Technological Displacement of Labor and Technological Unemployment

1933 ◽  
Vol 28 (181) ◽  
pp. 42
Author(s):  
Boris Stern
Keyword(s):  
Technological Unemployment ◽  
Technological Displacement

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.

Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

2021 ◽  
Vol 12 (4) ◽  
pp. 35-42
Author(s):  
Thomas Alan Woolman ◽  
Philip Lee
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Systems ◽  
Digital Communication ◽  
The United States ◽  
Learning Technologies ◽  
Technological Unemployment ◽  
Current State ◽  
Challenges And Opportunities ◽  
Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

Two sides, but not of the same coin: digitalization, productivity and unemployment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oxana Krutova ◽  
Pertti Koistinen ◽  
Tuuli Turja ◽  
Harri Melin ◽  
Tuomo Särkikoski
Keyword(s):  
Total Factor Productivity ◽  
Job Loss ◽  
Labour Productivity ◽  
Research Problem ◽  
Quality Of Work Life ◽  
Macro Level ◽  
Factor Productivity ◽  
Content Type ◽  
Technological Unemployment ◽  
Factors Of Production

PurposeThis paper aims to examine how input from the digital restructuring of the workplace and productivity affects the risk of job loss and unemployment.Design/methodology/approachRelying on the concepts of technological unemployment and the productivity paradox as well as the theory of skills-biased technological change, the analysis incorporated micro-level individual determinants of job loss, macro-level economic determinants of input and the contribution from traditional (machinery and equipment) vs innovative (ICT) factors of production. The model has been also controlled for “traditional” indicators of “outsiderness” in the labour market. The Quality of Work Life Survey, which is a broad-based national interview survey produced by Statistics Finland, for 2018, the latest year available (N = 4,110) has been used in the analysis. Binomial logistic regression has been applied in order to estimate the effects of individual- and macro-level factors on the risk of job loss.FindingsThe results support arguments for the divergence between effects from labour- vs total-factor productivity on the risks of job loss, as well as the divergence between effects for temporary (layoff) vs permanent job loss (dismissal or unemployment). While the contribution from “traditional” factors of production to labour productivity potentially decreases the risk of permanent job loss, input from “innovative” factors of production on total-factor productivity potentially causes adverse effects (e.g. growing risks of permanent job loss).Originality/valueThe paper contributes to the theoretical discussion about technological unemployment and productivity by means of including two different concepts into a single econometric model, thus enabling examination of the research problem in an innovative way.

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