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Record W2972098831 · doi:10.35120/kij280189t

AN EMPIRICAL INVESTIGATION OF SELECTED FACTORS DETERMINING THE LABOUR PRODUCTIVITY IN MACEDONIA

2018· article· en· W2972098831 on OpenAlexaboutno aff
Predrag Trpeski, Marijana Cvetanoska

Bibliographic record

VenueKnowledge International Journal · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityStandard of livingEconomicsLabour economicsWorkforceProduction (economics)Goods and servicesOrder (exchange)UnemploymentQuarter (Canadian coin)Demographic economicsEconomic growthEconomyMacroeconomicsGeographyMarket economy

Abstract

fetched live from OpenAlex

Labor productivity is a crucial determinant of one economy’s competitiveness, and it varies across different countries and areas. Productivity growth is important because it contributes to growth in output, income and living standards. There are only two measures which can be used for increasing the level of economic output: one is by applying more labor effort in the production process (such as more jobs) and the second through increases in the productivity of the workforce. Or in other words, it means bringing additional inputs into production; or increase productivity. As labor force growth slows and unemployment remains at relatively low levels, economies increasingly have to enhance productivity in order to maintain the high rates of output and income growth that have become common place over the past few decades. Although there are several reasons for differences in the level of economic development among countries, generally, we can start from the assumption that differences in economic development results from the differences in productivity. At the national level, higher productivity increases living standards as more real income improves people’s ability to consume and demand more goods and services whether they are necessities or luxuries, enjoy leisure, improve housing and education and contribute to social and environmental programs. Despite the significant productivity growth from 2002 to 2008, and again from 2014 to 2017, Macedonia still lags behind the EU average. Macedonia’s labour productivity has negative growth rate from 2017 upwards. It drops by 4.4% in the first quarter compared with a drop of 2.1% in the previous quarter. There are various countries specific case studies and various literature that are exploring the determinants of labour productivity growth in a particular country. This study intends to identify the potential determinants of labour productivity in Macedonia. Based on an extensive literature review, we identify several factors that determine Macedonia’s labour productivity. We quantify the relationship between the productivity growth and physical capital through gross capital formation, human capital through educational structure of employees, foreign direct investments and real wages. On the side of methodology, correlation and regression analysis for testing the relationship between the dependent variable and independent variables are used. The fundamental assumption for a clear econometric analysis is the stationarity of data time series and the regression analysis is followed by studying the stationarity of time series using Unit root test. The study is based on time series and the data on empirical analysis is taken from State Office of the Republic of Macedonia and World Bank. The sources of productivity are complex and they differ from country to country. While growth in productivity and in labour utilization are both sources of improvement in living standards, productivity growth can make a major contribution over the long term.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.051
GPT teacher head0.299
Teacher spread0.248 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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