Lecture 9: Wage inequality and employment polarization

Transcription

1 Lecture 9: Wage inequality and employment polarization Natalia Zinovyeva March 21, 2017 Lecture Slides 1 / 63

2 Sources: Today s lecture is partly based on the following two articles (available at MyCourses): Autor, David (2014a), Skills, Education, and the Rise of Earnings Inequality Among the Other 99 Percent, Science, 344 (6186), Autor, David (2014b), Polanyi s Paradox and the Shape of Employment Growth, NBER working paper / 63

3 Previous day: Increase in wage inequality (all over the board) Mostly explained by the increase in the skill premium Possible explanation: SBTC skilled and unskilled labor are gross substitutes Tinbergen s race between technology and education Katz and Murphy (1992) Other stories 3 / 63

4 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 4 / 63

5 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 5 / 63

6 Technology and Occupational Tasks The SBTC literature assumes that technology increases the productivity of all workers, but more strongly for skilled workers. Autor, Levy, and Murnane (QJE 2003) pointed out that this view of technology has major shortcomings: Technology may substitute for human labor instead of complementing it to different degrees. Exposure to technology arguably varies primarily according to occupational tasks, and not according to workers education. 6 / 63

7 Tacit versus Explicit Knowledge Michael Polanyi, The Tacit Dimension, 1966 We can know more than we can tell... The skill of a driver cannot be replaced by a thorough schooling in the theory of the motorcar; the knowledge I have of my own body differs altogether from the knowledge of its physiology. Two implications of Polanyi s Paradox: 1. Technical: Cannot automate what we don t explicitly understand E.g.: Tasks that require physical flexibility, judgement, intuition, persuasion, common sense. 2. Economic: Tasks that are not substituted by machines are typically complemented by machines E.g.: Economic statistics, medicine. But also construction work complemented by cranes, excavators, pneumatic nail guns, etc. 7 / 63

8 ALM (2003): Routine and Non-Routine Tasks ALM s key observation is that computers are good at doing routine tasks which are precisely specified work processes that are readily formalized and programmed in computer code. By contrast, computers struggle (so far!) to do non-routine tasks. These are often subdivided into two groups: abstract (= non-routine cognitive and interactive) tasks which involve problem solving, creativity, or complex decision making and (non-routine) manual tasks which involve basic verbal communication, visual recognition, or fine motor skills. 8 / 63

9 ALM (2003): Task Framework ALM (2003): Task Framework Routine and Non-Routine Tasks [ALM 2003] Figure: Routine and Non-Routine Wage Inequality Tasks [ALM 2003] 42/88 9 / 63

10 ALM (2003): Task Inputs ALM suggest that firms will use more computing capital as the price of computers falls over time. As a consequence, 1. the demand for routine tasks should decline (substituted by computers) 2. the demand for abstract tasks should increase (complemented by computers) 3. and what about manual tasks??? 10 / 63

11 Composition of Low-Skill Service Occupations, 2005 AD (2013): Low-Skill Service Occupations Composition of Service Occupations, 2005 [AD 2013] Figure: Source: Autor and Dorn (AER 2013) Wage Inequality 57/88 11 / 63

12 ALM (2003): Task Inputs ALM suggest that firms will use more computing capital as the price of computers falls over time. As a consequence, 1. the demand for routine tasks should decline (substituted by computers) 2. the demand for abstract tasks should increase (complemented by computers) 3. the authors make no clear prediction for manual tasks (neither substituted nor complemented) 12 / 63

13 ALM (2003): Task Data The Dictionary of Occupational Titles from the U.S. Bureau of Labor Statistics provides observational measures of job content in 318 occupations. Quantify the degree to which occupations involve routine, abstract, and manual tasks. 13 / 63

14 Task Inputs in the U.S. Economy 1296 QUARTERLY JOURNAL OF ECONOMICS Nonroutine analytic?? Nonroutine interactive?x? Nonroutine manual - -*? Routine cognitive -? - Routine manual Figure I Figure: Task Inputs in the U.S., [ALM 2003] By construction, each task variable has a mean of 50 centiles in Trends in Routine and Nonroutine Task Input, 1960 to 1998 Figure I is constructed using Dictionary of Occupational Titles [1977] task measures by gender and occupation paired to employment data for 1960 and 1970 Census and 1980, 1990, and 1998 Current Population Survey (CPS) samples. Data are aggregated to 1120 industry-gender-education cells by year, and each cell is assigned a value corresponding to its rank in the 1960 distribution of task input (calculated across the 1120, 1960 task cells). Plotted values depict the employment-weighted mean of each assigned percentile in the indicated year. See Subsequent points depict the employment-weighted mean of each assigned percentile over each decade. 14 / 63

