Aarabdeen and Alofaysan - 2023 - Basarili DX Ve Artan Istishdam
Aarabdeen and Alofaysan - 2023 - Basarili DX Ve Artan Istishdam
Aarabdeen and Alofaysan - 2023 - Basarili DX Ve Artan Istishdam
Article
Investigating the Impact of Digital Transformation on the Labor
Market in the Era of Changing Digital Transformation
Dynamics in Saudi Arabia
Masahina Sarabdeen * and Hind Alofaysan
Department of Economics, College of Business and Administration, Princess Nourah bint Abdulrahman
University, Riyadh 11617, Saudi Arabia
* Correspondence: msarabdeen@pnu.edu.sa
Abstract: In Saudi Arabia, limited studies have developed models related to measuring the impact of
the digital economy on the labor market. This model concerns the agricultural, service, and industrial
sectors in Saudi Arabia. This study further investigates the relationship between digitalization,
labor productivity, and unemployment using the ARDL error correction method for time-series data
obtained from the World Bank database for the period of 2001–2019. The findings of this study illus-
trate, digital variables such as fixed broadband subscriptions (LNFBS), mobile cellular subscriptions
(LNMCS), and computer, communications, and other services (LNCCO) do not significantly affect
the labor market in the agricultural sector. LNMCS and LNCCO do not influence the service sector.
However, they are negatively influencing the industrial sector and labor productivity. In contrast,
LNFBS has a positive impact on both the service and industrial sectors. Interestingly, all three digital
variables significantly reduce unemployment in the long run in Saudi Arabia. However, in the short
run, digitalization does not have a positive impact on the economy. This study hopes to benefit
policymakers in considering how to reorganize the socioeconomic infrastructure to balance economic
growth through greater technology and the utilization of the country’s human resources.
market capitalization, and many more. In the innovative era, the most effective strategies
are still in progress or uncertain (Walwei 2016).
An OECD report published in 2016 suggests the long-term effects of digitalization
on labor are ambiguous, as mechanical manipulation should be minimal. Meantime, it
is believed that digital competence has not resulted in the creation of modern jobs on a
large enough scale to replace traditional jobs. An additional 2018 OECD report shows that
digitalization and robotization do not constitute a threat to widespread employment in the
indefinite future (Nedelkoska and Quintini 2018).
The studies of Kvochko (2013) and Katz and Koutroumpis (2016) investigated the
impact of digital transformation on the labor market. The findings show that digital
transformation is expected to create 22% of new employment (760,000) by 2020 in the USA
alone, and 25,000 innovative jobs annually in Australia. Moreover, the study of Katz and
Koutroumpis (2016) revealed that a 1% increase in digitization of the consumption index
would lead to a 0.07% reduction in unemployment worldwide between 2004 and 2015. This
result is in line with the study of Kunming (2019) which found that every additional score
in the Digital China Index has prompted more than 660,000 job opportunities.
The effects of technological innovation on employment were investigated by Su et al.
(2022). A correlation was found between patents and jobs created between 2013 and
2021. Employment is positively impacted by technological innovation. Technological
innovation may also have a negative impact on employment because it tends to have a
greater substitution effect than a creation effect in Chinese society.
The study of Ping and Ying (2018) shows that the devastating impact of digitalization
on employment would in general require significant changes in working style, execu-
tives, and decision-making processes. As a result, a company’s lower costs of production
would increase its labor income. Therefore, an increase in income would increase expecta-
tions of living, expand labor efficiency, and advance the economic progress of events and
collective improvement.
A study by Aly (2020) reviewed the association between the digital revolution and em-
ployment among 25 developing countries in 2017. Malaysia, Chile, and China succeeded
in converting the digital revolution into more extensive working opportunities. How-
ever, Turkey, South Africa, and even Jordan were absent from creating the ideal number
of vacancies.
In the meantime, the study of Autor et al. (1998) shows that the demand for computers
and skilled laborers is high, which leads to polarization in the USA. The studies of Ace-
moglu and Autor (2011); Goos et al. (2014); Michaels et al. (2014); and Ju (2014) found that
as technology advances, the demand for “middle-skilled” labor declines while high and
“low-skilled” labors continue to grow. Moreover, Sachs and Kotlikoff (2019) propose that
insolent innovations accompany untalented work by youth, resulting in lesser earnings
for incompetent youth and impeded efforts to obtain skills. However, digitalization and
the demand for skilled labor have a positive impact, as digitalization and the exchange
have not yet prompted polarization of the work market among lower–middle-income
countries (Ugur and Mitra 2017). Meanwhile, the study of Banga and Velde (2018) shows
that digitalization does not affect the labor market in 12 African countries. At the same
time, the study of Arntz et al. (2016) found that pioneering digital innovations have a
minimal impact on absolute business rates yet lead to enormous developments in labor
among occupations and enterprises.
