International Journal of Technology 13(7) 1548-1557 (2022)

Received October 2022 / Revised November 2022 / Accepted December 2022

International Journal of Technology

http://ijtech.eng.ui.ac.id

Analyzing The Systemic Impact of Information Technology Development

Dynamics on Labor Market Transformation

Dmitry Rodionov1, Anastasia Gracheva1, Evgenii Konnikov1, Olga Konnikova2,

Darya Kryzhko1*
1Graduate School of Economics and Technologies, Peter the Great St. Petersburg Polytechnic University,
Saint Petersburg, 195251, Russia
2Department of Marketing, Saint-Petersburg State University of Economics, Saint Petersburg, 191023,

Russia

Abstract. Today, humanity is on the verge of the fourth industrial revolution. This can result in a
radical transformation of all aspects of society. Information technology is the core of the fourth
industrial revolution. The application variety of modern information technologies determines the
infinite vectors of their use, which ultimately become the overwhelming number of instruments for
life simplifing. Professional activity sphere is also being transformed under the influence of
information technology development. However, this transformation process is extremely
ambiguous. In connection with this specificity, the purpose of this study is a systematic analysis of
the influence of the information technology development dynamics on the transformation of the
labor market. The hypothesis assumes that there is a relationship between technology development
and changes in the labour market. This research examine digitalization impact on unemployment
level and the process of gradual extinction of certain professions. As the results authors defined
mathematical formalization of the alleged links and formulate the main vectors of labour market
transformation under the digital technologies development.

Keywords: ICT index; Information technology; Innovation index; Labor market; Unemployment
rate

1. Introduction
The labor market is one of the most significant economic institutions. Like any other
sphere, the labor market is constantly going through changes, and the rapid development
of information technology has a direct relation to it (Berawi, 2021). Routine, monotonous
work can be automated. There are many professions that are gradually transforming under
the influence of digital technologies spreading. Also, experts predict that developed
countries are going to lose up to 5 million jobs in the next five years alone due to digital
technology and robotization, and this number will only increase further on (Zaytsev et al.,
2021). RANEPA experts claim that 98% of drivers, 94% of accountants and economists,
72% of movers will be eventually replaced by robots (Semenets, 2019). The purpose of this
paper is to analyze systematically the impact of the dynamics of information technology
development on the transformation of the labor market. The hypothesis assumes that there
*Corresponding author’s email: darya.kryz@yandex.ru, Tel.: +7 (812) 534-73-31; Fax: +7 (812) 534-73-31
doi: 10.14716/ijtech.v13i7.6204
Rodionov et al. 1549

