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Eduvest �
Journal of Universal Studies Volume 4 Number 08, August, 2024 p- ISSN 2775-3735- e-ISSN 2775-3727 |
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THE EFFECT OF JKP ON THE PERCENTAGE OF NEET IN INDONESIA |
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Teger Ivo Bangun1,
Dwini Handayani2 1,2 Fakultas
Ekonomi dan Bisnis, Universitas Indonesia, Indonesia Email: : [email protected] |
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ABSTRACT |
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The NEET rate in Indonesia is the highest among other ASEAN countries
and has increased in 2022, coinciding with the first year JKP benefits can be
claimed. The purpose of this study is to see and analyze the effect of JKP on
the percentage of NEET in Indonesia. Using August 2022 sakernas data and 2021
podes data in the form of cross-section data, this study was conducted using
the Ordinary Least Square method. The analysis used in this research is
descriptive analysis and inferential analysis in the form of regression
analysis. The results of the analysis show that a 1% increase in the number
of youth JKP recipients in the district/city reduces the percentage of NEET
youth by 0.62% in 2022. Suggestions for the government are to increase JKP
participation among youth aged 15-24 years. Review the requirements, claim
procedures and obligations of JKP beneficiaries, to mitigate the risk of
moral hazard. |
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KEYWORDS |
JKP, NEET, OLS, sakernas, youth |
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International |
INTRODUCTION
Among other ASEAN countries, Indonesia has the
highest percentage of NEETs and stagnant above the twenty percent mark (D. N. Sari & Ahmad, 2021). The percentage of
NEET in Indonesia tends to be above 20%, the lowest in 2017 and experienced a
sharp increase in 2020. In 2021, it decreased, but in 2022 the NEET rate in
Indonesia increased again. (N. R. Sari et al., 2022)
The Open Unemployment Rate (TPT) is the percentage
of the number of unemployed people to the total labor
force. Based on BPS data, in February 2023, the TPT in Indonesia in the young
age group aged 15-24 years was the highest, reaching 16.46 percent. (Central Bureau of Statistics,
2023). Various efforts have been made by the government to overcome
unemployment in Indonesia, including providing job information, organizing job
training, supervising the improvement of the quality of education, etc. (Nurrahman, 2019). One of the
government's efforts to overcome the problem of unemployment, especially due to
termination of employment, is the Job Loss Guarantee program. In 2021 the
government launched the Job Loss Guarantee (JKP) program through Government
Regulation of the Republic of Indonesia Number 37 of 2023 concerning the
Implementation of the Job Loss Guarantee Program. JKP is a social security
provided to workers/laborers who experience
termination of employment in the form of cash benefits, access to labor market information and job training. (Government of the Republic of
Indonesia, 2021).
�� In Spain,
workers without unemployment benefit insurance have a faster transition time
back to work than workers with insurance. (Rebollo-Sanz & Garc�a-P�rez,
2015).. In general, workers who are laid off will try to find work as quickly
as possible because there is no more income and deplete existing savings.
Within the period of receiving and utilizing cash from the social security
program, there is a risk of an increase in unemployment if they delay looking
for a job because they are utilizing the benefits of the social security
program. As in Spain, the duration of benefits offered by the JKP program
affects the duration of the worker's unemployment (Hidayat et al., 2024).
Similarly, in Chile, workers who received the
unemployment benefit scheme saw an increase in the rate of job exit, an
acceleration of the time to layoff and an increase in the time with shorter
duration of work (Nagler, 2013). For some employers and workers in France,
unemployment benefits are seen as an early retirement scheme rather than as
insurance against the risk of (long-term) job loss (Baguelin & Delphine Remillon,
2014).
�� This study aims to examine and analyze whether the JKP program in
Indonesia has an effect on youth aged 15-24 who are NEET. The results of this
study will contribute to the literature on the impact of the JKP program on the
percentage of NEET. The results of this study can also serve as a
consideration/reference for policy makers in utilizing the JKP program to
reduce the percentage of NEET and improve the welfare of workers considering
that the NEET rate in Indonesia is the highest among other Asean
countries.
Employment
Decision
The decision to work or not is a decision on how to
use time (Ehrenberg et al., 2012) (Mankiw, 2018). If it is related to the labor force who are not working and not looking for work,
then these individuals have a variety of different reasons. The decision they
make has trade off, for example, if the individual chooses to relax rather than
work, then they will lose the opportunity to get income and welfare. If the
individual chooses work over leisure, he or she will lose time for leisure.
The indifference curve approach is used to explain
two commodities that are chosen by individuals, similar to what (Kumala, 2023)
& (Ridhwan, 2021) did in their research. However, in this study the author
uses different categories from previous studies. Ehrenberg (2012) then explains
that there are other things that influence individuals to choose work or
leisure preferences, namely the income effect. The income in question is in
addition to the salary earned after work. If income increases while salary is fixed
and the number of hours worked is fixed, then individuals tend to want to
increase hours for leisure. In line with Borjas (2013), that changes
(increases) in income from work can shift the indifference curve line upwards.