Despite this, the industrial revolution did not have the same impact on employment
across different sectors. A recent study by Chinoracký et al. (2019) examined OECD
countries’ employment in agricultural, services, and industrial sectors and the probability
of job automation. Sector-specific job automation risks were identified in the results. The
agricultural and industrial sectors are more susceptible to job automation than the service
sector. Therefore, countries that have a highly tensive labor force in agriculture and industry
will experience high risk from job automation.
Economies 2023, 11, 12 3 of 14
The literature is clear in showing that digitalization of the economy helps to boost
economic development by taking the skilled labor force while victimizing the low and
middle-skilled laborers. Several studies have been conducted in developed countries. The
impacts are different from country to country. However, there is still a lack of literature
in developing countries, specifically in the Middle East. There is no evidence to show
how digital transformation impacts job creation in Saudi Arabia. It is still debatable and
not predictable.
Saudi Arabia has prioritized the development of the digital economy, as it contributes
significantly to achieving one of the primary goals of “Vision 2030”, which is to create jobs.
The government is especially optimistic regarding decreasing the young unemployment
rate and expanding the participation of women in the workforce (SABR 2021).
On the other hand, it is noted that Saudi Arabia faces significant challenges in moving
towards a digital transformation or knowledge-based economy. First, there is a mismatch
in skills between jobs. Second, the unemployment rate among Saudis is 12.3%, and youth
unemployment and female unemployment were 25.55% and 42%, respectively, in 2019
(SAMA 2020). The high unemployment rate among Saudi youth remains a component of
the Saudi economy.
Considering the above challenges existing in the Saudi Arabian labor market, there is
a need to do in-depth research on to what extent digital transformation dynamics affect
the labor market by sectors in Saudi Arabia. Therefore, this research intends to develop a
model for investigating the impact of digital transformation on the labor market by sector
in Saudi Arabia. It will contribute to filling the knowledge gap.
To realize the objectives of this study, secondary data from the World Bank database
and digital reports has been utilized. Eviews were used as a research tool for data analysis.
It is useful to study the short- and long-term effects of digital transformation on the Saudi
labor market using the ARDL error correction method. It is expected that the findings of
this study will contribute to the empirical findings on the impact of digital transformation
on the labor market and will help monitor emerging labor market trends in Saudi Arabia.
Moreover, this study hopes to benefit policymakers in considering how to reorganize
the socioeconomic infrastructure. This is to balance economic growth through greater
technology and the utilization of the country’s human resources.
Having said that, this paper is structured as follows: A detailed introduction including
the impact of digital transformation on the labor market is discussed in Section 1 followed
by the methodology in Section 2. Section 2 provides details of data and model specification
and technical details on the statistical methods of the study. In Section 3, empirical findings
and discussions are presented while Sections 4 and 5 consist of the conclusion and the
policy implications followed by limitations and future research directions.
2. Methodology
Eviews software was used to measure the impact of the digital economy on the labor
market. This was done using secondary time-series data collected from the World Bank
from 2001 to 2019. ARDL error correction method was used, as it is useful to study the
short- and long-term effects of digital transformation on the Saudi labor market. Moreover,
the ARDL approach can easily be expanded to include multiple data and can accept general
lag patterns (Econometric Approach Report 2010). Therefore, ARDL approach was utilized
in this study.
This section is divided into two portions. First portion discusses the data and the
variables used in the model specification. Meanwhile, the second portion explains the
technical details of the statistical methods employed in the study.
The greater the error term activists, the faster the economies correct to the stable
growth rate. Moreover, in Models 2, 3, 4, and 5, the lagged error correction is negative
Economies 2023, 11, 12 8 of 14
and statistically significant. However, the coefficient of ECMt-1 representing the slow
adjustments toward equilibrium is corrected by 5%, 4%, 11%, and 4.8% in Models 2, 3, 4,
and 5, respectively.
Most of the digital transformation variables have shown negative implications in the
short run. In Model 1, LNGDPP and LNFBS have positive and LNCCO has a negative
relationship with LNLF_AGR at order 1. However, LNCCO shows a positive relationship
at lag order 0. In Model 2, only LNSE has a negative impact on LNLF_SER. In Models 2, 3,
4, and 5, the digital development variables LNCCO, LNFMS, and LNMCS are insignificant
in the short run. These results are consistent with the study of Duasa and Ramadan (2019).
However, in Model 4, LNMCS has a negative implication on LNGDPP. Meanwhile,
in Model 5, LNCCO has a negative implication on LNUNE in the short run. These out-
comes could be attributed to the country’s digital divide. The digital gap is a significant
difficulty for economies in the digital revolution period due to significant variations in the
development and quality of life between and within countries.
