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Record W101897640

Intersectoral Factor Movements: Do Adjustment Costs Matter for Welfare?

2012· article· en· W101897640 on OpenAlexaboutno aff
Dorothee Flaig, Harald Grethe, Scott McDonald, Khalid Siddig

Bibliographic record

VenueEcoMod2012 · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsWelfareProductivityLabour economicsUnemploymentDe factoFactor priceComputable general equilibriumTotal factor productivityMicroeconomicsMacroeconomicsMarket economy
DOInot available

Abstract

fetched live from OpenAlex

CGE models usually assume extremes of factor mobility: factors either move between industries without cost or they do not move at all. With the assumption of mobile factors, simulations lead to reallocation of factors among different sectors of the economy which often vary strongly in factor productivity. A standard assumption is that factors are employed in the new sector with the new sectors’ productivity. If factors move from less to more productive sectors, then an economy experiences a de facto increase in factor endowments due to the reallocation of factors. Separating the impacts of implicit increases in factor endowments from those arising from factor reallocation is therefore important to the interpretation of the results. Furthermore there is an extensive literature about the real world costs of factor reallocation between industries. If costs of reallocation exist, they should inhibit factor movement, hence neglecting reallocation costs should result in an overestimation of the size of factor movements. Several empirical studies (e.g. McLaughlin and Bils, 2001; Fallick, 1996) show that workers who change sectors can experience large and persistent losses in wages. Responsible for these losses are primarily two effects: lower incomes during unemployment and lower wages upon reemployment. The latter is caused by problems associated with transfering skills and time costs required for skill acquisition and learning processes in the new sector of employment. Tapp (2011) shows that the main source of costs is not the loss of a job, for a normal worker will find a new job relatively fast and thus losses in income and production are minor. But a large problem is reemployment at lower wage rates because of a failure in the transference of skills into the new sector . This is a persistent problem for the skill adjustment process that takes several years (Tapp, 2011; Fallick, 1996). Therefore this study aims at incorporating the costs of factor reallocation into a single country CGE-model and analysing the effects of different factor reallocation cost schemes on model results. We use the single country CGE model “STAGE” (McDonald, 2009), adapted to a Social Accounting Matrix (SAM) of Israel for the year 2004 (Siddig et al., 2011) which includes a highly disaggregated labour market. Reallocation costs of factors are mainly caused by problems in skill transfers between the different sectors. These sector specific worker skills are represented in the model by productivity differences. Each labour group is broken down into different sector blocks. Migration functions are implemented, allowing for migration between the sectoral blocks of a specific labour group. Further, reallocation costs are introduced through variation of the productivity of the migrating factor. We analyse three different assumptions: 1) the factors adopt the new sectors’ productivity and allocation costs are neglected, 2) the factors keep their original productivity, 3) the factors take on a productivity between these extremes. The effect of an increase in Palestinian labour supply on the Israeli economy is analysed under these different assumptions. Increased Palestinian labour supply increases domestic production and enhances economic growth in Israel. A liberalised labour market policy would widen the income gap between poor and rich households by increasing the factor income of rich household groups and by reducing the factor income to some poor household groups. However, the negative effect of decreasing factor income is smaller than positive effects accruing to all household groups from a decrease in prices and an increase in transfer income, resulting in welfare gains for all household groups in Israel. Effects are expected to be strongest when neglecting adjustment costs in the first assumption. Increased costs will decrease intentions to reallocate factors, thus effects from a more liberalised labour market policy are expected to be lower. The introduction of adjustment costs will hinder movement from lower paid jobs in less productive sectors to more productive sectors, the typical direction of adjustment. Regarding distribution we expect the results with more adjustment costs to be more pro rich and disadvantageous to the poor. Literature Fallick, Bruce (1996): A review of the recent empirical literature on displaced workers, Industrial and Labor Relations Review 50, 5–16. McDonald, S. (2009): STAGE Version 1: July 2007. Course documentation. McLaughlin, Kenneth, and Mark Bils (2001): Interindustry mobility and the cyclical upgrading of labor, ’ Journal of Labor Economics 19, 94–135. Siddig K, Flaig D, Luckmann J, Grethe H (2011): A 2004 Social Accounting Matrix for Israel, documentation of an economy-wide database with a focus on agriculture, the labour market and income distribution. Agricultural Economics Working Paper Series No. 20, Hohenheim, Germany. Tapp, S. (2011): Lost in transition: The costs and consequences of sectoral labour adjustment. Canadian Journal of Economics, 2011, 44, 1264-1296.

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)0.002
metaresearch head score (Gemma)0.014
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: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.007
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0390.001

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 head0.029
GPT teacher head0.251
Teacher spread0.222 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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