1
Skill-Biased Technical Change and Rising Wage Inequality
Student Name
Date
2
Introduction
Technological changes commonly bring about considerable changes in different dimensions of
the business world. From a Human Resource Management perspective, it is arguable that one of
the main impacts of Technological change is that it has resulted in the polarization of labour
markets (Violante, 2008). This is especially in terms of skill structure and wages. The theoretical
underpinnings of the impact of technological changes on workforce wages has been significantly
explored, especially since the 1980s. Different researchers and authors have presented different
perspectives and suppositions on the “Skill-biased technical change” (SBTC) hypothesis for
rising wage inequity (Esposito, and Stehrer, 2009). This paper seeks to explore how two different
paper explored the theory and hypothesis of SBTC and rising wage inequality. The two papers
that are explored in the paper are Katz and Murphy (1992) “Changes in Relative Wages, 19631987: Supply and Demand Factors”, and Michaels, Natraj, and Reenen, (2010) “Has ICT
polarised skilled demand: Evidence from eleven countries over 25 years”. The paper discusses
the approaches used by the authors in exploring the SBTC hypothesis for rising wage inequity,
and how their findings adds to existing debate on the topic.
Research Approach
Understanding the approach used by Katz and Murphy (1992), and Michaels, Natraj, and
Reenen, (2010) is important because it fosters the understanding of the difference in the outcome
of the research conducted by the authors. It is also important to know and understand the
approach used by the researchers because it is what influenced the research methods that they
used. In terms of approach, Katz and Murphy (1992) focused on exploring the hypothesis from
the perspective of demand and supply of a polarized job market. To this end, Katz and Murphy
3
(1992) explored wages inequality from the perspective of existing segmentation in the labour
market. Examples of the segmentation include skilled, and unskilled labour, as well as labour
based on age, gender, and race. Using labour market segmentation, Katz and Murphy (1992)
sought to explain the exact nature of wage inequality. Their approach was holistic in that it was
able to identify and explain the specific factors that prompt the wage inequality. For instance,
they were able to offer evidence that wage inequality in terms of gender and race was becoming
narrower. This is while wage inequality based on skill structure was becoming wider as a result
of technological changes. This was based on the data that they collected from various industries
and sectors. Their study also approached the issue from the perspective of the weekly wages that
full-time employees received and how this was linked to their skill levels. They also explored the
topic from the perspective of the amount of hours that the employees worked. It was using this
approach that the researchers were able to arrive at their outcomes.
Michaels, Natraj, and Reenen, (2010) used an approach that was dissimilar from that of Katz and
Murphy (1992). This is because their approach focused on exploring SBTC and its link to rising
wage inequality from the perspective of differences in tasks and roles performed by different
employees, and how these specific roles and tasks are linked to their levels of education. This
approach categorised tasks into two main types of tasks, which are, routine tasks, and nonroutine tasks which were analytical in nature. According to the approach taken by Michaels,
Natraj, and Reenen, (2010), routine tasks, for example work done in assembly lines, was done by
employees who were either unskilled, or semi-skilled. This is while the analytical non-routine
tasks, were performed by highly skilled laborers. According to Michaels, Natraj, and Reenen,
(2010), in the very least, the different types of tasks required different levels of efforts, skills, and
4
academic qualification and this justified the gap in the wages of skilled and unskilled laborers.
Michaels, Natraj, and Reenen, (2010) add that technological change brought an increase in the
availability of, and demand for tasks performed by skilled laborers. This in turn encouraged the
workforce to pursue high level education. Further incentive for high level skills in technological
skills which were fast becoming dominant in the 1980s through to the 2000s, was offering high
level payment and better working conditions for the skilled employees. According to Michaels,
Natraj, and Reenen, (2010), this is why the wage inequality continues to rise as a result of
technological changes. From a general perspective, it can be said that the approach taken by
Michaels, Natraj, and Reenen, (2010) considered the relationship between education, type of
work, and wages paid to the employees. This is while Katz and Murphy (1992) approached the
topic from a supply demand perspective of the availability of skilled labor.
Research Methods
When it comes to methodology, Katz and Murphy (1992) analysed information that they
collected from a series of 25 consecutive March Current Population Surveys (CPSs). This
included information on wage variation of different employees based on their gender, skills, age,
and race. They evaluated information that had been collected over a 20 year period, from 1967 to
1987. The authors were able to find that the most variation was experienced in the 1980s. This is
when the inequality that traditionally existed based on gender and race, was slowly narrowing.
