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The Automatability of Male and Female Jobs: Technological Unemployment, Skill Shift, and Precarious Work.

1. Introduction

The steady decline of unbiased access to decent work and the increase in discrimination constitute noticeable features of the current labor market. (Blustein et al., 2019) The digital refashioning of labor markets that encompasses automation of diverse tasks somewhat dissimilarly impacts jobs at the present time held by men and women. (Krieger-Boden and Sorgner, 2018) Professional activity in technological cities is defined by points of contact, typified by intense knowledge and cognizance of masculine culture that confines women's advancement. (Hardey, 2019)

2. Conceptual Framework and Literature Review

If the gender partiality that is deep-seated in the contemporary social order is not addressed directly, the subsequent realm of technology-driven work tends to aggravate gender fairness gaps. (Howcroft and Rubery, 2019) The automation of work operations, accelerated by digital technologies (Bolton et al., 2018; Freeman-Moir, 2017; Hardingham et al., 2018; Meila, 2018; Mihaila, 2018; Nica, 2018; Popescu Ljungholm, 2017; Schinckus, 2018; Vochozka et al., 2018), may influence women's labor market insertion by altering the necessity for standard women's jobs dissimilarly from that for standard men's jobs. (Krieger-Boden and Sorgner, 2018) Automation, by intensifying the need for capital, raises its price over a brief period of time, which reduces the possible output gains that can be accumulated by replacing the more cost-effective capital for the higher-priced labor in the automated tasks. Over a lengthy period of time, the price of capital continues to be stable, and therefore output gains will be more significant. (Acemoglu and Restrepo, 2018) The proliferation of precarious work quickly endangers individuals' capacity to sustain themselves, especially in economies that do not supply a feasible government aid or living wages. (Blustein et al., 2019)

3. Methodology and Empirical Analysis

Using and replicating data from Brookings Institution, CNBC, IWPR, McKinsey, PIAAC, and PwC, we performed analyses and made estimates regarding share of jobs with potential high rates of automation by worker characteristics (%, across countries), the number of women and men in occupations with low and high risk of automation, and in the total workforce (2014-2018, in millions), and share of tasks that could be automated with current technologies (%). The results of a study based on collected data and estimates provide support for our research model.

4. Results and Discussion

Technological developments will amplify the moderate demand for the tasks and skills they integrate, and diminish the corresponding demand for the ones they can substitute. (Krieger-Boden and Sorgner, 2018) Working can both satisfy and circumvent numerous of the demands that provide individuals a sense of aspiration, meaning, and enjoyment in their lives. Increasing insecurity and unpredictability at work generate significant discomfort for personnel and communities. (Blustein et al., 2019) Approaches and professional identities established around work and social connections in technological cities display the relevance of skilled communities and networks in backing women to handle non-acceptance and career obstacles. (Hardey, 2019) (Tables 1-4)

5. Conclusions and Implications

Cutting-edge advancements in the sphere of artificial intelligence provide groundbreaking prospects for women to enhance their involvement in the economic activity, thus considerably improving their financial and social self-governance. (Krieger-Boden and Sorgner, 2018) Low-skill automation coincides with tasks until now carried out by untrained labor being substituted by machines, while high-skill automation entails an innovative stage of robotization in which machines proceed participating in tasks in which trained personnel specialize. (Acemoglu and Restrepo, 2018) By dispossessing individuals of the protection and meaning that work may ideally offer, unstable work and joblessness reduce entitlement to the external and internal resources (Ciobanu et al., 2019; Grossman, 2018; Means, 2017; Mihaila et al., 2018; Mitea, 2018; Pilkington, 2017; Popescu Ljungholm, 2018a, b, c; Syaglova, 2017) that are required to handle the intricate challenges of life. (Blustein et al., 2019)

Funding

This paper was supported by Grant GE-4285981 from the San Francisco Center for IoT Economy, CA.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Pavol Kral

pavol.kral@fpedas.uniza. sk

Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovak Republic

Katarina Janoskova

katarina.janoskova@fpedas.uniza.sk

Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovak Republic

Ivana Podhorska

ivana.podhorska@fpedas.uniza.sk

Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovak Republic

Aurel Pera

aurelpera@yahoo.com

University of Craiova, Romania (corresponding author)