15 ALM (2003): Results Increases in abstract and decreases in routine labor input are particularly large 1. in industries that adopt more computer capital 2. in industry-education cells that rapidly computerize 3. in occupations that computerize These results corroborate the hypothesis that computers substitute for routine labor and complement abstract labor. 15 / 63

16 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 16 / 63

17 Goos and Manning (REStat 2007): Job Polarization Goos and Manning (REStat 2007) noticed an important feature of the ALM task analysis: The routine tasks which can be substituted by computers are usually found in jobs in the middle of the occupational spectrum. A reduction in routine jobs might therefore lead to employment polarization: 1. a falling employment share of occupations with intermediate education levels and wages (because these tend to be most intensive in routine tasks) 2. an increasing employment share of occupations with high education levels and wages (intensive in abstract tasks) 3. no dramatic change in the employment share of occupations with low education levels and wages (intensive in manual tasks) 17 / 63

18 (Acemoglu and Autor, 2011). Employment growth in these occupations was robust throughout the three decades plotted. Even in the deep recession and incomplete recovery between 2007 Employment Polarization in the U.S., [Autor and 2012, these occupations experienced almost no absolute decline in employment. 2014b] Figure 2. Percentage Changes in Employment by Major Occupational Category, Changes in Employment by Occupation, x Log Change in Employment Protective Service Food/Cleaning Service Personal Care Sales Office/Admin Production Operators/Laborers Technicians Managers Professionals Notes. 1980, 1990 and 2000 Census IPUMS files, American Community Survey combined file , and American Community Survey Sample includes the working- age (16-64) civilian non- institutionalized population. Employment is measured as full- time equivalent workers. In 1979 middle-skill occupations (Operators, Production, Office/Admin, Sales) accounted for 60% of employment. In 2012, only 46%. Moving leftward, the next four columns display employment growth in middle- skill 18 / 63

19 Figure 8 takes a closer look at this phenomenon by plotting the distribution of occupational employment changes among college- educated (panel A) and non- college (panel B) workers Employment Polarization in the U.S., [Autor across the three broad occupational categories above: manual- intensive, routine- intensive, and 2014b] abstract- intensive. 30 Figure 7. Smoothed Employment Changes by Occupational Skill Percentile, Smoothed Employment Changes by Skill Percentile Among All Workers 100 x Change in Employment Share Skill Percentile (Ranked by Occupation s 1979 Mean Log Wage) / 63

20 Employment Polarization EmploymentPolarizationinSixteen in the E.U. European Union Countries, % 15% Low Paying MiddlePaying High Paying 12% 9% 6% 3% 0% -3% -6% -4.9% -8.6% -8.5% -7.6% -6.7% -9% -10.9% -10.8% -10.7% -10.6% -10.6% -10.4% -10.3% -9.6% -12% -12.1% -12.0% -15% -14.9% -18% Goos, Manning and Salomons, 2014 Figure: European Union, [Goos/Manning/Solomons 2014] 20 / 63

21 Non-monotonicity in Employment Growth Across Skill Levels Most employment polarization graphs show that employment growth is largest in the highest skilled jobs. If we just distinguished between two groups of high-skill and low-skill jobs, then we would find a relative growth of high-skill employment. However, when we look at occupation terciles or deciles, then we realize that job losses are most concentrated towards the middle of the labor market and not at the bottom. 21 / 63

22 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 22 / 63

23 Impact of Automation on Earnings (i) 1. Substitution vs. complementarity of labor and computers Workers benefit if their job tasks are complemented, but not if tasks are substituted. 2. Elasticity of final demand Can either dampen or amplify the gains from automation E.g.: Agriculture vs. healthcare 3. Labor supply elasticity Can mitigate wage gains. E.g.: Why wages are not likely to rise rapidly in foodservice and haircutting? 23 / 63