is a systemic dependence between the development of digital technology, its

implementation in various areas and changes in the labor market: unemployment rate,
emergence of new professions, the gradual extinction of certain other professions.
Within the existing theoretical framework, it is possible to single out studies describing
the impact of information and communication technologies (hereinafter called ICT) on
employment at the moment in the world in general. Thus, in the work of Van-Roy et al.
(2018), the authors analyzed the changes in 20 thousand companies in 22 European
countries from 2003 to 2012 and as result showed that the positive impact of innovation
can only be observed in the high and medium technology production sector, while being
insignificant in low technology production and in the service sector. Dengler and Matthes
(2018) came to similar conclusion the study of routine occupations, which could be
replaced by computers or computer-controlled machines, in Germany. Only certain tasks
could be performed by machine labor, not the whole process. The impact of digital
transformation varies from occupation, the authors believe. There are many occupation
what cannot be performed by a computer. The potential to be replaced by a machine is high
in professions that do not require special skills, while this potential is lower in complex
professions. Also, Digilina and Teslenko (2019) concluded that it is inevitable that the labor
market is impacted by the ICT. New digital technologies replace human workers in
production, change the nature of their work and leisure time, distribute working time in a
different way. The authors conclude that the labor market will be affected by digital
technologies, mostly in the high-tech manufacturing sector. However, Garcia-Murillo et al.
(2018) come to slightly different results. Technological changes, in their opinion, will not
necessarily contribute to the transformation of the labor market, because the impact of
technology development, in the long run, is still unknown. The study also arrives at other
interesting conclusions: ICTs have helped move production from high-wage countries to
low-wage countries, and the development of digital networks facilitates labor migration,
putting pressure on wage in middle- and high-income countries, and developing wage
inequalities. The authors also looked at changes in education: new occupations require
higher levels of education, resulting in higher wages. Professions related to science,
engineering, mathematics, and technical fields are expected to see an increase in specialists.
Atalay et al. (2018) agree with the conclusions about income inequality that changes in the
labor market have caused. In a 2018 paper, they analyzed 4.2 million newspaper job ads
job to understand how ICT developments have affected hiring requirements for
recruitment applicants. It was information from newspapers such as the Boston Globe, New
York Times, and Wall Street Journal from 1960 to 2000. The authors came to the following
conclusions: the introduction of new technologies increased the share of non-routine
analytical tasks, which caused income inequality. It has to be noted, however, that ICT
development directions are extremely differentiated and essentially aggregate the totality
of technological solutions, perceived by consumers as an information resource designed to
minimize labor intensity and open new areas of consumption. The article by Dekle (2020)
analyzes Japan, which is an antipode of Russia in terms of automation and technology
implementation. In Japan they are not worried about the fact that robots will cause mass
unemployment, as they believe that robots, on the contrary, help due to the chronic
shortage of workers. The authors noted positive effect of robots on Japanese employment
and on aggregate demand by the data of last 35 years. Thus, we can formulate the following
key conclusion - only monotonous work can be replaced by machine labor. The more
automation increases the wider becomes inequality in wage rates. Moreover creating new
innovation products in technology sphere have contributed the production shift from high-
wage to low-wage countries.
1550 Analyzing The Systemic Impact of Information Technology Development Dynamics on Labor
Market Transformation

Separately, we should consider the studies arguing that the introduction and
proliferation of automated processes will only result in problems, mainly unemployment.
Thus, in an article by Garcia-Murillo (2018), the authors concluded that the current state of
transformation and automation will accelerate in the future. This process is hard to
influence, so the solution is not to resist these changes, but to mitigate the negative
consequences that they may entail. In the article by Digilina and Teslenko (2019), the
authors also concluded that the nature of the labor market will gradually change, which
makes it important at the administrative level to realize this new reality in time and to
neutralize the negative impact by making appropriate managerial decisions. The article by
Zemtsov et al. (2019) analyzed the Russian labor market and concluded which regions of
the country will be more affected by the transition to the digital economy.
In conclusion, we should consider sociological studies that analyze people's attitudes
towards working with robots. The study by Savela et al. (2021) aimed to research the
consequences of introducing robots into the work environment. The participants of the
study were asked to present a hypothetical situation in which they had to work in a team.
The number of robots varied across number of humans included in control group. The
result of the study suggests that when humans are a minority, they feel less comfortable,
which has adverse consequences in communication and productivity.
According to the results of the study of the existing theoretical basis, it can be argued
that the topic of the impact of ICT development on the labor market has been studied
primarily from one angle, while the issue of the impact of certain aspects of ICT
development on the population's perception of available professional development
perspectives, as well as the impact of negative changes in the labor market on the
information environment, haven't been studied as thoroughly. Moreover, the considered
issues were studied in isolation and do not allow forming a holistic understanding, which
is the purpose of this study.

2. Methodology
Based on the results of the theoretical study, a list of systematically related variables
can be compiled. It is assumed that technology development should influence changes in
the labor market. First of all, the ICT development index should be used as a kind of centroid
that can influence on other parameters or depend on them. ICT development is considered
via indicators such as innovation index, an index of government readiness to implement
ICT, government spending on research and development, robotization rate, and ICT
implementation feasibility. Also, in addition to ICT development indicators, the information
environment and its negative tone should be considered. It is assumed that we can measure
the changing of labor market index under the influence of ICT index. Figure 1 presents the
conceptual model of this study.
The most effective tool for testing this model is regression analysis. For the purposes
of the analysis, a sample of 15 countries leading in the ICT index was formed: Japan,
Germany, USA, South Korea, Iceland, Switzerland, Denmark, UK, Netherlands, Hong Kong,
New Zealand, Australia, Singapore, Sweden and Russia. Most of these countries are present
in the top 20 on the ICT index, and while Russia holds a lower position, it is included due to
its significance for the applied interpretation of the results of the study. However, it had
been decided not include more countries with lower ICT index in the sample. The reason is
that there are relatively small share of countries with hight ICT index. Thus, adding
additional data could affect on research results because there is huge difference between
the number of low and hight ICT index countries.
Rodionov et al. 1551

