Volume 4, Number 1, January 2023 e-ISSN: 2797-6068 and p-ISSN: 2777-0915
Email:
[email protected]
|
KEYWORDS Education; human development; population |
ABSTRACT The Human Development Index
(HDI) is part of the development goals, high HDI is not easy to achieve, it
takes the seriousness of the government to allocate budgets in the education
sector and health expenditures. Per capita income is also part of increasing
HDI. Central Sulawesi's HDI continues to increase from 2015-2019, the
distribution of the education, health, and per capita income sectors also continues
to increase. The purpose of this study was to determine the effect of
expenditure in the education, health, and per capita income sectors on HDI in
Central Sulawesi. The analysis tool used with panel data regression, the
results of the study showed: Education sector expenditure had a negative but
insignificant effect on the HDI of Central Sulawesi for the 2015-2019 period,
while health sector expenditure had a positive and significant effect on HDI
and per capita income had a negative and insignificant effect on the HDI of
Central Sulawesi for the 2015-2019 period |
INTRODUCTION
Development is an effort to
improve the welfare of a nation, a decent standard of living of the population
and a good quality of human resources (Mulyasari, 2016). Development becomes part of a process of continuous
state change to achieve national goals. The national goal is to promote just
and prosperous welfare for all Indonesians (Ahmadi, 2016).
(Sen, 1999) suggests that the human development approach is a human capabilities
approach. The concept of human capabilities approach is to find out the ability
that a person has to do something that is of value to use. Human ability is not
seen how much income they have, but how much the ability to achieve something
of use value (Sunaryo et al., 2016). Everyone has different abilities, to achieve goals, the
efforts made are also different. Education and health are very important to
advance human development in a country, each region is competing to improve
education and health to support the development of a region. Health and
education are the most fundamental forms of advancing human development in an
area. Education is a basis for human beings to support development, (Stiglitz & Meier, 2000).
Development goes well if it is
supported by quality education and human resources. The deterrent of
regional/regional development is a human being who is able to make a positive
contribution to the regional economy (Prawoto & Basuki, 2016). Based
on Permendagri No. 38 of 2018 concerning Guidelines
for the Preparation of the 2019 APBD, the minimum health budget allocated for
health is 10 percent of the APBD, while the education budget is 20 percent of
the National Budget (APBN). Data published by the Directorate General of
Financial Balance in 2019, shows the realization of the APBD for government
expenditure in the education and health sector in Central Sulawesi Province in
2015-2019 (Damodar, 2013).
Based on data from the
Directorate General of Financial Balance, in 2015 the realization of the
government expenditure budget for the education sector amounted to
Rp656,214,533,067 and in 2016 it decreased to Rp190,455,713,140 (growth -70.98
percent). The decrease in the education budget due to theuse
of the budget of the education function is a need for the district to be
included in the regional transfer budget. In 2017, it increased to
Rp1,286,840,398,332 or a growth of 575.66 percent. In 2018 the budget decreased
to Rp1,243,942,609,942 (-3.33 percent). In 2019 the budget increased to
Rp1,392,631,315,361 (11.95 percent). Directorate General of Financial Balance,
2019 (Andiny & Sari, 2018).
The government expenditure budget
for the health sector in 2015 amounted to Rp331,630,944,407 or an increase of
24.83 percent and in 2016 the expenditure amounted to Rp329,191,290,859 or
decreased compared to the previous year (-0.74 percent) (Handayani, 2015). In
2017 the budget growth was Rp381,951,315,196 or an increase of 16.03 percent.
In 2018 it was Rp424,150,984,865 or an increase of 11.05 percent. In 2019, the
health sector budget was IDR 503,775,510,742 or an increase of 18.77 percent.
The per capita income of Central Sulawesi regencies/cities showed an increase,
per capita income in 2015 amounted to Rp6,422,312 increased to Rp7,614,595 in
2017 until 2019 per capita income continued to increase (Wulan & Chotimah, 2017).