24 Impact of Automation on Earnings (ii) 1. Abstract task-intensive occupations (financial analysis, economic statistics, sales, legal cases, medical reports,...) Strong complementarity with new technologies Elastic demand of the services produced Relatively inelastic labor supply. 2. Manual task-intensive occupations (janitors, cleaners, drivers, flight attendants, food service workers, personal care assistants,...) Manual tasks are hard to automate: limited opportunities for either direct complementarity or substitution Relatively high income elasticity for its products The outputs of service occupations are not easily storable or transportable. Therefore, these occupations are not threatened by trade or offshoring. On the flip-side, low education and training requirements elastic labor supply 24 / 63

25 Autor and Dorn (AER 2013): Model Consider an economy that produces goods Y g and low-skill services Y s. There are four production inputs: 1. Abstract labor L a 2. Routine labor L r 3. Manual labor L m 4. Computer capital K which provides routine tasks 25 / 63

26 Autor and Dorn (AER 2013): Low-Skill Service Sector Production of low-skill services uses only manual labor L m : Y s = α s L m where α s > 0 is an efficiency parameter. 26 / 63

27 Autor and Dorn (AER 2013): Goods Sector Production of goods uses abstract tasks (provided by workers) and routine tasks (provided by workers and/or computers): with β, µ (0, 1). Y g = L 1 β a [(α r L r ) µ + (α k K) µ ] β/µ Assume σ r = 1 1 µ > 1 so that computer capital K is a relative substitute for routine labor. 27 / 63

28 Autor and Dorn (AER 2013): Workers Each high-skill worker has one unit of abstract skill and inelastically supplies abstract labor to the goods sector. Each low-skill worker has one unit of manual skill but there is heterogeneity in routine skill which takes values of 0 < η i 1. Unskilled workers will self-select into routine or manual labor depending on their relative skill endowment and relative skill prices. A worker with routine skill η i will supply routine labor whenever w m η i w r 28 / 63

29 Autor and Dorn (AER 2013): Capital Technological progress is modeled as over-time increase in the efficiency of production of K With time, the price of K decreases. 29 / 63

30 Autor and Dorn (AER 2013): Consumers All workers/consumers have identical CES utility functions defined over consumption of goods and services: where ρ < 1 u = (c ρ g + c ρ s )1/ρ The elasticity of substitution in consumption between goods and services is σ c = 1/(1 ρ). The model allows the authors analyzing how labor allocation and wages change in the long run when the price of computers tends towards zero. 30 / 63

31 Autor and Dorn (AER 2013): Summary of results Supply effect 1. Computer price falls. 2. The wage of routine labor falls because firms can buy cheap computer capital instead. 3. Low-skilled workers move from routine occupations into manual (service) occupations. 4. This worker reallocation increases employment but depresses wages in service occupations. 31 / 63

32 Autor and Dorn (AER 2013): Summary of results Demand effect 1. Computer price falls. 2. The relative cost of goods production decreases because of cheaper computer inputs, and the relative price of goods to services falls. 3. If goods are poor substitutes for services in consumption, then consumers will use part of the money that they save due to falling goods prices to buy more services. 4. Increasing demand for services raises both employment and wages in service occupations. 32 / 63

33 Autor and Dorn (AER 2013): Summary of results The model predicts that falling computer prices will: 1. raise wages and employment of high-skill workers in abstract tasks 2. lower wages and employment of low-skill workers in routine tasks 3. raise employment of low-skill workers in manual tasks (service occupations) while wages in manual tasks can either fall (due to supply effect) or increase (due to demand effect) 33 / 63

34 Wage Polarization in the U.S., [Autor 2014b] Figure 5. Changes in Mean Wages by Major Occupational Category among Full- Time, Full- Year Workers, x Log Change in Mean Weekly Wages Changes in Mean Weekly Wages by Occupation, Full Time Full Year Workers Personal Care Food/Cleaning Service Protective Service Operators/Laborers Production Office/Admin Sales Technicians Professionals Managers Notes. Calculated using 1980, 1990 and 2000 Census IPUMS files; American Community Survey combined file , American Community Survey Sample includes the working- age (16-64) civilian non- institutionalized population with 48+ annual weeks worked and 35+ usual weekly hours. Weekly wages are calculated as annual earnings divided by weeks worked. The right- hand two thirds of these wage figures look much like the plots of employment 34 / 63