This is while the inequality that was based on skills, more so technological skills, was widening
exponentially. To explain both the narrowing and the deepening wage inequality, Katz and
Murphy (1992) used a standard Mincerian augmented human capital regression. Through this
mathematical model, they were able to identify and explain the difference in wages in terms of
5
number of hours worked, by different kinds of labourers. The 25 consecutive CPS was used by
Katz and Murphy (1992) as a time series technique of econometric analysis, so that the authors
would be able to make comparison of wage data across a relevant period of time, thus identifying
impact of technological change on working hours, as well as wages.
Michaels, Natraj, and Reenen, (2010) used emperical based research methodology to investigate
how technological changes impacted on rising wage inequality. The main source of information
that was collected and analysed by Michaels, Natraj, and Reenen, (2010) was from EUKLEMS
database. The collected information mainly pertained to the difference in the wages that the
employees received based on their skill level. The skill level that they explored was based on the
level of education that the workers had attained. Essentially, unlike many other authors including
Katz and Murphy (1992), Michaels, Natraj, and Reenen, (2010) classified education level of the
employees into three levels. Katz and Murphy (1992) and most authors commonly used skilled
(educated) and unskilled (under-educated, or not educated), whereas Michaels, Natraj, and
Reenen (2010) classified workforce education level into the educated, semi-educated and highly
educated employees.
Michaels, Natraj, and Reenen, (2010) selected and used data from the EUKLEMS database
because the data in the database is constructed using information from the National Statistical
Office of different countries, including data on ICT capitol and the world’s most developed
economies as well as other OECD countries. The information has also been intergreted to each
country’s national accounts. The data in the dataset is from 1980-2004, with 1980 as the baseline
year because the inception of computerised technologies and foundation for constant
6
technological changes can trace their roots back to that year. The methodology used by
Michaels, Natraj, and Reenen (2010) follows an emperical strategy that explores how a rapid fall
in quality-adjusted ICT prices impacts on different industry pairs that rely on ICT.
Research Results, and Discussions
In their research findings, Katz and Murphy (1992) asserted that from the 1980s, considerable
technological change was experienced. The impact of the technological change is that it changed
the dynamics of supply and demand in the labour market. The explanation that they offer is that
technological changes required skilled workforce. Brasch, (2016) concurs with Katz and Murphy
(1992) and explains that ever since the industrial revolution, industry had always been the main
source of employment. Most of the work done in the industries was assembly line work, and
other tasks and roles that could be performed with skilled, as well as unskilled labourers
(Bogliacino, & Lucchese, 2016). Technological changes changed all this, as it resulted in
increase in the demand for skilled employees, and reduced demand for the unskilled labourers.
The number of skilled labourers could not successfully fill the existing demand. As a result,
employers started paying the skilled labourers higher wages, as well as make working
environment conducive for them so that they can be able to attract and retain the skilled
labourers (Okazawa, 2013). Inexorably, this resulted in growing wage inequality in terms of the
skills of the labourers.
Katz and Murphy (1992) further explored existing wage inequality in terms of other factors such
as age, gender and race. The outcome that the two arrived at is that older workers had more
experience and expertise and as such were accorded with wage increment. Wage increment was
7
also accrued to young employees who were skilled, and at the same time, the unskilled workers
experienced stagnated or even declined wages. This is what according to Katz and Murphy
(1992) set up the burgeoning wage inequality.
It is of note that authors like Card and DiNardo, (2002) do not agree with all of the assertions
that have been made by Katz and Murphy (1992). For instance, in their article, they assert that
wage inequality stabilised in the 1990s, and this was regardless of on-going technological
changes. The authors however do agree that wage inequality in terms of gender was slowly
closing, while the wage gap in terms of race stabilized. Using the assertions made by Katz and
Murphy (1992), Card and DiNardo, (2002) argued that wage inequality was not widening as
much as has been postulated because older more experienced employees continued to receive
pay rise, similar to the young educated employees. What the authors conclude is that their does
exist a wage gap, but it cannot be fully explained by the SBTC hypothesis according to what
Katz and Murphy (1992) presented. The findings of Taniguchi and Yamada, (2019) have
however been disputed by several other studies and evidence, for instance, the one presented by
Michaels, Natraj, and Reenen, (2010).
