Privatization of State-Owned Enterprises in Indonesian Manufacturing Industry

  • Maman SetiawanEmail author
  • Ernie Tisnawati Sule
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-31816-5_3673-1

Synonyms

Definition

State-owned enterprise (SOE) is an enterprise where the government has full, majority, or significant ownership of the firm enabling the government to fully control the firm. In Indonesia, the government can have a full control on the SOEs if it has at least 51% of the ownership share. The privatization is a mechanism applied by the government to transfer its ownership to the public or private firm. The privatization is supposed to improve the efficiency of the companies. The efficiency is related to efforts of the firm to reduce the waste in using the resources. The technical efficiency can be defined as the ability of the firm to expand its output using the same input or contracting its input using the same output.

Introduction

State-owned enterprises (SOEs) in the manufacturing sector contribute significantly to the Indonesia economy with its contribution about 10% to the total manufacturing output in the period of 1980–2015, on average. The number of the SOEs in the manufacturing industry also reached about 25% of the total number of SOEs in Indonesia in 2015 (see Setiawan and Tisnawati Sule 2018). With its significant contribution, the performance of the SOEs can significantly affect the Indonesian welfare.

Regarding the performance of the SOEs, three main theories i.e. property right theory, agency theory and public choice theory may explain the nature of the SOEs performance (see Bozec et al. 2002). Those theories suggest that the performance of the SOEs will not always be at the optimum level with a tendency to be lower than the private firms. Thus, most of the jurisdictions have been privatizing their SOEs to avoid the higher loss to the economy.

With respect to the privatization, Indonesian government has been privatizing the SOEs in the manufacturing sector since 1980s. In 1987, 7 SOEs operating in manufacturing and nonmanufacturing sectors were privatized. Also 52 SOEs having business in manufacturing, finance, agriculture, and service were planned to be privatized in 1989. Based on the Indonesian Law No. 19 year 2003 about the SOEs, the privatization can be done through three ways: (i) direct stock sales to the investor, (ii) initial public offering (IPO) through the stock market, and (iii) stock sales to the employee or management. The privatization causes the government to have less control to the privatized SOEs. The selling of the stock share to more than 50% will result in the loss of the government control on the SOEs.

The privatization is expected to increase the efficiency of the firms, since the privatized SOEs have less political interferences compared to the condition before the privatization. The political interferences in the SOEs may usually result in the poor choices of inputs, low quality of product, and less incentive for the managers (see Shleifer and Vishny 1994). Therefore, the privatization may improve the choice of the inputs and the quality of the products increasing the technical efficiency of the privatized SOEs.

Literature Review

Previous research has investigated the effect of the privatization on the technical efficiency of the state-owned enterprises. Chirwa (2001) investigated the privatization and its effect on the technical efficiency of the manufacturing sector in Malawi during the period 1970–1997. The technical efficiency is estimated using data envelopment analysis. The research found that the privatized firms had higher technical efficiency after the privatization. Al-Obaidan (2002) investigated the effect of the privatization on the efficiency of the 45 developing countries in 1980s. They applied frontier production function to account efficiency differences between countries with different degrees of the private sector contribution on the economy. They found that after the privatization the developing countries can increase the utility of their natural resource by 45%. Shi and Sun (2016) investigated the effect of the privatization in the China during the period 2001–2010. They applied regression model and Cobb-Douglass production function to estimate the firm performance and productivity, respectively. They found that the privatization improved efficiency and productivity in the China. Plane (1999) also investigated the effect of the privatization on the productive efficiency in the Cote d’Ivoire electric power company for the period 1959–1995. The research applied the stochastic production frontier to estimate the productive efficiency. The research found that the productive efficiency improved after the privatization because of the organizational innovations. In addition, Boussofiane et al. (1997) investigated the technical efficiency of nine privatized organizations in the UK in 1980. The research used data envelopment analysis to estimate the technical efficiency before and after privatization. The research found that the positive effect of the privatization on the technical efficiency applied only for some privatized organizations. Moreover, Yu and Fan (2008) investigated the effect of the privatization on the technical efficiency in the Taiwan’s Intercity Bus Services. They found that there is a significant increase in the technical efficiency of the firms after the privatization. The previous research explaining about the technical efficiency of the SOEs and its relation with the privatization in other countries had been investigated. It is still rare to find a same research in the Indonesian economy. Thus, it is important to investigate the technical efficiency and its changes after the privatization in the Indonesian economy.

