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THE INFLUENCE OF REGIONAL EXPENDITURE IN THE EDUCATION,

HEALTH, AND INVESTMENT SECTORS ON THE HUMAN DEVELOPMENT

INDEX IN EASTERN INDONESIA REGION

Nilam Nofiana Erwanti

, Wahyunadi

, Hadi Mahmudi

Faculty of Economics and Business, University of Mataram, East Nusa Tenggara, Indonesia

1,2,3

[email protected]

, [email protected]

ABSTRACT

This study aims to analyze the effect of Regional Expenditure on Education and Health derived from the

APBN and APBD, Domestic Investment, and Foreign Investment on the Human Development Index in

Eastern Indonesia. This study uses the panel data analysis method. The research data use time series data

from the 2015-2019 period and cross-section data on 13 provinces in Eastern Indonesia. The variables

used in this study are the HDI as the dependent variable, then the Education and Health Deconcentration

funds, Education and Health Function Expenditures, DDI and FDI Investment Realization as

independent variables. The results showed that regional expenditure in the field of education originating

from the APBN had a significant negative effect on HDI in Eastern Indonesia, regional expenditure in

the field of education originating from the APBD had a significant positive effect on HDI, regional

expenditure in the field of health originating from the APBN had a negative and insignificant effect on

HDI, regional expenditure in the field of health originating from the APBD had a significant positive

effect on HDI. DDI has a positive and insignificant effect on HDI. FDI partially has a negative and

insignificant effect on HDI.

Keywords: Regional Expenditure; Education; Health; Investment; Human Development Indeks

INTRODUCTION

In local government financial governance, some state expenditures have been regulated in

the amount based on the Law. Setting the amount of this expenditure is called mandatory

spending. The purpose of this mandatory spending is to reduce the problem of regional social

and economic inequality and is a form of government commitment in implementing the Law. The

types of mandatory spending in the Regional Budget (APBD) consist of: 1) education budget

allocation of 20% of the APBN or APBD. 2) health budget allocation of 5% of the state budget

and 10% of the regional budget.

The existence of mandatory spending can have a good impact on access to education. The

PISA (Programme For International Student Assessment) score, which is one of the indicators of

education performance in Indonesia, states that since 2001 it has not experienced a significant

improvement. Even in 2018, after almost 1 decade the government allocated 20% of the education

budget, around 52% of the students who became PISA samples were in the low performer

category in all three test subjects (literacy, mathematics and science), far lower than the

achievements of neighboring countries. PISA is highly dependent on the quality of

educators/teachers (Kurba, 2022).

The results of the Teacher Competency Test (UKG) released by the Ministry of Education

and Culture in 2019 also stated that only 4 percent of teachers scored 70 or higher (Kemendikbud,

2019). The World Bank and POSPERA stated that the low quality of education in Indonesia is

due to several aspects of the problem, namely the uneven distribution of allocation per student,

there are variations in education spending and performance between regions, inefficient spending,

and the contribution of education spending from PAD is declining and there is still a need for

synergy between central and local government spending.

In line with problems in the field of education, in the health sector, individual health efforts

through the National Health Insurance (JKN) need to be balanced with an increase in the budget

for public health efforts, especially the fulfillment of the Minimum Service Standard for Health

(SPM-BK) budget in order to achieve the expected results (Wiwin/IRO, 2020).

In the 2021 Regional Fiscal Review report published by the Directorate General of Treasury

of NTB Province, it is stated that there is a relationship between budget allocations for the fields

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of education, health, and economy (together) with HDI achievements in the regions and separates

the variables of health spending that are partially insignificant in forming HDI. However, each

variable has a positive effect on the formation of its own index. Furthermore, the insignificance

of the Health expenditure factors (together in the model) to HDI, but significant when modeled

alone against the health index illustrates that an evaluation of this expenditure allocation is needed

(Hendra & dkk., 2006).

Education and health are a combined investment in order to form quality Human Resources

(HR) that have an important role for a country's economy. Different patterns of economic growth

in each region can be seen from the difference in economic growth between developed regions

and less developed regions. The existence of this difference in economic growth will result in the

creation of income inequality between regions. Varying income inequality can be caused by low

human development, disparities in natural resources and unequal investment (Yanthi & Sutrisna,

2021).

Investment from both the private sector and the government can be said to be one of the

important elements in the economy of a country or region because investment has a relationship

with the sustainability of economic activities in the present and in the future. Based on its status,

investments include Domestic Investment (DDI) and Foreign Direct Investment (FDI).

In 2016 and 2017, the absorption of labor in FDI was greater than the workforce absorbed

in DDI. Meanwhile, from 2018 to 2020, DDI absorbed more workers than FDI (Zahara & Octavia,

2021). Investment will affect the improvement of public welfare, both public and private

investment, because investment will increase the number of workers absorbed. Along with

increasing income, the purchasing power of people who are part of the HDI component will also

increase. Economic growth accompanied by equal distribution of income will result in a higher

amount of HDI increase compared to economic growth that is not accompanied by equal

distribution of income in society.

