Skilled Labor, Unskilled Labor, and Experience Over Time

2016 ◽  
Author(s):  
Francesco Caselli
Keyword(s):  
Technical Change ◽  
The United States ◽  
Elasticity Of Substitution ◽  
Relative Wage ◽  
Unskilled Labor ◽  
Skilled Workers ◽  
Skill Bias ◽  
Unskilled Workers ◽  
Joint Behavior ◽  
Over Time

This chapter examines what the joint behavior of relative wage and relative supply reveal about the underlying changes in technology, with a focus on the United States. It distinguishes workers by two characteristics: skill and experience. It classifies the labor force into four kinds of workers: experienced skilled workers, inexperienced skilled workers, experienced unskilled workers, and inexperienced unskilled workers. The equation takes into account the quantities of unskilled inexperienced inputs, unskilled experienced inputs, skilled inexperienced inputs, and skilled experienced inputs, as well as the elasticity of substitution between unskilled inexperienced and unskilled experienced workers, and skilled inexperienced and skilled experienced ones. The results confirm many previous findings of a significant skill bias in technical change between 1960 and 2010, and also reveal an experience bias in technical change over roughly the same period, especially among skilled workers and since the 1980s.

Skills and Capital Over Time and Across Countries

2016 ◽  
Author(s):  
Francesco Caselli
Keyword(s):  
Technical Change ◽  
Relative Efficiency ◽  
The United States ◽  
Elasticity Of Substitution ◽  
Positive Trend ◽  
Technology Choice ◽  
Skilled Workers ◽  
Skill Bias ◽  
Unskilled Workers ◽  
Using Data

This chapter examines whether the trends in skill bias observed in the United States are common to other economies and extends the time series analysis to include capital. It first estimates, for each country, the skill bias from data on the relative supply of skills and the relative wages of skilled workers before constructing labor supply in units of equivalent unskilled workers. Finally, it calculates the augmentation coefficients using data on overall labor and capital shares. The results show that skill-biased technical change is a remarkably global phenomenon and that every country registers a positive trend in the relative efficiency of skilled labor. When the elasticity of substitution between two inputs is less than 1, technology choice shifts toward the input that becomes more scarce.

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.

Technology Differences over Space and Time

2016 ◽  
Author(s):  
Francesco Caselli
Keyword(s):  
Natural Resources ◽  
Technical Change ◽  
Technology Choice ◽  
Skilled Workers ◽  
Theoretical Understanding ◽  
Production Factors ◽  
Unskilled Workers ◽  
Flexible Framework ◽  
Rich Countries ◽  
Over Time

This book examines how countries use their productive resources—such as workers, skills, equipment and structures, and natural resources. It develops methods to assess the efficiency with which productive inputs are used, and how these efficiencies vary across countries and over time. The book finds that richer countries use skilled workers relatively more efficiently than unskilled workers, and equipment and structures relatively more efficiently than natural resources. They also are relatively more efficient users of labor than of capital. Technological change tends to make countries particularly efficient at using skills and less efficient at using capital. Technical change also favors experienced workers. In order to interpret and understand these findings, the book presents a theory of technology choice. In this theory, firms pick technologies that make the most efficient use of the most abundant production factors when these factors are good substitutes for the less abundant factors. Firms pick technologies that make the most of less abundant factors when other suitable factors are not available for substitution. For example, rich countries, where skilled workers are abundant, use skilled workers efficiently, as these are good substitutes for unskilled workers. This flexible framework can be applied to other pairs of inputs, over time, and across countries. The book has significant implications not only for the theoretical understanding of development and technological innovation, but also for government formulation of industrial policy and multinationals making decisions about what to invest in and where to make those investments.

DIRECTED STRUCTURAL CHANGE

2017 ◽  
Vol 23 (5) ◽  
pp. 1921-1958
Author(s):  
Robert F. Kane
Keyword(s):  
Structural Change ◽  
Technical Progress ◽  
Technical Change ◽  
Relative Wage ◽  
Skilled Labor ◽  
Unskilled Labor ◽  
Directed Technical Change ◽  
Unskilled Workers ◽  
The Stability ◽  
The Relationship

This paper extends the existing theories of directed technical change by allowing the factors of production, skilled, and unskilled workers, to be employed in both the skill-intensive and unskilled-intensive sectors. Consequently, the direction of technical progress and the sectoral allocation of factors are jointly determined. The feedback between technical progress and the allocation of factors leads to new results concerning structural change and directed technical change. An increase in the endowment of a factor leads to a dynamic reallocation of factors toward the sector that uses the factor intensively. The reallocation of factors also affects the stability properties of directed technical change. When the parameter conditions necessary for strong bias are satisfied, the interior regime (nonspecialization) is at most locally stable. More importantly, if the relative endowment of skilled labor becomes too high (low), the economy necessarily specializes in the production of skilled (unskilled)-labor-intensive goods. Last, the relationship between the relative endowment of skilled labor and the steady-state relative wage rate is not necessarily monotonic.