The HDI of Central Sulawesi has
increased from 66.76 in 2015 to 69.50 in 2019 (HDI per Central Sulawesi regency
by district, namely Morowali Regency 72.02, Poso Regency 71.40, North Morowali
Regency 68.45, Banggai Regency 70.36, Buol Regency 67.59, Sigi Regency
68.16, Donggala Regency 65.49, Parigi Moutong Regency 65.47, Toli-Toli
Regency 62.42, Banggai Islands Regency 65.13, Banggai Laut Regency 65.27, and
finally Tojo Una-una
Regency 64.52 (BPS Central Sulawesi Province, 2019) (Kahang, 2016).
Per capita income has a positive
relationship with HDI, as well as budget allocations in the education and
health sectors. Increasing the government's budget allocation in the education
and health sectors will
increase the productivity of the population further (Meylina, Nikensari, & Kuncara, 2013).
The Central Sulawesi government pays attention to the
increase in HDI in Central Sulawesi, the government's budget expenditure
allocated to the education and health sectors continues to increase from
2015-2019. Central Sulawesi's economic activities have driven an increase in
per capita income from year to year. The increase in education, health and per
capita income will increase the HDI in Central Sulawesi (Ichwan, Anam, Sir, Mertosono, &
Yunus, 2021).
Education expenditure is expected to increase the Average
Length of Schooling (RLS) and increase Life Expectancy (UHH) in Central
Sulawesi, as well as health and per capita income which increases play a role
in increasing HDI. Then research is needed to find out:
1.
How does the education sector expenditure affect
the HDI of the regencies/cities of Central Sulawesi Province for the 2015-2019
period?
2.
How does the health sector expenditure
affect the HDI of the regencies/cities of Central Sulawesi Province for the
2015-2019 period?
3.
How does per capita income affect the HDI
of regencies/cities of Central Sulawesi Province for the 2015-2019 period?
RESEARCH METHOD
The type of research used is quantitative research with an explanatory type
of research which aims to find an explanation of why an event or symptom
occurs. The end result of this study is an overview of the causal relationship.
�This study was conducted in Central Sulawesi Province, using the
observation year period 2015-2019.
The object of this study is government expenditure in
the education sector and the Health sector, as well as
per capita income as a free variable and the Human Development Index as a bound
variable.
The data used in this study is quantitative data.� Quantitative data is data in the form of
numbers or numbers. According to its form, quantitative data can be processed
or analyzed using calculation or statistical techniques. (Hammack & Anheier, 2013).
RESULT AND DISCUSSION
Based on BPS that the
HDI of Central Sulawesi for the 2015-2019 period showed an increase, the HDI
increased a sign of positive things. In 2015 the HDI was 66.76 and experienced
a continuous increase until 2018-2019 to 68.88 and 69.5. In 2019, it was
recorded that four regencies/cities had HDI above the HDI of Central Sulawesi,
namely Banggai Regency, Morowali
Regency, Poso Regency and Palu
City. Palu City with the highest HDI compared to
other districts is 69.50. Many economic activities are carried out in Palu City as the provincial capital, RRLS (Average School
Duration) and HLS (Harapan Lama sekolah) Palu City are the highest in Central Sulawesi.
��������� Development of Government
Expenditure in the Education Sector by Region in Central Sulawesi Province,
government expenditure a budget that has been given by the central government
to local governments to realize even better human development. Government
spending is also a tangible manifestation of the success of local governments
in realizing human development in their regions, this success is seen in the
form of realization. The larger the budget given by the government, the
positive impact on improving the quality of human development.
�������� In 2015 the expenditure of
the Central Sulawesi education sector amounted to Rp656,214,533,067 and
experienced a continuous increase, in 2017 the expenditure amounted to
Rp1,286,840,398,322 and in 2019 amounted to Rp1,392,631,315,361. It was
recorded that six districts/cities had their education budgets in a fluctuary, namely Buol Regency, Banggai Regency, Palu City, Tojo Una-una Regency, Sigi Regency, and Morowali
Regency.