Therefore, in this paper it was decided to focus on the leading countries in terms of
technology development. Let us examine the components of the conceptual model in more
detail. Data on the robotization rate, calculated per 10,000 workers, are aggregated within
the International Federation of Robotics resource.
The source of data on the ICT index is the International Telecommunication Union. This
index characterizes the level of ICT penetration and its uses in a country. This index consists
of several components: ICT accessibility, which accounts for 40% of the index, ICT usage,
40%, and ICT skills, 20%. Accessibility includes such factors as: fixed phone subscriptions
per 100 inhabitants, mobile phone subscriptions per 100 inhabitants, bandwidth (bits per
second) per user, the share of households with computers, the share of households with
internet access. ICT use consists of a proportion of people using the Internet, fixed high-
speed Internet subscriptions per 100 inhabitants, active mobile Internet subscriptions at
256 kbit/second or higher per 100 inhabitants. ICT skills include literacy rate, secondary
and tertiary education enrollment rates.

Figure 1 Conceptual model of the study

The robotization rate is considered in terms of positive or negative population
response (actual and potential representatives of the labor market). While the ICT index
characterizes the level of ICT penetration and application of ICT among the country's
population, which, in turn, determines the level of the population's immersion in the
current technological environment, and the ICT implementation readiness index reflects
the availability of ICT primarily for employers, the robotization rate reflects another unique
aspect – the potential awareness of the need to interact with automated solutions in the
scope of professional activity and the awareness of the decreasing value of low-skilled
labor, or labor that doesn't require competent interaction with ICT. In connection with the
above, we consider the use of this parameter as a separate indicator appropriate, which is
also confirmed by regression analysis.
Oxford Insights is calculated the government AI readiness only since 2017. The index
is calculated based on the digitalization index, the presence of startups associated with
artificial intelligence, the government efficiency index.
The global innovation index is calculated annually since 2011. The index includes many
factors, including the political environment in the country, the availability of a favorable
environment for business, various indicators of education, access to ICT, the degree of
market development, business and government contribution to research. Gross
expenditure on research and development includes the number of researchers, R&D
expenditure as a share of GDP and the quality of research institutions. This index serves as
an indicator of a country's commitment to technology development. ICT penetration
capacity show the opportunities for information technologies applying in all production
spheres over the country.
1552 Analyzing The Systemic Impact of Information Technology Development Dynamics on Labor
Market Transformation

