The Effect of Leverage, Inventory Intensity, Intensity of Fixed Assets, and Profitability on Tax Agresivity
(Case Study of Consumer Good Industry in the IDX from 2016-2018 Period)
Rukmini
Institute Technology Business AAS Indonesia E-mail: [email protected]
Abstract
This study determines the partial and simultaneous effects of leverage, inventory strength, fixed asset intensity, and tax aggressiveness on the profitability of consumer goods companies in the IDX from 2016 to 2018. This study is quantitative research, with a data source is taken from www.idx.co.id in 2016 to 2018. Regression analyses of logistics approaches were used in data analysis. The leverage is profiled in DAR, the inventory intensity is calculated by comparison of the total inventory to the total assets, the intensity of the assets remains dictated by comparing the total fixed assets to the total asset, and the profitability measured by ROA. The agretiviteness variable is measured by using ETR, which is a value of 1 when ETR> 25% and a value of 0 when ETR is <25%. The study's findings showed that inventory intensity and fixed asset intensity influenced the tax aggressiveness of the Consumer Goods industry. Meanwhile, tax aggressiveness in the consumer goods industry was unaffected by leverage or profitability.
Keywords: leverage, inventory intensity, profitability, fixed asset intensity, tax, aggressiveness
Introduction
One of the sectors of state income of the Republic of Indonesia comes from taxes. For Indonesia, state revenue originating from taxes is still the largest, which is around 70% of total government revenue. According to Zain (2005), tax imposition is given by the government to taxpayers as taxpayer participation to increase the growth rate of the country's development.
The company as a corporate taxpayer is obliged to pay taxes, paying taxes for the company is a burden that must be borne, because it reduces the net profit received. Therefore, the company tries to minimize costs incurred to earn a profit. Meanwhile, the government seeks to maximize income from the tax sector with the aim of maximum taxpayers for companies as a form of government-related corporate responsibility (Yoehana, 2013).
However, the government's efforts have not been maximized. This can be seen from the tax revenue for the last four years, namely 2014 - 2018, the tax revenue received by the government as stated in the APBN did not reach the target. Tax revenue for the five years can be showsn in table below:
Table 1. Tax Realization and Targets for 2015-2018
2015 2016 2017 2018
(In Trillion)
14640 Target
(Rp) Actual Percentage
1,246.1 1,143
91.73%
1,489.3 1,235.8
82.98%
1,283 1,147
1,424 1,315.9
92.4%
89.4%
Source: Director General of Taxes, 2014 - 2018. Data processed
Profit-oriented companies can always maximize profits and reduce all costs, including reducing tax burdens and even eliminating tax liabilities. Taxpayers often take advantage of the government's weakness in tax fraud. Lack of special attention from the government on tax collection policies can be an opportunity for companies to commit tax fraud. One of the opportunities taken by taxpayers is through tax avoidance, tax planning, and tax aggressiveness.
The factors that influence tax aggressiveness are inventory intensity, leverage, fixed asset intensity, and profitability (Savitri, 2017). The indication that the company is avoiding tax can be seen from the funding policy taken by the company. One of the funding policies is the company's leverage strategy, i.e. the amount of debt it takes on to fund its operations. The corporation will have to pay interest because the volume of debt has increased. The interest charged on this loan would be deducted from the company's net earnings, lowering tax payments accordingly achieve maximum benefits (Dharma, 2016). This opinion is following Purwanto's (2015) research which states that companies will take advantage of debt to carry out tax aggressiveness because the interest set as an expense can reduce taxes. This research is added by Dharma’research (2016) which prove that leverage will cause a decrease in the level of tax aggressiveness.
In addition to leverage, the company considers the intensity of inventory and the intensity of fixed assets as a company effort to avoid taxes. The high intensity of company inventories will create additional burdens for the company. The high inventory of a company arises due to inventory costs. These costs include expense costs, production costs, storage costs labor costs, general and administrative costs, and sales costs. All costs are recognized as expenses that will reduce the company's net profit and thus minimize tax payments (Savitri, 2017). Research by Noor et al (2010) found that inventory intensity affect the effective tax rate. This result is similar to Imelia’s research (2012) which states that the amount of tax payments of a company increases if there is no tax reduction factor in the company. The higher a company's inventory rate, the higher its profit margin. If the amount of company profit decreases, it decreases in the amount of company tax payments.
