Economy Research & Development
The Effect of Education Level on the Number of Unemployed in 11 Provinces of East Indonesia Region
(Panel Data 2012-2016)
By : Riller Katipana
Abstract
This study aims to see the effect of education level on the number of unemployed in 11 Provinces of Eastern Indonesia region. Where in the research indicators used to see the level of education is (Human Development Index) and (Open Unemployment Rate) for the number of unemployed in Eastern Indonesia region during the period 2012 -2016. In this research using panel data regression with common effect model, which from the research result showed that education level significantly influence to the number of unemployment in 11 provinces of eastern Indonesia region with the value of T-statistic equal to 0.002 < α 0.05.
As for the results of R-square value of 0.18 which indicates that the educational level factor is only able to explain the amount of unemployment is only 18%. So in this study showed that the number of unemployment that occurred not only influenced by the factors of education but influenced by many other factors outside this study.
Keywords: Education Level, Number of Unemployed, Panel Data Regression (CEM)
IDEAS, EconPapers, RePEc, NEP
I. INTRODUCTION
In this study, based on the data in which the problem of unemployment rate in 11 provinces in eastern Indonesia can be explained in the graph below as follows:
Figure 1
Unemployment Rate 11 Provinces
In Eastern Indonesia
2012-2016
 |
| Source :BPS Indonesia 2017 |
Based on figure
1 it can be explained that for unemployment rate in
2015 with peresentase of 4% -8% during period 2012-2016. As for provincial
level, the highest unemployment rate is found in Maluku Province, North
Sulawesi Province and Province West Papua on the first rank with average
percentage of 6% - 10%, followed by South Sulawesi Province and North Maluku
Province 4%-6% in second position then in third position in NTT, Central
Sulawesi, Gorontalo, West Sulawesi, Southeast Sulawesi and Papua. This shows
that of the 11 Provinces in Eastern Indonesia, the 5 Provinces have the largest
unemployment rate with a small employment rate when compared to the other 6 Provinces which still have a great opportunity in employment.
2.
Human Development Index
The issue of Human
Development Index can be explained as follows:
Figure.2
Human Development
Index Index 11 Provinces
In Eastern
Indonesia
2012-2016
 |
| Source:BPS Indonesia 2017 |
Based on Figure. 2, it can be
explained that the highest HDI rate during the period 2012-2016 in 11 provinces
in Eastern Indonesia the first highest position is in the provinces of north
Sulawesi, South Sulawesi and Southeast Sulawesi with percentage level of 69.31%
- 71.05% while in the second position is Central Sulawesi Province, Gorontalo,
Maluku and North Maluku with HDI 66-67%. While for the lowest HDI figures are in
NTT Province, West Sulawesi, Papua and West Papua that is equal to 58-63%. This
shows that the level of HDI, the population residing in the province with the
highest HDI rate has a high competitiveness when compared with the provinces
with the lowest HDI, so for the absorption of labor in the province with high
HDI figures have a very small chance because in the confrontation With a factor
of competition, while provinces with low HDI scores have job opportunities in
because there is no competition factor so that if you look at the problem of
unemployment rate in East Indonesia, the population in the province with the
highest HDI score will get a very big job opportunity if are distributed to
provinces with low HDI scores. In the sense that the quality of the population
of the developed regions will affect underserved areas.
II. METHOD
In this research the method used
is the OLS (Ordinary Least Square) regression method using panel data at a
significant level of α5%, where this test using statistical software Eviews 9
with equations which can be made is as follows:
PTit = a + βIPMit + eit
PT = Unemployment
Rate (Y)
IPM = Level of HDI (X)
a = Constanta / intercept
regression model
Î’ = regression coefficient
it = number of i-th observation
and time period to-t
e = error term
As for also in regression testing
by using panel data done through several stages namely:
1. Selection of Panel Data
Regression Estimation Model
In this research the selection of
panel data regression model consists of Chow Test, Hausmant Test and Langrange
Multiplier Test where the test can be explained as follows:
1.1 Chow Test
This test is used to select one
model on panel data regression, ie between fixed effect model with common effect model. The test procedure is as follows (Baltagi, 2005).
