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Record W3125537849 · doi:10.22004/ag.econ.20614

LABOR MARKET BEHAVIOR IN WASHINGTON: A COINTEGRATION APPROACH

2001· preprint· en· W3125537849 on OpenAlexaboutno aff
JunHo Yeo, Sung K. Ahn, David W. Holland

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2001
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCointegrationEconomicsPopulationQuarter (Canadian coin)Labour economicsDemographic economicsInvestment (military)Short runUnit (ring theory)Population growthEconometricsMonetary economicsDemographyPolitical scienceGeography

Abstract

fetched live from OpenAlex

In recent years, the research that investigates impact of employment on other labor related variables has a prominent place in regional science. Generally, it is well understood that new business investment brings changes in population, increased labor force participation rate and migration of new residents. There is mixed research results regarding the extent that new migrants tend to account for new employment. Bartik (1993) found that about one-quarter of the new jobs go to local workers because of the increase in the labor force participation rates of local residents in the long run. He considered the long run effects by estimating the effects of 1% job growth in a certain period on the labor force participation rate seventeen years after the period. In contrast Blanchard and Katz's (1992) research reaches the opposite conclusion - in five to seven years the employment response consists entirely of the migration of new migrants. Their finding is that long-run effect of the job growth on the labor force participation rate is negligible. In this study, from the cointegration time series analysis, we found a long run equilibrium relationship among population, labor force participation rate and employment, in which population is positively related to employment and negatively related to labor force participation rate. The long run effect of a unit change of labor force participation rate (1%) is a decrease of 73,880 in population and the long run effect of a unit change in employment (1000) is an increase of 2,190 in population. We decomposed the time series into stationary components and non-stationary components. The pattern of the stationary component of population is quite similar to that of labor force participation rate while that of employment shows a different fluctuation. From the decomposition, it was obvious that the pattern of stationary component of employment and net migration is quite similar, which means net migration is the short run, temporary response to employment change. The patterns of three years delayed stationary components of population are similar to that of employment and net migration, and the plots correspond to changing economic conditions. According to the change in economic conditions population responds three years later than employment and net migration. We interpreted the non-stationary component of labor force participation rate as reflecting the increasing trend of labor force participation rate in Washington mainly due to a considerable increase in the female labor force participation. The impulse responses of population, employment and labor force participation rate to a one standard deviation shock in employment show permanent increase effects. They settle at different equilibrium value after long term periods. The response of the labor force participation rate to an impulse in employment supports Bartik's finding. Obviously the result is the opposite of Blanchard-Katz's finding that the long-run effect of job growth on the labor force participation rate is negligible. However, since the effect of population is also significantly high, we doubt that the effect of increase in labor force participation rate according to the employment shock covers only local resident labor force.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.049
GPT teacher head0.222
Teacher spread0.173 · 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 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
Published2001
Admission routes1
Has abstractyes

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