Modeling

This research uses data envelopment analysis with a bootstrapping approach to estimate the technical efficiency. The data envelopment analysis requires very few assumptions about the properties of the production possibilities set, since it applies a mathematical linear programming to measure the efficiency (Setiawan et al. 2012). The technical efficiency score estimated by the DEA is based on the transformation of N inputs into the M outputs for all I firms. Suppose the column vectors xi and qi represent input and output for the i-th firm, respectively. The consecutive NxI input matrix, X, and the MxI output matrix, Q, represent the data for all I firms. The output-oriented DEA model (The output-oriented DEA estimates the technical inefficiency by having a proportional increase in output, given the same inputs. The rigidities in the inputs faced by the firms make this assumption relevant in the Indonesian economy (see Setiawan et al. 2012).) is applied by having the mathematical linear programming model as follows:
$$ {\displaystyle \begin{array}{l}{\max}_{\phi, \lambda }\ \phi, \\ {}\mathrm{st}\quad\quad -\phi {q}_i+ Q\lambda \ge 0,\\ {}\quad\quad {x}_i- X\lambda \ge 0,\\ {}\quad\quad I{1}^{\prime}\lambda =1\\ {}\quad\quad \lambda \ge 0,\\ {}\quad\quad \end{array}} $$
(1)
where ϕ − 1 can be defined as a proportional increase in outputs that could be achieved by the i-th firm or SOE given the same amount of input quantities. This research also applies a Farrell approach to avoid getting a negative efficiency score because of the high variabilities of inputs and outputs. This measures the technical efficiency to get an efficiency score which varies from 1 to infinity. The variable return to scale (VRS) is also applied to the DEA by having I1′λ = 1 as a convexity constraint with I1 as an Ix1 vector of ones and λ as an Ix1 vector of constants. Furthermore, the final technical efficiency score is calculated from 1/ϕ (\( \widehat{\delta}\left(x,y\right) \)) as a value in the unit interval as also applied by Setiawan et al. (2012).
The research also applies the DEA with a bootstrap technique of Simar and Wilson (1998) to get a robust estimate of the technical efficiency score. The bootstrapping approach uses a repeated simulation of the data generating process by using a resampling method and applying it to the original estimator. The simulated estimates can be identical to the sampling distribution of the original estimator. The biased-corrected efficiency score from the bootstrapped method is applied using the formula:
$$ {\displaystyle \begin{array}{c}\widehat{\widehat{\delta}}\left(x,y\right)=\widehat{\delta}\left(x,y\right)-{\mathrm{bias}}_B\left[\widehat{\delta}\left(x,y\right)\right]\\ {}=2\widehat{\delta}\left(x,y\right)-{B}^{-1}\sum \limits_{b=1}^B{\widehat{\delta}}_b^{\ast}\left(x,y\right)\end{array}} $$
(2)
$$ \mathrm{with}\ \mathrm{the}\ \mathrm{condition}\ \mathrm{of}\ \mathrm{sample}\ \mathrm{variance}\, {\widehat{\delta}}_b^{\ast}\left(x,y\right)\, <\, \frac{1}{3}{\left(\widehat{\mathrm{b}}{\mathrm{ias}}_B\left[\widehat{\delta}\left(x,y\right)\right]\right)}^2 $$
(3)

This research applies \( \widehat{\delta}\left(x,y\right) \) and \( \widehat{\widehat{\delta}}\left(x,y\right) \) representing the original and biased-corrected efficiency scores, respectively. The \( {\widehat{\delta}}_b^{\ast}\left(x,y\right) \) is a final bootstrap estimate of the efficiency score which is generated from B samples with b = 1,…,B.

This research also applies analysis of variance (ANOVA) to test the differences between average technical efficiency of the SOEs in the subsectors before and after privatization. ANOVA test is based on the variance analysis between the two groups of the subsectors. The higher the differences of the variance between the two groups, the higher the possibility of the test to reject the null hypothesis of the no differences of the variance between the two groups of the subsectors. The ANOVA will be applied for the groups of all subsectors, only subsectors with increasing technical efficiency after privatization and only subsectors with decreasing technical efficiency after privatization.

