Another stylized fact of economic development in China is that capital investment as a share of gross domestic product (GDP) is about 40% and substantially higher than many developed economies. So long as the depreciation rates of capital are not substantially di¤erent across countries, China is accumulating capital at a much faster rate than developed countries. From our theoretical analysis, we …nd that an increase in the stock of capital in the South relative to the North reduces o¤shoring. Intuitively, a larger stock of capital in China increases the wage rates of Chinese workers rendering o¤shoring to China less attractive. This decrease in o¤shoring is like a decrease in the supply of unskilled labor to Northern …rms triggering a market size e¤ect. Therefore, a larger stock of capital in the South also leads to skill-biasedtechnicalchange in the North. In other words, rapid capital accumulation and a decrease in unskilled labor in China could both contribute to skill-biasedtechnicalchange in the US.
During the past 40 years, wage inequality increased markedly in many industrialized countries. In that context, skill-biasedtechnicalchange (SBTC) proved to be a quite powerful explanation (e.g. Acemoglu and Autor, 2011; Goldin and Katz, 2008). Though, it is not SBTC directly that drives inequality; it is the increasing demand for skills induced by SBTC and a lack of supply to cope with it that determines wage disparities. This has been shown in several studies for the United States (e.g. Autor et al., 2008) and various other industrialized countries (e.g. Berman et al., 1998). Since the seminal contribution of Goldin and Katz (2008), the relationship between increasing demand for high-skilled workers and increases in their supply, is often referred to as ‘race between education and technology’. It also implies that inequality is not necessarily a by-
premium associated with each task) across occupations rather than allowing it to vary across occupations. This is consistent with the use of individual fixed-effects which control for variation originating from person specific heterogeneity and also variation associated with individuals who do not move across occupations or jobs over time. The specification alters the traditional Roy model by including a task-efficiency function that maps skills to occupational tasks. In doing this, we have assumed that sorting across occupations occurs through skill-task efficiency and that similarly skilled workers sort into occupations where the task requirements align with their skill. This assumption is represented through the maximization problem outlined in Equation 6.
As a result of our study, it appears that nonneutral technicalchange has been the rule for Turkish manufacturing. In 8 of 9 industries, tests rejected the hypothesis of Hicks- neutral technicalchange. In addition, skill neutral technological change hypothesis is strongly rejected in all industries, however the results do not show a statistically significant skillbiasedtechnicalchange. In 5 of 9 sectors, we observe a statistically insignificant skillbiased technological change hypothesis supporting coefficients. If we increase the level of significance to 0.1; then, in Manufacture of fabricated Metal product sector (38), we observe a statistically significant skillbiasedtechnicalchange with 0.1 level of significance. This can be reasonable since our sample size is really small meaning that power of our tests are low. If we look at the significant coefficients at either 0.05 or 0.01 level of significance, in 2 sectors (33 and 34) capital biased technological change is observed whereas in two (36 and 39), a technicalchange away from capital is seen.
The hypothesis of this paper is, therefore, the higher supply of skilled workers in US around 1970s has biased the technological progress which in turn has raised the wage differential between skilled workers in USA and abroad. As a result of greater wage differential, rational individuals who managed to cover the cost of mobility responded by migrating to USA which in turn has created further incentive for technicalchange to be skilled biased and raise the wage of skilled workers. The final consequence would be higher incentive for skill accumulation due to higher skilled wage premiums, and greater growth due to the availability of more input and concomitant involvement in production of final goods and innovation. The schematic form of the hypothesis of this paper is:
One well established debate concerns the importance of manufacturing in national economies. Within Europe the UK and Germany are often cited as exemplars of two different ‘varieties of capitalism’, a Liberal Market Economy and a Coordinated Market Economy respectively. In the former the institutional systems favour the development of services, whereas in the latter they favour high quality manufacturing. While the City of London is Europe’s only global financial centre, Germany is its manufacturing export powerhouse (Hall and Soskice (2001) for original formulation; for update and implications for employment see especially Bosch, Rubery and Lehndorff (2009)). In some accounts political decisions play a very direct role. The notorious failure of British industry to modernise in the third quarter of the last century, coupled with the country’s conflictual industrial relations, led to the Thatcher government’s decision to demolish the heartlands of British trade unionism not only in the publicly owned coal industry but also in broad swathes of privately owned manufacturing. The flip side was essentially a national wager on global financial services starting with the ‘Big Bang’ of financial deregulation in 1986. Despite conventional assumptions of the role of technical and scientific knowledge in economic growth, there was an actual decline in the number of engineers and scientists in the UK in the 1990s 3
The supply of skills is shaped by many variables, such as demographic trends, preferences and education shifts. Due to technological changes, workers may want to upgrade their skills, as the skill demand increases. Initially, technicalchange was viewed as factor-neutral, this is, improve- ments in the TFP leave marginal rates unchanged. However, empirically, we observe a rise in the skill premium, as well as the increase in skilled labor supply, as we show in section 2. Even with a higher supply of skilled people since 1970, wages for skilled people kept rising, which can be observed as pieces of evidence of skill-biased technological change. In fact, Acemoglu and Autor (2011) argue that technical changes are by its nature skill-biased.