The income effect is then used to analyze changes in the behavior
of youth affected by layoffs. Government policies through insurance programs
provide compensation to workers who cannot work, for example due to work
accidents that cause workers to be temporarily unable to work or work accidents
that cause permanent disability or layoffs (Ehrenberg et al., 2012).
Compensation for layoffs is then paid to workers because they have lost their
jobs and have not found work.
Ehrenberg (2012) explains that investment in labor knowledge and skills occurs in three stages. The
first stage is during early childhood, where there is the influence of parents,
the environment and elementary school. The second stage is adolescence, where
the individual gains knowledge as a student in high school and public
universities. The third stage is when entering the labor
market (the labor force who are actively looking for
work and those who are not actively looking for work) for example through on-the-job
training, or training conducted in preparation before looking for work. A
person's decision to improve skills is considered an investment for the future
in order to more easily enter the labor market and
get better income (Schultz, 1961) (Borjas, 2013) (Ehrenberg et al., 2012).
Not
In Employment, Education Or Training (NEET)
Based on ILO data, there is no international
standard for the definition of NEET. However, Eurostat, ILO and several other
organizations interpret the definition of NEET level as: the percentage of the
population of a given age group and sex who is not employed and not involved in
further education or training (Elder, 2015). It can be interpreted as the
percentage of the population of a given age group and sex who are not employed
and not involved in further education or training. The ILO then sets indicators
that meet the following: (i) those who are not
working (unemployed or inactive) and (ii) those who are not engaged in
education or training for at least four weeks prior to the survey.
The Indonesian Central Bureau of Statistics (BPS)
defines NEETs as the youth population who are not in school, work or training,
in other words, youth (15-24 years old) who are engaged in activities other
than school, work or training. BPS uses NEET data as a proxy for limited access
by young people to education, training and employment (BPS, 2022).
Employment
Policy
Unemployment Insurance, also known as Job Loss
Insurance (JKP), is part of social security. The original purpose of the
program was to help the unemployed, as a way for the government to cope with
the impact of a bad economy, one of which is termination of employment by
companies because they have to reduce production costs. The government tries to
guarantee workers' income when they are unemployed (Mankiw, 2019). With the job
loss guarantee, it is hoped that it will be able to maintain the purchasing power
affected by layoffs, but in a short-term context. The provisions of job loss
guarantee benefits differ in each country, both the benefit period, the
conditions for becoming a participant and the value of benefits received by
workers affected by layoffs.
The job loss guarantee has both negative and
positive effects, which can reduce the burden of layoffs but at the same time
also increase the number of unemployed (Mankiw, 2018). In addition to the
income effect, the individual will utilize the job loss guarantee to not rush
to accept a job or re-enter the workforce. However, according to (Mankiw, 2018)
if the individual is no longer eligible to receive job loss guarantee benefits,
then their efforts to find work increase dramatically so that the chances of getting
a job are greater. This behavior tends to affect the
unemployment rate in that period. In line with the opinion of (Blanchard, 2017)
that increasing job loss insurance benefits can increase unemployment because
the effect of unemployment is no longer considered as something painful because
it still gets income without working and can increase the duration of a
person's unemployment (Ehrenberg et al., 2012).
Another impact of job loss insurance is the
reservation wage. Reservation wage is the lowest wage that workers are willing
to accept in choosing a job (Kesternich et al.,
2022). The individual will only not accept a job if the wage offered is below
the lowest wage they set. The relationship with job loss guarantee is that
there is a tendency for an individual to refuse a job that does not meet the
criteria (skills or minimum expected wage) by utilizing the time lag obtained
from job security benefits. Rejection of a job offer with a wage below the
reservation wage is likely to increase the individual's unemployment period. (Ehrenberg et al., 2012)
Implementation
of JKP in Indonesia
The Job Loss Guarantee Program in Indonesia was
only launched in 2021 through Presidential Regulation No.37 of 2021, as a derivative
of Law Number 11 of 2020 concerning Job Creation, Article 82 and Article 5
paragraph (2) of the 1945 Constitution of the Republic of Indonesia. The
purpose of implementing the JKP Program is to maintain a decent standard of
living when workers/laborers lose their jobs. The
principle of implementing JKP is Social Insurance (President of the Republic of
Indonesia, 2020).�
Cash benefits are organized by BPJS Ketenagakerjaan, while job training and access to labor market information are organized by the central
government through the Ministry of Manpower of the Republic of Indonesia.
Meanwhile, the criteria for JKP beneficiaries are all participants with
indefinite term employment agreements (PKWTT) and specific time employment
agreements (PKWT) who experience layoffs provided that they meet the
eligibility requirements (at least 12 months of service in the last 24 months
before layoffs occur where 6 months of the 12 months of service are paid
consecutively) and are willing to work again. In other words, workers who
resign or stop working because the PKWT period has expired do not receive JKP
benefits. Other criteria that do not qualify as JKP beneficiaries are stopping
work due to resignation, permanent total disability, retirement age and death.
Empirical
Review
The criteria for individuals to enter the labor market are influenced by several variables. Among
them are demographic, socioeconomic and regional category variables. In
addition, there are several variables that determine a person's chances of
becoming NEET status. Previous studies generally explained the determinants of
NEET at the micro level, namely individuals, but in this study
it will be explained at the macro level, namely at the district/city level due
to limited information on Sakernas data for August
2022.
