Measuring Economic Insecurity and Vulnerability as part of Economic Well-being: Concepts and Context
Bibliographic record
Abstract
Worrying about future economic dangers subtracts from the well-being of individuals, hence measurement of economic insecurity should be part of the measurement of economic well- being. Because risk-averse individuals are worse off if they have to face uninsured economic hazards, and because ‘security’ has been defined as a basic human right, affluent societies have created complex systems of private insurance and public social protection to reduce the costs of economic hazards. However, the citizens of poor nations (i.e. most of humanity) typically find both private insurance and public social protection to be largely unavailable – their lives are both poorer and riskier. How should one measure the impact on well-being of economic insecurity and vulnerability in these very different contexts? In recent years, economic insecurity has been discussed by several authors (e.g. Bossert and d’Ambrosio (2009), Osberg (2009)). The “vulnerability” perspective on economic development (e.g. Dercon, 2005a, b) has also emphasized both the costs of unprotected hazards to individuals and the adverse implications for growth of the risk-avoidance strategies available to them. Unfortunately, the ‘economic insecurity’ and ‘vulnerability’ literatures have evolved in remarkable mutual isolation – Section 1 begins with a conceptual comparison and a discussion of the implications for measurement choices. Section 2 illustrates the measurement of economic insecurity and its importance to trends in relative economic well-being using OECD data on seven affluent countries since 1980. Section 3 then asks how one might estimate the level of economic security in a comparable way in the very different context of poor nations, and uses data from Tanzania in 2006-07 to illustrate that meaningful comparisons are possible. Section 4 concludes.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".