Jurnal Ekonomi & Studi Pembangunan Volume 22 Nomor 2, Oktober 2021 

Article Type: Research Paper 

Productivity Growth and Local Content 
Requirement of the Manufacturing Industry 
in Banten Province 

Diasitta Yusuf ¹, Muhammad Firdaus², and Alla Asmara2

Abstract: Productivity growth needs to be optimized not only to produce quality 
products, increase exports, and reduce dependence on imports but also to 
support the performance of the manufacturing sector. This study aims to 
determine the manufacturing industry map in Banten Province, measure 
manufacturing industry productivity growth, and analyze the effect of Local 
Content Requirement (LCR) on manufacturing industry productivity growth from 
2005 to 2017 by employing the use of K-Means cluster and panel data regression 
analysis. For this purpose, Total Factor Productivity (TFP) is the indicator used to 
measure productivity growth. The LCR is known as the percentage of local raw 
and auxiliary materials used in the production process. According to K-Means 
cluster analysis results, industrial companies are grouped into three clusters with 
different characteristics. The result of panel data regression analysis shows that 
the TFP of all manufacturing industry sub-sectors has increased and was 
influenced by LCR, total production, domestic investment, import, and provincial 
minimum wage. As a result, the government should integrate the LCR and other 
policies, such as the Indonesian National Standard, to increase productivity 
growth. 
Keywords: K-Means Cluster; LCR; Manufacturing Industry; Panel Data; TFP
JEL Classification: C38, L60, C33 

Introduction 

The economic structure based on the national long-term development plan 
from 2005 to 2025 was reinforced by placing the industrial sector as the 
driving force, supported by the agricultural, marine, and mining (Bappenas, 
2007). In the Java Island region, the manufacturing industry sector in West 
Java, Banten, and Central Java provinces has contributed the most to the 
value of Gross Regional Domestic Product (GRDP) from 2016 to 2018 
(Statistics Indonesia, 2019). The contribution of the manufacturing industry 
sector in Banten Province experienced the most decline with an average of 
0.71 percent per year. As a leading sector, the manufacturing industry 
sector in Banten Province has an important role in shaping the total value 
of GRDP and its regression has greatly produced an impact to its economy. 
From 2010 to 2018, the manufacturing industry sector had the most output 
value compared to other sectors, on average reaching 37.24 percent of the 
total GRDP of Banten Province with an average growth of 6.04 percent per 
year. 

AFFILIATION: 
1 Statistics of Bogor Regency, West 
Java, Indonesia 
2 Department of Economics, 
Faculty of Economics and 
Management, Institut Pertanian 
Bogor, West Java, Indonesia 

*CORRESPONDENCE:
diasitta_yusuf@apps.ipb.ac.id

THIS ARTICLE IS AVALILABLE IN: 
http://journal.umy.ac.id/index.php/esp  

DOI: 10.18196/jesp.v22i2.11369 

CITATION: 
Yusuf, D., Firdaus, M., & Asmara, 
A. (2021). Productivity Growth and 
Local Content Requirement of the 
Manufacturing Industry in Banten 
Province. Jurnal Ekonomi & Studi
Pembangunan, 22(2), 154-169. 

ARTICLE HISTORY 
Received: 
24 Mar 2021 
Revised: 
11 July 2021 
23 Aug 2021 
Accepted: 
09 Sep 2021 

https://www.scopus.com/authid/detail.uri?authorId=57204838013
https://scholar.google.co.id/citations?user=mIpPIlcAAAAJ&hl=en
https://bogorkab.bps.go.id/
https://bogorkab.bps.go.id/
https://ekonomi.ipb.ac.id/
https://ekonomi.ipb.ac.id/
https://ekonomi.ipb.ac.id/
https://ekonomi.ipb.ac.id/
mailto:diasitta_yusuf@apps.ipb.ac.id
http://journal.umy.ac.id/index.php/esp
https://journal.umy.ac.id/index.php/esp/article/view/11369
https://crossmark.crossref.org/dialog/?doi=10.18196/jesp.v22i1.7836&domain=pdf
https://crossmark.crossref.org/dialog/?doi=10.18196/jesp.v22i2.11369&domain=pdf


Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 155 

According to Rahmayani and Sugiyanto (2014), the economic growth of a region is 
determined by the level of productivity of its production factor components. Productivity 
means a combination of effectiveness in producing outputs and efficient use of inputs. 
Productivity measurements can be carried out on each input separately (partial) or all 
inputs (total). Total productivity measurement is carried out on several inputs by using 
the Total Factor Productivity (TFP). TFP is an indicator used to measure productivity 
growth and is part of the output which may not be explained by some of the inputs used 
in the production process (BPPT, 2012). Analysis of productivity growth in the 
manufacturing industry sector needs to be carried out to see the sustainability of output 
growth (Surjaningsih & Permono, 2014) and to identify sources of output growth that will 
help policymakers identify factors that contribute to industrial development (Mustapha 
et al., 2013).  
 
The manufacturing industry is an economic activity that carries out activities to change a 
basic good into finished or semi-finished goods, and or goods of less value to goods of 
higher value. Based on the number of workers, the manufacturing industry sector is 
divided into four groups: large industry, medium industry, small industry, and home 
industry (Statistics Indonesia, 2018). From 2010 to 2017 the number of Medium Large 
Industry (MLI) labor has always increased, but labor productivity has decreased in 2011, 
2016, and 2017. A reduction in labor productivity may partially affect total productivity 
and ultimately affect the contribution of the manufacturing industry sector to the GRDP 
of Banten Province. Industrial productivity needs to be optimized to produce better 
quality products, to increase added value and competitiveness. Strategies that can be 
taken to increase industrial competitiveness are to improve the quality of human 
resources and provide incentives to industries that can carry out and increase the 
proportion of raw materials (Nuraini & Setiartiti, 2017). Consequently, this will lead to an 
increase in exports while reducing dependence on imported products. The changes in 
exports and imports will affect economic growth in the region through production, 
consumption, and distribution (Astuti & Ayuningtyas, 2018). The value and growth of 
export and import of Banten Province are shown in Figure 1. 
 

