost in the acquired plants, which is not shared by similar plants
remaining in domestic hands. In the subsequent years, the divergence in performance
becomes even greater. By the end of the third year of foreign ownership, the
acquired plants enjoy a 34-percent productivity advantage over the control group.
The results are significant at the five percent level in the acquisition year and at the
one percent level in the following two years.
Table 3. Matching Results for Productivity
Effect of Foreign Acquisition Log TFP
Acquisition year(a) 0.147**
(0.065)
One year later(b) 0.259***
(0.068)
Two years later(c) 0.293***
(0.074)
n 185
Average Treatment Effect on the Treated (ATT), bootstrapped standard errors in parentheses.
n = number of matched acquisitions
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
(a) ATT =
()
(
)
∑∑ −nn
nn 1
control yearn acquisitio-pre
treated yearn acquisitio-pre
1
control yearn acquisitio
treated yearn acquisitio TFPln -TFPln
1
TFPln -TFPln
1
(b) ATT =
()
(
)
∑∑ −
++
nn
nn 1
control yearn acquisitio-pre
treated yearn acquisitio-pre
1
control 1yearn acquisitio
treated 1yearn acquisitio TFPln -TFPln
1
TFPln -TFPln
1
(c) ATT =
()
(
)
∑∑ −
++
nn
nn 1
control yearn acquisitio-pre
treated yearn acquisitio-pre
1
control 2yearn acquisitio
treated 2yearn acquisitio TFPln -TFPln
1
TFPln -TFPln
1
These figures are quite compelling. Performance improvements resulting from foreign
acquisitions are likely to be larger in developing countries where the productivity
gap between domestic plants and multinational companies is considerably greater.
Thus, our result of a 34 percentage-point productivity advantage over a three-year
horizon seems plausible when compared to the 14 percent improvement found by
17
Conyon et al. (2002) in the UK. It is also smaller than the 43 percent improvement
found by Evenett and Voicu (2003) in the Czech Republic.
(b) Extending the time horizon
To confirm that the observed productivity improvement is not a temporary
phenomenon, we extend the time horizon to cover two more years after the
acquisition. The difference-in-differences results presented in Table 4 indicate that
improvements experienced by acquired plants as a result of a foreign acquisition
continue in the third and fourth year after the acquisition. By the end of the fourth
year, the productivity gap between the acquired and the control plants widens to 40
percentage points. As extending the time horizon limits the size of the sample, in the
remainder of the study we will focus on the time horizon considered originally.
Table 4. Matching Results - Longer Horizon
Effect of Foreign Acquisition Log TFP Log TFP
Acquisition year 0.152**
(0.07)
0.098
(0.09)
One year later 0.275**
(0.08)
0.202**
(0.08)
Two years later 0.316***
(0.11)
0.248**
(0.11)
Three years later 0.382***
(0.11)
0.354***
(0.11)
Four years later 0.327***
(0.11)
n 152 108
Average Treatment Effect on the Treated. Bootstrapped std errors in parentheses.
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
It is worth pointing out that the observed effects of foreign ownership are driven by
an improved performance of the acquired plants rather than by a deterioration in
the situation of the control group. If we were to compare to the acquired plants to
the
average
performer in the same sector and year (rather than to the control
group), the advantage of foreign ownership would appear to be even greater. This
suggests that the propensity score matching performs well in constructing a suitable
control group.
(c) Removing the restriction on matching within sectors
To ensure that our matching results are not distorted by restricting the control
observations to come from the same sector and the same time period, below we
present the results obtained without imposing this constraint. As evident from Table
5, this modification leads to the same qualitative conclusions. Allowing out-of-sector
matching, however, produces somewhat smaller effects. The estimated productivity
18
advantage is almost identical regardless of whether the absolute TFP measure or the
TFP relative to the industry average in a given year is considered.29
Table 5. Matching Results for Productivity, not restricted within sector/year
Effect of Foreign Acquisition Relative TFP Log TFP
Acquisition year 0.134**
(0.06)
0.132**
(0.06)
One year later 0.225***
(0.06)
0.221***
(0.06)
Two years later 0.208***
(0.07)
0.215***
(0.06)
n 213 213
Average Treatment Effect on the Treated. Bootstrapped std errors in parentheses.
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
(d) Accounting for productivity trends prior to acquisition
The difference-in-differences approach removes plant-specific time-invariant effects,
however, unobservable but not time-invariant plant-specific characteristics still may
pose a main challenge in the analysis. While in some subfields of development
economics this issue is addressed by the use of randomized experiments, this is
unfortunately not an option in our context.
