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Lernmaterialien für Economic an der Leuphana Universität

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TESTE DEIN WISSEN

2. Job creation and destruction


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TESTE DEIN WISSEN

2.1 potential effects of automation


  • displacement of employees due to automation of jobs/task
  •  productivity effect: innovations increase firms‘ productivity, reduce costs and prices, demand increases, output increases; new/better products/services can be produced; the economy expands
    → labour demand increases–potentially also in sectors that do not adopt new technologies due to a multiplier effect
  • reinstatement effect: evolves either because new tasks are complementary to the new technologies or because the displacement effect increases the amount of labour that is available for performing new/more productive tasks→ more workers required to perform new tasks → labour demand increases
  • net effect: theoretically unclear → empirical question


2.2 Job creation and destruction on different levels


  • firm level: firms‘ technology investments did not reduce their net employment (displacement effects offset by technology induced firm-growth) in Germany
  • sector level: takes into account reallocation of workers between less and more innovative firms (additional use of robots between 1993 and 2007 raised labour
    productivity and value added, no effect on total hours worked in 17 OECD countries)
  • Regional level: net neutral effects of robots in German local labour markets between 1994 and 2014 (2.12 job losses in manufacturing per additional robot, compensated by rising service employment)
  • - small negative net effects of robots in US local labour markets between 1993 and 2007
  • - Productivity and reinstatement effects of robots apparently strong enough to compensat displacement effects in Germany, but somewhat weaker in the US (possible  employment protection and vocational training)
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TESTE DEIN WISSEN

Computerization in past decades

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TESTE DEIN WISSEN

computers follow procedures laid out by programmers
• programmers must fully understand the sequence of steps necessary to perform a task → algorithm
• easy for tasks like processing a company‘s payroll or tabulating the distribution of customers‘ age
• since 1945, computing power increased, on average, by 45% per year
→ drastic decline of the costs of computational tasks
→ substitution of routine tasks (=fully codified and automatable)
• calculations involved in simple bookkeeping
• execution of a repetitive physical operation in an unchanging environment (as in repetitive production tasks; assembly lines)
• but: many tasks exist that people understand only tacitly (and accomplish) with little effort, but for which none can summarize the “explicit rules” or procedures

• tasks that involve creativity
• writing an essay
• developing hypotheses
• “Polanyi’s paradox”: we know more than we can tell

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TESTE DEIN WISSEN

Two broad sets of tasks difficult to computerize (Autor, Levy & Murrane)

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TESTE DEIN WISSEN

abstract tasks (require problem-solving capabilities, intuition, creativity, persuasion)

  • characteristic of professional, technical and managerial occupations
  • workers with high levels of education and analytical capability
  •  place a premium on inductive reasoning, communications ability and expert mastery

manual tasks (require situational adaptability, visual and language recognition and in-person interactions)

  • characteristic of food preparation and serving jobs, cleaning and janitorial work, grounds cleaning and maintenance, in-person health assistance by home health aides, and numerous jobs in security and protective services workers who are physically adept and, in some cases, able to communicate fluently in spoken language
  • activities are not highly skilled, but present daunting challenges for automation
  • many outputs of these manual task jobs (haircuts, fresh meals, housecleaning) must be produced and performed largely on-site or in person (at least for now)
  • potential supply of workers who can perform these jobs is very large
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TESTE DEIN WISSEN

3. Employment effects

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TESTE DEIN WISSEN
  • Complementary effect dominates the substitution effect, the new technologies require more rather than less workers
  • Structural changes in employment
  • expanding abstract and interactive task intensive occupations tend to be high wage occupations
  • stagnating occupations located in the middle and at the lower end of the wage distribution
    mostly high-skilled and well-paid workers profit the most from digitalization, whereas middle- and lower-skilled workers fall further behind
    → digitalization and automation likely raise inequality in Germany


  •  automation potential ≠ employment effects suppose
  • worst case scenario: all jobs with “high automation potential” in Germany will be lost (12% of all jobs according to Arntz et al. 2016)
    → ≈45 million employed people in Germany in 2018 
  • → ≈5 million jobs lost (would not affect us that much) (1:30:00)
  • but: demographic change!