 
 

Figure 1 Value and Growth of Export and Import of Banten Province 2010-2017 
Source: Statistics Indonesia, 2018 

 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 156 

Based on Figure 1, it may be seen that from 2010 to 2017, the export value of the oil and 
gas sector has decreased by an average of 0.49 percent per year. Meanwhile, the export 
value of the non-oil and gas sector has increased. In this case, the average export value of 
the non-oil and gas sector for agricultural products increased by 48.42 percent, industrial 
products increased by 6.03 percent, and mining and other products increased by 89.01 
percent per year. Furthermore, the import value increased from 2010 to 2017. On 
average, the import value of consumer goods products increased by 117.53 percent, raw 
and auxiliary material products increased by 7.37 percent, and capital goods products 
increased by 8.42 percent per year. Overall, the export value of Banten Province increased 
by 4.90 percent per year or less than the increase in import value which reached 7.05 
percent per year. This means that Banten Province has a fairly high level of dependence 
on imported products, especially on products of raw and auxiliary materials. 
 
One of the government efforts made to increase industrial productivity, increase export, 
and reduce dependence on imported products at the same time is by establishing a Local 
Content Requirement (LCR) policy. Based on the Regulation of the Minister of Industry 
No.16/M-IND/PER/2/2011, LCR is defined as a limit or value that represents the level of 
local content requirement in a product or service. In the industrial sector, the LCR policy 
is carried out to strengthen, expand, and deepen the national industrial structure, both 
to the import substitution industry strategy and the export-oriented industrial strategy. 
Based on Government Regulation No.29/2018, domestic products that must be used in 
the procurement of goods and services must have a total LCR value and a Company 
Benefit Weight (CBW) value of at least 40 percent or an LCR value of at least 25 percent. 
 
Research results that related to productivity growth and local component requirement 
using cluster analysis and panel data were used as reference and comparison to this 
research. Several previous research on the factors affecting productivity growth has 
shown different results. The research of Budiwati and Yunanto (2013) shows that the 
factors that influence the Total Factor Productivity (TFP). are inflation, net export, R&D 
budgets, and the level of education of workers. On the other hand, Fazri et al. (2018) show 
that export, foreign investment, and domestic investment affect TFP. Research by De 
Souza and Da Cunha (2018) shows that productivity decreased in the industrial sector, 
while productivity increased in the agricultural sector and service sector. Several previous 
research on the effects and benefits of Local Content Requirement (LCR) policy has shown 
different results. The research of Negara (2016) shows that there is an effect of imported 
inputs on the level of productivity, added value, output, exports, and the number of jobs 
in the manufacturing sector in Indonesia. Kolstad and Kinyondo (2016) shows that the LCR 
policy has less influence on countries that already have natural resources than countries 
that have just discovered natural resources (oil). Research by Deringer et al. (2018) shows 
that the LCR policy in the heavy vehicle industry affects increasing output in Brazil, Russia, 
India, China, and South Africa (BRICS). At the same time, the increase in output has an 
adverse side effect such as changes in prices. Changes in prices will have a greater impact 
on the overall economic sector than the gains in industrial output in a particular sub-
sector. To fill in the gaps from the limitations of previous research, it is needed to conduct 
research that can see more thoroughly the effect of LCR and other factors on productivity 
growth. This research will show how the LCR influences the productivity of the 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 157 

manufacturing industry even at a more specific level, specifically at the sub-sector level 
of the manufacturing industry. 

 
Previous research has discussed the productivity growth of the manufacturing industry 
and LCR policy separately and has not seen the relationship. In this case, the discussion 
on the productivity growth of the manufacturing industry focused more at a regional level 
or on the industrial sector as a whole and was not specific to the industrial sub-sector 
level. The discussions on LCR policy also focused more on achieving LCR targets in 
industrial sectors. The novelty in this research is to see how the relationship between LCR 
policy and manufacturing industry productivity growth at a specific level, namely the 
manufacturing industry sub-sector. In addition, this study aims to determine how the 
manufacturing industry maps, measures the manufacturing industry growth and analyze 
the factors that affect the manufacturing industry productivity growth in Banten Province. 
The results of this research are expected to give recommendations to the government 
and business actors to decide the right LCR policy to increase productivity and develop 
the manufacturing industry sector. 
 
 

Research Method 
 
Data Types and Sources 
 
The data type used is secondary data with panel data of 836 Medium Large Industry (MLI) 
from 20 manufacturing industry sub-sectors from 2005 to 2017. The data sources used 
are from the annual MLI Survey conducted by Statistics Indonesia of Banten Province, and 
the Ministry of Investment. In this case, data related to output, input, export, import, LCR, 
and minimum wage were obtained from Statistics Indonesia. Data related to the value of 
a domestic and foreign investment were obtained from the Ministry of Investment. The 
data obtained are then selected and adjusted to the research method, resulting in 
balanced panel data. 
 
Research Variable 
 
This research used variables which were determined based on the research objectives. 
The variables used to determine the manufacturing industry map consist of five 
independent variables, which are output, input which is in the form of capital, labor, and 
intermediate inputs, and LCR from each MLI. The variables used to measure the 
manufacturing industry productivity growth consist of one dependent variable and three 
independent variables. The dependent variable is the output, and the dependent 
variables are capital, labor force, and intermediate input. The variables used to analyze 
the effect of LCR and other factors on manufacturing industrial productivity growth 
consist of one dependent variable and six independent variables. The dependent variable 
is TFP, and the dependent variables are LCR, total production, domestic investment, 
foreign investment, export, import, and minimum wage generated by the manufacturing 
sub-sector. The description of variables is shown in Table 1. 
 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 158 

Table 1 The Description of Variables 
No. Research Objectives Variables Definition 

1 Determine manufacturing industry 
map 

Y Output 
C Capital 
L Labor Force 
I Intermediate Input 

LCR Local Content Requirement 
2 Measure manufacturing industry 

productivity growth 
Y Output 
C Capital 
L Labor Force 
I Intermediate Input 

3 Analyze the effect of Local Content 
Requirement (LCR) and other factors 
on manufacturing industry 
productivity growth 

TFP Total Factor Productivity 
LCR Local Content Requirement 

PROD Total Production 
DIV Domestic Investment 
FIV Foreign Investment 
EXP Export 
IMP Import 

PMW Provincial Minimum Wage 

Source: Statistics Indonesia and Ministry of Investment. 
 
