Essays on welfare and debt : From impact evaluation in Kenya to Canadian housing markets
Bibliographic record
Abstract
This thesis is comprised of two independent essays on the topics of impact evaluation, and one essay on the housing wealth-effect. The essays address key questions on welfare and spending decisions made by households when subject to government assistance programs and increases in housing prices. The first essay deals with a large scale pro-poor government assistance program in Kenya. It studies the impact of extension services on rural households, to understand whether the SIDA-funded program led to sustainable improvements in the treated households’ livelihoods. The results suggest that the treated households increased fertilizer dosage, and had higher household expenditures. However, the treatment did not impact farming revenues and output. The second essay investigates a novel labelled cash transfer program in agriculture in Kenya. This essay documents the impacts of the program to draw a relationship between the treatment and farm output and revenue, as well as basic welfare indicators at the household level. The results show that while household expenditures were higher following the reception of the labelled cash transfer, farm yields and revenues were not improved by the intervention. The third essay analyses the relationship between housing prices and consumer debt in the Canadian province of British Columbia. Using administrative data and an implementation of the Arellano-Bond estimator, this essay shows that, even as residential property values climbed very rapidly, consumers did not engage in additional non-mortgage debt, in particular consumers who planned to stay in their home for the following twelve months.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".