35 Wage Polarization in the U.S., [Autor 2014b] Figure 6. Changes in Mean Wages by Occupational Skill Percentile among Full- Time, Full- Year Workers, Smoothed Wage Changes by Skill Percentile Among All Workers 100 x Log Change in Mean FTFY Wage Skill Percentile (Ranked by Occupation s 1979 Mean Log Wage) Notes. Calculated using 1980, 1990 and 2000 Census IPUMS files; American Community Survey combined file , American Community Survey The figure plots changes in mean log wages by 1980 occupational skill percentile rank using a locally weighted smoothing regression (bandwidth 0.8 with 100 observations), where skill percentiles are measured as the employment- weighted percentile rank of an occupation s mean log wage in the Census IPUMS percent extract. Sample includes the working- age (16-64) civilian non- institutionalized 35 / 63

36 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 36 / 63

37 Automation/computerization has progressed 37 / 63

38 The Future of Polanyi s Paradox 1. Automation and computerization have progressed 2. Polanyi s paradox remains central Explain what has not yet been accomplished Illuminates the contours of the technological frontier 3. Overcoming Polanyi s Paradox Machine Learning: Attempts an end-run around it 38 / 63

39 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 39 / 63

40 Machine Learning Machine learning Apply statistics and inductive reasoning to supply best guess answers in cases where formal procedures are unknown Problem Cannot program a machine to simulate a non-routine task by following a scripted procedure Solution Program a machine to master the task autonomously by studying successful examples of the task being carried out by others Process of exposure, training and reinforcement (atheoretical brute force technique) 40 / 63

41 Machine Learning Some examples Image recognition of a cat You can try to explain the machine how a cat looks like 41 / 63

42 What is a cat? 42 / 63

43 Is this also a cat? 43 / 63

44 And what about this? 44 / 63

45 Machine Learning Some examples Image recognition of a cat You can explain the machine how a cat looks like Or maybe you can provide examples! 45 / 63

46 Machine Learning How Many Computers to Identify a Cat? Google used an array of 16,000 processors to create a neural network with more than one billion connections They then fed it random thumbnails of images, one each extracted from 10 million YouTube videos. For some information on the latest developments in the field, you may want to watch this Ted Talk: How we re teaching computers to understand pictures 46 / 63

47 What is a cat? Images of Cats Images Successfully of CatsSuccessfulyRecognizedbyGoogle X Labs Team, using X Labs a Neural Network of 16,000 Team, Processors Using a NeuralNetworkof 16,000 Processors Le, Ranzato, Moga, Devin, Chen, Corrado, Dean and Ng, 2012 Figure: Source: Le, Ranzato, Moga, Devin, Chen, Corrado, Dean and Ng (2012) 47 / 63

48 Machine Learning Other examples Book/Music recommendations Amazon vs. the traditional bookseller, Spotify vs. record store Sound recognition Recognize different bird species 48 / 63

49 Machine Learning What are the limits? Image recognition of a chair it is not so much about how it looks like (e.g. it possesses legs, arms, a seat and a back) what makes an object a chair is that it is a device purpose-built for a human being to sit upon this purposiveness may be difficult for a machine learning algorithm to infer 49 / 63

50 Jobs that will definitely exist in 50 years [Autor 2015]] 1. Technical and creative experts and leaders 2. Medical professionals 3. Developers and testers of new ideas 4. Teachers, especially K Entertainers: Athletes, Musicians, Actors, Chefs, Comedians 6. Building and installation workers 7. Skilled repair workers 8. Personal helpers, coaches, assistants and consultants 50 / 63

51 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 51 / 63

52 Are we running out of jobs? Very old discussion... Luddites 52 / 63

53 Luddites in action Figure: Engraving from the Penny magazine, / 63

54 Are we running out of jobs? Lump of labor fallacy: an increase in labor productivity inevitably reduces employment because there is only a finite amount of work to do Green revolution In 1900, 41% of the US work force was employed in agriculture By 2000, the share has fallen to 2% But short-term losses sparked by rising productivity were eventually offset by subsequent employment gains in other sectors Industrial revolution Skilled artisanal labor replaced with unskilled factory labor But work force nowadays is higher than ever 54 / 63