Method of Analysis 
 
The analytical method used is descriptive and quantitative analysis. Descriptive analysis is 
used to provide an overview of Medium Large Industry (MLI) based on the manufacturing 
industry sub-sector. Descriptive analysis was also carried out by looking at the results of 
cluster analysis to determine the manufacturing industry map, apart from tables and 
figures. Quantitative analysis used in this research is panel data analysis. Panel data 
analysis is used to measure manufacturing industry productivity growth and analyze the 
effect of Local Content Requirement (LCR) and other factors on the manufacturing 
industry productivity growth in Banten Province. 
 
K-Means cluster analysis for the manufacturing industry map 
 
The cluster analysis method used is the K-Means cluster method based on the research of 
Setyaningsih (2012) and Karaca (2018). The clusters produced are groups with a high 
degree of similarity. Each cluster will contain very similar data. The stages carried out in 
the K-Means cluster analysis are as follows: (1) Determine the number of clusters to be 
formed, where the number of clusters must be less than the number of data (k <n); (2) 
Randomly determine the center point (centroid) for each cluster; (3) Calculate the 
distance between the data and the centroid in each cluster by using Euclidean Distance; 
(4) Grouping data based on the closest distance to the centroid; and (5) If all data already 
have clusters, and there are no data moving clusters, the cluster analysis process has been 
completed, or it is called convergent.  
 
 
 
 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 159 

Panel data regression analysis for the manufacturing industry productivity growth 
 
The variables used to measure the manufacturing industry productivity growth in Banten 
Province were determined based on research by Mayashinta and Firdaus (2014) and Fazri 
et al. (2018). The equation is written as follows: 
 

LnYit = 𝛼 + 𝛽₁ LnCit + 𝛽₂ LnLit + 𝛽₃ LnIit + 𝜀it      (1) 
 
Where:  
Yᵢₜ: Output Value (million IDR), Cᵢₜ: Capital Value (million IDR), Lᵢₜ: Total Labor Force 
(people), Iᵢₜ: Intermediate Input Value (million IDR), 𝛼: Constant, 𝛽₁, 𝛽₂, 𝛽₃,: Regression 
Coefficient, 𝜀: Error Term, i: 836 Medium Large Industry, t: Period 2005-2017. 
 
The manufacturing industry productivity growth is known throughout the Total Factor 
Productivity (TFP). The TFP concept employs a Cobb Douglas production function theory 
framework and a neoclassical growth model. TFP is known as residue, which is the amount 
of output growth remaining after calculating the growth of measurable factors (BPPT, 
2012). The equation is written as follows: 
 

TFPit = LnYit - 𝛽₁ LnCit - 𝛽₂ LnLit - 𝛽₃ LnIit       (2) 
 
Where:  
TFPᵢₜ : Productivity Growth (percent)  
 
Panel data regression analysis for the effect of local content requirement and other 
factors on the manufacturing industry productivity growth 
 
The variables used to analyze the effect of Local Content Requirement (LCR) and other 
factors on the manufacturing industry productivity growth in Banten Province were 
determined based on research by Mayashinta and Firdaus (2014) and Negara, (2016). The 
equation is written as follows: 
 
TFPit = α + β1LCRit + β2 LnPRODit + β3 LnDIVit + β4 LnFIVit + β5 LnEXPit + β6 LnIMPit + β7 
LnPMWit + 𝜀it          (3) 
 
Where:  
TFPit: Productivity Growth (percent), LCRit: Local Content Requirement (percent), PRODit: 
Total Production Value in Manufacturing Industry Sub-sector (million IDR), DIVit: Domestic 
Investment Value in Manufacturing Industry Sub-sector (million IDR), FIVit: Foreign 
Investment Value in Manufacturing Industry Sub-sector (thousand USD), EXPit: Export 
Value in Manufacturing Industry Subsector (thousand USD), IMPit: Import Value in 
Manufacturing Industry Subsector (million IDR), PMWit: Provincial Minimum Wage Value 
(IDR), α: Constant, 𝛽1, 𝛽2, 𝛽3, 𝛽4, β5, β6, β7: Regression Coefficient, 𝜀: Error Term, i: 20 
Manufacturing Industry Sub-sectors, t: Period 2005-2017. 
 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 160 

The best model selected between the Fixed Effects Model (FEM) and the Random Effects 
Model (REM) was done by using the Hausman test Firdaus, (2020). A classical regression 
assumption test was performed to ensure that the panel data model selected has 
provided the Best Linear Unbias Estimator (BLUE) prediction result. The interpretation of 
the panel data regression estimation results is carried out by looking at the statistical test 
results, by the simultaneous significance test (F-test), partial significance test (T-test), and 
the Goodness of Fit test that indicated by the coefficient of determination (R-Square). 
 
 

Result and Discussion 
 

Overview of Medium Large Industry  
 
The manufacturing industry sector provided the most contribution, with 37.39 percent of 
the total GRDP of Banten Province or 127 trillion IDR per year. After the manufacturing 
industry sector, the wholesale, retail trade and repair of cars and motorbikes sector 
contributed 13.35 percent or 45 trillion IDR. The construction sector contributed 8.85 
percent or 30 trillion IDR, and the real estate sector contributed 7.94 percent or 27 trillion 
IDR. Then the transportation and warehousing sectors contributed 6.36 percent or 22 
trillion IDR. 12 other sectors contributed 26.07 percent or 89 trillion IDR to the total GRDP 
of Banten Province. The manufacturing industry sub-sector that has the largest output 
value is the chemical and goods made of chemicals (ISIC 20), which is 95 trillion IDR per 
year or 24.29 percent (Statistics Indonesia, 2018).  
 
