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FACTORS AFFECTING BPK AUDIT FINDINGS ON LOCAL
GOVERNMENT FINANCIAL REPORTS IN WEST SULAWESI PROVINCE
FOR THE PERIOD 2020 TO 2022
Asa Jasmine Harimurti
1
, Jamaludin Iskak
2
Tarumanagara University West Java, Indonesia
[email protected]tar.ac.id
1
, jamaludini@fe.untar.ac.id
2
ABSTRACT
The Supreme Audit Agency (BPK) is the only institution authorized to carry out the task of examining
the management and financial responsibility of the state / region in accordance with applicable laws
and regulations. Central / local governments are required to submit their financial reports to BPK for
examination as a form of responsibility to the people, in this case represented by the House of
Representatives (DPR) / Regional People's Representative Council (DPRD). This study aims to
determine the factors that influence the number of BPK audit findings on the Internal Control System
(SPI) and compliance with applicable laws and regulations. Factors that are examined for their
influence in determining the number of BPK audit findings in this study are the size of local
government, the level of local wealth, fixed asset procurement, and the complexity of local
government. The data used is the Audit Report (LHP) on the examination of regional financial
statements in West Sulawesi Province issued by BPK from 2020 to 2022.
Keywords: Audit Findings; Audit Report; Financial Report Audit; Local Government Financial
Report; BPK; Local Government
INTRODUCTION
The issuance of Law No. 22 of 1999 concerning Regional Government, which was later
amended to Law No. 32 of (2004) concerning Regional Government, was the beginning of
changes in government administration from centralization to decentralization. Decentralization
is the principle in the administration of government marked by the division of authority and
the availability of adequate public space to interpret authority to lower government units or
called local governments (Thubany, 2005). Local governments are given the authority to carry
out local financial management, local development, and local services without any
interference from the central government. However, local governments must be accountable
for their authority to remain in accordance with existing laws and to create a good governance
system (Mahmod, 2013).
In order to be accountable for the management of the region, each year the Regional
Government is required to prepare a Regional Government Financial Report (LKPD) in
accordance with Government Accounting Standards based on Government Regulation
Number 71 of 2010 (2010). The LKPD must be submitted by the Regional Head to the
Supreme Audit Agency (BPK) no later than 3 (three) months after the end of the fiscal year as
mandated by Law Number 1 of (2004) concerning State Treasury. BPK as the external auditor
of local governments in Indonesia conducts an examination of LKPD, and provides opinions
and findings of the examination results on the fairness of information in the financial
statements compiled in the Audit Report (LHP). According to Indra, Iskak, and Khaq (2022),
the government external auditor acts as a third party in agency theory, which is able to bridge
the differences between agents and principals in the management and accountability of state /
regional finances (Christensen, Hail, & Leuz, 2021).
Based on the results of the BPK audit in the last three years, all Regional Governments
in West Sulawesi Province, totaling 7 entities, received an Unqualified Opinion (WTP) on
LKPD for the 2020 to 2022 fiscal years (Juanda, Setyawan, & Inata, 2023). Despite getting
WTP opinion, BPK still found a number of problems as outlined in the audit findings.
According to the Head of the West Sulawesi BPK Representative, Hery Ridwan, the BPK still
found several problems that need attention, inclon findings on the Internal Control System
(SPI) and compliance with laws and regulationsuding examinati (Anagnostopoulos, 2018).
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
Province for The Period 2020 to 2022
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This shows that although the Regional Government obtained WTP opinion, its financial
management has not been fully carried out in accordance with statutory provisions, and it is
necessary (Chang et al., 2020). to follow up on the recommendations of the problems
submitted by BPK (Anagnostopoulos, 2018).
BPK audit findings can be an interesting thing to study. Because there are several
research gaps from the results of previous studies, where from several studies there are
differences in research results, both positive and negative significant (Paranata, 2022a). On the
basis of this, the researcher is motivated to re-examine the determinants that affect the number
ofPKD audit findings in West Sulawesi. In terms of local government characteristics, namely,
the size of the local government as seen from the number of assets owned by the local
government, the level of regional wealth as seen from Regional Original Revenue (PAD), the
procurement of fixed assets as seen from capital expenditures, and the level of regional
complexity as seen from the number of Regional Work Units (SKPD) in the local government
(Masduki, Rindayati, & Mulatsih, 2022).
