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Human capital theory states that resources embodied in people, such as knowledge, skills and health, contribute to increases in earnings in the same way that physical capital raises output and income.

Activities such as education, on-the-job training and medical care affect individuals’ future earnings through the accumulation of human capital and are thus called investments in human capital. Although human capital theory originally was focused on explaining the substantial income growth in the U.S. even after controlling for growth in physical capital and labor, it also helps explain many aspects of workers' earnings over their lifetimes. Workers’ earnings increase with age at a decreasing rate. This positive and concave age-earnings profile can be explained by a lifetime human capital accumulation process in which investments are mostly concentrated at workers’ younger ages.

Investments in human capital when workers are young provide a longer period to recoup the returns, and such investments are less costly earlier in life because the opportunity costs of investing in human capital are lower when earnings are lower. Human capital theory also provides important insights into the patterns and changes i the distribution of earnings across workers.

Differences in schooling and labor market experience explain a significant fraction of the inequality in earnings. The explanatory power of the human capital model is even stronger when quality of schooling and the amount of on-the-job training are included in the analysis An important dimension of human capital theory is its distinction between general and specific human capital. General training increases a worker’s marginal productivity by the same amount in the firm providing the training as in other firms.

By contrast, specific training increases productivity more in the firm providing the training than when the worker is employed by another firm. For example, a training program of communication skills is general training because it raises trainees’ productivity in all firms that require a similar level of communication at work; on the other hand, a course in a specific software used only by firm X is specific training because it does not contribute much, if any, to the trainee’s productivity increase in firms other than X.

The distinctions between general training and specific training depend on the nature of training and on the extent to which employers require specific skills and training. For example, medical skill is specific human capital to the medical industry but general human capital to all hospitals. A mastery of Danish language is specific human capital to the Danish labor market but general human capital to all the firms in Denmark. If different countries are considered separate labor markets, even schooling, a typical general human capital in most cases, could become specific human capital. Knowledge about a country’s language, history and institutions is often of little value in another country.

Technology and Displaced Workers’ Earnings Losses

The employment and earnings consequences of job displacement have long been an important dimension in labor economics research, and concerns about earnings declines after job losses have led to many policy initiatives to assist displaced workers. Initially, the focus was on manufacturing and blue collar workers, where large-scale job displacements took place in the 1970s and 1980s. Over the last decade, an increased number of displacements occurred in high technology (hi-tech)
industries, which employ many highly educated and skilled employees.

In this chapter, hi-tech industries are defined as industries with intensive computer usage, with a large fraction of investment in hi-tech equipment, or with a high concentration of scientists and engineers. Some examples of hi-tech industries are communication, professional services, machinery manufacturing, and chemicals and allied products manufacturing. Displacements increased markedly in many hitech industries from the early 1990s to the beginning of the new century. For example, between 1993 and 2001, the displacement rate increased from eight percent to 12 percent in the communication industry, from 11 percent to 20 percent in the machinery manufacturing industry, and from 10 percent to 14 percent in the business services industry, calculated using data from the Displaced Worker Surveys (DWS)

During this time period, the displacement rate remained constant or declined in most low-tech industries where computer usage and concentration of scientists and engineers is low. Increases in displacement in hi-tech industries are driven by several forces. The rapid expansion in the IT sector followed by the “dot-com bubble burst” in 2000 increased the number of displacements among hi-tech workers. According to the Bureau of Labor Statistics (BLS) Mass Layoff Statistics, the number of mass layoff events in the IT sector tripled between 2000 and 2001, compared to an average of 38 percent growth for all sectors.

Other hi-tech industries also experienced above average increases in mass layoffs during this period. For example, mass layoffs increased by 86 percent in the chemical manufacturing industry, by 70 percent in professional and technical services, and by 49 percent in the finance and insurance industry. The 2000-2001 recession was followed by a period of only moderate employment growth, with employment in professional and business services, finance, and information sectors in 2003 at the same levels as in the 2000 pre-recession peak.

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Reference : Copyright by Xiaohan Hu, Doctor of Philosophy, 2007

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