Table 1
Development of Government Expenditure in the Education Sector by Region in
Central Sulawesi Province
Period 20l5-20l9 (billion rupiah)
|
No |
Districts/Cities |
2015 |
2016 |
2017 |
2018 |
2019 |
|
1 |
Banggai |
417 |
466 |
494 |
467 |
459 |
|
2 |
Banggai�Islands |
167 |
157 |
171 |
195 |
202 |
|
3 |
Buol |
231 |
34 |
228 |
225 |
239 |
|
4 |
Far-Far Away |
215 |
221 |
246 |
248 |
262 |
|
5 |
Donggala |
302 |
324 |
322 |
326 |
357 |
|
6 |
Morowali |
207 |
24 |
195 |
218 |
245 |
|
7 |
Poso |
343 |
45 |
327 |
329 |
361 |
|
8 |
Palu City |
457 |
460 |
372 |
357 |
365 |
|
9 |
Paringi Moutong |
356 |
339 |
401 |
433 |
466 |
|
10 |
Tojo One-One |
234 |
220 |
226 |
234 |
272 |
|
11 |
Sigi |
280 |
45 |
316 |
310 |
306 |
|
12 |
Banggai Laut |
69 |
81 |
88 |
110 |
113 |
|
13 |
North Morowali |
135 |
186 |
190 |
181 |
214 |
|
|
Central
Sulawesi |
656 |
190 |
1.286 |
1.243 |
1.392 |
Source: DPJK Central Sulawesi 2015-2019
�������� Table 2 shows the percentage
of government expenditure in the education sector, in 2019 Morowali,
Tojo Una-una, and North Morowali districts were the districts with higher growth in
education sector expenditures than provincial education sector expenditures.
The percentage growth of Morowali Regency is 12.4
percent, Tojo Una-una
Regency is 16.2 percent and North Morowali Regency is
18.2 percent, while Central Sulawesi's growth of 12 percent, education sector
expenditure will improve the quality of education of a district/city. However,
there are also districts with a percentage of growth that has not increased
(minus), namely Banggai Regency (-1.7 percent), and Sigi Regency (-1.3 percent).
Table 2
�Growth of Government Spending in the Education
Sector
By Region in Central
Sulawesi Province
Period 20l5-20l9
|
�No |
Districts/Cities |
2016 |
2017 |
2018 |
2019 |
|
1 |
Banggai |
11,8 |
6,0 |
-5,5 |
-1,7 |
|
2 |
Banggai Islands |
-6,0 |
8,9 |
14,0 |
3,6 |
|
3 |
Buol |
-85,3 |
570,6 |
-1,3 |
6,2 |
|
4 |
Far-Far Away |
2,8 |
11,3 |
0,8 |
5,6 |
|
5 |
Donggala |
7,3 |
-0,6 |
1,2 |
9,5 |
|
6 |
Morowali |
-88,4 |
712,5 |
11,8 |
12,4 |
|
7 |
Poso |
-86,9 |
626,7 |
0,6 |
9,7 |
|
8 |
Palu�City |
0,7 |
-19,1 |
-4,0 |
2,2 |
|
9 |
Paringi�Moutong |
-4,8 |
18,3 |
8,0 |
7,6 |
|
10 |
Tojo�One-One |
-6,0 |
2,7 |
3,5 |
16,2 |
|
11 |
Sigi |
-83,9 |
602,2 |
-1,9 |
-1,3 |
|
12 |
Banggai�Laut |
17,4 |
8,6 |
25,0 |
2,7 |
|
13 |
North Morowali |
37,8 |
2,2 |
-4,7 |
18,2 |
|
|
Central Sulawesi |
-71,0 |
576,8 |
-3,3 |
12,0 |
Data processed
���������� Based on
Table 3 shows government spending in the health sector. In 2015-2019, the
amount of central Sulawesi government spending continued to increase. The
government seeks to improve the health of the people of Central Sulawesi by
increasing spending on the health sector. Improving public health will reduce
infant mortality, and increase life expectancy.
