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Record W307207911

Commentary: Erica L. Groshen

2005· article· en· W307207911 on OpenAlexaboutno aff
Erica L. Groshen

Bibliographic record

VenueFederal Reserve Bank of New York Economic policy review · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)Labour economicsWageWorkforceEconomicsHuman capitalDeindustrializationImmigrationTechnological changeAffect (linguistics)Production (economics)Distribution (mathematics)BusinessMarket economyEconomyEconomic growthPolitical science
DOInot available

Abstract

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1. Introduction Motivated by concerns about the wage impact of deindustrialization and growing trade, Rene Morissette and Anick Johnson examine changes in the relative importance of well-paid jobs in Canada from 1981 to 2004. The authors analyze many dimensions of the issue using a mosaic of data sources, and have produced a thought-provoking paper with intriguingly mixed results. My comments use the parallel experience of the United States during the same period to find contrasts and commonalities that might clarify whether good jobs are indeed waning in Canada. 2. Why Wage Structures Change To begin, it is helpful to review why the distribution of wages might change. Employer influences, labor force composition, and institutions that mediate supply and demand are all reasons. Four fundamental shifts can affect the need for workers: trade activity, technological change, consumer tastes, and business conditions. The authors are particularly concerned about the influence of the first two shifts. Trade adds and eliminates jobs as it boosts production of exports, reduces production of import-competing goods, and expands transport and warehousing jobs. Technology affects which goods are produced and how they are made. However, consumer tastes--which reflect such characteristics as age, wealth, and fashion trends--and the business cycle also affect wages, as can workforce composition and institutional changes. On the worker side, wage changes can reflect differences in human capital, such as education, training, or skills, as well as the amount of competition faced from other workers, such as through demographics or immigration. Institutions that mediate supply and demand influences on wages also have an important effect. Government safety nets, such as unemployment insurance and transfer payments, can affect the willingness to work for a given wage. Retraining options can influence wages by enabling workers to upgrade their skills. Finally, union negotiations can also have an effect on wages. How similar are trends in these influences across Canada and the United States? The strongest similarities probably relate to technology, consumer tastes, and trade. These sister economies use much the same technologies and are increasing their trade with the rest of the world and with each other. Populations in both countries are also aging and growing wealthier. The countries differ, however, in terms of the depth of the 1990 and 2001 recessions and their institutional labor market practices. With regard to the latter, unionism is higher in Canada, and the social safety net of unemployment insurance, training options, and other transfer payments is wider. These institutional differences are likely to result in more rigid wages in Canada. 3. Canadian and U.S. Labor Market Trends A comparison with labor markets in the United States may shed light on the causes of trends in Canada, as many influences on wages have been the same across both countries, while others have differed. Accordingly, we examine five labor market trends: unemployment rates, mean wages, wages of new workers versus those of incumbents, pension plan participation, and the share of temporary jobs. Recent unemployment rates have been higher in Canada than in the United States. The two countries began the 1980s with almost identical unemployment rates of 7 to 8 percent. However, the 1980s recession proved to be much deeper in Canada. By 1984, the Canadian unemployment rate exceeded the U.S. rate by about 4 percentage points. A differential of 3 to 4 percentage points persisted until around 2000, when the milder recession in Canada narrowed it to about 2 percentage points. In contrast, real wage patterns have been steadier and stronger in Canada throughout most of the past two decades. Morissette and Johnson find that median real wages in Canada have been stable for the past twenty years. …

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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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.372
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.358
Teacher spread0.298 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2005
Admission routes1
Has abstractyes

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