Creative Occupations, County-Level Earnings and the U.S. Rural-Urban Wage gap/Des Occupations Creatives, Les Revenues Au Niveau Des Comtes et la Disparite Salariale Entre Milieu Urbain et Rural Aux E.-U
Bibliographic record
Abstract
Abstract This paper investigates the relationships among county-level earnings, employment in occupations and the U.S. rural-urban wage gap. We find that the proportion of county-level employment in Florida's has a positive effect on average earnings. However, much of this creativity earnings premium appears to be driven by technology-based segments of the such as computer and mathematical, architecture and engineering, and scientific occupations. Differences in the proportions of workers between metropolitan and nonmetropolitan counties contribute 11.5 percent to the U.S. rural-urban wage gap. Resume Dans cet article, une analyse est presentee des rapports entre les revenus au niveau des comtes, l'emploi dans des occupations creatives et la disparite salariale entre milieu urbain et rural aux E.-U. Nous avons observe que la proportion de l'emploi au niveau des comtes dans le noyau super-creatif de Floride a un effet positif sur les salaries moyens. Toutefois, une partie importante de cette prime en termes de revenues semblent etre sous-tendue par les segments technologiques du noyau super-creatif, tells que les emplois dans les domaines de l'informatique et des mathematiques, de l'architecture et de l'ingenierie et de la science. Les differences dans les proportions des travailleurs cr6atifs entre les comtes metropolitains et non metropolitains comptent pour 11,5% de la disparite salariale entre les milieux urbain et rural aux E.-U. Introduction In his bestselling book The Rise of the Creative Class, Richard Florida argues that the attraction of workers is a key contributor to regional economic development. One look at Table 1, reproduced from the book, is all the evidence that many people would need to agree. Members of Florida's creative class earned an average of $20,000 more per year than individuals in his working (e.g., production operations, transportation) and service (e.g., clerical workers) classes. These figures, and Florida's account of the impressive growth of workers in the United States, have regional policymakers searching for ways to attract and retain scientists, writers, engineers and artists--individuals who figure prominently in Florida's class. (1) However, as noted by Florida (2002a: xiv), the growth of the economy may lead to massive tensions and disruptions. A key challenge facing policymakers is the rising inequality in places with a high proportion of workers in the economy (Florida 2002a; Peck 2005). This is caused, in part, by the expansion of lower-paying service jobs fueled by the growing demands of workers. In addition, although Wojan (2006) and McGranahan and Wojan (forthcoming) have uncovered vibrant artistic and economies in some rural places, the economy tends to have its strongest roots in large cities. Thus, along with the growth in intra-regional inequality discussed by Florida (2002a) and Peck (2005), the expansion of the economy may also contribute to the wage gap between rural and urban areas. This paper investigates the relationships among county-level earnings, employment in occupations and the U.S. rural-urban wage gap. Our empirical analysis, which uses data on a large sample of U.S, counties, centres around three interrelated topics of inquiry. First, we examine the extent to which the proportion of employment in Florida's super-creative core affects county-level earnings. (2) Second, given the diversity of occupations within the core, we narrow our focus to the relationship between county-level earnings and employment in individual segments of the economy. The purpose of this analysis is to determine whether the workforce earnings premium is mainly the result of a high proportion of employment in a few select occupations. Third, we use our results to examine how disparities in the relative size of the economy between metropolitan and non-metropolitan counties may be contributing to the U. …
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".