AN EMPIRICAL INVESTIGATION OF SELECTED FACTORS DETERMINING THE LABOUR PRODUCTIVITY IN MACEDONIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it