00074918.2014.938407
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Ownership and Energy Efficiency in Indonesian
Manufacturing
Eric D. Ramstetter & Dionisius Narjoko
To cite this article: Eric D. Ramstetter & Dionisius Narjoko (2014) Ownership and Energy
Efficiency in Indonesian Manufacturing, Bulletin of Indonesian Economic Studies, 50:2,
255-276, DOI: 10.1080/00074918.2014.938407
To link to this article: http://dx.doi.org/10.1080/00074918.2014.938407
Published online: 30 Jul 2014.
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Date: 17 January 2016, At: 23:28
Bulletin of Indonesian Economic Studies, Vol. 50, No. 2, 2014: 255–76
OWNERSHIP AND ENERGY EFFICIENCY IN INDONESIAN
MANUFACTURING
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:28 17 January 2016
Eric D. Ramstetter*
International Centre for the Study of East Asian Development; Kyushu University
Dionisius Narjoko*
Economic Research Institute for ASEAN and East Asia
Using 1996 and 2006 census data on medium-large plants in Indonesian manufacturing, we examine whether foreign multinational enterprises (MNEs) and stateowned enterprises (SOEs) used purchased energy more eficiently than local, private plants, inding that the correlation between plant ownership and total energy
intensity, gas intensity, and coal intensity was generally weak in both years. Second,
we ask whether energy eficiency in private plants was affected by the presence of
MNEs or SOEs in high-energy-consuming industries. In 1996, private energy intensities were often positively correlated with the presence of SOEs and majorityforeign MNEs and negatively correlated with the presence of wholly foreign or
minority-foreign MNEs, but in 2006 the corresponding results differed substantially. This suggests that ownership-related differentials in energy intensity and
intra-industry energy-intensity spillovers are not pronounced. If policymakers are
concerned with improving energy eficiency in Indonesian manufacturing, plant
ownership should not be a major consideration.
Keywords: ownership, multinational corporations, energy eficiency, manufacturing
JEL classiication: F23, K32, L32, L33, L60, O53, Q40
INTRODUCTION
The consumption of purchased energy generates much of the pollution (mainly
air pollution) emitted by manufacturing plants in Indonesia. Improving energy
eficiency or conservation is thus an effective way of limiting pollution by
* We are grateful to the Japan Society for the Promotion of Sciences for inancial assistance
(grant 22530255 for the project Ownership and Firm- or Plant-Level Energy Eficiency in
Southeast Asia) and to the International Centre for the Study of East Asian Development
and the Economic Research Institute for ASEAN and East Asia for logistic support. We
thank Kornkarun Cheewatrakoolpong, Kenichi Imai, Kozo Kiyota, Lin See Yan, Kiichiro
Fukusaku, Sadayuki Takii, Siang Leng Wong, Chih-Hai Yang, and Naoyuki Yoshino for
discussing related papers on Indonesia, Malaysia, and Thailand. Two referees provided
valuable comments, as did several other participants in the 9th Australasian Development
Economics Workshop on 6–7 June 2013 and previous 2012 and 2013 conferences where related papers were presented. Other project participants (Shahrazat Binti Haji Ahmad and
Archanun Kohpaiboon) and Juthathip Jongwanich also provided important input. The authors are solely responsible for the content of this article.
ISSN 0007-4918 print/ISSN 1472-7234 online/14/000255-22
http://dx.doi.org/10.1080/00074918.2014.938407
© 2014 Indonesia Project ANU
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256
Eric D. Ramstetter and Dionisius Narjoko
manufacturers. In this article we use plant-level data underlying the Indonesian
manufacturing censuses (Statistik Industri) of 1996 and 2006 to analyse whether
plants controlled by foreign multinational enterprises (MNEs) or state-owned
enterprises (SOEs) used purchased energy (deined as electricity and fuel) more
eficiently than medium-large local, private plants. We then ask whether energy
intensities (ratios of purchased energy to output), in local, private plants were correlated with the shares of MNEs and SOEs in Indonesia’s high-energy-consuming
industries.
A 2003 study by Eskeland and Harrison is one of the few to use microdata
to investigate the consumption of purchased energy in developing economies. It
inds that ‘foreign plants are signiicantly more energy eficient and use cleaner
types of energy’ than their local peers in Côte d’Ivoire, Mexico, and Venezuela
(Eskeland and Harrison 2003, 21). He (2006, 228) provides evidence that foreign
direct investment (FDI) enterprises in China ‘produce with higher pollution [sulfur dioxide] eficiency’, but that stronger environmental regulation has simultaneously, though moderately, deterred FDI among Chinese provinces. Earnhart and
Lizal (2006) analyse the effects of inancial performance and privatisation on the
environmental performance of Czech irms, their results indicating that foreign
ownership was usually an insigniicant determinant of pollution.
As detailed in the following literature review, MNEs, in particular, are also
thought to be a source of positive spillovers on private irms. Theory suggests
that a greater MNE presence in an industry can increase competition and labour
mobility among MNEs and local, private plants, as well as strengthen linkages
that foster higher productivity in local plants. Previous studies generally indicate positive productivity spillovers from MNEs in Indonesia. If the presence of
MNEs has affected productivity in private plants, it may have also affected energy
eficiency (the inverse of energy productivity). This article describes the data and
patterns of energy intensities, as well as SOE and MNE presence, and analyses the
statistical signiicance of MNE–private and SOE–private energy differentials and
energy spillovers, after accounting for the scale, input mix, and technical characteristics of plants.
MULTINATIONAL AND STATE ENTERPRISES AND ENERGY EFFICIENCY
This section surveys the relevant literature, explaining why MNEs are usually
expected to be more eficient than SOEs and discussing the implications of productivity spillovers for possible spillovers of energy eficiency.
Productivity Differentials and Energy Eficiency
Recent theoretical analyses have drawn attention to the role of knowledge-based,
intangible assets (terminology from Markusen 1991) in MNEs. Most observers
agree that MNEs tend to have relatively large amounts of technological knowledge and marketing expertise, strong international networks, and generally
sophisticated and effective management.1 These characteristics are evidenced
by relatively high research and development (R&D) intensities (ratios to sales or
1. Caves (2007) and Dunning and Lundan (2008) provide thorough literature reviews. See
also Markusen’s (2002) study.
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Ownership and Energy Eficiency in Indonesian Manufacturing
257
output), large proportions of patent applications and approvals, high advertising–
sales ratios, and a high dependence on international trade.
If MNEs have relatively large knowledge-based, intangible asset stocks, they
should be more eficient producers than non-MNEs in some respect. They may
become more energy eficient, for example, or pollute less, as part of efforts to
increase demand among consumers and minimise production costs. Moreover,
because MNEs tend to be R&D- and patent-intensive, and because energy conservation technologies often require sophisticated technological inputs, it is logical
that MNEs should be both better able to and more motivated to conserve energy
than other irms.2
The theoretical rationale for expecting MNEs to be more productive than other
irms is convincing. However, in Southeast Asia’s developing economies the
empirical evidence on productivity differentials between foreign MNEs and local
irms (most of which are not MNEs) is often ambiguous. For example, studies of
manufacturing in Malaysia (Oguchi et al. 2002; Haji Ahmad 2010) and Thailand
(Ramstetter 2004, 2006) suggest that productivity differentials tend to be small
and often statistically insigniicant. Similar evidence for Indonesia (Takii 2004,
2006) and Vietnam (Ramstetter and Phan 2008, 2013) suggests that productivity differentials between MNEs and local plants are more commonly statistically
signiicant, although these differentials are often statistically insigniicant when
using a translog function (allowing for lexible assumptions about scale and factor
substitution) and disaggregating plants by industry (allowing for differences in
production function slopes among industries, as well as the constant).
Many economists expect SOEs, in contrast with MNEs, to be less productive
than private plants, largely because SOEs are thought to have fewer incentives
for pursuing proits and eficiency. The empirical evidence is mixed, with several
studies inding that SOEs are less productive than private irms and several inding the opposite.3 In Indonesia, SOEs have played large roles in several industries,
such as basic metals (table 1), and are generally perceived to be ineficient. This is
one reason for the privatisation of many SOEs during 1996–2006. Hartono, Irawan,
and Achsani (2011) ind that local, private plants tend to have signiicantly higher
energy intensities than SOEs in Indonesian manufacturing and that MNE–SOE
differentials were not signiicant in samples of all manufacturing plants 2002–6.
Productivity Spillovers and the Potential for Energy-Eficiency Spillovers
The presence of MNEs in an industry can increase the productivity of local plants
by creating spillovers through at least three major channels: (a) by fostering linkages between MNEs and local plants, (b) by stimulating labour mobility between
MNEs and local plants, and (c) by creating a demonstration or competition effect.
First, direct linkages are usually backward linkages created when MNEs source
raw materials, parts, or services from local plants. MNEs can also create forward
2. For example, evidence from Cole, Elliott, and Shimamoto (2006) suggests that Japanese
irms with FDI and international trade pollute less and manage emissions better than irms
without FDI or trade.
3. See the studies of Aharoni (2000), Djankov and Murrell (2002), and Stretton and Orchard
(1994) for surveys; Jefferson and Su (2006) for Chinese evidence; and Brown, Earle, and
Telegdy (2004, 2006) for Eastern European evidence.
TABLE 1 Total Energy Expenditures; Private Energy Intensities; and Shares of StateOwned and Multinational Enterprises in Labour, Energy Expenditures, and Output
SOE shares
MNE shares
(% of industry subtotals) (% of industry subtotals)
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Industry, by year
1996
Manufacturing
12 highest energy consumers
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral
products
Basic metals
Electronics-related
machinery
Motor vehicles
Other transportation
machinery
2006
Manufacturing
12 highest energy consumers
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral
products
Basic metals
Electronics-related
machinery
Motor vehicles
Other transportation
machinery
1
(Rp bn)
2
(%)
7,309
6,786
804
1,521
75
474
631
631
399
5.37
5.96
5.83
4.94
2.24
4.65
5.38
5.07
5.68
6.92
8.42
22.31
3.13
0.95
1.16
6.40
12.37
9.97
11.66
12.24
12.37
3.62
0.17
0.64
8.51
25.48
3.17
8.88
9.99
10.09
2.47
0.80
0.78
4.65
13.97
3.23
18.11
16.50
9.82
13.22
23.08
9.20
18.64
18.97
11.86
21.58
21.12
19.65
17.45
24.94
8.58
30.79
27.02
16.13
27.15
27.22
17.72
20.01
28.05
11.54
31.40
38.03
20.83
1,274
542
15.61
5.61
6.79
14.44
15.54
29.07
13.11
46.04
11.86
20.66
20.31
20.54
25.35
22.91
256
80
3.19
3.97
1.85
0.47
23.28
0.78
3.44
0.09
51.29
29.00
39.94
41.31
55.90
57.66
99
4.39
38.58
30.02
15.69
19.45
15.84
14.40
56,841
52,635
7,652
7,810
1,938
1,523
5,197
7,316
3,125
5.83
6.54
6.49
6.82
3.70
6.05
5.90
6.25
6.57
5.52
6.31
11.04
2.10
3.07
0.73
17.09
9.00
8.19
8.77
9.19
7.62
1.15
3.35
0.37
10.24
3.34
7.06
6.48
7.08
7.19
1.37
2.91
0.31
13.42
14.91
4.29
24.97
24.83
17.33
17.25
30.43
12.94
18.31
21.51
22.32
28.54
28.56
21.80
23.65
25.36
16.58
29.93
25.69
22.84
35.79
36.73
28.67
32.05
35.90
17.79
30.32
33.44
27.87
7,379
4,316
14.52
5.36
7.95
4.70
34.88
4.72
26.64
3.27
21.96
20.48
30.34
11.29
29.37
15.43
1,610
2,827
3.76
5.11
1.40
–
0.09
–
0.33
–
65.58
54.74
66.34
87.23
71.95
81.31
1,943
6.17
24.08
16.27
12.84
32.97
18.81
61.84
Labour Energy Output Labour Energy Output
Source: Authors’ calculations based on data from BPS.
