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Record W2754658457 · doi:10.14288/1.0394831

Essays in empirical macroeconomics

2020· article· en· W2754658457 on OpenAlexaff
Mengying Wei

Bibliographic record

VenuecIRcle (University of British Columbia) · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomicsKeynesian economicsMacroeconomics

Abstract

fetched live from OpenAlex

This thesis presents three chapters in empirical macroeconomics. The first chapter studies how the mortgage expansions in the early 2000s affect U.S. regional economies by estimating its impact on the local labor market from 2003 to 2017. Using a plausible exogenous measure of the credit supply shock, I find that counties with higher credit supply shocks have not seen significant changes in local unemployment but have shown slower wage growth. While the high-credit counties did not experience significantly different changes in local labor markets in the expansion period, they did experience larger increases in unemployment in the recession, but also recovered faster after the recession, summing to a zero net effect in the long run. Meanwhile, these counties experienced a slowdown in wage growth since the recession, resulting in a depressed wage level until 2017. Additionally, the wage decline was accompanied by a decrease in the employment share of young firms. In Chapter 2, I propose a mechanism to explain how mortgage market fluctuations affected the labor market, slowed down wage growth, and led to labor reallocation. I introduce two financial constraints, one on the household side and the other on the production side, both tied to the collateral values of houses. I show that changes in household borrowing constraints affect housing prices and thereby affect firms’ financial condition. When working capital constraint binds, mortgage market fluctuations affect firms' labor demand, which led to labor reallocation from financially constrained to unconstrained firms and a decline in wage. In Chapter 3, we study how small and micro enterprises (SMPE) respond to the policy in reducing the corporate income tax rate in China. Using gradual increases in the qualifying threshold for SMPEs during 2010-2016 as a natural experiment, we find that the rate cut led to significant increases in sales growth, investment, and productivity of affected SMPE firms. We further show that the rate cut induced micro-sized firms to enter the market.

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)0.009
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.038
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.009
Science and technology studies0.0020.005
Scholarly communication0.0070.008
Open science0.0010.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0260.010

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 head0.027
GPT teacher head0.177
Teacher spread0.150 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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