As a step toward addressing this issue, we account for pre-acquisition trends in plant
performance in the matching stage. We construct a new control group based on a
new propensity score including the
productivity change in the period preceding the
acquisition
in addition to the
productivity level
and all other variables used in Table
2.30 This requires one additional observation per plant and thus reduces the sample
size. The difference-in-differences approach applied to the newly created matched
sample produces no statistically significant divergence between the treated and the
control group in the year when FDI is received. A statistically significant difference
is found, however, in the first and the second year following the acquisition (see
Table 6). Thus this robustness check supports our conclusion that FDI recipients
outperform plants remaining in domestic hands.
The effects found in Table 6 are smaller than those obtained earlier, amounting to a
22-percent difference within three years as opposed to a 34-percent divergence. The
difference in magnitudes, however, appears to be driven by the fact that for many
acquired plants we do not observe productivity two years before the acquisition and
29 Note that in this case it makes sense to consider both absolute and relative TFP measures because
sectoral averages do not cancel out as the treated and the control observations may belong to different
sectors.
30 The productivity change is calculated as the first difference of log TFP in the pre-acquisition period
while the level refers to the log of TFP relative to the sector/year average in that same year. The latter
normalization is done in order to assure comparability (recall that TFP estimates come from
regressions performed at the sectoral level). Neither the productivity change nor the productivity level,
however, appear to be statistically significant in the probit model.
19
are thus unable to include them in this robustness check. When we reproduce the
results of Table 3 restricting the sample to the 99 plants for which such information
is available (see column 2 in Table 6), the estimated effects closely resemble those
presented in the first column of Table 6.
Table 6. Sample Matched on Lagged TFP Growth
Effect of Foreign Acquisition
Log TFP
(matched on lagged TFP growth)
Log TFP
(matching corresponding to Table 3)
Acquisition year 0.034
(0.08)
0.035
(0.07)
One year later 0.185**
(0.08)
0.168*
(0.09)
Two years later 0.201**
(0.10)
0.181*
(0.09)
n 99 99
Average Treatment Effect on the Treated. Bootstrapped std errors in parentheses. *, **, *** indicate statistical
significance at the 10, 5 and 1% level, respectively.
(e) Evidence of restructuring
If our findings of improved productivity are due to FDI, we would expect to observe
foreign owners introduce other changes to plant operations. Indeed we find evidence
that acquired plants undergo a restructuring process. As illustrated in Table 7,
acquired plants grow much faster between the pre- and the post-acquisition period
than the control establishments, implying that foreign ownership helps them increase
their output and employment. Further, the acquired plants increase employee wages
faster than the control group.31 In addition, plants receiving FDI see a larger rise in
their investment outlays relative to establishments remaining in domestic hands. All
of the mentioned effects are statistically significant throughout the period
considered. They are also consistent with the anecdotal evidence mentioned in the
introduction.
Table 7. Matching Results for Output, Employment, Wages, Investment
Effect of Foreign Acquisition Log Output Log Employment Log Wages Log Investment
Acquisition year 0.665***
(0.14)
0.318***
(0.08)
0.397***
(0.09)
1.561***
(0.52)
One year later 0.781***
(0.16)
0.311***
(0.08)
0.382***
(0.10)
1.509**
(0.64)
Two years later 0.826***
(0.16)
0.331**
(0.10)
0.407***
(0.10)
1.069*
(0.64)
n 185 185 185 185
Average Treatment Effect on the Treated. Bootstrapped std errors in parentheses.
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
31 This is to be expected as the existing literature has documented that foreign establishments tend to
pay higher wages than domestic plants. See Sjoholm and Lipsey (2003 and 2004) for a careful analysis
of the Indonesian case.
20
The results also indicate that foreign ownership affects participation of the acquired
plants in international markets. As illustrated in Table 8, a foreign acquisition leads
to an increase the share of output exported by 11 percentage points in the year of
acquisition and by an additional 3 percentage points in the following year.32 A
similar pattern is observed with respect to the reliance on imported inputs. In the
acquisition year, treated plants increase the share of imported inputs by 8
percentage points more than the control group. Two years later this difference
increases to 12.7 percentage points. Increased reliance on imported intermediates
and the ability to enter or expand the presence in foreign markets also suggest that
profound changes to the production process may be taking place in the acquired
plants.
Table 8. Matching Results for Export Ratio and Ratio of Imported Inputs
Effect of Foreign Acquisition Exports/Sales Imported
Inputs/Inputs
Acquisition year 11.43**
(5.07)
8.32*
(3.37)
One year later 14.20**
(5.67)
10.25**
(4.02)
Two years later 14.26**
(5.88)
12.71***
(3.92)
n 133 185
Average Treatment Effect on the Treated. Bootstrapped std errors in parentheses.
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
To sum up, we observe significant changes in the way that plants are managed once
they receive FDI. The foreign acquisition seems to unleash an acquired plant’s
growth potential both in terms of productivity and size. The improvements
materialize quickly and continue over time. Acquisitions also raise investment and
wages and intensify the plants’ participation in the global economy.