Note: SOE = state-owned enterprise. MNE = multinational enterprise. 1 = total energy expenditures.
2 = private energy intensities. – = no plants in category. Industry deinitions differ between 1996 and
2006. See the text for detailed deinitions of industries and ownership groups.
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Ownership and Energy Eficiency in Indonesian Manufacturing
259
linkages by supplying local plants with intermediate goods (materials, parts, or
services) or inal goods of superior quality to those produced locally. Second,
MNEs often require relatively skilled workers, such as middle-level technicians
and managers, and seek to recruit them from local irms. Local plants also try to
woo workers from MNEs, and some MNE workers resign after realising that they
have obtained the skills to start their own irm. Third, the entry or expansion of
foreign MNEs usually increases competitive pressure on local plants that produce
goods or services similar to those of the MNEs. The increased competition can
motivate local irms to develop or upgrade technology, cut input costs, or expand
marketing efforts.
Much of the existing research on spillovers focuses on intra-industry productivity spillovers, although more recent studies examine inter-industry spillovers.
Several reviews emphasise that there is only mixed evidence of productivity
spillovers (Görg and Strobl 2001; Fan 2002; Görg and Greenaway 2004; Lipsey
and Sjöholm 2005; Pessoa 2007).4 Moreover, studies of Asian economies (reviewed
below) suggest that estimates of spillovers vary substantially depending on the
economies and industry groups studied, the measure of foreign presence used (for
example, whether measured as shares of employment, output, or ixed assets),
and the estimation methodology. Cross-sectional methodologies tend to result in
larger estimates of spillovers, but recent studies generally use ixed-effects estimators when panel data are available.5
Some of the earliest research on spillovers from MNEs in Asian host economies examined Indonesia. Cross-sectional evidence for 1980 and 1991 from Blomström and Sjöholm (1999) and Sjöholm (1999a, 1999b) indicates that productivity
spillovers tended to be positive and stronger in industries in which competition
among local plants was relatively intense and within regions with diversiied
industrial structures. Subsequent panel analysis for 1990–95 (Takii 2005, 2006)
suggests that positive intra-industry spillovers were more prevalent in industries
with small technical gaps and in which minority foreign MNEs had relatively
large shares. Blalock and Gertler (2008) ind strong evidence of productivity
gains, greater competition, and lower prices among local irms in markets that
supplied foreign entrants in 1988–2006. Suyanto, Salim, and Bloch (2009) analyse
spillovers in chemical and pharmaceutical plants in 1998–2000, inding positive
productivity spillovers from MNEs that are larger in more competitive industries
and in local plants with R&D. Results from Lipsey and Sjöholm (2004, 2006) and
Sjöholm and Lipsey (2006) also suggest the existence of positive wage spillovers.
For Malaysia, Khalifah and Adam (2009) ind that productivity spillovers were
positive when the presence of MNEs was measured as the share of value added or
ixed assets in 2000–4, but insigniicant or negative when measured as the share of
labour. Haji Ahmad (2010, chapter 6) uses the same dataset and inds that evidence
of signiicant spillovers was rare and that results varied among industry groups.
4. Mebratie and Van Bergeijk’s (2013) meta-analysis argues that accounting for irm heterogeneity in R&D and exporting changes many ambiguous results and provides relatively
strong evidence of positive spillovers.
5. In general, ixed-effects panel estimates are preferable because they control for unobserved characteristics among local plants or irms and because they are less vulnerable to
simultaneity problems that may arise if MNEs are attracted to high-productivity industries.
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260
Eric D. Ramstetter and Dionisius Narjoko
For Thailand in 1996, industry-level results from Kohpaiboon (2006a, 2006b) and
plant-level results from Ramstetter (2006) and Movshuk and Matusoka-Movshuk
(2006) suggest positive productivity and wage spillovers from MNEs. Using a
more limited sample of manufacturing irms in 2001–3, Kohpaiboon (2009) inds
positive horizontal spillovers in industries in which import protection was relatively low.
For Vietnam, Pham’s (2008) estimates generally suggest that positive spillovers were largest in Hanoi and Ho Chi Minh City, and from MNEs that were
not wholly foreign. For 2000–5, Nguyen (2008) inds that both horizontal and
vertical spillovers were generally positive, and largest in more advanced regions
and in more sophisticated local irms. Nguyen et al. (2008) inds that backward,
vertical spillovers were positive in manufacturing and that horizontal spillovers
were positive in services. Le and Pomfret (2011) ind positive backward spillovers
in manufacturing but negative and strong horizontal spillovers to private irms,
domestic-oriented irms, irms without R&D, and irms in low-technology industries. Ramstetter and Phan (2008) ind positive spillovers from MNEs to private
irms in cross-sections, but Ramstetter and Phan (2013) ind no signiicant spillovers in unbalanced panels for 2001–6.
DATA SOURCES, ENERGY CONSUMPTION, AND ENERGY INTENSITIES
This article uses data from industrial censuses for 1996 and 2006 because they are
comprehensive and contain details on energy expenditures and labour quality
that other annual surveys do not. Because a number of plants are jointly owned
by two or more irms (which may be MNEs, SOEs, and/or private, joint ventures
with foreign shares of 33% or more) are classiied as MNEs and non-MNE joint
ventures with state shares of 33% or more are classiied as SOEs. The cut-off for
deining MNEs is higher than the IMF standard (having foreign shares of 10% or
more), but we know of no similar standard for deining SOEs and we need to be
clear.
The irst column of table 1 shows total energy (fuel and electricity) expenditures
in manufacturing and by the 12 highest-energy-consuming industries, generally
deined by the two-digit level of Indonesia’s Standard Industrial Classiication
(ISIC) Revision 3. The industry deinitions for 1996 are based on ISIC Revision
2 and differ from the 2006 deinitions, so caution is necessary when interpreting
industry-level trends.6 The data refer to purchased energy (most of which is fuel,
and which accounts for the bulk of the energy used by most plants) and do not
account for energy generated or sold.7
6. It is impossible to construct a precise correspondence between the two revisions, because
several detailed categories (that is, at the four- or ive-digit level) in one classiication are
split among detailed categories in the other. Ramstetter and Narjoko (2012, appendix table
8) give detailed deinitions.
7. Self-generated electricity is important for some plants. Purchased electricity accounted
for 42% of total purchased energy in 1996 and 47% in 2006; if we assume that plant-level
prices of purchased and self-generated electricity were identical, the ratios of self-generated
electricity to total purchased energy were 24% in 1996 and 9% in 2006. These ratios varied
only slightly among ownership groups in 1996 (23% for private plants, 25% for MNEs, and
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Ownership and Energy Eficiency in Indonesian Manufacturing
261
Calculations of electricity prices (unit values) by source are also of interest,
because they give hints about the quality of the data and because SOEs are said
to receive cheap electricity from the state provider (Perusahaan Listrik Negara
[PLN]) and because they give hints into the quality of the data. In 2006, SOEs
and MNEs paid much lower electricity prices to PLN in 2006 than private plants,
but in 1996 they paid slightly higher prices to PLN.8 Non-PLN electricity was a
relatively small source for all ownership groups in 1996 and for private plants and
SOEs in 2006, but it composed two-ifths of the total for MNEs in 2006. Prices of
non-PLN electricity, however, were several times higher for MNEs than for private plants or SOEs in 1996. Thus both ownership-related variations in electricity
prices and sources of purchased electricity were much smaller in 1996 than in
2006. To some extent this is probably realistic, relecting PLN’s shrinking inluence and the privatisation of SOEs. Yet the substantial price differentials in 2006
may also relect a deterioration of data quality, which is also observed in other
variables such as ixed asset (capital) stocks.
Nominal energy expenditures increased greatly between 1996 and 2006,
largely because of inlation (table 1).9 In this article we focus on the 12 highestenergy-consuming industries, which together accounted for 93% of total energy
expenditures and caused the most energy-related pollution in Indonesian manufacturing.10 Among these industries, textiles, chemicals, paper products, food and
beverages, and non-metallic mineral products accounted for 66% of total purchased energy in 1996 and 62% in 2006.
The third to eighth columns of table 1 show the shares of labour, energy, and
output held by SOEs and MNEs in 1996 and in 2006 in each of these 12 industries,
in these industries as a group, and in manufacturing overall. Nearly all shares
were larger in the group of 12 industries than in manufacturing. On one hand,
most SOE shares fell between 1996 and 2006, relecting the privatisation of several
SOEs after the 1997–98 Asian inancial crisis. On the other hand, most MNE shares
27% for SOEs), but were much lower for SOEs in 2006 (2%) than for MNEs (10%) or private
plants (15%). The ratios of electricity sold to total purchased energy were much smaller—
under 0.5% for all ownership groups in 1996 and 2006 (authors’ calculations based on BPS
data from various years).
8. In 1996, SOEs paid 6% more than private plants for PLN electricity and MNEs 4% more,
but these price differentials were –82% and –31% in 2006. For non-PLN energy, corresponding energy prices differentials were –8% for SOEs and 21% for MNEs in 1996, and –32%
and 393% in 2006. Non-PLN shares of purchased electricity were 1% for private plants,
14% for SOEs, and 5% for MNEs in 1996, but 9%, 0.2%, and 40%, respectively, in 2006. PLN
shares of purchased electricity quantities were 99% for private plants, 86% for SOEs, and
95% in MNEs in 1996, and 91%, 100%, and 60%, respectively, in 2006 (authors’ calculations
based on BPS data from various years).
9. According to national accounts estimates, manufacturing GDP increased 6.7-fold in
1996–2006 if measured in current prices, but the manufacturing GDP delator increased
4.9-fold while real manufacturing GDP increased only 1.4-fold. The growth of the manufacturing GDP delator peaked in 1998 (60%), but this measure of manufacturing inlation
was also high in 2000–2001 (20%–27%), and in 1997, 1999, and 2005–6 (13%–17%; Asian
Development Bank 2004, 2011).
10. These analyses focus on industries that accounted for 3% or more of expenditures in
1996 or 2006 (or both) for each type of fuel examined (total energy, natural gas, and coal).
