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Title: Income Growth is Related to Complexity
Citation Type: Miscellaneous
Publication Year: 2015
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Abstract: That wages grow over lifetime is a well-known economic fact. Ever since the seminal work of Jacob Mincer (1974) lifetime earnings growth has been attributed to the investment in human capital, which was thought to take place primarily in the form of education and on-the-job training. The data show that, taking into account compositional differences of the workforce, average wage rate grows by 23% in the first 10 years since employment, 15% in the subsequent 10 years, 10% after that and finally halts with virtually no growth before retirement typically starts. While wages in all occupations follow this general pattern, there is considerable unevenness in the magnitude of growth (fig. 1). Wages increase most in occupations that require considerable skill, such as managers and professionals, and least in low skilled occupations like craft and elementary workers. Traditionally the existence of wage curves was attributed to declining training (Ben-Porath 1967). In order to explain such differences across occupations, three explanations can be put forth. First, training in some of them may be much more prevalent. Second, higher wage growth may be a return on considerable education investment necessary to undertake more demanding occupations. Lastly, workers in some occupations may be faster learners. In a seminal paper Kenneth Arrow (1962) suggested that learning-by-doing may be a major source of productivity growth and talked about two of its types: learning by repetition and learning by problem solving. The latter type is arguably responsible for the bulk of lifetime productivity growth, and occupations where this type of learning is prevalent are conducive to fast improvements of skills and productivity. This policy brief discusses the role of lifelong learning in the form of learning by problem solving and job complexity in determining earnings and the kind of employment.
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Authors: Patt, Alexander
Publisher: LLLightineuorpe
Data Collections: IPUMS USA
Topics: Labor Force and Occupational Structure, Other
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