Gross Regional Domestic Product (GRDP) and NEET are
two concepts related to the economy and labor force
of a region. GRDP can be used to determine the economic condition of a region
in a certain period (BPS, n.d.-b). If associated with employment opportunities,
GRDP can reflect the level of economic welfare of a region. An increase in GRDP
allows for an increase in employment opportunities (Chodijah,
2007), potentially reducing the NEET rate.
Youth from poor families tend to face limited
access to education. Poverty is one of the reasons why people leave school
early (Bardak et al., 2015). High school dropout rates that can affect youth
aged 15-24 years have NEET status. In addition to access to education, youth
from poor families also tend to experience limited access to training, because
most training as one of the provisions to enter the world of work tends to be
paid. On the other hand, youth from poor families tend to accept job offers without
considering reservation wages. This is because youth from poor families tend to
have no alternative sources of income other than wages, in contrast to youth
from well-off families where their families can still help them with their
daily needs.
One of the direct effects of education (as human
capital) is a greater chance of getting a better job (Schultz, 1961). Youth
aged 15-24 years who have higher education tend to have a smaller chance of
becoming NEET because they have better employment opportunities, that is, the
opportunity to enter the world of work by having greater human capital. Human
capital in this case is the skills/knowledge gained from previous education.
A person who is married tends to have more
responsibilities than before marriage. So to fulfill the needs of their household, one of them has to
work and the other takes care of the household. In general, those who take care
of the household are women and men go to work, but in recent decades there has
been an increase in the work participation rate of married women (Ehrenberg et
al., 2012) but married women are more likely to be NEET than young women aged
15-24 years who have never married (D. N. Sari & Ahmad, 2021).�����
The unemployment rate is the percentage of the labor force that is not working (Mankiw, 2018). In
contrast, NEET status refers to a group of individuals, especially young
people, who are not working, not engaged in formal education, and not in
training. In other words, someone who falls into the NEET category can also be
counted as part of the unemployment rate. Based on Mankiw's (2018) explanation
above, unemployment benefits (JKP) can increase the number of unemployed. If it
is related to NEET, the higher the unemployment rate, the higher the chance for
youth aged 15-24 years to become NEET status.
Since the Covid pandemic, many activities that were
previously carried out offline have now shifted to online, especially learning
activities, both education and training (Organization For
Economic Co-Operation And Development, 2020). The pandemic period lasts long
enough, so learning activities cannot continue to stop. In the end, learning
organizers made policy breakthroughs or innovations in organizing learning in
the midst of the Covid pandemic last time. E-learning (online learning) has
become an important learning method since the Covid pandemic (Alhumaid et al.,
2020). Training organizers also provide options for trainees to choose whether
to take offline or online training.
Online activities can be carried out inseparably
from the presence or absence of signals in the area of the training organizer
and training participants. Compared to before, 4G/LTE signals increasingly
support the performance of mobile internet services, so that video conferencing
services and other features can be accessed with better internet speeds (Telkomsel, n.d.). So with the
4G/LTE signal, online learning, including training, as well as training and
working from home, is increasingly possible.
In this study, the number of senior high schools in
regencies/cities in Indonesia will be used as one of the control variables. One
of the NEET variables is not being in school. One of the reasons the number of
high schools is used as a control variable is based on research conducted (Saputri & Setyodhono, 2019),
that the high school portion is the largest portion of young workers with NEET
status.
Youth who work in the informal sector tend to be
more likely to become NEET status, working in the informal sector should not
require special skills so that it does not require high human capital so that
there is no competency development through further education or training. Based
on the Law of the Republic of Indonesia Number 25 of 1997 concerning labor, informal sector workers are workers who work in
informal sector employment relationships by receiving wages or compensation
(Government of the Republic of Indonesia, 1997).
The status of youth who are not in school, not
working and not in training is closely related to the availability of adequate
infrastructure, access to the labor market will be
easier if there is more demand for labor in the area.
In this case, in Indonesia, there are differences between Java and Outer Java,
based on BPS data for industry, more are located on the island of Java.
Therefore, the difference between Java and Outer Java has an opportunity for
youth aged 15-24 years to become NEET status.���� ����
Data
Type and Source
This research will use secondary data sourced from labor data, namely the National Labor Force Survey (Sakernas) for the August 2022 period, Podes
data in 2021 and publications sourced from the Central Statistics Agency, in
the form of cross-section data. Sakernas is a special survey to collect employment
data to determine employment opportunities and their relationship to education,
number of hours worked, type of work, employment and employment status,
unemployment and underemployment as well as the population covered by the non-labor force (those who go to school, take care of the house
or do other activities). (BPS, n.d.).
Unit
of Analysis
The unit of analysis in this study is all
districts/cities in Indonesia. This study does not use individual units of
analysis because the data sources used in this study cannot capture data on
participants who are not working, studying or training and have previous JKP
membership.
