
"Homeland economics" will make the world poorer
- Bias Rating
- Reliability
25% ReliableLimited
- Policy Leaning
40% Somewhat Right
- Politician Portrayal
-27% Negative
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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
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- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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-100%
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100%
Conservative

Contributing sentiments towards policy:
62% : The firm says that production at its first plant in Arizona will be delayed until 2025 due to a shortage of specialist workers.61% : Governments believe so, and are quick to point out perceived successes.
54% : Governments are also finding that their promises of money today create expectations of more money tomorrow.
48% : Governments also want to incentivise production of rare earths, batteries and solar panels.
48% : "Attempts to create competitive advantage through government direction and support were generally unsuccessful," Geoffrey Owen of the London School of Economics argues in a review of post-war European industrial policy.
47% : The gleaming factory has workers from all walks of life, from young black women to grizzled Trump voters.
46% : Mr Chauhan and his co-authors, discussing India, "cannot reject the possibility that [the scheme] is an enormous and possibly misdirected transfer of public resources to large domestic and foreign firms".
45% : In a new paper Nathan Lane of Oxford University studies the impact of South Korea's seminal industrial push -- the Heavy Chemical and Industry () drive of 1973-79, in which the government introduced policies including cheap credit to boost production and exports.
30% : As in the 1990s, government attempts to boost their chip industries are running into trouble.
*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.