8. Addressing Alternative Explanations
As argued earlier, the fact that multinational corporations are characterized by
large endowments of intangible assets, high productivity and a willingness to invest
in staff training suggests that the observed productivity improvements associated
with foreign acquisitions are likely to result from the introduction of new
technologies and management techniques and restructuring of plant operations.
There exist, however, other explanations which could potentially be consistent with
32 This increase in the average export share is a result of both increased export intensity of previously
exporting plants and of plants entering foreign markets for the first time after the acquisition. The
reduction in the sample size is due to the unavailability of information on exports in the Census data
before 1990.
21
the observed productivity improvements. In this section we explore their
plausibility.
(a) Can the results be explained by valuation?
A valuation effect stemming, for instance, from a change in accounting procedures or
from an introduction of a brand name, is not a likely explanation for the observed
patterns. First, such an effect would lead to a one-time jump in the observed
productivity. This is clearly not the case in our sample as we observe a sustained
productivity growth over a three-year period. Second, a mere valuation effect would
not explain changes in other aspects of plant operations, such as employment,
participation in the global economy and so on. Third, it is difficult to argue that our
findings are purely due to the introduction of a parent company’s brand name
without any changes being made to the products to which the trademark is then
applied. In most cases, the fear of a brand-name erosion would make foreign owners
hesitant to apply their trademark to a product unless they are absolutely sure that
the company-wide quality standards have been met. Further, royalty payments for
the use of the parent company’s brand name would be reflected in the company’s
accounts. Yet, the results from the difference-in-differences approach suggest that
the acquired plants do not diverge from the control group in terms of royalty
payments made (see Table 9).
Table 9. Matching Results for Royalty Payments
Effect of Foreign Acquisition Royalty Payments
Acquisition year 0.308
(0.58)
One year later 1.286*
(0.71)
Two years later 1.195
(0.74)
n 60
Average Treatment Effect on the Treated. Bootstrapped std
errors in parentheses. *, **, *** indicate statistical significance
at the 10, 5 and 1% level, respectively.
Fourth, while transfer pricing could potentially influence our findings, we believe
that this is an unlikely explanation. Accounting statements in Indonesia are
prepared according to well-established accounting standards, which are directly
based on the U.S. Generally Accepted Accounting Principles (GAAP) (Asian
Development Bank 2003, p.97). This suggests that the quality of accounting is
reasonably high, particularly in a developing country context. The degree to which
transfer pricing motives can introduce measurement errors is limited by these
accounting standards. In any event, the incentives for transfer pricing are probably
small, because corporate taxes in Indonesia are not much different from those in
22
OECD countries.33 Nevertheless, to rule out transfer pricing as the underlying reason
for our findings, we limit our sample to acquired plants which do not report any
transactions that could be used as a vehicle for transfer pricing (i.e., imports,
exports or royalty payments). As reflected in Table 10 below, our earlier conclusions
are confirmed even with the restricted sample size. The results suggests that even
plants that do not engage in any foreign transactions experience a large and
statistically significant productivity improvement (relative to the control group)
following a foreign acquisition.
Table 10. Matching Results for Subsample of Plants with no Foreign Transactions
Effect of Foreign Acquisition Log TFP
(No foreign trade in t=0 and
t=1)
Log TFP
(No foreign trade in t=0 to
t=2)
Log TFP
(No foreign trade and no
royalties in t=0 to t=2)
Acquisition year 0.339*
(0.17)
0.355*
(0.21)
0.257
(0.19)
One year later 0.352**
(0.15)
0.323*
(0.18)
0.216
(0.21)
Two years later 0.532**
(0.21)
0.602**
(0.24)
0.553**
(0.26)
n 25 21 16
Average Treatment Effect on the Treated. Bootstrapped std errors in parentheses.
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
(b) Could the observed changes be due to foreign acquisitions lessening credit
constraints?
While the transfer of know-how and technology accompanied by improvements in
management is a plausible explanation for the results presented so far, benefits from
foreign ownership could also work through easier access to financing. It is possible
that foreign investors pick plants that would have done well in the absence of
foreign ownership, had they had sufficient access to credit. For instance, foreigners
may choose to invest in local plants that have recently developed a potentially
successful new product or identified a promising investment opportunity but are
unable to take advantage of it due to lack of financing. If this were the case, the sole
impact of foreign investment would be provision of financing rather than transfer of
knowledge. To address this possibility we accounted in the construction of the
propensity score for having a bank loan as well for investment undertaken by the
plant during the year preceding a foreign acquisition (see Table 2). Our matching
analysis is thus conditional on these two variables. Neither of the two factors,
however, appears to be a statistically significant predictor of a foreign acquisition.