2. Research Method Population and Sample
This study uses consumer goods companies in the IDX from 2016-2018 with total 52 companies.
As for how to determine the sample the writer uses purposive sampling to get a representative sample in line with the study conducted out. This is done by taking the subject, not based on a specific goal (Sugiyono, 2017). Based on a population of 52 companies, 34 companies do not meet these criteria because they do not report financial reports during the study period. So the study sample was 19 companies (19 companies x 3 years of observation) with 57 observations of financial statements. Rukmini et al. (2020) state that several parties need the services of a company that prepares financial statements. These are the final product of an accounting procedure that follows commonly agreed with accounting rules (Fauzi & Rukmini, 2018).
Data Analysis Method 1. Descriptive Statistics Test
This research uses this test to see variables' average value (mean), minimum value,
maximum value, and standard deviation (Ghozali, 2016) 2. The Goodness of Fit Test
The Hosmer and Lemeshow Goodness of Fit Test was used to determine the validity of the regression model. If this statistical value of Fit is greater than 0.05, the model cannot be discarded, implying that it can estimate the observation value or that it is adopted because it follows the observation results (Ghozali, 2016)
Hypothesis test
1. Logistic Regression Analysis
Since the dependent variable in the model is a categorical one, it is a logistic regression model (variable dichotomy), giving a value of 1 for agricultural companies that do not experience tax aggressiveness and a value of 0 for agricultural companies that experience tax aggressiveness.
Based on the general model of logistic analysis obtained for the prediction of tax aggressiveness for industrial consumer goods companies listed on the IDX are as follows (Nirwana, 2015):
E (Y) = β0 + β1X1 + β2X2 + β3X3 + β4X4
If entered into the logistic analysis equation in this study can be translated into the following formula:
AP = bo + b1Lev + b2IP + b3IAT + b4Prof
2. T-test
This measure was used to determine the degree of importance of independent variable's effect on the dependent variable (Ghozali, 2016). The conclusion drawn in this t-test is to look at the significance value (α) provided that:
3. Overall Model Test (Simultaneous)
This evaluation determines whether the hypothesized model suits the data or not. The final model fit (overall model fit) will be used to see how all independent variables in logistic regression influence the dependent variable at the same time, much as the F test in linear regression. The overall model fittest is based on the statistical value -2LL or the LR value. A simultaneous test of logistic model regression coefficients is calculated from the difference in the -2LL value between models consisting only of constants and models that are estimated from constants and independent variables (Widarjono, 2010).
For the model to fit with the data, it must be accepted. Likelihood-based estimates were used. The likelihood L of the model is the probability that the proposed model correctly reflects
14642 the input information. Between the original value of -2LogL and the value of -2LogL in the next step representing the input results, there is a decrease in value. Between the original value of - 2LogL and the current value of -2LogL, there is a reduction in value. It then reveals that the hypothesized model suits the data in the next phase. The discovery of probability (-2LogL) reveals a more accurate regression model or one that is hypothesized to match the results (Ghozali, 2016).
4. Coefficient of Determination (Nagelkerke R2)
This test sees how much the independent variables' uncertainty can explain the variability of the dependent variable. The determination’s coefficient is a variation on the Nagelkerke coefficient that ensures the value ranges between 0 and 1. This is accomplished by dividing the maximum value by the Nagelkerke R Square value. A value close to one indicates that the independent variables have all information required to estimate the dependent variable's variance. For multiple regression, the coefficient of a decision may be represented as the R Square value.