In their recent paper, Funk and Vogel (2004), show that technological change does not have to be skillbiased and conclude that instead of being assumed, the skill bias technicalchange can be an equilibrium result. The same feature can be found in Acemoglu (2002), where he describes technological change bias as a function of both prices and stocks of skills, with opposed results in terms of technological change. The price e ﬀ ect inducing innovation towards the scarce factor, the stock eﬀect induces innovations towards the abundant one. Both Funk and Vogel (2004) and Acemoglu (2002), point out that technological change is not per se skillbiased and give as an example the unskilled biasedtechnicalchange that took place in the late 18th and early 19th century, where mass production replaced the artisan.
same through three key elements — non- profit initiatives, profit enterprises, and consumers. Currently, there are many skilling opportunities approached by the government, the private sector and the collaboration between the two. The current focus of skill development has shifted to the learner and his/her requirements and expectations from vocational education and training; in order to empower the working population and other citizens of the country, it is essential to put emphasis upon skill development (Knowledge paper, 2012). India has the world's second largest population. The PGR for the country is 1.25. A very large number of India's population, about 50%, is below the age group of 24. This provides the nation with a large workforce for many decades, helping in its growth. The government is training a 400 million-workforce, which is larger than the
The paper begins by using EUKLEMS data to investigate the impact of ICT on demand for various types of workers, including a split by gender, three skill groups and age. We use the specification in O’Mahony, Robinson and Vecchi (2008) in this first analysis and consider results for 9 countries and 11 industry groups. These first results suggest there may be a bias against older male workers and specifically older males with university education arising from ICT. The second part of the paper attempts to delve more into the reasons why this might have occurred, focusing on training and organisational changes. This analysis uses data from the EU Labour Force Survey (EU LFS) on training matched with EUKLEMS data. We consider whether training combined with ICT use affects wage premiums. We then consider if lower training for older workers appears to be driven by reluctance on the part of firms to train these people or reluctance of the workers themselves to undertake training.
According to Sabellah (2010) and Aremu (1998) good performance does not only make students who perform well in a particular subject develop a positive interest towards the subject but also motivates them to work hard that area. In contrast, poor performance has a tendency to retract the learner’s efforts from studying the subject. It was found in this study that the students are happy about their performance in technical works. This implies that they have positive attitude towards the subject. Positive attitude has been found in studies to have consequential link with performance (Burrow, 1978; Otami, 2012; Myint & Goh, 2001; Chui-Seng, 2004; Mucherah, 2008). The findings in this study agree with the above studies as it revealed that there were positive relationships between students’ attitude in technicalskill acquisition and their performance. This is evident from the attitude and performance mean scores of 4.3333 and 69.8376 representing 86.67% and 69.84% respectively. These gave an ‘r’ value of 0.366 which was a positive correlation implying that attitude of the students stirs up their interest towards technicalskill and their tendency to grab much from what was taught (Table 3). This consequently affected their performance positively.
Binswanger (1974) also employs the translog cost function in his estimations, looking at pooled data from 39 US states at different time periods, only consider- ing labor as a whole. This study is the first to explicitly look at the biases of tech- nological change and shows that, at least for short enough time periods, a linear trend is a good approximation to technological change. Jorgenson and Fraumeni (1983) use a translog price function with a linear trend for technology to look at the biases of technology and find distinct patterns of factor using and factor saving biases of technology across industries. They look at different industries, but do not differentiate labor by skill level. The linear approximation of technol- ogy still is commonly used, with the exception of Jin and Jorgenson (2010), who consider technological change a latent variable in the estimation process and use Kalman filter techniques to back it out. They look at US industries, but do not divide labor into several types.
It still seems likely that a change in the characteristics of technological change affects wages and wage inequality, although the mechanism behind the effect is most likely not SBTC. In many studies on wage inequality, there seems to be an implicit assumption that workers are differentiated only by skills that can be acquired by any worker at the same cost. That is, workers are heterogeneous only in whether they intentionally acquire skills or not. However, this view seems to be too simple and seems to neglect some important elements in worker heterogeneity. Under the assumption that the source of heterogeneity of workers is only acquirable skills, the number of skilled workers will be determined at equilibrium where the “net” income (income minus cost of acquiring skills) is equalized across workers. At equilibrium, the net incomes of skilled and unskilled workers become identical. Hence, the difference in wages between them must always be equal to the cost of acquiring skills, but this seems unrealistic. Therefore, it seems highly likely that workers are heterogeneous before they acquire skills; this scenario seems more realistic, because each individual worker is in fact different from all other workers. In this paper, I focus on a different source of worker heterogeneity to examine wage inequality based on the model of total factor productivity (TFP) shown by Harashima (2009, 2016).