33 This conclusion is based on the corporate tax rates reported in the
Global Competitiveness Report
(1996). The comparison takes into account statutory tax rates as we have no information about tax
incentives that may have been granted on a case-by-case basis.
23
To take this issue even further, we employ an alternative matching technique where
we match plants on a Mahalanobis distance measure of the propensity score and the
value of investment in the year of ownership change. This allows us to construct a
new control group with the following characteristics: (i) similarity to the treament
group in terms of observable characteristics (considered earlier) prior to the
acquisition, and (ii) similarity in terms of investment undertaken
in the year when
foreign investment is received.
The logic behind this exercise is that if plants from
the same industry with similar observable characteristics exhibit a similar
investment pattern in the same year, something other than credit constraints should
be responsible for a divergence in performance. The results from the difference-in-
differences approach applied to this new control group are presented in Table 11.
They are very similar to those obtained earlier which suggests that credit constraints
are unlikely to be driving our results.
Table 11. Matching on Mahalanobis Distance including Investment at t=0
Effect of Foreign Acquisition Log Relative TFP Log TFP
Acquisition year 0.158***
(0.06)
0.168***
(0.05)
One year later 0.258***
(0.06)
0.277***
(0.07)
Two years later 0.267***
(0.08)
0.294***
(0.07)
n 119 119
Average Treatment Effect on the Treated. Bootstrapped std errors in parentheses.
*, **, *** indicate statistical significance at the 10, 5 and 1% level, respectively.
Finally, we check whether the acquired plants experienced a larger increase in the
amount of outstanding loans than the control group. This does not appear to be the
case. When we consider the value of outstanding loans (both domestic and foreign)
normalized by the plant output, and we do not find a statistically significant
difference between the two groups (see Table 12).34
34 To remove outliers, we drop plants with the loan to output ratio above 10.
24
Table 12. Matching Results for Loans/Sales
Effect of Foreign Acquisition Loans/Sales
Acquisition year -0.055
(0.07)
One year later -0.042
(0.08)
Two years later -0.038
(0.08)
n 179
Average Treatment Effect on the Treated. Bootstrapped std
errors in parentheses. *, **, *** indicate statistical significance
at the 10, 5 and 1% level, respectively.
(c) Can capacity utilization explain the observed productivity improvements?
In order to ascertain whether the changes taking place in the acquired plants are
part of a long-term restructuring process, or whether they are short-term expansions
of the production scale resulting from the provision of new markets, we also apply
the matching analysis to the self-reported information on capacity utilization. As
evident from Table 13 below, changes in capacity utilization alone cannot explain
the improvements in performance experienced by plants receiving FDI. In the year
of acquisition, there is no statistically significant divergence in capacity utilization
between the two groups. In the subsequent year, FDI recipients increase their
capacity utilization relative to the control group, but two years after the acquisition
the difference disappears. Even in the period where the effect is significant at the 10
percent level, however, the average increase in capacity utilization amounts to only
8 percentage points, from 65 to 73 percent.
Table 13. Matching Results for Capacity Utilization
Effect of Foreign Acquisition Capacity Utilization
(%)
Acquisition year 4.32
(4.62)
One year later 9.89*
(5.35)
Two years later 8.12
(5.50)
n 133
Average Treatment Effect on the Treated. Bootstrapped std
errors in parentheses. *, **, *** indicate statistical significance
at the 10, 5 and 1% level, respectively.
Neither are our findings due solely to scale economies. The production functions
estimated at the sectoral level indicate that in 77 percent of cases (or 48 out of 62
sectors) constant returns to scale cannot be rejected. Thus, we conclude that the
25
results are consistent with foreign investors inducing deep structural changes in the
way the acquired plants are run and cannot be explained by economies of scale.35
(d) Are we picking up the exporter effect?
Our results could potentially reflect the improvements stemming from a plant’s
effort to prepare for entering export markets, rather than the effect of FDI. To
eliminate this possibility, we restrict our attention to the acquired plants that do not
export in the acquisition year or the following years. Then we compare the
performance of this subsample to the corresponding control plants in the same
manner as we did before. This modification results in a very small change to the
magnitude of the effect. As before, in all periods considered FDI recipients
outperform plants remaining under domestic ownership (see Table 14).
Table 14. Matching Results for the Subsample of Plants with no Exports
Effect of Foreign Acquisition Log TFP
Acquisition year 0.164**
(0.08)
One year later 0.239***
(0.08)
Two years later 0.295***
(0.10)
n 102
Average Treatment Effect on the Treated. Bootstrapped std
errors in parentheses. *, **, *** indicate statistical significance
at the 10, 5 and 1% level, respectively.
(e) Is it foreign ownership per se or acquisitions in general?