Research
Variables
Table
1. Research Variables
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No. |
Variables |
Symbol |
Measurement
Scale |
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Dependent Variable |
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1 |
NEET |
numeric |
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Independent
Variable |
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2 |
Percentage of youth
aged 15-24 who own JKP to the total number of youth aged 15-24 years
old |
jkp |
numeric |
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Control
Variables |
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|||
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3 |
Gross regional domestic product |
ln_pdrb |
numeric |
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4 |
Percentage of poor population |
poor_22 |
numeric |
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5 |
Percentage of youth aged 15-24 with higher
education to total youth aged 15-24 |
educ |
numeric |
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6 |
Percentage of married young women aged 15-24 years to the total
number of young men aged 15-24 years |
pr_kwn |
numeric |
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7 |
Unemployment rate |
unemp_rate |
numeric |
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8 |
Average villages with 4G/LTE signal in
districts/cities |
ada_4g |
numeric |
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9 |
Number of senior high schools in the district/city |
total_sma |
numeric |
|
|
10 |
numeric |
|||
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11 |
Java and Outer Java Region |
Java |
dummy |
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RESEARCH
METHOD
Descriptive Analysis
Descriptive analysis is used to describe the object under study,
explain or describe the sample data that has been collected but does not intend
to make conclusions that apply generally because it only intends to describe
the data. The presentation of descriptive analysis data in this study is in the
form of tabulation.
Inferential Analysis
The inferential analysis used in this study is regression analysis,
which aims to analyze the extent to which the
percentage of youth aged 15-24 years who own JKP to the total number of youth aged 15-24 years affects the percentage of NEET to the
number of youth aged 15-24 years. The purpose of this research is to see and analyze the extent to which the independent variable
affects the dependent variable so that the most appropriate inferential
analysis to use is regression analysis.
Regression Equation
The regression � model to be used
is multiple linear regression which is made in a model as follows:
NEETi
= β0 + β1jkpi +�
β2ln_pdrbi +� β3poor_22i
+� β4educi +�
��
β5pr_kwni� + β6unemp_ratei
+ β7ada_4gi + β8total_smai +
�
β9persentaseinformal_pemudai + β10jawai + εi
Description:
i���������������������� ����������� = index for the i-th district/city
NEET������������� ����������� = percentage of NEETs to total youth
aged 15-24 years old
β0������� ����������� ����������� = intercept
jkp������������������ ����������� = percentage of
youth who own JKP to total youth 15-24
�� years old
ln_pdrb����������� ����������� =
gross regional domestic product����������������������������������������
poor_22���������� ����������� = percentage of poor people
educ���������������� ����������� = percentage of
youth aged 15-24 years with education high
�� against the
number of youth aged 15-24 years
pr_kwn ���������� ����������� = percentage of female youth aged
15-24 years old marriage
�� to the number of youth aged 15-24 years
unemp_rate���� ���������� = unemployment rate
ada_4g ���������� ����������� =
average villages with 4G/LTE signal in district/city
total_sma�������� ����������� =
number of SMAs in the district/city�����������
percentage informal��� = percentage of
youth aged15-24 who are employed in
�� informal sector youth to total
youth aged 15-24 years old
�� who works
Java���� ����������������������� = Java and outside Java
�εi�������������������� ����������� = unobserved factors (things that affect Y but are not recognized
entered into the model)
RESULT AND DISCUSSION
Research Results and Analysis
Descriptive
Analysis Results
Table 2. Descriptive Statistics
|
�Variable |
�Obs |
�Mean |
�Std. Dev. |
�Min |
�Max |
|
�NEET |
514 |
21.951 |
6.973 |
2.976 |
46.65 |
|
�jkp |
514 |
.477 |
.709 |
0 |
6.731 |
|
�ln_pdrb |
514 |
29.869 |
1.281 |
25.747 |
33.809 |
|
�poor_22 |
514 |
11.682 |
7.274 |
2.28 |
42.03 |
|
�educ |
514 |
3.499 |
2.206 |
0 |
14.583 |
|
�pr_kwn |
514 |
9.027 |
3.946 |
.617 |
25.856 |
|
�unemp_rate |
514 |
4.324 |
2.28 |
.147 |
11.087 |
|
�ada_4g |
514 |
.754 |
.231 |
0 |
1 |
|
�total_sma |
514 |
75.095 |
81.535 |
2 |
731 |
|
�percentage of informal~a |
514 |
96.938 |
2.169 |
89 |
100 |
|
�Java |
514 |
.232 |
.422 |
0 |
1 |
|
|
|||||
Source: Sakernas August 2022, podes
2021 and BPS publications (reprocessed)
Table 2 is the descriptive statistics of the dependent and independent
variables used, which consists of the number of observations, mean value,
standard deviation as well as the minimum and maximum values of each variable
used. The dependent variable used is the percentage of NEET to the total number
of youth aged 15-24 years, symbolized NEET. The
independent variable is the percentage of youth aged 15-24 years who own JKP to
the total number of youth aged 15-24 years who are
given the symbol jkp.