Based on the number of Medium Large Industry (MLI), the largest population of MLI 
comes from the four manufacturing industry sub-sectors, which are the rubber, rubber 
and plastic goods; the food industry; the chemical and goods made of chemical; and the 
metal goods, non-machinery and equipment (ISIC 22, 10, 20 and 25), since this is 
composed of as many as 1067 MLI or 42.43 percent of the total companies. From 2005 to 
2017, MLI in Banten Province increased by 55.25 percent. The number of manufacturing 
industries used in this research was 33.24 percent of the total MLI in 2017. Most MLI 
samples came from the manufacturing industry sub-sector with ISIC 22, 20, 25, and 10, 
that is 367 MLI or 43.90 percent of the total MLI sample used. The composition of the 
sample used in this research is sufficient and comparable to the population of MLI in 
Banten Province. 
 
Overview of the Local Content Requirement Policy 
 
The implementation of the Local Content Requirement (LCR) policy for companies does 
not only apply to the industrial sector but also to government-run projects. To support 
the production process in the industrial sector, the government may grant import licenses 
for companies that already have an LCR certificate. Besides, the LCR certificate also 
functions for companies wishing to act as providers of goods and services in government 
strategic projects. The LCR certificate is issued by a surveyor institute designated by the 
Ministry of Industry, such as PT. Surveyor Indonesia and PT. Sucofindo.  
 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 161 

Manufacturing Industry Map 
 
K-Means cluster analysis is used to map the manufacturing industry in Banten Province 
based on the characteristics equation owned by MLI. The data used in cluster analysis is 
MLI data in 2017. The number of clusters used is three clusters. The results of the K-Means 
cluster analysis are shown in Table 2. 
 
Table 2 Results of the K-Means Cluster Analysis of Manufacturing Industry 

Variabel 1st Cluster:  2nd Cluster: 3rd Cluster: 

695 MLI  21 MLI   120 MLI  

Output  (0.34) 0.48 1.90 
Capital (0.19) 4.96 0.23 
Labor Force (0.28) 0.86 1.47 
Intermediate Input (0.32) 0.27 1.82 
Local Content Requirement 0.06 -0.31 -0.29 

 
Based on Table 2, the largest number of Medium Large Industry (MLI) is in cluster one 
which is 83.13 percent of the total number of MLI in Banten Province. While the number 
of MLI is at least in cluster two, which is 2.51 percent of the total number of MLI in Banten 
Province. In general, the results of the analysis of each cluster may be explained as 
follows: 
 
1. The first cluster consists of 83.13 percent of MLI in Banten Province. Based on the 

estimation results, the total productivity component of the MLI in the first cluster is 
below the average, and the percentage of Local Content Requirement (LCR) is above 
the average compared to other clusters; 
 

2. The second cluster consists of 2.51 percent of MLI in Banten Province. Based on the 
estimation results, the total productivity component of MLI in the second cluster is on 
average, and the percentage of LCR is below the average compared to others; and 
 

3. The third cluster consists of 14.35 percent of MLI in Banten Province. Based on the 
estimation results, the total productivity component of the MLI in the third cluster is 
above the average and the percentage of LCR is on average compared to others. 

 
Overall, the results of the cluster K-Means analysis show that the companies in the first 
cluster are MLI with low productivity component values and high LCR percentages. 
Companies in the second and third clusters are MLI with high productivity component 
values and low LCR percentages. Thus, it may be seen that most of the MLI in Banten 
Province have low productivity component values and high LCR percentages. This 
condition may occur because it is suspected that the domestic components used, such as 
local raw and auxiliary materials, are low-quality products. As a result, the increase in the 
use of local raw and auxiliary materials did not lead to an increase in the output value 
produced by each MLI, which is indicated by the value of the low productivity component. 
 
 
 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 162 

Cluster characteristics 
 
The characteristics of each cluster are known based on the distribution of the number of 
MLI according to the administrative area, company location, and manufacturing industry 
sub-sector (2-digits of ISIC), as shown in Figure 2. Based on the Regency/City 
administrative area, the largest number of MLI in all clusters came from the Regency and 
the City of Tangerang. Based on company location, the largest number of MLI in all 
clusters are outside industrial areas. Based on the manufacturing industry sub-sector (2-
digits of ISIC), the largest number of MLI in the first and third clusters came from the 
rubber, rubber, and plastic goods industrial sub-sector (ISIC 22) and the chemical and 
goods made of chemicals industrial sub-sector (ISIC 20). The second cluster came from 
the chemical and goods made of chemicals industrial sub-sector (ISIC 20) and the metal 
goods, non-machinery, and equipment industrial sub-sector (ISIC 25). 
 

 
 
Figure 2 Numbers of Medium Large Industry by Administrative Area, Company Location, 

and Manufacturing Industry Sub-sector based on ISIC 
 
The characteristics of each cluster are also known based on capital status, business scale, 
Local Content Requirement (LCR) percentage, and the Indonesian National Standard (SNI) 
provisions, as shown in Figure 3. Based on capital status, the largest number of Medium 
Large Industry (MLI) in all clusters is a domestic investment. Based on the business scale, 
the largest number of MLI in the first cluster is medium scale industry, while in the second 
and third clusters, the largest number of MLI is large scale industry. Based on the 
percentage of LCR, the largest number of MLI in all clusters has met the minimum 
requirement, which is 25 percent of LCR. Based on SNI provisions, the highest number of 
MLI in all clusters are MLI that do not have an SNI certificate. 
 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 163 

 
 

Figure 3 Numbers of Medium Large Industry by Capital Status, Business Scale, 
Percentage of LCR, and Provisions of SNI 

