the effects of automation. how the development of new technologies affects the change in the...
TRANSCRIPT
Dominik BatorskiMarek Błażewicz
University of Warsaw
The Effects of Automation
How the development of new technologies affects the change in
the popularity of various professions.
Technological progress has been rapid
Technology is likely to dramatically reshape labour markets in the long run and to cause reallocations in the types of skills that the workers of tomorrow will need.
The battle between man and machines goes back centuries. In the early 1800s it was the Luddites smashing weaving machines.
Automation
• These days retail staff worry about automatic checkouts.
• Narrative Science, in Chicago automate the writing of reports and is used by Forbes, a business magazine, to cover basic financial stories.
• Many firms use computers to answer telephones.
Sooner or later taxi drivers will be fretting over self-driving cars.
The end of work
The end of work was predicted20 years ago (Rifkin 1995)
“We are entering a new phase in world history—one in which fewer and fewer workers will be needed to produce the goods and services for the global population.”
Analogies to the Industrial Revolution
“The role of humans as the most important factor of production is bound to diminish in the same way that the role of horses in agricultural production was first diminished and then eliminated by the introduction of tractors.”
Wassily Leontief (1983)
Why people still matters?
Frank Levy i Richard Murnane(2004) The New Division of Labor
• Levy and Murnane argued that pattern recognition and complex communication were the two broad areas where humans would continue to hold the high ground over digital labor.
• However, this has not always proved to be the case.
Background• McAfee and Brynjolfsson
predict dramatic economic shifts to result from the coming of the ‘Second Machine Age’. • The technology will dramatically
reshape the kind of skills required by workers.
• The automation of jobs threatens not just routine tasks with rule-based activities but also, increasingly, jobs defined by pattern recognition and non-routine cognitive tasks.
Background
• Stewart, De and Cole (2015) argue that the debate has been skewed towards the job-destroying effects of technological change, which are more easily observed than its creative aspects.
• Others think that we are entering a new era of low economic growth where new technological developments will have less impact than past ones (Gordon).
Questions
• Are computers and machines taking our jobs? or are they merely easing our workload?
• Who will win and who will lose from the impact of new technology onto old areas of employment?
The Future of Work
• Frey and Osborne (2013) combine elements from the laboureconomics literature with techniques from machine learning to estimate how ‘computerisable’ different jobs are.
• The gist of their approach is to modify the theoretical model of Autor et al. (2003) by identifying three engineering bottlenecks that prevent the automation of given jobs – these are creative intelligence, social intelligence and perception and manipulation tasks. They classify 702 occupations according to the degree to which these bottlenecks persist.
• Using these classifications, they estimate the probability (or risk) of computerisation – this means that the job is “potentially automatable over some unspecified number of years, perhaps a decade or two”.
• According to their calculations, 33% of working American population are located in the low risk occupations, while 19% of them are at medium and 47% at high risk of automation.
Probability of automation
• Frey and Osborne (2013) used detailed job characteristics (including skills and abilities required, and tasks performed) from O*NET database. The variables were chosen, according to their importance in the automation process (perception and manipulation, creativity, social intelligence).
• 70 randomly chosen occupations (out of 702 with an unique 6 - digit SOC code) were labeled as "automatable" or not.
• Then, a discriminant analysis using Gaussian process classifiers was performed, resulting with remaining 632 being assigned a probability of automation (Frey, Osborne, 2013).
• They provide a table of job’s probability of computerisationand the Standard Occupational Classification (SOC) code associated with the job.
Probability of automation
… cont.
SOC to ISCO
• The computerisation risks we use are exactly the same as in Frey and Osborne paper but we needed to translate them to an International Standard Classification of Occupations (ISCO) classification commonly used in most European countries.
• We used a crosswalk proposed by the Bureau of Labor Statistics (BLS, 2012).
