Essays on Skilled Labor Force and Structural Transformations

Abstract

[eng] Besides introduction and conclusions, the thesis is composed by three theoretical and empirical chapters that investigate the allocation of skilled workers during structural transformation. Specifically, the second chapter of the thesis, Structural Change and the Income of Nations, looks at the necessary condition that has to exist during structural transformation for skilled workers to be allocated in high TFP sectors. Skilled workers might end up in either high or low TFP sectors, according to two opposite theories of structural change --skill-biased structural transformation and stagnant structural Transformation--. It is shown that the existence of directed technical change is the necessary condition to achieve skill-biased structural transformation and, therefore, skilled workers are allocated to high TFP sectors. Macrodata and microdata evidence are used to identify the existence of directed technical change. In the macrodata approach an increasing relative TFP of skilled versus unskilled sectors reveals the existence of directed technical change. While in the microdata approach the interaction term between tertiary education and high TFP sectors in a GLS estimation of wages reveals it. The results of the macrodata approach suggest that directed technical change has existed in the U.S., France and South Korea given an increasing relative TFP of skilled versus unskilled sectors. Then, skilled workers have been allocated to these sectors during structural transformation leading the economy towards high income levels. This finding is supported at micro level in which the coefficient of the interaction term between tertiary education and high TFP sectors is the highest and significant in the U.S., South Korea and France. Wages increase more if a worker has tertiary education and works in the High-Tech Industry (12.7% more in the U.S., 53.7% more in South Korea and 16.2% more in France) or in the Skilled Market Services (12.8% more in the U.S., 27.5% more in South Korea and 8.3% more in France) rather than in the Unskilled Services. Then, a skilled worker employed at a company in one of these sectors has the highest wage and does not have any incentive to move towards another sector. As a result, the existence of directed technical change in high TFP sectors is confirmed in the U.S., South Korea and France. The third chapter, Understanding Women: the Preference for the Skilled Non-Market Services Sector, studies those characteristics that make skilled women move towards the Skilled Non-Market Services. Namely, in this joint work with Fernando Sánchez-Losada not only a multisector analysis is carried out but also a gender composition within them. It is a known fact that skilled female participation in labor markets has increased over time, but skilled men share is still higher than skilled women share in all economic sectors, except in the Skilled Non-Market Services sector. Why do skilled women end up working mostly in this sector during structural transformation? Our main hypothesis is that specific characteristics of this sector match empowered women's preferences and, then, this phenomenon is explained. Using data from the U.S. Labor Input File and the U.S. ASEC supplement to the CPS we identify three relevant characteristics of this sector: a small gender wage gap, low number of hours to work with a relatively high compensation, and better demographic indicators. A theoretical model that focuses on the preferences of an empowered woman is built. The first relevant characteristic of this sector matches the fact that as gender wage gap increases the fraction of her wage mass devoted to satisfy family consumption decreases in the theoretical model. The largest weight of “own” leisure and family consumption on utility matches the second relevant characteristic. The third characteristic matches the fact that in order for the fraction of her wage mass devoted to satisfy family consumption to increase, she has to work more hours and, then, she faces a high cost: a lower number of children. Then, the trade-off between marriage, having children and participating in the labor market is more favorable for those women in the Skilled Non-Market Services sector since it offers them lower hours to work and a relatively high compensation per hour worked. In order to support these findings a microdata approach is developed. Using data from U.S. ASEC supplement to the CPS the probability to end up working in the Skilled Non-Market Services sector is estimated. Other relevant characteristics that strength skilled women's preferences towards this sector are identified, i.e. a long run stability and job flexibility. Then, skilled women find a balanced trade-off between family and working life in this sector. The fourth chapter, The Role of Intangible Capital in Structural Transformation, provides a theoretical framework for understanding why the High-Tech Industry sector uses less services inputs from other sectors than the Low-Tech Industry sector. Specifically, I focus my analysis on skilled workers and those characteristic services of industry: software and databases capital services, R&D capital services, and other intellectual property products (OIPP) capital services --known as intangible capital— Given the nature of intangible capital, the need for skilled workers might increase as rms decide to invest in this type of capital. In this chapter, through the theoretical framework mentioned above, I study how the elasticity of substitution between intangibles and skilled workers determines whether a sector is skill-biased. I find that intangibles and skilled workers are complements in high TFP sectors --High-Tech Industry-- while they are substitutes in low TFP sectors --Low-Tech Industry--. Then, as investment in intangible capital increases, allocation of skilled workers rises in high TFP sectors. This finding is in line with those in Marrocu et al. (2009) and Chiavari and Goraya (2021) and argue that firms within high TFP sectors are those who decide to produce intangible capital internally, i.e. they decide to hire skilled workers instead of using services of other sectors as inputs in their production. Opposite results are found for rms in low TFP sectors. Moreover, I provide a microdata approach where the impact of a R&D unit in total cost of production in India is analyzed.