Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2021 Vol: 20 Issue: 2

The Impact of Economic Growth in the Primary, Secondary and Tertiary Sectors on Employment in West Kalimantan, Indonesia

Jamaliah, Universitas Tanjungpura

Elyta, UniversitasTanjungpura

Abstract

These study pursuits to analyze monetary growth in the primary, secondary, and tertiary employment sectors in West Kalimantan Province. The approach used in this study is quantitative. This study is classed as explanatory research (showing cause and effect). The data used are secondary in the Province of West Kalimantan in 2013-2015 obtained from data published by the Central Statistics Agency of West Kalimantan. The evaluation used is multiple regression evaluation of panel data. The results showed that the primary and secondary sectors' economic growth had a high quality and full-size effect. The monetary gain of the tertiary industry had a destructive and sizeable impact on the increase in employment. The economic development of the primary sector had a positive effect on work. In contrast, the secondary sector had a negative and sizeable impact, while the tertiary industry had a better and insignificant effect. The model variables together affect about 81.50 percent of employment, while other variables influence 18.40 percent.

Keywords

Economic Growth, Employment, Economic Development.

Introduction

The primary determinant of the financial boom is associated with the call for items and offerings from outdoor the place. This assumption presents know-how that a region can inspire an economic increase in it, in which the part must have a leading sector capable of winning the opposition with inside the equal area as different areas so that the leading site can meet regional wishes or meet the call for from out of doors the place or in exports (Arsyad, 2010). A good economy in a sector can support the surrounding community's lives and assist the Government in increasing state revenue (Elyta et al., 2020 a & b). In the modern era, the latest energy use dynamics have encompassed many terms, such as economic growth, technological advances, and trade openness. These have been the subject of debate over the past few years. Economic growth reflects economic activities. If, in a certain period, the economy has experienced positive change, it means that economic activities in that period have increased. Meanwhile, if, in a certain period, the economy has experienced negative growth, it means that economic activities in that period have slowed down.

In economic development, development can be seen from the government apparatus's performance, both at the Government's main level at the village government level (Martoyo et al., 2020). Today, monetary increase and monetary improvement have attracted worldwide researchers' eye in carrying out their tasks in a detailed interpretation of policies' implementation on the development agenda in some underdeveloped and developing countries. The history of development in several developing countries over the last two decades has shown that high economic growth is still difficult to achieve due to several problems such as poverty, income distribution inequality, and unemployment. The difficulty of economic growth in each region is inseparable from the lack of employment. This results in the need for expansion in work.

Employment is fundamental to human life, consisting of economic and social aspects. Labour is one of the supporting economic development factors in a country that aims to achieve equitable economic growth. However, employment is still an unsolvable problem. It happens because the population increases faster than the number of jobs. Engagement can be related to the balanced interaction between the demand for labour and the supply of work. The labour demand market and the labour supply market simultaneously establish a flat wage level and balanced labour utilization.

Meanwhile, an explanation from other experts that employment is the recruitment of workers to carry out their duties is a situation that describes the state of job availability to be filled with job seekers (Todaro & Smith, 2015). The labor economy model related to investment, economic growth, and employment is the Macro Output Model. The macro model is an output job that focuses on the correlation between capital accumulation, industrial output growth, and job creation. The main concern of this growth model is policies to increase national output through capital accumulation. This model is related to the level of employment opportunities with the GNP rate growth, so this model implies that by maximizing its GNP growth, a country can maximize employment.

Research conducted in China has found that sustainable development in enhancing its economic growth is by playing a significant function in expanding the monetary scale, increasing workers’ productivity, and benefiting the economy through technology. Besides, innovation and technology switch help boost industrial structures that sell optimization and improve industrial systems and contribute to sustainable economic development. Like China, economic growth in Taiwan is also dominated by technological sophistication (Yi et al., 2019). Whereas in Taiwan, it has been found that industrial employment from foreign human resources are the most efficient form of international technology transfer and assimilation of new knowledge. In its innovation of the implementation of economic improvement, Taiwan collaborates with Japan by supporting high-tech staff companies from the Japanese industrial world. This collaboration's resulting impact provides an expansion for the workforce in Taiwan (Tabata, 2012).

Contrary to these two countries, South Africa has been affected by excessive unemployment and low employment rates over the past twenty years. The new data released by the South Africa statistics agency show that the unemployment rate is still increasing. Unemployment in South Africa expanded from 27.2% in 2018 to 29.0% in 2019, at the same time as the employment fee reduced from 43.1% to 42.4% over the equal period. This employment data set is astonishing as compared to different developing countries. Of all these countries, South Africa has the highest and growing unemployment rates, followed by Greece and Turkey. Colombia, Brazil, and South Africa have the lowest minimum wages, namely under $300. South Africa also has the lowest index of Human Capital, followed by Brazil and Turkey. In terms of employment rates, South Africa and Argentina have the lowest, at 42.6% and 42.3%, respectively, followed by Turkey. The countries with the highest labor cost index are Turkey and South Africa (Habanabakize et al., 2019).