While our data set does not allow us to test directly whether the observed
productivity improvements stem from foreign ownership
per se
or would result from
any (domestic or foreign) acquisition, we believe that the former explanation is
much more plausible for several reasons. First, as explained earlier in the literature
review, multinational corporations are characterized by large endowments of
intangible assets relative to other firms in developed and, even more so, developing
countries. Thus foreign acquisitions present a greater potential for technology and
know-how transfer to the acquired plants than domestic takeovers.
Second, domestic M&A activities in Indonesia were quite limited during the time
period considered in our study. According to the Securities Data Corporation
Mergers and Acquisitions Database, there were only 47 domestic acquisitions
between 1988 (the first year of data availability) and 1994, of which only 19 took
place in manufacturing sectors.
35 Little is known about the relationship between plant-level scale economies and multinationality. The
available evidence suggests, however, a negative association (see references in Markusen 1995).
26
Third, as illustrated in the Appendix, the few cases of cases of domestic M&As in
manufacturing, for which data are available, suggest that foreign acquisitions may
be associated with greater performance improvements than domestic takeovers. This
view is also supported by the evidence from Malaysia presented by Fauzias and
Shamsubaridah (1995) who find a statistically significant decline in the performance
(measured in terms of earnings per share and return to capital) of establishments
acquired by domestic companies.
Further evidence on the differential effect of domestic and foreign acquisitions comes
from information on privatization episodes. Our data set does not allow us to
identify changes in ownership if both the new and old owners are private Indonesian
entities. However, we can observe previously state-owned plants being sold to
domestic or foreign owners. We use this fact to compare the performance of formerly
state-owned plants that were sold to foreign owners (treatment group) with that of
plants sold to domestic interests (control group). Again the difference-in-differences
approach is used. To create the control group we model the probability of a state-
owned plant being privatized into foreign rather than domestic plants. Privatization
is defined as a change leading to the public (central and/or local government)
ownership share dropping to less than 20 percent. The explanatory variables in the
probit model are the same as those listed in Table 2 with the exception of the public
ownership dummy. As illustrated in Table 15 below, we find that previously state-
owned plants acquired by foreign investors outperform those sold to domestic
interests. The divergence in performance is statistically significant in the first and
second year following the privatization. In the second year, the estimated advantage
is equal to 35 percentage points which is only one percentage point higher than the
effect estimated in our basic specification in Table 3.
27
Table 15. Matching Results for Privatization Cases
(not restricted within sector/year)
From Public to Foreign Private vs. Domestic Private
Effect of Foreign Acquisition Log Relative TFP
Acquisition year 0.241
(0.16)
One year later 0.392**
(0.17)
Two years later 0.303**
(0.146)
n 39
Average Treatment Effect on the Treated. Bootstrapped std
errors in parentheses. *, **, *** indicate statistical significance
at the 10, 5 and 1% level, respectively.
(f) Are our results driven by the methodology chosen?
To eliminate the possibility that our results are driven by the methodology chosen,
we use an approach employed by several existing studies (Griffith 1999; Harris 2002;
Benfratello and Sembenelli 2002). We apply a GMM system estimator, proposed by
Blundell and Bond (1999), to estimate a production function including a binary
variable for foreign ownership. The production function is estimated separately for
62 industries of the Indonesian manufacturing sector. If foreign ownership has a
positive impact on plant productivity, we expect to find a positive coefficient on the
FDI variable.
The definitions of variables used in the estimation are the same as those employed
earlier, except for the additional FDI dummy. Real output is the dependent variable
and the explanatory variables include production labor, non-production labor,
materials and capital as well as the FDI dummy. All variables on the right hand
side (including FDI) are considered potentially endogenous and are instrumented by
levels lagged 3 to 6 periods in the differenced equation and by differences lagged 2 to
6 periods in the levels equation.
A summary of the GMM results is presented in Table 16 below. The coefficient on
the FDI variable shows a positive sign in 55 of the 62 industries. 73 percent of the
acquired plants analyzed in Section 7 belong to industries where this effect is
positive and significant at the 10 percent level. The estimated magnitude of the
effect on the plant productivity averages at around 26 percentage points with the
median effect of 23 percentage points. These estimates are broadly in line with the
results presented in Section 7. The GMM results hence confirm our previous results
that foreign ownership
per se
has a significant impact on plant productivity.
28
Table16.GMMSystemResults(Blundell/Bond1999):
FDIIndicatorinProductionFunction
NumberofindustrieswithFDI62
IndustrieswithpositivesignforFDI55
IndustrieswithpositivesignforFDI,significantat10%level39
IndustrieswithSargan‐Testnotrejectedat5%level44
IndustrieswithSecond‐OrderAutocorrelationrejectedat5%level55
NumberofFDIRecipients185
NumberofFDIRecipientsinIndustrieswithpositiveandsignificantsignforFDI149
AveragemagnitudeoftheestimatedeffectofFDIonplantproductivity26%pts
OverallNumberofObservationsinEstimation99,964
9. Conclusions
A large empirical literature searches for the evidence of knowledge spillovers from
foreign direct investment. Implicit to this analysis lies the assumption that foreign
ownership
per se
conveys some intangible advantages whose proximity can be
beneficial to domestic firms. Yet there is no robust empirical confirmation that this
assumption holds.
