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Record W7009749283

Essays on modern economic issues in international trade, exchange rates and housing

2019· article· en· W7009749283 on OpenAlexaboutno aff

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHymenoptera taxonomy and phylogeny
Canadian institutionsnot available
Fundersnot available
KeywordsEndogeneityAggregate dataBilateral tradeRanking (information retrieval)Quality (philosophy)Index (typography)Exchange rateEconometric modelComparative advantage
DOInot available

Abstract

fetched live from OpenAlex

The common theme of the three papers in this thesis is the focus on the impact of data choices on empirical research in Economics. Such choices can be about the source of data; should we source the data from country A or country B in a bilateral trade relation? Is there a way to reconcile the discrepancies in international trade data? In investigating the impact of exchange rate on trade, should we choose high-frequency or low-frequency data? What does the choice of a certain frequency imply for the econometric analysis? In assessing the impact of housing wealth on household consumption, what are the benefits of choosing household-level data? How can we take advantage of aggregate data on house prices to circumvent the endogeneity arising from household-specific confounding factors? This thesis shows that data choices can strongly affect our conclusions regarding several modern economic issues. The first paper is titled ‘Reconciling International Trade Data.’ International trade data are filled with discrepancies–where two countries report different values of trade with each other. We develop an index for ranking countries’ data quality based on the following notion: the more a country’s reports on bilateral trade differ from the corresponding reports of its partners, the more likely it is a low-quality reporter. We calculate the comparative quality for each country’s imports and exports separately for every year from 1962 to 2016. We reconcile international trade data through picking the value reported by the country with higher quality in every bilateral flow. The findings include: (a) global trade was under-reported by roughly 5% over the past five years as countries with low data quality under-report both, their imports and exports; (b) erroneous reporting is prevalent among low-quality reporters; (c) importers’ data are less likely to be in error; (d) the level of development and corruption are possible determinants of trade data quality; (e) low-quality reporters are 14% more open to trade using reconciled data than using self-reported data (f) China tends to under-report its exports and over-report its imports, while there is only a small difference between US self-reported and reconciled data. The reconciled trade dataset is made freely available for future studies to use. The second paper is titled ‘Why You Should Use High Frequency Data to Test the Impact of Exchange Rate on Trade.’ The paper suggests that testing the impact of exchange rate on trade should be done using high frequency data. Using different data frequencies for identical periods and specifications between the US and Canada, the paper shows that low frequency data suppresses and distorts the evidence of the impact of exchange rate on trade in the short-run and the long-run. The third paper is titled: ‘Housing Leverage and Consumption Expenditure: Evidence from New Zealand Microdata.’ The paper investigates how household debt affects the marginal propensity to consume out of housing wealth. The paper uses New Zealand household-level data on spending, income, and debt over the period 2006–2016. The main empirical challenge is to identify exogenous variation in house prices to determine how consumption evolves with movements in household wealth. This identification problem is complicated by the presence of unobserved household characteristics that are correlated with housing wealth. The paper uses a detailed house sale dataset to derive local average house prices and use it as an instrument. The empirical results show that the estimated elasticity of consumption spending to housing wealth is about 0.22%. In dollar terms, the average marginal propensity to consume out of a one-dollar increase in housing wealth is around 2.2 cents. The empirical results confirm that household indebtedness, especially mortgage debt, acts as a drag on consumption spending, not only through the debt overhang channel, but also through influencing the collateral channel of the housing wealth effect.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0020.007
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0110.002

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.

Opus teacher head0.033
GPT teacher head0.284
Teacher spread0.251 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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