The role of the government in improving the Human Development Index also affects

economic growth and vice versa. The rate of regional economic growth is positively and

significantly influenced by human development which is expected to improve the quality of

human life (Mirza, 2012).

Indonesia has experienced a fairly good increase in HDI in the last 12 years, namely in the

range of 70 to less than 80 since 2016. Until 2021, the ten provinces that achieved the high HDI

category were DKI Jakarta, DI Yogjakarta, East Kalimantan, Riau Islands, Bali, North Sulawesi,

Riau, Banten, West Sumatra and West Java while the ten provinces with the lowest HDI

respectively were Papua, West Papua, NTT, West Sulawesi, West Kalimantan, NTB, North

Maluku, Gorontalo and Central Sulawesi. There is a considerable difference between the western

and eastern regions of Indonesia (Gianie, 2022).

In the western part of Indonesia, which includes Sumatra, Kalimantan and Java, the average

HDI is slightly higher than the national figure of 72.86. However, in eastern Indonesia which

includes Nusa Tenggara, Sulawesi, the Maluku cluster and Papua, the HDI figure is below the

national figure, which is 68.95. The average HDI of eastern Indonesia in 2021 is almost the same

as the average HDI of western Indonesia in 2012 which was at 68.50. (BPS)

This shows, from the dimension of time, the results of development are not enjoyed equally

by all Indonesian people. It can be seen that there is a fairly wide gap in human quality between

the western and eastern regions. The achievement of HDI shows the quality of human

development in a region while the indication of development carried out by the government to

improve the quality of its society can be seen from the growth rate of HDI.

This study aims to analyze the influence of Regional Expenditure in Education and Health

both from the APBN and APBD, to analyze the influence of DDI and FDI Investment on the Human

Development Index in Eastern Indonesia from 2015 to 2019.

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RESEARCH METHOD

The type of research that the author uses is quantitative descriptive research that describes

a phenomenon that occurs carefully based on the characteristics and facts that occur (Azwar,

1998). This type of research approach was chosen because the design in this study calculates and

presents how much the relationship and influence between the independent variable and the

dependent variable.

This research was conducted in 13 provinces located in Eastern Indonesia. The provinces

in question are 1) Bali, 2) West Nusa Tenggara, 3) East Nusa Tenggara, 4) North Sulawesi, 5)

Gorontalo, 6) Central Sulawesi, 7) West Sulawesi, 8) South Sulawesi, 9) Southeast Sulawesi, 10)

North Maluku, 11) Maluku, 12) West Papua, 13) Papua.

The author uses secondary data types obtained from publications released by the Central

Statistics Agency, Directorate General of Financial Balance, Investment Agency of the Ministry

of Investment, Annex to the Regulation of the Minister of Education and Culture on

deconcentration funds, and Annex to the Regulation of the Minister of Health on deconcentration

funds. The author disaggregated the data from 2015 to 2019.

The dependent variable used in this study is the Human Development Index (HDI) and the

independent variables used in this study amounted to 6 (six) variables, namely:

1. Education Expenditures Derived from the State Budget represented by the Education

Deconcentration Fund (PN)

2. Expenditure in the Education Sector sourced from the Regional Budget (APBD) (PD)

3. Expenditure in the Health Sector sourced from the State Budget (APBN) represented by

Health Decentralization Fund (KN)

4. Expenditure in the Health Sector sourced from the Regional Budget (APBD) (KD)

5. Domestic Investment (DDI)

6. Foreign Direct Investment (FDI)

This study used panel data. Panel data is a data set that contains sample data of individuals

(households, companies, districts / cities, etc.) in a certain period of time. In other words, panel

data is a combination of time-series data and cross-individual data (cross-section)(Ekananda,

2016). The final equation to be used in panel data regression is as follows:

𝐿𝑜𝑔$

(

𝐼𝑃𝑀

)

=$∝ +𝛽

𝐿𝑜𝑔(𝑃𝑁) + 𝛽

𝐿𝑜𝑔(𝑃𝐷) + 𝛽

𝐿𝑜𝑔(𝐾𝑁) + 𝛽

𝐿𝑜𝑔(𝐾𝐷) + 𝛽

𝐿𝑜𝑔(𝐷𝐷𝐼)

+ 𝛽

𝐿𝑜𝑔(𝐹𝐷𝐼)

Information:

Log : Logaritma

α : Constant

β : Variable Regression Coefficient X

-X

PN : Education Expenditures Derived from the State Budget represented by the Fund

Deconcentration of Education

PD : Expenditure in the Education Sector sourced from the Regional Budget (APBD)

KN : Expenditure in the Health Sector sourced from the State Budget (APBN) represented

by Health Decentralization Fund

KD : Expenditure in the Health Sector sourced from the Regional Budget (APBD)

DDI : Domestic Investment

FDI : Foreign Direct Investment

In analyzing regression models using panel data can be done with several approaches,

namely: a) Common Effect Model (CEM) Approach; b) Fixed Effect Model (FEM) Approach; c)

Random Effect Model (REM) Approach. To determine which method/approach is good in

estimating panel data regression, there are several procedures that can be done, namely: :

1. Test Chow. The chow test is a test used to select the appropriate method between the Common

Effect or Fixed Effect Model. If the F-count value is greater than the F of the table then it is

considered significant, it means rejecting H0. In other words, accept Ha which states that

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estimates with the Fixed Effect Model are better than estimates with the Common Effect

Model.

