Three Essays on Environmental Economics and on Credit Market Imperfections
Bibliographic record
Abstract
This dissertation contains three essays on environmental economics and on credit market imperfections. The literature on carbon tax incidence generally finds that carbon taxes have a regressive impact on the distribution of income. The main reason for that finding stems from the fact that poor households spend a larger share of their total expenditure on energy products than the rich households do. This literature, however, has ignored the impact of carbon taxes on income stemming from changes in relative factor prices. Yet, changes in household welfare depend not only on variations in commodity prices, but also on changes in income. Chapter 1 provides a comprehensive analysis of the distributional impact of carbon taxes on inequality by considering both demand-side and supply-side channels. We use a multi-sector, multi-household general equilibrium model to analyze the distributional impact of carbon taxes on inequality. Using equivalent income as the household welfare metric, we apply the Shapley value and concentration index approaches to decomposing household inequality. Our simulation results suggest that carbon taxes exert a larger negative impact on the income of the rich than that of the poor, and are thereby progressive. On the other hand, when assessed from the use side alone (i.e., commodity prices alone), our results confirm previous findings, whereas carbon taxes are regressive. However, due to the stronger incidence of carbon taxes on inequality from the income side, our results suggest that the carbon tax tends to reduce inequality. These findings further suggest that the traditional approach of assessing the impact of carbon taxes on inequality through changes in commodity prices alone may be misleading. Chapter 2 investigates the economic impacts of creating an emissions bubble between Canada and the US in a context of subglobal participation in efforts to reduce pollution with market based-instruments. One of the advantages of an emissions bubble is that it can be beneficial to countries that differ in their production and consumption patterns. To address the competitiveness issue that arises from the free-rider problem in the area of climate-change mitigation, we consider the imposition of a border tax adjustment (BTA) - a commonly suggested solution in the literature. We develop a detailed multisector and multi-regional general equilibrium model to analyze the welfare, aggregate, sectoral and trade impacts of the formation of an emissions bubble between Canada and the US with and without BTA. Our simulation results suggest that, in the absence of BTA, the creation of the bubble would make both countries better off through a positive terms-of-trade effect, and more importantly, through a significant reduction in Canada’s marginal abatement cost. The benefits of these positive effects would spill over to the non-participating countries, leading them to increase their trade shares in non-emissions-intensive goods. Moreover, the simulation results also indicate that a unilateral implementation of a BTA by any one of the two countries is welfare deteriorating in the imposing country and welfare improving in the other. In contrast, a joint implementation of a BTA by the two countries would make Canada better off and the US worse off. Chapter 3 shows that learning by lending is a potential channel of understanding the business cycle fluctuation under an imperfect credit market. An endogenous link among the learning parameter, lending rates, and the size of investment makes it possible to generate an internal propagation even due to a temporary shock. The main finding of this chapter is the explanation of how ex post non-financial factors such as information losses by individual agents in a credit market may account for a persistence in real indicators such as capital stock and output.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".