https://jiae.ub.ac.id/
156
MARKET POWER AND FIRMS’ PERFORMANCE: A CASE OF INDONESIAN MANUFACTURING INDUSTRY
Janaska Nurrachmat
Faculty of Economics and Business, University of Indonesia Corresponding author:
Abstract Purpose
This study aims to investigate the relationship between market power and firms’ performance in the Indonesian manufacturing industry.
Design/methodology/approach
Using the Statistik Industri Besar dan Sedang from BPS we extract the data about the market share and productivity of each firm that will represent market power and the firms’
performance respectively. The dataset also allows us to apply dynamic panel data that might address the endogeneity and reverse causality problem which could occur in the estimation.
Findings
The results suggest that market power has an inverted U-shaped relationship with firms’
productivity. Further analysis shows similar conditions also occur in all selected industries except automotive.
Research limitations/implications
This study could help policymakers if they want to influence firms’ performance based on their market share.
Originality/value
This paper applies the DPD method to address the endogeneity problem that might occur in the previous studies.
Keywords: Market Power, Firm’s performance, Productivity, Industry
ARTICLEHISTORY
Received : March 15, 2023 Published : August 31, 2023 HOWTOCITE
Nurrachmat, J. (2023). Market Power and Firms’
Performance: A Case of Indonesian Manufacturing Industry. Journal of Indonesian Applied Economics, 11(2), 156-168.
DOI: doi.org/10.21776/ub.jiae.2023.011.02.4
157 1. INTRODUCTION
An excellent firm’s performance is not only wished by the firm’s owner or its worker but also by the government. The aggregated firms’ performance could represent the country’s productivity which is a very important indicator of the economy. Higher productivity can generate higher income and higher income means better prosperity. The main theory in industrial organization argues that firms’ performance is significantly determined by the market structure and/or its market power (Pervan et al., 2019). A firm that has higher market power can exert its power to influence the market situation, so it always be favorable for the firm. However, this situation may not always be desired. Such a setting can push other firms to exit the market and prevent new entrants. It could also damage the welfare of consumers because a firm with higher power can set the price or quantity freely to get the best outcome without any significant repercussions.
Figure 1. Share of manufacturing output in Indonesian GDP 2012-2019
Source: BPS (2023)
The manufacturing industry is a very important economic sector for a developing country such as Indonesia. Figure 1 shows the percentage of manufacturing output related to Indonesian GDP from year 2012 until 2019. The data shows that the average share is 36% and the percentage always exceeding 30% on each observational year. The highest share was achieved in 2016 (48%) whereas the lowest share was recorded in year 2012 and 2014. Manufacturing sector also play significant role in Indonesian labour dynamic.
Figure 2 shows that for the past five years before the pandemic manufacturing sector contributed around 14% of total employment in Indonesia. Kreuser & Newman (2018) also argues that manufacturing sector is highy crucial in determining economic growth and job creation.
Figure 2. Labour contribution from manufacturing industry
Source: BPS (2023)
31% 32% 31% 33%
48%
37% 41% 39%
2012 2013 2014 2015 2016 2017 2018 2019
13.53 13.41
14.51 14.68 14.91
2015 2016 2017 2018 2019
158
The vast amount of available human resources and abundant resources of renewable energy could give Indonesia a strategic advantage in the manufacturing industry in the future. Some studies also shows other positive aspects which is delivered by higher firm’s performance, such as higher innovation (Bartelsman et al., 2019) and better environmental awareness among firm (Huang, 2021). These are the reason why the study of manufacturing industry is important in Indonesia. This study will investigate the relationship between market power and firms’ performance in various manufacturing industries in Indonesia. The research questions are does individual market power affects firm’s performance? How market share affects it? Is the relationship negative/positive or is there a U-shape/inverted U-shaped relationship between them? This study will ultimately help the authority formulate a policy when they want to influence firms’ performance in the manufacturing sector. This study can also help formulating a decision, whether a low/highly concentrated market must be intervened, if we want to influence the firm’s performance in that market.
2. LITERATURE REVIEW
Similar studies that examined the relationship between market structure, and/or market power, and firms’ performance have been conducted several times in Indonesia.
