Small Business Lending and Economic Well-Being in Texas Counties: A Test with Community Reinvestment Act Data
Bibliographic record
Abstract
Utilizing the Capitals framework we examine the impact of Reinvestment (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 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 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 ----------------------------------------------------------- Resume A l'aide cadre Capitaux Communautaires, nous examinons l'impact positif economique du Reinvestment Act (CRA) qui a agi comme preteur aux petites entreprises au Texas en 1999-2000. Nous combinons les donnees diverses sources, incluant celles des institutions financieres federales Conseil d'Analyse Comte (FFIEC an anglais) concernant les donnees annuelles globales publiques Comte—collectees sous la directive Community Reinvestment act de 1977—et nous utilisons GeoDa pour modeliser l'impact des prets aux petites entreprises chaque Comte Texas entre 1996 et 1999, l'indice pauvrete Comte 1999, un revenu median par famille, un coefficient revenu Gini inegal, 2000 revenu par habitant et 2000 salaire non-agricole par travailleur. En controlant les autres aspects cadre Capitaux communautaires, les resultats montrent des effets positifs sur les petites entreprises pretant a deux niveaux dans le revenu—au niveau salaire chaque employe non-agricole, et par revenu capital. De plus, nous trouvons que les prets aux petites entreprises 1996-1999 reduisent la pauvrete et les inegalites revenu dans les Comtes les plus ruraux Texas. Les implications theoriques, principe et recherche sont discutees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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; 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".