Since this analysis was conducted at the district/city level, the
number of observations in this study was 514 districts/cities in Indonesia. The
average percentage of NEET at the district/city level is 21.95% with a minimum
value of 2.97% and a highest value of 46.65%. Meanwhile, the percentage of
youth receiving JKP contributions in 514 districts/cities is 0.47% with the
highest value of 6.73% and the lowest value of 0%. This means that of the 514
districts/cities in Indonesia, there are still districts/cities where youth are
not yet one of the beneficiaries of JKP. In the poverty variable, the average
percentage of poverty in the 514 districts/cities was 11.68% with the lowest
poverty percentage of 2.28% and the highest of 42.03%.
When analyzed according to the percentage of
highly educated youth from 514 districts/cities, the average percentage of
highly educated youth is 3.49%, with the highest percentage of 14.58% and the
lowest value of 0%. The average married female youth is 9.02% with the lowest
value of 0.61% and the highest value of 25.85%. Meanwhile, the average
unemployment rate in the 514 districts/cities was 4.32% with the lowest value
of 0% and the highest value of 11.08%. The other two variables calculated using
the 2021 Village Potential (Podes) values are the
average number of villages with 4G/LTE signal in the district/city and the
total number of high schools/equivalents in a district/city. In terms of the
average number of villages with 4G/LTE signal in the district/city, the average
number in the district/city is 0.75%, the lowest is 0 with no 4G/LTE signal and
the highest is 1 with 4G/LTE signal. In addition, for the number of senior high
schools, the average number of senior high schools is 75 units of senior high
school/equivalent with the highest number in a district/city of 731 senior high
schools and the lowest is only 2 senior high schools/equivalent. The percentage
of informal workers after 15-24 years old is 96% with the highest percentage
being 100%. From these results, it can be seen that young workers are still
dominant in the informal sector.
The first control variable in this study is gross regional domestic
product which is given the symbol ln_pdrb. In this
variable, the natural logarithm transformation is carried out to create a liner
relationship, namely changing the data measurement scale to another form so
that the assumptions of the analysis are met. The gross regional domestic
product data used is the gross regional domestic
product of districts/cities throughout Indonesia in 2022 obtained from the data
of the Central Bureau of Statistics
. The second control variable is the percentage of poor people in
districts/municipalities throughout Indonesia in 2022 obtained from data from
the Central Bureau of Statistics. The third variable is the percentage of youth
aged 15-24 years with higher education to the total number of youth aged 15-24 years. From the Sakernas
data for the August 2022 period used in this study, there are districts/cities
where there are no youth aged 15-24 years with higher education, namely Raja
Ampat, Paniai, Puncak Jaya,
Bintang Mountains, Supiori, Membrano
Raya, Nduga, Yalimo, Puncak, Intan Jaya and Deiyai.
The fourth variable is the percentage of married young women aged 15-24
years to the total number of young men aged 15-24 years. The fifth variable is unemployment rate . The data used is the
unemployment rate of districts/cities throughout Indonesia in 2022 obtained
from Sakernas data in August 2022. The sixth variable
is the average village with 4G/LTE signal in the district/city. The data source
for the average village with 4G/LTE signal in the district/city uses Podes data in 2021. The 2021 Podes
data is used because the Sakernas data does not yet
have information about 4G/LTE in the questionnaire questions. The second reason
for selecting the 2021 Podes is that at the time of
the research, the last available Podes data was 2021.
Based on information on the BPS website, Podes
data collection is routinely carried out 3 times in a period of ten years to
support the activities of the Population Census, Agricultural Census, or
Economic Census. (Central Bureau of Statistics, n.d.). The following is the distribution of 4G/LTE
signals in Indonesia based on districts/cities in Indonesia in 2021:
Figure 1: Distribution of 4G/LTE Signal in
Indonesia by District/City Year 2021 Source: Podes 2021 (reprocessed)
The seventh variable is the number of senior high schools (SMA) in each
district/city across Indonesia. The data source also uses the 2021 Podes data, then the 2021 Podes
data is merged with the August 2022 Sakernas
data. The eighth variable is the percentage of youth aged 15-24 years who work
in the informal sector. The data source for this variable uses data from Sakernas August 2022. Then the ninth variable is Java and
outside Java. The data source for this variable also uses data from Sakernas August 2022. Based on Sakernas
data for August 2022, there are several districts/cities in Indonesia where all
youth aged 15-24 years work in the informal sector.
Table 3. Percentage and Number Distribution of NEET
Youth by Individual Characteristics
|
Variables |
% |
�Total |
|
Youth |
|
|
|
Yes |
20,9% |
�������� 157.442 |
|
No |
79,1% |
�������� 595.246 |
|
Total |
|
�������� 752.688 |
|
Gender |
|
|
|
Male |
51.85% |
81.630 |
|
Female |
48.15% |
75.812 |
|
Total |
|
157.442 |
|
Married women |
|
|
|
Yes |
18,2% |
����������� 13.694 |
|
Unmarried |
81,8% |
����������� 61.477 |
|
Living divorce |
0,8% |
���������������� 589 |
|
Death divorce |
0,1% |
������������������� 52 |
|
Total |
|
����������� 75.812 |
|
Education |
|
|
|
More |
82.60% |
130.184 |
|
High |
17.31% |
������������� 27.000 |
|
Total |
|
�������� 157.442 |
|
JKP |
|
|
|
Yes |
0,5% |
���������������� 829 |
|
No |
99,5% |
�������� 156.613 |
|
Total |
|
�������� 157.442 |
|
LAYOFFS |
|
|
|
Yes |
0,3% |
���������������� 435 |
|
No |
99,7% |
�������� 157.007 |
|
Total |
|
�������� 157.442 |
|
NEET |
|
|
|
Yes |
21,9% |
����������� 34.525 |
|
No |
78,1% |
�������� 122.917 |
|
Total |
|
�������� 157.442 |
Source: Sakernas August 2022 (reprocessed)
Based on Sakernas data for August 2022, the
number of people over 25 years old is greater than youth aged 15-24 years.