On the other hand, Malaysia, Indonesia's neighboring country, is also improving its economy by making various efforts and steps with prolonged impact. Malaysia and Indonesia's position, which have strategic borders and similar cultural potentials, facilitates the process of cooperation between the two parties. This will indirectly positively impact equitable regional development (Elyta et al., 2020 a & b). In Malaysia, economic growth is more directed at the public and private sectors. The personal industry (except the agricultural sector) is likely to contribute to its development compared to authorities' investment. Also, Malaysia is taking steps to encourage technology transfer to be in line with the implementation of economic improvement and generate positive impacts on economic growth (Kuppusamy et al., 2009).

Compared to these countries, Indonesia is also increasing its economic growth by making various efforts covering various sectors. As it is known, Indonesia is still high with poverty and unemployment rates, so this makes it essential for the Government to focus on improvements in people's lives so that they can achieve high values in economic growth and be able to compete with other developing countries. Expansion in employment also needs to be considered in improving the economy. On the other hand, the various existing sectors are an essential part to be improved to positively impact life, both in rural and urban areas.

Studies have examined the correlation between economic growth, the environment, and fuel consumption in the empirical literature on fuel and environmental economics. However, economic growth is classified into three groups in its distribution, namely economic growth in the primary, secondary, and tertiary sectors. These three sectors are sectors that can absorb labour. The increasing development of the primary, secondary, and tertiary sectors should affect West Kalimantan Province employment. However, this is inversely proportional to jobs in West Kalimantan Province, which does not show consistency in work in this Province of Indonesia. Therefore, this study aims to analyse monetary growth in employment in West Kalimantan, Indonesia.

Literature Review

The method of monetary improvement is characterized via way of means of economic growth, specifically with inside the pre-needful degree of take-off, specifically the begin of a technological revolution in agriculture, so that the productiveness of the rural quarter will boom however can result in extra green use of hard work so that the hard work absorbed can be smaller. In this sense, sustainable improvement calls for improvement to satisfy the gift desires without implementing a chance to destiny generations' wishes. This worries the progress of the economy, resources, and environmental protection. The capacity and impact of a region's sustainable improvement and resistance to turbulence are limited and affected via means of many factors. Among these, an era is one of the most vital forces for regional resilience and sustainable improvement. Also, it plays a large position in increasing economic scale, increasing hard work productiveness, and monetary benefits.

Moreover, Rostow said the innovation and generation transfer help accelerate the adjustment of industrial structures that sell optimization and improvement of industrial systems and contribute to the sustainability of economic development (Jhingan, 2018). Economic growth is the development of economic activities that causes an increase in goods and services produced by the community and an increase in people's welfare. Economic growth is a critical macroeconomic goal. This is based on three reasons. First, the population is always increasing. This increase in population means that the workforce is growing continuously. Economic growth will be able to employ the force. If the economic growth that can be created is smaller than the workforce increase, it will encourage unemployment. Second, as long as wants and needs are always unlimited, the economy must continually produce more goods and services to meet these wants and needs. Third, efforts to achieve economic equality (economic stability) through income redistribution will be easier to accomplish during high economic growth periods. Financial growth problems can be seen as macroeconomic issues in the long run. From one period to another, the country's ability to produce goods and services will increase. This capacity increase is due to production factors that will always increase in quantity and quality.

Today's contemporary-day financial increase relies upon locating new methods to transport ships, mild rooms, control humans, or make more significant durable footwear. Institutional reform, developing more excellent inclusive institutions (Acemoglu & Robinson, 2015), especially those who allow efficient innovation and new agencies, facilitate the efficient design and new agencies and markets that generate financial increase and growth living standards. Developing nations with confined sources commonly conscious of growing guidelines to growth funding levels (Porter & Kramer, 2019). Although the funding price is vital to inspire financial increase and employment, its quantity is confined. There has to be sustainable funding fulfillment and improved productiveness for balanced economic development (Ahluwalia, 2002). In the modern era of globalization, the agricultural region, which's conventional, is slowly transferring closer to the producing enterprise region, which makes use of and applies advanced generation and significant capital but makes use of only a small quantity of hard work.