 
Overall, it may be seen that the differences in MLI characteristics in each cluster occur in 
terms of administrative areas, industrial sub-sectors based on ISIC, and business scale. 
Characteristics of the first cluster are MLI with a medium industry business scale, which is 
located in all districts and cities, with the main sub-sector being the rubber, rubber and 
plastic goods industry sub-sector (ISIC 22), and the chemical and goods made of chemical 
industry sub-sector (ISIC 20). Characteristics of the second cluster are the MLI with a large 
industry business scale, which is located in regency and large cities only, with the main 
sub-sector being the chemical and goods made of chemical industry sub-sector (ISIC 20) 
and the metal goods, machines, and equipment industry sub-sector (ISIC 25). 
Characteristics of the third cluster are the MLI with a large industry business scale, which 
is located in regency and large cities only, with the main sub-sector is the rubber, rubber, 
and plastic goods industry sub-sector (ISIC 22) and chemical and goods made of chemical 
industry sub-sector (ISIC 20). Given that the results of this cluster analysis have not been 
able to show the relationship between the effect of LCR on the productivity growth of the 
manufacturing industry, especially over a relatively long period,further analysis is needed, 
specifically through the use of panel data regression analysis. 
 
Manufacturing Industry Productivity Growth 
 
Based on Hausman's test results, the resulting probability value is 0.73 which is greater 
than the α value of 5 percent (0.05). Thus, it may be concluded that REM is the best model. 
The correlation value generated by the variable capital, labor force, and intermediate 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 164 

input is less than 0.80. Hence, it is safe to say that there is no multicollinearity problem. 
Based on the heteroscedasticity test results, the probability value generated is 0.00. This 
means that there is a heteroscedasticity problem. Based on the autocorrelation test 
results, the resulting probability value is 0.50. Consequently, we could posit that there is 
no autocorrelation problem. With a violation of one of the classical regression 
assumptions in the model, correction is needed, by using a model that is robust to 
violations of this assumption. The results of panel data regression with a robust model are 
shown in Table 3. 
 
Table 3 Panel Data Regression Results for The Manufacturing Industry Productivity 
Growth 

Variables Coefficient Standard Error z-statistics 

Constant 5.45685 1.521209 3.59 
Capital  (0.0069606)  0.0074406 (0.94) 
Labor Force  0.3691084  0.3524136 1.05 
Intermediate Input  0.5391041*  0.0625052 8.62 

Note: Significant at * α= 1%. 
 
Based on Table 3, the F-test results show that the probability value is 0.00, which means 
that simultaneously, the variables of capital, labor force, and intermediate input have a 
significant effect on output. The Z-test results show that the probability value of the 
variable capital, labor force, and intermediate input is 0.35; 0.29; and 0.00, which means 
that the intermediate input variable has a significant effect on output. The Goodness of 
Fit test results shows that the coefficient of determination is 0.8278, which means that 
82.78 percent of the output diversity may be explained by the variables used. These 
results are in line with the research conducted by Mayashinta and Firdaus, (2014) and 
Fazri et al., (2018) which shows that the value of the raw material coefficient is greater 
than the other variable coefficients. To describe the condition of the manufacturing 
industry in Banten Province, Total Factor Productivity (TFP) measurement in this research 
was carried out by grouping each Medium Large Industry (MLI) into 20 manufacturing 
industry sub-sectors. The TFP values in each manufacturing industry sub-sector are shown 
in Table 4.  
 
Based on Table 4, the average TFP of the manufacturing industry has increased in all 
industrial sub-sectors. Based on the manufacturing industry sub-sector, from 2006 to 
2017 the highest average TFP occurred in the chemical and goods made of chemicals 
industrial sub-sector (ISIC 20) at 4.61 percent per year. The lowest average TFP occurred 
in the recording media reproduction and printing industrial sub-sector (ISIC 18) at 3.45 
percent per year. The highest TFP value in the chemical and goods made of chemicals 
industrial sub-sector (ISIC 20) is thought to occur because the output value produced by 
the industrial sub-sector is the largest compared to others. The lowest TFP value in the 
recording media reproduction and printing industrial sub-sectors (ISIC 18) is presumed 
because the output value produced by the industrial sub-sector is the smallest compared 
to others. 
 
Overall, from 2006 to 2017 the highest TFP occurred in 2015 and the lowest TFP occurred 
in 2006 with the manufacturing industry sector in Banten Province with average 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 165 

productivity growth of 4.39 percent per year. This means that the resulting output is 4.39 
times the input used by MLI in Banten Province. A positive TFP value indicates that the 
output growth that occurs is greater than the input growth. A positive TFP may indicate 
that the use of production factors is quite efficient. 
 
Table 4 Total Factor Productivity Values by Manufacturing Industry Sub-sector (percent) 

ISIC  Total Factor Productivity (TFP) Average 

2006 2008 2010 2012 2014 2015 2016 2017 

10,11 3.69 4 4.1 4.03 4.27 4.27 4.15 4.42 4.08 
13 3.78 4.06 4.19 3.93 4.09 4.14 4.32 4.35 4.09 
14 3.29 4.59 3.64 3.69 3.82 3.85 3.97 4.26 3.91 
15 3.5 3.51 3.58 3.97 4.04 4.24 3.92 3.9 3.75 
16 3.36 3.52 3.47 3.2 3.71 3.7 3.7 3.93 3.57 
17 3.75 3.78 4.1 4.23 4.07 4.25 4.04 4.13 4.03 
18 3.11 2.96 3.34 3.46 3.77 3.56 3.5 3.99 3.45 
19 3.6 3.54 3.16 3.51 3.59 3.69 3.55 4.22 3.55 
20 4.4 4.37 4.43 4.85 5.16 5.02 4.47 4.43 4.61 
21 3.42 3.44 3.55 3.88 4.02 4.05 3.93 3.89 3.71 
22 3.67 3.83 3.8 3.88 4.12 4.33 4.41 4.15 3.98 
23 3.62 3.53 3.85 4.34 4.26 4.25 4.3 4.19 3.97 
24 4.18 4.27 4.13 4.42 4.66 4.33 4.17 4.29 4.37 
25 3.81 4.03 4.18 4.34 4.23 4.23 4.19 4.21 4.14 
26 3.91 3.56 3.49 3.95 4.16 4.35 3.65 3.85 3.79 
27 4.03 3.8 3.93 4.14 4.41 4.22 4.58 4.13 4.07 
28 3,63 3.62 3.77 4.07 4.73 5.12 4.06 4.02 3.99 
29 3,81 4.14 3.92 4.28 4.52 4.69 4.77 4.56 4.18 
30 3,60 3.86 3.99 4.07 4.26 4.31 4.39 4.49 4.04 