Such as the studies of Nurwati et al. (2014), Pahlevi & Ruslan, (2019), and Maghfuriyah et al., (2019). Those studies followed the same framework, analyzing the effect of market structure which was proxied by the Herfindahl-Hirschman Index or Lerner index on firms’
performance in the banking industry, which was proxied by return on asset (ROA) and return on investment (ROE). Arintoko et al, (2021) also conducted a similar study. They analyzed the impact of market structure and power on firms’ performance in insurance industry.
The results of the studies are very interesting; Nurwati and her colleagues stated that market structure was negatively correlated with ROE. Pahlevi & Ruslan found similar results that market structure had a negative effect on ROA but they also found that the Lerner index was positively correlated with ROA. Meanwhile, Arintoko and his co- researcher claimed that market structure did not have any effect at all on the firms’
performance. All of the studies mentioned above used the data from the financial statement, which is only issued by listed companies, to represent both dependent and independent variables and they chose the service industry as their observation. To differentiate with those studies and also to fill the gap in current literature of market power dynamic, this research will use different dataset and different measurement for firms’
performance which is productivity and also this research will focus on manufacturing industry.
Similar studies also had been conducted in other countries. Dai et al. (2018) examined the relationship between market power and productivity of food & tobacco industry in China. They discovered that market power had negative correlation with productivity. Rodríguez-Castelán et al. (2019) also carried out a research about market concentration and productivity in Mexico. The result of their study was that market concentration had negative effect on productivity. However, the impact varied across industry, even the negative effect could be completely compensated if the industry had decent exposure to trade market. Similar study in Europe is carried out by Battiati et al.
(2021) they found that market power which was proxied by price markup could lead to an increase of productivity. From the results of study that mentioned before, we can summarize that the relationship between those two factors still ambiguous. The research in Indonesian also limited in observation, which is only examine listed firm. Therefore, it is important to conduct another research with other dataset, better definition of both
159 variables, and better method.
3. RESEARCH METHOD
Catacutan (2022) & Jovanovic (2018) have developed a method to capture firm’s performance using a decomposition of total factor productivity (TFP). The decomposition is derived from the Cobb-Douglas equation:
𝑌𝑖 = 𝐴𝑖+ 𝐾𝑖𝛼+ 𝐿𝛽𝑖 + 𝑀𝑖𝛾 (1)
Where 𝑌𝑖 is the output of firm, 𝐾𝑖𝛼 is the capital stock, 𝐿𝛽𝑖 describes labor, 𝑀𝑖𝛾 explains material, and 𝐴𝑖 is the unexplained factor that describes total factor productivity.
Transforming to natural logarithm and moving 𝐴𝑖 to the left-hand side we will have:
𝐿𝑛𝐴̂𝑖 = 𝐿𝑛𝑌𝑖− 𝛼̂𝑙𝑛𝐾𝑖− 𝛽̂𝑙𝑛𝐿𝑖− 𝛾̂𝑙𝑛𝑀𝑖 (2)
Empirically to obtain the TFP value we first conduct a simple linear regression or panel regression based on equation 2. Thus, we will have:
𝑙𝑛𝑂𝑢𝑡𝑝𝑢𝑡𝑖𝑡 = 𝛼 + 𝛽1𝑙𝑛𝐾𝑖𝑡+ 𝛽2𝑙𝑛𝐿𝑖𝑡+ 𝛽3𝑙𝑛𝑀𝑖+ 𝜀𝑖𝑡 (3)
We then take the predicted residual of each firm, which represents the productivity or a share of output that cannot be explained by the inputs. After that a regression formula for this research can be formulated such as:
𝑙𝑛𝑇𝐹𝑃_𝑌𝑖𝑡 = 𝛼 + 𝛽𝑀𝑆𝑖𝑗𝑡+ 𝛾𝑋𝑖𝑡+ 𝜀𝑖𝑡 (4)
Where the dependent variable is the predicted value of TFP based on Output. In this research market power is defined by share of output of firm 𝑖 in industry 𝑗 which will serve as our main independent variable and 𝑋𝑖𝑡 is a set of control variable. Sahoo &
Pramanik (2017) suggested that variable value-added might be better representation for productivity rather than Output or Sales. Therefore, we can formulate another regression equation that will be also estimated in this study:
𝑙𝑛𝑇𝐹𝑃_𝑉𝐴𝑖𝑡 = 𝛼 + 𝛽𝑀𝑆𝑖𝑗𝑡+ 𝛾𝑋𝑖𝑡 + 𝜀𝑖𝑡 (5)
The data used in this study is Statistik Industri from BPS. The dataset is a survey panel that covers the detailed information of medium and large manufacturing industry from all region in Indonesia. In this study five industries are selected as the observation.