Meanwhile, the provider region is slowly starting to decline because of the minimal position of buyers in growing the provider region, so that the more substantial role of the economic and provider sectors is anticipated to inspire the countrywide and nearby economies, particularly in soaking up hard work and opening up as many new jobs as possible, therefore having an instantaneous effect on lowering unemployment. Indonesia is presently acknowledged to have a big populace in which there may be an inclination to enjoy extra hard work. A maximum of Indonesia's people is in rural regions and works within the agricultural area. Excess hard work in a single part will contribute to increased output, and hard work delivers in different sectors. The absorption of extra hard work with inside the business region (contemporary-day region) via way of means of the casual area will purpose a sluggish growth with inside the salary rate in the rural areas, and this may lessen the disparity of income among rural and concrete regions so that an extra delivery of people does now no longer purpose problems for financial increase. Extra hard work becomes the capital to collect income, assuming that the shift of hard work from the conventional to the contemporary-day region runs smoothly and in no way becomes excessive.

Several troubles with inside Indonesia's economic system, including the supply of employment, are the primary troubles. This stems from unequal populace boom and employment costs. Also, the primary triggering component is the necessities imposed through the economic and provider sectors instead of the workforce's dearth of abilities. If they're adequately trained, it can assist the manufacturing manner (items and offerings). In any other case, it will become a financial burden. This reason for the authorities' unique interest is to enhance the economic system by noting the significance of employment for the network to increase the local financial system. Various efforts to improve humans' welfare consist of numerous elements and factors, including the economic system, socio-politics, and culture. Almost all areas in Indonesia experience a similar problem, specifically with inside the hard work zone wherein this zone cannot accommodate a wide variety of people because of the lack of process opportunities. Besides, to create excessive employment opportunities, there has to be a boom in manufacturing in financial sports in this example. This may be accomplished through growing funding due to the fact funding is step one in manufacturing sports. Labour has a critical position with inside the manufacturing manner as the boom in all sectors of the economic system cannot be separated from the contribution of hard work. This is because there's a close correlation between capital and hard work in manufacturing sports. It has been defined that the waft of hard work is known as a critical channel for expertise switch among corporations.

However, competing arguments offer specific factors for this mechanism. First, variations in productiveness among supply and receiving companies had been located to inspire this excess; second, preceding proof shows that the waft of hard work from multinational companies affords productiveness profits for corporations; and third, industry connectedness throughout corporations are considered critical due to the fact industry-unique abilities affect organizational gaining knowledge of and manufacturing (Mankiw, 2010). Employment is the recruitment of people to perform their obligations as they're or a state of affairs that describes the country of process availability to be packed with process seekers. On the alternative hand, the hard work marketplace shows that hard work is susceptible (relative to supply) for knowledgeable and uneducated people. In addition to various participation measures indicating unsuspecting hard work call for, the employment shape itself shows that process introduction is nonetheless susceptible. Although the composition of the economic system has shifted far from the dominant roles of the presidency and public employers, there was a little boom in formal wage employment in the non-public zone. In addition to constrained employment dynamism, there's also little proof of structural extrude throughout industries (Lefouili & Jeon, 2015). The economic boom harms unemployment following the law recommend by Arthur Okun. A high financial crash is predicted to create an output boom so that plenty of people are needed to seize up with the boom in output capacity (Arsyad, 2010).

The impact of adjustments in output at the unemployment fee can be longer for areas with proper hard work marketplace safety regulations. Meanwhile, this impact can be sizeable if carried out in regions that apply susceptible safety policies (Economou & Psarianos, 2016). The economic boom is typically observed through the introduction of recent jobs. As the financial system grows, there's a boom with inside the manufacturing of products and offerings. When this happens, the want for hard work to provide items and offerings will boom. Economic boom and unemployment have a strong correlation because the running populace contributes to producing articles and offerings, even as unemployment does now no longer. Also, misplacing inflation will reason a country's financial boom to endanger its economic boom (Kuncoro, 2003).

The shift of hard work occurs because of output growth produced through the secondary and tertiary sectors. There is a shift of hard work from the number one zone to the secondary and tertiary sectors due to the fact all people with inside the number one zone to produce an equal quantity of output. Thus, the wages acquired through people with inside the number one zone are decided primarily based totally on the standard product as compared to the marginal made of hard work with inside the secondary and tertiary sectors. The wages supplied through the tertiary and secondary sectors are better than the number one sectors, so that humans are competing to discover paintings with inside the secondary and tertiary sectors. The secondary zone includes manufacturing, electricity, fuel line and smooth water, and buildings. The second location in growing countrywide earnings has now no longer modified plenty, and the adjustments had been inconsistent. The effects of studies associated with the Secondary Sector Indirect Effect at the Human Development Index via Employment display a positive correlation. There is an impact of the secondary zone at the absorption of unionized hard work, together with the actual zone financial boom via the output of the construction, trade, transportation, and communique sectors that is enormous for the employment so that that extra hard work may be absorbed. Conversely, a decline with inside the secondary zone will lessen engagement.

