scholarly journals Unpacking Skill Bias: Automation and New Tasks

2020 ◽  
Author(s):  
Daron Acemoglu ◽  
Pascual Restrepo
Keyword(s):  
Skill Bias

Conclusions

2016 ◽  
Author(s):  
Francesco Caselli
Keyword(s):  
Technical Change ◽  
Unskilled Labor ◽  
Skill Bias ◽  
Skilled And Unskilled Labor ◽  
Over Time

This chapter concludes that the book has presented evidence showing that technology and technical change are more flexible than generally allowed. The efficiency of different factors changes across countries and over time at different rates. Indeed, in some instances the efficiency with which one factor is used can decline while the efficiency of others increases. Since the 1990s, it has been increasingly clear that technical change tends to have a skill bias, but this book's findings reveal that nonneutralities are much more pervasive than that. They also occur across countries, and not just over time. Furthermore, they invest a broader set of inputs: not only skilled and unskilled labor, but also experienced and inexperienced workers, natural and reproducible capital, and a broad labor aggregate and a broad capital aggregate. The book has merely scratched the surface of the likely patterns of nonneutrality that exist across countries and over time.

Skilled and Unskilled Labor

2016 ◽  
Author(s):  
Francesco Caselli
Keyword(s):  
Relative Efficiency ◽  
Wage Rate ◽  
School Quality ◽  
Elasticity Of Substitution ◽  
Perfect Competition ◽  
Relative Wage ◽  
Unskilled Labor ◽  
Skilled Worker ◽  
Skill Bias ◽  
Skilled And Unskilled Labor

This chapter examines how skilled and unskilled labor vary across countries by taking into account the wage rate for skilled labor and the wage rate for unskilled labor, based on the assumption that labor markets approximate conditions of perfect competition. The equation to be used implies that the relative wage of a skilled worker is decreasing with the relative supply of skills. However, for a given supply of skills the relative wage also depends on the relative efficiency with which skills are used. The chapter first estimates the skill bias, the relative supply of skills, and the skill premium before deriving a calibrated value for the elasticity of substitution. It then presents the key empirical results for the skill bias in technology across countries and goes on to discuss alternative skill thresholds. It also considers the implications of differences in school quality and the implications of capital–skill complementarity.

Brain Drain from Israel and Skill-Biased Global Immigration Policy

2018 ◽  
Author(s):  
Assaf Razin
Keyword(s):  
United States ◽  
Welfare State ◽  
The United States ◽  
Welfare States ◽  
University Graduates ◽  
Skill Bias ◽  
Skilled Immigrants ◽  
The World ◽  
Per Capita ◽  
Generous Welfare

A more generous welfare state (particularly with an aging population) has financing needs that immigrants could fill. With high-skilled immigrants more likely to pay in rather than draw on the welfare state, more generous welfare states are more inclined to try to attract high skilled. Israel ranks third in the world in the number of university graduates per capita, after the United States and the Netherlands. It possesses the highest per capita number of scientists in the world, with 135 for every 10,000 citizens (compared to 85 per 10,000 in the United States). Israelis took full advantage of the immigration skill bias. When examining the index for educated émigrés, i.e., those with a college degree, the average index is 12.41 and Israel's index is more than three times higher, 41.45. Using this index, Israel is higher than Portugal and the gap between Israel and Ireland (49.09) narrows considerably.

The Skill Bias of the US Trade Deficit

2017 ◽  
pp. 197-221 ◽  
Author(s):  
Rosario Crinò ◽  
Paolo Epifani
Keyword(s):  
Trade Deficit ◽  
Skill Bias ◽  
The Us

scholarly journals Why Has Urban Inequality Increased?

2018 ◽  
Vol 10 (4) ◽  
pp. 1-42 ◽  
Author(s):  
Nathaniel Baum-Snow ◽  
Matthew Freedman ◽  
Ronni Pavan
Keyword(s):  
Production Function ◽  
Wage Inequality ◽  
Strong Evidence ◽  
Technical Change ◽  
Agglomeration Economies ◽  
Unskilled Labor ◽  
Skill Bias ◽  
Urban Inequality

This paper examines mechanisms driving the more rapid increases in wage inequality in larger cities between 1980 and 2007. Production function estimates indicate strong evidence of capital–skill complementarity and increases in the skill bias of agglomeration economies in the context of rapid skill-biased technical change. Immigration shocks are the source of identifying variation across cities in changes to the relative supply of skilled versus unskilled labor. Estimates indicate that changes in the factor biases of agglomeration economies rationalize at least 80 percent of the more rapid increases in wage inequality in larger cities. (JEL J24, J31, O33, R23)

scholarly journals Taking the skill bias out of global migration

2020 ◽  
Vol 142 ◽  
pp. 102317 ◽  
Author(s):  
Costanza Biavaschi ◽  
Michał Burzyński ◽  
Benjamin Elsner ◽  
Joël Machado
Keyword(s):  
Skill Bias ◽  
Global Migration

Do Urban Agglomeration Effects and Household Amenities have a Skill Bias?*

2004 ◽  
Vol 44 (2) ◽  
pp. 201-224 ◽  
Author(s):  
Dwight W. Adamson ◽  
David E. Clark ◽  
Mark D. Partridge
Keyword(s):  
Urban Agglomeration ◽  
Skill Bias ◽  
Agglomeration Effects
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