The interest of the population in professions with a high level of automation can be
expressed by the dynamics of Google search for vacancies in these professions. Five
professions were selected based on the automation index for 2018 developed by the
University of Hawaii (Hawai’i Career Explorer, 2020). These occupations are electrician,
farmer, dishwasher, gardener, and logger. The research was done using Google Trends. The
information for these five occupations from 2005 to 2019 was collected using official
language of each country concidered. The trend in popularity for each profession was
calculated as the level of interest in the topic in relation to the highest score in the table for
a particular region and time period. 100 points means the highest level of popularity of a
query, 50 - a query that is half as popular as the first case, 0 points mean insufficient data
about the query in question. After collection, year average was taken, and data were
generated for fifteen countries for five occupations from 2005 to 2019, then average for all
occupations was taken, i.e., generated an interest index expressed through Google searches
for vacancies of occupations with high level of automation for selected countries from 2005
to 2019.
The unemployment rate is the ratio of unemployed to the labor force (the sum of
employed and unemployed), defined as a percentage.
Let us take a closer look at the negative tone of the information environment in the
news when the word "Automation" is mentioned. The analysis of the information
environment allows us to assess how the tone of the news has changed over time (Rudskaya
et al., 2020). The data search algorithm can be divided into 2 stages. The first stage is the
formation of the primary data set. At this stage, a news array is collected in accordance with
the analyzed time period, from 2005 to 2020. The source of primary information is Google
News, as this platform is popular at the Internet. For parsing this data, the programming
language Python 3 and the library GoogleNews can be used. With the help of this library, it
is possible to cover through a massive amount of news headlines in the period and the
language of interest. The second step is to analyze the tone of the received information. At
this stage, the collected news headlines are tonally analyzed by three metrics using
Dostoevsky library (Veselov, 2018): negative, positive, neutral. The average value for each
year is also calculated.
The labor market index is a coefficient calculated on the basis of changes in the
indicators characterizing the labor market by country, calculated by the OECD -
Organization for Economic Cooperation and Development. It characterizes the labor
market state, determined by the job seeker activity level relative to the employer demand.
The higher the value of the labor market index, the more favorable the situation is for the
employers, as a high value indicates high job seeker activity and low demand from the
employers, which ensures finding a qualified specialist easily. The index calculation base is
100. It defines the expected mean value of the normalized index time series with monthly
values over five years. A sufficient set of data ensures the representativeness of the sample,
as it includes all kinds of data (stable situations, rapid growth, stagnation, and crisis). The
index consists of several indicators, which determine the state of the labor market, namely
the unemployment rate, the number of unemployed and the wage index. In order to balance
mathematical operations with indicators of different nature, we should normalize the time
series of each indicator. This index is calculated for each of the countries based on open
data.
It must be noted, that while the unemployment rate is used in labor market index
calculation, it is also an important indicator on its own. It indicates not just one of the
aspects of the labor market, but the consequences of the socioeconomic environment
expressed within the labor market. This nature of the indicator determines the contrast of
Rodionov et al. 1553

its impact on the information environment, which is shown in the last considered
regression equation. The given set of indicators is aggregated in a single summary table
(Table 1).
Table 1 Indicator summary table
Indicator Designation Units Type Sources
Number of robots per 10000
x1 Coefficient Exogenous Robotic Density IFR
workers – robotization rate
Endogenous
ICT index x2 Factor ICT Index
– exogenous
Government ICT implementation
v1 Factor Exogenous Oxford Insights
readiness index
Global Innovation Index v2 Factor Exogenous Global Innovation Index
Gross expenditure on R&D v3 Coefficient Exogenous Global Innovation Index
Feasibility of ICT implementation x3 Factor Exogenous Global Innovation Index
Level of
Public interest in professions Endogenous Google trends,
y1 interest
with a high level of automation – exogenous Automation Index
(points)
Unemployment rate y2 % Exogenous Macrotrends
Negative tone of information
f1 Coefficient Endogenous Dostoevsky
environment in Google News
Labor market index z Coefficient Endogenous OECD
The reliability level is determined at 90% due to data specifics since most model`s
indicators are indices and can be similar to each other. Significant level for each indicator
should not exceed a value equal to the difference between one and the level of reliability.
Therefore, each characteristic-factors with a value greater than 0.1, will be excluded from
the model one by one, since they will not affect the result-factors. There is no specific value
of R2 for this model that will be acceptable as well as approximation error.

3. Results and Discussion

According to the results of the regression analysis, the indicators of robotization rate
and the feasibility of introducing ICT were removed from the model. These indicators do
not have a significant impact on the result of modeling. The results of regression analysis
for each equation are shown in Table 2.
Table 2 Regression results
Multiple R R- Adjusted R- Standard Coeff.
squared squared error t-statistics P-value
Equation 1 0.7926 0.6282 0.5268 0.3096
Intercept 0.7778 5.6843 7.3135 1.52E-05
𝑣3(𝑡−1) 0.0201 -0.0371 -1.8418 0.0926
𝑣3(𝑡−2) 0.0221 0.0529 1.9418 0,.782
𝑣2(𝑡−2) 0.0153 0.0428 2.8011 0.0172
Equation 2 0.9368 0.8777 0.8427 0.1997
Intercept 0.2963 16.5771 55.9538 1.53E-10
𝑥2 0.0502 -0.1607 -3.1979 0.0151
𝑥1 0.0026 -0.0075 -2.5025 0.0408
Equation 3 0.8571 0.7346 0.6938 0.0306
Intercept 0.0471 0.0247 0.5232 0.6096
𝑥1 1.1702 -4.1199 -3.5205 0.0038
𝑥2 0.0039 0.0227 5.8479 5.71E-05
Equation 4 0.9317 0.8681 0.8516 0.1584
Intercept 1.6562 -3.8176 -2.3049 0.0501
𝑥2 0.0166 0.12033 7.2564 8.75E-05
1554 Analyzing The Systemic Impact of Information Technology Development Dynamics on Labor
Market Transformation