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TESTE DEIN WISSEN

Automation, jobs & tasks

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TESTE DEIN WISSEN

automation doesn‘t eliminate all jobs
• tends to eliminate tedious and repetitive tasks
- manual: washing clothes, drying dishes, mowing lawn, digging holes
- cognitive: making change for purchase, memorizing maps, adding numbers
• only if you eliminate all the tasks associated with a particular job, you eliminate that job
• but: that is rare
• 270 detailed occupations listed in the 1950 US Census
• 1 of them has to been eliminated due to automation (elevator operator)


• most jobs are more complicated than we think
• O*NET data base contains a rich set of variables that describe work and worker characteristics, including skill requirements
• example of landscaping and groundskeeping workers
some of those tasks can be automated, but can all of them be automated = what fraction

Lösung ausblenden
TESTE DEIN WISSEN

Automation potential ≠ employment effects

Lösung anzeigen
TESTE DEIN WISSEN

occupation-level studies overestimate automation potentials
• share of automatable jobs drops to about 9% for the U.S. (comparable figures in other countries) once job-level variation of tasks within occupations is taken into account
• still: insights from such estimates remain limited because estimated automation potentials only capture whether a job – given its contemporaneous task structure – could theoretically be done by a machine or not
the estimates tells nothing about actual job losses or employment effects

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TESTE DEIN WISSEN

3 Reasons why automation potential ≠ employment effects 


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TESTE DEIN WISSEN

1. Technological Diffusion

2. Job creation and destruction

3. Employment effects

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TESTE DEIN WISSEN

Development of the labour market (Overview)

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TESTE DEIN WISSEN
  • 1935: “Thinking Machine Replace the Thinker
  • same now: “Smart robot could soon steal your job”
  • there have always been strong changes in the Employment-to-population ratio (shows the proportion of a country's working-age population that is employed)

USA

  • shared increased in the last 70 years roughly from 56% to 65 % in the year 2000
  • first decline = 1980 dotcom bubble
  • second decline = 2008 in the financial crisis
  • increase to again 61 % in 2019

EU 

  • ratio was larger in 2017 than in 1992
  • also a decline in EU in 2008 for financial crisis

Germany

  • nowadays around 58% → more people in employment than ever in the past
  • even though there are more robots
  •  no decline within the last 30 years
  • negative trend on the German labor market in the late 1990s
  • but then strong positive trend
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TESTE DEIN WISSEN

Polarization in the european labour market: employment

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TESTE DEIN WISSEN

• increase in the number of people employed in job that tend to be low skilled (food, cleaning, personal care protective service etc.)
• increase in more high skilled jobs
• decrease in the middle skilled jobs
• polarization = more jobs at the lower end of the skill distribution and more jobs on the higher end of the skill distribution + job losses in the middle of the skill distribution

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TESTE DEIN WISSEN

What will happen in the future? (Autor)

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TESTE DEIN WISSEN

machine learning (ML) could eventually solve Polanyi‘s paradox (“we know more than we can tell”) by applying statistics and inductive reasoning
• even if we are unable to program a machine to “simulate” a nonroutine task by following an algorithm, we may nevertheless be able to program a machine to master the task autonomously by studying successful examples of the task being carried out by others
• through a process of exposure, training, and reinforcement, machine learning algorithms may potentially infer how to accomplish tasks that have proved dauntingly challenging to codify with explicit procedures

Lösung ausblenden
TESTE DEIN WISSEN

Change in the Employment Shares/Jobs

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TESTE DEIN WISSEN

Structural Change in the Employment Share

  • less Agricutlure, Operatives/laborers, skilled blue collar (craft), but therefor increase in professional/technical, Managers and Service → higher need for high skilled jobs and less need for low skilled jobs

The Spreadsheet Apocalypse

  • Jobs in book keeping such as accounting and auditing clerks plummeted after the introduction of spreadsheet software, but jobs in management analysts and financials increased significantly

 ATM and bank sellers

  • as more ATMS were installed in the US the number of tellers employed didn’t drop
  • the jobs changed in the sense they had other things to do, but it is not that everyone lost their jobs
Lösung ausblenden
TESTE DEIN WISSEN

Frey and Osborne (2017)

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TESTE DEIN WISSEN

 47% of total US employment is at risk of automation in the next one or two decades
• definition of an occupation as “at high risk” of automation if the estimated automation potential ≥ 70%
• Authors calculate the technical possibility of automating a job (could be interpreted as automation potential, but does NOT capture the probability that a job is actually automated)


