Education
The Effects of Education On Productivity and Growth

The Effects of Education On Productivity and Growth

The objective of this first part is to report on works examining the effects of education on productivity. From the outset, the analysis of the economic role of education followed two parallel paths, that microeconomics (with the theory of human capital) and that of macroeconomics (with international empirical work on economic growth). Their purpose is however common: to define and measure the return on investment in human capital for society. Both types of approaches rely on the use of statistical techniques to determine how an increase in educational attainment affects individual income or growth at the macroeconomic level.

 

The Returns to Education

In human capital theory, education is seen as an investment that the individual makes in building productive capital. This learning is expensive, but in return, the knowledge acquired brings gains to the individual when it is applied in the context of professional activities. The wage return to education can then be estimated from these effects on perceived wages. The point of reference for the extensive literature on returns to education is the “Mincer equation”. The analysis of the relationship between training and salary consists of relating the salary to three groups of variables: variables describing initial training, variables describing experience (and seniority), and finally a third, heterogeneous group, intended to take into account other factors influencing the salary (individual characteristics: sex, profession…; characteristics of the company: branch of activity, size, profit…). As the emphasis is on the first two groups of factors – training and experience – the third, when present, is only there to arrive at “pure” estimates of the effects of training and education. ‘experience. The return to an additional year of study is thus measured by its effect on the salary. Empirical estimates of the return to education range from 5% to 15%, depending on the era and country. As the emphasis is on the first two groups of factors – training and experience – the third, when present, is only there to arrive at “pure” estimates of the effects of training and education. ‘experience. The return to an additional year of study is thus measured by its effect on the salary. Empirical estimates of the return to education range from 5% to 15%, depending on the era and country. As the emphasis is on the first two groups of factors – training and experience – the third, when present, is only there to arrive at “pure” estimates of the effects of training and education. ‘experience. The return to an additional year of study is thus measured by its effect on the salary. Empirical estimates of the return to education range from 5% to 15%, depending on the era and country.

The returns to education have evolved over the past 40 years. Thus, in the United States, it decreased during the 1970s and increased during the 1980s, generating a U-shaped temporal pattern. In Europe, in general, the return to education followed a pattern. U-shaped temporal similar to that of the United States, but shifted in time. In the 1970s, the return to education was lower than in the 1960s. In the 1980s, this return continued to decline but began to rise again in the 1990s (Denny, Harmon & Lyndon 2002). For France, the work of Selz & Thélot (2003) shows that the profitability of education decreases between 1962 and 1984 and then stabilizes.

 

The Macroeconomic Approach: The Effects of Growth

Beginning in the 1960s, macroeconomists analyzed the contribution of education to aggregate economic growth. The aim was to quantify the proportion of the growth in production that can be directly attributed to the increase in the level of training. The first series of international studies showed that education had the expected positive effect, the rise in the level of training explaining on average one-fifth of the increase in production by workers (Temple 2001). The second series of studies, using more sophisticated econometric techniques, however, produced somewhat contradictory results, which even led some researchers to explicitly question the relationship between education and growth (Pritchett 1999). These last years, everything seems to show that these negative results were largely due to poor quality data as well as to various econometric problems (De La Fuente & Cyclone 2003). Recent studies that mobilize improved data sets show that investment in education does have a substantial impact on productivity growth. Thus, Krueger & Lindale (2001) highlight a significant role in the growth of both the accumulation and the initial level of human capital in a panel of 110 countries observed between 1960 and 1990. The effect measured is close to the microeconomic returns mentioned above. Cyclone 2003). Recent studies that mobilize improved data sets show that investment in education does have a substantial impact on productivity growth. Thus, Krueger & Lindale (2001) highlight a significant role in the growth of both the accumulation and the initial level of human capital in a panel of 110 countries observed between 1960 and 1990. The effect measured is close to the microeconomic returns mentioned above. Ciccone 2003). Recent studies that mobilize improved data sets show that investment in education does have a substantial impact on productivity growth. Thus, Krueger & Lindahl (2001) highlight a significant role in the growth of both the accumulation and the initial level of human capital in a panel of 110 countries observed between 1960 and 1990. The effect measured is close to the microeconomic returns mentioned above.