• Finally, we tested the relation between the original probabilities and those computed by aggregating categories by ISCO groups. The correlation was r = .94(weighted by the total number of employed in the US)
Probabilities: from SOC to ISCO
Relation between probabilities form the original paper (Frey & Osborne, 2013) and those determined by the weighted mean of SOC groups which are a part of the same ISCO category.
Data: BKL dataset
• A BKL study (Study of Human Capital in Poland) is an annual research on the labor market in Poland.
• It provides the detailed description of the participants occupation, which we reduced to a 4 digit ISCO code.
• In case of unemployed people, a previously performed job was ascribe to them as their occupation for the purpose of analysis.
• Data collected over the period of 5 years (2010, 2011, 2012, 2013, 2014) provided us with 83809 observations.
• Before further steps, farmers were excluded from the sample.
The risk of automation in Poland
Frey and Osborne (2013) proposed a cut - off points for determining low, medium and high risk of automation at p = .3 and p = .7 risk respectively.
47
3530
5547
20
22
19
20
23
3343
51
2530
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
US UK London Poland Warsaw
Perc
ent
of
emp
loye
d
high risk medium low risk
The risk of automation in US, UK and in Poland
Probability of automation within working and unemployed
Relation between probability of automation and unemployment
Occupations and skills
• ISCO classification is constructed to reflect the increasing level of skills required to perform the jobs.
• Widely used method proposes the division of ISCO occupations into three groups (Neujobs, 2014):• high-skills jobs (ISCO beginning with
1, 2 or 3): professionals, managers, technicians and associate professionals,
• medium-skills jobs (ISCO 4, 5 ,7 and 8): clerks, service workers, craft and trades, plant and machine operators,
• low-skills jobs: elementary occupations (9).
High skills jobs are usually those which are not easy to automate.
Predicting unemployment
Multiple logistic regression models using:• skills required (models 1-3),
• probability of automation (models 2 - 3)
• and their interaction (model 3)
as predictors of unemployment, with other factors as controlling variables:
• size of the city of residence,
• respondent age and squared age,
• education level,
• whether respondent is a university student,
• gender,
• country region,
• year of completing the survey (2010, 2011, 2012, 2013 or 2014).
Results
Status:
unemployed = 1Model 1 Model 2 Full model
B S.E. B S.E. B S.E.
(Intercept) 0.30 (0.08) -0.19 (0.09) -3.25 (0.28)
medium skills job -0.56 (0.04) -0.53 (0.04) 2.55 (0.28)
high skills job -0.65 (0.06) -0.39 (0.06) 2.95 (0.28)
probability of automation 0.62 (0.06) 4.54 (0.33)
medium skills * probability -3.90 (0.34)
high skills * probability -4.53 (0.35)
… … … …
Nagelkerke 𝑅2 .119 .123 .131
Note: The effect of skills is positive in the 3rd model, but it is only because the interaction effect is included in the model.
Those who worked in the low skill jobswere more likely to be unemployed.
Probability of automation positively predicted unemployment.
Interaction between automation and skills in predicting unemployment
Probabilities on y axis are fitted by the model, meaning that the effect of other factors is accounted for.
Conclusions
• Probability of automation is a significant predictor of unemployment,
• but the effect depended on the level of skills required for the job, • if a job required low skills, chance of being unemployed rises
dramatically with the probability of automation. • For the high skill group, there was no relation between
required skills and automation.
• Workers with high skills are not affected by computerization. It doesn’t mean that their job is not affected but they can more easily adapt or move to another sector, where their skills are still required.
Consequences of technologicalunemployment – an anecdoteThe consequences of the problem of technologicalunemployment are summarized in a classic though possibly apocryphal story:
Ford CEO Henry Ford II and United Automobile Workers president Walter Reuther are jointly touring a modern auto plant.
Ford jokingly jabs at Reuther: “Walter, how are you going to get these robots to pay UAW dues?”
Not missing a beat, Reuther responds: “Henry, how are you going to get them to buy your cars?”