Ho: Common effect Model (CEM)
Ha: Fixed effect Model (FEM)
If the value of chi-square >
0.05 then Ho in receipt Ha rejected
If the value of chi-sqaure
< 0.05 then Ho in rejected Ha in receipt
1.2. Hausmant test
This test is used to select random
effect model with fixed effect model.
This test works by testing whether there
is a correlation between the error in the model (composite error) with one or
more explanatory variables (independent) in the model.
Ho: Random effect Model (REM)
Ha: Fixed effect Model (FEM)
If the value of chi-square >
0.05 then Ho in receipt Ha rejected
If the value of chi-square
< 0.05 then Ho in Ha rejected in receipt
1.3. Langrange Multiplier Test (LM)
This test is used to select a
common effects model with a fixed effect model (random effect model).
The hypothesis in this test can be made as follows:
Ho: Common effect Model (CEM)
Ha: Random effect Model (REM)
If the value of Breusch-pagan >
0.05 then Ho in receipt Ha rejected
If the value of Breusch-pagan
<0.05 then Ho in rejected Ha in receipt
1.4. Estimation of Panel Data Regression Model
1. Chow Test
 |
| Source : Eviews 9 (2017) |
From Chow Test Result obtained that significance value α < 0.05 which shows take hypothetical model that in use is Fixed Effect Model (FEM) better than Common Effect Model.
2. Hausmant Test
 |
| Source : Eviews 9 (2017) |
.
3. Langrange Multiplier Test (LM)
 |
| Source : Eviews 9 (2017) |
From LM test results obtained that the significance value α <0.05 which shows take the model hypothesis in use is model Random Effect Model (REM) is better than the Common Effect Model (CEM). So for that based on test results can be used the common effect model with random effects in this study.
4. Common Effect Model Regression (CEM)
 |
| Source: Eviews 9 (2017) |
From the estimation result can be made regression equation as follows:
PTit = -9.82 + 0.23IPMit + eit
So from the equation can be explained statistically that the regression coefficient of 0.23 or 23% indicating if the HDI or education level increased by 1% then will reduce the unemployment rate with a constant rate of 9.82% in 11 Provinces in Eastern Indonesia during the period 2012 -2016.
5. Test of significance
- T-StatisticsFrom the results of T-Statistics test in the p-value value of 0.0012 and <0.05 which indicates that the variable of HDI or education level has a positive and significant effect on the unemployment rate variables in 11 provinces in Eastern Indonesia.
- Coefficient of Determination (R2)From the test results of determination coefficient in the value of R-Square (R2) of 0.18 which indicates that the variable of HDI or Level of Education is able to explain the Unemployment Variables in 11 provinces in Eastern Indonesia by 18% and the remaining 82% in explained by other factors Outside of this study.
IV. DISCUSSION
The results showed that overall from the results of regression testing in can be the result of the influence of education level to the level of unemployment in 11 Provinces in East Indonesia very significant with the value of the coefficient of influence of 0.23 or 23% indicating that if the problem level of education is resolved it will reduce the unemployment rate of 9.83%. This proves that the level of education measured by the Human Development Index approach is an important factor in overcoming the problem of unemployment in Eastern Indonesia Region. This is in because the education is one of the capital to improve the quality of human resources the better the education of a person will be the better the level of work in. but this is not too effective. If it is seen from the research results, the R-Square value for the HDI variable or the education level is only 18% in explaining the problem of unemployment rate and the remaining 82% is explained by other factors, which also proves that the unemployment problem is not only caused by Educational factors but also caused by other factors such as social factors, competition quality of human resources (HR), or the problem of infrastructure or the number of jobs available in a given area.