Due to the study limitations, while the regression equations (1) and (2) are valid for all
the 15 considered countries, the model including the negative tone of the information
environment indicator, was only calculated for Russia. This is caused by both limitations in
data collection toolset and the fact that it's impossible to reliably evaluate the tone for a
country's information environment without knowing the specifics of said environment, that
country's culture and language. The validated conceptual model is presented in Figure 2.

Figure 2 Validated conceptual model of the study

The following regression equations described this system of relations:
𝑥2 = 5,68 + 0,043 ∗ 𝑣2(𝑡−2) − 0,037 ∗ 𝑣3(𝑡−1) + 0,05 ∗ 𝑣3(𝑡−2) (1)
𝑦1 = 16,58 − 0,0065 ∗ 𝑥1 − 0,16 ∗ 𝑥2 (2)
𝑓 = 0,025 − 4,12 ∗ 𝑥1 + 0,023 ∗ 𝑥2 (3)
𝑧 = −3,82 + 0,12 ∗ 𝑥2 (4)
Based on system of equations we should indicate each result:
1. First equation. As the innovation index increases, the ICT index also increases. The
innovation index is a combination of factors that determine a country's position in
technological sophistication which are calculated according to established methodology
Global Innovation Index. This index includes, among others, the political environment of
the country, the presence of a business-friendly environment, various indicators of
education, Internet accessibility for the population, availability of electronics, market
development, and business and government contribution to research. So, when there is a
favorable political environment that motivates and promotes the implementation of
information technologies in all spheres of human life, the level of use of these technologies,
which is reflected in the ICT index, increases.
The effect of government interest, which is expressed in R&D expenditure, on the ICT
index has a negative regression coefficient, but this does not mean that when expenditure
increases, the index decreases. In this case, the coefficient is negative because the impact
has a time lag of more than one year. This proves the positive value of the index in period
(t-2).
The impact of the innovation index and R&D expenditure on the ICT index was
examined at the metalevel. No explicit development and dependence of the analytical
criteria was found in the period under consideration in the analyzed countries. Stable
fluctuations around a central value are present. It can be connected to influences of other
factors which have not been analyzed in this example. It is recommended to take a larger
sample of countries for further study. Since the relationship is rather weak, it is not useful
to change these factors to influence on the outcome, the ICT index. It is necessary to analyze
the resulting indicator and its components in more detail in order to develop a more robust
model with stronger relationships. Also there is a significant time lag between the allocation
of resources for R&D and the changes in the ICT index which should be taken into account
in process of results interpretation.
Rodionov et al. 1555