There are no districts/cities where youth aged 15-24 years are more dominant. While
from the total labor force, the proportion of youth
aged 15-24 years is around 30.156%. The total number of JKP owners is 13,912
people, while youth aged 15-24 years own JKP 829 people. In Table 3, the number
of youth aged 15-24 years amounted to 157,442 people
or around 20.92%. The percentage of people over 24 years old amounted to
595,246 or around 79.08%. The proportion of youth aged 14-24 years with NEET
status amounted to 34,525 or around 21.93% of the total number of youth aged 15-24 years of 157,442 people. This figure is
considered high because it is above the 20% mark. The following is the
distribution of NEET in Indonesia by district/city in 2022:
Figure 2. Distribution of NEETs in Indonesia by
District/City Year 2022 Source: Sakernas August 2022 (reprocessed)
The number of males outnumbered females by 51.85% of the total number
of youth aged 15-24 years, which amounted to 157,442
people. Looking at the marital status of women aged 15-24 years. Unmarried 15-24 year old females have a larger proportion of 81.09%
compared to married 15-24 year old females which is 18.06%. For youth with
higher education, the number is less than other education. Higher education is
calculated from Diploma I and above, with a total of 27,000 people while youth
who do not have higher education amounted to 130,184 people.
The number of youth aged 15-24 years who
became JKP participants amounted to 829 people or around 0.53% of the total
number of youth aged 15-24 years which amounted to 157,442 people. Youth aged
15-24 years who become JKP participants in 2022 are relatively small compared to
the number of youth aged 15-24 years. At the age of
15-18 years, some of them are still students in high school so they have not
become JKP participants, because the requirements to become a JKP participant
must work, have a minimum contribution period of twelve months in the last
twenty-four months and pay contributions for six consecutive months in an
orderly manner before experiencing layoffs.
Based on Sakernas data for August 2022, there
are youth aged 15-24 years in the district/city who have not yet participated
in the JKP program. Meanwhile, youth with layoff status amounted to 435 people
with a proportion of 0.5%. The following is the distribution of JKP participation
of youth aged 15-24 years in Indonesia by district / city in 2022:
Figure 3: Distribution of JKP membership for 15-24 year old in Indonesia by district/city in 2022.
Source: Sakernas August
2022 (reprocessed)
There are 236 districts/cities in Indonesia that do not have youth aged
15-24 years participating in JKP. Then the number of districts/cities that have
youth aged 15-24 years who are JKP participants is 278 districts/cities. Most
of these districts/cities are outside Java. When viewed based on regional
characteristics, Java has more industrial sectors, as well as more formal
employment opportunities. As one of the requirements to become a participant in
the JKP program, the company where you work must meet several criteria,
including being a participant in at least three social security programs, where
jobs that have a minimum membership of 3 programs are formal sector companies.
Results of Inferential Analysis and
Discussion
Table 4. Regression Results
|
�NEET |
�Coef. |
�St.Err. |
�t-value |
�p-value |
�[95%
Conf |
�Interval] |
�Sig |
|||
|
jkp |
-.624 |
.333 |
-1.88 |
.061 |
-1.278 |
.029 |
* |
|||
|
ln_pdrb |
-.671 |
.345 |
-1.95 |
.052 |
-1.349 |
.007 |
* |
|||
|
poor_22 |
-.146 |
.058 |
-2.51 |
.012 |
-.26 |
-.032 |
** |
|||
|
educ |
-.731 |
.148 |
-4.92 |
0 |
-1.022 |
-.439 |
*** |
|||
|
pr_kwn |
.636 |
.087 |
7.29 |
0 |
.465 |
.808 |
*** |
|||
|
unemp_rate |
1.597 |
.155 |
10.29 |
0 |
1.292 |
1.901 |
*** |
|||
|
ada_4g |
1.446 |
1.673 |
0.86 |
.388 |
-1.84 |
4.732 |
|
|||
|
total_sma |
.008 |
.004 |
2.16 |
.031 |
.001 |
.016 |
** |
|||
|
percentage of informal~a |
.526 |
.131 |
4.01 |
0 |
.269 |
.784 |
*** |
|||
|
Java |
-2.719 |
.716 |
-3.80 |
0 |
-4.126 |
-1.313 |
*** |
|||
|
Constant |
-18.218 |
16.073 |
-1.13 |
.258 |
-49.797 |
13.361 |
|
|||
|
|
||||||||||
|
Mean dependent
var |
21.951 |
SD dependent var |
6.973 |
|
||||||
|
R-squared |
0.325 |
Number of obs� |
514 |
|
||||||
|
F-test� |
30.269 |
Prob > F |
0.000 |
|
||||||
|
Akaike crit.