Data

This research uses establishment-level data to estimate the technical efficiency of the firms pooling both SOEs and non-SOEs. To compare the technical efficiencies of the SOEs in the subsectors between pre- and post-privatization, the research only takes the technical efficiency scores of the SOEs. The technical efficiency of the subsectors is obtained by averaging the technical efficiency of the SOEs in the subsectors. The data are sourced from the Annual Manufacturing Survey provided by the Indonesian Bureau of Central Statistics (BPS) covering the period from 1980 to 2015 in which the data of the SOEs can be obtained.

There are about 400 subsectors in the Indonesian manufacturing industry. Each subsector may include both SOEs and non-SOEs in the period from 1980 until 2015. The subsectors having less than 30 observations are combined into a new subsector in the same digit of ISIC 4 (four) or 3 (three) level to have at least 30 observations. (For example, subsectors of 10110, 10120, and 10130 were combined into a new subsector of 10100 to have at least 30 observations in every single year.) Also the subsectors without having the SOEs during the period 1980–2015 are dropped. Finally, this research uses 146 subsectors classified into 5-digit International Standard Industrial Classification (ISIC) level. This research defined the privatization as the changes of the government ownership on the SOEs to become less than 51%. The government has no dominant control anymore on the SOEs if its stock ownership is less than 51%.

The firms in the manufacturing industry transform labor, raw material, and fixed capital such as machines, equipment, etc. into the output (see Setiawan et al. 2012). Production output is defined as the value of gross output produced by the firms and deflated by the Wholesale Price Index (WPI). This research also applies a proxy of labor efficiency units to represent the labor. (We define the labor efficiency units as used by Setiawan et al. (2012): L = Number of production worker + number of other worker* (\( \frac{\mathrm{Salary}\, \mathrm{of}\ \mathrm{other}\ \mathrm{worker}}{\mathrm{Salary}\ \mathrm{of}\ \mathrm{production}\ \mathrm{worker}} \))) Raw material is represented by the total costs of domestic and imported materials (The raw materials include not only materials but also other costs related to the production such as electricity and fuel cost.) and is deflated by Wholesale Price Index (WPI), respectively. Fixed assets measure the fixed capital which is deflated by the WPI of machinery (excluding electrical products), transport equipment, residential, and nonresidential buildings.

Table 1 shows the descriptive statistics of the variables including input and output variables used for the technical efficiency estimation using the DEA across firms and subsector period 1980–2015. Table 1 shows that all variables had high variability. This is shown by the high standard deviation for every variable. The high variation of data can be caused by the long period of 1980–2015. The high variability of the data can also be caused by the variation across the subsectors. Moreover, fixed capital had the highest variation of the data among the other inputs and output variables with the mean and standard deviation of 1.020*107 and 6.300*109, respectively. In spite of this, the high variability of data will not affect the estimation of the technical efficiency significantly, since the estimation of the technical efficiency is applied for every year in each subsector. Furthermore, the DEA estimation which applies the VRS approach naturally benchmarks each firm to the best practice of the other firms with the similar sizes.
Table 1

Descriptive statistics of the variables across firms and subsectors, 1980–2015

Variable

Mean

Standard deviation

Maximum

Minimum

Output (in thousands Rupiah)

386114.700

3691963.000

7.950*108

102.802

Labor (person index)

816.904

8618.203

174992.100

4.000

Capital (in thousand Rupiah)

1.020*107

6.300*109

4.25*1012

100.000

Material (in thousand Rupiah)

287855.000

2391415.000

6.440*108

100.037

Source: Authors’ calculation

Results

Table 2 shows the number of privatized SOEs in the Indonesian manufacturing industry during the period 1980–2015. The number of the privatized SOEs was based on the establishment data. Table 2 shows that there was a significant increase of the number of privatized SOEs from the interval period of 1980–1990 to the interval period of 1991–2000. The number of privatized SOEs was 45 in the interval period 1980–1990, and the number increased to 71 SOEs in the interval period of 1991–2000. The number of privatized firm was the largest in the interval period of 1991–2000. This may be an indication that the plan of the privatization in 1989 went as the plan in 1990s. Furthermore, this might also happen because the privatization was the priority program stated in the letter of intent (LOI) between Indonesia and the International Monetary Fund as a return of the support from IMF to resolve the economic crisis in Indonesia occurring during the period 1997/1998. The number of privatized firms decreased in the interval period of 2001–2010 compared to the previous interval period, but the number of privatized SOEs increased again in the interval period of 2011–2015. The average of the number of privatized SOEs was 53 during the period 1980–2015. The fluctuation of the number of the privatized firms was also related to the different regulations as well as the different regimes in the Indonesian government during the period.
Table 2

