Understanding generational differences for financial inclusion in Kenya
Bibliographic record
Abstract
Purpose The purpose of this paper is to estimate the effect the five different generations and the key financial inclusion indicators of gender, education and location (rural–urban) in exacerbating disparities in financial inclusion in Kenya. This paper considers whether the five generational cohort groups in Kenya differ on the financial inclusion determinants and behaviour as predicted by common generational stereotypes. Design/methodology/approach The authors applied a multinomial logistic regression approach to nationally representative household survey data from Kenya to estimate the effect that key financial inclusion indicators have on belonging to one of the five generations: Z, Y, X, baby boomers and traditionalists. Findings The authors found significant links between all tested variables and financial inclusion. The authors found an access gap between Generations X and Y, with the latter being more prone to access and use financial services and products. These differences are compounded by gender and rurality. People in rural locations and women generally were found to have less access to financial services and products, thus causing significant exclusion of a large proportion of the population. Practical implications The research has important implications for governments, financial institutions and educational providers, notably on targeted policies and programmes that strategically aim to eliminate disparities and promote greater financial inclusion, denoting the value of such variables as generational differences and gender inclusivity. Originality/value This paper deepens the understanding of differences that can divide generations on financial inclusion.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".