This study fills this gap in the literature by examining the causal relationship
between foreign ownership and plant productivity using a Census of Indonesian
Manufacturing Plants. Our aim is to distinguish between the possibility of foreign
investors acquiring above-average performers (the gifted kids explanations) and
genuine performance improvements resulting from foreign ownership (the pushy
parent hypothesis). To make a clear distinction between correlation and causality,
our analysis focuses on plants that change from domestic to foreign ownership and
combines the difference-in-differences approach with a propensity score matching.
The results suggest that foreign ownership brings significant benefits to Indonesian
plants. The acquired plants experience a faster growth in total factor productivity
than their counterparts remaining in domestic hands. They also grow faster in terms
of output and employment, invest more and increase employee wages faster. Finally,
they become more integrated into the international economy, both in terms of
exports and in terms of sourcing inputs from abroad.
Many developing countries strive to attract FDI inflows in the hope of stimulating
economic growth through knowledge transfer associated with foreign investment.
Recently, the
Economist
magazine pushed this view even further by stating that
29
“the fate of the [Indonesian] economy rests on attracting foreign investment.”36 The
positive view of FDI and benefits it may bring to Indonesia and other developing
countries are reinforced by the results of this study which indicate that foreign
investors outperform indigenous plants and that foreign ownership
per se
lies at the
root of this advantage. This finding is important as the existence of a positive direct
effect is a precondition for knowledge spillovers from FDI.
36 “Time to deliver: A survey of Indonesia.” December 11, 2004, p. 4.
30
References
Aitken, Brian and Ann Harrison (1999). Do Domestic Firms Benefit from Direct
Foreign Investment? Evidence from Venezuela. American Economic Review,
v89, n3: 605-618.
Asian Development Bank (2003). Diagnostic Study of Accounting and Auditing
Practices (Private Sector): Republic of Indonesia. Manila, Philippines.
Available online at http://www.adb.org.
Arellano, Manuel and Stephen Bond (1991). Some Tests of Specification for Panel
Data: Monte Carlo Evidence and an Application to Employment Equations.
Review of Economic Studies v58, n2: 277-97.
Arnold, Jens Matthias and Katrin Hussinger (2005a). Exports vs. FDI. Empirical
evidence using German firm level data. ZEW Working Paper, Centre for
European Economic Research, Mannheim, Germany.
Arnold, Jens Matthias and Katrin Hussinger (2005b). Export Behavior and Firm
Productivity in German Manufacturing. A Firm-level Analysis. Forthcoming
in Review of World Economics/Weltwirtschafliches Archiv
,
v141, n2.
Barba Navaretti, Giorgio and Castellani, Davide (2004), "Investments Abroad and
Performance at Home: Evidence from Italian Multinationals". CEPR
Discussion Paper No. 4284.
Barba Navaretti, Giorgio and A.J. Venables with F. Barry, K. Ekholm, A. Falzoni,
J. Haaland, K-H. Midelfart, & A. Turrini (2004). Multinational firms in the
world economy, Princeton University Press (forthcoming).
Becker, Sascha and Andrea Ichino (2002). “Estimation of Average Treatment Effects
Based on Propensity Scores,” Stata Journal, v2, n4: 358-377.
Benfratello, Luigi and Alessandro Sembenelli (2002). Foreign Ownership and
Productivity: Is the Direction of Causality So Obvious? Centro Studi Luca
d'Agliano Development Studies Working Paper 166.
Biro Pusat Statistik (BPS). Statistical Yearbook Indonesia, various editions,
Jakarta.
Blundell, Richard and Steve Bond (1999). GMM Estimation with Persistent Panel
Data: An Application to Production Functions. Working Paper W99/04,
Institute for Fiscal Studies, London.
Blundell, Richard and Monica Costa Dias (2000). Evaluation Methods for Non-
Experimental Data. Fiscal Studies v21, n4: 427-68.
Chari, Anusha, Paige Parker Ouimet and Linda Tesar. 2004. Acquiring Control in
Emerging Markets: Evidence from the Stock Market, NBER Working Paper
10872.
Conyon, Martin J., Sourafel Girma, Steve Thompson and Peter Wright (2002). The
Productivity and Wage Effects of Foreign Acquisition in the United
Kingdom. Journal of Industrial Economics v50, n1: 85-102.
31
Djankov, Simeon and Bernard Hoekman (2000). Foreign Investment and
Productivity Growth in Czech Enterprises. World Bank Economic Review
v14, n1: 49-64.