The results of the pairwise Granger causality tests show that unidirectional causality
takes place among the variables. In Models 1 and 3, there is no causal relationship between
the variables. In Model 2, LNLF_SER has a relationship with LNSE but the LNSE has
no Granger cause with LNLF_SER. Thus, we can conclude that there is a unidirectional
relationship between these variables. In Model 4, LNGDPP has a relationship with LNCCO,
LNFBS, and LNMCS, but LNCCO, LNFBS, and LNMCS have no Granger cause with
LNGDPP. This shows that there is a unidirectional relationship between these variables. In
Model 5, LNFBS has a relationship with LNUNE. However, LNUNE has no Granger cause
with LNFBS. LNUNE has a relationship with LNMCS and LNGDPP. However, the LNMCS
and LNGDPP have no Granger cause with LNUNE. Therefore, there is a unidirectional
relationship between these variables (refer to Appendix D).
Moreover, the results of the diagnostic tests indicate the stability of the specified
models of the study, as shown in Table 5.
4. Conclusions
For a country’s economic growth and flexibility, technological capabilities are essential.
As a result, a country’s economy must comprehend its current state as well as the trajectory
of its technological development and its economic influence. The purpose of this study was
to examine the impact of digital transformation on the labor market in a variety of sectors.
In the short-term, digital transformation in the labor market has negative effects probably
because of the nation’s digital divide, which affects network availability and connectivity.
However, in the long run, digital transformation in the labor market is profound in Saudi
Arabia. The main findings of this study are:
• LNGDPP significantly affects the labor market in the agricultural sector. However, the
digital variables do not significantly affect the labor market in the agricultural sector.
• An increase in labor productivity (LNGDPP) by 1% would decrease the demand for
labor by 0.65%. Meanwhile, an increase in digital development, LNFBS, by 1% would
increase the demand for labor by 0.03% in the service sector.
Economies 2023, 11, 12 9 of 14
5. Policy Implications
Saudi Arabia ranked second among the G20 countries in technological competitive-
ness, up 20 places from the previous year according to the Digital Riser Report (Digital
Riser Report 2021). This advancement reflects the ambition and progress of Saudi Arabia’s
strategy in developing the country’s telecommunications infrastructures. Since 2016, when
Saudi Vision 2030 was initiated, several digital programs have been administered in collab-
oration between the government and service providers to improve telecommunications
infrastructure, both fixed and mobile, and to optimize fixed and mobile broadband network
performance to reduce the digital divide between densely populated and rural areas.
In 2017, Saudi Arabia’s Ministry of Communications and Information Technology made
an agreement with IBM to teach and qualify more than 38,000 people in information and com-
munication technology (ICT) programs over the next four years through 30 new educational
institutions. Around 19,000 trainees were projected to receive certification in the profession by
2020. The ministry’s fundamental concerns, particularly “the shortage of specialized human
capital” and “low user skills in the communication and information technology industry,”
was addressed through a deal with IBM. Through the ministry, “the Kingdom launched
five upcoming programs involving the training, qualification, and recruiting of ICT experts”
(Saudi Arabia: Political, Economic & Social Development Report 2017).
Furthermore, in 2021, the Saudi government established the Digital Government
Authority to regulate the work of digital government in its agencies and to develop a
technologically advanced and proactive government capable of providing highly efficient
electronic services, such as e-education, e-government, and e-commerce to consumers,
enterprises, and society. The government aims at accelerating digital transformation by
adopting and implementing telecommunication systems and ICT technology. This would
provide access to the internet for all regardless of their economic status.
The more extensive the use of ICT and other computerized apparatuses, the more
enlightening and effective the residents will be. The Saudi Master Plan 2030 should achieve
its goal by cooperating with the International Telecommunication Union (ITU), portable
administrators, banks, retailers, and other specialist organizations.
This joint effort will improve worldwide interoperability and drive economies of scale
to increase opposition and interest in ICT ventures in the area. A strong administrative
strategy is also necessary to stimulate competition in the ICT markets of the locale. A
government could direct the market to ensure that the positive ramifications of digital
change on the way of life are acknowledged in the short and long term.
This research concludes with a call for active state intervention in promoting R&D,
investing in infrastructure and education, and introducing regulatory practices that ensure
that technology-induced organizational arrangements generate decent jobs while remaining
mindful of possible government overreach with new technologies. Saudi Arabia, the use
of digitization can constitute a catalyst for sustainable development in the post oil area
and become a key pillar of transparency, welfare, and improving citizens’ access to public
services. To accomplish digital transformation, Saudi Arabia must base the economy on
the recognition that education plays a key role in this phase, and the Saudi government
should make greater investments in human capital to enhance skills among youth.