2. The Hausman test is performed to determine the best method between FEM and REM. This

test follows the distribution of chi-square at free degrees (k-1). If the chi square > 0.05 then

Ho is accepted and the right choice of method is REM while if the chi-square < 0.05 then Ho

is rejected and the right choice of method is FEM

Furthermore, the model hypothesis test was carried out to identify whether the regression

coefficient obtained in this study was significant (had an effect between one variable and another).

The t- Statistical test is performed to determine the effect of the independent variable partially

(each variable) on the dependent variable. The F-Statistics test is performed to determine whether

all independent variables simultaneously (together) have an effect on the dependent variable.

The determinant coefficient test (R2) is an important measure and should be considered in

conducting regression analysis because the value of the determinant coefficient can inform

whether or not the estimated regression model is good. In other words, the coefficient of

determinance can measure how close the estimated regression line is to the actual data. The value

of the coefficient of determinance reflects how much the model variation of the dependent variable

(Y) can be described by the independent variable (X).

Uji Asumsi Klasik yang digunakan dalam penelitian ini adalah

1) Normality Test. The normality test aims to determine the distribution of data in variables

to be used in research and should be done before the data is processed based on research

models. The Jarque Berra (JB) test is one of the normality tests of the goodness of fit test

through eviews software which measures whether the skewness and kurtosis of the sample

are in accordance with the normal distribution.

2) Multicollinearity Test. Multicollinearity is the presence of perfect linear relationships

among variables that explain the regression model. To measure the occurrence of

multicollinearity in regression models seen from the correlation coefficient between each

independent variable. If the coefficient > 0.80, then in the regression model

multicollinearity occurs

3) Heterokedasticity Test. The heteroscedasticity test is a test to see if in the regression model

there is an inequality of variance from the residual of one observation to another. There are

two ways to detect the presence or absence of heterokedasticity, namely the graph method

and the statistical test method (formal test). The relative graph method is carried out, namely

by finding a residual graph with graph requirements that do not cross the limit (500 and -

500), meaning that the residual variants are the same and it can be concluded that there are

no symptoms of heterocedasticity or pass the heteroscedasticity test (Napitupulu et al.,

2021).

RESULTS AND DISCUSSION

Based on the panel data regression model approach using the Common Effect Model, Fixed

Effect Model, and Random Effect Model approaches and the tests that have been carried out,

namely the Chow Test and the Hausman Test show that the more appropriate regression model

to be used in this study is the Fixed Effect Model. The regression results from the test are

presented in Table below.

Table 1 Chow Test

Effects Test

Statistic

d.f.

Prob.

Cross-section F

209,102392

(12,46)

0,0000

Cross-section Chi-square

261,121615

0,0000

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Table 2 Hausman Test

Test Summary

Chi-Sq. Statistic

Chi-Sq. d.f.

Prob.

Cross-section random

14,889011

0,0211

Table 3 Fixed Effect Model

Variable

Coefficient

Std. Error

t-Statistic

Prob.

4.091118

0.303831

13.46509

0.0000

-0.013617

0.004508

-3.020353

0.0041

0.012645

0.004746

2.664140

0.0106

-0.005546

0.006009

-0.922904

0.3609

0.008382

0.005015

1.671430

0.1014

DDI

0.000355

0.001037

0.342803

0.7333

FDI

-0.000508

0.001490

-0.340942

0.7347

Source: Data processing results with Eviews

The regression line equation is obtained as follows:

𝐿𝑜𝑔$

(

𝐻𝐷𝐼

)

= $4,091118 − 0,013617$𝐿𝑜𝑔(𝑃𝑁)

+ 0,012645𝐿𝑜𝑔$(𝑃𝐷)

− 0,005546$𝐿𝑜𝑔(𝐾𝑁)

+ 0,008382$𝐿𝑜𝑔(𝐾𝐷)

+ 0,000355$𝐿𝑜𝑔(𝐷𝐷𝐼)

− 0,000508$𝐿𝑜𝑔(𝐹𝐷𝐼)

By looking at each value of the coefficient, the influence of independent variables on the

dependent variable can be explained as follows:

1. The regression coefficient value of the education budget derived from the APBN (PN) is -

0.013617, meaning that when the state budget in the education sector (PN) represented by

the education deconcentration fund increases by 1%, there is a decrease in HDI by 1.3%

and vice versa, if there is a decrease in state spending in the education sector (PN), HDI

increases by 1.3%.

2. The regression coefficient value of the variable regional budget in education (PD) of

0.012645 means that when the regional budget in education (PD) increases by 1%, HDI

increases by 1.26% and vice versa, namely if there is a decrease in regional expenditure in

education (PD), HDI decreases by 1.26%.

3. The regression coefficient value of the state budget in the Health sector (KN) of -0.005546

means that when the state budget in the Health sector (KN) increases by 1%, there is a

decrease in HDI by 0.55% and vice versa, namely if there is a decrease in the state budget

in the Health sector (KN) by 1%, the HDI increases by 0.55%.