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262
Eric D. Ramstetter and Dionisius Narjoko
increased, partially because the inancial crisis increased debts of many Indonesian irms and led to large declines in asset prices and the rupiah. The ensuing ire
sale made mergers and acquisitions, as well as new investments, relatively cheap
for MNEs. In addition, Indonesian policymakers stepped up implementation of
reforms mandated by FDI policy changes in the mid-1990s, reducing restrictions
on foreign ownership and simplifying approval procedures for FDI. Shares of
heavily foreign MNEs (foreign shares of 90% or more) in labour, output, and purchased energy all increased conspicuously.11
SOEs accounted for more than one-ifth of the energy expenditures in chemicals, basic metals, electronics-related machinery, and other transportation machinery in 1996, and in non-metallic mineral products in 2006 (table 1). MNE shares
exceeded one-ifth of the energy expenditures in most of the highest-energyconsuming industries, including basic metals in 1996; food and beverages, textiles, and rubber and plastics in 2006; and apparel, paper products, chemicals,
non-metallic mineral products, electronics-related machinery, and motor vehicles
in both years. SOEs and MNEs generally accounted for larger shares of energy
and output than labour; that is, they tended to have higher energy–labour and
output–labour ratios than private plants. Energy shares were usually smaller than
output shares in MNEs, indicating that they tended to have lower energy intensities than private plants, while the reverse was true for SOEs.
Mean energy intensities, measured as the ratio of total energy expenditures to
output, were generally lower in MNEs and higher in SOEs than in private plants
(table 2). If we combine plants from the 12 highest-energy-consuming industries,
the mean energy intensity of private plants was 6.0% in 1996 and 6.5% in 2006
(table 1). The mean energy intensity of MNEs was 2.4 percentage points lower in
1996 and 1.3 percentage points lower in 2006, while that of SOEs was 1.1 and 0.2
percentage points higher. Heavily foreign MNEs had the lowest energy intensities
among MNEs (2.6 and 1.4 percentage points lower than private plants). The differentials were smallest in absolute value for minority-foreign MNEs (33%–49%
foreign shares) and of intermediate size for majority-foreign MNEs (50%–89% foreign shares).
Energy intensities and MNE–private differentials varied greatly among industries and ownership groups (tables 1 and 2). For example, both private energy
intensities and negative MNE–private differentials were relatively large in nonmetallic mineral products. In contrast, SOEs and minority- and majority-foreign
MNEs all had higher energy intensities than private plants in textiles in both
years. Most MNE–private differentials were negative in both years (10–11 industries in 1996, 8–9 industries in 2006). In other words, MNEs tended to have lower
energy intensities than private plants at the industry level, but the size of differentials varied among industries. SOE–private differentials were negative in six of
the twelve (1996) and six of the eleven (2006) relevant, high-energy-consuming
industries.12
11. Heavily foreign shares of both labour and output were only 6%–7% in 1996 (for both
total manufacturing and the 12 highest-energy-consuming industries, but they more than
doubled, to 15%–16% of labour and 20%–21% of output, by 2006 (Ramstetter and Narjoko
2012, table 2).
12. There were no SOEs in the 2006 sample of motor vehicle plants.
TABLE 2 Difference in Intensities of Mean Total Energy, Natural Gas, and Coal
between Private Plants and State-Owned Enterprises or Multinational Enterprises
(percentage points)
MNEs
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Industry, by fuel and year
Minority Majority Heavily
foreign foreign foreign
SOEs
All
Total energy
12 highest energy consumers, 1996
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral products
Basic metals
Electronics-related machinery
Motor vehicles
Other transportation machinery
12 highest energy consumers, 2006
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral products
Basic metals
Electronics-related machinery
Motor vehicles
Other transportation machinery
1.110
1.785
4.417
–1.218
–0.700
7.782
3.234
–2.600
–3.096
–0.083
1.334
1.540
–2.140
0.224
0.717
4.315
4.630
–1.275
2.543
–1.922
–2.746
–2.775
1.466
–2.747
–
–1.659
–2.429
–2.463
0.310
–0.309
–1.460
1.417
–2.544
–2.359
–5.821
–1.187
–0.248
–1.537
–1.637
–1.253
–1.259
0.405
1.834
–1.985
–0.272
–2.170
–1.097
–4.293
0.054
0.175
–0.426
–0.295
–2.195
–2.207
0.097
–0.930
–2.284
–2.337
–1.942
–2.837
–2.059
–3.367
–1.175
–1.603
–1.795
–0.208
5.238
2.211
–2.087
–3.392
–3.088
–2.017
–2.246
–5.326
–1.087
–2.620
4.704
–5.228
–2.375
–2.362
0.805
–0.512
–1.276
1.682
–2.652
–2.648
–7.570
–0.690
–0.212
–1.278
–1.494
–1.040
–0.589
1.910
5.509
–2.713
2.941
–2.769
–1.834
–5.884
–0.549
–0.935
–1.398
–1.811
–2.584
–2.748
–0.210
–0.097
–1.340
2.764
–2.658
–1.953
–5.698
–1.089
–0.138
–2.487
–1.721
–1.434
–2.460
–0.477
1.595
–1.631
–0.556
–1.850
–0.705
–2.526
0.590
0.442
–0.930
0.463
Natural gas
8 highest gas consumers, 1996
8 highest gas consumers, 2006
–0.027
0.006
0.103
0.152
0.112
0.058
0.218
0.225
–0.087
0.128
Coal
4 highest coal consumers, 1996
4 highest coal consumers, 2006
0.216
0.252
0.039
0.112
0.277
0.702
0.005
0.134
–0.018
0.020
Source: Authors’ calculations based on data from BPS.
Note: MNE = multinational enterprise. SOE = state-owned enterprise. – = no plants in category. See
the text for detailed deinitions of ownership groups and note that industry deinitions differ between
1996 and 2006. The eight highest gas-consuming industries were food and beverages, textiles, paper
products, chemicals, rubber and plastic products, non-metallic mineral products, basic metals , and
fabricated metals. The four highest coal-consuming industries were textiles, paper products, chemicals, and non-metallic mineral products.
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264
Eric D. Ramstetter and Dionisius Narjoko
The aggregate energy intensities at the top of table 2 do not relect the important possibility that MNEs and private plants may consume different energy
mixes and thus have different effects on the environment even if their aggregate energy intensities are similar. For example, Eskeland and Harrison (2003)
assume that electricity consumption is cleaner than fuel consumption and ind
that MNEs tend to consume more electricity than non-MNEs. However, electricity
is not unambiguously clean in Indonesia; coal (a dirty fuel) generated large and
increasing shares of electricity in 2000 and 2005. In contrast, natural-gas usage is
relatively clean. Purchases of both fuels were small and concentrated in only a few
industries.13 Natural-gas expenditures accounted for more than 3% of purchased
fuels in only eight industries in either 1996 or 2006, and coal shares exceeded 3%
of the total in only four industries.14
Mean natural-gas intensities (ratios of expenditures to output) of private plants
in high-energy-consuming industries were only 0.10% in 1996 and 0.08% in
2006, while mean coal intensities were 0.04% and 0.23%, respectively (Ramstetter and Narjoko 2012, table 2). When all high-energy-consuming industries were
combined, mean MNE–private differentials were positive for both types of fuel,
except for a negative differential in gas intensities for heavily foreign MNEs in
1996 (table 2). MNEs used large amounts of both clean and dirty fuels per unit of
output, on average. In SOEs, coal intensities were large in both years, while gas
intensities were small in 1996 but increased to a level similar to that of private
plants in 2006.
In short, the energy intensities in table 2 are consistent with Eskeland and Harrison (2003), suggesting that MNEs purchase less total energy per unit of output
than private plants. MNEs also purchase more relatively clean gas per unit of
output than private plants—as well as more coal, which is a relatively dirty fuel.
Such simple comparisons, however, mask plant-level differences in scale, factor
usage, and technology that may affect the relation between ownership and energy
intensities.
ENERGY INTENSITIES AFTER ACCOUNTING FOR SCALE AND
FACTOR USAGE
We examine the relation between ownership and energy intensities after accounting for scale and other factor usage by estimating a factor demand model ‘based
on a translog approximation to a production function’ taken from Eskeland
and Harrison (2003, 16–18). The model speciies the share of the energy factor’s
income (expenditure) in gross output as a function of the logs of other factor
13. According to the Asia Paciic Energy Research Centre (2009), natural gas and hydropower together generated 36% of Indonesia’s electricity in 2000 and 23% in 2005, while
accounting for 22% and 18% of the primary energy supply, respectively. Coal generated
37% of electricity in 2000 and 41% in 2005, while its share of the primary energy supply rose
from 7% to 13%, respectively. Oil generated 40% and 39% of electricity and accounted for
30% and 19% of the primary energy supply. See Ramstetter and Narjoko’s (2012) study for
detailed analyses of MNE–private and SOE–private differentials in electricity intensities.
14. Natural gas accounted for 7% of total energy in 1996 and 3% in 2006; coal accounted for
5% and 6%, respectively (Ramstetter and Narjoko 2012, appendix tables 1a, 1d, 1e).
Ownership and Energy Eficiency in Indonesian Manufacturing
265
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inputs (other intermediate consumption, ixed assets, and labour); the log of the
quantity of the energy input; and factors related to a plant’s technological sophistication. Indonesian manufacturing census data include four indicators of technological sophistication that may affect energy intensities: plant vintage, the ratio of
R&D expenditures to gross output, shares of moderately educated workers in the
workforce, and shares of highly educated workers in the workforce.15
To determine whether energy intensities in MNEs or SOEs differed signiicantly from those in private plants, we introduce dummy variables for MNEs and
SOEs. The simplest version of the model assumes that MNE–private differentials
were the same for all MNE ownership groups:
EPij = a0 + a1(LLij ) + a2(LK ij ) + a3(LM ij ) + a4(LEij ) + a5(SM ij ) + a6(SH ij ) +
a7(RDij ) + a8(YRij ) + a9(DSij ) + a10(DFij ) + UAij
(1)
where the dummy DFij equals one if plant i in industry j was an MNE and zero
otherwise; the dummy DSij equals one if i was an SOE and zero otherwise; EPij is
the energy intensity or ratio of energy expenditures to gross output of i (%); LEij is
the natural log of the quantity of energy used in i + 1 (kilowatt hours of electricity for total energy, kilograms for coal, cubic meters for natural gas); LLij is the
natural log of the number of workers in i (number); LKij is the natural log of the
ixed assets, less depreciation at year-end, of i (Rp thousand); LMij is the natural
log intermediate consumption, excluding energy, of i (Rp thousand); RDij is the
ratio of R&D expenditures to gross output of i (%); SMij is the percentage of workers with secondary education in i; SHij is the percentage of workers with tertiary
education in i; YRij is the vintage, or the number of years of operation of i; and UAij
is the error term.
The coeficient a10 is the percentage point differential (comparable to the differentials in table 2) of energy intensities between MNEs and local, private plants,
after accounting for scale and factor usage and the four indicators of technological sophistication. We can test if MNE–private differentials varied among MNE
ownership groups by estimating the following:
EPij = b0 + b1(LLij ) + b2(LK ij ) + b3(LM ij ) + b4(LEij ) + b5(SM ij ) + b6(SH ij ) +
b7(RDij ) + b8(YRij ) + b9(DSij ) + b10(DFMIN ij ) + b11(DFMAJ ij ) +
b12(DFHVYij ) + UBij
(2)
where (other variables as deined above) the dummy DFMINij equals one if i was
a minority-foreign MNE and zero otherwise; the dummy DFMAJij equals one if
i was a majority-foreign MNE and zero otherwise; the dummy DFHVYij equals
one if i was a heavily foreign MNE and zero otherwise; and UBij is the error term.
The coeficients b10, b11, and b12 are the percentage point differentials between
minority-foreign, majority-foreign, and heavily foreign MNEs, on the one hand,
15. Eskeland and Harrison’s (2003) model includes the R&D ratio and plant vintage. It also
includes machinery imports, but that variable is not available in the Indonesian census
data.