Table 3
Development of
Government Expenditure in the Health Sector by Region
In Central Sulawesi
Province for the period 20l5-20l9 (billion rupiah)
|
No |
Districts/Cities |
2015 |
2016 |
2017 |
2018 |
2019 |
|
1 |
Banggai |
192 |
225 |
296 |
293 |
399 |
|
2 |
Banggai Islands |
64 |
67 |
93 |
105 |
152 |
|
3 |
Buol |
104 |
41 |
134 |
188 |
178 |
|
4 |
Far-Far Away |
150 |
139 |
255 |
195 |
279 |
|
5 |
Donggala |
119 |
136 |
114 |
126 |
214 |
|
6 |
Morowali |
113 |
21 |
163 |
189 |
248 |
|
7 |
Poso |
146 |
- |
163 |
208 |
235 |
|
8 |
Palu City |
244 |
309 |
277 |
256 |
273 |
|
9 |
Paringi Moutong |
178 |
260 |
262 |
257 |
358 |
|
10 |
Tojo One-One |
140 |
150 |
178 |
172 |
231 |
|
11 |
Sigi |
125 |
65 |
173 |
156 |
184 |
|
12 |
Banggai Laut |
57 |
71 |
62 |
82 |
94 |
|
13 |
North Morowali |
110 |
108 |
127 |
177 |
201 |
|
|
Central
Sulawesi |
313 |
329 |
381 |
424 |
503 |
Source: DPJK Central Sulawesi 2015-2019
����������� The Health
Budget will create a better health service and it will reduce infant mortality
and extend a person's life expectancy. Improving public health has a positive
impact on the economy because people will work more productively. Based on data
from the Central Sulawesi Regional Budget, health sector spending continues to
increase in the 2015-2019 period In 2015 the
distribution of the health sector amounted to Rp331,630,944,407 increased to Rp381,951,315,196
in 2017 to 2019 to Rp503,775,510,742.�
The highest health sector expenditures are Banggai
Regency, Parigi Maoutong Regency, Toli-toli
Regency, Morowali Regency, and Palu
City.
��������
Table 4
Health Sector
Government Spending Growth
By Region in Central
Sulawesi Province for the period 20l5-2019 (%)
|
No |
Districts/Cities |
2016 |
2017 |
2018 |
2019 |
|
1 |
Banggai |
17,2 |
31,6 |
-1,0 |
36,2 |
|
2 |
Banggai Islands |
4,7 |
38,8 |
12,9 |
44,8 |
|
3 |
Buol |
-60,6 |
226,8 |
40,3 |
-5,3 |
|
4 |
Far-Far Away |
-7,3 |
83,5 |
-23,5 |
43,1 |
|
5 |
Donggala |
14,3 |
-16,2 |
10,5 |
69,8 |
|
6 |
Morowali |
-81,4 |
676,2 |
16,0 |
31,2 |
|
7 |
Poso |
27,6 |
13,0 |
||
|
8 |
Palu City |
26,6 |
-10,4 |
-7,6 |
6,6 |
|
9 |
Paringi Moutong |
46,1 |
0,8 |
-1,9 |
39,3 |
|
10 |
Tojo One-One |
7,1 |
18,7 |
-3,4 |
34,3 |
|
11 |
Sigi |
-48,0 |
166,2 |
-9,8 |
17,9 |
|
12 |
Banggai Laut |
24,6 |
-12,7 |
32,3 |
14,6 |
|
13 |
North Morowali |
-1,8 |
17,6 |
39,4 |
13,6 |
|
|
Central
Sulawesi |
5,1 |
15,8 |
11,3 |
18,6 |
�������������
Table 4 shows that 2019
district/city expenditure experienced growth compared to 2018, there were 9
positive health expenditure growth districts, namely Banggai
Regency, Banggai Islands
Regency, Toli-toli Regency, Donggala
Regency, Morowali Regency, Parigi Moutong
Regency, Tojo Una-una
Regency, and Sigi Regency. This shows the seriousness of the district/city government in improving
public health. The government realizes that health is part of improving
people's lives in the future.
Model Test
�������� The selection of the best
model begins with several approaches. There are three paneled data analysis
approaches, namely the Common Effect Model, the Fixed Effect Model, and the Random
Effect Model. After processing the data, obtain the following
results:
1.