3. Results and Discussion 3.1 Result
1. Test Eligibility of the Regression Model (goodness of fit test)
The Goodness of Fit Test by Hosmer and Lemeshow was used to determine the validity of the regression model. H0 cannot be discarded if the predictive value of this model is greater than 0.05, implying that the model will estimate the value of the observation or that the model is adopted since it is under observation results (Ghozali, 2016). The regression model viability test is used to test the null hypothesis that empirical evidence will fit or complement the model and that there will be no differences between the model and the data, indicating that the model is fit. This is the test table:
Table 2. Model Feasibility Test
The significance value is 0.285, which is greater than 0.05, as seen in Table 2. It implies the model can be said to be fit and acceptable.
2. Hypothesis Testing
a. Logistic Regression Analysis
The analysis used logistic regression analysis. The independent variable (Y) is tax aggressiveness and the independent variable Leverage (X1), Supply Intensity (X2), Fixed Asset Intensity (X3), and profitability (X5). Logistic regression is used because the dependent variable (Y) is a dummy. There are only two decisions, namely“Yes” and “No” decisions to carry out tax aggressiveness. The calculation of logistic regression analysis was carried out using SPSS Version 26.
Table 3. Logistic Regression Analysis
Table 3 produces the logistic regression analysis equation as follows:
AP = bo + b1Lev + b2IP + b3IAT + b4Prof
AP = 0.758 + (-0.003) Lev + 0.355 IP + 0.292 IAT + (-0.002) Prof AP = 0.758 - 0.003 Lev + 0.355 IP + 0.292 IAT -0.002 Prof
The meaning of the logistic regression equation above is that the presence or absence of tax aggressiveness can be calculated using this equation. The tax aggressiveness probability figure is 0-1. If the value of the equation is generated approaching numbers1, it means that the company is likely to engage in large tax aggressiveness. Meanwhile, if the outcome is close to 0, it indicates that the likelihood of aggression is strong, which is affected by the variables identified in this analysis. The value of the logistic regression coefficient in table 3 is as follows:
1) The leverage vector (X1) has a logistic regression coefficient value of -0.003, show the negative result on the regression coefficient value represents the opposite relationship between X and Y, so the increase in leverage, the less tax aggressiveness.
2) The inventory intensity vector (X2) has a logistic regression coefficient of 0.355, show a positive result on the regression coefficient value represents a parallel relationship between X and Y, so the increase in inventory intensity, the more tax aggressiveness increases.
3) The fixed assets sensitivity vector (X3) has a logistic regression coefficient of 0.292, show the positive results on the regression coefficient value represent the parallel relationship between X and Y, so the increasing intensity of fixed assets, the increased tax aggressiveness.
4) The profitability vector (X4) has a logistic regression coefficient of -0.002, show the negative result on the regression coefficient value symbolizes the opposite relationship between X and Y, so the increase in profitability, the less tax aggressiveness.
b. T-test (Individual Parameters Significant Test)
This test is to assess the significance of each independent variable's impact on the dependent variable (Ghozali, 2016). The conclusion drawn in this t-test is to look at t count> t table and significance (α) provided that:
Based on table 3, the significant test of individual parameters can be described as follows:
1) Leverage has a 0.981 meaning level above 0.05. This means that tax aggressiveness is unaffected by leverage. H0 has been accepted, and the first hypothesis has been reported. Leverage has a huge impact on tax aggressiveness and has been dismissed (not proven correct).
2) The relevance value of inventory intensity is 0.012, which is less than 0.05. As a result, inventory intensity affects tax aggressiveness. When the company can manage the inventory intensity well,
14644 it will cause the company to do tax aggressiveness 1.427 times compared to companies with bad inventory intensity. It implies that H0 is rejected and the second hypothesis states that inventory intensity has an effect significant to Tax Aggressiveness accepted (proven to exist).
3) Fixed asset intensity has a meaning value of 0.003 below 0.05. This means that the amount of fixed assets affects tax aggressiveness. When the company can manage the fixed asset intensity properly, it will cause the company to tend to tax aggressiveness of 1.339 times compared to companies with poorly fixed asset intensity. It can be concluded that H0 is rejected and the third hypothesis states that the intensity of fixed assets has an effect significant to Tax Aggressiveness accepted (proven to exist).
