Inequality and Technology: The Future of Jobs and Social Policy Omar - PowerPoint PPT Presentation

Inequality and Technology: The Future of Jobs and Social Policy Omar Arias, World Bank European Investment Bank , Luxembourg 2017

Technological breakthroughs are speeding up First phone First website First iPhone Internet of 1991 2007 call 1876 things 115 16 years years Mobile Internet Hargreaves’ GM’s Unimate Google’s Machine Jenny 1764 1962 Schaft 2010 intelligence 198 48 Advan- years years ced robotics SOURCE: McKinsey Global Institute analysis

An unprecedented pace of penetration 100 improved water mobile phone 80 improved sanitation 60 secondary school enrollment 40 internet 20 mobile broadband 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Note: Mobile phone and mobile broadband subscriptions, internet users, improved water and sanitation are per 100 individuals. Net secondary school enrollment is the percent of the relevant age group. Sources: World Bank, WDR on Internet and Development Team based on World Development Indicators and ITU data.

High, and growing use … 2.95 billion 3.63 billion 2.03 billion 42% 50% 28% Source: wearesocial.sg, September 2014

… Across developed and developing countries Mobile Penetration, January 2014 151 90 129 % 101 % % % 92 72 112 % % % 89 109 % 67 % % 94 124 % % Source: wearesocial.sg, January 2014

Alth though h access ss and use se of dig igit ital te techn hnol ologies, ies, esp special cially t the he in inte ternet, et, is is ve very unequal Africa: Percentage of individuals who report ever using the internet (%) (2012) 23 20 18 12 11 10 8 4 Men Women Urban Rural Age 15-24 Age 45+ Upper 60% * Bottom 40%* Source : WDR 2016, based on Research ICT Africa RIA survey.

Improving welfare and reducing poverty? Exacerbating inequality? Threats of Disruption and Growing Opportunities Exclusion

The increasing use of digital technologies is creating jobs … Contribution of ICT sector to total employment (circa 2012) Source : OECD 2017.

But t st stil ill l a sm a smal all sh shar are e of em f employ loyment, ment, whi hile le ICT intensity ensity of f jobs jo s is s lar arger Contribution of ICT to employment (2012-2013) 30 Share of employment 25 20 (%) 15 10 5 0 ICT Sector ICT Occupations ICT Intensive Source : WDR 2016, based on STEP household surveys. OECD definitions for ICT sector and occupations.

Impact act on em employment loyment an and ea earnings ings in ineq equality uality is is a ba a balance ance bet etween ween two wo fo forces rces Technology But technology can substitute complements others (labor- some saving) Workers (skill- biased) What matters is whether the task is ROUTINE (and can thus be automated) or NON-ROUTINE

Work k is is becomin ming g mo more in inte tensi sive ve in in no non-rou outin tine e sk skil ills, a s, and labor ma markets ets are polariz izing ing Skills-intensity of Employment (simple cross-country average by type of occupation) (2000-2012) OECD countries Developing countries 50 50 45 45 Share in total employment (%) Share in total employment (%) 40 40 35 35 30 30 25 25 20 20 Non-routine cognitive or inter-personal Non-routine cognitive or inter-personal Routine cognitive or manual Routine cognitive or manual Non-routine manual Non-routine manual Source: WDR 2016, based on ILO KILM data . Skills classification follows Autor (2014).

Dem emand of of skills lls is shifting ting to toward ards s job obs re requi uiring ring bot oth non on-routine routine cog ognitive itive/tec /techni hnical cal an and soc ocio io-em emot otional ional skill lls Source : Deming (2015), “The Growing Importance of Social Skills in the Labor Market”, NBER WP No. 21473

Labor r markets s in in the he devel eloping ping world are also becomin ing polarized ized (shi hift t towards rds non- routi tine ne skill ills) Change in employment shares in selected developing countries: 1993-2010 2 Annual average change in employment share 1.5 1 (percentage points) 0.5 0 -0.5 -1 -1.5 -2 High-skilled occupations (intensive in non-routine cognitive and interpsersonal skills) Middle-skilled occupations (intensive in routine cognitive and manual skills) Low-skilled occupations (intensive in non-routine manual skills) Source : WDR 2016, based on ILO KILM data. For China, data from the Population Census for 2000 vs 2010.