Selection of the research population in the Regional Government in West Sulawesi
Province by taking the observation year for three years, namely 2020 to 2022 (Nguyen,
Nguyen, & Dai Vo, 2022). Based on this, the research is entitled "Factors Affecting BPK
Audit Findings on Local Government Financial Reports in West Sulawesi Province for the
Period 2020 to 2022".
RESEARCH METHOD
This research uses secondary data that has been provided and published by other parties,
both in the form of financial data and non-financial data (Salehi & Arianpoor, 2021). Financial
data is obtained from the Local Government Financial Report (LKPD) which has been audited
and published by BPK in the form of an Examiner Results Report. In addition to financial
data, the Examination Results Report (LHP) also shows other non-financial data, namely the
number of audit findings of the Audit Board on Local Government Financial Reports (Salehi
& Arianpoor, 2021).
This study uses quantitative research with hypothesis testing, which is research that
explains phenomena in the form of relationships between variables (Indriantoro & Supomo,
2002). This study is to determine whether the variables of regional size (assets), regional
wealth level (PAD), procurement of fixed assets (capital expenditure realization), and regional
complexity (number of SKPD) have an influence on the findings of the Audit Board
examination of Local Government Financial Reports in Provinces and Districts in West
Sulawesi (Bahfiarti, 2020). The popupation of this study is Provinces and Regencies in West
Sulawesi from 2020 to 2022, with a total sample of 21 using the purposive sampling method
(Bahfiarti, 2020).
Operasional Variabel
Variabel Dependen
The dependent variable is the variable that is the main concern of the researcher.
Through analysis of the dependent variable it is possible to find the answer to a problem
(Sekaran, 2009). The dependent variable in this study was the findings of the CPC
examination. The findings of the Audit Board (BPK) examination in this study are seen from
the number of findings or violations that occur (Paranata, 2022b).
Audit Board (BPK) examination findings = number of cases of examination
findings
Independent Variables
Independent variables are variables that affect the dependent variable, where the
influence that appears can be positive or negative (Sekaran, 2009). The independent variables
used in this study are as follows:
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
Province for The Period 2020 to 2022
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Local Government Size
The size of the organization refers to how large the organization is.
Districts/Municipalities with larger total assets will be more complex in maintaining and
managing their assets (Suhardjanto et al., 2010). The size of local government in this study is
measured by the total assets owned by the government because assets are a relatively more
stable measure (Alqahtani & Mayes, 2018). To avoid high data variability, the total asset data
will be transformed into a natural logarithm (Singh & Sharma, 2016).
Local Government Size = Ln (Total Assets)
Regional Wealth Level
The level of regional wealth describes the ability and independence of the region based
on the amount of PAD produced by the area. PAD is sourced from local tax revenues, regional
levy proceeds, segregated regional wealth management results and other legitimate local
original revenues (Xia & Song, 2017).PAD is measured by the total realization of PAD
contained in the Budget Realization Report (LRA) (Shalehah, Handiani, Wahyunita, Faizah, &
Oktaviana, 2022). The level of regional wealth is calculated by comparing the PAD obtained
with the total regional income (Musaazi et al., 2015).
Regional Wealth Level = Local Original Revenue / Total Revenue
Addition of Regional Fixed Assets
The addition of regional fixed assets is closely related to the implementation of capital
expenditure (Wang, Zhang, Fu, Tan, & Chen, 2021). Capital expenditure is a Regional
Government expenditure whose benefits exceed 1 (one) fiscal year and will increase regional
assets or wealth and will subsequently increase routine expenditures such as maintenance costs
in the general administrative expenditure group (PP Number 71 of 2010) (2010). Capital
expenditure consists of capital expenditure on land, equipment and machinery, buildings and
buildings, roads, irrigation and networks, and other physical capital expenditures (Asiedu,
Sadekla, & Bokpin, 2020). Capital expenditure in this study is measured from the total
realization of capital expenditure. The addition of regional fixed assets is calculated by
comparing the realization of capital expenditure with the total regional expenditure (Fan, Yu,
Li, Xu, & Zhang, 2022).
Additional Regional Fixed Assets = Realization of capital expenditure / total expenditure
Kompleksitas Daerah
The complexity of local government can be seen from several aspects, one measure of
the complexity of a regional government through the number of SKPD (Masduki et al., 2022).