Number of privatized firms, 1980–2015

Period

Average number of privatized firms in Indonesian manufacturing sector

1980–1990

45

1991–2000

71

2001–2010

46

2011–2015

47

1980–2015

53

Source: Authors’ calculation

Table 3 shows the average technical efficiency of the privatized and non-privatized SOEs across subsectors during the period 1980–2015. The average technical efficiency of the privatized SOEs was larger than the average technical efficiency of the non-privatized SOEs during the period 1980–2015. In the interval period of 1980–2015, the average technical efficiencies of the privatized and non-privatized SOEs were 0.51 and 0.48, respectively. The average technical efficiency of the SOEs declined for both privatized and non-privatized SOEs during the period of estimation. For example, the average technical efficiencies of the SOEs were 0.60 and 0.55 for privatized and non-privatized SOEs in the interval period of 1980–1990, respectively. The average technical efficiency decreased to 0.45 for both privatized and non-privatized SOEs in the interval period of 2011–2015, respectively.
Table 3

Technical efficiency of the SOEs before and after privatization across subsectors, 1980–2015

Period

Technical efficiency before privatization

Technical efficiency after privatization

1980–1990

0.55

0.60

1991–2000

0.46

0.50

2001–2010

0.45

0.46

2011–2015

0.45

0.45

1980–2015

0.48

0.51

Source: Authors’ calculation

Table 4 shows the percentage of the subsectors in which the privatized SOEs exhibited increasing and decreasing average technical efficiencies after the privatization. Percentage of the subsectors with increasing technical efficiency after privatization was 53%. The percentage of the subsectors with increasing technical efficiency was larger than the percentage of the subsectors with decreasing technical efficiency which reached about 47%. Although more than half of the subsectors experienced increasing technical efficiency after the privatization, the privatization might not always guarantee that privatized SOEs will be more technically efficient. Okten and Arin (2006) suggested that other environmental factors could also influence the effect of the privatization on the efficiency.
Table 4

Percentage of subsectors with increasing and decreasing technical efficiencies after privatization of the SOEs period 1980–2015

Subsectors

% of privatized firms

Subsectors with increasing technical efficiency after privatization

53

Subsectors with decreasing technical efficiency after the privatization

47

Source: Authors’ calculation

Table 5 shows the percentage of the subsectors with increasing and decreasing technical efficiency after the privatization classified into low technology level, medium-low technology level, medium-high technology level, and high technology level. The classification is based on the grouping from the United Nations Industrial Development Organization (UNIDO). Table 5 shows that the percentage of the subsectors having decreasing technical efficiency after the privatization were 54.41% coming from the subsectors with low level of technology. Only 50% of the subsectors with increasing technical efficiency after privatization were classified as the subsectors with low level of technology.
Table 5

Percentage of subsectors with increasing and decreasing technical efficiency (TE) after privatization based on the groups of technology level

Technology level

Subsectors with increasing TE (%)

Subsectors with decreasing TE (%)

Low-level

50.00

54.41

Medium-low

30.77

22.06

Medium-high

14.10

16.18

High

5.13

7.35

Source: Authors’ calculation

Table 6 shows 20 of the 78 subsectors in which the privatized SOEs exhibited increasing average technical efficiency after the privatization, and they had the largest average technical efficiencies after the privatization. From Table 6, it shows that subsectors classified as medium-high technology industry, medium-low technology industry, and low technology industry were four subsectors, eight subsectors, and eight subsectors. This indicates that 60% of the 20 subsectors exhibiting increasing average technical efficiency came from the subsectors with medium-low and medium-high technology industry.
Table 6

20 of the 78 subsectors in which the privatized SOEs exhibits increasing average technical efficiency after the privatization