Doms, Mark E. and J. Bradford Jensen (1998). Comparing Wages, Skills, and
Productivity between Domestically and Foreign-Owned Manufacturing
Establishments in the United States. In: Geography and ownership as bases
for economic accounting : 235-55. NBER Studies in Income and Wealth, vol.
59. Chicago and London: University of Chicago Press.
Dunning, John H. (1993). Multinational Enterprises and the Global Economy.
Wokingham, England: Addison-Wesley Publishing Company.
Evenett, Simon and Alex Voicu (2003). Picking Winners or Creating Them?
Revising the Benefits of FDI in the Czech Republic, Oxford University,
mimeo.
Fauzias, Mat Nor and Shamsubaridah Ramlee (1995). Post Performance of
Malaysian Acquired Firms: A Preliminary Study. Jurnal Pengurusan v14,
n1:11-25.
Filer, R., O. Schneider and J. Svejnar (1995). Wage and non-wage labour cost in the
Czech Republic: The impact of fringe benefits, CERGE-EI Working Paper
No. 77, Prague.
Girma, Sourafel and Holger Görg (2003). Multinationals’ productivity advantage:
Scale or Technology? GEP Research Paper No. 2002/07. University of
Nottingham.
Girma, Sourafel, Holger Görg and Eric Strobl (2004). Exports, international
investment, and plant performance: Evidence from a non-parametric test.
Economics Letters, v83, n3: 317-324.
Girma, Sourafel, David Greenaway and Richard Kneller (2003). Export Market Exit
and Performance Dynamics: A Causality Analysis of Matched Firms.
Economics Letters v80, n2: 181-87
Goeltom, Miranda S. (1995). Indonesia's financial liberalization: An empirical
analysis of 1981-88 panel data. Current Economic Affairs Series. Singapore:
Institute of Southeast Asian Studies, ASEAN Economic Research Unit.
Gopinath, Gita and John Romalis (2005). The Value of Foreign Ownership,
University of Chicago, mimeo.
Görg, Holger and Eric Strobl (2001). “Multinational Companies and Productivity
Spillovers: A Meta-Analysis.” Economic Journal, v111, n475: 723—739.
Griffith, Rachel (1999). Using the ARD Establishment Level Data to Look at
Foreign Ownership and Productivity in the United Kingdom Economic
Journal v109, n456.
Griffith, Rachel and Helen Simpson (2001). Characteristics of foreign-owned firms in
British manufacturing. Institute for Fiscal Studies, IFS Working Papers:
W01/10.
32
Haddad, Mona and Ann Harrison (1993). Are There Positive Spillovers from Direct
Foreign Investment? Evidence from Panel Data for Morocco. Journal of
Development Economics v42, n1: 51-74.
Harris, Richard D. (2002). Foreign Ownership and Productivity in the United
Kingdom--Some Issues When Using the ARD Establishment Level Data.
Scottish Journal of Political Economy v49, n3: 318-35.
Harris, Richard and Catherine Robinson (2003). Foreign Ownership and
Productivity in the United Kingdom Estimates for U.K. Manufacturing
Using the ARD. Review of Industrial Organization v22, n3.
Harris, John R., Fabio Schiantarelli and Miranda G. Siregar (1994). The Effect of
Financial Liberalization on the Capital Structure and Investment Decisions
of Indonesian Manufacturing Establishments. World Bank Economic Review
v8, n1: 17-47.
Heckman, James J. (1979). Sample Selection Bias as a Specification Error.
Econometrica v47, n1 : 153-61.
Helpman, Elhanan, Marc J. Melitz and Stephen R. Yeaple (2004). Export Versus
FDI with Heterogeneous Firms, American Economic Review, v94, n1: 300-
316.
Hill, Hal (2000). The Indonesian economy (Second edition). Cambridge; New York
and Melbourne: Cambridge University Press.
International Finance Corporation (1997). Lessons of Experience: Foreign Direct
Investment, Washington DC.
Javorcik, Beata Smarzynska. (2004). “Does Foreign Direct Investment Increase the
Productivity of Domestic Firms? In Search of Spillovers through Backward
Linkages,” American Economic Review, v93, n3: 605-627.
Kertesi, G. and J. Köllö. (2001). A gazdasági átalakulás két szakasza és az emberi
töke átértékelödése, (Two phases of economic transformation and the
revaluation of human capital).
Közgazdasági Szemle
v47: 897-919.
Leuven, Edwin and Barbara Sianesi (2003). "PSMATCH2: Stata module to perform
full Mahalanobis and propensity score matching, common support graphing,
and covariate imbalance testing".
http://ideas.repec.org/c/boc/bocode/s432001.html. Version 1.2.3.
Levinsohn, Jamesand Amil Petrin (2003). Estimating Production Functions Using
Inputs to Control for Unobservables Review of Economic Studies v70, n2:
317-41.
