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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
Alternatives
An unprecedented pace of penetration
0
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
Although access and use of digital technologies, especially the internet, is
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 still a small share of employment, while ICT intensity of
jobs is larger
Contribution of ICT to employment
(2012-2013)
Source: WDR 2016, based on STEP household surveys. OECD definitions for ICT sector and occupations.
0
5
10
15
20
25
30
Sh
are
of
em
plo
ym
en
t
(%)
ICT Sector ICT Occupations ICT Intensive
Impact on employment and earnings inequality is a balance
between two forces
Technology
complements
some
Workers (skill-
biased)
But technology
can substitute
others (labor-
saving)
What matters is whether the task is ROUTINE (and can thus be
automated) or NON-ROUTINE
Work is becoming more intensive in non-routine skills, and labor markets
are polarizing
Skills-intensity of Employment (simple cross-country average by type of occupation)
(2000-2012)
20
25
30
35
40
45
50
Sh
are
in t
ota
l em
plo
ym
en
t (%
)
OECD countries
Non-routine cognitive or inter-personal
Routine cognitive or manual
Non-routine manual
20
25
30
35
40
45
50
Sh
are
in t
ota
l em
plo
ym
en
t (%
)
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).
Demand of skills is shifting towards jobs requiring both non-routine cognitive/technical
and socio-emotional skills
Source: Deming (2015), “The Growing Importance of Social Skills in the Labor Market”, NBER WP No. 21473
Labor markets in the developing world are also becoming polarized (shift towards non-
routine skills)
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
0.5
1
1.5
2
An
nu
al a
ve
rag
e c
ha
ng
e in
em
plo
ym
en
t sh
are
(pe
rce
nta
ge
po
ints
)
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 one notable exception to labor market polarization trends – China- 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
0
.01
.02
.03
.04
.05
.06
stand
ard
devia
tion
from
200
0 m
ean
2000 2005 2010 2015year
-.06
-.05-
.04-
.03
-.02-
.01
0
.01
.02
.03
.04
.05
.06
stan
dard
dev
iatio
n fro
m 2
000
mea
n
2000 2005 2010 2015year
-.06 -
.05-
.04-
.03-
.02-
.01
0
.01
.02
.03
.04
.05
.06
stan
dard
dev
iatio
n fro
m 2
000
mea
n
2000 2005 2010 2015year
-.06
-.05
-.04
-.03
-.02
-.01
0
.01
.02
.03
.04
.05
.06
stan
dard
dev
iatio
n fro
m 2
000
mea
n
2000 2005 2010 2015year
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
or 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).
0
20
40
60
80
100
HR
V
CY
P
LVA
MLT
LTU
CH
N
OEC
D
ALB
THA
RO
U
EC
U
CR
I
MY
S
MU
S
ZA
F
SR
B
PA
N
GTM
AR
G
SLV
BG
R
SY
C
ETH
UK
R
PSE
GEO
KSV
Sh
are
of
em
plo
ym
en
t th
at
ca
n b
e a
uto
ma
ted
(%)
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
Newer technologies (e.g, robots) can have net negative labor impacts by
displacing workers altogether
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)
of exposure to robots in the US (%)
Technological change is one key driver of the fall in labor shares in
output across 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
0
5
10
PO
L
MEX
HU
N
EST
BH
R
SV
N
LTU
ZA
F
NO
R
LUX
FSM
NA
M
LVA
NZL
CH
N
FIN
TUN
AR
G
SV
K
GER
AU
T
SW
E
FR
A
ITA
AU
S
TWN
CA
N
JP
N
DN
K
CH
E
USA
NLD BEL
CZE
ESP
MA
C
SG
P
GB
R
PR
T
BO
L
TUR
AR
M
CO
L
KEN
THA
CR
I
ISL
BLR
MD
A
KO
R
UK
R
BR
A
Lab
or
sha
re tre
nd
s, p
erc
en
tag
e p
oin
ts e
ve
ry 1
0 y
ea
rs
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!
CHECK OUT THE
WORLD DEVELOPMENT REPORT 2016