4. The regression coefficient value of the regional budget in the Health sector (KD) is

0.008382, meaning that when the regional budget in the Health sector (KD) increases by

1%, the HDI increases by 0.83% and will also apply vice versa, namely if there is a decrease

in the regional budget in the Health sector (KD), the HDI decreases by 0.83%.

5. The regression coefficient value of domestic investment (DDI) of 0.000355 means that

when domestic investment (DDI) increases by 1%, HDI will increase by 0.03% and vice

versa, namely if there is a decrease in domestic investment investment by 1%, HDI

decreases by 0.03%

6. The regression coefficient value of foreign direct investment (FDI) of -0.000508 means

that when foreign investment (FDI) increases by 1%, there is a decrease in HDI by 0.05%

and vice versa, namely if there is a decrease in foreign investment investment by 1%, HDI

increases by 0.05%.

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Table 4 T-statistical test, F-statistical test and Coefficient of determination

Variable

Coefficient

Std. Error

t-Statistic

Prob.

4.091118

0.303831

13.46509

0.0000

-0.013617

0.004508

-3.020353

0.0041

0.012645

0.004746

2.664140

0.0106

-0.005546

0.006009

-0.922904

0.3609

0.008382

0.005015

1.671430

0.1014

DDI

0.000355

0.001037

0.342803

0.7333

FDI

-0.000508

0.001490

-0.340942

0.7347

Effects Specification

Cross-section fixed (dummy variables)

R-squared

0.990347

Mean dependent var

4.206168

Adjusted R-squared

0.986570

S.D. dependent var

0.060232

S.E. of regression

0.006980

Akaike info criterion

-6.852616

Sum squared resid

0.002241

Schwarz criterion

-6.217026

Log likelihood

241.7100

Hannan-Quinn criter.

-6.601835

F-statistic

262.1871

Durbin-Watson stat

1.591009

Prob(F-statistic)

0.000000

Source: Data processing results with Eviews

The F-statistical test presented in Table 2 yields a value of F-statistic probability smaller than

the significance level (0.0000 < 0.05). Thus, it can be concluded that the regression model formed

is feasible to use and the independent variables which include PN, PD, KN, KD, DDI and FDI

have a simultaneous influence on the dependent variable HDI, while the t-static test produces

regression results as follows:

a. The probability value of the PN variable is smaller than the significance level (0.0041 < 0.05)

with a t-statistic value of –0.013617, meaning that the PN variable (Regional expenditure in

education derived from the State Budget) partially has a significant negative influence on

HDI .

b. The probability value of the PD variable is smaller than the significance level (0.0106 < 0.05)

with a t-statistic value of 0.012645, meaning that the PD variable (Regional expenditure in

education derived from the APBD) partially has a significant positive influence on HDI.

c. The probability value of the KN variable is greater than the significance level (0.3609 > 0.05)

with a t-statistic value of -0.005546, meaning that the KN variable (Regional expenditure in

the Health sector derived from the State Budget) partially has a negative insignificant

influence on HDI.

d. The probability value of the KD variable is greater than the significance level (0.1014 > 0.05)

with a t-statistic value of 0.008382, meaning that the KD variable (Regional expenditure in

the Health sector derived from the Regional Budget) partially has a negative insignificant

influence on HDI.

e. The probability value of the DDI variable is greater than the significance level (0.7333 >

0.05) with a t-statistic value of 0.000355, meaning that the DDI (Domestic Investment)

variable partially has a positive insignificant influence on HDI.

f. The probability value of the FDI variable is greater than the significance level (0.7347 >

0.05) with a t-statistic value of -0.000508, meaning that the FDI (Foreign Investment)

variable partially has a positive insignificant influence on HDI.

The Adjusted r-squared value in Table 2 is 0.987930. This means that the variation in HDI

by 98% can be explained by variables PN, PD, KN, KD, DDI and FDI while the remaining 2% is

explained by other variables outside the model.

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Figure 1 Normality Test Result

Source: Data processing results with Eviews

Based on the results of the normality test in Figure 1 shows that the Jarque-Bera probability value

of 0.394828 is greater than the significance level of 0.05 so that the data is normally distributed.

Table 5 Heterokedasticity Test

Variable

Coefficient

Std. Error

t-Statistic

Prob.

0,052020

0,061401

0,847222

0,4013

-0,000364

0,000911

-0,399097

0,6917

0,000212

0,000959

0,220715

0,8263

-0,000829

0,001214

-0,682871

0,4981

-0,000968

0,001013

-0,954849

0,3446

DDI

5,06E-05

0,000210

0,241360

0,8103

FDI

-0,000108

0,000301

-0,358777

0,7214

Source: Data processing results with Eviews

Based on heterokedasticity testing in Table 3 using the statistical test method, it was obtained that

all probability values of the independent variable were greater than the significance level of 0.05

so that it was concluded that heteroscedasticity did not occur.

Figure 2 Multicollinearity Test Table

Source: Data processing results with Eviews

Based on multicollinearity testing using Pearson Corelation in Table 4, it is known that the value

of the coefficient between variables is smaller than 0.8. Then it can be concluded that the data

does not have a multicollinearity problem.