The number of SKPD is one of the considerations in seeing the level of public service needs in
an area. According to Puspitasari (2013), complexity is based on an individual's perception of
the difficulty of a task or job. Complexity shows how many SKPD Regional Apparatus Work
Units there are in the area (Sari, Ikhwal, & Soufyan, 2023).
Regional Complexity = Number of Regional Equipment Work Units (SKPD)
Data Analysis Methods
The analysis method used to prove the hypothesis is panel data regression analysis (Liu,
Ren, Cheng, & Wang, 2020). The processing of this research data will use the Eviews
statistical test tool. Panel data is a combination of cross section data and time series data
(Guliyev, 2023).
Descriptive Statistics
Descriptive statistics were used to briefly describe the variables in this study.
Descriptive analysis is carried out to find out the picture of the data to be analyzed. Ghozali
(2019) mentioned that the analytical tools used in descriptive statistical tests include the mean
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
Province for The Period 2020 to 2022
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value, standard deviation, variance and statistical range. The mean is used to estimate the
estimated average population size of a sample (Clayson, Carbine, Baldwin, & Larson, 2019).
Standard deviation is used to assess the mean dispersion of a sample. Maximum-minimum is
used to see the minimum and maximum values of the population (Li, Yang, Wang, & Lin,
2018). This needs to be done to see the overall picture of the samples that have been
successfully collected and are eligible to be sampled in research (Li et al., 2018).
Analysis Regresi Data Panel
According to Widarjono (2018), based on the problems faced and the characteristics of
existing data, in panel data regression estimation techniques there are three techniques
(models) that can be used, namely:
1. Model Common Effect
This technique is the simplest technique to estimate the parameters of the panel data
model, namely by combining cross section and time series data as a whole without looking at
the difference in time and entities (individuals) (Kabdrakhmanova, Memon, & Saurbayeva,
2021). The approach that is often used is the Ordinary Least Square (OLS) method. This
model ignores differences in individual dimensions or time or in other words the behavior of
data between individuals is the same in various periods of time (Henningsen & Henningsen,
2019).
2. Model Fixed Effect
This technique uses dummy variables to capture intercept differences between
individuals. However, this method brings the disadvantage of reducing degrees of freedom
which ultimately reduces the efficiency of parameters. The Fixed Effect model approach
assumes that the intercepts of each individual are different while the slopes between
individuals are fixed (the same).
3. Model Random Effect
A random effect model is a method that will estimate panel data in which interference
variables may be interconnected over time and between individuals (Yu et al., 2020). The
technique used is to add error terms that may appear in relationships between time and
between entities. The OLS method technique cannot be used to obtain an efficient estimator,
so it is more appropriate to use the Generalized Least Square (GLS) method.
To determine the panel data model reprogressed with the common effect method, fixed
effect method or random effect method, regression testing of panel data was carried out with
the Chow test and Hausman test.
1. Chow Test
This test is conducted to choose between a common effect or fixed effect model. The
hypotheses to be used are:
H0 : Model Common Effect
H1 : Model Fixed Effect
If the value of Chow Statistics (F-stat) is greater than the F of the table, then the null
hypothesis is rejected. In Eviews if the p-value is < α then go H0 and accept H1 so that the
model used is a fixed effect model, vice versa.
2. Hausman Test
This test is carried out to choose between fixed effect or random effect models. The
hypotheses used are:
H0 ; Model Random Effect
H1 : Model Fixed Effect
If the Hausman statistics are greater than the chi-square table then there is enough
evidence to reject the null hypothesis so that the model chosen is fixed effect, and vice versa.
In eviews if the p-value is < α then reject H0 and accept H1 so that the model used is a fixed
effect model, the opposite happens.
The panel data regression equation model is as follows:
TP it = β0 + β1TAit +β2PADit + β3BMit + β3SKPDit + ε
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
Province for The Period 2020 to 2022
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Information:
TP = Examination findings
β0 = Constant
β1, β2, β3 = Regression coefficient of the independent variable
TA = Local Government size
PAD = Raise Regional Wealth
BM = addition of regional fixed assets
SKPD = The complexity of the area
i = Provinces/Regencies in West Sulawesi
t = Specific time (2020 - 2022)
ε = Error coefficient
Uji Model
Model feasibility testing is the initial stage of identifying regression models that are
estimated to be feasible or not. The feasibility test of the model was carried out to measure the
accuracy of the sample regression function in calculating the actual value statistically
(Ghozali, 2019). This test can be measured from the coefficient of determination (R2),
statistical test t, and statistical test F.
