The article examines the general trends of automation of professions according to various estimates. Global trends in the structure of employment by profession are also analyzed. The output per employee and the dynamics of this indicator by country for the period from 2011 to 2018 are considered. As a result, the corresponding trends in the labor market for that period were determined

Bogdanova, E.

Pankova, A.

Digital Library of Polotsk State University (Репозиторий Полоцкого государственного университета)

ELECTRONIC COLLECTED MATERIALS OF XII JUNIOR RESEARCHERS’ CONFERENCE           2020 Economics  55 UDC  331  THE ANALYSIS OF GLOBAL TRENDS ON LABOUR MARKET    A. PANKOVA, E. BOGDANOVA Polotsk State University, Belarus  The article examines the general trends of automation of professions according to various estimates. Global trends in the structure of employment by profession are also analyzed. The output per employee and the dynamics of this indicator by country for the period from 2011 to 2018 are considered. As a result, the corresponding trends in the labor market for that period were determined.    More than a half of all current jobs are expected to either change significantly or disappear completely. This is partly the result of technological changes in computing, mechanics, and biochemistry, where a much larger range of tasks is at risk of replacing humans with robots compared to previous waves of technological changes. Let's look at the summary results of some studies in the field of replacing people with robots, which are given in the report of the International labor organization in 2017, in table 1.  Table 1. – Estimates of the impact of digital technologies on employment  Organization Estimate University of Oxford 47% of workers in the United States are at high risk of replacing jobs with automa-tion. Pricewaterhouse Coopers 38% of jobs in the US, 30% of jobs in the UK, 21% in Japan, and 35% in Germany are at risk of automation. International labour organization ASEAN-5*: 56% of jobs are at risk of automation  in the next 20 years. McKinsey 60% of all professions have at least 30%  technically automated operations. OECD OECD average**: 9 % of jobs at high risk of automation over the next five years. Low risk of full automation, but a significant share (50-70 %) of automated tasks. Roland Berger Western Europe: by 2035, 8.3 million jobs will be lost in industry, but at the same time, 10 million new jobs will be created will be created in the service sector. World bank Two-thirds of all jobs in developing countries  subject to automation.  * - countries: Indonesia, Malaysia, Philippines, Thailand, Vietnam.  ** - OECD-Organization for economic cooperation and development (OECD)  Source: [1]  This report rightly notes that great care should be taken when interpreting these estimates, since many studies consider the likelihood that the work can be automated, rather than the likelihood that it (or the tasks in it) will be automated. Due to the significant financial costs associated with the introduction of advanced technol-ogies, the difference between “may be” and “will be” will be significant, especially in developing countries. In addition, destroying certain tasks within a profession does not necessarily mean that the entire profession will disappear. Employees will be required to adapt to a new work environment where they work together with ma-chines and robots. Thus, some of the current estimates of technological unemployment may be overstated [1]. We analyzed the dynamics of the structure of the distribution of the labor force by profession in some countries (in total of 38 countries from all regions of the world: USA, China, Germany, Argentina, South Africa, Russia, etc.) for the period from 2011 to 2018. In accordance with the methodology of the International labor organization, the following groups of professions have been distinguished: 1) managers; 2) specialists; 3) technical specialists and junior specialists; 4) office employees (clerks); 5) service and sales workers; 6) artisans and related professions; 7) plant and machine operators, assemblers; 8) elementary professions and skilled workers in agriculture, forestry, and fisheries. ELECTRONIC COLLECTED MATERIALS OF XII JUNIOR RESEARCHERS’ CONFERENCE           2020 Economics  56 Categories 1-3 according to the methodology of the international labor organization belong to professions with a high level of skills, 4-7 − to middle-level professions, elementary − to the minimum level, qualified workers in agriculture, forestry and fisheries − to the average level [3]. Also, we examined the rates of labor productivity for the same countries during the corresponding period. In table 2, we analyze the output per employee, calculated in constant 2011 prices in US dollars for the period from 2011 to 2018.  