Obviously, online personal profiles can help people connect with others of similar backgrounds and provide valuable resources for businesses, especially for personnel resource managers to find talents (Yang et al., 2011a; Guy et al., 2010). In the profiles, the personal skill information is the most impor- tant aspect to reflect the expertise of a person. However, few social platforms allow users to manually attach such personal skill information into their personal profiles. For example, in our collected dataset, 91.8% skills appear less than 10 times. Even the distribution of the top 10 frequently occurring skills is asymmetric, and only 43.1% people attach skills on their profiles. For this regard, it is highly desirable to develop reliable methods to automatically infer personal skills for personal profiles.
towards each other and the ball-carrier deciding whether to side-step right or left (Week 1, Day 4). The coaching style during this week is prescriptive i.e. emphasising the specific techniques for safe and effective contact. The second week builds on week 1, and aims to develop the players’ technical capacity and refine or remediate techniques. Week 2 starts with another highly structured 1 v 1 drill; however, the ball-carrier and tackler move toward each other (no direction change from ball-carrier). For each shoulder (right and left) and type of tackle (front-on and side-on), the player engages in a set of 8 contact events (players engage in 10-15 contact events during a match (46-48). Before and between each set, players perform a physical conditioning block to induce fatigue. Week 2, Day 2, the skill load as well as the physical load of the skill session is reduced to correct and fine-tune the player’s contact technique. On Week 2, Day 4, players engage in a close contact/wrestling type drill to further build technical capacity.
varied which involves multi-disciplinary activities being carried out at the same time. The construction project manager being the focal point of all the activities, this necessities him/her to be competent and able to execute their available resources in an efficient way. Due recent trends in the construction sector showing increased time frame and cost expenditure for a given project which are attributable to various factors with the skill of the construction project manager being one of them. The aim of this study is to identify and categorise the skills and competence level of project manager for shouldering the risks and responsibilities of a construction project in a safe and efficient manner. A total of 58 questionnaires were drawn from various stake holders of the construction industry through which the frequency counts were calculated and the corresponding relative importance index of the skills were listed. The results showed the various parameters such as decision making, communication and leadership skill as the most important skill for a construction project manager. The implications of this results are discussed, herein
We set g = 0.15 for the 1958 birth cohort and g = 0.30 for the 1970 birth cohort. In Table 1, we calibrate da dc / for Case 1 and find that, for all values of m which satisfy this case, the average ability gap between those with and those without a college education falls by about 14%. This is not a trivial change, though the extent to which this might impact on estimates of the college wage premium will depend on the return to ability. In Table 2, we consider a calibration for Case 3, the intermediate case. 3 In this case, when the distribution is symmetric the doubling in g is associated with an 11% fall in the average ability gap – similar to that in Case 1. However, as the distribution becomes increasingly negatively-skewed, the extent of the fall in the gap diminishes until, for m = 0.8 , the impact of the increase in g is a (small) rise in the gap. As g grows further – as has been the case in the UK – it becomes more likely that a rise in g might lead to an increase in the average ability gap.
urd cultivation, non-availability of plant protection equipments, lack of skill about plant protection measures, inadequate knowledge about soil treatment, biased Agriculture supervisors and high cost of improved seeds, micro-nutrients, fungicides were important constraints expressed by the beneficiary farmers in the adoption of recommended urd interventions in the study area.
Abstract. Assessments of ocean data assimilation (DA) sys- tems and observing system design experiments typically rely on identical or nonidentical twin experiments. The identical twin approach has been recognized as yielding biased impact assessments in atmospheric predictions, but these shortcom- ings are not sufficiently appreciated for oceanic DA appli- cations. Here we present the first direct comparison of the nonidentical and identical twin approaches in an ocean DA application. We assess the assimilation impact for both ap- proaches in a DA system for the Gulf of Mexico that uses the ensemble Kalman filter. Our comparisons show that, despite a reasonable error growth rate in both approaches, the iden- tical twin produces a biasedskill assessment, overestimating the improvement from assimilating sea surface height and sea surface temperature observations while underestimating the value of assimilating temperature and salinity profiles. Such biases can lead to an undervaluation of some observing assets (in this case profilers) and thus a misguided distribu- tion of observing system investments.