Profitability has a substantial value of 0.228 that is greater than or equal to 0.05. This means that profitability has little impact on tax aggressiveness. H0 is confirmed, although the fourth theory, that profitability has a major impact on tax aggressiveness, is dismissed (not proven correct).
c. Test Whole Model (Simultaneous)
The total model fit test is a test that is used to see whether the independent variables in logistic regression are affecting the dependent variable at the same time. The estimates used are Likelihood-based. The model's likelihood L is the chance that the hypothesized model accurately represents the input details. Between the original value of -2LogL and the value of -2LogL in the next step representing the input results, there is a decrease in value. Tables 4.5 and 4.6 below have more information:
Table 4. A value of -2LL which only consists of Constants
Table 5. The value of -2LL which consists of Constants and Variables
The SPSS output results in Table 4 are the log-likelihood values which consist of only constants. While table 5 is the value of -2 likelihood which consists of constant and independent variables. The -log likelihood value which only includes a constant is 72.563. Meanwhile, the Likerhood value which consists of constant and independent variables is 52.246. The comparison of the two loglikelihood values is 20.317. As shown in this Chi-Square table:
Table 6. Comparison table of -2LL values
Table 7 is a comparison display of the -2Log likelihood values consisting of constants only (table 4.5) with the -2Log likelihood values consisting of constants and independent variables (Table 4.6). This comparison follows the chi-square distribution. The chi-square value is 20.316 with df 4. A significant model of 0.000 is obtained using table 4.7. Since this amount is less than 0.05, the fifth theory, which says that debt, inventory intensity, fixed asset intensity, and profitability both have a substantial impact on tax aggressiveness, is rejected (proven to be true).
d. Coefficient of Determination (Nagelkerke R2)
This test is to see how well the independent variable will describe the dependent variable.
Table 7. Coefficient of Determination
To see the percentage of effect of the independent variables together on the dependent variable, use the coefficient of determination (Nagelkerke).
Formula = Value Nagelkerke x 100%
= 0.416 x 100%
= 41.6%
So, the variable Leverage (X1), Supply Intensity (X2), Fixed Asset Intensity (X3), and profitability (X5) can affect tax aggressiveness by 41.6% while 58.4% is influenced by other variables not examined such as good governance.
3.1.Discussion
1. Leverage (X1) on Tax Aggressiveness
For a large 0.981 rather than 0.05, leverage has a coefficient value of -0.003. Leverage does not seem to have a substantial impact on tax aggressiveness, according to the findings. This
14646 is because of the relationship leverage with this tax aggressiveness that both high and low levels leverage does not influence to take tax aggressive actions because debt is not the main reason for reducing taxes. Besides, with the high level of debt of a company, the management will be more selective in managing finances. Management will not take the risk of tax aggressiveness because this debt is used in large amounts, it can cause losses for the company. This research is following research conducted by Ardisamartha (2015).
2. Inventory Intensity (X2) on Tax Aggressiveness
Inventory intensity has a coefficient value of 0.355, with a significant value of 0.012 that is smaller than 0.05. It implies the inventory intensity has a significant effect on tax aggressiveness. Companies that invest in inventory in warehouses will cause the formation of inventory maintenance and storage costs which will increase the number of company expenses, so it can reduce company profits. Companies with a high inventory intensity would be more competitive in their approach to the tax load they encounter. Companies that have high inventory intensity will also be able to carry out cost efficiency so that company profits can increase. This research is following the research conducted by Ardisamartha (2015).
3. Fixed Asset Intensity (X3) on Tax Aggressiveness
Fixed asset intensity has a coefficient value of 0.292, with a significant 0.003 less than 0.05. The intensity of capital assets, it is argued, has a substantial impact on tax aggressiveness.
This is because the higher the company's capital asset intensity, the higher the depreciation cost would be, and the higher the depreciation expense, the lower the company's costs in fiscal reconciliation would be. Other than that Shareholders want managers to be able to generate and manage profits on these fixed assets properly so that the costs incurred to pay taxes are not too large. The results above show that companies with large fixed assets have a large depreciation expense which will affect the tax burden because they reduce income and/or increase commercial costs. This study is consistent with previous studies written by Ardisamartha (2015).