Test Coefficient of Determination (R2)
The R2 value is used to measure the level of the model's ability to explain the variation
of the independent variable (Hair Jr, Howard, & Nitzl, 2020). The value of the coefficient of
determination is between zero and one. A small R2 value means that the ability of independent
variables to explain dependent variable variation is very limited. A value close to one means
that the independent variables provide almost all the information needed to predict the
variation of the dependent variable (Ghozali, 2019). But because R2 contains a fundamental
weakness, namely bias towards the number of independent variables entered into the model,
this study used adjusted R2 ranging between zero and one.
Simultaneous Significance Test (Statistical Test F)
The F test basically shows whether the independent variables included in the model
have a joint influence on the dependent variable (Ghozali, 2019). The criteria of simultaneous
significance are as follows:
a. If the significance > 0.05 then H0 is accepted, meaning that together the independent
variables have no significant effect on the dependent variable.
b. If the significance < 0.05 then H0 is rejected, meaning that together the independent
variables have a significant influence on the dependent variable.
Individual Parameter Significance Test (Satistic Test t)
This test aims to show how far the influence of one explanatory variable (independent)
individually in explaining the variation of the dependent variable. Comparing the p-value with
a significance level of 0.05, it can be determined whether H0 is rejected or accepted (H0 is
accepted if the p-value > 0.05, rejected if the p-value < 0.05). The criteria for the significance
of the hypothesis are as follows:
a. If the significance > 0.05 then H0 is accepted, meaning that there is no significant
influence between the independent variable and the dependent variable.
b. If the significance < 0.05 then H0 is rejected, meaning that there is a significant
influence between the independent variable and the dependent variable.
RESULT AND DISCUSSION
Description of Research Data
The main object of this research is the financial statements of all Local Governments in
West Sulawesi Province that have been audited by BPK for the period 2020 to 2022. West
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
Province for The Period 2020 to 2022
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Sulawesi Province has 7 entities, consisting of 1 Provincial Government and 6 District
Governments, so the research objects totaled 21 LKPDs.Analisis Deskriptif.
The results of descriptive analysis in this study are illustrated in table 1 as follows:
Table 1 Descriptive Analysis Results
TP
TA
PAD
BM
SKPD
Mean
14.95238
2.05E+12
1.11E+12
1.02E+12
42.38095
Median
15.00000
2.14E+12
9.23E+11
8.95E+11
43.00000
Maximum
21.00000
3.34E+12
2.02E+12
1.86E+12
48.00000
Minimum
8.000000
1.28E+12
6.12E+11
5.51E+11
34.00000
Std. Dev.
3.866215
6.06E+11
4.40E+11
4.18E+11
4.005948
Based on the results of the descriptive analysis above, the mean, median, maximum,
minimum and standard deviation values for all research variables are obtained as follows:
1. The Inspection Findings (TP) variable obtained a mean value of 14,952, a median value
of 15,000, a maximum value of 21,000, a minimum value of 8,000 and a standard
deviation value of 3,866.
2. The Local Government Size variable (TA) obtained a mean value of 2.05E+12, a
median value of 2.14E+12, a maximum value of 3.34E+12, a minimum value of
1.28E+12 and a standard deviation value of 6.06E+11.
3. The Regional Wealth Level (PAD) variable obtained a mean value of 1.11E+12, a
median value of 9.23E+11, a maximum value of 2.02E+12, a minimum value of
6.12E+11 and a standard deviation value of 4.40E+11.
4. The Regional Fixed Asset Addition (BM) variable obtained a mean value of 1.02E+12,
a median value of 8.95E+11, a maximum value of 1.86E+12, a minimum value of
5.51E+11 and a standard deviation value of 4.18E+11.
5. The Regional Complexity Variable (SKPD) obtained a mean value of 42.381, a median
value of 43.000, a maximum value of 48.000, a minimum value of 34.000 and a
standard deviation value of 4.006.4.006.