1. The Effect of Education Expenditure from the State Budget on HDI

The results obtained from the Fixed Effect Model regression result in a probability value of

-0.010 -0.005 0.000 0.005 0.010

Series: Standardized Residuals

Sample 2015 2019

Observations 65

Mean -5.87e-19

Median 3.76e-05

Maximum 0.013923

Minimum -0.010074

Std. Dev. 0.005918

Skewness 0.237994

Kurtosis 2.321995

Jarque-Bera 1.858610

Probability 0.394828

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the state budget variable in the education sector (PN) smaller than the significance level

(0.0041<0.05) with a regression coefficient value of the state budget in the education sector (PN)

of -0.013617 meaning that the state budget in the education sector represented by the Education

Deconcentration fund has a significant negative influence on HDI in Eastern Indonesia Province

2015-2019.

Deconcentration Funding is implemented to finance activities that are the authority of the

Central Government in regions where the characteristics of the activities are non-physical

activities that produce outputs (output indicators) that do not add fixed assets, such as

coordination, planning, facilitation, training, counseling, coaching, supervision, and control

activities (Peraturan Pemerintah Nomor 7 Tahun 2008 Tentang Dekonsentrasi Dan Tugas

Pembantuan, 2008). Ministries/Agencies must pay attention to the balance of funding in the

regions in site planning and budgets for Deconcentration programs and activities.

Based on the mandate of laws and regulations, the Ministry of Finance is obliged to formulate

the balance of funding in the regions (Peraturan Menteri Keuangan Nomor Tentang Perubahan

Atas Peraturan Menteri Nomor 156/PMK.07/2008 Tentang Pedoman Pengelolaan Dana

Dekonsentrasi Dan Tugas Pembantuan, 2010). The funding balance in question is reviewed from

two variables, namely the Regional Funding Capability (KPD) variable which describes the

potential financial capacity in the region and the community development variable in the region

represented through the Human Development Index (HDI) (Kementerian Keuangan, 2019) so that

from the calculation of these two variables will group the regions into four quadrants.

a. Quadrant I regions are intended for areas that have real IKPD per capita above the national

average and HDI above the national HDI. This area is categorized as a non-priority area.

b. Quadrant II regions are intended for areas that have real IKPD per capita below the national

average and HDI above the national HDI. This area is categorized as priority II area.

c. Quadrant III regions are intended for areas that have real IKPD per capita below the national

average and HDI below the national HDI. This area is categorized as priority area I.

d. Quadrant IV regions are intended for regions that have real IKPD per capita above the

national average and HDI below the national HDI. This area is categorized as a non-

priority area.

The regions that receive the largest allocation of deconcentration funds are those that have

funding capabilities below the national average and have HDI that is below the national HDI. The

area in question is an area that is included in the Priority I and Priority II regional groups. As for

the recommendation of the Minister of Finance regarding the Deconcentration fund, it is stated

that areas included in other quadrant groups are still allocated by Ministries/Institutions, but the

portion is relatively small when compared to priority areas.

Source: Appendix to the Ministry of Education and Culture on Deconcentration Funds, processed.

Figure 3

Eastern Indonesia Education Deconcentration Budget, 2015-2019

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Based on the recommendation of the Minister of Finance on the balance of funding in the

regions, provinces located in Eastern Indonesia as a whole are categorized as Priority I and II

regions. This area is the area with the largest allocation of Deconcentration funds than areas with

other categories. Figure 2 shows that if the amount of education deconcentration funds during the

2015-2019 period is averaged, Papua Province is the province with the largest number of

recipients of deconcentration funds in Eastern Indonesia. Papua Province is also the province with

the lowest HDI with an average HDI of 59.06 points during the observation period. The results

showed that the largest allocation of recipients of Deconcentration funds were areas with low

fiscal capacity.

In Figure 2, the allocation of Education Deconcentration funds during the observation year

has generally decreased. The decrease in the Education Deconcentration fund budget resulted

from the gradual transfer of deconcentration funds into Special Allocation Funds (DAK) as

mandated in Article 108 paragraph (1) of Law Number 33 of 2004 concerning Financial Balance

Between the Central Government and Regional Governments (Undang-Undang Nomor 33 Tahun

2004 Tentang Perimbangan Keuangan Antara Pemerintah Pusat Dan Pemerintahan Daerah,

2004). This diversion is due to the bias of the implementation of relations between the center and

the regions where in practice deconcentration funds are mostly used to fund affairs that have been

decentralized to the regions (Undang-Undang Nomor 33 Tahun 2004 Tentang Perimbangan

Keuangan Antara Pemerintah Pusat Dan Pemerintahan Daerah, 2004).

Moreover. The Government Regulation on Deconcentration states that most of the

deconcentration funds are used for non-physical activities and a small part can be used for

supporting activities in the form of procurement of goods that can produce fixed assets (Peraturan

Pemerintah Nomor 7 Tahun 2008 Tentang Dekonsentrasi Dan Tugas Pembantuan, 2008).