Methodological drawbacks
• focus on occupational level
→ assumption: everyone within the same occupation conducts exactly the same tasks as described in the O*NET data base.
→ however, tasks do vary substantially between workers of the same occupation (Autor and Handel 2013)


Spitz-Oener (2016):
→ average occupational task structures do not sufficiently mirror task heterogeneity within occupations → overestimation of automation potentials


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Q:

2. Job creation and destruction


A:

2.1 potential effects of automation


  • displacement of employees due to automation of jobs/task
  •  productivity effect: innovations increase firms‘ productivity, reduce costs and prices, demand increases, output increases; new/better products/services can be produced; the economy expands
    → labour demand increases–potentially also in sectors that do not adopt new technologies due to a multiplier effect
  • reinstatement effect: evolves either because new tasks are complementary to the new technologies or because the displacement effect increases the amount of labour that is available for performing new/more productive tasks→ more workers required to perform new tasks → labour demand increases
  • net effect: theoretically unclear → empirical question


2.2 Job creation and destruction on different levels


  • firm level: firms‘ technology investments did not reduce their net employment (displacement effects offset by technology induced firm-growth) in Germany
  • sector level: takes into account reallocation of workers between less and more innovative firms (additional use of robots between 1993 and 2007 raised labour
    productivity and value added, no effect on total hours worked in 17 OECD countries)
  • Regional level: net neutral effects of robots in German local labour markets between 1994 and 2014 (2.12 job losses in manufacturing per additional robot, compensated by rising service employment)
  • - small negative net effects of robots in US local labour markets between 1993 and 2007
  • - Productivity and reinstatement effects of robots apparently strong enough to compensat displacement effects in Germany, but somewhat weaker in the US (possible  employment protection and vocational training)
Q:

Computerization in past decades

A:

computers follow procedures laid out by programmers
• programmers must fully understand the sequence of steps necessary to perform a task → algorithm
• easy for tasks like processing a company‘s payroll or tabulating the distribution of customers‘ age
• since 1945, computing power increased, on average, by 45% per year
→ drastic decline of the costs of computational tasks
→ substitution of routine tasks (=fully codified and automatable)
• calculations involved in simple bookkeeping
• execution of a repetitive physical operation in an unchanging environment (as in repetitive production tasks; assembly lines)
• but: many tasks exist that people understand only tacitly (and accomplish) with little effort, but for which none can summarize the “explicit rules” or procedures

• tasks that involve creativity
• writing an essay
• developing hypotheses
• “Polanyi’s paradox”: we know more than we can tell

Q:

Two broad sets of tasks difficult to computerize (Autor, Levy & Murrane)

A:

abstract tasks (require problem-solving capabilities, intuition, creativity, persuasion)

  • characteristic of professional, technical and managerial occupations
  • workers with high levels of education and analytical capability
  •  place a premium on inductive reasoning, communications ability and expert mastery

manual tasks (require situational adaptability, visual and language recognition and in-person interactions)

  • characteristic of food preparation and serving jobs, cleaning and janitorial work, grounds cleaning and maintenance, in-person health assistance by home health aides, and numerous jobs in security and protective services workers who are physically adept and, in some cases, able to communicate fluently in spoken language
  • activities are not highly skilled, but present daunting challenges for automation
  • many outputs of these manual task jobs (haircuts, fresh meals, housecleaning) must be produced and performed largely on-site or in person (at least for now)
  • potential supply of workers who can perform these jobs is very large
Q:

3. Employment effects

A:
  • Complementary effect dominates the substitution effect, the new technologies require more rather than less workers
  • Structural changes in employment
  • expanding abstract and interactive task intensive occupations tend to be high wage occupations
  • stagnating occupations located in the middle and at the lower end of the wage distribution
    mostly high-skilled and well-paid workers profit the most from digitalization, whereas middle- and lower-skilled workers fall further behind
    → digitalization and automation likely raise inequality in Germany


  •  automation potential ≠ employment effects suppose
  • worst case scenario: all jobs with “high automation potential” in Germany will be lost (12% of all jobs according to Arntz et al. 2016)
    → ≈45 million employed people in Germany in 2018 
  • → ≈5 million jobs lost (would not affect us that much) (1:30:00)
  • but: demographic change!