They are Food & beverage, Chemical, Textile, Automotive, and Electronic. The aforementioned industries are selected due to the high contribution from total manufacturing industry (BPS, 2023). Each of selected industry has distinctive nature with each other, this can also help us to observe the heterogeneity in manufacturing industry.
Table 1 shows the definition the variable that we inlcude to estimate the regression.
The variable such as fixed asset, expenditure, employment etc. are chosen to be control variable. Based on previous reseach from Catacutan (2022) , Jovanovic (2018) and Rodríguez-Castelán et al. (2019) they stated that such variable can be applied as control to reduce the omitted variable bias problem that might occur when estimating impact on firm’s pefromance.
160
Table 1. Variable definition
Variable Definition
1 Output Total Output from Firm
2 Input Total Input from Firm
3 Value-added Output subtracted by Input
4 Market share Total output firm i divided by total output sub-industry j 5 Max market share (in %) Maximum market share a Firm had in a year in selected sub-
industry
6 HHI Herfindahl-Hirschman Index
7 TFP VA Total factor productivity measured by Value-added 8 TFP sales Total factor productivity measured by Output 9 Fixed asset Total fixed asset owned by Firm
10 Salary expenditure Labor cost from Firm 11 Energy expenditure Energy cost from firm
12 Employment Total Employment
13 Imported material (in %) The percentage of imported intermediate goods in a firm 15 Foreign ownership (0/1) 1 if the firm has foreign ownership, 0 Otherwise
Source: Statistik Industri (2010-2015, Processed)
As mentioned in previous chapter this research will observe five industry groups.
The classification is conducted according to two digit of ISIC-code rev. 3 number 24 (UN, 2008). In our dataset for one-year observation, 5436 firms are classified in the selected industries. The distribution is displayed in table 2, as we can see in table that industry with the largest number of players is Food and beverage, which comprise more than 50% of the whole sample. One of the features of the available dataset also allows us to breakdown each sector within each industry. We then break it down the grouping once more following 4-digit ISIC. As a result, we have around 46 different sub-industry within our five main industries1.
Table 2. Distribution of selected industry
Industry N
Automotive 168
Chemical 567
Electronic 289
F&B 2931
Textile 1481
Total 5436
Source: Statistik Industri (2010-2015, Processed)
Other advantage of current data set is that we could apply panel data technique. The characteristic of panel data is; when we can get same information from the same individual (can be person, firm, country, etc.) in different multiple time period (Wooldridge, 2016). The application of panel data could help us to get better estimation
1 Appendix Table 6
161
result. Therefore, this study observed selected firms in multiple periods (2010-2015).
Table 3 below shows the descriptive statistics for six years average. For the row one until eight, the numbers are in million Rupiah and the numbers are already adjusted with Wholesale price index (WPI) on each industry group.
Table 3. Descriptive statistics (six years average)
Industry Automotive Chemical Electronic F&B Textile
1 Value-added 345900 168838 190661 56856 16693
2 Output 611444 397311 440223 154031 45608
3 Input 265544 228473 249562 97175 28915
4 Fixed asset 526381 66818 115957 334020 19104
5 Salary expenditure 22246 11497 25689 4311 3407
6 Energy expenditure 6697 15033 10706 3345 1712
7 TFP VA 4,58 3,42 173,97 1,68 2,43
8 TFP sales 2,05 1,80 278,20 1,32 3,21
9 Employment 318 190 415 128 136
10 Imported material (in %) 22,78 27,19 30,82 2,37 6,66 11 Foreign ownership (0/1) 0,279 0,200 0,458 0,042 0,044 12 Max market share (in %) 48,035 46,661 16,718 18,455 9,585
13 HHI 1145 1069 328 193 167
Source: Analysed from Statistik Industri 2010-2015
From the table we can see that Automotive is the industry group with highest average of value added, output, and fixed asset in six years span. The electronic spent the highest salary, whereas the chemical industry has the highest spending on energy. If we look at the productivity aspect (row seven and nine) the electronic industry is the most productive industry among the group. Row 10 explains about the usage percentage of imported intermediate goods, while row 11 shows if a firm has a foreign ownership in its shareholder. Electronic industry has the highest percentage of imported material usage and around 45% of electronic firms has a foreign influence in their ownership structure.