Meanwhile, the authority's regulations regularly have harmful impacts, authorities applications are appropriately designed, and rules can promote process boom. Policies that enhance the commercial enterprise environment, as correctly as adjusted subsidies, tax exemptions, and monetary assistance, can help corporations carry out better (Hansen et al., 2009). In addition to the authorities, non-governmental organizations (NGOs) also can offer to assist applications for employment boom. When small manufacturing corporations receive NGO assistance, giving them more entry to overseas markets, they get 15-25% better profits (Atkin et al., 2017). Financial assistance from the authorities or NGOs may be critical. However, the effect of microfinance is reasonably small internationally. Therefore, coverage makers need to know no longer be too enticed through the amount of financial boom; however, extra importantly, they have to be aware of its shape and quality. According to UNDP, the financial crash will become lame or disabled if the economic system as an entire grows; however, it does now no longer enlarge employment opportunities.

The hypothesis is a quick solution to reply to the hassle formulation. Based on the conceptual framework above, the studies speculation may be drawn as follows (Figure 1):

Figure 1 Conceptual Framework

1. Economic increase in the prior quarter influences the rise of employment in West Kalimantan Province.

2. Secondary quarter monetary increase influences the increase of employment in West Kalimantan Province.

3. Economic increase in the tertiary quarter has an impact on the rise of employment in West Kalimantan Province.

Methodology

The method used in this research is the quantitative method. The quantitative approach is research using numerical data (numbers). This research is classified as explanatory research. It was conducted in West Kalimantan Province, with the Central Statistics Agency (BPS) timeframe, namely from 2013-2015. The type of data used in this research is secondary data obtained from the Central Statistics Agency website and official agencies such as publications from the Central Statistics Agencies at the regional (District) and provincial levels. The analysis method used in this research is multiple regression models, and panel data with the help of Eviews 9, panel data (pooled data) or what is also called longitudinal data is a combination of cross-section and time-series data.

Systematically, the multiple regression models and the panel data can be explained as follows:

image

Where:

Y : Employment Growth

X1 : Primary Sector Economic Growth

X2 : Secondary Sector Economic Growth

X3 : Tertiary Sector Economic Growth

α : Constant

β1, β2, β3 : Regression Coefficient

e : Error Term.

Results and Discussion

To determine the regression model in this study, first simulate the equation of three models: the Comment Effect model, the Fixed Effect model, and the Random Effect model. From the three models, one type of model is chosen to be used in the regression equation. The selected model is a model that has a significantly better value. And this study shows that the Fixed Effect model has a better significance value so that the results of Fixed Effect modeling are used to estimate the regression in this study. The following shows the processed data results for the three models that have been processed using e-views 9 (Tables 1-8).

