Small Business Lending and Economic Well-Being in Texas Counties: A Test with Community Reinvestment Act Data
Bibliographic record
Abstract
Utilizing the Community Capitals framework we examine the impact of Community Reinvestment Act (CRA) reported small business lending on the economic well-being of Texas counties in 1999–2000. We combine data from multiple data sources, including the County Federal Financial Institutions Examination Council (FFIEC) annual county Aggregate and Disclosure data—collected under directive of the 1977 Community Reinvestment Act—and use GeoDa to model the impact of small business lending in each Texas county from 1996–1999 on the 1999 county poverty rate, median family income, Gini income inequality coefficient, 2000 per capita income and 2000 nonfarm earnings per worker. Controlling for other dimensions of the Community Capitals Framework, the results show positive effects of small business lending on two income measures—per worker nonfarm earnings, and per capita income. Furthermore, we find the small business lending from 1996–1999 reduced poverty and income inequality in the most rural Texas counties. Implications for theory, policy, and research are discussed. Keywords: community development; small business; financing; rural ----------------------------------------------------------- Résumé À l'aide du cadre de Capitaux Communautaires, nous examinons l'impact positif économique du" Community Reinvestment Act" (CRA) qui a agi comme prêteur aux petites entreprises au Texas en 1999-2000. Nous combinons les données de diverses sources, incluant celles des institutions financières fédérales du Conseil d'Analyse du Comté (FFIEC an anglais) concernant les données annuelles globales publiques du Comté—collectées sous la directive du "Community Reinvestment act "de 1977—et nous utilisons GeoDa pour modéliser l'impact des prêts aux petites entreprises de chaque Comté du Texas entre 1996 et 1999, l'indice de pauvreté du Comté de 1999, un revenu médian par famille, un coefficient du revenu de Gini inégal, 2000 de revenu par habitant et 2000 de salaire non-agricole par travailleur. En contrôlant les autres aspects du cadre de Capitaux communautaires, les résultats montrent des effets positifs sur les petites entreprises prêtant à deux niveaux dans le revenu—au niveau du salaire de chaque employé non-agricole, et par revenu de capital. De plus, nous trouvons que les prêts aux petites entreprises de 1996-1999 réduisent la pauvreté et les inégalités de revenu dans les Comtés les plus ruraux du Texas. Les implications théoriques, de principe et de recherche sont discutées.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".