Demand for domestic help services: Evidence from a natural experiment
Bibliographic record
Abstract
We investigate how an increase in subsidies for purchasing domestic help services affects the consumption of individuals who need assistance to live at home. Drawing on administrative data, we analyse the impact of a reform implemented in Quebec (Canada) in 2016, which made the program more generous for a subgroup of beneficiaries. For this purpose, we estimate a difference-in-differences lognormal hurdle model. We derive the corresponding average treatment effect on the treated for this class of non-linear models. Our results suggest that the price elasticity of the demand for subsidized domestic help services for the treated is around 0.74. The elasticity of monthly purchase frequency (0.59) is much larger than the elasticity of monthly purchase intensity (0.14). Based on our results, we determine a floor to the marginal external benefit required for the reform to be socially worth adopting. • We study the price elasticity of the demand for subsidized domestic help services. • We derive the ATT for lognormal hurdle models, and the corresponding estimated elasticity. • Reductions in out-of-pocket costs lead to an increase in the consumption of domestic help services. • The response in terms of the frequency of purchase is four times larger than that in terms of the hours consumed, conditional on consuming any. • Based on a cost–benefit analysis and our results, we determine a floor to the marginal external benefit required for increased subsidies to be socially worth adopting.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".