Table 1 Processed Results Common Effect no Weight Regression
Variable Coefficient Std. Error t-Statistic Prob.
C 3.961100 21.99303 0.180107 0.8580
PEPRIM? 1.501663 1.431404 1.049084 0.3008
PESEK? 0.117213 2.206786 0.053115 0.9579
PETER? -1.105807 2.825222 -0.391405 0.6977
R-squared 0.029651 Mean dependent var   3.156133
Adjusted R-squared -0.046956 S.D. dependent var   20.18016
S.E. of regression 20.64851 Akaike info criterion   8.983556
Sum squared resid 16201.72 Schwarz criterion   9.149049
Log-likelihood -184.6547 Hannan-Quinn criteria.   9.044216
F-statistic 0.387051 Durbin-Watson stat   2.932090
Prob(F-statistic) 0.762947      
Table 2 Common Effect Cross Section Weight
Linear estimation after one-step weighting matrix
Variable Coefficient Std. Error t-Statistic Prob.
C 0.441030 7.294253 0.060463 0.9521
PEPRIM? 0.567996 0.736486 0.771225 0.4453
PESEK? 0.501307 0.785266 0.638392 0.5270
PETER? -0.342937 1.203039 -0.285059 0.7771
Weighted Statistics
R-squared 0.029131 Mean dependent var   6.916340
Adjusted R-squared -0.047516 S.D. dependent var   21.00686
S.E. of regression 20.14462 Sum squared resid   15420.62
F-statistic 0.380066 Durbin-Watson stat   2.248230
Prob(F-statistic) 0.767907      
Unweighted Statistics
R-squared 0.016729 Mean dependent var   3.156133
Sum squared resid 16417.48 Durbin-Watson stat   2.885316
Table 3 Fixed Effect no Weight
Variable Coefficient Std. Error t-Statistic Prob.
C 4.916552 34.43809 0.142765 0.8876
PEPRIM? 4.298035 2.201739 1.952109 0.0622
PESEK? 5.480973 4.644219 1.180171 0.2490
PETER? -8.264709 4.801296 -1.721350 0.0975
Fixed Effects (Cross)        
BKY--C -10.93418      
KH--C 6.391629      
KP--C -6.484861      
KR--C 12.87595      
KS--C -28.00148      
KTP--C 33.22327      
KU--C -5.385604      
LDK--C 6.367146      
MLW--C -5.487770      
MPW--C 6.309969      
SBS--C -8.050094      
SGU—C 20.52242      
SKD—C -16.24954      
STG—C -5.096849      
Effects Specification  
Cross-section fixed (dummy variables)  
R-squared 0.241273 Mean dependent var   3.156133
Adjusted R-squared -0.244313 S.D. dependent var   20.18016
S.E. of regression 22.51072 Akaike info criterion   9.356590
Sum squared resid 12668.31 Schwarz criterion   10.05993
Log-likelihood -179.4884 Hannan-Quinn criteria.   9.614393
F-statistic 0.496869 Durbin-Watson stat   3.558244
Prob(F-statistic) 0.925278      
Table 4 Fixed Effect Cross Section Weight
Linear estimation after one-step weighting matrix
Variable Coefficient Std. Error t-Statistic Prob.
C -4.148027 13.80505 -0.300472 0.7663
PEPRIM? 3.714864 0.679068 5.470531 0.0000
PESEK? 6.010411 1.066325 5.636567 0.0000
PETER? -7.037948 2.676266 -2.629764 0.0144
Fixed Effects (Cross)        
BKY--C -10.49689      
KH--C 5.935129      
KP--C -6.049394      
KR--C 10.82537      
KS--C -27.25499      
KTP--C 29.07043      
KU--C -4.685628      
LDK--C 7.148118      
MLW--C -5.272855      
MPW--C 8.592155      
SBS--C -7.705865      
SGU--C 22.04855      
SKD--C -16.24912      
STG--C -5.905006      
Effects Specification
Cross-section fixed (dummy variables)
Weighted Statistics
R-squared 0.815056 Mean dependent var   11.07364
Adjusted R-squared 0.696692 S.D. dependent var   45.92768
S.E. of regression 22.07732 Sum squared resid   12185.20
F-statistic 6.885997 Durbin-Watson stat   2.774614
Prob(F-statistic) 0.000012      
  Unweighted Statistics
R-squared 0.237210 Mean dependent var 3.156133
Sum squared resid 12736.15 Durbin-Watson stat 3.493306
Table 5 Random Swamy Arora
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 3.961100 23.97649 0.165208 0.8697
PEPRIM? 1.501663 1.560497 0.962298 0.3420
PESEK? 0.117213 2.405808 0.048721 0.9614
PETER? -1.105807 3.080018 -0.359026 0.7216
Random Effects (Cross)        
BKY--C 0.000000      
KH--C 0.000000      
KP--C 0.000000      
KR--C 0.000000      
KS--C 0.000000      
KTP--C 0.000000      
KU--C 0.000000      
LDK--C 0.000000      
MLW--C 0.000000      
MPW--C 0.000000      
SBS--C 0.000000      
SGU--C 0.000000      
SKD--C 0.000000      
STG--C 0.000000      
Effects Specification    
      S.D. Rho
Cross-section random 0.000000 0.0000
Idiosyncratic random 22.51072 1.0000
  Weighted Statistics    
R-squared 0.029651 Mean dependent var   3.156133
Adjusted R-squared -0.046956 S.D. dependent var   20.18016
S.E. of regression 20.64851 Sum squared resid   16201.72
F-statistic 0.387051 Durbin-Watson stat   2.932090
Prob(F-statistic) 0.762947      
  Unweighted Statistics    
R-squared 0.029651 Mean dependent var   3.156133
Sum squared resid 16201.72 Durbin-Watson stat   2.932090
Table 6 Random Wallace Hussain
Wallace and Hussain estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 3.961100 24.84529 0.159431 0.8742
PEPRIM? 1.501663 1.617042 0.928648 0.3589
PESEK? 0.117213 2.492983 0.047017 0.9627
PETER? -1.105807 3.191623 -0.346472 0.7309
Random Effects (Cross)        
BKY--C 0.000000      
KH--C 0.000000      
KP--C 0.000000      
KR--C 0.000000      
KS--C 0.000000      
KTP--C 0.000000      
KU--C 0.000000      
LDK--C 0.000000      
MLW--C 0.000000      
MPW--C 0.000000      
SBS--C 0.000000      
SGU--C 0.000000      
SKD--C 0.000000      
STG--C 0.000000      
  Effects Specification    
      S.D. Rho
Cross-section random 0.000000 0.0000
Idiosyncratic random 23.32641 1.0000
  Weighted Statistics    
R-squared 0.029651 Mean dependent var 3.156133
Adjusted R-squared -0.046956 S.D. dependent var 20.18016
S.E. of regression 20.64851 Sum squared resid 16201.72
F-statistic 0.387051 Durbin-Watson stat 2.932090
Prob(F-statistic) 0.762947      
  Unweighted Statistics    
R-squared 0.029651 Mean dependent var 3.156133
Sum squared resid 16201.72 Durbin-Watson stat 2.932090
Table 7 Random Effect Wansbeek Kaptyen
Wansbeck and Kapteyn estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 4.257799 24.36960 0.174718 0.8622
PEPRIM? 1.560910 1.581835 0.986772 0.3300
PESEK? 0.194292 2.468018 0.078724 0.9377
PETER? -1.264126 3.134762 -0.403261 0.6890
Random Effects (Cross)        
BKY--C -0.859191      
KH--C -0.029633      
KP--C 0.145240      
KR--C 0.637607      
KS--C -0.579255      
KTP—C 0.775979      
KU—C -0.559254      
LDK—C 0.638048      
MLW—C -0.205273      
MPW—C 0.278485      
SBS—C -0.347385      
SGU—C 0.439768      
SKD—C -0.239667      
STG—C -0.095467      
  Effects Specification    
      S.D. Rho
Cross-section random 3.555574 0.0243
Idiosyncratic random 22.51072 0.9757
  Weighted Statistics    
R-squared 0.031738 Mean dependent var 3.044265
Adjusted R-squared -0.044703 S.D. dependent var 20.11380
S.E. of regression 20.55846 Sum squared resid 16060.71
F-statistic 0.415195 Durbin-Watson stat 2.952482
Prob(F-statistic) 0.743066      
  Unweighted Statistics    
R-squared 0.029517 Mean dependent var 3.156133
Sum squared resid 16203.94 Durbin-Watson stat 2.926384
Table 8 Multiple Regression Results with Fixed Effect Weight
Variable Coefficient Probability
Constant -4.148027 0.7663
Primary sector economic growth 3,714864 0.0000
Secondary sector economic growth 6,010411 0.0000
Tertiary sector economic growth -7.037948 0.0144