266
Eric D. Ramstetter and Dionisius Narjoko
and private plants, on the other, after accounting for scale and other factor usage
and the four indicators of technological sophistication.
To determine whether the presence of MNEs or SOEs affected energy eficiency
in local plants, we restrict the sample to private plants and replace the ownership
dummies in equations (1) and (2) with the corresponding MNE or SOE shares of
labour or output:
EPij = c0 + c1(LLij ) + c 2(LK ij ) + c3(LM ij ) + c 4(LEij ) + c5(SM ij ) + c6(SH ij ) +
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:28 17 January 2016
c7(RDij ) + c8(YRij ) + c9 ( SSj ) + c10 ( SFj ) + UCij
(3)
EPij = d 0 + d1(LLij ) + d 2(LK ij ) + d 3(LM ij ) + d 4(LEij ) + d 5(SM ij ) + d 6(SH ij ) +
d 7(RDij ) + d 8(YRij ) + d 9(SS j ) + d10(SFMIN j ) + d11(SFMAJ j ) +
d12(SFHVY j ) + UDij
(4)
where (other variables as deined above) SFj is the share of MNEs in the labour or
output of industry j (%), SSj that of SOEs, SFMIN j that of minority-foreign MNEs,
SFMAJ j that of majority-foreign MNEs, and SFHVY j that of heavily foreign MNEs.
The error terms are UCij and UDij.
Energy requirements differ greatly among manufacturing industries, so the
determinants of energy intensities (including SOE–private and MNE–private
differentials) are also likely to have differed similarly. We therefore compare the
industry-level estimates of equations (1) and (2) with estimates in samples of the
12 highest-energy-consuming industries combined. We account for more detailed,
industry-related differences in intercepts in all regressions by adding industry
dummies at the four-digit level, when possible.16 When estimating equations
(3) and (4), we limit the industry dummies to the 12 highest-energy-consuming
industries because we measure the shares of SOEs and MNEs at the three- or
four-digit level. We include regional dummies to account for the effects of plant
location on intercepts.17 Because the data cover two years, including a period of
severe economic crisis and large structural change, we estimate the models in the
cross-section using ordinary least squares with robust standard errors to control
for heteroskedasticity. The lack of panel data and usable instruments in the crosssectional data limits our ability to address simultaneity.
Even after we exclude plants that reported extreme values of production or
the average product of labour, 28%–33% of plants in high-energy-consuming
16. One industry, motor vehicles, was a four-digit category in ISIC Revision 2 (1996). In
other industries, it is sometimes necessary to combine four-digit categories with few observations or ambiguous deinitions.
17. We use Jakarta as the reference region and regional dummies to identify plants in ive
regions—Sumatra, West Java, Central Java (including Yogyakarta), East Java, and East Indonesia (including Nusa Tenggara, Kalimantan, Sulawesi, Maluku, and Irian Jaya). We
omitted plants in East Indonesia from some estimates of equations (1) and (2), to avoid
perfect correlations with ownership dummies (paper products in 1996, basic metals in
2006, and electronics-related machinery and motor vehicles in both years [Ramstetter and
Narjoko 2012, appendix tables 3, 6, 7]).
Ownership and Energy Eficiency in Indonesian Manufacturing
267
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industries did not report data on ixed assets in 1996 and 43%–48% did not do so
in 2006. More plants reported year-end capital than initial capital. Estimates using
initial capital are preferable because they reduce the possibility of simultaneity,
but samples using year-end capital are up to 15% larger.18 Yet even the smallest
samples—such as total energy and natural gas in basic metals (105 plants in 1996
and 130 in 2006)—are large enough to facilitate reasonable inference.
Differentials in Energy Intensities
The quantity of total energy is not measurable, so we use as a proxy the quantity of electricity (as in Eskeland and Harrison 2003). We measure quantities of
other energy types directly, in homogeneous units. The performance of the model
varies substantially, depending on the energy intensity, period, and industry
(Ramstetter and Narjoko 2012, appendix tables 3, 6, 7). In the samples of plants
combined, for example, the model is effective in explaining variations in total
energy and coal intensities in 1996 (R-squared of 0.59 or higher) but less effective
in explaining variations in gas intensities in 1996 and 2006 and coal intensities in
2006 (R-squared of 0.36 or lower). As expected, explanatory power across industries also varied substantially. However, even the lowest R-squared (0.18 for total
energy in other transportation machinery in 2006) was not unusually low for such
cross-sections.
In almost all estimates of equation (1) for total energy, the coeficients on labour
and the energy quantity were positive and at least weakly signiicant at the 5%
level or better, while the coeficients on intermediate consumption (excluding the
energy input in question) were negative and signiicant. In estimates of equation (2), however, the coeficients on intermediate consumption and the energy
quantity differed in sign depending on the industry. Moreover, the coeficient on
the energy quantity was positive and signiicant in most gas and coal estimates
(Ramstetter and Narjoko 2012, appendix tables 3, 6, 7). Higher shares of workers
with secondary education were positively and signiicantly correlated with total
energy intensities in slightly under half of the estimates. Other indicators of technological sophistication were not generally signiicant determinants of energy
intensities, and the correlations were weakest for gas and coal.
Our estimates of total energy intensities for all 12 industries combined (the
top panel of table 3) suggest that all SOE–private and MNE–private differentials
were statistically insigniicant in 1996. The positive differential between majorityforeign MNEs and private plants was the only weakly signiicant (at the 10% level
or better) differential in 2006. Tests of the hypothesis that MNE–private differentials differed among MNE ownership groups are rejected at either the 10% level
(initial capital) or the standard 5% level (year-end capital), suggesting that equation (2) is preferable to equation (1). Yet none of the differentials in equation (2) are
signiicant at the standard 5% level or better.
As the data in table 2 suggest, ownership-related differentials and other slope
coeficients sometimes differed greatly among industries. However, industry-level
18. Ramstetter and Narjoko (2012, 2013) report in appendix tables the results of estimates
in large samples that omit capital. Choosing or omitting the capital variable does not have
a predictable effect on estimates of MNE–private differentials, SOE–private differentials,
or spillovers.
TABLE 3 Estimates of Total-Energy-Intensity Differentials from Equations (1) and (2)
1996
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Industry, equation, differential
12 largest energy consumers (combined)
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
Food & beverages
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
Textiles
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
2006
Initial
capital
Year-end
capital
Initial
capital
Year-end
capital
0.3017
–0.2025
0.2310
–0.1559
0.8454
–0.0402
0.9661
–0.2190
0.2933
–0.7328
–0.3950
0.2410
2.00
0.2248
–0.7374
–0.2375
0.1384
1.42
0.8515
–1.1381
0.8494*
–0.2539
2.47*
0.9739
–1.4010
0.7814*
–0.4524
3.46**
0.2359
0.0513
0.2538
0.0050
0.9146
1.2665
1.5051*
1.3189*
0.9220
1.3248
2.3492***
0.6327
0.97
1.5114*
0.9381
2.5458***
0.6780
1.23
0.2393
–1.9068***
0.3133
0.5225
6.38***
0.2520
–2.1417***
0.2574
0.4949
7.22***
2.7380**
0.3220
2.2935*
0.3325
9.9839*
–0.4717
10.0477*
–0.8486
2.7336**
0.5273
-0.1142
0.6843
0.18
2.2921*
0.5265
0.1157
0.5051
0.05
9.9905*
0.8189
0.3407
-1.0928
1.68
10.0573*
0.5328
0.0848
–1.5074**
5.01***
Apparel
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
–0.7441*
0.5943*
–0.8285*
0.7893**
–2.1992***
–0.0768
–2.2944
–0.0239
–0.7462*
–0.0553
–0.4448
1.3593***
4.60**
–0.8334*
0.0339
0.2950
1.2726***
2.11
–2.1987***
–1.1901*
0.2149
–0.0287
1.31
–2.2911
–1.2595
0.2807
0.0029
1.35
Wood products
Equation (1)
SOE–private
MNE–private
0.7242
–0.5021
0.6830
–0.0605
4.4512
–0.2232
4.5320
–0.4600
TABLE 3 (continued)
1996
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Industry, equation, differential
Initial
capital
2006
Year-end
capital
Initial
capital
Year-end
capital
Paper products (East Indonesian and minority-foreign plants omitted for 1996)
Equation (1)
SOE–private
6.1690
6.4640*
–2.3363
MNE–private
0.4859
1.4496
1.4167
Equation (2)
SOE–private
6.2196
6.4340*
–2.1258
Minority-foreign MNE–private
–
–
–2.9980*
Majority–foreign MNE–private
–0.3370
1.6077
3.2408
Heavily foreign MNE–private
4.3113
1.0823
1.4229
Wald equality test
1.38
0.03
2.44*
–2.4872
–5.3068**
2.9849
0.6207
3.19**
Chemicals
Equation (1)
SOE–private
MNE–private
4.1615**
–0.0622
Rubber & plastic products
Equation (1)
SOE–private
MNE–private
Non–metallic mineral products,
Equation (1)
SOE–private
MNE–private
3.5609*
0.1954
0.7666
–0.2882
1.1918
–0.4983
–0.9482
–0.3007
–1.1290*
–0.6996
–0.4337
0.2159
–1.0144
0.1563
–0.8752
–3.0950*
–0.9236
–2.9576*
6.4073
–2.2913
6.8196
–1.6446
–
–1.5212
–
–2.2187*
0.1732
0.1105
0.4092
–0.2242
–0.4547
–2.7168**
–1.0595
–1.0354
–0.3054
–0.7599
0.5313
Basic metals (SOE & East Indonesian plants omitted for 2006)
Equation (1)
SOE–private
–4.1974*
–2.5143
MNE–private
–0.2194
–0.4278
Electronics–related machinery (SOE & East Indonesian plants omitted)
Equation (1)
MNE–private
0.9084**
0.8955**
Motor vehicles (SOE & East Indonesian plants omitted)
Equation (1)
MNE–private
0.8239
Other transportation machinery
Equation (1)
SOE–private
MNE–private
–2.7228
0.4231
–2.1724*
0.2072
Note: SOE = state-owned enterprise. MNE = multinational enterprise. – = no plants in category. Wald
tests address the hypothesis that MNE–private differentials are equal for all MNE groups and associated p-values. Equation (2) results are omitted for industries if all Wald tests of MNE group equality
for the industry are not signiicant at the 10% level. For other slope coeficients and equation statistics,
see Ramstetter and Narjoko (2012, appendix table 3).
* p < 0.1; ** p < 0.05; *** p < 0.01.
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270
Eric D. Ramstetter and Dionisius Narjoko
results were consistent with estimates for the 12 highest-energy-consuming industries, suggesting that the correlation between ownership and total energy intensities was generally weak (table 3). For example, all ownership coeficients were
insigniicant (at the 5% level or better) in paper products in 1996; in chemicals,
electronics-related machinery, and other transportation machinery in 2006; and
in wood, rubber and plastics, non-metallic mineral products, basic metals, and
motor vehicles in both years. We observed negative and signiicant (at the 5%
level) differentials for minority-foreign plants in food and beverages and for SOEs
in other transportation machinery in 1
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Ownership and Energy Efficiency in Indonesian
Manufacturing
Eric D. Ramstetter & Dionisius Narjoko
To cite this article: Eric D. Ramstetter & Dionisius Narjoko (2014) Ownership and Energy
Efficiency in Indonesian Manufacturing, Bulletin of Indonesian Economic Studies, 50:2,
255-276, DOI: 10.1080/00074918.2014.938407
To link to this article: http://dx.doi.org/10.1080/00074918.2014.938407
Published online: 30 Jul 2014.