The last row exhibits the average number of Herfindahl-Hirschman index (HHI) of each sub sectors. The HHI is the sum of squared market share in an industry which has a usual value from 0-10000 (Rodríguez-Castelán et al., 2019). Pavic et al., (2016) stated that HHI value under 1500 is considered efficient competition. Value of 1500-2500 can be considered as moderately concentrated market. Whereas, a HHI value over 2500 is classified as highly concentrated market. In this table it is shown that there is no highly concentrated industry, all industry seems to feature decent competition. However, we could get different result when we examined it further on the sub-industry level2.
The usual panel regression on equation four and five could suffer under endogeneity and reverse causality problem. It was possible that the dependent variable also influenced the independent variable; firm with better performance tend to have more market power.
To overcome that problem the regression method with instrumental variable (IV) or dynamic panel data (DPD) could be applied. With the available dataset DPD model could be easily applied. In dynamic panel data setting, the lag of the dependent variable is added in the model as a new explanatory variable. The lagged dependent variable does not need much attention. However, putting that lag can be important to control for the dynamic
2 Appendix Table 7
162
process. (Brañas-Garza et al., 2011). Study from Chen et al., (2018) also stated that the relationship between market power and firms’ productivity was not completely linear.
Therefore, in this study the main independent variable was adjusted in squared form, as displayed with the equation six and seven.
𝑙𝑛𝑇𝐹𝑃_𝑌𝑖𝑡 = 𝑙𝑛𝑇𝐹𝑃_𝑌𝑖𝑡−1+ 𝛼𝑀𝑆𝑖𝑡 + 𝛽𝑀𝑆2𝑖𝑡+ 𝛾𝑋𝑖𝑡+ 𝜀𝑖 (6) 𝑙𝑛𝑇𝐹𝑃_𝑉𝐴𝑖𝑡 = 𝑙𝑛𝑇𝐹𝑃_𝑉𝐴𝑖𝑡−1+ 𝛼𝑀𝑆𝑖𝑡+ 𝛽𝑀𝑆2𝑖𝑡+ 𝛾𝑋𝑖𝑡+ 𝜀𝑖 (7) 4. FINDINGS
The estimation with DPD has different approach, the estimation is conducted through generalized method of moments (GMM). There are two common estimation methods in dynamic panel data; GMM difference and system GMM. The former uses the lags in differences as an instrument, whereas the latter use the lags in differences and level as an instrument (Labra Lillo & Torrecillas, 2018). The estimation with system GMM allows us to carried out estimation with small period of time, which is more suitable for this study3.
Table 4. Estimation results
Output (1) Value-added (2)
A B A B
GMM SYS GMM SYS IV GMM SYS GMM SYS IV
L1. dep.var. 0,2765*** 0,2774*** 0,2943*** 0,2978***
market share 0,0477*** 0,0491*** 0,0535*** 0,0565***
market share_sq -0,00056*** -0,00056*** -0,00055*** -0,00054***
employment -0,03*** -0,0399*** -0,00226 -0,0122
fixed asset -0,00481** -0,0041* -0,0052 -0,00476
energy expenditure 0,0236*** 0,0281*** -0,0206*** -0,013*
imported material 0,00038 0,00034 -0,00004 0,00011
foreign ownership (0/1) 0,0982* 0,1163** 0,1051 0,1423
N 23954 23954 23954 23954
*, **, *** significant on 10%,5%,1% respectively Source: Analysed from Statistik industri 2010-2015
Table 4 shows the result of the estimation results. The dependent variable as well as control variable employment, fixed asset and energy expenditure are represented in natural logarithm while, the market share is defined in percentage. Column A displays estimation with system GMM method, while column B displays similar estimation with instrument on the main independent variable. The instrument is the lagged variable of market share (Market share & Market share in squared form). All models show that market power has positive relation with firms’ productivity, however in a certain point the effect is
3 The estimation GMM-difference is not suitable. All models have failed to reject H0 from Sargan-test (P-
value=0,0000) and serial correlation exists in model with first lag and second lag. (Ar(2) = 0,0000). There is also inconsistency in the coefficient of the first lag of dependent variable when it estimated using Pooled OLS, Fixed Effect and GMM-difference.
163
diminishing. This result is similar with the study of Chen et al. (2018) which showed that in GMM with IV-setting the concentration ratio, which is a proxy of market power, has a inverted U-shape relation with firm’s productivity.