Where, Dependent Variable: PTK?

Method: Pooled Least Squares

Date: 08/17/19 Time: 18:16

Sample: 1 3

Included observations: 3

Cross-sections included: 14

Total pool (balanced) observations: 42

Multiple linear regression analysis is an analytical tool used to determine the effect of the dependent variable (Table 8). The estimation result of numerous regression used in this study is multiple regression with the expected effect weight model.

The results of the calculation on the multiple regression coefficients obtained the following equation:

image

Based on this equation shows that based on the regression results, the constant is equal to-4.148027. This value means that if the primary sector economic growth (X1), secondary sector economic growth (X2), tertiary sector economic growth (X3) changes, the development of labor absorption in West Kalimantan is-4.148027. If economic growth in the primary sector increases by 1%, then employment gain will increase by 3.714864%. If the secondary sector economic growth increases by 1%, the employment growth will increase by 6.010411%. If the tertiary sector's economic development rises by 1%, the absorption of labor will decrease by 7.037948%.

A statistical t-test is a test tool used to partially determine the independent variable's effect on the dependent variable. Following are the results of the statistical t-test (Table 9).

Table 9 Statistical T-Test Results
Variable T-statistics Prob Significant Level Information Direction of Relationships
Primary sector economic growth 5,470531 0.0000 0.05 Significant Positive
Secondary sector economic growth 5,636567 0.0000 0.05 Significant Positive
Tertiary sector economic growth -2.629764 0.0144 0.05 Significant Negative

Based on the estimation results above, the results can be explained. Table 9 shows the primary sector economic growth variable has a positive and significant direction of the relationship, which is seen from the probability value of 0.000 or less than the significant level of 0.05%. This means that the primary sector economic growth variable has an influence on employment in West Kalimantan Province. Table 9 shows that the secondary sector economic growth variable has a positive and significant direction of the relationship, which is seen from the probability value of 0,000 or less than the significant level of 0.05%. This means that the secondary sector economic growth variable has an influence on employment in West Kalimantan Province. Table 9 shows that the tertiary sector economic growth variable has a negative and significant direction of the relationship, which is seen from the probability value of 0.0144 or less than the significant level of 0.05%. This means that the tertiary sector economic growth variable influences labor absorption in West Kalimantan Province.