Octav Negurita

octavnegurita@yahoo.com

Spiru Haret University, Constanta, Romania

How to cite: Kral, Pavol, Katarina Janoskova, Ivana Podhorska, Aurel Pera, and Octav Negurita (2019). "The Automatability of Male and Female Jobs: Technological Unemployment, Skill Shift, and Precarious Work," Journal of Research in Gender Studies 9(1): 146-152. doi:10.22381/JRGS9120197

Received 12 March 2019 * Received in revised form 5 July 2019

Accepted 7 July 2019 * Available online 10 July 2019
Table 1 Share of tasks that could be automated with current
technologies (%)

Gender

Men                     42.9
Women                   39.9
Age
16-24                   49.8
25-54                   41.2
55-64                   41.4
65+                     39.8
Race/Ethnicity
Hispanic                48.2
American Indian         45.4
Black                   44.6
White                   40.2
Asian/Pacific Islander  39.7

Sources: Brookings Institution; CNBC; our 2018 data.

Table 2 Share of jobs with potential high rates of automation by worker
characteristics (%, across countries)

             Gender      Age group           Education level
             Women  Men  Young  Core  Older  Low  Medium  High

Slovakia     41     49   48     44    47     56   54      20
Slovenia     37     50   52     42    46     64   49      15
Lithuania    32     56   52     41    44     59   52      22
Czech        39     44   51     42    45     59   48      13
Republic
Italy        33     45   44     41    40     46   44      18
USA          39     40   41     38    42     48   47      23
France       34     43   43     37    41     53   42      16
Germany      36     40   46     37    38     49   44      11
Austria      34     38   43     33    37     47   38      23
Spain        30     40   35     36    33     45   40      16
Poland       25     41   36     31    39     41   44      15
Turkey       21     37   42     32    36     39   37       9
Ireland      28     36   32     33    34     39   40      13
Netherlands  30     34   36     29    35     48   38      12
UK           27     35   34     29    37     48   37      13
Cyprus       28     35   29     31    33     40   39      13
Belgium      25     37   40     29    34     46   35      11
Denmark      28     34   27     29    38     42   34      12
Israel       27     33   36     28    32     46   38      14
Chile        23     33   29     28    31     36   30       7
Singapore    30     25   26     24    34     48   32      11
Norway       24     30   27     23    33     42   32      10
Sweden       21     31   27     22    31     40   30       9
New          24     27   31     24    27     41   29      13
Zealand
Japan        24     26   31     27    22     33   29      14
Russia       14     35   23     24    29     41   32      13
Greece       20     28   21     26    22     25   31      12
Finland      17     31   18     23    27     41   28       7
South        19     26   31     23    22     26   27      10
Korea

Sources: PIAAC; PwC; our 2018 data.

Table 3 The number of women and men in occupations with low and high
risk of automation, and in the total workforce (2014-2018, in millions)

                                  Women  Men

Workers in low-risk occupations   22.6   20.3
Workers in high-risk occupations  71.4   78.4
Workers in the total workforce    22.1   15.8

Sources: IWPR; our 2018 data.

Table 4 Jobs at risk of being displaced by automation by 2030

                Million FTE  % of female  Million FTE  % of male
                             employment                employment

Canada           2.3         24            3.2         28
France           3.1         22            3.1         23
Germany          4.2         21            5.3         22
Japan            6.3         24            9.4         24
United Kingdom   3.4         22            4.2         24
United States   19.3         24           20.3         26
China           52.4         15           66.2         15
India           12.1         10           44.1         12
Mexico           3.2         17            6.2         18
South Africa     1.2         18            2.1         22

Sources: ILO; NSS; INEGI; China Population Census; Statistics South
Africa; CPS IPUMs; ONS; Japan National Survey; Eurostat; Statistics
Canada; McKinsey; our estimates.
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Author:Kral, Pavol; Janoskova, Katarina; Podhorska, Ivana; Pera, Aurel; Negurita, Octav
Publication:Journal of Research in Gender Studies
Geographic Code:4EXSV
Date:Jul 1, 2019
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