JSM 2013- Business and Economic Statistics Section A Multi-dimensional Measure of Economic Well-Being for the U.S.: The Material Condition Index October 2013
Bibliographic record
Abstract
When measuring economic well-being, a decision first needs to be made regarding what is to be measured and then second, how. Focusing on one dimension of economic wellbeing may provide an incomplete picture of the economic well-being of individuals and households. A recent call for the integration and multi-dimensional measurement of income, consumption and wealth has been published by the OECD (2013a), building on the recommendations of Stiglitz, Sen, and Fitoussi (2009). The OECD report notes that multi-dimensional measurement is a new field of statistics; the report describes several measures within this new field. One of these is a central tendency measure penalized for dispersion in the distributions of the dimensions under consideration. This particular measure draws on the work by Ruiz (2011) with a mapping of income, consumption, and wealth into a single index, the Material Condition Index (MCI). The purpose of the current study is to determine whether it is feasible to operationalize the Ruiz method using U.S. data. In this study, we apply the Ruiz method and test whether the joint distribution of income, consumption, and wealth produces a different picture of economic well-being than any of the three dimensions alone. Data from the 2009 quarter two through 2012 quarter one U.S. Consumer Expenditure Interview Survey are used. Our results suggest that the method can be applied to U.S. data but only under a certain assumption, that income, consumption, and wealth must be positive. Such a restriction limits the applicability of the method; future research will investigate relaxing this assumption. However, aside from this restriction, we find that the MCI provides a more complete picture of economic well-being than any of the three dimensions of economic well-being alone.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.085 | 0.058 |
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".