The estimation with orientation of value added as TFP factor shows larger magnitude but the difference is inconsiderable. Other differences are shown in the control variables, Employment and fixed asset are negatively correlated with TFP in model 1, while in model 2 they have no impact. Energy expenditure is significant in both model, however it has different effect in each model. Imported intermediate goods has no effect on all model and foreign ownership only significant in model 1.
It is not neglectable that each industry has different nature, which indicate that heterogeneity might exist. Therefore, we break down the estimation in each industry for further analysis. Table 5 displays the estimation results. It is found that in most industries, the market power has similar impact on productivity as in the previous estimation, where the productivity increase if a firm capture more market share but at some point, the rate of increase will slow down. The automotive industry is the only sub-sample where no correlation is founded between market share and firm productivity in all estimation model.
In table 3 it is displayed that on average, the automotive industry had the highest level of HHI but the average HHI from Chemical industry is not signficantly different from auntomotive industry yet we have similar estimation from the effect of market share on productivity.
Table 5. Estimation result by industry
Output Value added
Industry GMM SYS GMM SYS IV GMM SYS GMM SYS IV
F&B N=13460
Market Share 0,0626*** 0,0675*** 0,0684*** 0,0791***
Market Share_SQ -0,00078*** -0,00088*** -0,00067* -0,00091***
Textile N=6083
Market Share 0,0956*** 0,0836*** 0,1058*** 0,0858***
Market Share_SQ -0,00131*** -0,00133*** -0,00124*** -0,00120***
Chemical N=2480
Market Share 0,0234*** 0,0288*** 0,0330** 0,0374***
Market Share_SQ -0,00032*** -0,00040*** -0,00040* -0,00051***
Electronic
N=1040
Market Share 0,0465*** 0,0476*** 0,0574*** 0,0636***
Market Share_SQ -0,00049*** -0,00046*** -0,00050* -0,00052***
Automotive N=711
Market Share 0,0166 0,0113 0,0164 0,035
Market Share_SQ -0,00015 -0,00013 -0,00019 -0,00048
*, **, *** significant on 10%,5%,1% respectively Source: Analysed from Statistik Industri 2010-2015
The possible reason might be the number of firms and the number of sub-industry within that firm. It is also found that there is a small difference in result between the measurement of TFP with output and value added, which implicated that the might be no consensus yet in choosing value-added over output in capturing the total factor productivity of firm. Recent research from Kong et al. (2022) claimed that positive correlation existed between market power and TFP among the firm larger market power.
This information is also relevant for the analysis.The proportion of large firm in a sub- industry may play a role in determining the impact. Other variable that explains regional macroeconomic data may also influence the effect of market power on certain industry.
164 5. CONCLUSION(S)
This study investigates the relationship between market power and firms’
performance in manufacturing industries in Indonesia. Market power is described as the share of individual firm’s output on total sub-industry output, while firm’s performance is proxied by total factor productivity. To address the reverse causality problem the estimation method Dynamic panel data is applied. The results suggest that market power has inverted U-shaped relationship with the performance of the firm. Further analysis shows that several industries such as Food & beverage, Textile, Chemical, and Electronic have similar dynamic, whereas in Automotive industry market share has no significant impact at all.
For possible future studies it is recommended to add more observation from different industry, especially from the service sector. It is also heavily recommended to find the threshold where the market power start to slow down. This extension of the study can answer several policy questions such as, how incentive should be distributed? which firm should get better support? Should we limit the growth of one firm? so they would not harm the market and consumer. This policy tool can be very useful for the government and other stakeholder to achive better market dynamic in manufacturing industry.
For estimation strategy other TFP measurement such as from Olley-Pakes and Levinsohn-Petrin could be applied. It is stated that the measurement can solve bias problem that can occur when using TFP through linear regression (Curzi & Olper, 2012).
Measuring productivity directly can also serve as a proxy for firm’s performance. In that case estimation method such as Data envelopment analysis (DEA) or Stochastic frontier analysis (SFA) can be applied (Majumdar & Asgari, 2017). Applying external instrument also might deliver better result in addressing endogeneity problem and reverse causality but finding such IV could be troublesome and time-consuming.
6. REFERENCES
Arintoko, Ahmad, A. A., & Habibah, S. N. (2021). Market structure and determinants of firm profitability on general insurance industry in Indonesia. Studies in Business and Economics, 16(1).