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Date: 17 January 2016, At: 23:28
Bulletin of Indonesian Economic Studies, Vol. 50, No. 2, 2014: 255–76
OWNERSHIP AND ENERGY EFFICIENCY IN INDONESIAN
MANUFACTURING
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:28 17 January 2016
Eric D. Ramstetter*
International Centre for the Study of East Asian Development; Kyushu University
Dionisius Narjoko*
Economic Research Institute for ASEAN and East Asia
Using 1996 and 2006 census data on medium-large plants in Indonesian manufacturing, we examine whether foreign multinational enterprises (MNEs) and stateowned enterprises (SOEs) used purchased energy more eficiently than local, private plants, inding that the correlation between plant ownership and total energy
intensity, gas intensity, and coal intensity was generally weak in both years. Second,
we ask whether energy eficiency in private plants was affected by the presence of
MNEs or SOEs in high-energy-consuming industries. In 1996, private energy intensities were often positively correlated with the presence of SOEs and majorityforeign MNEs and negatively correlated with the presence of wholly foreign or
minority-foreign MNEs, but in 2006 the corresponding results differed substantially. This suggests that ownership-related differentials in energy intensity and
intra-industry energy-intensity spillovers are not pronounced. If policymakers are
concerned with improving energy eficiency in Indonesian manufacturing, plant
ownership should not be a major consideration.
Keywords: ownership, multinational corporations, energy eficiency, manufacturing
JEL classiication: F23, K32, L32, L33, L60, O53, Q40
INTRODUCTION
The consumption of purchased energy generates much of the pollution (mainly
air pollution) emitted by manufacturing plants in Indonesia. Improving energy
eficiency or conservation is thus an effective way of limiting pollution by
* We are grateful to the Japan Society for the Promotion of Sciences for inancial assistance
(grant 22530255 for the project Ownership and Firm- or Plant-Level Energy Eficiency in
Southeast Asia) and to the International Centre for the Study of East Asian Development
and the Economic Research Institute for ASEAN and East Asia for logistic support. We
thank Kornkarun Cheewatrakoolpong, Kenichi Imai, Kozo Kiyota, Lin See Yan, Kiichiro
Fukusaku, Sadayuki Takii, Siang Leng Wong, Chih-Hai Yang, and Naoyuki Yoshino for
discussing related papers on Indonesia, Malaysia, and Thailand. Two referees provided
valuable comments, as did several other participants in the 9th Australasian Development
Economics Workshop on 6–7 June 2013 and previous 2012 and 2013 conferences where related papers were presented. Other project participants (Shahrazat Binti Haji Ahmad and
Archanun Kohpaiboon) and Juthathip Jongwanich also provided important input. The authors are solely responsible for the content of this article.
ISSN 0007-4918 print/ISSN 1472-7234 online/14/000255-22
http://dx.doi.org/10.1080/00074918.2014.938407
© 2014 Indonesia Project ANU
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256
Eric D. Ramstetter and Dionisius Narjoko
manufacturers. In this article we use plant-level data underlying the Indonesian
manufacturing censuses (Statistik Industri) of 1996 and 2006 to analyse whether
plants controlled by foreign multinational enterprises (MNEs) or state-owned
enterprises (SOEs) used purchased energy (deined as electricity and fuel) more
eficiently than medium-large local, private plants. We then ask whether energy
intensities (ratios of purchased energy to output), in local, private plants were correlated with the shares of MNEs and SOEs in Indonesia’s high-energy-consuming
industries.
A 2003 study by Eskeland and Harrison is one of the few to use microdata
to investigate the consumption of purchased energy in developing economies. It
inds that ‘foreign plants are signiicantly more energy eficient and use cleaner
types of energy’ than their local peers in Côte d’Ivoire, Mexico, and Venezuela
(Eskeland and Harrison 2003, 21). He (2006, 228) provides evidence that foreign
direct investment (FDI) enterprises in China ‘produce with higher pollution [sulfur dioxide] eficiency’, but that stronger environmental regulation has simultaneously, though moderately, deterred FDI among Chinese provinces. Earnhart and
Lizal (2006) analyse the effects of inancial performance and privatisation on the
environmental performance of Czech irms, their results indicating that foreign
ownership was usually an insigniicant determinant of pollution.
As detailed in the following literature review, MNEs, in particular, are also
thought to be a source of positive spillovers on private irms. Theory suggests
that a greater MNE presence in an industry can increase competition and labour
mobility among MNEs and local, private plants, as well as strengthen linkages
that foster higher productivity in local plants. Previous studies generally indicate positive productivity spillovers from MNEs in Indonesia. If the presence of
MNEs has affected productivity in private plants, it may have also affected energy
eficiency (the inverse of energy productivity). This article describes the data and
patterns of energy intensities, as well as SOE and MNE presence, and analyses the
statistical signiicance of MNE–private and SOE–private energy differentials and
energy spillovers, after accounting for the scale, input mix, and technical characteristics of plants.
MULTINATIONAL AND STATE ENTERPRISES AND ENERGY EFFICIENCY
This section surveys the relevant literature, explaining why MNEs are usually
expected to be more eficient than SOEs and discussing the implications of productivity spillovers for possible spillovers of energy eficiency.
Productivity Differentials and Energy Eficiency
Recent theoretical analyses have drawn attention to the role of knowledge-based,
intangible assets (terminology from Markusen 1991) in MNEs. Most observers
agree that MNEs tend to have relatively large amounts of technological knowledge and marketing expertise, strong international networks, and generally
sophisticated and effective management.1 These characteristics are evidenced
by relatively high research and development (R&D) intensities (ratios to sales or
1. Caves (2007) and Dunning and Lundan (2008) provide thorough literature reviews. See
also Markusen’s (2002) study.
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Ownership and Energy Eficiency in Indonesian Manufacturing
257
output), large proportions of patent applications and approvals, high advertising–
sales ratios, and a high dependence on international trade.
If MNEs have relatively large knowledge-based, intangible asset stocks, they
should be more eficient producers than non-MNEs in some respect. They may
become more energy eficient, for example, or pollute less, as part of efforts to
increase demand among consumers and minimise production costs. Moreover,
because MNEs tend to be R&D- and patent-intensive, and because energy conservation technologies often require sophisticated technological inputs, it is logical
that MNEs should be both better able to and more motivated to conserve energy
than other irms.2
The theoretical rationale for expecting MNEs to be more productive than other
irms is convincing. However, in Southeast Asia’s developing economies the
empirical evidence on productivity differentials between foreign MNEs and local
irms (most of which are not MNEs) is often ambiguous. For example, studies of
manufacturing in Malaysia (Oguchi et al. 2002; Haji Ahmad 2010) and Thailand
(Ramstetter 2004, 2006) suggest that productivity differentials tend to be small
and often statistically insigniicant. Similar evidence for Indonesia (Takii 2004,
2006) and Vietnam (Ramstetter and Phan 2008, 2013) suggests that productivity differentials between MNEs and local plants are more commonly statistically
signiicant, although these differentials are often statistically insigniicant when
using a translog function (allowing for lexible assumptions about scale and factor
substitution) and disaggregating plants by industry (allowing for differences in
production function slopes among industries, as well as the constant).
Many economists expect SOEs, in contrast with MNEs, to be less productive
than private plants, largely because SOEs are thought to have fewer incentives
for pursuing proits and eficiency. The empirical evidence is mixed, with several
studies inding that SOEs are less productive than private irms and several inding the opposite.3 In Indonesia, SOEs have played large roles in several industries,
such as basic metals (table 1), and are generally perceived to be ineficient. This is
one reason for the privatisation of many SOEs during 1996–2006. Hartono, Irawan,
and Achsani (2011) ind that local, private plants tend to have signiicantly higher
energy intensities than SOEs in Indonesian manufacturing and that MNE–SOE
differentials were not signiicant in samples of all manufacturing plants 2002–6.
Productivity Spillovers and the Potential for Energy-Eficiency Spillovers
The presence of MNEs in an industry can increase the productivity of local plants
by creating spillovers through at least three major channels: (a) by fostering linkages between MNEs and local plants, (b) by stimulating labour mobility between
MNEs and local plants, and (c) by creating a demonstration or competition effect.
First, direct linkages are usually backward linkages created when MNEs source
raw materials, parts, or services from local plants. MNEs can also create forward
2. For example, evidence from Cole, Elliott, and Shimamoto (2006) suggests that Japanese
irms with FDI and international trade pollute less and manage emissions better than irms
without FDI or trade.
3. See the studies of Aharoni (2000), Djankov and Murrell (2002), and Stretton and Orchard
(1994) for surveys; Jefferson and Su (2006) for Chinese evidence; and Brown, Earle, and
Telegdy (2004, 2006) for Eastern European evidence.
TABLE 1 Total Energy Expenditures; Private Energy Intensities; and Shares of StateOwned and Multinational Enterprises in Labour, Energy Expenditures, and Output
SOE shares
MNE shares
(% of industry subtotals) (% of industry subtotals)
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Industry, by year
1996
Manufacturing
12 highest energy consumers
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral
products
Basic metals
Electronics-related
machinery
Motor vehicles
Other transportation
machinery
2006
Manufacturing
12 highest energy consumers
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral
products
Basic metals
Electronics-related
machinery
Motor vehicles
Other transportation
machinery
1
(Rp bn)
2
(%)
7,309
6,786
804
1,521
75
474
631
631
399
5.37
5.96
5.83
4.94
2.24
4.65
5.38
5.07
5.68
6.92
8.42
22.31
3.13
0.95
1.16
6.40
12.37
9.97
11.66
12.24
12.37
3.62
0.17
0.64
8.51
25.48
3.17
8.88
9.99
10.09
2.47
0.80
0.78
4.65
13.97
3.23
18.11
16.50
9.82
13.22
23.08
9.20
18.64
18.97
11.86
21.58
21.12
19.65
17.45
24.94
8.58
30.79
27.02
16.13
27.15
27.22
17.72
20.01
28.05
11.54
31.40
38.03
20.83
1,274
542
15.61
5.61
6.79
14.44
15.54
29.07
13.11
46.04
11.86
20.66
20.31
20.54
25.35
22.91
256
80
3.19
3.97
1.85
0.47
23.28
0.78
3.44
0.09
51.29
29.00
39.94
41.31
55.90
57.66
99
4.39
38.58
30.02
15.69
19.45
15.84
14.40
56,841
52,635
7,652
7,810
1,938
1,523
5,197
7,316
3,125
5.83
6.54
6.49
6.82
3.70
6.05
5.90
6.25
6.57
5.52
6.31
11.04
2.10
3.07
0.73
17.09
9.00
8.19
8.77
9.19
7.62
1.15
3.35
0.37
10.24
3.34
7.06
6.48
7.08
7.19
1.37
2.91
0.31
13.42
14.91
4.29
24.97
24.83
17.33
17.25
30.43
12.94
18.31
21.51
22.32
28.54
28.56
21.80
23.65
25.36
16.58
29.93
25.69
22.84
35.79
36.73
28.67
32.05
35.90
17.79
30.32
33.44
27.87
7,379
4,316
14.52
5.36
7.95
4.70
34.88
4.72
26.64
3.27
21.96
20.48
30.34
11.29
29.37
15.43
1,610
2,827
3.76
5.11
1.40
–
0.09
–
0.33
–
65.58
54.74
66.34
87.23
71.95
81.31
1,943
6.17
24.08
16.27
12.84
32.97
18.81
61.84
Labour Energy Output Labour Energy Output
Source: Authors’ calculations based on data from BPS.
