Abstract
This chapter uses the data from the Firm Survey (2002, Technological change and skill development: A comparative study of chemical and metal medium and large scale enterprises in the UAE. April, 2002) to examine skill indicators, their implications and relationships with average wages, and with upskilling (ICT training) and technology (ICT), ICT and input–output indicators at the micro/firm level. The findings illustrate the low skill levels –due to the excessive share of unskilled foreign workers– and the implications on skills mismatch, public-private duality and productivity decline across private firms. These findings verify our hypotheses regarding the implications of the interaction between the deficient educational system and high use of unskilled foreign workers. These findings then confirm our first hypothesis, concerning the pressing need for upskilling, particularly within the private sector. The results show positive correlations between actual and required education, experience and average wages. These results verify part of the fourth hypothesis that an increase in skill level and firm size lead to improved relationships between actual and required education, and between actual education, experience and wages. The findings with respect to the positive complementary relationships between skill, technology (ICT) and upskilling (ICT training) and between computers, telecommunications and ICT training are consistent with the findings in the new growth literature. We illustrate and corroborate part of the fourth hypothesis that an increase in skill level and firm size lead to an improvement in the complementary relationships between skill, upskilling and technology (ICT). Taken together, all these results imply the importance of a good education for bridging differences between firms and also for enhancing skill, technology and upskilling complementarity at the micro level. These findings seem consistent with the endogenous growth framework and stylized facts concerning the relationships between human capital, technical progress and upskilling and our theoretical framework. In addition, our results verify part of the fourth hypothesis concerning the relationships between actual and required education and experience and between actual education, experience and wages and the relationships between technology (ICT), skill and upskilling (ICT training). The results corroborate the fifth hypothesis regarding the inconclusive effect of ICT at the micro level.
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- 1.
All data, information and analysis in this chapter are based on the results covering 26 firms obtained from the Firm Survey (2002).
- 2.
We classify the educational qualifications of workers into three groups: high skilled (H) with postgraduate, university and diploma degree (more than twelve years of schooling), medium skilled (M) with secondary education (twelve years of schooling) and low skilled (L) with less than secondary education (less than twelve years of schooling). We define the occupational status according to five categories, including white-collar high (managers, professionals, management executives, scientists, technicians and engineers); white-collar low (clerical and administrative); blue collar high (skilled craftsmen); blue-collar low (plant machinery operators, assemblers and elementary occupation) and other workers. We define the required qualifications by required years of schooling including: postgraduate/ Ph.D. (19–20 years); professional, MSc./ postgraduate (18 years); university graduate (16 years); diploma (14 years); higher secondary schooling (12 years); and less than secondary schooling (less than 12 years). We measure the average wages by average monthly wages (in Dirhams, the UAE national currency), and average years of experience by both actual and required average years of experience for both educational and occupational definition respectively.
- 3.
ICT is the sum of total expenses on computers, telecommunications, training, maintenance and other related items.
- 4.
We measure output by the total sales value because the measurement units of sales value is unified (in local currency) across firms, while the measurement units of output in physical terms (tonne, litre, etc.) varies across firms.
- 5.
We use few observations in the estimated equations, due to limited availability of reliable data covering these indicators, because some of the respondent firms were particularly reluctant to provide adequate reliable quantitative data covering these indicators.
- 6.
We use a modified definition of the diversification index developed by Utton (1979). We define the diversification index by output/ sales diversification Di = [P1 + 2P2 + 3P3 + 4P4] −1/ 2], where Pi refers to the percentage share of diversified sale product in total sale products within firms. Ranked from large to small, when Di = 1, Di =4 and 1 < Di < 4, it implies complete specialization, complete diversification and some degree of diversification respectively. We apply the same definition for employment diversification index (cf. Utton, pp. 15–16, 104–105).
- 7.
In Figs. 6.1, 6.2, and 6.3, the horizontal axis defines firms, industry, size (chemical/metal, large/medium), and skill level (high (H), medium (M) and low (L)). The vertical axis defines the intensity/share of H, M and L across firms. The information in the right margin defines the distribution of workers in Figs. 6.1 and 6.2, and the average required years of education in Fig. 6.3.
- 8.
White collar (WC) includes white collar high and low. Blue collar (BC) includes blue collar high and low.
- 9.
Our definition of actual education refers to educational attainment classified under three groups: high (post secondary) educational attainment: university degree and above (16 years of schooling); medium educational attainment: secondary education (12 years of schooling); and low educational attainment: less than secondary education (9 years of schooling). We define the required education by the translated merged required qualifications for each occupation group defined by average years of schooling. The occupational classification includes the following five categories/ groups: (1) Managers, professional, management executive, scientific, technical and engineers; (2) Clerical and administrative; (3) Skilled craftsmen; (4) Plant machinery operators, assemblers and elementary occupation; and (5) Other workers. We translate the required qualifications associated with each occupational class into average years of schooling and group them in the following way: (1) PhD/postgraduate (19–20 years); (2) Professional, MSc./ postgraduates (18 years); (3) University graduate (16 years); (4) Diploma (14 years); (5) Higher/ Secondary Schooling (12 years) and (6) Less than Secondary Schooling (9 years). We then merge the required qualifications into three groups, assuming that the high occupation group includes both the first and second occupation categories, the medium occupation group includes both the third and fourth occupation categories and, finally, the low occupation group includes the fifth occupation category. We then use this definition to compare between the required education for each occupation class and actual/ attained education, and we assume that the difference between these indicates the presence of skills mismatch between jobs requirements and educational attainment.
- 10.
Due to the small number of observations on the declining trend of labour productivity, our results should be interpreted carefully as probably this may not be the only case; other possible explanations are either the steady or increasing trends amongst the non-respondent firms.
- 11.
In Table 6.3 we limit our analysis of the productivity decline to compare only the change in labour productivity over the period 1999–2000 and 2000–2001 across 14 of the respondent firms. Since our data only reflects skill levels for the year 2001, but does not reflect the change in skill levels over the period 1999–2001. That means we cannot compare the change in productivity with the change in skill levels, so as to attribute the declining trend in productivity over the period 1999–2001 to the declining trend in skill levels.
- 12.
- 13.
From the Firm Survey (2002) we find that the proportion of high skilled wages/low skilled wages accounts for 7.5, 6.9, 8.1, 6.3 and 8.4 for all firms, chemical, metal, large and medium size firms respectively.
- 14.
Firms reported the use of different types of new technologies such as mass petrochemicals plants, advanced process controls, gas plants installation, CNC machines, new advanced machines and ICT.
- 15.
This result is consistent with SBTC theorem and our earlier findings indicating that wages are increasing in education and biased against unskilled workers.
- 16.
Moreover, other factors are: the expected increases in market share, turnover, sales, adoption of international standards and enhancement of production, advanced control systems, shortage of manpower, competition, increasing motivation to reduce costs, achieving high standard precision work, improving productivity, quality of work and demand for more specialized skills in IT.
- 17.
Except in 2000, where the correlations between ICT and both output and profit are negative.
- 18.
At the aggregate level, when using the most recent data (2002) on the share of spending on ICT relative to GDP across four Gulf countries: Bahrain, Saudi Arabia, UAE, and Kuwait, we find an inconclusive effect of ICT. Because the share of spending on ICT/GDP shows a significant positive correlation with GDP, but a significant negative correlation with GDP per capita across the four Gulf countries.
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Nour, S.M. (2013). Relationship Between Skill, Technology and Input–Output Indicators. In: Technological Change and Skill Development in Arab Gulf Countries. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-01916-1_6
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