Q:

Automation, jobs & tasks

A:

automation doesn‘t eliminate all jobs
• tends to eliminate tedious and repetitive tasks
- manual: washing clothes, drying dishes, mowing lawn, digging holes
- cognitive: making change for purchase, memorizing maps, adding numbers
• only if you eliminate all the tasks associated with a particular job, you eliminate that job
• but: that is rare
• 270 detailed occupations listed in the 1950 US Census
• 1 of them has to been eliminated due to automation (elevator operator)


• most jobs are more complicated than we think
• O*NET data base contains a rich set of variables that describe work and worker characteristics, including skill requirements
• example of landscaping and groundskeeping workers
some of those tasks can be automated, but can all of them be automated = what fraction

Mehr Karteikarten anzeigen
Q:

Automation potential ≠ employment effects

A:

occupation-level studies overestimate automation potentials
• share of automatable jobs drops to about 9% for the U.S. (comparable figures in other countries) once job-level variation of tasks within occupations is taken into account
• still: insights from such estimates remain limited because estimated automation potentials only capture whether a job – given its contemporaneous task structure – could theoretically be done by a machine or not
the estimates tells nothing about actual job losses or employment effects

Q:

3 Reasons why automation potential ≠ employment effects 


A:

1. Technological Diffusion

2. Job creation and destruction

3. Employment effects

Q:

Development of the labour market (Overview)

A:
  • 1935: “Thinking Machine Replace the Thinker
  • same now: “Smart robot could soon steal your job”
  • there have always been strong changes in the Employment-to-population ratio (shows the proportion of a country's working-age population that is employed)

USA

  • shared increased in the last 70 years roughly from 56% to 65 % in the year 2000
  • first decline = 1980 dotcom bubble
  • second decline = 2008 in the financial crisis
  • increase to again 61 % in 2019

EU 

  • ratio was larger in 2017 than in 1992
  • also a decline in EU in 2008 for financial crisis

Germany

  • nowadays around 58% → more people in employment than ever in the past
  • even though there are more robots
  •  no decline within the last 30 years
  • negative trend on the German labor market in the late 1990s
  • but then strong positive trend
Q:

Polarization in the european labour market: employment

A:

• increase in the number of people employed in job that tend to be low skilled (food, cleaning, personal care protective service etc.)
• increase in more high skilled jobs
• decrease in the middle skilled jobs
• polarization = more jobs at the lower end of the skill distribution and more jobs on the higher end of the skill distribution + job losses in the middle of the skill distribution

Q:

What will happen in the future? (Autor)

A:

machine learning (ML) could eventually solve Polanyi‘s paradox (“we know more than we can tell”) by applying statistics and inductive reasoning
• even if we are unable to program a machine to “simulate” a nonroutine task by following an algorithm, we may nevertheless be able to program a machine to master the task autonomously by studying successful examples of the task being carried out by others
• through a process of exposure, training, and reinforcement, machine learning algorithms may potentially infer how to accomplish tasks that have proved dauntingly challenging to codify with explicit procedures

Q:

Change in the Employment Shares/Jobs

A:

Structural Change in the Employment Share

  • less Agricutlure, Operatives/laborers, skilled blue collar (craft), but therefor increase in professional/technical, Managers and Service → higher need for high skilled jobs and less need for low skilled jobs

The Spreadsheet Apocalypse

  • Jobs in book keeping such as accounting and auditing clerks plummeted after the introduction of spreadsheet software, but jobs in management analysts and financials increased significantly

 ATM and bank sellers

  • as more ATMS were installed in the US the number of tellers employed didn’t drop
  • the jobs changed in the sense they had other things to do, but it is not that everyone lost their jobs
Q:

Frey and Osborne (2017)

A:

 47% of total US employment is at risk of automation in the next one or two decades
• definition of an occupation as “at high risk” of automation if the estimated automation potential ≥ 70%
• Authors calculate the technical possibility of automating a job (could be interpreted as automation potential, but does NOT capture the probability that a job is actually automated)


Methodological drawbacks
• focus on occupational level
→ assumption: everyone within the same occupation conducts exactly the same tasks as described in the O*NET data base.
→ however, tasks do vary substantially between workers of the same occupation (Autor and Handel 2013)


Spitz-Oener (2016):
→ average occupational task structures do not sufficiently mirror task heterogeneity within occupations → overestimation of automation potentials


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