However, in the proportion of each type of expenditure nationally, it is stated that most of the

allocation of deconcentration funds is used for goods spending (Kementerian Keuangan, 2019).

The tendency of goods spending in the allocation of deconcentration funds is the influence

of the implementation of Minister of Finance Regulation Number 156 / PMK.07 / 2008 as

amended by Minister of Finance Regulation Number 248 / PMK.07 / 2010 concerning Guidelines

for Management of Deconcentration Funds and Assistance Duties. The Minister of Finance

regulation regulates the use of goods expenditure for expenditure of goods supporting

deconcentration activities (Peraturan Menteri Keuangan Nomor 156/PMK.07/2008 Tentang

Pedoman Pengelolaan Dana Dekonsentrasi Dan Tugas Pembantuan, 2008).

The overlapping rules and funding in the administration of affairs result in inefficient

government spending so that it has not had an impact on human development in the regions. This

is supported by research by Purwanti, et al (2016) which states that the Education Deconcentration

Fund is not effective in reducing school dropout rates in the regions and in the presence or absence

of the Government Deconcentration Fund can overcome the dropout rate with the APBD

(Permana & Purwati, 2013).

2. The Effect of Education Expenditure from the Regional Budget on HDI

The results obtained from the Fixed Effect Model regression produce a probability value of

the PD variable smaller than the significance level (0.0106 < 0.05) with a regression coefficient

value of the regional budget for Education (PD) of 0.012645, meaning that the regional budget

(PD) has a significant positive influence on HDI in the Eastern Province of Indonesia in 2015-

2019.

The budget for education functions in the APBD of each Province in Eastern Indonesia

during the period 2015-2019 presented in Figure 1.3, continues to increase. In general, the

realization of the budget performance of the education function is closely related to the policy

strategy for achieving the education development targets pursued by the Government.

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Source: Directorate General of Financial Balance Publication

Figure 4

Shopping for Education Function in Eastern Indonesia, 2015-2019

In line with the increase in regional expenditures in the Education Sector in Eastern Indonesia

in the period 2015-2019, the indicators forming HDI, School Length Expectations (HLS) and

Average School Length (RLS) in Eastern Indonesia were also recorded to continue to increase

from year to year with an average increase in HLS of 0.47 years and RLS of 0.63 years. With

more and more children attending school with a higher level of education, it will have an impact

on improving human capital in Eastern Indonesia.

The results of this study are supported by research conducted by Maryozi, et al which states

that education spending can encourage an increase in HDI in the regions (Maryozi et al., 2022).

Purwanti, et al (2016) who also examined the relationship between the APBD and

Deconcentration Fund with the School Dropout Rate concluded that there was a relationship /

correlation between the APBD and the dropout rate while the Deconcentration Fund did not affect

the School Dropout Rate. Through the Wilcoxon difference test, the Regional Budget is more

effective in reducing the dropout rate than the Deconcentration Fund. In the presence or absence

of the Deconcentration Fund, the Central Java Government can overcome the School Dropout

Rate with the Regional Budget (Permana & Purwati, 2013).

3. The Effect of Health Expenditure from the State Budget on HDI

The results obtained from the Fixed Effect Model regression produce a probability value of

the KN variable greater than the significance level (0.3609 > 0.05) with a coefficient value of the

state budget in the Health sector (KN) of -0.005546 meaning that the state budget in the Health

sector represented by the Health Deconcentration fund has a negative influence on HDI in the

Eastern Province of Indonesia in 2015-2019 but insignificantly.

Just like the education deconcentration fund, the determination of site plans and budgets for

programs and activities to be concentrated is prepared by taking into account the country's

financial capacity, the balance of funding in the regions, and regional development needs

(Peraturan Pemerintah Nomor 7 Tahun 2008 Tentang Dekonsentrasi Dan Tugas Pembantuan,

2008). The balance of funding in the regions is reviewed from two variables, namely the Regional

Funding Capability (KPD) variable which describes the potential financial capacity in the regions

and the community development variable in the regions represented through the Human

Development Index (HDI) (Kementerian Keuangan, 2019).

The amount of Health Deconcentration funds in Provinces in Eastern Indonesia as shown in

Figure 4, fluctuates every year. If the amount of health deconcentration funds over a period of five

years is averaged, Papua Province is the province with the largest number of recipients of Health

Deconcentration funds during the observation period and it is known that Papua Province is the

province with the lowest average HDI, which is 59.06 points during the 2015-2019 period

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However, the lowest average Life Expectancy (UHH) during the observation period was in West

Sulawesi Province.

Source: Appendix to the Ministry of Health on Deconcentration Funds

Figure 5

Health Deconcentration Budget Table in Eastern Indonesia, 2015-2019

Although this study showed an insignificant association between health deconcentration

funds and HDI with a negative direction of the relationship, it cannot be concluded that

deconcentration funds directly cause a decrease in HDI. There are other factors that might

influence HDI levels in the area, such as other social, economic, and policy factors not covered in

the study.