4. Profitability (X4) on Tax Aggressiveness
Profitability has a value coefficient of -0.002 significantly 0.954greater than 0.05.
Leverage does not seem to have a substantial impact on tax aggressiveness, according to the findings. This is because the manager will not take risks. After all, tax aggressiveness can burden the company, including the burden that must be paid to the tax consultant, time spent on completion of tax audits, reputation fines, and fines paid to tax authorities. This study is consistent with previous studies written by Ardyansah (2014).
5. Leverage (X1), Supply Intensity (X2), Fixed Asset Intensity (X3), and profitability (X5)Tax Aggressiveness
Comparison of the chi-square value of 20.316 with df 4, the model is significant amounting to0,000. Because this value is less than 0.05, it is concluded that simultaneously leverage, inventory intensity, fixed assets intensity, and profitability have a significant effect on tax aggressiveness. The corporation will have to pay interest because the volume of debt has increased. The interest cost paid on this loan would be deducted from the company's net earnings, lowering tax costs and allowing the company to maximize profits. Besides, the high intensity of the company's inventory will cause additional expenses for the company as well as the proportion in which fixed assets have posts for the company to add expenses, namely the depreciation expense incurred by fixed assets as a deduction of income, if the fixed assets are greater, the resulting profit will be. getting smaller, because of the depreciation expense contained in fixed
assets which can reduce profits. The decreasing taxable profit of the company will reduce the tax payable by the company. However, on the other hand, with high profitability, the company will always fulfill its obligations, including paying taxes. This study is consistent with previous studies written by Dharma (2016) and Savitri (2017).
6. Percentage of the effect of Leverage (X1), Supply Intensity (X2), Fixed Asset Intensity (X3), and profitability (X5) on Tax Aggressiveness
Score the coefficient of determination (Nagelkerke) of 0.416. It was concluded that Leverage (X1), Supply Intensity (X2), Fixed Asset Intensity (X3), and profitability (X5) variables can influence tax aggressiveness by 41.6% while 58.4% is influenced by other variables not examined such as good governance.
2. Conclusion
Several conclusions can be reached based on the findings of the study and discussion:
1) Variable leverage has a coefficient value of -0.003 with a significant 0.981 greater than 0.05. It is concluded that the first hypothesis is rejected. This means that leverage does not have a significant effect on tax aggressiveness in industrial consumer goods companies listed on the Indonesian stock exchange 2016-2018.
2) Variable Inventory intensity has a coefficient value of 0.355, with a significant value of 0.012 that is smaller than 0.05. It is concluded that the second hypothesis is accepted. That is, the intensity of supplies has a significant effect on tax aggressiveness in consumer goods industry companies listed on the Indonesian stock exchange 2016-2018.
3) Variable the intensity of fixed assets has a coefficient value of 0.292, with a significant 0.003 less than 0.05. It is concluded that the third hypothesis is accepted. That is, the intensity of fixed assets has a significant effect on tax aggressiveness in consumer goods industry companies listed on the Indonesian stock exchange 2016-2018.
4) Variable profitability has a value coefficient of -0.002 with a significant 0.954 greater than 0.05. It is concluded that the fourth hypothesis is rejected. This means that leverage does not have a significant effect on tax aggressiveness in industrial consumer goods companies in the IDX from 2016 to 2018.
5) Tax aggressiveness is influenced by the leverage variable, inventory intensity, fixed asset intensity, and profitability. The chi-square value of 20.316 is compared to df 4, which has a significant model of 0.000. Since this result is less than 0.05, the fifth hypothesis is assumed to be true. This means that in the IDX from 2016 to 2018, debt, inventory intensity, fixed asset value, and profitability both have a huge impact on tax aggressiveness in industrial consumer goods firms.
6) Score the coefficient of determination (Nagelkerke) of 0.416. It is concluded that the variable Leverage (X1), Supply Intensity (X2), Fixed Asset Intensity (X3), and profitability (X5) can affect tax aggressiveness by 41.6% while 58.4% is influenced by other variables not examined such as good governance.
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