The he one nota table exceptio tion n to to l labor ma market et polariz ization tion tr trends s – Chi hina- is is no longer… Changes in the Skills-intensity of Employment in China (2000-2015) .01 .02 .03 .04 .05 .06 .01 .02 .03 .04 .05 .06 Non-routine Non-routine cognitive analytical interpersonal 0 0 -.06-.05-.04-.03-.02-.01 -.06-.05-.04-.03-.02-.01 2000 2005 2010 2015 2000 2005 2010 2015 year year .01 .02 .03 .04 .05 .06 .01 .02 .03 .04 .05 .06 Routine cognitive Routine manual 0 0 -.06-.05-.04-.03-.02-.01 -.06-.05-.04-.03-.02-.01 2000 2005 2010 2015 2000 2005 2010 2015 year year Use of robots in China is up 60% between 2010-15 Source: Park and du Yang 2017, forthcoming for China’s Sources of Growth Study.

Digital technologies are expected to take on or transform many jobs 50% : Probability that a child in the developing world will find a job in an occupation as they exist today 100 Share of employment that can be automated 80 Estimated share of employment that is susceptible to automation (%) 60 (%) 40 20 0 HRV CYP LVA MLT LTU CHN OECD ALB THA ROU ECU CRI MYS MUS ZAF SRB PAN GTM ARG SLV BGR SYC ETH UKR PSE GEO KSV Adjusted (technological feasability + adoption time lags) Source: World Bank 2016, based on household surveys, the Income Distribution Database (I2D2),ILO Laborsta database, China’s Population Census, Frey and Osborne 2013, and Comin and Hobjin (2010).

Ne Newer r techno chnologie ies s (e.g, , robots) ts) can ha have net negative ative labor im impacts s by dis ispl placing cing workers s altoget ether her Estimated impacts on employment (left) and wages (right) of exposure to robots in the US (%) Source: WDR 2016 team, based on household surveys, the Income Distribution Database (I2D2),ILO Laborsta database, China’s Population Census, Frey and Osborne 2013, and Comin and Hobjin (2010).

Techn hnol ological ical ch change ge is is on one key driv iver r of th the fall in in labor sha shares s in in outp tput t across ss th the world 10 Trends in labor shares in output every 10 years since 1975 Labor share trends, percentage points every 10 years 5 0 -5 -10 -15 POL MEX HUN EST BHR SVN LTU ZAF NOR LUX FSM NAM LVA NZL CHN FIN TUN ARG SVK GER AUT SWE FRA ITA AUS TWN CAN JPN DNK CHE USA NLD BEL CZE ESP MAC SGP GBR PRT BOL TUR ARM COL KEN THA CRI ISL BLR MDA KOR UKR BRA Source : Karabarbounis, L. and B. Neiman (2013) Note : The figure shows estimated trends in the labor share for all countries in data set with at least 15 years of data starting in 1975. Trend coefficients are reported in units per 10 years (i.e., a value of 5 means a 5 percentage point decline every 10 years).

Policies have to adapt to new realities…  Technology changes the skills required to succeed in a modern economy.  Technology also accelerates the pace of change, making skills obsolete more quickly and opening up new opportunities.  Technology further changes the world of work, introducing new forms of work and allowing for more flexible work arrangements but also eroding traditional employer-employee and social protection schemes.

Policy Implications: Strengthening life long-learning and training programs  Equip future workers with the skills that are complementary to technology: foundational (cognitive and socio-emotional) skills, digital skills;  Schools need to shift from rote learning to nurturing “learning to learn”  Refocus training programs to equip workers with both foundational and technical skills  Improve incentives for life-long learning  For individuals  Training accounts  For industries  Work with sector-wide trade and employer unions to co-finance training and retraining in sector-specific, but not firm-specific training  For firms  Subsidies for firms to provide non-firm, non-sector specific training

Policy Implications: Rethinking social protection schemes – shift to protections delinked from the job  Technology can make too strict labor regulations more binding (need for workforce reorganization)  All individuals should be registered in the same social insurance system, regardless of where they work, with subsidies for the poor or low-wage earners.  Strengthen the link between employment services, post- secondary educational institutions and the private sector using technology throughout the service chain  Raises several policy issues:  How to move away from an insurance system designed with little careers disruptions and stable formal employment in mind?  How to support workers that are not able to work or earn enough to afford a basic standard of leaving and coverage? Universal basic income?  How to finance social insurance if labor taxation becomes less desirable?

And hopefully we can avoid this! Source : The New Yorker; Cartoon by Zachary Kanin.

Thank you! CHECK OUT THE WORLD DEVELOPMENT REPORT 2016