Panel Data Regression Analysis
Model Estimation Stage
In estimating the panel regression model, there are three approaches that are often used,
including the Common Effect Model (CEM), Fixed Effect Model (FEM), Random Effect
Model (REM):
Table 2 Common Effect Model (CEM) table output:
Dependent Variable: TP
Method: Panel Least Squares
Date: 09/12/23 Time: 15:21
Sample: 2020 2022
Periods included: 3
Cross-sections included: 7
Total panel (balanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
12.86556
10.08716
1.275440
0.2204
TA
1.71E-12
3.31E-12
0.517938
0.6116
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
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PAD
-1.57E-11
1.12E-11
-1.395383
0.1820
BM
1.67E-11
1.04E-11
1.609315
0.1271
SKPD
-0.026573
0.230052
-0.115508
0.9095
R-squared
0.182172
Mean dependent var
14.95238
Adjusted R-squared
-0.022285
S.D. dependent var
3.866215
S.E. of regression
3.909058
Akaike info criterion
5.768727
Sum squared resid
244.4917
Schwarz criterion
6.017422
Log likelihood
-55.57163
Hannan-Quinn criter.
5.822700
F-statistic
0.891002
Durbin-Watson stat
2.547024
Prob(F-statistic)
0.491777
Table 3 Output tabel Fixed Effect Model (FEM):
Dependent Variable: TP
Method: Panel Least Squares
Date: 09/12/23 Time: 15:22
Sample: 2020 2022
Periods included: 3
Cross-sections included: 7
Total panel (balanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
51.82811
33.04753
1.568290
0.1479
TA
2.27E-11
9.93E-12
2.282638
0.0456
PAD
-4.77E-11
1.92E-11
-2.481400
0.0325
BM
-4.65E-11
1.62E-11
-2.880769
0.0164
SKPD
0.406601
0.458044
0.887689
0.3956
Effects Specification
Cross-section fixed (dummy variables)
R-squared
0.761776
Mean dependent var
14.95238
Adjusted R-squared
0.523552
S.D. dependent var
3.866215
S.E. of regression
2.668664
Akaike info criterion
5.106714
Sum squared resid
71.21766
Schwarz criterion
5.653845
Log likelihood
-42.62050
Hannan-Quinn criter.
5.225456
F-statistic
3.197728
Durbin-Watson stat
3.578826
Prob(F-statistic)
0.040324
Table 4 Random Effect Model (REM)
Dependent Variable: TP
Method: Panel EGLS (Cross-section random effects)
Date: 09/12/23 Time: 15:23
Sample: 2020 2022
Periods included: 3
Cross-sections included: 7
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
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Total panel (balanced) observations: 21
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
14.41596
9.177825
1.570738
0.1358
TA
2.48E-12
2.96E-12
0.836657
0.4151
PAD
-1.28E-11
8.23E-12
-1.556959
0.1390
BM
1.25E-11
7.89E-12
1.581803
0.1333
SKPD
-0.073219
0.204225
-0.358521
0.7246
Effects Specification
S.D.
Rho
Cross-section random
1.560376
0.2548
Idiosyncratic random
2.668664
0.7452
Weighted Statistics
R-squared
0.118426
Mean dependent var
10.50582
Adjusted R-squared
-0.101968
S.D. dependent var
3.522605
S.E. of regression
3.697843
Sum squared resid
218.7847
F-statistic
0.537337
Durbin-Watson stat
2.653061
Prob(F-statistic)
0.710441
Unweighted Statistics
R-squared
0.162782
Mean dependent var
14.95238
Sum squared resid
250.2884
Durbin-Watson stat
2.319121
Selection of the right panel data regression model
Selection of panel data regression model is an analysis stage to determine the best
estimation method between common effect, fixed effect and random effect (Ullah, Akhtar, &
Zaefarian, 2018).
Chow Test
The Chow test aims to determine the choice of model that is better used between common
effect and fixed effect.
H0 : CEM model is selected (prob > 0.05)
H1 : FEM model is selected (prob < 0.05)
Table 5 Output tabel Uji Chow:
Redundant Fixed Effects Tests
Equation: MODEL_FEM
Test cross-section fixed effects
Effects Test
Statistic
d.f.
Prob.
Cross-section F
4.055036
(6,10)
0.0253
Cross-section Chi-square
25.902255
6
0.0002
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
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Cross-section fixed effects test equation:
Dependent Variable: TP
Method: Panel Least Squares
Date: 09/12/23 Time: 15:23
Sample: 2020 2022
Periods included: 3
Cross-sections included: 7
Total panel (balanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
12.86556
10.08716
1.275440
0.2204
TA
1.71E-12
3.31E-12
0.517938
0.6116
PAD
-1.57E-11
1.12E-11
-1.395383
0.1820
BM
1.67E-11
1.04E-11
1.609315
0.1271
SKPD
-0.026573
0.230052
-0.115508
0.9095
R-squared
0.182172
Mean dependent var
14.95238
Adjusted R-squared
-0.022285
S.D. dependent var
3.866215
S.E. of regression
3.909058
Akaike info criterion
5.768727
Sum squared resid
244.4917
Schwarz criterion
6.017422
Log likelihood
-55.57163
Hannan-Quinn criter.