It should be theoretically if HDI increases then unemployment will decrease and vice versa, but in reality it is inversely as described in this study based on data of BPS 2017 in figure 1 & 2, shows some provinces in eastern Indonesia that have HDI level The highest but also has a high unemployment rate while for regions or provinces that have the lowest HDI has a low unemployment rate. Seeing this theoretically can be described as follows:
1. Provinces with the highest HDI or educational level but has a large unemployment rate such as North Sulawesi, Maluku and West Papua Provinces from 11 Provinces in Eastern Indonesia Region. Theoretically the unemployment caused by Meyer, (1977) and Collins (1979) explains that the phenomenon of unemployment like this caused by the quality of work specified by the company itself. Where the level of education is not always in accordance with the quality of work, so that people who are educated High or low does not vary the quality in handling the same job if it is determined after the company's criteria. This is consistent with the phenomenon in Maluku Province, North Sulawesi and West Papua where the level of education or individual quality is not as secure as a person in getting a job. Meaning that if a person has a good criteria in accordance with the company or agency in the set even though not have the level of both the dosage and good quality can be hired in accordance with the criteria specified in the institutions and companies that exist in the area. This is what makes the rate of unemployment uneducated in these three provinces. In addition, according to Clignet (1980), who found symptoms of increased unemployment educated, among others due to the desire to choose a safe job of risk. Thus the educated workforce prefers to choose unemployed rather than get jobs that are not in accordance with their wishes. This means that in this case someone prefer jobs that generate greater income, and more comfortable in carrying out its work, whereas if the see the problem of unemployment that can be resolved if each individual can perform activities that productivity that can generate added value in the any such area. However, with the problem of income level with social status that makes the individual behavior prefer to idle and wait for the right time to get a permanent job, as happened in those three Provinces most of the society more oriented on the type of work that is civil servant when compared with entrepreneurship .
2. Provinces with HDI a good level of education and a low unemployment rate of South Sulawesi Province, Gorontalo, West Sulawesi, Southeast Sulawesi and North Maluku, theoretically can be explained that Education is positioned as a means to improve welfare through the utilization of employment opportunities there is. In other words, the ultimate goal of the educational program for the educational user community is the achievement of job opportunities expected by Jefran, (2001) means from the exposure The provinces in this category have the population at the level of education in accordance with the desired work, So from the existing data can be explained that the low unemployment rate resulted in the availability of sufficient employment for the population in the area, this is in accordance with the results of research that if the level of education increases will significantly affect the decline in unemployment.
3. Provinces with HDI or low education level consisting of NTT and Papua Provinces for education level with low unemployment rate, theoretically can be explained that the low unemployment rate is caused by the number of people who work with low education level or not have a level of education altogether, more if compared with the number of unemployed who are in these areas. So it is also in accordance with the results of research that the level of education is also significant enough in addressing unemployment means there is a great opportunity for employment opportunities for both provinces even though the overall population there has a low level of education. But have a job opportunity that is so great if done the process of investment in the area. The lack of education in due to lack of infrastructure and the inadequate quality of human resources in these areas. So if viewed from the competition of employment opportunities, this area can be an alternative to open new jobs with the arrival of quality-quality human resources are superior to outside the area.
V. CONCLUSION
From the results of existing research it can be concluded that for the influence of education level on unemployment in 11 provisnsi in East Indonesia. Shows that the level of education has a significant effect on the regression coefficient value of 0.23 or 23% of the unemployment problem, although the explanatory variable strength is only 18% but able to explain the phenomenon of unemployment rate and education problem in East Indonesia. To see the strength of explanatory variables there are other factors of 82% outside of this study, which shows that the problem of unemployment in Eastern Indonesia is not only caused by educational factors, but also can be viewed based on reality that consists of behavioral factors Social, human resource quality competition with the number of companies absorbing labor in these areas for 11 Provinces in Eastern Indonesia during the period 2012-2016.
Reference
Baltagi, B. H. (2005). Econometrics Analysis of Panel Data (3rd ed). Chicester,
England: John Wiley & Sons Ltd.
BPS, (2017) Indonesian Central Bureau of Statistics, Aces through www.bps.go.id
Jefran, (2001), "Divergences of Productivity and Wage rates: Indonesian manufacturing Competitiveness and the Role of Labor Market", Maximizing the Gains from Deregulation, Jakarta
Meyer. (1977). Employee Turnover: An Empirical and Methodological Assessment. Journal of Vocational Behavior ,.
UNDP. (2004). Human Development Report, 2004. New York: Oxford University Press.
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