Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 3 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 4 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 5 https://isni.org/isni/0000000404811396, International Monetary Fund

References

Annex:

Table A.1

– Automation and the Future of Work: Baseline, Odds Ratios

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Standard errors in parentheseStandard errors in parentheses ***p<0.01, **p<0.05, *p<0.1 ***p<0.01, **p<0.05, *p<0.1

This table reports odds ratios from a set of regression where the dependent variable is automation – a variable that captures the perception of how survey respondents view the impact of new technologies on their own future, and takes the value negative (1), neutral (2), or positive (3).

Table A.2

– Automation and the Future of Work: Labor Market Characteristics, Averages

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Standard errors in parentheses ***p<0.01, **p<0.05, *p<0.1

This table reports a set of regression where the dependent variable is automation – a variable that captures the perception of how survey respondents view the impact of new technologies on their own future, and takes the value negative (1), neutral (2), or positive (3). In addition to the original specification, columns 1 and 2 include labor market characteristics (period averages) that proxy degree of automation and degree of protection in an economy individually. Column 3 jointly estimates them.

1

We are grateful to Jorg Decressin, Laura Papi, Emil Stavrev, Petia Topalova, other members of the Emerging Economies Division, participants in the departmental seminar of the IMF’s European Department, and Chadi Abdallah for their helpful suggestions.

3

We use the term automation to refer broadly to new technologies in the workplace—such as automation and artificial intelligence as posed as a question in the survey

4

This paper analyzed the responses of 3600 persons interviewed from 1994 through early 1998 via the nationwide Survey of Economic Expectations. It found that subjective probabilities of job loss tend to decrease with schooling and subjective probabilities of good search outcomes tend to increase with schooling; hence composite job insecurity tends to decrease with schooling. Self-employed workers see themselves as facing less job insecurity than do those who work for others.

6

For section 5, we use this variable as an independent factor explaining the worker’s attitudes towards reskilling and government policies. We use dummies generated as follows: automation_pos takes value 1 when automation equals 3 (positive), and takes value 0 otherwise; automation_neg takes value 1 when automation equals 1 (negative) and takes value 0 otherwise.

7

While the questions are not exactly comparable across surveys, positive perceptions about automation and the future of work seem to be higher among respondents in BCG (2018) survey than in previous surveys mentioned in Table 1.

8

Contingent workers in this survey would include the following: self-employed (e.g. a tradesperson, independent professional, freelancer) or small entrepreneurs with no or less than 5 employees; temporary workers employed by staffing agencies (e.g. Randstad, Adecco) or similar companies; company owners with 5 or more employees are also considered contingent workers; and unemployed workers.

9

Non-continent workers in this survey would include the following: Salaried employee in a large company (50 employees or more); and salaried employee in a small company (less than 50 employees).

10

This result is probably driven by the fact that there are more contingent workers in emerging economies (particularly in India and Indonesia), and they overall exhibit a positive view towards the impact of new technologies on the future of work. As a result, the country fixed effects absorb the statistical significance of the dummy variable. We therefore drop this variable in subsequent regressions.

11

Similar results are obtained when we run the regressions including a dummy variable to identify workers of the gig economy. Only 4 percent of workers declare gig or platform economy as a primary source of income, and 13 percent report it as a secondary source of income. Gig-economy workers are younger (60 percent are between 18 and 35 years old) and more educated (43 percent are college educated) than the sample average. Again, in this case the effect of Indian and Indonesian respondents (a large majority of which are young, educated and welcoming technological change) is significant.

12

Our country groupings in this section are the following: Europe (Germany, France, Spain, Sweden); Advanced (Germany, France, Spain, Sweden, the US, the UK, and Japan); and Emerging (India, Indonesia, China, and Brazil).

13

The CBR Labour Regulation Index Dataset (’CBR-LRI’) provides data on labor laws in 117 countries. For our purposes, we focus on the area of labor law coded in the dataset relating to dismissal. We use a simple average of 9 variables under section C (from 16 to 24) on the Regulation of dismissal which includes Legally mandated notice period, Legally mandated redundancy compensation, Minimum qualifying period of service for normal case of unjust dismissal, Law imposes procedural constraints on dismissal, Law imposes substantive constraints on dismissal, Reinstatement normal remedy for unfair dismissal, Notification of dismissal, Redundancy selection, and Priority in re-employment. Please see Adams, Z., Bishop, L. and Deacon, S. (2016) CBR Labour Regulation Index (Dataset of 117 Countries) (Cambridge: Centre for Business Research) for further details.

14

We also run regressions whereby labor market characteristics are defined by levels of unemployment and spending in active labor market policies (ALMPs). None of these variables are significant determinants of the views that survey respondents have about the future of work. For brevity, these additional tests are not reported, but are available from authors upon request.

15

We cannot include country or country-industry pair fixed effects in this specification since our key explanatory variables of interest, proxy for exposure to new technology and labor protection, is at the country level.

Automation, Skills and the Future of Work: What do Workers Think?
Author: Mr. Carlos Mulas-Granados, Richard Varghese, Vizhdan Boranova, Alice deChalendar, and Judith Wallenstein