Levinsohn, James, Amil Petrin and Brian Poi (2004). Production Function
Estimation in Stata Using Inputs to Control for Unobservables. Stata
Journal, v4, n1.
Lipsey, Robert E. and Fredrik Sjoholm (2003). Foreign Firms and Indonesian
Manufacturing Wages: An Analysis with Panel Data , NBER Working Paper
9417.
33
Lipsey, Robert E. and Fredrik Sjoholm (2004). Foreign direct investment, education
and wages in Indonesian manufacturing Journal of Development Economics,
v73, n1: 415-422.
Markusen, James R. (1995). “The Boundaries of Multinational Enterprises and the
Theory of International Trade,” Journal of Economic
Perspectives, v9, n2:
169-189.
Olley, G. Steven and Ariel Pakes (1996). The Dynamics of Productivity in the
Telecommunications Equipment Industry. Econometrica v64, n6: 1263-97.
Ramachandaram, Vijaya (1993). “Technology transfer, Firm Ownership, and
Investment in Human Capital,” Review of Economics and Statistics 75(4):
664-670.
Rosenbaum, Paul R.and Donald B.Rubin (1984). Estimating the Effects Caused by
Treatments: Comment [On the Nature and Discovery of Structure]. Journal
of the American Statistical Association v79, n385: 26-28.
Saggi, Kamal (2002). Trade, Foreign Direct Investment, and International
Technology Transfer: A Survey. World Bank Research Observer v17, n2:
191-235.
World Bank (1997). Malaysia: Enterprise Training, Technology, and Productivity.
World Bank. Washington, DC.
34
Figures
Figure A2. Foreign Ownership Share after Acquisition
0.05 .1 .15 .2
Fraction
20 40 60 80 100
Foreign Capital Share After Receiving FDI
Figure A1. Net FDI Inflows to Indonesia, as a % of GDP
0
0
.5
1
1
.5
2
2
.5
3
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
S
ource:
W
orld Bank Develo
p
ment Indicators
35
Fire A3. Distribution of Foreign Acquisitions by Year
0.1 .2 .3
Fraction
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
Distribution of Foreign Acquisitions over years
Figure A4. Distribution of Foreign Acquisitions by Sector
Textiles
Metal
Chemical
Machinery
Minerals
Food
Wood
Paper
36
Appendix
Additional Evidence on Domestic vs. Foreign Acquisitions in Indonesia
The Securities Data Corporation Mergers and Acquisitions Database lists 47
domestic acquisitions in Indonesia between 1988 (the first year of data availability)
and 1994 (the last year in which acquisitions are considered in our sample), of which
19 took place in manufacturing sectors. For 6 of the 19 cases we managed to obtain
additional data from the
Worldscope
database (1995 release). As
Worldscope
does
not contain sufficient information to calculate TFP figures, in the table below we
consider the evolution of the ratio of cost of goods sold (COGS) to sales, which gives
some indication of the efficiency with which inputs are being utilized. The COGS is
defined as the wage and material costs. The ratio of COGS to sales is normalized by
the average value observed in a given industry and year. The industry averages are
calculated based on the data from the Indonesian Census of Manufacturing. For
comparison purposes, we calculate the analogous figures for the plants acquired by
foreign investors, considered in Section 7.
The available information, albeit limited, suggests that foreign acquisitions may be
associated with greater performance improvements than domestic takeovers. A
performance improvement is defined as a decrease in the ratio of COGS to sales
(relative to the industry average). Out of six firms considered, only two experience a
decline in the ratio and the decline does not take place until two year after the
acquisition. Thus on average a domestic acquisition is associated with a
deterioration in firm performance. In contrast, plants which undergo foreign
acquisitions (considered in Section 7) experience on average a decline in the ratio in
the year of the acquisition as well as in the following period. Two years after the
takeover the ratio increases slightly but remains well below the pre-acquisition
period.
Table 1A. Cost of Goods Sold over Sales, normalized by the industry average
t-1 t=0 t+1 t+2
Domestic Acquisition 1 0.593 0.912 1.214 1.269
Domestic Acquisition 2 1.153 1.149 1.176 1.116
Domestic Acquisition 3 1.062 1.026 1.087 1.102
Average of 1- 3 0.936 1.029 1.159 1.162
Domestic Acquisition 4 0.561 0.726 0.833
Domestic Acquisition 5 0.811 0.961 0.924
Domestic Acquisition 6 0.736 0.974 1.068
Average of 4 - 6 0.703 0.887 0.942
Overall average (1 — 6) 0.866 1.023 1.052
Foreign Acquisitions 0.939 0.925 0.885 0.901
The figures on domestic acquisition come from the
Worldscope
database, while the figures
for foreign acquisitions are from the Indonesian Census of Manufacturing.