�Common Effect Model (CEM)
Table 5
�Common Effect Model Estimation Results
|
Coefficient |
Prob. |
|
|
C |
2.915309 |
0.0000 |
|
Education |
-0.026283 |
0.2344 |
|
Health |
0.074564 |
0.0061 |
|
Percapita |
0.003971 |
0.2696 |
|
R-squared |
0.176333 |
|
|
Adjusted
R-squared |
0.135825 |
|
|
S.E. of
regression |
0.060420 |
|
|
Sum
squared resid |
0.222689 |
|
|
Log
likelihood |
92.25097 |
|
|
F-statistic |
4.353031 |
|
|
Prob(F-statistic) |
0.007633 |
|
�������������������������������� Data
processed
The results of the common effect model estimate show that partially
the education sector expenditure variable has a negative (-0.02683) and
insignificant (prob 0.2344) influence. Health and per capita have a positive
effect on HDI, health has a positive effect but not significance, while per
capita income has a positive but not significant effect. The Adjusted R-squared
number of 0.14 or 14 percent of independent variables can explain the dependent
variables.
2.
�Fixed Effect Model (FEM) Approach
The results of the Fixed Effect
Model estimate show that partially the educational variable has a negative
and insignificant relationship (prob 0.1471) to the HDI. The health variable is
positively and significantly related to the HDI, the coefficient value ��is
0.023139 while the prob is 0.0005. The per capita income variable is negatively
and significantly related to the HDI, the coefficient value -0.002732
and the prob 0.0004. The coefficient number shows 98 percent of independent
variables can explain the dependent variable. Or the ability of the education,
health, and per capita income variables to explain the HDI of 98 percent and
the remaining or residual 2 percent is explained by other variables that are
not included looking at the effect on HDI.
Table 6
�Fixed Effect Model Estimation Results
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
3.822105 |
0.107931 |
35.41245 |
0.0000 |
|
Education |
-0.006580 |
0.004466 |
-1.473206 |
0.1471 |
|
Health |
0.023139 |
0.006198 |
3.733470 |
0.0005 |
|
Percapita |
-0.002732 |
0.000712 |
-3.834716 |
0.0004 |
|
|
Effects Specification |
|
|
|
|
Cross-section fixed (dummy variables) |
|
|||
|
R-squared |
0.980927 |
|
|
|
|
Adjusted R-squared |
0.975089 |
|
|
|
|
S.E. of regression |
0.010258 |
|
|
|
|
Sum squared resid |
0.005157 |
|
|
|
|
Log likelihood |
214.6300 |
|
|
|
|
F-statistic |
168.0082 |
|
|
|
|
Prob(F-statistic) |
0.000000 |
|
|
|
3.
Random Effect Model
(REM) Test
The estimation results convince the Random Effect Model that
education has a negative and significant influence on HDI. Health variables
have a positive and significant effect on HDI. Table 7 of the Adjusted R �
squared coefficient of 0.501820 shows the ability of the education, health,
and per capita income variables to explain the HDI of 51 percent and the
remaining or residual 49 is explained by other variables that were not included
and studied looking at their effect on HDI.
Table 9
�Random Effect Model Estimation Results
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
3.805472 |
|
|
0.0000 |
|
Education |
-0.006799 |
|
|
0.1326 |
|
Kesehatan |
0.023958 |
|
|
0.0003 |
|
Percapita |
-0.002643 |
|
|
0.0004 |
|
|
Effects
Specification |
|
|
|
|
|
|
|
S.D. |
Rho |
|
Cross-section random |
0.057953 |
0.9696 |
||
|
Idiosyncratic random |
0.010258 |
0.0304 |
||
|
|
Weighted
Statistics |
|
|
|
|
R-squared |
0.525172 |
|
|
|
|
Adjusted R-squared |
0.501820 |
|
|
|
|
S.E. of regression |
0.010466 |
|
|
|
|
F-statistic |
22.48923 |
|
|
|
|
Prob(F-statistic) |
0.000000 |
|
|
|
Data processed
The next step is to perform a Model Test, a model test is carried out
before analyzing the influence of independent variables on the dependent
variables. Test models are also used looking at the best models used in
multiple regression. There are three model tests carried out, namely Chou
Test, Hausman Test, Lagrage Multiplier Test (LM).