(AIC) |
3273.767 |
Bayesian crit.
(BIC) |
3320.431 |
|
||||||
|
*** p<.01, ** p<.05, * p<.1 |
||||||||||
|
|
||||||||||
The
inferential analysis used in this study is regression analysis to see and analyze the extent to which the independent variable
affects the dependent variable. The dependent variable in this study is the
percentage of NEET to the total number of youth aged
15-24 years. While the independent variable is the percentage of youth aged
15-24 years who own JKP to the total number of youth
aged 15-24 years.
The
regression results found that the main independent variable in this study, JKP,
has a negative correlation with the NEET percentage. Other control variables
such as the percentage of poor population, percentage of highly educated youth,
and regional variables also have a negative influence on the percentage of
NEET. Some control variables are found to have a positive effect such as the
unemployment rate, the percentage of the female population aged 15-24 years who
are married, the number of high schools in the district, and the percentage of
youth working in the informal sector. Other variables such as gross regional
domestic product and number of bts were found to be
insignificant.
The
JKP variable as the main independent variable is found in the regression
results to be significant and has a negative effect on the percentage of NEET.
A 1% increase in the number of youth receiving JKP in
the district decreases the percentage of NEET youth by 0.62% in 2022. This
result is different from the literature review and previous research which
found that JKP can increase the likelihood of a person having NEET status
through increasing the probability of being unemployed (Mankiw, 2018). (Mankiw, 2018). The following
figure shows the relationship between NEET and JKP based on Sakernas
data processing in August 2022:
Figure 4. Percentage of NEET and JKP by District/City Year 2022
Source: Sakernas
August 2022 (reprocessed)
From
Figure 4, it can be seen that the ten districts/cities with the highest NEET,
when looking at the percentage of youth participating in the JKP program, the
districts/cities that have high NEET to districts/cities with the results of
the NEET and JKP correlation test are negative. The
lowest NEET does not necessarily cause the JKP participation of youth aged
15-24 years to increase as well. The regression results of the gross
regional domestic product (GRDP) control variable are significant and in line with
the literature review. When associated with employment opportunities, GRDP can
reflect the level of economic welfare of a region. An increase in GRDP allows
for an increase in employment opportunities (Chodijah,
2007) thus potentially reducing the NEET rate.
Furthermore,
the variable percentage of poor people from the regression results appears to
reduce the percentage of NEET, in contrast to the literature review. A 1%
increase in poverty in the district/city reduces the NEET percentage by 0.14%
in 2022. Poverty is one of the reasons why people leave school early (Bardak et
al., 2015). A high dropout rate can affect youth aged 15-24 years to have NEET
status. In addition to access to education, youth from poor families also tend
to experience limited access to training, because most training as one of the
provisions to enter the world of work tends to be paid. However, if examined
further, individuals from lower economic families, if they get a job
opportunity or job offer, tend to accept without delay because they ignore reservation
wages.
The
regression result of the variable percentage of youth aged 15-24 years with
higher education in the number of youth aged 15-24
years has a negative direction, i.e. an increase of 1% of youth aged 15-24
years in the district decreases the percentage of NEET by 0.73% in 2022.
According to previous literature, one of the direct impacts of education (as
human capital) is a greater chance of getting a better job (Schultz, 1961).
Youth aged 15-24 years old who are highly educated tend to have a smaller
chance of becoming NEET because they have better employment opportunities, that
is, the opportunity to enter the world of work by having greater human capital.
Human capital in this case is the skills/knowledge gained from previous
education. In line with the research results (Febria et al., 2022) (Citra, 2022) (Gaffari, Abrar, 2019).
The
regression result of the variable percentage of the number of female youth aged 15-24 years married to the number of adolescents
has a positive direction on the percentage of NEET. A 1% increase in the number
of young women aged 15-24 years married in the district/city increases the
percentage of NEETs by 0.63% in 2022. In line with previous research findings
that married women are more likely to be NEET than young women aged 15-24 years
who have never married (D. N. Sari & Ahmad, 2021). In Model 5, there was a
fairly high change in r square compared to the previous model, after the
variable of married women was included in the model. That married women tend to
be NEET cares cared for because they take care of the household, in line
with the research of (Citra, 2022) (Febria et al., 2022).
The
regression result of the unemployment rate has a positive direction on the NEET
percentage. That a 1% increase in unemployment in the district increases the
percentage of NEET by 1.59%. in 2022. The unemployment rate is the percentage
of the labor force that is not working (Mankiw,
2018). If it is associated with NEET, the higher the unemployment rate, the
higher the chance of youth aged 15-24 years to become NEET status. When
conducting regression in model 6, there is a fairly high increase in r square
compared to the previous model. The following are the results of the
analysis of how JKP relates to the unemployment rate based on the graph:
Figure 5: Percentage of JKP and Unemployment Rate by District/City Year
2022
Source: Sakernas
August 2022 (reprocessed)
Figure
5 shows the top five districts/municipalities in Indonesia with the highest
unemployment rates, consisting of Ambon City, Tebing
Tinggi City, Pontianak, Serang and Jayapura. The
correlation test is positive, but the graph shows that Serang
has the highest JKP percentage at 2.92%, but Jayapura has the lowest
unemployment rate at 10.26%. Then Kota Tebing Tinggi
has no youth aged 15-24 years who are JKP participants or 0%.