The statistical f test is used to simultaneously determine the independent variable's effect on the dependent variable. Following are the results of the statistical f test:

Based on Table 10, which shows that the probability value of the f-test statistic of 0.000012 is less than the significant level of 0.05, it can be concluded that the economic growth of the primary, secondary and tertiary sectors has a significant effect on employment in West Kalimantan Province.

Table 10 F Statistical Test Results
F statistic 6.885997
Probability 0.000012

The coefficient of determination (R2) is an indicator used to determine the percentage change in the dependent variable caused by the independent variable.

Based on Table 11, the coefficient of determination (R2) is 0.815056; this indicates that the percentage of economic growth variables in the primary, secondary and tertiary sectors on employment is 81.5056%.

Table 11 Determination Coeficin Results (R2)
R-squared 0.815056
Adjusted R-squared 0.696692

In choosing the panel information regression version, three fashions are to be utilized in figuring out which version is quality to be used in estimating the panel information. The three forms of techniques are the Common Effect version, Fixed Effect version, and Random Effects version. One kind needs to be decided on for use in the panel information regression equation of the three fashions. This regression was accomplished in 14 districts/towns in West Kalimantan Province from 2013 to 2015. The regression analysis outcomes showed that the variable of the number one-quarter increase in part had a subtle and widespread impact on the rise of employment in West Kalimantan Province. Based on the test outcomes accomplished using the Eviews nine information processing device program, the coefficient of the number one-quarter variable is 3.714864. The t-statistical probability is 0.0000, which is smaller than the widespread stage used (α = 0.05). The hypothesis that the increase in the number one quarter affects the rise in employment is accepted. The correlation between the number one quarter and the increase in employment is correlated with the variable coefficient value. If the rural quarter has extended by 1%, then the employment increase will boom through 3.714864%.

The significance of exertions in a place as a monetary drive may be visible from the labour Force Participation Rate (LFPR). The regional economic increase begins with growth in bodily capital, activity creation, and development in manufacturing ability, and improvement of the era to achieve welfare. It may be discovered that capital and exertions are the essential elements riding the economic system of an area or United States that converts inputs into outputs in the shape of products and services. Currently, employment with inside the commercial region remains to experience an increase in Indonesia. In this period, the proportion of employment growth is much less than the balance of monetary gain. This situation is much less favourable for the economic system due to the fact it could illustrate that GDP increase, in particular, with inside the commercial region, has now no longer performed a decisive function in growing the activities of the commercial area so that the use of recent employees or the opening of new jobs in this region remains small.

During the commentary period, the commercial region exertions elasticity becomes extra within the “inelastic” category. Best in 2015 and 2019, it becomes with inside the "elastic" type. The GDP increase within the commercial region isn't always very encouraging in reaction to the rise within the variety of employees in this region. The above situation occurs because employees within the retail area are changed through the manufacturing system within current excessive-tech (capital intensive) machines. The low exceptional of human sources is a problem, at the same time as Indonesia has pretty lots of capability sources. Modernization of the commercial region has a manufacturing mechanism, which immediately influences the elasticity of employment within the commercial area.

Since 2017, the variety of massive and medium scale commercial agencies in Indonesia has decreased, affecting employment in Indonesia. Based on the consequences of Indonesia's economic census records, the commercial region best absorbs approximately 10% of the full team of workers in the United States. Besides, excessive running hours within the commercial region are related to low salary fees and pressure them to work extra hours. This indicates a method to growth output (productiveness) within the commercial area, pursued by lowering the variety of employees and growing running hours. It appears that the shift of exertions is driven extra towards companies that can be accessed without problems, requiring no age, education, skills, and capital so that growth in productiveness is low. These empirical findings are in keeping with the former studies. The increase of the number one and secondary sectors immediately influences employment while the carrier region does now no longer affect work. The expansion of the secondary region and carrier region directly impacts the welfare of the network and indirectly influences human beings' interest through employment (Darma et al., 2020).

The primary sector has a positive and significant correlation because it can absorb labor with low productivity. It is also the driving force for all existing sectors, so employment occurs directly in the primary industry. The primary industry has a significant effect on other sectors and contributes to work (Baderi, 2014). The indirect impact of primary sector growth on employment in West Kalimantan is the production process, from the transportation of input and output goods to the marketing of goods produced by the primary sector, which absorbs labor. Primary sector growth is significant for West Kalimantan's economy because it is the most crucial sector for the Province's economy. According to the West Kalimantan Provincial Government’s record, 60% of the population depends on agricultural activities (Satyagraha, 2017).

