Is it possible to measure the amount of knowledge in quantity? How can we rank the laborers based upon their skills? These and many other ones are directly related to one of the components of production, human capital. Like any other production, production of energy requires human capital, especially as the energy resources and alternatives of existing energy routes diversify. This makes human capital important to the energy industry and as the evolution of technology continues, some of the existing human capital become obsolete.
By putting some questions on human capital in the center, the statistical analysis makes it possible to draw some inferences about human capital growth by modeling. Initially, to define human capital, according to OECD, human capital is people’s skills, learning, talents, and attributes, which implies the accumulation of knowledge is positively correlated with the level of human capital. Therefore, to measure the human capital level for a population for a particular time, we may look at the number of patents taken out by the population and citation for the population until the time we want to measure; the more patents and citations, the more we want to measure human capital level is.
In this article, we shall be doing a literature review for some models of human capital growth based upon several patents and citations.
Firstly, for Akçiğit, Acemoğlu, and Çelik, managers are the ones who manage innovations; thus, the number of patents for innovations is directly related to manager’s success. In particular, the younger CEOs, the more citations per patent are. However, the "CEO effect" does not necessarily happen immediately after a change of CEOs due to the process of research and patenting. With the help of their model, instead of just looking at the new CEO's human capital, we should think of the event "changing CEO ."The change to a younger CEO creates such an effect, not the CEOs themselves directly, and the model result is that switching to younger CEOs generates more radical innovations both after and shortly before the switch.
Secondly, according to the model of Chari and Hopenhayn, which is a vintage human capital model in which each technology requires vintage-specific skill, young workers entering new technologies invest when they are young and reap the benefits of their human capital when they are old by stating that unskilled workers’ wages go up with vintage. To explain, "vintage" is a kind of relativity between generations in particular, as time elapses, the generation gap makes the older one vintage for the younger ones. Algebraic computations of the model aside, since while technology changes, the novelties require an adaptation and a new level of human capital, as the newcomers to the labor market are seen as unskilled, their investments in the new technologies are going to return them as a higher wage as vintage increases. The statement is also supported by the model by MacDonald and Weisbach, which predicts that the income of the young will exceed the income of the old because, in addition to the younger generation's technology-specific human capital and other kinds of features, they are learning by doing.
At this point, the diffusion rate of a new technology ought to be considered; according to them, technologies diffuse slowly, and the rate of technology arrival is positively correlated to the rate of diffusion. Therefore, an improvement in technology actually does not quickly get the technology-specific human capital off; nonetheless, keeping trying to increase the number of patents and their citations will increase the diffusion rate and, correspondingly, the rate of being obsolete the technology-specific human capital of older generations.
Now, through these models, we have a ground on which we can think of. One of the most popular ways to increase innovations and so human capital is giving the incentive to innovate. According to Akçiğit and Ateş, for example, lower trade barriers increase private companies’ incentives to innovate by increasing the competition in the market. As exemplified by their model, when both countries reduce tariff levels, competition intensifies for a large chunk of firms, incentivizing them to innovate, which reduces the magnitude of underinvestment in R&D and the need for aggressive R&D subsidies. In addition to that, protectionist policies in favor of national firms, in the long run, remove their incentives to innovate. Moreover, Akçiğit, Aghion, and Villavede’s model claim that taxing capital reduces innovation incentives for a given labor supply.
In this article, we have seen a short history of the models regarding human capital growth, which is for many people who do not have enough knowledge in economics too abstract to comprehend and measure. In these models, researchers used data such as the number of patents per firm and citations based upon the definition of human capital to measure it and see its growth. After that, with the help of these models, it has become possible to understand how incentives work in this variable of economic growth, human capital. Especially when such industries where human capital may play an integral role in growth as energy is thought of, the importance of understanding how incentives work becomes more clear.