4. The Effect of Health Sector Spending from the Regional Budget on HDI

The results obtained from the Fixed Effect Model regression produce a probability value of

the KD variable greater than the significance level (0.1014 > 0.05) with a regional budget

coefficient value in the Health (KD) of 0.008382, meaning that the regional budget for Health

(KD) has a positive influence on HDI in the Eastern Province of Indonesia in 2015-2019 but

insignificantly.

Source: Directorate General of Financial Balance Publication

Figure 6

Shopping for Health Functions in Eastern Indonesia, 2015-2019

The budget on the health function in each province in Eastern Indonesia during 2015-2019

presented in Figure 5 shows fluctuating trends but in general, the health function budget has

increased from year to year. The increase in the budget allocation of the health function is driven

The Influence of Regional Expenditure In The Education, Health, And Investment Sectors On The

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by the government's commitment to implement the National Health Insurance (JKN) program

through the provision of health insurance to the poor and indigent (Contribution Assistance

Recipients / PBI) and it is known that the PBI JKN budget in the 2015-2019 period experienced

an average growth of 7.4% so that the adjustment of JKN contribution premiums will be the focus

of policy in the use of state spending in the health budget column (Astutik, 2019).

Based on the components of HDI formation, the health dimension represented by Life

Expectancy (UHH) is an indicator that can reflect the degree of health of an area related to the

availability of infrastructure, ease of access to health services, and health quality. Looking at the

development of UHH in 13 Provinces of Eastern Indonesia, UHH in each Province of Eastern

Indonesia continues to increase during the period of 2015-2019 with an average growth of 0.26

percent per year.

However, even if the regional budget increases for health functions, the amount may still be

limited to meet all health needs in the area. There are many health factors that are not widely

explained by Life Expectancy, including::

a. Quality of Life. Although UHH provides an idea of how long a person is expected to live, it

does not directly reflect their quality of life. Factors such as physical and mental well-being,

happiness levels, levels of discomfort or disability, and quality of social relationships also

play a role in a person's quality of life, which is not always directly correlated with age.

b. Disease. Although a person may live longer, they may experience chronic illness or

significant disability that affects their quality of life.

c. Environmental and Lifestyle Factors. Environmental factors such as air quality, water,

sanitation, food safety, and work environment also have a significant effect on public health.

In addition, lifestyles such as diet, physical activity, smoking habits, alcohol consumption,

and exposure to stress also have a major impact on a person's health that cannot be explained

by life expectancy alone.

Under these conditions, despite the increase in budget, the impact is not significant in addressing

all health problems that affect HDI.

5. The Effect of Domestic Investment on HDI

The results obtained from the Fixed Effect Model regression produce a DDI variable

probability value greater than the significance level (0.7333 > 0.05) with a Domestic Investment

(DDI) coefficient value of 0.000355 meaning that Domestic Investment (DDI) has a positive

influence on HDI in Eastern Indonesia Province in 2015-2019 but insignificantly.

Looking at the development of DDI investment in 13 Provinces of Eastern Indonesia during

the period 2015-2019 shown in Table 1.5, it fluctuates greatly every year. It was noted that the

increase in investment value only occurred on the island of Bali-Nusa Tenggara. While the

Sulawesi and Maluku-Papua island groups fluctuate. In the Eastern Island group of Indonesia,

there has been a considerable increase in the number of projects, but in terms of investment value

absorbed, it has decreased

Table 6

Development of DDI Investment Realization by Island (Billion Rupiah)

Province

Development of Investment Realization in Domestic Investment

2015

2016

2017

2018

2019

Project

Investment

Project

Investment

Project

Investment

Project

Investment

Project

Investment

Sumatera

939

37.752

1.415

39.824

1.693

46.464

2.293

57.820

5.762

90.492

Jawa

3.155

103.758

4.336

126.354

5.555

166.000

6.350

176.953

17.691

202.494

Bali Nusa

2.894

156

2.647

223

7.088

357

9.930

1.428

14.665

Kalimantan

433

20.007

832

33.588

850

30.234

1.087

56.957

8.694

52.705

Sulawesi

421

13.668

687

33.588

459

10.085

634

23.499

1.660

24.230

Maluku

Papua

1.387

251

2.480

3.445

216

1.914

Jumlah

5.100

179.466

7.511

236.253

8.838

262.351

10.815

328.605

35.451

386.498

Source: Investment Coordinating Board

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The increase in the number of projects on DDI investment has the potential to affect per

capita income in the economy. The increase in the number of DDI projects contributes to job

creation and productivity improvement. With increased productivity, companies can produce

more goods and services at a more cost efficient which will then lead to an increase in the revenue

of both the company and employees.

Several reasons for the increase in the number of projects are quite large, but in terms of

investment value absorbed actually decreased, namely:

a. Project Scale. A large number of projects does not necessarily mean that every project

has a large scale. If the projects undertaken are small-scale, for example small projects

at the individual or small business level, the overall investment value may not experience

a significant increase.

b. Return on investment. Although the number of projects increases, the return on

investment of individual projects may not be very high. If the projects have a low rate of

return, the overall investment value will not rise significantly.

c. Project Type. The projects undertaken may be of the same or similar type, so the value

of the investment does not vary too much. For example, if the projects undertaken relate

only to a particular industry or a limited sector, the value of the investment may not

experience a significant increase.

d. Risk Level. A large number of projects may indicate scattered investments, but if they

carry a high risk, investors may be reluctant to allocate large funds. This can cause the

investment value to remain stable even though the number of projects increases.

e. Economic conditions. External factors such as unstable economic conditions or slow

growth can affect the overall value of the investment. If the economy experiences a

slowdown, investors may be more cautious in allocating funds, so that the value of

investments does not rise even though the number of projects increases.

