Three Essays on Applied Microeconomics
Bibliographic record
Abstract
This dissertation contains three chapters in the field of applied microeconomics. Specifically, they address research questions about the demand for (il)legal marijuana and firms' responses to financial audits. \n \nIn the first chapter, I assess the effect of recreational marijuana legalization on the black market's presence by studying Uruguay's marijuana market. This country's legalization aimed to reduce the drug trafficking market's presence and societal costs. However, post-legalization, a third of Uruguayan marijuana users still buy from drug dealers. In this chapter, I estimate a novel demand model in a post-legalized environment that includes access selection/limitations, alternative choices regarding the source (legal or drug trafficking), and individual-level prices. I use these estimates to identify tools that steer the demand to the legal market. Counterfactuals show that a 10% price reduction increases legal marijuana use by 9%, but primarily driven by new users. Reducing access to the drug trafficking market decreases the use of both legal and illegal marijuana, emphasizing access's role in demand. In contrast, widespread legal marijuana access leads to a 17% increase in legal use, with half coming from the drug trafficking market. Understanding consumer substitutions between (il)legal options is crucial for policies targeting black market reduction. \n \nI the second chapter, joint with Tania Guerra Rosero, we analyze if the government should harness private agents to deliver public services. We assess this in the context of the tax administration in Ecuador where the government collaborates with third-party auditors to increase tax compliance. Large firms in Ecuador are required to have third-party audits of their yearly balance sheets and income statements. Auditors review the financial statements and prepare a tax compliance report for the Ecuadorian Tax Agency. We exploit a reform that significantly reduced the asset threshold determining the audit obligation and first document a large bunching response. Second, we provide suggestive evidence that bunching firms reduce their assets through reductions in the debts of their clients (accounts receivable) and in the short-term debts with their suppliers (accounts payable). Third, we use a donut-hole Regression Discontinuity Design to explore the effects of the audits on the audited firms. Our results indicate that firms reduce their reported costs and expenses by 24% and compensate for this with a reduction in reported revenues of 23%. Firms also reduce their net income by 32%. This suggests that governments should not rely on private agents to conduct tax audits. \n \nIn the third chapter, I assess the price elasticities of different forms of marijuana (inhalants and edibles) and how these elasticities vary based on potency preference. Using individual-level data from surveys conducted between 2020 and 2022 in Canada, I estimate a two-level nested logit model where individuals first decide whether to use marijuana and then select the form (edible or inhalant). The results indicate a positive correlation between marijuana forms' valuations and reveal that individuals with a preference for high THC potency obtain a lower utility for edibles over inhalants. Additionally, the study finds that edibles exhibit larger own-price elasticities, in absolute terms, compared to inhalants. Regarding inhalants, individuals with a preference for high THC potency are less price sensitive than individual without this taste. These findings can be useful for public policy when designing pricing strategies in curbing excessive consumption of more potent and harmful marijuana products.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".