Bartelsman, E. J., Falk, M., Hagsten, E., & Polder, M. (2019). Productivity, technological innovations and broadband connectivity: Firm-level evidence for ten European countries. Eurasian Business Review, 9(1), 25–48. https://doi.org/10.1007/s40821- 018-0113-0
Battiati, C., Jona-Lasinio, C., Marvasi, E., & Sopranzetti, S. (2021). Market power and productivity trends in the European economies: A Macroecnomic perspective.
LUISS Working Paper, 1.
BPS. (2023). Industri Besar dan Sedang. Industri Besar Dan Sedang.
https://www.bps.go.id/subject/9/industri-besar-dan- sedang.html#subjekViewTab3.html
Brañas-Garza, P., Bucheli, M., & García-Muñoz, T. (2011). Dynamic panel data: A useful technique in experiments. ResearchGate.
Catacutan, Z. (2022). Determinants of total factor productivity across Philippine manufacturing firms: A simultaneous quantile regression analysis. International
165
Journal of Economics, Business and Accounting Research (IJEBAR), 6(2), 648.
https://doi.org/10.29040/ijebar.v6i2.4691
Chen, C., Polemis, M., & Stengos, T. (2018). On the examination of non-linear
relationship between market structure and performance in the US manufacturing industry. Economics Letters, 164, 1–4. https://doi.org/10.1016/j.econlet.2017.12.030 Curzi, D., & Olper, A. (2012). Export behavior of Italian food firms: Does product quality matter? Food Policy, 37(5), 493–503. https://doi.org/10.1016/j.foodpol.2012.05.004 Dai, J., Li, X., & Cai, H. (2018). Market power, scale economy and productivity: The case
of China’s food and tobacco industry. China Agricultural Economic Review, 10(2), 313–322. https://doi.org/10.1108/CAER-03-2017-0040
Huang, D. Z. X. (2021). Environmental, social and governance (ESG) activity and firm performance: A review and consolidation. Accounting & Finance, 61(1), 335–360.
https://doi.org/10.1111/acfi.12569
Jovanovic, B. (2018). Productivity analysis by using firm-level data: The case of Macedonia. Working Paper, 5.
Kong, X., Xu, J., & Zhang, Y. (2022). Industry competition and firm productivity:
Evidence from the antitrust policy in China. Finance Research Letters, 47, 103001.
https://doi.org/10.1016/j.frl.2022.103001
Kreuser, C. F., & Newman, C. (2018). Total Factor Productivity in South African Manufacturing Firms. South African Journal of Economics, 86, 40–78.
https://doi.org/10.1111/saje.12179
Labra Lillo, R., & Torrecillas, C. (2018). Estimating dynamic Panel data. A practical approach to perform long panels. Revista Colombiana de Estadística, 41(1), 31–52.
https://doi.org/10.15446/rce.v41n1.61885
Maghfuriyah, A., Azam, S. M. F., & Shukri, S. (2019). Market structure and Islamic banking performance in Indonesia: An error correction model. Management Science Letters, 1407–1418. https://doi.org/10.5267/j.msl.2019.5.010
Majumdar, S., & Asgari, B. (2017). Performance Analysis of Listed Companies in the UAE-Using DEA Malmquist Index Approach. American Journal of Operations Research, 07(02), 133–151. https://doi.org/10.4236/ajor.2017.72010
Nurwati, E., Azam Achsani, N., Hafidhuddin, D., & Nuryartono, N. (2014). Market Structure and Bank Performance: Empirical Evidence of Islamic Banking in Indonesia. Asian Social Science, 10(10), p105.
https://doi.org/10.5539/ass.v10n10p105
Pahlevi, C., & Ruslan, A. (2019). Effect of Market Structure and Financial Characteristics on Bank Performance in Indonesia. International Journal of Academic Research in Accounting, Finance and Management Sciences, 9(3), Pages 128-139.
https://doi.org/10.6007/IJARAFMS/v9-i3/6347
166
Pavic, I., Galetic, F., & Piplica, D. (2016). Similarities and Differences between the CR and HHI as an Indicator of Market Concentration and Market Power. British Journal of Economics, Management & Trade, 13(1), 1–8.
https://doi.org/10.9734/BJEMT/2016/23193
Pervan, M., Pervan, I., & Ćurak, M. (2019). Determinants of firm profitability in the Croatian manufacturing industry: Evidence from dynamic panel analysis. Economic Research-Ekonomska Istraživanja, 32(1), 968–981.
https://doi.org/10.1080/1331677X.2019.1583587
Rodríguez-Castelán, C., López-Calva, L.-F., & Barriga-Cabanillas, O. (2019). Market Concentration, Trade Exposure, and Productivity in Developing Countries:
Evidence from Mexico.