5.822700
F-statistic
0.891002
Durbin-Watson stat
2.547024
Prob(F-statistic)
0.491777
Based on the table above, the p-value of the cross-section chi-square is 0.000 < α =
0.05, so H0 is rejected, which means that the fixed effect model is better used than the
common effect model.
Hausman Test
This test is used to determine the choice of a better model between fixed effect and
random effect.
H0 : REM model is selected (prob > 0.05)
H1 : FEM model is selected (prob < 0.05)
Table 6 Output tabel uji Hausman:
Correlated Random Effects - Hausman Test
Equation: MODEL_REM
Test cross-section random effects
Test Summary
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
Cross-section random
18.720570
4
0.0009
Cross-section random effects test comparisons:
Variable
Fixed
Random
Var(Diff.)
Prob.
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
Province for The Period 2020 to 2022
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TA
0.000000
0.000000
0.000000
0.0332
PAD
-0.000000
-0.000000
0.000000
0.0446
BM
-0.000000
0.000000
0.000000
0.0000
SKPD
0.406601
-0.073219
0.168097
0.2419
Cross-section random effects test equation:
Dependent Variable: TP
Method: Panel Least Squares
Date: 09/12/23 Time: 15:23
Sample: 2020 2022
Periods included: 3
Cross-sections included: 7
Total panel (balanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
51.82811
33.04753
1.568290
0.1479
TA
2.27E-11
9.93E-12
2.282638
0.0456
PAD
-4.77E-11
1.92E-11
-2.481400
0.0325
BM
-4.65E-11
1.62E-11
-2.880769
0.0164
SKPD
0.406601
0.458044
0.887689
0.3956
Effects Specification
Cross-section fixed (dummy variables)
R-squared
0.761776
Mean dependent var
14.95238
Adjusted R-squared
0.523552
S.D. dependent var
3.866215
S.E. of regression
2.668664
Akaike info criterion
5.106714
Sum squared resid
71.21766
Schwarz criterion
5.653845
Log likelihood
-42.62050
Hannan-Quinn criter.
5.225456
F-statistic
3.197728
Durbin-Watson stat
3.578826
Prob(F-statistic)
0.040324
Based on the table above, it shows that the p-value of 0.0310 < α = 0.05, which means that H0
is rejected, so the fixed effect model is better to use.
Lagrange Multiplier Test
This test is used to determine the choice of a better model between common effect and
random effect
H0 : CEM model is selected (prob > 0.05)
H1 : REM model is selected (prob < 0.05)
Table 7 Output tabel uji Lagrange Multiplier:
Lagrange Multiplier Tests for Random Effects
Null hypotheses: No effects
Alternative hypotheses: Two-sided (Breusch-Pagan) and one-
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
Province for The Period 2020 to 2022
1136 Return: Study of Management, Economic And Business, Vol 2 (11), November 2023
sided
(all others) alternatives
Test Hypothesis
Cross-section
Time
Both
Breusch-Pagan
1.098286
0.465116
1.563403
(0.2946)
(0.4952)
(0.2112)
Honda
-1.047992
-0.681994
-1.223285
--
--
--
King-Wu
-1.047992
-0.681994
-1.114620
--
--
--
Standardized Honda
-0.076500
-0.293225
-3.583686
--
--
--
Standardized King-
Wu
-0.076500
-0.293225
-3.269787
--
--
--
Gourierioux, et al.*
--
--
0.000000
(>= 0.10)
*Mixed chi-square asymptotic critical values:
1%
7.289
5%
4.321
10%
2.952
Based on the table above, it shows that the Breusch-Pagan (Both) p-value is 0.211> α = 0.05,
which means that H0 is accepted, so the common effect model is better used.
Based on the three model tests that have been carried out, the best model to be used in this
study is the fixed effect model.
