Should Governments Tax Companies’ Use of Robots? Automated Workers, Technological Unemployment, and Wage Inequality

2019 ◽  
Vol 14 (2) ◽  
pp. 64 ◽  
Keyword(s):  
Wage Inequality ◽  
Technological Unemployment

Is technological change a devourer of jobs?

2017 ◽  
pp. 111-140 ◽  
Author(s):  
R. Kapeliushnikov
Keyword(s):  
Technological Change ◽  
Critical Analysis ◽  
New Technologies ◽  
Employment Growth ◽  
Foreseeable Future ◽  
Employment Effects ◽  
Technological Unemployment ◽  
Micro Level ◽  
Task Sets ◽  
The Impact

The paper provides a critical analysis of the idea of technological unemployment. The overview of the existing literature on the employment effects of technological change shows that on the micro-level there exists strong and positive relationship between innovations and employment growth in firms; on the sectoral level this correlation becomes ambiguous; on the macro-level the impact of new technologies seems to be positive or neutral. This implies that fears of explosive growth of technological unemployment in the foreseeable future are exaggerated. Our analysis further suggests that new technologies affect mostly the structure of employment rather than its level. Additionally we argue that automation and digitalisation would change mostly task sets within particular occupations rather than distribution of workers by occupations.

Econometric Analysis of Returns to Education of Rural and Urban Labour Markets Workers in Tamil Nadu

2020 ◽  
pp. 336-344
Keyword(s):  
Wage Inequality ◽  
Tamil Nadu ◽  
Returns To Education ◽  
Labour Markets ◽  
Wage Distribution ◽  
Rural Market ◽  
Rural And Urban ◽  
Rate Of Returns ◽  
Education Groups ◽  
Quantile Regression Method

The objective of this study was to empirically evaluate the returns to education of rural and urban labour markets workers in Tamil Nadu using the IHDS data with appropriate Econometric models. First, the present study estimated the earning functions of the rural and urban market's workers by OLS technique and standard Mincerian earning functions. Secondly, the quantile regression method was also used to examine the evolution of wage inequality. The findings of the study showed that the effects of education and experience on the log of hourly wages were positive, and these coefficients were statistically significant. The returns to education increased with the level of education and differed among the workers of rural and urban labour markets. The results showed that the rates of returns to primary, middle and higher secondary were higher in the urban market, whereas those of secondary and graduation were higher in the rural market. The study revealed that the effect of education was not the same across the rural and urban wage distribution. The rate of returns differed considerably within education groups across different quantiles of the wage distribution.

The Impact of International Trade on Wage Inequality in Argentina (1998-2006)

2010 ◽  
Author(s):  
Claudio Fernández Macor ◽  
Néstor Perticarari ◽  
Carlos Beltrán
Keyword(s):  
International Trade ◽  
Wage Inequality ◽  
The Impact
Sign in / Sign up

Export Citation Format

Share Document