The per capita expenditure indicator shows that per capita expenditure during 2015-2019

in each province in Eastern Indonesia did not experience a significant increase. Based on the

sector, the realization of Domestic Investment Investment in 13 Provinces of Eastern Indonesia

during the period 2015-2019 states that the Istic, Gas and Water (LGA) sector is the sector with

the highest investment value in Eastern Indonesia (BKPM, 2022).

BPS data in February 2022 recorded 135.61 million working people. The majority is

absorbed in the agricultural sector by 29.96% while in the Electricity, Water and Gas sector has

the least absorption of the working population at 0.23% (Annur, 2022). The data shows that the

Electricity, Gas and Water sector has a fairly small contribution in employment in Indonesia

compared to other sectors.

The insignificant influence between DDI and HDI investment in 13 Eastern Indonesia

Provinces can be caused by the imbalance in investment distribution where investment allocation

is uneven across sectors and regions, another factor that causes insignificant influence between

DDI and HDI investment in 13 Eastern Indonesia Provinces is dependence on natural resources.

If DDI's investment is based on the exploitation of natural resources, such as mines or the energy

sector, its impact on human development may be limited. The revenues generated from this sector

may not be fully reflected in the increase in HDI, especially if those revenues are not fairly

distributed or if adverse environmental impacts are not properly addressed.

6. The Effect of Foreign Direct Investment on HDI

The results obtained from the Fixed Effect Model regression produce a probability value of

the FDI variable greater than the significance level (0.7347 > 0.05) with a Foreign Direct

Investment (FDI) coefficient value of -0.000508 meaning that Foreign Direct Investment (FDI)

has a negative influence on HDI in the Eastern Province of Indonesia in 2015-2019 but

insignificantly. This rejects the research hypothesis that there is a significant negative influence

between Foreign Capital Security Investment and the Human Development Index.

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Table 7

Development of FDI Investment Value Realization, by Island (US$ Million), 2015-2019

Province

Perkembangan Realisasi Investasi Penanaman Modal Asing

2015

2016

2017

2018

2019

Project

Investment

Project

Investment

Project

Investment

Project

Investment

Project

Investment

Sumatera

1.884

3.733

2.964

5.665

2.529

5.497

2.356

4.839

3.809

4.251

Jawa

12.177

15.433

12.060

14.772

19.475

16.761

15.533

17.046

19.801

15.476

Bali Nusa

1.309

1.265

2.171

948

2.199

1.158

2.316

1.354

4.029

824

Kalimantan

1.195

5.843

1.630

2.589

1.101

2.887

914

1.954

1.438

2.131

Sulawesi

834

1.560

1.084

2.765

669

2.887

608

2.324

918

3.497

Maluku-

Papua

339

1.442

412

2.225

284

2.449

245

1.790

359

2.029

Total

17.738

29.276

20.321

28.964

26.257

31.640

21.972

29.308

30.354

28.208

Source: Investment Coordinating Board

The development of foreign investment into Indonesia presented in Table 6 shows fluctuating

movements every year. In 2016, foreign investment on the islands of Bali and Nusa Tenggara had

experienced a significant decline and increased again in 2017-2018. In 2019, the value of foreign

investment in Bali and Nusa Tenggara Island again experienced a sharp decline. This situation is

inversely proportional to the number of projects in Bali and Nusa Tenggara Provinces which

actually continue to increase from year to year

Based on the sector, the Basic Metal, Metal Goods, Non-Machinery and Equipment Industry

is the sector with the highest FDI investment realization in Eastern Indonesia, but in its

development has not had a direct impact on the increase in the human development index (HDI).

One reason is because the metal and mining industries tend to require high technical skills in

which some positions

CONCLUSION

Based on the results of panel data analysis that has been carried out using the Fixed Effect

Model (FEM) approach, it can be concluded that the state budget in the education sector (KN)

represented in the Education Deconcentration Fund has a significant influence, but has a negative

relationship with HDI in Eastern Indonesia in 2015-2019. The regional budget in the field of

education (PD) has a significant influence on the direction of positive relations with HDI in

Eastern Indonesia in 2015-2019. The state budget in the health sector (KN) represented in the

Health Deconcentration Fund has an insignificant influence on the direction of negative relations

with HDI in Eastern Indonesia in 2015-2019. The regional budget in the health sector (KD) has

an insignificant effect but has a positive relationship direction for HDI in Eastern Indonesia in

2015-2019.

Domestic Investment Investment has an insignificant effect but has a positive relationship

direction for HDI in Eastern Indonesia in 2015-2019. Foreign Direct Investment has an

insignificant influence on the direction of negative relations with HDI in Eastern Indonesia in

2015-2019.

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