1. Chow Test
�The Chow test is used to determine the best model between CEM and FEM. If H0 CEM is
the best model. The results of the Chow Test are as follows:
Table 10
�Chow Test Results
|
Effects
Test |
Statistic |
D.F. |
Prob. |
|
Cross-section
F |
172.258378 |
(12,49) |
0.0000 |
|
Cross-section
Chi-square |
244.758151 |
12 |
0.0000 |
Data processed
The probability value of F of 0.000 is less than the value of α
(smaller than alpha 5 percent), H0 is rejected then the best model is FEM.
2. ���� Uji Hausman
Test the thirst to see the best
model between FEM and REM. The hypothesis of this test is H0 then the best
model is REM. Hausman test results� are as follows:
Table 11
Hausman Test Results
|
Test
Summary |
Chi-Sq. Statistic |
Chi-Sq. d.f. |
Prob. |
|
Cross-section
random |
5.495045 |
3 |
0.1389 |
��� Data processed
���������� The best
models between FEM and REM are shown from their probability values, if the
value is less than the alpha of 5 percent then the FEM of the best model. Table
11 shows a probability value of 0.1389 greater than 0.05. So that H0 is
accepted, the best model REM compared to FEM.
3. ���� Lagrange Multiplier
(LM) Test
LM tests were performed to find the best model between CEM -REM. This test
was carried out because in the previous test it chose two different models. H0
= CEM and H1 = FEM
Table 12
Lagrange Multiplier Test Results
|
Null (no rand. effect) |
Cross-section |
Period |
Both |
|
|
Alternative |
One-sided |
One-sided |
|
|
|
Breusch-Pagan |
106.8028 |
1.723428 |
108.5262 |
|
|
|
(0.0000) |
(0.1893) |
(0.0000) |
|
|
Honda |
10.33454 |
-1.312794 |
6.379340 |
|
|
|
(0.0000) |
(0.9054) |
(0.0000) |
|
��������� Data processed
The LM test results refer to a
statistical LM value greater than the Chi-Square statistical critical
value then H0 is rejected. If the statistical LM value is smaller
than the statistical value. The Breusch-Pagan probability value of 0.0000 is less than the α of 0.05. LM value < critical
value, REM models are better than FEM.
Based on the results of regression analysis, the data panel shows REM as
the best model. Thenthe equation is as follows:
����� HDI = 3.805472
� 0.006799education + 0.023958health �0.002643perkapita+μit
The equation above
shows that if education, health and per capita income are considered constant,
the HDI will increase by 3.805472 percent. The
relationship between independent variables to dependent variables is
illustrated from the equation, the education variable will reduce the HDI by
0.006799 percent assuming the other variables are considered constant. While
the health variable gives a positive relationship to the HDI assuming other
variables do not change, if the health variable increases by 1
percent then the HDI increases by 0.023958 percent. For the per capita
income variable has a negative relationship to HDI, When
per capita income increases by 1 percent, the HDI decreases by 0.002643
percent.
Hypothesis Test
Simultaneous Hypothesis Test (f-statistical
test)
This test is used to determine whether free variables
have a significant influence on bound or synchronous variables. The test
results were shown by
Table 13
f-Statistical Test Results
|
F-statistic |
2.248.923 |
|
Prob(F-statistic) |
0.000000 |
��������� Data
processed
The results of the F test analysis that the variables of education, health,
per capita income simultaneously have a significant effect on the HDI variable
(probability < 5 percent) which is 0.0000.
t-Statistical test
Table
t-Statistical Test Results
|
Variable |
Coefficient |
Standard Error |
t - Statistics |
Probability |
|
C |
3.805.472 |
0.108504 |
3.507.212 |
0.0000 |
|
Education |
-0.006799 |
0.004460 |
-1.524.501 |
0.1326 |
|
Health |
0.023958 |
0.006175 |
3.879.828 |
0.0003 |
|
Percapita |
-0.002643 |
0.000711 |
-3.719.380 |
0.0004 |
���� Data processed
Statistical t�test to determine the effect of each independent variable on
the dependent variable. The education variable has a negative but not
significant effect (prob 0.1326), while the health variable has a positive and
significant effect on HDI, the per capita income variable has a negative and
significant effect on HDI.
Discussion
Effect of Education, Health, Per capita income on HDI��
The Effect of Education on HDI
Education plays a big role in the development of social economic life by
increasing knowledge, skills, productivity, so that education is able to
produce a quality workforce. HDI measures a country's socio-economic
development achievement, linking between attainments in education, health, and
per capita real income, Todaro, 2009.