Note: SOE = state-owned enterprise. MNE = multinational enterprise. 1 = total energy expenditures.
2 = private energy intensities. – = no plants in category. Industry deinitions differ between 1996 and
2006. See the text for detailed deinitions of industries and ownership groups.
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Ownership and Energy Eficiency in Indonesian Manufacturing
259
linkages by supplying local plants with intermediate goods (materials, parts, or
services) or inal goods of superior quality to those produced locally. Second,
MNEs often require relatively skilled workers, such as middle-level technicians
and managers, and seek to recruit them from local irms. Local plants also try to
woo workers from MNEs, and some MNE workers resign after realising that they
have obtained the skills to start their own irm. Third, the entry or expansion of
foreign MNEs usually increases competitive pressure on local plants that produce
goods or services similar to those of the MNEs. The increased competition can
motivate local irms to develop or upgrade technology, cut input costs, or expand
marketing efforts.
Much of the existing research on spillovers focuses on intra-industry productivity spillovers, although more recent studies examine inter-industry spillovers.
Several reviews emphasise that there is only mixed evidence of productivity
spillovers (Görg and Strobl 2001; Fan 2002; Görg and Greenaway 2004; Lipsey
and Sjöholm 2005; Pessoa 2007).4 Moreover, studies of Asian economies (reviewed
below) suggest that estimates of spillovers vary substantially depending on the
economies and industry groups studied, the measure of foreign presence used (for
example, whether measured as shares of employment, output, or ixed assets),
and the estimation methodology. Cross-sectional methodologies tend to result in
larger estimates of spillovers, but recent studies generally use ixed-effects estimators when panel data are available.5
Some of the earliest research on spillovers from MNEs in Asian host economies examined Indonesia. Cross-sectional evidence for 1980 and 1991 from Blomström and Sjöholm (1999) and Sjöholm (1999a, 1999b) indicates that productivity
spillovers tended to be positive and stronger in industries in which competition
among local plants was relatively intense and within regions with diversiied
industrial structures. Subsequent panel analysis for 1990–95 (Takii 2005, 2006)
suggests that positive intra-industry spillovers were more prevalent in industries
with small technical gaps and in which minority foreign MNEs had relatively
large shares. Blalock and Gertler (2008) ind strong evidence of productivity
gains, greater competition, and lower prices among local irms in markets that
supplied foreign entrants in 1988–2006. Suyanto, Salim, and Bloch (2009) analyse
spillovers in chemical and pharmaceutical plants in 1998–2000, inding positive
productivity spillovers from MNEs that are larger in more competitive industries
and in local plants with R&D. Results from Lipsey and Sjöholm (2004, 2006) and
Sjöholm and Lipsey (2006) also suggest the existence of positive wage spillovers.
For Malaysia, Khalifah and Adam (2009) ind that productivity spillovers were
positive when the presence of MNEs was measured as the share of value added or
ixed assets in 2000–4, but insigniicant or negative when measured as the share of
labour. Haji Ahmad (2010, chapter 6) uses the same dataset and inds that evidence
of signiicant spillovers was rare and that results varied among industry groups.
4. Mebratie and Van Bergeijk’s (2013) meta-analysis argues that accounting for irm heterogeneity in R&D and exporting changes many ambiguous results and provides relatively
strong evidence of positive spillovers.
5. In general, ixed-effects panel estimates are preferable because they control for unobserved characteristics among local plants or irms and because they are less vulnerable to
simultaneity problems that may arise if MNEs are attracted to high-productivity industries.
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260
Eric D. Ramstetter and Dionisius Narjoko
For Thailand in 1996, industry-level results from Kohpaiboon (2006a, 2006b) and
plant-level results from Ramstetter (2006) and Movshuk and Matusoka-Movshuk
(2006) suggest positive productivity and wage spillovers from MNEs. Using a
more limited sample of manufacturing irms in 2001–3, Kohpaiboon (2009) inds
positive horizontal spillovers in industries in which import protection was relatively low.
For Vietnam, Pham’s (2008) estimates generally suggest that positive spillovers were largest in Hanoi and Ho Chi Minh City, and from MNEs that were
not wholly foreign. For 2000–5, Nguyen (2008) inds that both horizontal and
vertical spillovers were generally positive, and largest in more advanced regions
and in more sophisticated local irms. Nguyen et al. (2008) inds that backward,
vertical spillovers were positive in manufacturing and that horizontal spillovers
were positive in services. Le and Pomfret (2011) ind positive backward spillovers
in manufacturing but negative and strong horizontal spillovers to private irms,
domestic-oriented irms, irms without R&D, and irms in low-technology industries. Ramstetter and Phan (2008) ind positive spillovers from MNEs to private
irms in cross-sections, but Ramstetter and Phan (2013) ind no signiicant spillovers in unbalanced panels for 2001–6.
DATA SOURCES, ENERGY CONSUMPTION, AND ENERGY INTENSITIES
This article uses data from industrial censuses for 1996 and 2006 because they are
comprehensive and contain details on energy expenditures and labour quality
that other annual surveys do not. Because a number of plants are jointly owned
by two or more irms (which may be MNEs, SOEs, and/or private, joint ventures
with foreign shares of 33% or more) are classiied as MNEs and non-MNE joint
ventures with state shares of 33% or more are classiied as SOEs. The cut-off for
deining MNEs is higher than the IMF standard (having foreign shares of 10% or
more), but we know of no similar standard for deining SOEs and we need to be
clear.
The irst column of table 1 shows total energy (fuel and electricity) expenditures
in manufacturing and by the 12 highest-energy-consuming industries, generally
deined by the two-digit level of Indonesia’s Standard Industrial Classiication
(ISIC) Revision 3. The industry deinitions for 1996 are based on ISIC Revision
2 and differ from the 2006 deinitions, so caution is necessary when interpreting
industry-level trends.6 The data refer to purchased energy (most of which is fuel,
and which accounts for the bulk of the energy used by most plants) and do not
account for energy generated or sold.7
6. It is impossible to construct a precise correspondence between the two revisions, because
several detailed categories (that is, at the four- or ive-digit level) in one classiication are
split among detailed categories in the other. Ramstetter and Narjoko (2012, appendix table
8) give detailed deinitions.
7. Self-generated electricity is important for some plants. Purchased electricity accounted
for 42% of total purchased energy in 1996 and 47% in 2006; if we assume that plant-level
prices of purchased and self-generated electricity were identical, the ratios of self-generated
electricity to total purchased energy were 24% in 1996 and 9% in 2006. These ratios varied
only slightly among ownership groups in 1996 (23% for private plants, 25% for MNEs, and
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Ownership and Energy Eficiency in Indonesian Manufacturing
261
Calculations of electricity prices (unit values) by source are also of interest,
because they give hints about the quality of the data and because SOEs are said
to receive cheap electricity from the state provider (Perusahaan Listrik Negara
[PLN]) and because they give hints into the quality of the data. In 2006, SOEs
and MNEs paid much lower electricity prices to PLN in 2006 than private plants,
but in 1996 they paid slightly higher prices to PLN.8 Non-PLN electricity was a
relatively small source for all ownership groups in 1996 and for private plants and
SOEs in 2006, but it composed two-ifths of the total for MNEs in 2006. Prices of
non-PLN electricity, however, were several times higher for MNEs than for private plants or SOEs in 1996. Thus both ownership-related variations in electricity
prices and sources of purchased electricity were much smaller in 1996 than in
2006. To some extent this is probably realistic, relecting PLN’s shrinking inluence and the privatisation of SOEs. Yet the substantial price differentials in 2006
may also relect a deterioration of data quality, which is also observed in other
variables such as ixed asset (capital) stocks.
Nominal energy expenditures increased greatly between 1996 and 2006,
largely because of inlation (table 1).9 In this article we focus on the 12 highestenergy-consuming industries, which together accounted for 93% of total energy
expenditures and caused the most energy-related pollution in Indonesian manufacturing.10 Among these industries, textiles, chemicals, paper products, food and
beverages, and non-metallic mineral products accounted for 66% of total purchased energy in 1996 and 62% in 2006.
The third to eighth columns of table 1 show the shares of labour, energy, and
output held by SOEs and MNEs in 1996 and in 2006 in each of these 12 industries,
in these industries as a group, and in manufacturing overall. Nearly all shares
were larger in the group of 12 industries than in manufacturing. On one hand,
most SOE shares fell between 1996 and 2006, relecting the privatisation of several
SOEs after the 1997–98 Asian inancial crisis. On the other hand, most MNE shares
27% for SOEs), but were much lower for SOEs in 2006 (2%) than for MNEs (10%) or private
plants (15%). The ratios of electricity sold to total purchased energy were much smaller—
under 0.5% for all ownership groups in 1996 and 2006 (authors’ calculations based on BPS
data from various years).
8. In 1996, SOEs paid 6% more than private plants for PLN electricity and MNEs 4% more,
but these price differentials were –82% and –31% in 2006. For non-PLN energy, corresponding energy prices differentials were –8% for SOEs and 21% for MNEs in 1996, and –32%
and 393% in 2006. Non-PLN shares of purchased electricity were 1% for private plants,
14% for SOEs, and 5% for MNEs in 1996, but 9%, 0.2%, and 40%, respectively, in 2006. PLN
shares of purchased electricity quantities were 99% for private plants, 86% for SOEs, and
95% in MNEs in 1996, and 91%, 100%, and 60%, respectively, in 2006 (authors’ calculations
based on BPS data from various years).
9. According to national accounts estimates, manufacturing GDP increased 6.7-fold in
1996–2006 if measured in current prices, but the manufacturing GDP delator increased
4.9-fold while real manufacturing GDP increased only 1.4-fold. The growth of the manufacturing GDP delator peaked in 1998 (60%), but this measure of manufacturing inlation
was also high in 2000–2001 (20%–27%), and in 1997, 1999, and 2005–6 (13%–17%; Asian
Development Bank 2004, 2011).
10. These analyses focus on industries that accounted for 3% or more of expenditures in
1996 or 2006 (or both) for each type of fuel examined (total energy, natural gas, and coal).
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262
Eric D. Ramstetter and Dionisius Narjoko
increased, partially because the inancial crisis increased debts of many Indonesian irms and led to large declines in asset prices and the rupiah. The ensuing ire
sale made mergers and acquisitions, as well as new investments, relatively cheap
for MNEs. In addition, Indonesian policymakers stepped up implementation of
reforms mandated by FDI policy changes in the mid-1990s, reducing restrictions
on foreign ownership and simplifying approval procedures for FDI. Shares of
heavily foreign MNEs (foreign shares of 90% or more) in labour, output, and purchased energy all increased conspicuously.11
SOEs accounted for more than one-ifth of the energy expenditures in chemicals, basic metals, electronics-related machinery, and other transportation machinery in 1996, and in non-metallic mineral products in 2006 (table 1). MNE shares
exceeded one-ifth of the energy expenditures in most of the highest-energyconsuming industries, including basic metals in 1996; food and beverages, textiles, and rubber and plastics in 2006; and apparel, paper products, chemicals,
non-metallic mineral products, electronics-related machinery, and motor vehicles
in both years. SOEs and MNEs generally accounted for larger shares of energy
and output than labour; that is, they tended to have higher energy–labour and
output–labour ratios than private plants. Energy shares were usually smaller than
output shares in MNEs, indicating that they tended to have lower energy intensities than private plants, while the reverse was true for SOEs.
