Impact of Financial Inclusion on Consumption Expenditure in Kenya
Bibliographic record
Abstract
This study looked at the impact of financial inclusion on households’ welfare in Kenya based on both the single (transactionary, credit, savings and investment, insurance and pension) and composite measures (portfolio usage) of financial inclusion. The study used repeated household Financial Access datasets for the period 2009 to 2016 to run five autoregressive distribution models to capture the welfare impact. Estimation results established that the impact of financial inclusion on household welfare varies by product with the credit channel taking the lions share. A shift from non-usage (control) to usage (treatment) of financial services (zero one change) among the sampled respondents raises household welfare by 126, 110 and 49 percent with respect to credit, transactionary and insurance products respectively ceteris paribus. Conversely, a counterfactual assessment revealed a 56, 52 and 33 percent drop in welfare from the non-usage of credit, transactionary and insurance products respectively. Portfolio usage of financial services as captured by the index of financial inclusion raises household welfare by 347 percent other factors held constant. Given the positive welfare impact of financial inclusion, the study recommends increase in the range of formal financial products to increase competition in financial markets lowering transaction costs for welfare improvement. Policies targeting welfare improvement through finance should also be aligned to specific financial inclusion transmission channels to be more effective as opposed to blanket proposals.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".