Table 2. – Output per employee US dollars for the period from 2011 to 2018    Source: own development based on data from the ILOSTAT database [2]  Also, to continue the analysis, let's look at the change in the output per employee by country over the analyzed period in table 3. Country 2011 2012 2013 2014 2015 2016 2017 2018Qatar 174190 169057 164912 161633 158960 157675 156433 158013Saudi Arabia 138657 136474 132619 131617 130505 128546 123786 123506United States 108667 109142 109753 110642 112080 111941 112677 114990Belgium 101055 100975 100868 101584 102617 103133 103229 103712Sweden 89536 88769 88871 90521 93597 95488 96449 98265Italy 95753 93740 94495 94859 95228 94866 95245 95991France 90155 90199 90883 92352 93151 93704 94504 95846Austria 90058 89771 89290 90048 90672 91395 93508 95137Finland 88281 86736 87027 86803 87329 88820 90277 91937Australia 82623 85011 86659 88322 88757 89983 90194 91559Germany 86557 86373 85870 86898 87942 88612 89748 91358Canada 80520 80976 81874 83735 83893 84553 85696 86437Spain 80465 81812 82645 82844 83538 83980 84530 85510United Kingdom 77546 77725 78316 78933 79627 80164 80848 81334Japan 72143 73422 74234 73962 74642 74617 75235 76419Turkey 60227 61542 64678 66878 68644 68896 71389 73147Korea, Republic of 63144 63569 64546 65295 66478 67821 69179 70802Czech Republic 61496 60734 59802 60905 63353 63724 65469 67719Poland 52806 53560 54314 54891 56280 56894 58502 60538Latvia 46162 47135 47556 48678 49575 50764 52725 55844Russian Federation48744 50108 51324 51726 50663 50669 51813 53012Chile 45714 47207 48171 48475 48861 49119 49239 50669Kazakhstan 42053 43528 45604 46918 47095 47347 49019 50619Argentina 46784 45869 46581 45361 46003 44634 45357 46753Mexico 39002 39031 38903 39567 39792 40063 40066 40163Turkmenistan 25727 28013 30343 32874 34381 36037 37755 39540Egypt 33506 33601 33595 33926 35917 36804 37439 38285Belarus 33953 34440 34704 35284 33987 33254 34304 35758Azerbaijan 33733 33785 35099 35120 34905 33202 32779 33307Brazil 33430 33273 33738 33438 32328 32041 32254 32578Thailand 22970 24587 25791 26231 27089 28201 29225 30115China 18347 19732 21209 22719 24267 25878 27645 29499Venezuela, Bolivarian Republic of39882 41569 41730 39238 36464 32896 29706 27550Indonesia 19465 20011 20967 21679 22589 23441 23933 24849Georgia 15216 16123 17035 17486 17845 18675 19736 20733Ukraine 19047 19262 19094 18816 17001 17631 18294 19095Tajikistan 8965 9395 9841 10222 10550 10992 11502 11936Congo, Democratic Republic of the2036 2154 2262 2390 2472 2453 2457 2467ELECTRONIC COLLECTED MATERIALS OF XII JUNIOR RESEARCHERS’ CONFERENCE           2020 Economics  57 Table 3. – Dynamics of changes in output per employee in USD for 2011-2018  Country Absolute change Total change 2012 to 2011 2013 to 2012 2014 to 2013 2015 to 2014 2016 to 2015 2017 to 2016 2018 to 2017 Turkmenistan 2286 2330 2531 1507 1656 1718 1785 13813 Turkey 1315 3136 2200 1766 252 2493 1758 12920 China 1385 1477 1510 1548 1611 1767 1854 11152 Latvia 973 421 1122 897 1189 1961 3119 9682 Australia 2388 1648 1663 435 1226 211 1365 8936 Sweden -767 102 1650 3076 1891 961 1816 8729 Kazakhstan 1475 2076 1314 177 252 1672 1600 8566 Poland 754 754 577 1389 614 1608 2036 7732 Korea, Republic of 425 977 749 1183 1343 1358 1623 7658 Thailand 1617 1204 440 858 1112 1024 890 7145 United States 475 611 889 1438 -139 736 2313 6323 Czech Republic -762 -932 1103 2448 371 1745 2250 6223 Canada 456 898 1861 158 660 1143 741 5917 France 44 684 1469 799 553 800 1342 5691 Georgia 907 912 451 359 830 1061 997 5517 Indonesia 546 956 712 910 852 492 916 5384 Austria -287 -481 758 624 723 2113 1629 5079 Spain 1347 833 199 694 442 550 980 5045 Chile 1493 964 304 386 258 120 1430 4955 Germany -184 -503 1028 1044 670 1136 1610 4801 Egypt 95 -6 331 1991 887 635 846 4779 Japan 1279 812 -272 680 -25 618 1184 4276 Russian Federation 1364 1216 402 -1063 6 1144 1199 4268 United Kingdom 179 591 617 694 537 684 486 3788 Finland -1545 291 -224 526 1491 1457 1660 3656 Tajikistan 430 446 381 328 442 510 434 2971 Belgium -80 -107 716 1033 516 96 483 2657 Belarus 487 264 580 -1297 -733 1050 1454 1805 Mexico 29 -128 664 225 271 3 97 1161 Congo, Dem. 118 108 128 82 -19 4 10 431 Italy -2013 755 364 369 -362 379 746 238 Ukraine 215 -168 -278 -1815 630 663 801 48 Argentina -915 712 -1220 642 -1369 723 1396 -31 Azerbaijan 52 1314 21 -215 -1703 -423 528 -426 Brazil -157 465 -300 -1110 -287 213 324 -852 Venezuela 1687 161 -2492 -2774 -3568 -3190 -2156 -12332 Saudi Arabia -2183 -3855 -1002 -1112 -1959 -4760 -280 -15151 Qatar -5133 -4145 -3279 -2673 -1285 -1242 1580 -16177  Source: own development based on data from the ILOSTAT database [2]  ELECTRONIC COLLECTED MATERIALS OF XII JUNIOR RESEARCHERS’ CONFERENCE           2020 Economics  58 According to the results, the following can be noted: − if we leave out Qatar and Saudi Arabia, the output per employee in our sample is the highest in the United States, then in the EU countries: Belgium, Sweden, Italy, and France. This indicator can be considered as an indi-cator of the overall state of the economy and its stability, since, for example, China, which is comparable in total GDP with the United States, is significantly inferior to a number of countries in output per employee. It is obvious that more output per employee is achieved mainly by automating technological processes: whether it is the pro-cess of assembling a car, or processing large amounts of information. Because digital technologies allow one per-son who knows how to handle them to replace several workers who perform routine non-creative tasks; − based on the analysis of the dynamics of this indicator, it is possible to clearly idenfy countries that are developing in terms of technology – countries where output per employee has significantly increased over the period 2011-2018. These include Turkmenistan, Turkey, China, Latvia, Australia, and Sweden in our sample; − the impact of the 2015 crisis can be clearly seen in the CIS, where output per employee in almost all countries decreased or had minimal growth; − countries with a high share of commodity exports in the output structure - Qatar and Saudi Arabia, despite the largest indicators of total gross output per employee, showed a stable decline in this indicator for the period 2011-2018. The impact of commodity prices (primarily oil) can also be traced to the economies of other countries: Russia, Venezuela, and Kazakhstan. Conclusion. Based on the conducted analysis we can draw the following conclusions for 2011-2018:  − the category of "elementary occupations and skilled workers of agriculture, forestry and fisheries" in the structure is reduced: to 30 countries – a decline for the rest of the minimum increase to a maximum of 0.8%, and the increase in this area is observed, including in highly developed countries France, Italy, USA; − the highest growth in the employment structure (in almost all countries) is observed in the category of "professionals" - these are highly qualified workers in a particular area. This indicates a certain trend in employ-ment, which can be formulated as follows: increasingly complex technological processes require the presence of highly qualified specialists in their field in the country; − for the category of "managers" - as highly qualified managers, there is no clear trend to determine (in 20 countries it increased, for the rest 18 − decreased), but it is worth noting that these changes are less than 1 % in one direction or another; − the categories of "office workers", "operators", and "artisans" have not pronounced but still general downward trend in the employment structure, while "sales and service workers" often get more space in the overall ratio of employed in the economy. This can also be explained by another general trend, that the market has a maximum focus on the consumer, respectively, requires more "salesmen".  REFERENCES  1. Inception Report for the Global Commission on the Future of Work. 2017 // International Labour Organization [Электронный ресурс]. – Режим доступа: https://www.ilo.org/ wcmsp5/groups/public/---dgreports/---cabi-net/documents/publication/wcms_ 591502.pdf. – Дата доступа: 06.02.2020. 2. База данных ILOSTAT // Международная организация труда [Электронный ресурс]. – Режим доступа: https://ilostat.ilo.org/. – Дата доступа: 06.02.2020. 3. Методика определения показателей // Международная организация труда [Электронный ресурс]. – https://ilostat.ilo.org/resources/methods/description-employment-by-occupation/. – Дата доступа: 06.02.2020.    

The analysis of global trends on labour market

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The analysis of global trends on labour market

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