Mean energy intensities, measured as the ratio of total energy expenditures to
output, were generally lower in MNEs and higher in SOEs than in private plants
(table 2). If we combine plants from the 12 highest-energy-consuming industries,
the mean energy intensity of private plants was 6.0% in 1996 and 6.5% in 2006
(table 1). The mean energy intensity of MNEs was 2.4 percentage points lower in
1996 and 1.3 percentage points lower in 2006, while that of SOEs was 1.1 and 0.2
percentage points higher. Heavily foreign MNEs had the lowest energy intensities
among MNEs (2.6 and 1.4 percentage points lower than private plants). The differentials were smallest in absolute value for minority-foreign MNEs (33%–49%
foreign shares) and of intermediate size for majority-foreign MNEs (50%–89% foreign shares).
Energy intensities and MNE–private differentials varied greatly among industries and ownership groups (tables 1 and 2). For example, both private energy
intensities and negative MNE–private differentials were relatively large in nonmetallic mineral products. In contrast, SOEs and minority- and majority-foreign
MNEs all had higher energy intensities than private plants in textiles in both
years. Most MNE–private differentials were negative in both years (10–11 industries in 1996, 8–9 industries in 2006). In other words, MNEs tended to have lower
energy intensities than private plants at the industry level, but the size of differentials varied among industries. SOE–private differentials were negative in six of
the twelve (1996) and six of the eleven (2006) relevant, high-energy-consuming
industries.12
11. Heavily foreign shares of both labour and output were only 6%–7% in 1996 (for both
total manufacturing and the 12 highest-energy-consuming industries, but they more than
doubled, to 15%–16% of labour and 20%–21% of output, by 2006 (Ramstetter and Narjoko
2012, table 2).
12. There were no SOEs in the 2006 sample of motor vehicle plants.
TABLE 2 Difference in Intensities of Mean Total Energy, Natural Gas, and Coal
between Private Plants and State-Owned Enterprises or Multinational Enterprises
(percentage points)
MNEs
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Industry, by fuel and year
Minority Majority Heavily
foreign foreign foreign
SOEs
All
Total energy
12 highest energy consumers, 1996
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral products
Basic metals
Electronics-related machinery
Motor vehicles
Other transportation machinery
12 highest energy consumers, 2006
Food & beverages
Textiles
Apparel
Wood products
Paper products
Chemicals
Rubber & plastic products
Non-metallic mineral products
Basic metals
Electronics-related machinery
Motor vehicles
Other transportation machinery
1.110
1.785
4.417
–1.218
–0.700
7.782
3.234
–2.600
–3.096
–0.083
1.334
1.540
–2.140
0.224
0.717
4.315
4.630
–1.275
2.543
–1.922
–2.746
–2.775
1.466
–2.747
–
–1.659
–2.429
–2.463
0.310
–0.309
–1.460
1.417
–2.544
–2.359
–5.821
–1.187
–0.248
–1.537
–1.637
–1.253
–1.259
0.405
1.834
–1.985
–0.272
–2.170
–1.097
–4.293
0.054
0.175
–0.426
–0.295
–2.195
–2.207
0.097
–0.930
–2.284
–2.337
–1.942
–2.837
–2.059
–3.367
–1.175
–1.603
–1.795
–0.208
5.238
2.211
–2.087
–3.392
–3.088
–2.017
–2.246
–5.326
–1.087
–2.620
4.704
–5.228
–2.375
–2.362
0.805
–0.512
–1.276
1.682
–2.652
–2.648
–7.570
–0.690
–0.212
–1.278
–1.494
–1.040
–0.589
1.910
5.509
–2.713
2.941
–2.769
–1.834
–5.884
–0.549
–0.935
–1.398
–1.811
–2.584
–2.748
–0.210
–0.097
–1.340
2.764
–2.658
–1.953
–5.698
–1.089
–0.138
–2.487
–1.721
–1.434
–2.460
–0.477
1.595
–1.631
–0.556
–1.850
–0.705
–2.526
0.590
0.442
–0.930
0.463
Natural gas
8 highest gas consumers, 1996
8 highest gas consumers, 2006
–0.027
0.006
0.103
0.152
0.112
0.058
0.218
0.225
–0.087
0.128
Coal
4 highest coal consumers, 1996
4 highest coal consumers, 2006
0.216
0.252
0.039
0.112
0.277
0.702
0.005
0.134
–0.018
0.020
Source: Authors’ calculations based on data from BPS.
Note: MNE = multinational enterprise. SOE = state-owned enterprise. – = no plants in category. See
the text for detailed deinitions of ownership groups and note that industry deinitions differ between
1996 and 2006. The eight highest gas-consuming industries were food and beverages, textiles, paper
products, chemicals, rubber and plastic products, non-metallic mineral products, basic metals , and
fabricated metals. The four highest coal-consuming industries were textiles, paper products, chemicals, and non-metallic mineral products.
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264
Eric D. Ramstetter and Dionisius Narjoko
The aggregate energy intensities at the top of table 2 do not relect the important possibility that MNEs and private plants may consume different energy
mixes and thus have different effects on the environment even if their aggregate energy intensities are similar. For example, Eskeland and Harrison (2003)
assume that electricity consumption is cleaner than fuel consumption and ind
that MNEs tend to consume more electricity than non-MNEs. However, electricity
is not unambiguously clean in Indonesia; coal (a dirty fuel) generated large and
increasing shares of electricity in 2000 and 2005. In contrast, natural-gas usage is
relatively clean. Purchases of both fuels were small and concentrated in only a few
industries.13 Natural-gas expenditures accounted for more than 3% of purchased
fuels in only eight industries in either 1996 or 2006, and coal shares exceeded 3%
of the total in only four industries.14
Mean natural-gas intensities (ratios of expenditures to output) of private plants
in high-energy-consuming industries were only 0.10% in 1996 and 0.08% in
2006, while mean coal intensities were 0.04% and 0.23%, respectively (Ramstetter and Narjoko 2012, table 2). When all high-energy-consuming industries were
combined, mean MNE–private differentials were positive for both types of fuel,
except for a negative differential in gas intensities for heavily foreign MNEs in
1996 (table 2). MNEs used large amounts of both clean and dirty fuels per unit of
output, on average. In SOEs, coal intensities were large in both years, while gas
intensities were small in 1996 but increased to a level similar to that of private
plants in 2006.
In short, the energy intensities in table 2 are consistent with Eskeland and Harrison (2003), suggesting that MNEs purchase less total energy per unit of output
than private plants. MNEs also purchase more relatively clean gas per unit of
output than private plants—as well as more coal, which is a relatively dirty fuel.
Such simple comparisons, however, mask plant-level differences in scale, factor
usage, and technology that may affect the relation between ownership and energy
intensities.
ENERGY INTENSITIES AFTER ACCOUNTING FOR SCALE AND
FACTOR USAGE
We examine the relation between ownership and energy intensities after accounting for scale and other factor usage by estimating a factor demand model ‘based
on a translog approximation to a production function’ taken from Eskeland
and Harrison (2003, 16–18). The model speciies the share of the energy factor’s
income (expenditure) in gross output as a function of the logs of other factor
13. According to the Asia Paciic Energy Research Centre (2009), natural gas and hydropower together generated 36% of Indonesia’s electricity in 2000 and 23% in 2005, while
accounting for 22% and 18% of the primary energy supply, respectively. Coal generated
37% of electricity in 2000 and 41% in 2005, while its share of the primary energy supply rose
from 7% to 13%, respectively. Oil generated 40% and 39% of electricity and accounted for
30% and 19% of the primary energy supply. See Ramstetter and Narjoko’s (2012) study for
detailed analyses of MNE–private and SOE–private differentials in electricity intensities.
14. Natural gas accounted for 7% of total energy in 1996 and 3% in 2006; coal accounted for
5% and 6%, respectively (Ramstetter and Narjoko 2012, appendix tables 1a, 1d, 1e).
Ownership and Energy Eficiency in Indonesian Manufacturing
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inputs (other intermediate consumption, ixed assets, and labour); the log of the
quantity of the energy input; and factors related to a plant’s technological sophistication. Indonesian manufacturing census data include four indicators of technological sophistication that may affect energy intensities: plant vintage, the ratio of
R&D expenditures to gross output, shares of moderately educated workers in the
workforce, and shares of highly educated workers in the workforce.15
To determine whether energy intensities in MNEs or SOEs differed signiicantly from those in private plants, we introduce dummy variables for MNEs and
SOEs. The simplest version of the model assumes that MNE–private differentials
were the same for all MNE ownership groups:
EPij = a0 + a1(LLij ) + a2(LK ij ) + a3(LM ij ) + a4(LEij ) + a5(SM ij ) + a6(SH ij ) +
a7(RDij ) + a8(YRij ) + a9(DSij ) + a10(DFij ) + UAij
(1)
where the dummy DFij equals one if plant i in industry j was an MNE and zero
otherwise; the dummy DSij equals one if i was an SOE and zero otherwise; EPij is
the energy intensity or ratio of energy expenditures to gross output of i (%); LEij is
the natural log of the quantity of energy used in i + 1 (kilowatt hours of electricity for total energy, kilograms for coal, cubic meters for natural gas); LLij is the
natural log of the number of workers in i (number); LKij is the natural log of the
ixed assets, less depreciation at year-end, of i (Rp thousand); LMij is the natural
log intermediate consumption, excluding energy, of i (Rp thousand); RDij is the
ratio of R&D expenditures to gross output of i (%); SMij is the percentage of workers with secondary education in i; SHij is the percentage of workers with tertiary
education in i; YRij is the vintage, or the number of years of operation of i; and UAij
is the error term.
The coeficient a10 is the percentage point differential (comparable to the differentials in table 2) of energy intensities between MNEs and local, private plants,
after accounting for scale and factor usage and the four indicators of technological sophistication. We can test if MNE–private differentials varied among MNE
ownership groups by estimating the following:
EPij = b0 + b1(LLij ) + b2(LK ij ) + b3(LM ij ) + b4(LEij ) + b5(SM ij ) + b6(SH ij ) +
b7(RDij ) + b8(YRij ) + b9(DSij ) + b10(DFMIN ij ) + b11(DFMAJ ij ) +
b12(DFHVYij ) + UBij
(2)
where (other variables as deined above) the dummy DFMINij equals one if i was
a minority-foreign MNE and zero otherwise; the dummy DFMAJij equals one if
i was a majority-foreign MNE and zero otherwise; the dummy DFHVYij equals
one if i was a heavily foreign MNE and zero otherwise; and UBij is the error term.
The coeficients b10, b11, and b12 are the percentage point differentials between
minority-foreign, majority-foreign, and heavily foreign MNEs, on the one hand,
15. Eskeland and Harrison’s (2003) model includes the R&D ratio and plant vintage. It also
includes machinery imports, but that variable is not available in the Indonesian census
data.