Economies 2023, 11, 12 10 of 14
Author Contributions: Conceptualization, M.S. and H.A.; methodology, M.S.; software, M.S.; vali-
dation, M.S., H.A.; formal analysis, M.S.; investigation, H.A.; resources, H.A.; data curation, M.S.;
writing—original draft preparation, M.S.; writing—review and editing, H.A.; visualization, M.S.;
supervision, H.A.; project administration, M.S. and H.A.; All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Appendix B
Model 1
Test Statistic Value Sign I(0) I(1)
F-statistic 3.840882 10% 2.08 3
k 5 5% 2.39 3.38
2.5% 2.7 3.73
1% 3.06 4.15
Model 2
F-statistic 4.027924 10% 2.08 3
k 5 5% 2.39 3.38
2.5% 2.7 3.73
1% 3.06 4.15
Model 3
F-statistic 3.773361 10% 2.08 3
k 5 5% 2.39 3.38
2.5% 2.7 3.73
1% 3.06 4.15
Model 4
F-statistic 8.177814 10% 2.08 3
k 5 5% 2.39 3.38
2.5% 2.7 3.73
1% 3.06 4.15
Model 5
F-statistic 4.854579 10% 1.99 2.94
k 6 5% 2.27 3.28
2.5% 2.55 3.61
1% 2.88 3.99
Appendix C
Model specification:
Model (1)
P
∆logLFAGRt = α0 + ∑ γi∆logLFAGRt−i
i =1
q q
+ ∑ α1 ∆logGDPPt−1 + ∑ α2 ∆logCCOt−i
i =0 i =0
q q q
+ ∑ α3 ∆logSEt−i + ∑ α4 ∆logFBSt−i + ∑ α5 ∆logMCSt−i
i =0 i =0 i =0
+ β 1 logLFAGRt−1 + β 2 logGDPPt−1 + β 3 logCCOt−1
+ β 4 logSEt−1 + β 5 logFBSt−1 + β 6 logMCSt−1 + Ut
Economies 2023, 11, 12 12 of 14
Model (2)
P
∆logLFSERt = α0 + ∑ γi∆logLFSERt−i
i =1
q q q q
+ ∑ α1 ∆logGDPPt−1 + ∑ α2 ∆logCCOt−i + ∑ α3 ∆logSEt−i + ∑ α4 ∆logFBSt−i
i =0 i =0 i =0 i =0
q
+ ∑ α5 ∆logMCSt−i + β 1 logLFI NDt−1 + β 2 logGDPPt−1 + β 3 logCCOt−1 + β 4 logSEt−1
i =0
+ β 5 logFBSt−1 + β 6 logMCSt−1 + Ut
Model (3)
P
∆logLFI NDt = α0 + ∑ γi∆logLFI NDt−i
i =1
q q q q
+ ∑ α1 ∆logGDPPt−1 + ∑ α2 ∆logCCOt−i + ∑ α3 ∆logSEt−i + ∑ α4 ∆logFBSt−i
i =0 i =0 i =0 i =0
q
+ ∑ α5 ∆logMCSt−i + β 1 logLFI NDt−1 + β 2 logGDPPt−1 + β 3 logCCOt−1 + β 4 logSEt−1
i =0
+ β 5 logFBSt−1 + β 6 logMCSt−1 + Ut
Model (4)
P
∆logGDPPt = α0 + ∑ γi∆logGDPPt−i
i =1
q q q
+ ∑ α1 ∆logTLFt−1 + ∑ α2 ∆logCCOt−i + ∑ α3 ∆logSEt−i
i =0 i =0 i =0
q q
+ ∑ α4 ∆logFBSt−i + ∑ α5 ∆logMCSt−i + β 1 logGDPPt−1
i =0 i =0
+ β 2 logTLFt−1 + β 3 logCCOt−1 + β 4 logSEt−1
+ β 5 logFBSt−1 + β 6 logMCSt−1 + Ut
Model (5)
P
∆logUNEt = α0 + ∑ γi∆logUNEt−i
i =1
q q
+ ∑ α1 ∆logGDPPt−1 + ∑ α2 ∆logCCOt−i
i =0 i =0
q q q
+ ∑ α3 ∆logSEt−i + ∑ α4 ∆logFBSt−i + ∑ α5 ∆logMCSt−i
i =0 i =0 i =0
q
+ ∑ α6 ∆logTLFt−i + β 1 logUNEt−1 + β 2 logGDPPt−1
i =0
+ β 3 logCCOt−1 + β 4 logSEt−1 + β 5 logFBSt−1
+ β 6 logMCSt−1 + β 7 logTLFt−1 + Ut
Appendix D
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