ISIC

Industry

Technology level

Technical efficiency before privatization

Technical efficiency after privatization

10623

Glucose

Low

0.663

1.000

21020

Traditional medicine

Medium-high

0.843

1.000

25940

Metal furniture

Medium-low

0.862

1.000

23950

Concrete

Medium-low

0.802

0.959

25990

Metallic goods

Medium-low

0.752

0.959

24102

Steel rolling

Medium low

0.850

0.934

10392

Soybean – tahu

Low

0.647

0.913

20290

Other basic chemistry goods

Medium-high

0.570

0.890

20111

Khor and Alchaly

Medium-high

0.490

0.875

23922

Clay tiles

Medium-low

0.527

0.864

20220

Paint, printed ink and Lacquer

Medium-high

0.462

0.779

24101

Iron and steel making

Medium-low

0.714

0.771

13942

Rope goods

Low

0.723

0.765

20115

Organic, farm

Medium-high

0.608

0.760

14201

Finished clothes from leather convection

Low

0.636

0.744

10792

Cakes

Low

0.665

0.739

10611

Rice and seed milling, cleaning

Low

0.717

0.729

10293

Frozen other aquatic biota

Low

0.708

0.722

25930

Cutting equipment

Medium-low

0.664

0.718

13111

Preparation textile

Low

0.479

0.716

Source: Authors’ calculation

Table 7 shows the 20 of the 68 subsectors in which the privatized SOEs exhibited decreasing average technical efficiency after the privatization. The 20 subsectors also had the lowest-average technical efficiency score during the period 1980–2015. The subsectors classified as high technology level, medium-high technology level, medium-low technology level, and low technology level were 1 subsector, 4 subsectors, 3 subsectors, and 12 subsectors. From Table 7 it is seen that the 20 subsectors having decreasing technical efficiency after the privatization dominantly came from subsectors classified as low technology level. About 60% of the 20 subsectors with the decreasing technical efficiency came from the subsectors classified with the low technology level.
Table 7

20 of the 68 subsectors in which the privatized SOEs exhibits decreasing average technical efficiency after the privatization

ISIC

Industry

High-tech

Technical efficiency before privatization

Technical efficiency after privatization

11035

Wine and other liquor

Low

0.499

0.265

13912

Embroidery textile

Low

0.299

0.265

20231

Soap, detergents

Medium-high

0.324

0.274

16212

Laminated plywood

Low

0.291

0.282

17021

Paper nec

Low

0.320

0.294

31000

Furniture, rattan, and others

Low

0.323

0.300

23929

Porcelain prod, structural

Medium-low

0.330

0.306

10745

Macaroni, noodles

Low

0.342

0.307

23940

Gips and gips prod

Medium-Low

0.484

0.307

12011

Cigarettes, clove

Low

0.352

0.308

10612

Coffee cleaning

Low

0.356

0.318

10620

Other palm scratch

Low

0.409

0.320

22210

Plastic prod, structural

Medium-low

0.489

0.326

10800

Animal feed and concentrates

Low

0.373

0.329

27110

Electrical motor and generator

Medium-high

0.370

0.335

22122

Remilled rubber

Medium-low

0.488

0.341

15120

Leather goods for technical and Industrial use

Low

0.403

0.344

13132

Finished textiles

Low

0.364

0.352

28190

Machinery and equipment for other general uses

High

0.364

0.355

20110

Other basic chemistry industries

Medium-high

0.415

0.362

Source: Authors’ calculation

Tables 6 and 7 may give an indication that privatization most likely had positive effect on the technical efficiency on the industry with higher level of technology. The privatization of the firms in the higher level of technology may boost the economies of scale because of the improved inputs and outputs boosting the technical efficiency. Nevertheless, the privatized SOEs exhibiting decreasing technical efficiency may consider other environmental factors that can affect the technical efficiency (see Setiawan and Tisnawati Sule 2018).

Table 8 shows ANOVA test to determine whether there is a difference between technical efficiency before privatization of the SOEs and technical efficiency after privatization of the SOEs. The ANOVA test was based on the subsector level comparing between the technical efficiency of the subsectors before the SOEs were privatized and after the SOEs were privatized. The technical efficiency of the subsectors is the average technical efficiency of the SOEs in the respective subsector. The ANOVA test was also applied for both groups of subsectors in which the privatized SOEs exhibited decreasing and increasing average technical efficiency after the privatization.
Table 8

ANOVA test of the average technical efficiency before and after privatization period 1980–2015

