The economic costs of protests and policy recommendations for preventing them: the case of Hong Kong
Bibliographic record
Abstract
Purpose This paper aims to examine the economic costs of protests at micro-to-firm, market sector and aggregate levels. This paper then develops institutional policy recommendations for allaying these costs. Design/methodology/approach This paper conducts a case study of the anti-extradition bill protests in Hong Kong by examining news articles, online discussions and economic indices from the Hong Kong Census and Statistics Department. This paper further develops policy insights from an analysis of the Hong Kong Basic Law (the city’s mini-constitution) and insights from economic research. Findings This paper discovers that the protests may have caused overall volatility in firms, market sectors and the overall economy, measured in production disruptions, revenue losses and declines in employment. Among Hong Kong’s four major industries, the most severely stunted market sectors were tourism and retail, as well as trading and logistics, whereas financial services and professional and producer services experienced mixed effects. This paper develops two institutional policy recommendations for government and corporate policymaking for reducing volatility and ultimately safeguarding economic growth: the separation of political ideology and economics; the systematic use of public opinion analytics to pre-test the reception of policies. Practical implications Corporate strategists and policymakers would benefit from and advance the economy by better insulating business decision-making from political biases and by investing in public opinion analytics. Originality/value Much of economic theory treats social transformations as externalities. This paper adopts a different approach by foregrounding the role that social transformations play in shaping the economy. To this end, to the best of the author’s knowledge, this paper is among the first to examine the anti-extradition bill protests of Hong Kong, arguably the most significant and widespread protests in the city’s and the region’s history.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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".