Namibia's Tax Inequities and Economic Vulnerabilities
- Bias Rating
- Reliability
N/AN/A
- Policy Leaning
-48% Medium Left
- Politician Portrayal
N/A
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Bias Score Analysis
The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
Sentiments
-7% Negative
- Liberal
- Conservative
Sentence | Sentiment | Bias |
---|---|---|
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
Extremely
Liberal
Very
Liberal
Moderately
Liberal
Somewhat Liberal
Center
Somewhat Conservative
Moderately
Conservative
Very
Conservative
Extremely
Conservative
-100%
Liberal
100%
Conservative

Contributing sentiments towards policy:
64% : Fostering sustainable economic growth: Create an enabling environment for informal operators to transition to the formal sector through support for small and medium-sized enterprises (SMEs), regulatory simplification, and investment incentives.53% : Newly graduated public service employees face significant challenges due to this tax structure.
51% : The inability to effectively collect tax from the large informal sector restricts government revenue, limiting its ability to invest in essential services like education, healthcare, and social safety nets.
47% : Relieving the tax burden of low-income public servants: Increase tax thresholds and provide targeted credits for low-income workers, such as teachers and police officers.
46% : Strengthening Namibia Revenue Agency's (Namra) capacity: Invest in technology and skilled personnel for the Namra to close compliance gaps and improve tax administration efficiency.
42% : TAX REFORM Addressing these inequities requires a multi-pronged approach that balances tax collection with support for the informal sector.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.