By Theo Anthony. Edited by Arjun Chandrasekar.
Overview
In the present-day workforce, many industries are implementing redundancies of unskilled workers, mostly due to automation; in fact, a study conducted by Professor Carl Frey at Oxford University found that there is a negative correlation between the skill level of a particular job and it is susceptibility to automation. Consequently, redundancies of particular jobs mean the demand is almost negligible, and hence, no return on skills; therefore, wage inequality increases.
First, the UK medical industry is being faced with an 18% probability of automation of higher-skilled jobs, such as medical practitioners, but, over 50% increase in automation portability for unskilled jobs such as care-workers.
Second, in the United States, 90% of the median annual salaries of the top 30 fastest declining occupations represent the top salaries of unskilled workers in the United States. These include, but are not limited to, travel agents, postal service mail sorters, telephone operators. Almost all of the top 30 fastest declining U.S. jobs are declining due to rising automation within these particular industries. For example, travel agents are being outsourced due to notable travel websites, such as Airbnb, which saw a growth rate of 153% between 2009 and 2019.
The demand for different types of workers, skilled and unskilled, varies greatly. If certain skills are somewhat rare and heavily in demand in a particular region, then the return will be high, and as a result, inequality will be high. For example, California is one of the U.S. states where registered nurses have a projected salary growth of 10.44% over the next ten years. Whereas jobs in California’s fast food industry grew over 16% in size between April and December of 2020, but the salaries of jobs in the fast-food industry are ranked the fifth-lowest in the state.
Additionally, noticeable wage discrepancies have resulted from a prominent labor market failure; discrimination. Across the 22 OECD (Organization for Economic Co-operation and Development) countries that participated in PIAAC (Program for the International Assessment of Adult Competencies), wages are 18% higher for men than for women; 36% higher for older workers (aged 50–56) than for younger ones (aged 16–29); 20% higher for workers with at least one parent with higher education than for those whose parents have only a lower secondary education; and 15% higher for workers who are native-born than for those who are foreign-born. Recent analysis shows that differences in skills can account, on average, for 83% of the wage gap between workers with different levels of parental education and 72% of the wage gap between native- and foreign-born workers. By contrast, skills explain a much smaller (but still substantial) part of the wage gap between men and women (23%)—suggesting that factors others than skills are responsible for the bulk of gender wage disparities.
Discrimination has long been a labor market failure; by examining data from three periods – 1979-81, 1989-91, 1997-99 – to construct estimates of mean log wages relative to the years of experience for workers with either a high school or college degree. The results are shown in Figures 3 and 4. 3 shows the experience profiles for men with 12 years of education in the 3 time periods.
Figure 3:
For women with a high school education (Figure 4), the experience profile shifted downward for younger workers over the 1980s, leading to a rise in the conventional return to experience, especially for women with 10 to 20 years of experience (i.e., those ages 28 to 38). The experience profiles for college-educated women in Figure 4 also show a rise in returns to experience, although for the college group most of the gain was concentrated among women with under 10 years of experience.
These documented profiles outline the clear history of gender disparity between male and female workers. This again proves that in light of technological change – heightened in the period 1997-99 during the peak of the internet bubble – other factors, such as discrimination, have led to high wage inequality.
Figure 4:
Lastly, another factor that differentiates wage inequality among countries is development. More specifically, in terms of human development. A study conducted by Arzu Alvan, of Eastern Mediterranean University, concluded that “income distribution tends to be fairer if HDI is greater than or equal to 0.80 together with high levels of GDP and per capita income.”
In addition, the quality of education systems varies so much across countries, that a year of education in one country may produce a very different amount of skill than a year of education in another country. The relative human development, in terms of education, evidently has a noticeable effect on wage inequality, as seen in Figure 5.
Figure 5:
Conclusion
In conclusion, I disagree that technological change means that the wage gap between the skilled and unskilled will simply keep growing. I believe that the many factors such as differences in human development within different countries, and the fact that supply and demand of different jobs, which seems to exhibit a somewhat stochastic nature, overshadow the continuous growth that society has seen with technology. The sheer number of factors that influence the wages of skilled or unskilled outweigh the standalone technological influence. I also believe that technological change may soon enough expand job opportunities to unskilled professions, in the same way, that advances in technology have delivered opportunities to skilled workers!
