Determinants of effective Tax Rate of the top 45 Largest listed companies of Indonesia
Bibliographic record
Abstract
The capital inflows and outflows of a country are closely related to the established tax rate policy. Tax rate is one of important factors in investment decisions. Evidence that there are variations in effective tax rates amongs firms draw attention of researchers to understand the impact of tax policies on corporate tax burdens (Gupta and Newberry, 1997; Molloy, 1998). Effective tax rate is a dependent variable that is commonly used as a proxy to measure corporate tax burden. This study examined corporate effective tax rates (ETRs) of the top 45 largest listed companies of Indonesia within 2009-2014 (after tax reform of 2008, to be exact). We used two types of ETR1 and ETR2 measures as dependent variables. The first type is the ratio of current income tax expense divided by income before interest and taxes and the second type is the ratio of total income tax expense (current tax expense plus deferred tax expense) divided by income before interest and taxes (Noor et al. 2008).We also used some of independent variables related to firms’characteristics, such as firm size, capital intensity, leverage, returns on assets, and inventory intensity. The statistical results reveal that all independent variables contributed to ETR1 and ETR2 except the capital intensity is not contributed to ETR2. However, the findings provide support for the tax policy on corporate actual tax burdens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".