Promoting Tax Compliance in the Democratic Republic of Congo: Lessons Learned from the Republic of Korea as Case Study
Bibliographic record
Abstract
Tax revenues are primary sources of funding for public expenditure in most developing countries. Therefore, tax administration as a focal point of economic policies should receive keen attention. This raises the following important questions: 1) what constitutes an efficient tax system? and 2) how can the tax system be designed to generate optimal revenue to finance public spending and promote economic development? Many developed countries, including the United States, Canada, South Korea, and China, have made significant efforts to establish effective tax management systems using available solutions. According to official reports from international organizations, African countries generally rank poorly in tax sector management. Challenges include inefficient tax collection methods, structural and functional complexities, lack of a tax culture, and crucially, underutilization of information technology as a tool to modernize the tax system. This paper strongly encourages developing countries to learn from achievements and best practices of other countries rather than reinventing the wheel. It specifically analyzes the existing tax management system of the Democratic Republic of Congo (DRC), provides a comprehensive review of the Korean tax system, and summarizes key lessons learned. A critical assessment of organizational, functional, and structural challenges was carried out using analytical and descriptive methodologies. The case study of South Korea is particularly insightful. The design of Korea's tax system reflects its unique structure, function, and policy goals, which have evolved in tandem with its economic development policies. However, the author advises caution when considering Korean fiscal policies due to their unique contexts. This paper proposed a customized tax framework for the DRC, which could also be applicable to other developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".