If the Prospect of Some Occupations Are Stagnating With Technological Advancement? A Task Attribute Approach to Detect Employment Vulnerability

Two distinct trends can prove the existence of technological unemployment in the US. First, there are more open jobs than the number of unemployed persons looking for a job, and second, the shift of the Beveridge curve. There have been many attempts to find the cause of technological unemployment. However, all of these approaches fail when it comes to evaluating the impact of modern technologies on employment future. This study hypothesizes that rather than looking into skill requirement or routine non-routine discrimination of tasks, a holistic approach is required to predict which occupations are going to be vulnerable with the advent of this 4th industrial revolution, i.e., widespread application of AI, ML algorithms, and Robotics. Three critical attributes are considered: bottleneck, hazardous, and routine. Forty-five relevant attributes are chosen from the O*NET database that can define these three types of tasks. Performing Principal Axis Factor Analysis, and K-medoid clustering, the study discovers a list of 367 vulnerable occupations. The study further analyzes the last nine years of national employment data and finds that over the previous four years, the growth of vulnerable occupations is only half than that of non-vulnerable ones despite the long rally of economic expansion.