55 the sorting, filing, checking, calculating, remembering, comparing, okaying skills that are the Are special we preserve losing of the the office race worker.. against.. In the end, the as machines machine? continue to invade society, duplicating greater and greater numbers of social tasks, it is human labor itself at least, as we now think of labor that is gradually rendered redundant (pp ). Figure 1. Chicago Booth IGM Expert Poll: Impact of Automation on Employment and Wages 70% 60% A. Advancing Automation has Not Historically Reduced Employment in the United States... 63% 50% 40% 30% 25% 20% 10% 0% 8% 4% 0% Strongly Disagree Disagree Uncertain Agree Strongly Agree 5 The three threats perceived by the ad hoc committee were: the cybernation revolution; the weaponry revolution; and the human rights revolution. 55 / 63

56 Outline Occupational Tasks and Labor Market Polarization Technology and Occupational Tasks Employment Polarization Impact on Wages The Future Machine Learning Are we running out of jobs? Is Wage Inequality a Problem? 56 / 63

57 Is Wage Inequality a Problem? Rising wage inequality has drawn considerable attention from media, politics, and the general public. The labor market outcomes of High School Dropouts and High School Graduates are very worrying 57 / 63

58 Is Wage Inequality a Problem? There are many other different angles to look at this question: Incentives: High wages for skilled workers provide desirable incentives for investment in education and reward people whose work may spur future economic growth (managers, scientists, etc.). Social peace: Growing inequality may spur criminality and unrest. Social equality: Large income differentials may be perceived as unfair. Democracy: Potential risk of political capture. Fiscal concerns: Transfer expenses of a country increase when the wages of low-income earners fall. Economic mobility: Earnings inequality might lead to low economic mobility. 58 / 63

59 Great Gatsby Curve Generational Earnings Elasticity (Less Mobility >) Sweden Finland Denmark Norway Germany New Zealand Canada Italy France Japan Australia United Kingdom United States Income Inequality (More Inequality >) Figure: Earnings Inequality and Economic Mobility: Cross-national relationships [Corak 2013] 59 / 63

60 No Decline in Intergenerational Income Mobility So far no for decline U.S. Children in intergenerational Born 1971 income 1974 mobility vs (at 1982 least in U.S.) Mean Child Income Rank Parent Income Rank Mean percentile income rank of children at ages vs. percentile rank of their parents for three groups of birth cohorts: , , and Figure: Mean percentile income rank of children at ages vs. percentile rank of their parents for three groups of birth cohorts: , , and [Chetty, Hendren, Kline, Kline, Saez andsaez Turnerand 2014 Turner 2014] 60 / 63

61 No Decline in Intergenerational Educational Mobility... or in for intergenerational U.S. Children Born educational mobility vs Percent in College at Age 19 20% 40% 60% 80% 100% Parent Income Rank Figure: Mean Meanpercentile percentileincome incomerank rankof ofchildren at at ages vs. percentile percentile rank of rank their of parents their parents for three for three groups groups of birth of birth cohorts: cohorts: , , and , , and [Chetty, Hendren, Kline, Saez and Turner 2014] Chetty, Hendren, Kline, Saez and Turner / 63

62 But nonetheless the future is worrying Per Capita Enrichment Expenditures on Children [Duncan and Murnane, 2011] Per Capita Enrichment Expenditures on Children ($2008) Top versus Bottom Quartileof Households $10,000 $7,500 Top incomequintile Bottomincome quintile 6,975 8,872 $5,000 5,650 $2, ,536 1,264 1,173 1,315 $ Source: Duncan and Murnane, / 63

63 Conclusion 1. Rising inequality is mostly (not entirely) about skills Still a meritocracy rather than a pure plutocracy 2. Rising skill returns largely due to two forces: Slowing supply of new college graduates after 1980 Secularly rising demand for human expertise, creativity, adaptability 3. Recent technological changes lead to job polarization But does not necessarily raise wages for service workers 4. Intergenerational mobility has not (yet) declined But reason for worry : Labor market Marriage market Adverse impact on children 63 / 63