31,32,33 3,34 3.32 3.53 3.53 3.91 4.12 4.62 4.02 3.69 

Note: International Standard Industrial Classification (ISIC) Code as follow: 10, 11 Food and drink; 
13 Textiles; 14 finished clothes; 15 Leather, articles of leather and footwear; 16 Wood, articles of 
wood and cork (excluding furniture) and wicker articles of bamboo, rattan, and the like; 17 Paper 
and paper articles; 18 Recording media reproduction and printing; 19 Products from coal and 
petroleum refining; 20 Chemicals and goods made of chemicals; 21 Pharmacy, chemical medicinal 
products, and traditional medicines; 22 Rubber, rubber and plastic goods; 23 Non-metal minerals; 
24 Base metals; 25 Metal goods, non-machinery and equipment; 26 Computers, electronic and 
optical goods; 27 Electrical equipment; 28 Machinery and equipment not included in other; 29 
Motor vehicles, trailers, and semi-trailers; 30 Other means of transportation; 31, 32, 33 Furniture, 
other processing, repair services, and installation of machines and equipment. 

 
Effect of Local Content Requirement and Other Factors on the Manufacturing Industry 
Productivity Growth 
 
Based on Hausman's test results, the resulting probability value is 0.93 and is greater than 
the α value of 5 percent (0.05). Then, it may be concluded that REM is the best model. 
The correlation value generated by the variables Local Content Requirement (LCR), 
production, domestic investment, foreign investment, export, import, and Provincial 
Minimum Wage (PMW) is smaller than 0.80. With this, we could conclude that there is no 
multicollinearity problem. Based on the heteroscedasticity test results, the probability 
value generated is 0.00. Thus, there is a heteroscedasticity problem. Based on the 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 166 

autocorrelation test results, the resulting probability value is 0.06. Hence, there is no 
autocorrelation problem. With a violation of one of the classical regression assumptions 
in the model, correction is needed, by using a model that is robust to violations of this 
assumption. The results of panel data regression with a robust model are shown in Table 
5. 
 
Table 5 Panel Data Regression Results for the Effect of Local Content Requirement and 
Others Factor on The Manufacturing Industry Productivity Growth 

Variables Coefficient Standard Error z-statistics 

Constant  (2.336716)  0.8154068  (2.87) 
Local Content Requirement  0.0036988*  0.0036988  3.25 
Total Production  0.1812462*  0.1812462  5.41 
Domestic Investment  0.0054558***  0.0054558  1.84 
Foreign Investment  0.0029283  0.0029283  0.91 
Export  (0.0312951)  (0.0312951)  (1.53) 
Import  0.0249503**  0.0249503  2.61 
Provincial Minimum Wage  0.2414062*  0.2414062  3.30 

Note: Significant at * α= 1%, ** α = 5%, *** α = 10% 
 
Based on Table 5, the F-test results show that the probability value is 0.00, which means 
that simultaneously, the variables of Local Content Requirement (LCR), total production, 
domestic investment, foreign investment, export, import, and Provincial Minimum Wage 
(PMW) have a significant effect on the Total Factor Productivity (TFP). The Z-test results 
show that the probability value of the variables LCR, total production, domestic 
investment, foreign investment, export, import, and PMW is 0.00; 0.00; 0.06, 0.36; 0.13; 
0.00; and 0.00, which means that the variables of LCR, production, domestic investment, 
import, and PMW have a significant effect on the TFP. The Goodness of Fit test results 
shows that the coefficient of determination is 0.6215, which means that 62.15 percent of 
TFP diversity can be explained by the variables used. 
 
The results of the panel data regression show that LCR, total production, domestic 
investment, import, and PMW have a significant positive effect on the TFP following the 
research hypothesis. The positive effect of LCR is in line with research conducted by 
Negara (2016) and Deringer et al. (2018), who show that an increase in LCR in terms of 
raw material input will support an increase in output. The positive effect of total 
production is in line with research conducted by Mayashinta and Firdaus (2014), which 
shows that an increase in total production in the manufacturing industry sub-sector may 
increase the efficiency of using production factors or technology absorption. The positive 
effect of domestic investment is in line with research conducted by Fazri et al. (2018), 
which shows that increasing domestic investment will allow Medium Large Industry (MLI) 
to do more innovations. The positive effect of import is in line with research conducted 
by Negara (2016), which shows that increasing import in terms of raw materials will 
provide better access to foreign knowledge and technology for local industries. The 
positive effect of PMW is in line with research conducted by Gehringer et al. (2015) and 
Kim (2015), which shows that an increase in PMW will have a positive effect on improving 
the quality of the labor force so that it may support an increase in output. Thus, an 
increase in LCR, total production, domestic investment, import, and PMW may 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 167 

simultaneously produce higher quality and highly competitive products, thereby 
increasing productivity growth in the manufacturing sub-sector in Banten Province.  
 
The panel data regression results also show that the biggest factor influencing the 
productivity growth of the manufacturing industry is the value of PMW and total 
production. The smallest factor that influences the productivity growth of the 
manufacturing industry is LCR. This result is not appropriate to the objectives of the LCR 
policy for the manufacturing industry. The LCR policy is expected to have a big impact on 
industrial productivity growth. This is thought to have occurred because the LCR 
components used as raw materials and local support did not have sufficient quality to 
support increased output and productivity growth in the manufacturing industry. 
 