The positive correlation between the primary sector and employment growth is caused by the shift of labour from the primary to the secondary industry. Still, this shift of work does not cause production in the primary sector to fall (Tambunan, 2015). The results of economic growth in the agricultural industry have a significant effect on employment. When income inequality is too high in an area, economic growth does not trickle down to the poor, so relying on economic growth alone does not guarantee poverty reduction (Nindi & Odhiambo, 2015). One of the possible underlying factors is the low productivity of West Kalimantan’s people who work in the agricultural sector. Understandably, most of them live below the poverty line, particularly those living in rural areas. The limited capital and resources they have are unable to increase their income, and the narrow land ownership makes people only work as agricultural workers, not as landowners.

The regression analysis results showed that the secondary sector growth variable partially had a positive and significant effect on employment in West Kalimantan Province. Based on the results of the test carried out using the e-views nine tool, the coefficient of the secondary sector variable is 6.010411, and the probability t-statistic is 0.0000, which is smaller than the significant level used (α = 0.05) so that the hypothesis which states that secondary sector growth affects employment is accepted. The test results showed that the secondary sector and work are positively correlated when viewed from the variable coefficient value. This means that if the secondary industry has increased by 1%, employment will increase by 6,010411%.

A positive and significant correlation between the growth of the secondary sector and employment in West Kalimantan is that society has transformed from a traditional to a modern one. This is because the secondary sector offers higher wages compared to the conventional industry. If the secondary sector carries out the production process of both goods and services, it requires labour. Economic growth in the secondary sector will stimulate economic growth in the primary sector, which provides raw materials for secondary sector production needs; if economic growth in the secondary industry increases, economic growth in the primary sector will simultaneously increase. The role of the secondary industry is significant because it can absorb extensive labour.

The economy's structure will shift from being dominated by the agricultural sector to non-primary sectors, such as the industrial sector (Tambunan, 2015). This study shows that the industrial sector affects employment. Therefore, economic growth with equal distribution will encourage regions to catch up, minimize the gap between the poor and the rich, and ultimately reduce the poverty rate. The Government must optimize the agricultural sub-sectors' potential by increasing human resources' capacity, especially education, providing capital and production facilities to marketing commodities. Also, it is necessary to develop other sectors that support the agricultural industry.

The investment figure in the trade sector in Pontianak City contributed 18.61% and was the highest of all sectors, followed by the manufacturing industry by 16.56% (Satyagraha, 2017). The increase in economic growth in the trade, hotel, and restaurant sectors affected employment (Prastyadewi et al., 2013). Based on the results of research conducted using e-views 9, the coefficient of the tertiary sector variable is -7.037948, and the probability of t-statistic is 0.0144, which is smaller than the significant level (α = 0.05) so that the hypothesis which states that the growth of the tertiary sector affects the employment is accepted. The test results also showed that the tertiary industry and jobs are negatively correlated when viewed from the variable coefficient value. If the tertiary sector has increased by 1%, then employment will also decrease by -7.037948%. The negative and significant correlation is that the tertiary industry's economic growth has not absorbed labour. After all, the tertiary sector can only develop in urban areas and strategic areas such as in the regions. This is because the foreign investment that enters urban areas is quite large, so that it has the potential to create new jobs and absorb labour and the resulting output also increases. This study's results encouraged a shift from the primary to the secondary and the tertiary sector due to changes in demand from basic needs, namely clothing, food, and shelter, into a request that focuses on manufacturing and services.

Conclusion

The impact of the economic boom with inside the number one zone showed a giant and tremendous impact on employment in West Kalimantan in 2013-2015. This is due to the fact economic boom with inside the number one zone is a zone that absorbs a whole lot of exertions, and the humans of West Kalimantan by an extensive work with inside the agricultural area. Moreover, the economic boom's impact within the secondary zone showed a giant and tremendous effect on employment in West Kalimantan in 2013-2015. This is because the commercial area gives excessive wages so that humans pick to work with inside the secondary zone. Furthermore, the economic boom's impact on the tertiary site showed giant and adverse effects on employment in West Kalimantan in 2013-2015. This is because the tertiary zone is the handiest capable of increase in city and strategic areas.

Based on the conclusion, it is recommended that the West Kalimantan Government should make policies that prioritize labour-intensive employment because the skills of the labour in West Kalimantan have not adapted to technological tools. Therefore, the three main sectors can be positively proportional to jobs in West Kalimantan. Furthermore, the West Kalimantan Government should make policies that prioritize the balanced growth of the three main sectors so that the three main sectors' growth can have a positive effect on employment in West Kalimantan. Also, the Government should continue to increase investment and export volume from year to year, especially the investment that can absorb labour and, at the same time, boost economic growth in West Kalimantan Province.

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