Essays on the Welfare Implications of Fiscal Policies over the Business Cycle in Heterogeneous-Agent Models
Bibliographic record
Abstract
This dissertation investigates the welfare implications of fiscal policy in business cycle models with rich household heterogeneity. It demonstrates that understanding heterogeneity in households’ consumption–saving behavior—both empirically and theoretically—is essential for designing effective fiscal policy. To address the theoretical challenges, the study also proposes a computational method for efficiently solving heterogeneous-agent models with aggregate shocks, which are workhorses of modern macroeconomic analysis. Chapter 1 examines the optimal design of countercyclical transfers. One-time stimulus checks are widely used during recessions, but expectations of future transfers alter households’ precautionary saving. I develop a two-asset heterogeneous-agent model with aggregate shocks, solved globally using the method in Chapter 3. The optimal policy provides an additional $1,800 per recessionary year relative to the baseline, with welfare gains from reduced consumption risk outweighing lower long-run capital. Effects are highly uneven: indebted and poor Hand-to-Mouth households benefit most, wealthy Hand-to-Mouth households lose, and Savers are neutral. Computationally, I show that standard forecasting-rule solutions can misestimate aggregate capital in recessions, leading to understated transfer cyclicality and welfare gains. Chapter 2 evaluates Canada’s Tax-Free Savings Account (TFSA), introduced in 2009. Using administrative tax data in a regression discontinuity design, I find that TFSAs raised the share of households with liquid savings by 3.5 percentage points. A structural model shows welfare gains of 0.2859% in consumption-equivalent terms. The policy encourages liquid saving, which increases long-run capital and consumption while reducing volatility. The main insurance value arises from smoothing individual, rather than aggregate, consumption fluctuations. Chapter 3 develops a new global solution method for heterogeneous-agent models with aggregate shocks. The algorithm blends finite difference methods with deep learning to overcome the curse of dimensionality. It iteratively updates guesses of derivatives and decision rules, solves value functions, and uses machine learning to extract cross-state sensitivities. The method is flexible and applicable to a wide range of models, particularly for studying fiscal and monetary policies where inequality interacts with aggregate outcomes. Its usefulness is illustrated by applications to the classic Aiyagari (1994) and Krusell and Smith (1998) models.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".