Financial Inclusion of Rural and Urban Households and the Dodd-Frank Act
Bibliographic record
Abstract
This paper examines the consequences of the Dodd–Frank Wall Street Reform and Consumer Protection Act of 2010 on financial inclusion in rural areas. The Act imposed changes in the U.S. banking industry that contributed to closures or consolidation of smaller community banks, mostly in the rural areas, that could not sustain the higher regulatory burden. We evaluate whether the Act had differential impacts on the financial inclusion of rural and urban unbanked households. Financial inclusion is measured by the utilization of banking services such as checking or savings account and by relying less on Alternative Financial Services (AFS). We employ the Changes-in-Changes quantile model to establish if rural unbanked households were more affected relative to their urban counterparts and provide robustness checks through ordered and binomial logistic regressions. We analyze both the short- and the long-term impacts of the Act using household-level data from the FDIC National Surveys of Unbanked and Underbanked Households. Results indicate that rural unbanked households on average were more likely to plan to open a bank account shortly after 2010 but the magnitude of the effect decreased in long-term. The rural unbanked households did not use more AFS services for credit and transaction purposes than urban households in the short term. However, in the long term, they increased their use of AFS for credit relative to their urban counterparts, likely because they were less able to obtain credit from banks. The policy implications point at the need to promote technologies that may help close the rural-urban financial inclusion gap and indentify a potential for combination of Fintech and banking services provision.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".