Transformation

Methodology

The aim of the paper is to analyze, define and characterize the impact of digital technologies on the labor market and potential impacts of digital technologies on labor market in the transport industry. In order to achieve this goal, at the introduction we defined the context in which the subject was dealt with. As part of the analysis of the current state, we applied secondary research, which consisted of data collection and processing methods and their subsequent analysis and synthesis. The main sources of realized secondary research are the publications of foreign authors and research reports from the OECD and the World Economic Forum.

In the research, we conducted, we examined the correlation and the trend between the selected data we obtained from the OECD (2018) statistical databases and which we also used to analyze the current state. Data of employment and unemployment are from year 2013, same as data on jobs with high risk automation which are from year 2013. Countries whose data we taken into account are: Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, Korea, Lithuania, Netherlands, New Zealand, Norway, Poland, Slovak Republic, Slovenia, Spain, Sweden, Turkey, United Kingdom and United States. Second part of research is dedicated to characterizing trends that may have an impact on the labor market in the transport industry specifically in the freight transport sector.

4. Results

As we can see in Figure 3, in OECD countries with high unemployment rate, the level of jobs at high risk automation increases, which can also be confirmed by the trend line with an upward tendency. Average number of jobs at high risk in selected OECD countries is at the rate 13,5 % and at this rate the average unemployment is 9,3 %. Correlation coefficient of unemployment rate and percentage of jobs at high risk of automation in selected countries is 0,5298. This means that the correlation between selected variables have moderate positive correlation. In another words unemployment rate in selected countries is correlated to the percentage of jobs at high risk of automation by 52,98 %. Countries, whose unemployment can be mostly affected by job automation are Slovakia, Greece, Spain and Slovenia and on the other hand countries who have low unemployment and also have low risk of job automation and therefore whose labor market can be less affected by job automation are Scandinavian countries such as Norway, Finland, Sweden and Korea.

As we can see in Figure 4, in the selected OECD countries, the lower the employment rate is, the higher is the risk of job automation. This is also proven by the fact that the trend line has downward tendency. Average values for the OECD are 13,5 % for the risk of jobs automation at the employment rate of 65,6 %. Correlation coefficient of employment rate and percentage of jobs at high risk of automation has value of -0,5822. Coefficient value indicates that there is moderate negative correlation between examined variables. This relation can be interpreted as following: employment rate in selected countries that are members of OECD is correlated to the percentage of jobs that are at the high risk automation by -58,22 %. Countries whose employment can be mostly affected by automation are Slovakia, Slovenia, Greece and Spain. On the other hand employment of countries such as Norway, Finland, Sweden, Netherlands and USA may not be significantly affected by job automation.

Figure 3. Relationship between unemployment rate and jobs at high risk of automation.

Relationship between employment rate and percentage of jobs at high risk of automation is stated in figure 4.

Figure 4. Relationship between employment rate and jobs at high risk of automation.

On the basis of statistical measurements, the results of which can be seen in Figures 3 and 4, we can state that automation can have a major impact on employment and unemployment. From a perspective of unemployment, in countries with high unemployment rate there is a high risk associated with job automation. Countries with low unemployment have a relative lesser risk associated with the automation of jobs. From the point of view of employment, in countries where there is low employment, the risk of job automation is rising and therefore, on the other hand, the higher the employment rate, the risk of job automation decreases (Madlenakova and Madlenak 2015). Countries, whose employment respectively unemployment can be highly affected by automation are in both cases Slovakia, Slovenia, Greece and Spain. Rates of employment and unemployment of countries such as Norway, Sweden and Finland face the lowest risk of decrease in the case of employment and increase in the case of unemployment.

Thus, we can state that automation, which is an integral part of technological progress, may have a significant impact on the labor market in selected OECD countries in the future. This impact has the character of an increasing

unemployment and decreasing employment. Impact of automation can be crucial in countries where the unemployment is high and the employment is low. It is therefore necessary for countries to prepare themselves for the potential risks associated with the impact of digital technologies on the labor market.

The second of part of our research was dedicated to the analysis of implications of digitalization of labor market of freight transport sector. The transport sector directly employs millions of drivers. If other areas such as taxis, buses and delivery services are included, numbers are much larger. In OECD countries, there are typically between 3 to 6
% of total employed working in the sector. According to Blix (2017) the freight labor market is now subject to several developments that could have impact on employment and on the businesses that support or rely on the transport sector. Blix in his study further discusses scenarios for how work could develop in the transport sector. These scenarios are designed for the area of autonomous vehicles.
First scenario “Medium skill, medium autonomy” is the least disruptive scenario for the transport labor market. In this scenario the share of autonomous vehicles is limited. This can happen for a variety of reasons, for example, if regulation presents hurdles that are costly to overcome or in situations where human flexibility is more cost effective than multi-purpose automation. The experience from the labor market as a whole is that manual non-routine jobs are much less subject to automation. Perhaps it will be possible to automate the loading and unloading of cargo at specialized stations at or outside cities. But even so, many transports have final destinations in places inside cities where full automation could prove costly.
Second scenario “Low skill, high autonomy” can be thought of as one where a large share of the current tasks is still performed by humans, but the skills needed to perform the tasks are lower. For example, a human driver is less able to keep the same speed of the optimal speed for fuel consumption compared to machines. Software can calculate the most fuel efficient driving in ways that are impossible for humans. Such technology is already available in commercial fleets today and when fully implemented, further reduces the need for human driving. In this scenario, human work will be concentrated around non-routine work that is costly to automate. Some work in the sector will even require higher skills than today. For example, repairing machines and vehicles could require a combination of software skills as well as understanding of mechanics. But diagnostics and communication technologies also imply that those possessing higher skills may not need to be on-site. In the same way that specialized doctors can follow a surgery on video-link from afar, contingency work and repairs on vehicles could also be done from off-site in combination with lower skilled labor.
Third scenario “High skill, high autonomy” points out that if the freight transport labor increasingly goes towards automation, the decline in the amount of jobs could be similar to how jobs in the manufacturing industry have declined over time. Due to network effects from digital technologies, the changes could be much more rapid than before. The driving forces behind a decline in jobs could stem from: substantially better safety records than human drivers, lower labor costs and less traffic congestions if transport logistics improved and vehicles also drive during the night. There is controversy about the safety of autonomous vehicles and especially about the ethical and moral choices that must be programmed in advance. There is less controversy that the total amount of accidents could be substantially fewer. An established track record of better safety could lead to an accelerated impetus for automation. As regards labor costs, a human on board a fully autonomous vehicles with a solid track record would be rather expensive. If there is very little for the driver to do, such jobs will remain mainly if regulation requires it. In combination with platooning, one could consider one driver accompanying several trucks. The labor cost of one driver servicing several trucks could still be cost effective. Of course, in a platoon with five trucks, only one driver might remain employed. Acceptance for autonomous trucks could also increase if transports were made off-peak hours, such as during the nights, thereby alleviating daytime congestion. Better logistics and use of existing capacity would reduce costs of congestion.
In general, the ultimate effects of work on the freight transport labor market are likely to a large degree depend on the skill level of the jobs. A fully autonomous fleet will require a few specialized and high skilled workers to operate, but the amount of less skilled work could decline dramatically.

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