The
regression result of the average villages with 4G/LTE signal in the
district/city is not significant in the model used and has a positive direction
towards the percentage of NEET. A 1% increase in the average number of villages
with 4G/LTE signal in the district/city increases the NEET percentage by 1.44%
in 2022. The presence of 4G/LTE signal should make it easier for individuals to
participate in online activities, be it school, training or work. However, from
the results of this study, the presence of a 4G/LTE signal actually increases
the chances of youth becoming NEET status.
The
regression result of the number of senior high schools in the district has a
positive direction on the percentage of NEET. That a 1% increase in the number
of high schools in the district/city increases the percentage of NEET by 0.08%
in 2022. In line with research conducted (Saputri
& Setyodhono, 2019), that the high school portion
is the largest portion of young workers with NEET status. With more high
schools in the district/city, the number of youth who
experience the transition from school to work is more, although individuals who
attend high school in a district/city may come from other districts/cities that
have limited quotas or access to these schools. Therefore, this study uses data
on the total number of senior high schools in the district/city, not the
percentage of villages that have a senior high school or not and then takes the
average as is used in the 4G/LTE variable.
The
regional factor is also found to have a negative effect, where districts/cities
in Java Island have a smaller probability of NEET percentage of 2.71% in 2022
than districts/cities outside Java Island. This illustrates the condition
mentioned by BPS that the most job vacancies in Indonesia in 2021 are still
dominated by Java Island. Which means, the probability of youth living in Java
Island to get a job and not fall into the NEET group is greater than youth
living outside Java Island.
The
higher the percentage of youth working in the informal sector, found in this
study, will increase the percentage of NEET. An increase in the percentage of
youth working in the informal sector by 1% can also increase the percentage of
NEET in the district/city by 0.52% in 2022. This result is not in line with
what ILO (2017) states that in developing countries, youth cannot become
unemployed without a good social security system, so they are forced to engage
in work in the informal sector. This can then reduce the percentage of NEETs.
CONCLUSION
NEET is the percentage of the youth population
that is not in school, work or training, in other words, youth (15-24 years
old) who are doing other activities outside of school, work or training. This
study examines and analyzes the effect of JKP on the percentage of NEET in
Indonesia. The unit of analysis in this study is 514 districts/cities
throughout Indonesia, using data from Sakernas August 2022, Podes 2021 and BPS
publications. The main finding of this study is that a 1% increase in the
number of youth receiving JKP in districts/cities reduces the percentage of
NEET youth by 0.62% in 2022.
There are 236 districts/cities where JKP
participation among youth aged 15-24 is low, including in Java including
Trenggalek, Tulung Agung, Blitar, and Pati. The data shows that Java Island has
a larger industrial sector and formal employment than other islands in
Indonesia. Then for the eastern region of Indonesia, there are districts /
cities where there is no JKP participation from all age groups, including
Puncak Jaya, Yahukimo, Nduga, and Intan Jaya.
There is a control variable that has a
positive effect on the percentage of NEET in Indonesia, namely the status of
married women, which has been used by previous researchers with the same
regression results, namely a positive effect on the percentage of NEET. In
other words, this variable has been one of the causal factors that has not been
resolved in reducing the NEET rate since the last few studies. Although
actually based on the proportion of male gender has a larger proportion, which
is 51.85%. There are 36 districts/cities where all youth aged 15-24 years work
in the informal sector, that youth who work in the informal sector tend to be
more likely to become NEET status.
Many studies related to NEET have been
conducted both in other countries and in Indonesia but tend to use individual
characteristics as the dominant variable used. This research is expected to
provide another perspective for the government in making policies to reduce the
number of NEETs in Indonesia. One of them is utilizing the Job Loss Guarantee
(JKP) program. By maximizing the benefits of the JKP program, targeting more
potential membership, especially youth aged 15-24 years. Likewise, for
regencies / cities with zero JKP membership (all age groups), BPJS
Ketenagakerjaan should examine the potential for membership in the region. Reflecting on the experience
of other countries, one of the obstacles in implementing JKP is moral hazard. Participants tend to
prolong their unemployment period and take advantage of the income effects of
the JKP program until the maximum time limit for receiving benefits.
The percentage of NEETs who were laid off from
the data is low, areas where JKP is high indicate the probability that they can
get JKP, so the probability of being NEET status is lower. JKP is one of the
policies that can increase the probability of someone re-entering the
workforce, through the benefits of free training and labor
market information. JKP benefits are provided to workers who have been laid off
and are committed to returning to work. Youth aged 15-24 years old who are not
yet working (unemployed), not in education and not in training and have never
worked before should be given other programs that can increase their human
capital in order to increase the chances of entering the labor
market so that the probability of becoming NEET status can be reduced. For
example, by providing competency development training with a broader scope, so
that one of the NEET indicators can be reduced.
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