 

Education: At The Heart of Innovation Phenomena

Beyond the problems of estimating the effects of education on growth, recent macroeconomic developments have made it possible to renew reflection and better specify the role of education and the mechanisms through which it could have an impact. productive value (Gurgand 2000). Thus, certain models resulting from the theories of endogenous growth no longer consider education as a factor of production, but as a factor of innovation. Other models emphasize that education increases productivity less than the capacity of individuals to adapt to changes in the economic tuition environment (Benhabib & Spiegel 1994). Education would promote the effectiveness of learning behaviours in an unstable universe. This approach revives a more “dynamic” view of the role of education in economic growth, which had been developed in an embryonic way by Nelson & Phelps (1966). The latter, taking the example of the dissemination of innovations in the agricultural field, had shown that it is the most educated farmers who are the first to adopt new products and processes. Thus, we can conclude that the level of education affects long-term growth through its effects on the speed of adaptation to technological change (Aghion & Cohen 2004). had shown that the most educated farmers are the first to adopt new products and processes. Thus, we can conclude that the level of education affects long-term growth through its effects on the speed of adaptation to technological change (Aghion & Cohen 2004). had shown that the most educated farmers are the first to adopt new products and processes. Thus, we can conclude that the level of education affects long-term growth through its effects on the speed of adaptation to technological change (Aghion & Cohen 2004).

  • This work provides a certain number of elements of understanding both empirical – the evolution of the rates of return to education – and theoretical – the role of education in the diffusion of technological innovations. However, several questions remain unanswered.

 

New Technologies and Skills Development

  • Isn’t the evolution of the return on investment observed in recent years attributable to the emergence of an information society? Doesn’t the rise in returns to education and training comes from the diffusion of new information technologies which would increase labour productivity? Even if it is still difficult today to assess the effects of NICTs on the total productivity of factors, there is nevertheless little doubt that a new technological wave is now affecting the production processes of the whole of the world. ‘economy. How do these technological changes affect the nature of work and employment, how do they alter the skills required and, accordingly,

 

Nick: A Non-Neutral Technological Change

  • Over a long period of time, researchers observe an increase in demand for human capital. However, this phenomenon has accelerated markedly. Author & al. (1998) show that the relative demand for skilled labour has increased more rapidly over the last twenty-five years (1970-1996) than during the previous thirty (1940-1970). Companies have replaced the least skilled workers with a skilled workforce at an unprecedented rate. The increase is even more marked in the years 1980-1990. This is mainly due to technological developments, and in particular to information technologies, which require a more qualified workforce. Such developments concern the United States but they appear in most of the developed countries. Krueger’s study (1993), often cited as a reference on these questions, shows that employees, with equal characteristics, who use computers in their work activity have higher wages by 10 to 15% higher than those who do not. Moreover, he shows that the expansion of these computer tools can be attributed to between one-third and one-half of the increase in rates of return to education in the 1980s. Numerous studies have subsequently confirmed the importance of IT on the demand for skilled labour in the 1990s (Green & al. 2000).

 

The computer or the pen?

  • The direct causal relationship between the use of information technology and wage increases has, however, been discussed, notably by Dinar do & Pischke (1997). In response to Krueger’s analysis, these authors resort to much more comprehensive data on the type of tools used by German workers in their work activity. They use the same technique as Krueger to estimate the salary differential associated with the use of a calculator, a telephone, pens, an office chair … This allows them to show that the premium associated with the use of these different instruments is at least as broad as that related to the use of computers. Since they do not believe that employees enjoy special rewards for using a pen or chair, they come to doubt the validity of the exercise and therefore question the interpretation of the effect of the use of computers on wages. In fact, the results suggest that employees who use computers have unobserved skills that are valued in the labour market but that may not have much to do with computer skills.

Labor sociologists, relying on detailed case studies, have in fact shown for some time now that new modes of the organization have important consequences on the qualification of employees (De Coster & Pichault 1994). These organizational changes lead to an expansion of the tasks of operators and an accentuation of the collective nature of the work linked to the imprecision of individual work (managing hazards, solving problems, etc.). We are also witnessing the multiplication of tasks involving the manipulation of signals, symbols and codes. While the Taylorist organization reduced work to the repetition of simple and prescribed tasks, the worker now has to deal more with complex tasks characterized by multiplicity,

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