266
Eric D. Ramstetter and Dionisius Narjoko
and private plants, on the other, after accounting for scale and other factor usage
and the four indicators of technological sophistication.
To determine whether the presence of MNEs or SOEs affected energy eficiency
in local plants, we restrict the sample to private plants and replace the ownership
dummies in equations (1) and (2) with the corresponding MNE or SOE shares of
labour or output:
EPij = c0 + c1(LLij ) + c 2(LK ij ) + c3(LM ij ) + c 4(LEij ) + c5(SM ij ) + c6(SH ij ) +
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c7(RDij ) + c8(YRij ) + c9 ( SSj ) + c10 ( SFj ) + UCij
(3)
EPij = d 0 + d1(LLij ) + d 2(LK ij ) + d 3(LM ij ) + d 4(LEij ) + d 5(SM ij ) + d 6(SH ij ) +
d 7(RDij ) + d 8(YRij ) + d 9(SS j ) + d10(SFMIN j ) + d11(SFMAJ j ) +
d12(SFHVY j ) + UDij
(4)
where (other variables as deined above) SFj is the share of MNEs in the labour or
output of industry j (%), SSj that of SOEs, SFMIN j that of minority-foreign MNEs,
SFMAJ j that of majority-foreign MNEs, and SFHVY j that of heavily foreign MNEs.
The error terms are UCij and UDij.
Energy requirements differ greatly among manufacturing industries, so the
determinants of energy intensities (including SOE–private and MNE–private
differentials) are also likely to have differed similarly. We therefore compare the
industry-level estimates of equations (1) and (2) with estimates in samples of the
12 highest-energy-consuming industries combined. We account for more detailed,
industry-related differences in intercepts in all regressions by adding industry
dummies at the four-digit level, when possible.16 When estimating equations
(3) and (4), we limit the industry dummies to the 12 highest-energy-consuming
industries because we measure the shares of SOEs and MNEs at the three- or
four-digit level. We include regional dummies to account for the effects of plant
location on intercepts.17 Because the data cover two years, including a period of
severe economic crisis and large structural change, we estimate the models in the
cross-section using ordinary least squares with robust standard errors to control
for heteroskedasticity. The lack of panel data and usable instruments in the crosssectional data limits our ability to address simultaneity.
Even after we exclude plants that reported extreme values of production or
the average product of labour, 28%–33% of plants in high-energy-consuming
16. One industry, motor vehicles, was a four-digit category in ISIC Revision 2 (1996). In
other industries, it is sometimes necessary to combine four-digit categories with few observations or ambiguous deinitions.
17. We use Jakarta as the reference region and regional dummies to identify plants in ive
regions—Sumatra, West Java, Central Java (including Yogyakarta), East Java, and East Indonesia (including Nusa Tenggara, Kalimantan, Sulawesi, Maluku, and Irian Jaya). We
omitted plants in East Indonesia from some estimates of equations (1) and (2), to avoid
perfect correlations with ownership dummies (paper products in 1996, basic metals in
2006, and electronics-related machinery and motor vehicles in both years [Ramstetter and
Narjoko 2012, appendix tables 3, 6, 7]).
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industries did not report data on ixed assets in 1996 and 43%–48% did not do so
in 2006. More plants reported year-end capital than initial capital. Estimates using
initial capital are preferable because they reduce the possibility of simultaneity,
but samples using year-end capital are up to 15% larger.18 Yet even the smallest
samples—such as total energy and natural gas in basic metals (105 plants in 1996
and 130 in 2006)—are large enough to facilitate reasonable inference.
Differentials in Energy Intensities
The quantity of total energy is not measurable, so we use as a proxy the quantity of electricity (as in Eskeland and Harrison 2003). We measure quantities of
other energy types directly, in homogeneous units. The performance of the model
varies substantially, depending on the energy intensity, period, and industry
(Ramstetter and Narjoko 2012, appendix tables 3, 6, 7). In the samples of plants
combined, for example, the model is effective in explaining variations in total
energy and coal intensities in 1996 (R-squared of 0.59 or higher) but less effective
in explaining variations in gas intensities in 1996 and 2006 and coal intensities in
2006 (R-squared of 0.36 or lower). As expected, explanatory power across industries also varied substantially. However, even the lowest R-squared (0.18 for total
energy in other transportation machinery in 2006) was not unusually low for such
cross-sections.
In almost all estimates of equation (1) for total energy, the coeficients on labour
and the energy quantity were positive and at least weakly signiicant at the 5%
level or better, while the coeficients on intermediate consumption (excluding the
energy input in question) were negative and signiicant. In estimates of equation (2), however, the coeficients on intermediate consumption and the energy
quantity differed in sign depending on the industry. Moreover, the coeficient on
the energy quantity was positive and signiicant in most gas and coal estimates
(Ramstetter and Narjoko 2012, appendix tables 3, 6, 7). Higher shares of workers
with secondary education were positively and signiicantly correlated with total
energy intensities in slightly under half of the estimates. Other indicators of technological sophistication were not generally signiicant determinants of energy
intensities, and the correlations were weakest for gas and coal.
Our estimates of total energy intensities for all 12 industries combined (the
top panel of table 3) suggest that all SOE–private and MNE–private differentials
were statistically insigniicant in 1996. The positive differential between majorityforeign MNEs and private plants was the only weakly signiicant (at the 10% level
or better) differential in 2006. Tests of the hypothesis that MNE–private differentials differed among MNE ownership groups are rejected at either the 10% level
(initial capital) or the standard 5% level (year-end capital), suggesting that equation (2) is preferable to equation (1). Yet none of the differentials in equation (2) are
signiicant at the standard 5% level or better.
As the data in table 2 suggest, ownership-related differentials and other slope
coeficients sometimes differed greatly among industries. However, industry-level
18. Ramstetter and Narjoko (2012, 2013) report in appendix tables the results of estimates
in large samples that omit capital. Choosing or omitting the capital variable does not have
a predictable effect on estimates of MNE–private differentials, SOE–private differentials,
or spillovers.
TABLE 3 Estimates of Total-Energy-Intensity Differentials from Equations (1) and (2)
1996
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Industry, equation, differential
12 largest energy consumers (combined)
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
Food & beverages
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
Textiles
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
2006
Initial
capital
Year-end
capital
Initial
capital
Year-end
capital
0.3017
–0.2025
0.2310
–0.1559
0.8454
–0.0402
0.9661
–0.2190
0.2933
–0.7328
–0.3950
0.2410
2.00
0.2248
–0.7374
–0.2375
0.1384
1.42
0.8515
–1.1381
0.8494*
–0.2539
2.47*
0.9739
–1.4010
0.7814*
–0.4524
3.46**
0.2359
0.0513
0.2538
0.0050
0.9146
1.2665
1.5051*
1.3189*
0.9220
1.3248
2.3492***
0.6327
0.97
1.5114*
0.9381
2.5458***
0.6780
1.23
0.2393
–1.9068***
0.3133
0.5225
6.38***
0.2520
–2.1417***
0.2574
0.4949
7.22***
2.7380**
0.3220
2.2935*
0.3325
9.9839*
–0.4717
10.0477*
–0.8486
2.7336**
0.5273
-0.1142
0.6843
0.18
2.2921*
0.5265
0.1157
0.5051
0.05
9.9905*
0.8189
0.3407
-1.0928
1.68
10.0573*
0.5328
0.0848
–1.5074**
5.01***
Apparel
Equation (1)
SOE–private
MNE–private
Equation (2)
SOE–private
Minority-foreign MNE–private
Majority-foreign MNE–private
Heavily foreign MNE–private
Wald equality test
–0.7441*
0.5943*
–0.8285*
0.7893**
–2.1992***
–0.0768
–2.2944
–0.0239
–0.7462*
–0.0553
–0.4448
1.3593***
4.60**
–0.8334*
0.0339
0.2950
1.2726***
2.11
–2.1987***
–1.1901*
0.2149
–0.0287
1.31
–2.2911
–1.2595
0.2807
0.0029
1.35
Wood products
Equation (1)
SOE–private
MNE–private
0.7242
–0.5021
0.6830
–0.0605
4.4512
–0.2232
4.5320
–0.4600
TABLE 3 (continued)
1996
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Industry, equation, differential
Initial
capital
2006
Year-end
capital
Initial
capital
Year-end
capital
Paper products (East Indonesian and minority-foreign plants omitted for 1996)
Equation (1)
SOE–private
6.1690
6.4640*
–2.3363
MNE–private
0.4859
1.4496
1.4167
Equation (2)
SOE–private
6.2196
6.4340*
–2.1258
Minority-foreign MNE–private
–
–
–2.9980*
Majority–foreign MNE–private
–0.3370
1.6077
3.2408
Heavily foreign MNE–private
4.3113
1.0823
1.4229
Wald equality test
1.38
0.03
2.44*
–2.4872
–5.3068**
2.9849
0.6207
3.19**
Chemicals
Equation (1)
SOE–private
MNE–private
4.1615**
–0.0622
Rubber & plastic products
Equation (1)
SOE–private
MNE–private
Non–metallic mineral products,
Equation (1)
SOE–private
MNE–private
3.5609*
0.1954
0.7666
–0.2882
1.1918
–0.4983
–0.9482
–0.3007
–1.1290*
–0.6996
–0.4337
0.2159
–1.0144
0.1563
–0.8752
–3.0950*
–0.9236
–2.9576*
6.4073
–2.2913
6.8196
–1.6446
–
–1.5212
–
–2.2187*
0.1732
0.1105
0.4092
–0.2242
–0.4547
–2.7168**
–1.0595
–1.0354
–0.3054
–0.7599
0.5313
Basic metals (SOE & East Indonesian plants omitted for 2006)
Equation (1)
SOE–private
–4.1974*
–2.5143
MNE–private
–0.2194
–0.4278
Electronics–related machinery (SOE & East Indonesian plants omitted)
Equation (1)
MNE–private
0.9084**
0.8955**
Motor vehicles (SOE & East Indonesian plants omitted)
Equation (1)
MNE–private
0.8239
Other transportation machinery
Equation (1)
SOE–private
MNE–private
–2.7228
0.4231
–2.1724*
0.2072
Note: SOE = state-owned enterprise. MNE = multinational enterprise. – = no plants in category. Wald
tests address the hypothesis that MNE–private differentials are equal for all MNE groups and associated p-values. Equation (2) results are omitted for industries if all Wald tests of MNE group equality
for the industry are not signiicant at the 10% level. For other slope coeficients and equation statistics,
see Ramstetter and Narjoko (2012, appendix table 3).
* p < 0.1; ** p < 0.05; *** p < 0.01.
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270
Eric D. Ramstetter and Dionisius Narjoko
results were consistent with estimates for the 12 highest-energy-consuming industries, suggesting that the correlation between ownership and total energy intensities was generally weak (table 3). For example, all ownership coeficients were
insigniicant (at the 5% level or better) in paper products in 1996; in chemicals,
electronics-related machinery, and other transportation machinery in 2006; and
in wood, rubber and plastics, non-metallic mineral products, basic metals, and
motor vehicles in both years. We observed negative and signiicant (at the 5%
level) differentials for minority-foreign plants in food and beverages and for SOEs
in other transportation machinery in 1