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Record W7132898297

Homo Satiabilis: Satiability, Inequality, and Markups

2024· dissertation· W7132898297 on OpenAlexaff
Dylan Stephen Gowans

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldEnergy
TopicEnergy, Economy, and Technology Trends
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConsumption (sociology)Argument (complex analysis)Order (exchange)Competition (biology)Market powerMarket structureSupply sideMarginal utility
DOInot available

Abstract

fetched live from OpenAlex

This dissertation makes two related arguments: one about markups and one about the structure of consumption and preferences. The main argument regards markups. There have been significant changes to markups since the 1980s: an increase in the average resulting from an increase in the right tail of the markup distribution, with the median relatively unchanged (De Loecker et al. 2020). Whereas most previous explanations rely on changes on the supply side -- changing levels of competition or market power -- I suggest that it is at least partly the result of changes on the demand side brought about by increasing income inequality. Because of declining marginal utility, rich consumers have lower price elasticities than poor consumers. Thus, luxury products, which cater more to the rich, will tend to have higher markups. Moreoever, when the composition of consumers change because of an increase in income inequality, the composition of demand changes resulting in markups which change differently for different products. Luxury, high-markup products will tend to see an increase in rich consumers and fewer median income consumers, leading to lower average elasticities and higher markups. The number of poor consumers factors very little into the decision of these firms, as they make up such a small portion of their market shares. Conversely, basic, low-markup products, which tend to have equal shares of rich and poor consumers, will see much smaller changes in their markups. The secondary argument -- although it comes first in the order of the dissertation -- regards the structure of demand. The argument about markups relies heavily on certain assumptions -- that price sensitivity declines with income, and that rich and poor consumers purchase different bundles of goods -- while the numerical importance of this channel relies heavily on the structure of demand -- how much and in what ways do consumption bundles vary across incomes? Investigating these features of demand leads me to conclude that preferences are satiable, creating a hierarchical demand structure. It is this satiability which lends its name to the dissertation's title. The structure of the dissertation then is as follows. In Chapter 2, I examine the empirical features of consumption, income, and markups. Using a dataset of retail markups based on the Nielsen Homescan Database, I show that rich consumers tend to pay higher markups, suggesting that rich consumers may be less price sensitive than poor consumers. Next, I show some facts which lend credence to preferences being satiable: (i) inferior products are ubiquitous in the dataset -- a fact difficult to reconcile without satiability, and (ii) for a given category of goods, increases in expenditure are mainly the result of increasing average prices paid, rather than increasing physical quantities. In Chapter 3, I present a model of satiable preferences which is able to explain the facts presented in Chapter 2. I show that, once these preferences are aggregated, they resemble a discrete choice model. However, whereas a discrete choice model is formulated for a single sector, the model presented here must take into account the macroeconomic characteristics of the model, represented by an endogenous marginal utility of money. Chapter 4 then does the heavy lifting regarding our question of the effects of income inequality on the distribution of markups. Here, I calibrate the model presented in Chapter 3 to match the data for 2016. Then, I shock the model by changing the distribution of income to that in 1983, median-adjusted. Moving from the 1983 equilibrium to the 2016 equilibrium, the average markup is higher, and this is brought about by an increase in the right tail of the markup distribution, with the left tail relatively unchanged. The model is able to generate about 25\% of the change in the average markup which we see empirically. I also explore the important welfare implications of the model. As well as the usual aggregate welfare costs of income inequality which come from a concave utility function, this model suggests that endogenous changes in markups may cause additional costs. The welfare analysis finds that the additional costs of changing markups is almost equally important to reducing welfare as the effects coming from a concave utility function. However, these costs would be even greater if all markups increase equally; the fact that they increase most for the rich lessens the increase in real income inequality to some extent.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.007
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.001

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.

Opus teacher head0.018
GPT teacher head0.320
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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