����������� The achievement of good
human quality is the goal of development, development requires qualified human
resources for the implementation of the economy so that development goals are
achieved. Government spending in the education, health, per capita income
sectors can have a positive impact on increasing HDI in Central Sulawesi. This
research shows that education negatively affects HDI in Central Sulawesi,
investment in the education sector is a long-term investment, the human
resources of the population will be seen in the future, for example the next 5
years or more.� This research is in
accordance with research conducted by (Koelmans
et al., 2019).
�Spending in the education sector
increases the Old School Hope (HLS) in Central Sulawesi Province from year to
year. Based on BPS, the Central Sulawesi HLS in 2015-2019 continues to
increase, but HLS growth is declining. In 2016 HLS grew by 1.57 years, in 2017
it was 0.69 years and in 2019 it was 0.07 years.
When viewed from the length of time of education there is an increase, for
example in 2015 by 12.72 it increased to 13.04 in 2017 and in 2019 by 13.14. Palu City has an HLS of 16.22, the highest HLS among other
districts in Central Sulawesi. This is because the budget that is utilized in
the higher education sector, Palu City provides good
educational facilities and quality human resources.
�������� ������� Average School Length (RRLS) by
district/city in Central Sulawesi showed an increase from 2015 � 2019.� In 2015 the RRLS of 2.97 years increased to
8.29 years and in 2019 it increased to 8.75 years. This achievement is due to
the seriousness of local governments to improve the quality of education. In
2019 Palu City has a high RRLS of 11.60 years or
class 2 high school. There are two districts with very low RRLS, namely Donggala Regency (RRLS 7.86 years) and Parigi Moutong Regency (RRLS 7.47 years).
Effect of Health on HDI
����������� Health
is one of the determinants of HDI, an increase in spending in the health sector
will increase the degree of health. If the degree of health increases, the HDI
will increase and will have a positive impact on economic growth. Based on the
results of the study, health sector spending has a positive and significant
influence on HDI in Central Sulawesi.
��� In the previous description, it was stated
that government spending in the health sector in 2015-2019 showed an increase,
in 2015 health expenditure amounted to Rp331,630,944,401 decreased in 2016 (-0.61),
expenditure amounted to Rp329,191,290,859. But in 2017 - 2019 expenditure
continued to increase, in 2017 it was Rp381,951,315,198 to Rp424,150,984,866 or
a growth of 11.04 percent. The increasing expenditure of the health sector also
increased the HDI, in 2015 the HDI of 66.76 increased to 67.47 in 2016. In 2017
� 2019 HDI continues to increase from year to year. In 2017 it was 68.88 and in
2019 it was 69.50.
The central government allocates
health budgets to each region, the increased HDI is an indicator of the success
of local governments in managing health budgets. This research is in line with
the research of Chotimah, 2017, and Adiny, Sari, 2018.
Effect of Per capita Income on HDI
Per capita income is the average income of people who are in a country or
geographical area.� Central Sulawesi's
per capita income is obtained by dividing Central Sulawesi's GRDP by the total
population in Central Sulawesi. Per capita income is used to determine the
welfare of a region. An increase in per capita income will increase the HDI,
but based on the results of research the per capita income of Central Sulawesi
is negatively and significantly related to the HDI of Central Sulawesi. The
increase in income in Central Sulawesi is uneven, and there is still a group of
people who are less concerned about education. The increase in per capita
income should be followed by an increase in HDI but this is not in accordance
with the conditions in Central Sulawesi.
�Increased economic growth will be
followed by an increase in per capita income. Per capita income is expected to
be enjoyed by the whole community, so that everyone is able to shop to meet the
needs of life, food and non-food such as health and education.
CONCLUSION
Education Sector Expenditure has
a negative but not significant effect on the HDI of Central Sulawesi for the
2015-2019 Period
Health Sector Expenditure has a
positive and significant effect on the HDI of Central Sulawesi for the 2015-2019
Period
Per capita income has a negative
and insignificant effect on the HDI of Central Sulawesi for the 2015-2019
period.
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