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 The World Technology Frontier

2006 ◽  
Vol 96 (3) ◽  
pp. 499-522 ◽  
Author(s):  
Francesco Caselli ◽  
Wilbur John Coleman
Keyword(s):  
Skilled Labor ◽  
Simple Explanation ◽  
Unskilled Labor ◽  
Skilled Workers ◽  
Poor Countries ◽  
Aggregate Production Function ◽  
Unskilled Workers ◽  
Cross Country ◽  
Rich Countries ◽  
Lower Income Countries

We study cross-country differences in the aggregate production function when skilled and unskilled labor are imperfect substitutes. We find that there is a skill bias in cross-country technology differences. Higher-income countries use skilled labor more efficiently than lower-income countries, while they use unskilled labor relatively and, possibly, absolutely less efficiently. We also propose a simple explanation for our findings: rich countries, which are skilled-labor abundant, choose technologies that are best suited to skilled workers; poor countries, which are unskilled-labor abundant, choose technologies more appropriate to unskilled workers. We discuss alternative explanations, such as capital-skill complementarity and differences in schooling quality.

scholarly journals It's the Market: The Broad-Based Rise in the Return to Top Talent

2013 ◽  
Vol 27 (3) ◽  
pp. 35-56 ◽  
Author(s):  
Steven N Kaplan ◽  
Joshua Rauh
Keyword(s):  
Social Norms ◽  
Technical Change ◽  
The United States ◽  
Income Levels ◽  
Income Share ◽  
Managerial Power ◽  
The Us ◽  
Biased Technological Change ◽  
To Receive ◽  
Over Time

One explanation that has been proposed for rising inequality is that technical change allows highly talented individuals, or “superstars” to manage or perform on a larger scale, applying their talent to greater pools of resources and reaching larger numbers of people, thus becoming more productive and higher paid. Others argue that managerial power has increased in a way that allows those at the top to receive higher pay, that social norms against higher pay levels have broken down, or that tax policy affects the distribution of surpluses between employers and employees. We offer evidence bearing on the different theories explaining the rise in inequality in the United States over recent decades. First we look the increase in pay at the highest income levels across occupations. We consider the income share of the top 1 percent over time. And we turn to evidence on inequality of wealth at the top. In looking at the wealthiest Americans, we find that those in the Forbes 400 are less likely to have inherited their wealth or to have grown up wealthy. The Forbes 400 of today also are those who were able to access education while young and apply their skills to the most scalable industries: technology, finance, and mass retail. We believe that the US evidence on income and wealth shares for the top 1 percent is most consistent with a “superstar”-style explanation rooted in the importance of scale and skill-biased technological change. It is less consistent with an argument that the gains to the top 1 percent are rooted in greater managerial power or changes in social norms about what managers should earn.

scholarly journals Emigration, Immigration, and Skill Formation: The Case of a Midstream Country

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Kenji Kondoh
Keyword(s):  
Immigration Policy ◽  
Wage Rate ◽  
Income Tax ◽  
National Income ◽  
Developed Countries ◽  
Unskilled Labor ◽  
Small Country ◽  
Skilled Workers ◽  
Skill Formation ◽  
Unskilled Workers

This study theoretically investigates the economy of a small country that exports skilled labor to higher developed countries and simultaneously imports unskilled labor from lower developed countries. Compared with the free immigration case, if this country adopts an optimally controlled immigration policy by imposing income tax on immigrants to maximize national income, skills formation is negatively affected and the number of domestic unskilled workers increases. Moreover, under certain conditions, we can assert the counter-intuitive possibility that the wage rate of domestic unskilled workers may decrease but that of skilled workers may increase owing to the restriction of foreign unskilled workers.

scholarly journals Unpacking Skill Bias: Automation and New Tasks

2020 ◽  
Vol 110 ◽  
pp. 356-361
Author(s):  
Daron Acemoglu ◽  
Pascual Restrepo
Keyword(s):  
Technical Change ◽  
Canonical Model ◽  
Skill Premium ◽  
Real Wages ◽  
Skill Bias ◽  
The Past ◽  
Level Data ◽  
Unskilled Workers ◽  
Productivity Gains ◽  
Industry Level Data

We extend the canonical model of skill-biased technical change by modeling the allocation of tasks to factors and allowing for automation and the creation of new tasks. In our model, factor prices depend on the set of tasks they perform. Automation can reduce real wages and generate sizable changes in inequality associated with small productivity gains. New tasks can increase or reduce inequality depending on whether they are performed by skilled or unskilled workers. Industry-level data suggest that automation significantly contributed to the rising skill premium, while new tasks reduced inequality in the past but have contributed to inequality recently.

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