2. Second equation. The effect of the ICT index on the population's interest in
professions with a high level of automation has a negative regression coefficient. When the
ICT index increases, the interest decreases, which is logical. The value of the coefficient is
less than 1, i.e. the strength of influence is low. This may be due to the specifics of the
resulting indicator. Population interest is expressed through Google Trends, in scores.
Inaccuracies that may have arisen due to the specifics of each language and search queries
may have affected the value of the regression coefficient. But nevertheless, the relationship
is negative, and its presence is confirmed.
Robotization rate, which is calculated by the number of robots per 10,000 workers,
affects on public interest inversely. When robots increase, the interest decreases. The
weakness of this relationship, as well as with the relationship to the ICT index, is logically
justified by the specifics of the resulting indicator. This relationship is the weakest but at
the same time it has a surprisingly low approximation error. The presence of a weak
relationship can be justified by the indirect effect on robotization rate on interest in highly
automated occupations.
3. Third equation. There is a negative correlation between the population's interest in
highly automated occupations and the negative tone of the information environment. If
there are decreasing number of searches for vacancies in highly automated professions,
then negative tone of the information environment increases. This effect can be shown by
negativity appearing in the news in case of "Automation" headline mentioned. The
presumed presence of an inverse relationship of these indicators was confirmed. The active
process of human replacement by machine labour lead to more negative human perception
of ICT development. Examples of occupations that either no longer require human labor or
have such a tendency are given in the introduction. People who are left out of work begin
to show negative emotions about the cause of their unemployment. Routine occupations
tend to replace human labor with machine labor, as stated in the theoretical rationale for
the problem. Often, these jobs don't require higher education. For people who have lost
their jobs, it is not easy to find a new one in the same field, and finding one in other fields
requires requalification, which is difficult. But the value of the regression coefficient is
lower than one. This equation was considered on the example of Russia. Information
technologies have not yet been implemented in Russia as widely as, for example, in the
countries leading in the ICT development index. But even so, the relationship between the
indicators is present, though not strong.
The increased level of unemployment lead to decreasing negative tone at the
mentioning of the word "Automation". The illogical presence of a negative, relatively strong
regression coefficient may be justified by the fact that this dependence has been considered
only in the Russian market. First, automation has not come to the Russian labor market
widely enough, and there is no mass unemployment associated with it. Second,
unemployment is affected by many factors unrelated to the development of technology:
seasonality, crises, negative political environment, pandemics, high birth rate and so on.
Over time, the relationship between the unemployment rate and negative attitudes towards
automation may increase, but this requires a more detailed analysis of the problem.
4. Fourth equation. The impact of the ICT index on the labor market index has a positive
regression coefficient, because when the ICT index increases, the labor market index also
increases. The relationship is not strong, but still present. This suggests that information
and communication technology development does affect on the labor market, what can be
expressed by labor market index changing. The presence of a weak correlation can be
justified by the fact that the labor market is influenced by many other factors, the
development of information technology being just one of them. In general, the presence of
1556 Analyzing The Systemic Impact of Information Technology Development Dynamics on Labor
Market Transformation

the relationship confirms the impact of the factor on the result. For a more detailed analysis,
it may be worth considering a multiple regression rather than a pairwise regression, where
the labor market is affected by many factors related to the development of technology.

4. Conclusions
The paper studies the influence of information and communication technologies (ICT)
on the transformation of the labor market. Several cases of American, European and
Russian companies that implement ICT (for example, robotization) to replace routine labor
functions are analyzed. The growing popularity of this trend is also proven by search
queries analysis in the Google Trends system (for example, such as Robotic Process
Automation) over the past 5-7 years. In all the considered cases, there has been a steady
increase in the popularity of queries connected with informatization. At the same time,
literature review shows the ambiguity of the impact of the ICT on the development of the
labor market from an economic and social point of view, in particular, a number of scholars
have proved that such an impact strongly depends on the specifics of industry and the
degree of its technological development, as well as the average age of workers involved in
the labor market. This research was made for endeavor to prove the information and
communication technologies role in the labor market development. The indicators in the
model are the Labor market index, the ICT index (consisting of 4 components). The novelty
of the study is the introduction into data model for information environment surrounding
labor resources. The results of the statistical analysis showed that the ICT index is
influenced by the global innovation index and the gross expenditure on R&D. The ICT index,
in turn, affects the Labor market index and public interest in professions with a high level
of automation. The latter indicator has proven to be related to the tonality of the
information environment, aggregating information about workplace automation and
measured using sentiment analysis of the news agenda in the Google search engine. The
research limitation consists on fact that database partly included results of information
environment analysis in the context of subject area. The dynamics of consumer requests in
the information environment needs to be constantly monitored, since it can undergo
significant transformation due to the influence of many exogenous factors. The directions
for further research are the specification of the study by countries and industries
(especially in the context of high-tech, mid-tech and low-tech industries), as well as the
search and introduction into the model of a larger number of indicators that affect the
measured values.

Acknowledgments
The research was partially funded by the Ministry of Science and Higher Education of
the Russian Federation under the strategic academic leadership program ‘Priority 2030’
(agreement 075-15-2021-1333, dated 30 September 2021).

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