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

115 years 198 years

Technological breakthroughs are speeding up

Mobile Internet First phone call 1876 First website 1991 First iPhone 2007 16 years Hargreaves’ Jenny 1764 GM’s Unimate 1962 Google’s Schaft 2010 48 years

SOURCE: McKinsey Global Institute analysis

Internet of things Machine intelligence Advan- ced robotics

An unprecedented pace of penetration

20 40 60 80 100

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

improved water improved sanitation secondary school enrollment mobile phone internet mobile broadband

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

42%

3.63 billion

50%

2.03 billion

28%

Source: wearesocial.sg, September 2014

… Across developed and developing countries

101 % 89 % 67 % 129 % 151 % 112 % 124 % 90 % 72 % 92 % 109 % 94 %

Mobile Penetration, January 2014 Source: wearesocial.sg, January 2014

Alth though h access ss and use se of dig igit ital te techn hnol

• logies,

ies, esp special cially t the he in inte ternet, et, is is ve very unequal

18 12 23 10 20 8 11 4

Men Women Urban Rural Age 15-24 Age 45+ Upper 60% * Bottom 40%*

Africa: Percentage of individuals who report ever using the internet (%) (2012)

Source: WDR 2016, based on Research ICT Africa RIA survey.

Improving welfare and reducing poverty? Exacerbating inequality?

Growing Opportunities

Threats of Disruption and Exclusion

The increasing use of digital technologies is creating jobs …

Source: OECD 2017.

Contribution of ICT sector to total employment (circa 2012)

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 jo jobs s is s lar arger

Contribution of ICT to employment (2012-2013)

Source: WDR 2016, based on STEP household surveys. OECD definitions for ICT sector and occupations.

5 10 15 20 25 30 Share of employment (%) ICT Sector ICT Occupations ICT Intensive

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 complements some Workers (skill- biased) But technology can substitute

• thers (labor-

saving)

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

• utin

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)

20 25 30 35 40 45 50 Share in total employment (%)

OECD countries

Non-routine cognitive or inter-personal Routine cognitive or manual Non-routine manual

20 25 30 35 40 45 50 Share in total employment (%)

Developing countries

Non-routine cognitive or inter-personal Routine cognitive or manual Non-routine manual

Source: WDR 2016, based on ILO KILM data. Skills classification follows Autor (2014).

Dem emand of

• f skills

lls is shifting ting to toward ards s job

• bs re

requi uiring ring bot

• th non
• n-routine

routine cog

• gnitive

itive/tec /techni hnical cal an and soc

• cio

io-em emot

• tional

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)

Source: WDR 2016, based on ILO KILM data. For China, data from the Population Census for 2000 vs 2010.

Change in employment shares in selected developing countries: 1993-2010

• 2
• 1.5
• 1
• 0.5

0.5 1 1.5 2

Annual average change in employment share (percentage points)

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)

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)

Source: Park and du Yang 2017, forthcoming for China’s Sources of Growth Study.

• .06-.05-.04-.03-.02-.01

.01 .02 .03 .04 .05 .06 2000 2005 2010 2015 year

• .06-.05-.04-.03-.02-.01

.01 .02 .03 .04 .05 .06 2000 2005 2010 2015 year

• .06-.05-.04-.03-.02-.01

.01 .02 .03 .04 .05 .06 2000 2005 2010 2015 year

• .06-.05-.04-.03-.02-.01

.01 .02 .03 .04 .05 .06 2000 2005 2010 2015 year

Non-routine cognitive analytical Non-routine interpersonal Routine cognitive Routine manual

Use of robots in China is up 60% between 2010-15

Digital technologies are expected to take on

• r transform many jobs

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).

20 40 60 80 100 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 Share of employment that can be automated (%) Adjusted (technological feasability + adoption time lags)

Estimated share of employment that is susceptible to automation (%)

50%: Probability that a child in the developing world will find a job in an occupation

as they exist today

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

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).

Estimated impacts on employment (left) and wages (right)

• f exposure to robots in the US (%)

Techn hnol

• logical

ical ch change ge is is on

• ne key driv

iver r of th the fall in in labor sha shares s in in

• utp

tput t across ss th the world

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).
• 15
• 10
• 5

5 10 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 Labor share trends, percentage points every 10 years

Trends in labor shares in output every 10 years since 1975

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?

Source: The New Yorker; Cartoon by Zachary Kanin.

And hopefully we can avoid this!

Thank you!

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