Model Interpretation
Based on the three model tests that have been carried out, the best model to be used in
this study is the fixed effect model, so the interpretation of the fixed effect model is as
follows:
Table 8 Simultaneous Significance Test
Effects Specification
Cross-section fixed (dummy variables)
R-squared
0.761776
Mean dependent var
14.95238
Adjusted R-squared
0.523552
S.D. dependent var
3.866215
S.E. of regression
2.668664
Akaike info criterion
5.106714
Sum squared resid
71.21766
Schwarz criterion
5.653845
Log likelihood
-42.62050
Hannan-Quinn criter.
5.225456
F-statistic
3.197728
Durbin-Watson stat
3.578826
Prob(F-statistic)
0.040324
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Based on the output table of the fixed effect model, the prob value is 0.040 <0.05 so it can be
concluded that there is a significant effect simultaneously between the Size of the Local
Government (TA), the Level of Regional Wealth (PAD), the Addition of Regional Fixed
Assets (BM), and Regional Complexity (SKPD) on Audit Findings (TP).
Table 9 Coefficient of determination
Effects Specification
Cross-section fixed (dummy variables)
R-squared
0.761776
Mean dependent var
14.95238
Adjusted R-squared
0.523552
S.D. dependent var
3.866215
S.E. of regression
2.668664
Akaike info criterion
5.106714
Sum squared resid
71.21766
Schwarz criterion
5.653845
Log likelihood
-42.62050
Hannan-Quinn criter.
5.225456
F-statistic
3.197728
Durbin-Watson stat
3.578826
Prob(F-statistic)
0.040324
Based on the output of the common effect model table, the R2 value is 0.762 (76.2%), so it
can be concluded that the variable Checking Findings (Tp) Can Be Explained By The
Variables Of Daerah Government Size (Ta), Daerah Wealth Level (Pad), Addition Of Daerah
Fixed Assets (Bm), and Regional Complexity (SKPD) by 0.762 (76.2%), while the rest is
influenced by other variables outside the model.
Table 10 Partial Significance Test
Dependent Variable: TP
Method: Panel Least Squares
Date: 09/12/23 Time: 15:22
Sample: 2020 2022
Periods included: 3
Cross-sections included: 7
Total panel (balanced) observations: 21
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
51.82811
33.04753
1.568290
0.1479
TA
2.27E-11
9.93E-12
2.282638
0.0456
PAD
-4.77E-11
1.92E-11
-2.481400
0.0325
BM
-4.65E-11
1.62E-11
-2.880769
0.0164
SKPD
0.406601
0.458044
0.887689
0.3956
Effects Specification
Cross-section fixed (dummy variables)
R-squared
0.761776
Mean dependent var
14.95238
Adjusted R-squared
0.523552
S.D. dependent var
3.866215
S.E. of regression
2.668664
Akaike info criterion
5.106714
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
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Sum squared resid
71.21766
Schwarz criterion
5.653845
Log likelihood
-42.62050
Hannan-Quinn criter.
5.225456
F-statistic
3.197728
Durbin-Watson stat
3.578826
Prob(F-statistic)
0.040324
From the test results above, it can be concluded as follows:
1. Testing the influence between Local Government Size (TA) on Audit Findings (TP)
obtained a coefficient value of 2.27E-11 with a significance value of 0.046, because
the significance value <0.05, there is a significant influence between Local
Government Size (TA) on Audit Findings (TP). Given that the regression coefficient
is positive, it indicates that the relationship between the two is positive, meaning that
assuming other independent variables remain, an increase in Local Government Size
(TA) of one unit will affect the increase in Audit Findings (TP) by 2.27E-11 units, and
vice versa.
2. Testing the effect between the Level of Regional Wealth (PAD) on Audit Findings
(TP) obtained a coefficient value of -4.77E-11 with a significance value of 0.033,
because the significance value <0.05, there is a significant influence between the
Level of Regional Wealth (PAD) on Audit Findings (TP). Given that the regression
coefficient is negative, it indicates that the relationship between the two is negative,
meaning that assuming other independent variables remain, an increase in the level of
regional wealth (PAD) of one unit will affect the decrease in Audit Findings (TP) by
2.27E-11 units, and vice versa.
3. Testing the effect between the Addition of Regional Fixed Assets (BM) on Audit
Findings (TP) obtained a coefficient value of -4.65E-11 with a significance value of
0.016, because the significance value <0.05, there is a significant influence between
the Addition of Regional Fixed Assets (BM) on Audit Findings (TP). Given that the
regression coefficient is negative indicates that the relationship between the two is
negative, meaning that assuming other independent variables are fixed, an increase in
the addition of Regional Fixed Assets (BM) of one unit will affect the decrease in
Audit Findings (TP) by -4.65E-11 units, and vice versa.