Sahoo, B. B., & Pramanik, A. K. (2017). Value Added: Technique for Corporate Performance Measurement under Social Perspective. KINERJA, 21(1), 109–128.
https://doi.org/10.24002/kinerja.v21i1.1038
UN. (2008). International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4. United Nations. https://doi.org/10.18356/8722852c-en
Wooldridge, J. M. (2016). Introductory econometrics: A modern approach (Sixth edition, student edition). Cengage Learning.
167 7. APPENDIX
Table 6. Observation by ISIC 4-digit ISIC-
Code
Description N ISIC-
Code
Description N
Food and Beverages Chemical
1511 Meat products 42 2411 Basic Chemical 117
1512 Fish products 340 2412 Fertilizer 45
1513 Fruit & vegetables 47 2413 Basic plastic 29
1514 Oils and fats 247 2421 Pesticide 19
1520 Dairy product 18 2422 Paints 75
1531 Grain mill 318 2423 Pharmaceuticals 122
1532 Starches and starch products
124 2424 Soap and detergent 70
1533 Animal feeds 32 2429 Other chemicals 78
1541 Bakery 363 2430 Man-made fibres 12
1542 Sugar 45 Total 567
1543 Cocoa 38
1544 Pasta, Noodles, etc. 186 Electronic
1549 Other food products 993 3000 Computer 4
1551 Alcohol 6 3110 Electric motor, generators,
transformers
19
1552 Wines 1 3120 Electricity distribution and
control
30 1554 Soft drinks & mineral
Water
131 3130 Insulated wire and cable 46
Total 2931 3140 Battery 14
3150 Lamps and lighting 15
Textiles 3190 Other electrical equipment 14
1711 Preparation of textiles fibres
522 3210 Electronic component 110
1712 Finishing of textiles 400 3220 Transmitter 6
1721 Textile Article 94 3230 Receivers 31
1722 Carpet and rugs 8 Total 289
1723 Cordage, rope, etc. 51
1729 Other textiles 164 Vehicles
1730 Knitted and crocheted fabrics
154 3410 Motor vehicle 8
1740 Other fabrics 88 3420 Body and trailers 48
Total 1481 3430 Parts of motor vehicles 112
Total 168
Source: Processed from Statistik Industri
168
Table 7. HHI by ISIC 4-digit ISIC-
Code
Description HHI ISIC-
Code
Description HHI
Food and Beverages Chemical
1511 Meat products 1622 2411 Basic Chemical 1096
1512 Fish products 740 2412 Fertilizer 3425
1513 Fruit & vegetables 2033 2413 Basic plastic 3812
1514 Oils and fats 517 2421 Pesticide 1823
1520 Dairy product 2938 2422 Paints 818
1531 Grain mill 2169 2423 Pharmaceuticals 514
1532 Starches and starch
products 912
2424 Soap and detergent
956
1533 Animal feeds 1517 2429 Other chemicals 1498
1541 Bakery 380 2430 Man-made fibres 2735
1543 Cocoa 2665
1544 Pasta, Noodles, etc. 699 Electronic
1549 Other food products 529 3000 Computer 4361
1551 Alcohol
3634
3110 Electric motor, generators,
transformers 1724
1552 Wines
10000
3120 Electricity distribution and
control 1562
1554 Soft drinks & mineral
Water 567
3130 Insulated wire and cable
1251
3150 Lamps and lighting 3466
Textiles 3190 Other electrical equipment 14
1711 Preparation of textiles
fibres 339
3210 Electronic component
683
1712 Finishing of textiles 795 3220 Transmitter 3985
1721 Textile Article 1741 3230 Receivers 2505
1723 Cordage, rope, etc. 1440
1729 Other textiles 447 Vehicles
1730 Knitted and crocheted
fabrics 612
3410 Motor vehicle
3782
1740 Other fabrics 1150 3420 Body and trailers 1379
3430 Parts of motor vehicles 387
Source : Processed from Statistik Industri