Group

DF

F-statistics

All subsectors

df1 = 1; df2 = 290

0.010

Subsectors in which the privatized SOEs exhibits decreasing average technical efficiency after the privatization

df1 = 1; df2 = 134

11.697***

Subsectors in which the privatized SOEs exhibits increasing average technical efficiency after the privatization

df1 = 1; df2 = 154

14.195***

Source: Authors’ calculation

Note: *** denotes significance of the test statistic at the 1% critical level

From Table 8, it is shown that there were no significant differences of the average technical efficiency between before and after privatization for all subsectors at the 10% critical level. In spite of this, the results were different if the tests were based on the only subsectors with increase technical efficiency after privatization or the only subsectors with decreasing technical efficiency after the privatization. Table 7 shows that there were significant differences of the technical efficiency before and after privatization for the two groups at the 1% critical level. The subsectors in which the privatized SOEs exhibited decreasing or increasing average technical efficiency after the privatization had a different average technical efficiency significantly compared to the average technical efficiency before the privatization. From the Table 7 and previous tables, it is concluded that more than half of the privatized SOEs improved their efficiencies in the respective subsectors significantly, but the other privatized SOEs worsen their efficiencies in the respective subsectors significantly.

Conclusions

This research investigates the impact of the privatization on the technical efficiency of the privatized SOEs in the Indonesian manufacturing industry. This research uses the data from the Indonesian Bureau of Central Statistics during the period 1980–2015. The ANOVA test is also applied to test whether there is a difference of the technical efficiency of the subsectors between pre- and post-privatization.

This research found that the technical efficiency of the SOEs in the Indonesian manufacturing sector was low. Furthermore, the improvement of the average technical efficiency after the privatization happens not to all SOEs. The ANOVA tests suggest that the privatization may increase or decrease the technical efficiency of the SOEs significantly in the subsectors after the privatization. These results may suggest the government to carefully consider the SOEs that will be privatized. The SOEs should be feasible and able to give a good impact on the efficiency as well as on the market after the privatization.

Cross-References

References

  1. Al-Obaidan AM (2002) Efficiency effect of privatization in the developing countries. Appl Econ 34(1):111–117CrossRefGoogle Scholar
  2. Boussofiane A, Martin S, Parker D (1997) The impact on technical efficiency of the UK privatization programme. Appl Econ 29(3):297–310CrossRefGoogle Scholar
  3. Bozec R, Breton G, Cote L (2002) The performance of state owned enterprises revisited. Financ Account Manag 18(4):383–407CrossRefGoogle Scholar
  4. Chirwa EW (2001) Privatization and technical efficiency: evidence from the manufacturing sector in Malawi. African Development Review 13(2):276–307Google Scholar
  5. Okten C, Arin KP (2006) The effects of privatization on efficiency: how does privatization work? World Dev 34(9):1537–1556CrossRefGoogle Scholar
  6. Plane P (1999) Privatization, technical efficiency and welfare consequences: the case of the Cote d’Ivoire electricity company (CIE). World Dev 27(2):343–360CrossRefGoogle Scholar
  7. Setiawan M, Tisnawati Sule E (2018) Technical efficiency and its determinants of the SOEs in the Indonesian manufacturing sector. Working Papers in Economics and Development Studies (WoPEDS) No. 201802, Department of Economics, Padjadjaran University, Available at https://econpapers.repec.org/paper/unpwpaper/201802.htm
  8. Setiawan M, Emvalomatis G, Oude Lansink A (2012) The relationship between technical efficiency and industrial concentration: evidence from the Indonesian food and beverages industry. J Asian Econ 23(4):466–475CrossRefGoogle Scholar
  9. Shi W, Sun J (2016) The impact of privatization on the efficiency and profitability: evidence from Chinese listed firms, 2001–2010. Econ Transit 24(3):393–420CrossRefGoogle Scholar
  10. Shleifer A, Vishny R (1994) Politicians and firms. Q J Econ 109:995–1025CrossRefGoogle Scholar
  11. Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44(1):49–61CrossRefGoogle Scholar
  12. Yu M-M, Fan C-K (2008) The effects of privatization on return to the dollar: a case study on technical efficiency, and price distortions of Taiwan’s intercity bus services. Transp Res A Policy Pract 42(6):935–950. ElsevierCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Economics and BusinessPadjadjaran UniversityBandungIndonesia