The panel data regression estimation results show that the export variable has no 
significant effect on the Total Factor Productivity (TFP), which is not following the research 
hypothesis but in line with research conducted by Kim (2015) and Yalcinkaya et al. (2016). 
Kim (2015) shows that export has no effect on the TFP in the durable goods and 
consumables industry sector in Japan. Yalcinkaya et al. (2016) show that only export 
products with high technology may have a positive influence on TPF. No significant effect 
of export may occur because the largest export in Banten Province is the export of semi-
finished goods, which may not provide maximum profit. In this case, the industrial sub-
sector that has the largest export value is the chemical and goods made of chemicals (ISIC 
20) with organic chemical products. 
 
The panel data regression estimation results show that the foreign investment variable 
has no significant effect on the TFP, which is not following the research hypothesis but in 
line with research conducted by Budiwati and Yunanto (2013). The insignificant influence 
of foreign investment may occur because the percentage of Medium Large Industry (MLI) 
with foreign investment status in Banten Province is relatively small compared to 
domestic investment, which only reaches 15.32 percent, so it does not have a significant 
effect on the productivity growth of the manufacturing industry. According to the 
research of Jayanti & Muqorobin (2017), one of the efforts that can be made to increase 
the realization of investment value is to create a conducive business climate and increase 
the competitiveness of the regional economy. 
 
 

Conclusion 
 

The results of this study contribute to making improvements for the Local Content 
Requirement (LCR) policy on productivity growth in the manufacturing industry. The 
contribution given by this recommendation is beneficial for the government and business 
actors in such a sub-sector. To increase productivity growth, it is recommended to 
determine the priority industrial sub-sectors that are suitable with LCR policy. 
 
The manufacturing industry in Banten Province is mapped into three clusters. The cluster 
industry with large scale MLI members has a relatively high productivity component value 
and a relatively low percentage of LCR. Meanwhile, the cluster industry with medium scale 



Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 168 

MLI members has a relatively low productivity component value and a relatively high 
percentage of LCR. All cluster industries show similar characteristics in locations outside 
the industrial area, domestic investment status, the percentage of LCR that has met the 
minimum requirement and does not have an Indonesian National Standard (SNI) 
certificate. 
 
The productivity growth of the manufacturing industry in Banten Province is positive. The 
highest productivity growth occurred in chemicals and goods made of chemicals by the 
industrial sub-sector (ISIC 20). The productivity growth of the manufacturing industry is 
influenced positively by LCR, total production, domestic investment, import, and PMW. 
The percentage of LCR has a significant effect, but with the smallest value compared to 
other factors. This condition shows that LCR policy has not been able to play an important 
role in the productivity growth of the manufacturing industry. 
 
The government should increase the percentage of LCR in the large scale industry and 
improve the quality of local raw and auxiliary materials used in the medium scale industry. 
It is recommended for the government to integrate the LCR and SNI policy. This is because 
the government also needs to regulate the location of MLI to be in industrial estates, and 
improve the investment system to increase foreign investment contributions. Further 
research may use labor force and machines for specific LCR components and explore the 
other benefits or impacts of LCR policy in the manufacturing industry sector. 
 
 

References 
 

Astuti, I., & Ayuningtyas, F. (2018). Pengaruh ekspor dan impor terhadap pertumbuhan 
ekonomi di Indonesia. Jurnal Ekonomi & Studi Pembangunan, 19(1), 1-10. 
https://doi.org/10.18196/jesp.19.1.3836  

BPPT. (2012). Peranan Teknologi Dalam Pertumbuhan Ekonomi Indonesia : Pendekatan 
Total Factor Productivity. Badan Pengkajian dan Penerapan Teknologi. 

Budiwati, S., & Yunanto, A. (2013). Analisis pertumbuhan total factor productivity Indonesia 
dan faktor-faktor yang mempengaruhinya. Jurnal Kebijakan Ekonomi, 9(1), 57-71. 
http://dx.doi.org/10.21002/jke.v9i1.17  

De Souza, T. A. A., & Da Cunha, M. S. (2018). Performance of Brazilian total factor 
productivity from 2004 to 2014: a sectoral and regional analysis. Journal of Economic 
Structures, 7(1), 1–18. https://doi.org/10.1186/s40008-018-0122-2  

Deringer, H., Erixon, F., Lamprecht, P., & van der Marel, E. (2018). The economic impact of local 
content requirements: a case study of heavy vehicles. Retrieved from 
https://ecipe.org/publications/the-economic-impact-of-local-content-requirements/  

Fazri, M., Siregar, H., & Nuryartono, N. (2018). Efisiensi teknis, pertumbuhan teknologi dan 
total faktor produktivitas pada industri menengah dan besar di Indonesia. Jurnal 
Ekonomi dan Kebijakan Pembangunan, 6(1), 1–20. https://doi.org/10.29244/jekp.6.1.1-
20  

Firdaus, M. (2020). Aplikasi ekonometrika dengan E-views, Stata, dan R. IPB Press. 
Gehringer, A., Martínez-Zarzoso, I., & Nowak-Lehmann Danzinger, F. (2015). What are the 

drivers of total factor productivity in the European Union? Economics of Innovation and 
New Technology, 25(4), 406–434. https://doi.org/10.1080/10438599.2015.1067007  

https://doi.org/10.18196/jesp.19.1.3836
http://dx.doi.org/10.21002/jke.v9i1.17
https://doi.org/10.1186/s40008-018-0122-2
https://ecipe.org/publications/the-economic-impact-of-local-content-requirements/
https://doi.org/10.29244/jekp.6.1.1-20
https://doi.org/10.29244/jekp.6.1.1-20
https://doi.org/10.1080/10438599.2015.1067007


Yusuf, Firdaus, & Asmara 
Productivity Growth and Local Content Requirement … 

 
 

