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Record W4289004328 · doi:10.7302/6295

Three Essays in the Economics of Taxation

2022· article· en· W4289004328 on OpenAlexaboutno aff

Bibliographic record

VenueDeep Blue (University of Michigan) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsPositive economicsNeoclassical economicsPublic economicsMathematical economics

Abstract

fetched live from OpenAlex

This dissertation presents three essays advancing the theory of taxation. The first chapter presents a new method for evaluating proposed reforms of progressive piecewise linear tax schedules. Typically, estimates of the elasticity of taxable income (ETI) are used to predict taxpayer responses to changes in tax rates and/or tax bracket thresholds. In this chapter, I show that elasticities are not always needed for this task; the ``bunching mass'' at a bracket threshold (the share of taxpayers locating there) is a sufficient statistic for the revenue effect of behavioral responses to small changes of the threshold. Building on this finding, revenue forecasting and welfare analysis of threshold changes can be conducted using the pre-reform distribution of taxable income alone. I apply these results in an analysis of the Earned Income Tax Credit, an exercise which motivates extensions addressing taxpayer optimization errors, tax rate heterogeneity, large reforms, and income and participation effects. My approach complements existing bunching methods: it avoids key limitations of bunching-based ETI estimation, but addresses a relatively narrower set of policy questions. The second chapter explores the evolution of economic inequality and political inequality in a democratic society where these two types of inequality mutually reinforcing. I introduce a simple dynamic model of democratic redistribution where, in each period, two candidates compete in an election by proposing how a fixed amount of income will be divided amongst a group of citizens in the next period (i.e. pure redistribution policy). Campaign spending is financed by citizen political donations, leading to inequality of political influence favoring wealthier citizens. This creates a feedback loop through which the current distribution of income affects the future distribution. If the marginal dollar of income yields a sufficiently large increase in political influence, long run convergence to a plutocratic equilibrium can occur for arbitrarily small levels of initial economic inequality. The opposite scenario is also possible: a society which is initially extremely unequal may nonetheless be destined for egalitarianism. The long run distribution of income can exhibit extreme sensitivity to initial conditions: tiny differences in initial inequality may determine whether democratic redistribution leads to plutocracy or egalitarianism. Turning to a version of the model where elections are fought over a nonlinear income tax, I show that the same conditions that determine whether convergence to egalitarianism occurs in the pure redistribution model also dictate whether taxation of the rich is possible in the nonlinear tax model. The third chapter employs a variant of the election model from the second chapter to examine the optimal tax treatment of political contributions. Adopting the normative stance that inequality of political influence is undesirable, I characterize the optimal nonlinear tax schedule on political donations. Sufficient statistics for optimal policy include not only donation demand elasticities, but also the marginal efficacy of campaign spending, and the effect of taxes on the sensitivity of donations to candidate policy platforms. Using numerical simulations, I provide proof-of-concept results showing that this framework can rationalize real world policies such as the nonlinear subsidy schedules present in Canada. These feature generous marginal rates of subsidy on the first dollar of political donations, with rates of subsidy declines in donation amount.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.163
Teacher spread0.142 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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