4. Testing the effect between Regional Complexity (SKPD) on Audit Findings (TP)
obtained a coefficient value of 0.407 with a significance value of 0.396, because the
significance value> 0.05, there is no significant effect between Regional Complexity
(SKPD) on Audit Findings (TP). This means that assuming the other independent
variables are constant, an increase or decrease in Regional Complexity (SKPD) of one
unit will not affect the increase or decrease in Audit Findings (TP).
Hypothesis Testing Results
Based on the results of calculations using the Path Analysis approach, the results of
hypothesis testing are obtained as presented below:
Hipotesis 1. Hypothesis 1. Local Government Size (TA) has a significant effect on Audit
Findings (TP) is accepted. Testing the effect between Local Government Size (TA) on
Audit Findings (TP) obtained a coefficient value of 2.27E-11 with a significance value of
0.046, because the significance value <0.05, there is a significant influence between Local
Government Size (TA) on Audit Findings (TP). Given that the regression coefficient is
positive, it indicates that the relationship between the two is positive, meaning that
assuming other independent variables remain, an increase in Local Government Size (TA)
of one unit will affect the increase in Audit Findings (TP) by 2.27E-11 units, and vice
versa.
Factors Affecting BPK Audit Findings on Local Government Financial Reports in West Sulawesi
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Hipotesis 2. The level of Regional Wealth (PAD) has a significant effect on Audit Findings
(TP) is accepted. Testing the effect between the Level of Regional Wealth (PAD) on Audit
Findings (TP) obtained a coefficient value of -4.77E-11 with a significance value of
0.033, because the significance value <0.05, there is a significant effect between the Level
of Regional Wealth (PAD) on Audit Findings (TP). Given that the regression coefficient is
negative, it indicates that the relationship between the two is negative, meaning that
assuming other independent variables remain, an increase in the level of regional wealth
(PAD) of one unit will affect the decrease in Audit Findings (TP) by 2.27E-11 units, and
vice versa.
Hipotesis 3. The addition of Regional Fixed Assets (BM) has a significant effect on Audit
Findings (TP) is accepted. Testing the effect between the addition of Regional Fixed
Assets (BM) on Audit Findings (TP) obtained a coefficient value of -4.65E-11 with a
significance value of 0.016, because the significance value <0.05, there is a significant
effect between the addition of Regional Fixed Assets (BM) on Audit Findings (TP). Given
that the regression coefficient is negative indicates that the relationship between the two is
negative, meaning that assuming other independent variables are fixed, an increase in the
addition of Regional Fixed Assets (BM) of one unit will affect the decrease in Audit
Findings (TP) by -4.65E-11 units, and vice versa.
Hipotesis 4. Hypothesis 4. Testing the effect between Regional Complexity (SKPD) has
no significant effect on Audit Findings (TP) is accepted. Testing the effect between
Regional Complexity (SKPD) on Audit Findings (TP) obtained a coefficient value of
0.407 with a significance value of 0.396, because the significance value> 0.05, there is no
significant effect between Regional Complexity (SKPD) on Audit Findings (TP). This
means that assuming the other independent variables are constant, an increase or decrease
in Regional Complexity (SKPD) of one unit will not affect the increase or decrease in
Audit Findings (TP).
Hipotesis 5. Local Government Size (TA), Regional Wealth Level (PAD), Regional Fixed
Asset Addition (BM), and Regional Complexity (SKPD) simultaneously have a
significant effect on Audit Findings (TP) is accepted. Based on the output of the fixed
effect model table, the prob value is 0.040 <0.05 so it can be concluded that there is a
simultaneous significant effect between Local Government Size (TA), Regional Wealth
Level (PAD), Regional Fixed Asset Additions (BM), and Regional Complexity (SKPD)
on Audit Findings (TP).
CONCLUSION
The results of this study show evidence that the number of BPK audit findings is
significantly and moderately influenced by 3 of the 4 research variables. These variables
include the size of local government proxied by total assets, the level of regional wealth
proxied by local revenue, and the addition of fixed assets proxied by capital expenditure.
Meanwhile, the variable of local government complexity, which is proxied by the number of
Local Government Work Units (SKPD), has no effect on the number of BPK audit findings on
Local Government Financial Reports in West Sulawesi Province from 2020 to 2022.
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