Jurnal Ekonomi & Studi Pembangunan, 2021 | 169 

Jayanti, P., & Muqorobin, M. (2017). Analisis strategi dan program peningkatan daya saing 
pada industri unggulan provinsi Jawa Tengah dalam menghadapi Masyarakat Ekonomi 
Asean (MEA). Jurnal Ekonomi & Studi Pembangunan, 18(1), 52-61. 
https://doi.org/10.18196/jesp.18.1.3952  

Karaca, Z. (2018). The cluster analysis in the manufacturing industry with K-Means method: 
an application for Turkey. Eurasian Journal of Economics and Finance, 6(3), 1–12. 
https://doi.org/10.15604/ejef.2018.06.03.001  

Kim, S. (2015). Factor determinants of total factor productivity growth for the Japanese 
manufacturing industry. Contemporary Economic Policy, 34(3), 572–586. 
https://doi.org/10.1111/coep.12152  

Kolstad, I., & Kinyondo, A. (2016). Alternatives to local content requirements in resource-
rich countries. Oxford Development Studies, 45(4), 409–423. 
https://doi.org/10.1080/13600818.2016.1262836  

Mayashinta, W., & Firdaus, M. (2014). Faktor-faktor yang memengaruhi total factor 
productivity industri pertanian Indonesia periode 1981-2010. Jurnal Manajemen & 
Agribisnis, 10(2), 90–97. Retrieved from 
https://journal.ipb.ac.id/index.php/jmagr/article/view/8480  

Ministry of National Development Planning/National Development Planning Agency 
(Bappenas). Dokumen Perencanaan dan Pelaksanaan RPJPN 2005-2025. Retrieved 
from https://www.bappenas.go.id/id/data-dan-informasi-utama/dokumen-
perencanaan-dan-pelaksanaan/dokumen-rencana-pembangunan-nasional/rpjp-2005-
2025/rpjpn-2005-2025/  

Mustapha, N. H. N., Hashim, N. M. H., & Yacob, R. (2013). Technical components of total 
factor productivity growth in Malaysian manufacturing industry. Applied Mathematics, 
4(9), 1251–1259. https://doi.org/10.4236/am.2013.49169  

Negara, S. D. (2016). The impact of local content requirements on the Indonesian manufacturing industry. 
ISEAS Yusof Ishak Institute Economics. Retrieved from 
http://hdl.handle.net/11540/6716  

Nuraini, R., & Setiartiti, L. (2017). Strategi pengembangan kota Magelang sebagai kawasan 
andalan di provinsi Jawa Tengah. Jurnal Ekonomi & Studi Pembangunan, 18(2), 173-182. 
https://doi.org/10.18196/jesp.18.2.4048  

Rahmayani, D., & Sugiyanto, F. (2014). Total Factor Productivity (TFP) Sebagai kekuatan 
pertumbuhan ekonomi. Prosiding Seminar Nasional & Sidang Pleno ISEI XVII, 149–159. 

Setyaningsih, S. (2012). Using cluster analysis study to examine the successful performance 
entrepreneur in Indonesia. Procedia Economics and Finance, 4, 286–298. 
https://doi.org/10.1016/s2212-5671(12)00343-7  

Statistics Indonesia. (2018). Statistik Industri Besar dan Sedang (IBS) di Provinsi Banten 2010-2017. 
Badan Pusat Statistik. 

Statistics Indonesia. (2019). Produk Domestik Regional Bruto Provinsi-provinsi di Indonesia menurut 
lapangan usaha 2014-2018. Badan Pusat Statistik. 

Surjaningsih, N., & Permono, B. P. (2014). Dinamika total factor productivity industri besar 
dan sedang Indonesia. Buletin Ekonomi Moneter dan Perbankan, 16(3), 277–308. 
https://doi.org/10.21098/bemp.v16i3.46  

Yalcinkaya, O., Aydin, H. I., & Siriner, I. (2016). Macroeconomic determinants of total factor 
productivity: new generation panel data analysis on OECD countries (1996- 2015). 
Annals of the Economy Series, 6, 4–16. Retrieved from 
https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.utgjiu.ro%2
Frevista%2Fec%2Fpdf%2F2016-
06%2F01_HALIL.pdf;h=repec:cbu:jrnlec:y:2016:v:6:p:4-16  

https://doi.org/10.18196/jesp.18.1.3952
https://doi.org/10.15604/ejef.2018.06.03.001
https://doi.org/10.1111/coep.12152
https://doi.org/10.1080/13600818.2016.1262836
https://journal.ipb.ac.id/index.php/jmagr/article/view/8480
https://www.bappenas.go.id/id/data-dan-informasi-utama/dokumen-perencanaan-dan-pelaksanaan/dokumen-rencana-pembangunan-nasional/rpjp-2005-2025/rpjpn-2005-2025/
https://www.bappenas.go.id/id/data-dan-informasi-utama/dokumen-perencanaan-dan-pelaksanaan/dokumen-rencana-pembangunan-nasional/rpjp-2005-2025/rpjpn-2005-2025/
https://www.bappenas.go.id/id/data-dan-informasi-utama/dokumen-perencanaan-dan-pelaksanaan/dokumen-rencana-pembangunan-nasional/rpjp-2005-2025/rpjpn-2005-2025/
https://doi.org/10.4236/am.2013.49169
http://hdl.handle.net/11540/6716
https://doi.org/10.18196/jesp.18.2.4048
https://doi.org/10.1016/s2212-5671(12)00343-7
https://doi.org/10.21098/bemp.v16i3.46
https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.utgjiu.ro%2Frevista%2Fec%2Fpdf%2F2016-06%2F01_HALIL.pdf;h=repec:cbu:jrnlec:y:2016:v:6:p:4-16
https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.utgjiu.ro%2Frevista%2Fec%2Fpdf%2F2016-06%2F01_HALIL.pdf;h=repec:cbu:jrnlec:y:2016:v:6:p:4-16
https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.utgjiu.ro%2Frevista%2Fec%2Fpdf%2F2016-06%2F01_HALIL.pdf;h=repec:cbu:jrnlec:y:2016:v:6:p:4-16