Michaels, Natraj, and Reenen, (2010) discovered that what technological changes did is that it
substituted capital labour with computerised technologies. The connotation of this is that demand
for unskilled labour unavoidably went done since computerisation made the work done by
unskilled labour easier. Behar, (2016) supports this assertion by explaining that computerisation
enhanced automation and it ensured that automation was spread to other industries and sectors
beyond manufacturing. This therefore meant that demand for unskilled labour declined sharply,
8
especially because the technological changes offered cheaper costs of operations and services to
organisations. Michaels, Natraj, and Reenen, (2010) explained in their findings that education is
associated with skill and that it is a significant bias factor based on the specific role that is played
by different employees. According to the authors, employees with technological skills accrued
the organisations that they work for with competitive advantages, and as a result the
organisations offered the employees with better wages. At the same time, demand for unskilled
labourers was sharply declining, to discourage the unskilled labourers, employers reduced the
amount of wages paid to the unskilled labourers (Hühne, and Herzer, 2017). Evidently this meant
a growing wage gap in regards to skill level. As already mentioned, there is a wage gap when it
comes to skill level, and that the skill levels are associated with education levels. Goos,
Manning, and Salomons (2014) points out that the link between education and skill is the main
reason for continued wage inequality.
Conclusion
From the above discussions, it can be concluded that both Katz and Murphy (1992), and
Michaels, Natraj, and Reenen, (2010) acknowledge that technological changes favours skilled
labour. Organisations are willing to pay high wages to skilled labourers who are able to accord
them with competitive advantages. This coupled with the declining demand for unskilled labour
is what explains the growing wage gap between the skilled and unskilled workers. The main
difference between what Katz and Murphy (1992), and Michaels, Natraj, and Reenen, (2010)
presented in their studies is that Katz and Murphy (1992) explored more of the supply and
demand factors associated with technological skills and wages. This is while Michaels, Natraj,
and Reenen, (2010) focused more on explaining how influenced by technological changes,
9
various tasks demanded more of skilled workers, and less of unskilled workers, thus influenced
by educational and skills level, the wage inequality widened.
10
References
Behar, A. (2016). The endogenous skill bias of technical change and wage inequality in
developing countries. The Journal of International Trade & Economic Development. Volume 25,
Issue 8, Pages 1101-1121. https://doi.org/10.1080/09638199.2016.1193887.
Bogliacino, F. and Lucchese, M. (2016) ‘Endogenous skill biased technical change: testing for
demand pull effect’, Industrial & Corporate Change, 25(2), pp. 227–243. Doi:
10.1093/icc/dtv010.
Brasch, T. (2016) ‘Identifying the sector bias of technical change’, Empirical Economics, 50(2),
pp. 595–621. doi: 10.1007/s00181-015-0938-7.
Card, D., and DiNardo, J. E. (2002). Skill Biased Technological Change and Rising Wage
Inequality: Some Problems and Puzzles. NBER Working Paper No. 8769 v20, 733-783. (DOI):
10.3386/w8769.
Esposito, P. and Stehrer, R. (2009) ‘The sector bias of skill-biased technical change and the
rising skill premium in transition economies’, Empirica, 36(3), pp. 351–364. doi:
10.1007/s10663-008-9097-9.
Goos, M., Manning, A., and Salomons, A. (2014). Explaining Job Polarization: Routine-Biased
Technological Change and Offshoring. American Economic Review 104(8), pp. 2509-2526. DOI:
10.1257/aer.104.8.2509.
Hühne, P. and Herzer, D. (2017) ‘Is inequality an inevitable by-product of skill-biased technical
change?’ Applied Economics Letters, 24(18), pp. 1346–1350. Doi:
10.1080/13504851.2017.1279259.
11
Jones, J. B. and Yang, F. (2016) ‘Skill-biased technical change and the cost of higher education’,
Journal of Labor Economics, (3), p. 621-663.
Katz, L. F., and Murphy, K. M. (1992). Changes in Relative Wages, 1963-1987: Supply and
Demand Factors. The Quarterly Journal of Economics, Vol. 107, No. 1., pp. 35-78.
http://links.jstor.org/sici?sici=00335533%28199202%29107%3A1%3C35%3ACIRW1S%3E2.0.
CO%3B2-O.
Michaels, G., Natraj, A., and Reenen, J. V. (2010). Has ICT Polarized Skill Demand? Evidence
from Eleven Countries over 25 years. Review of Economics and Statistics. Vol. 96, No. 1, Pages
60-77.
Okazawa, R. (2013) ‘Skill-biased technical change, educational choice, and labor market
polarization: the U.S. versus Europe’, Journal of Economic Inequality, 11(3), pp. 321–342. doi:
10.1007/s10888-012-9223-6.
Taniguchi, H., and Yamada, K. (2019). ICT Capital-Skill Complementarity and Wage
Inequality: Evidence from Fourteen OECD Countries. The Economic Journal.
Violante, G. L. (2008). Skill-Biased Technical Change. In G. L. Violante, The New Palgrave
Dictionary of Economics (pp. pp.1-6. DOI: 10.1057/978-1-349-95121-5_2388-1). New York:
Palgrave .