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EXPANDING OPPORTUNITIES<br />
135<br />
Box 2.10 Digital technologies and economic opportunities: A gender<br />
lens (continued)<br />
the internet could actually delay necessary reforms. For<br />
example, home-based work could help connect women<br />
to work in environments where social norms or child care<br />
responsibilities are a barrier to women working outside<br />
the home. But if working outside the home continues to be<br />
seen as unacceptable for women or if there is no availability<br />
of affordable child care, technology could end up delaying<br />
fundamental reforms. Addressing these underlying constraints<br />
remains key to the gender and overall economic<br />
agenda.<br />
a. World Bank 2011.<br />
b. World Bank 2014b. In Rwanda, the Land Tenure Regularization Programme demarcated and digitized 10 million plots. Households that registered their<br />
land were more likely to invest in it, and this effect was twice as strong for female-headed households (Ali, Deininger, and Goldstein 2014).<br />
c. Oster and Millett 2013.<br />
d. Rendall 2010; Weiberg 2000.<br />
e. Black and Spitz-Oener 2007; Rendall 2010; Weiberg 2000.<br />
f. Rendall 2010; Autor and Price 2013; Black and Spitz-Oener 2007.<br />
g. WDR 2016 team, based on STEP household surveys (World Bank, various years).<br />
h. Cortes and others 2014.<br />
i. La Ferrara, Chong, and Duryea 2012; Jensen and Oster 2009.<br />
j. Seol and Santos 2015.<br />
k. Aker and others 2014.<br />
l. GSMA 2015.<br />
m. Gomez (2014) shows, for developing countries, that women—unlike men—prefer using the internet in public libraries rather than in private cybercafés<br />
because they are safer and despite poorer service.<br />
n. WDR 2016 team calculations, based on Research ICT Africa surveys (various years).<br />
o. Intel and Dalberg Global Development Advisors 2012.<br />
p. Klonner and Nolen 2010.<br />
better-paying jobs, skills need to be upgraded. Current<br />
and future workers need to develop the lifelong cognitive,<br />
technical, and socioemotional skills required<br />
of a well-educated worker in the 21st century. Workers<br />
also need to be capable of processing the everincreasing<br />
information available on the internet.<br />
Building these skills requires actions affecting all<br />
relevant environments for learning: families, schools,<br />
universities, training systems, and firms. Given the<br />
speed of technological changes, these skills will also<br />
require constant updating throughout the life cycle as<br />
workers prepare for careers that last more than one<br />
job. Digital technologies themselves can help (sector<br />
focus 2 and chapter 5). Complementary reforms are<br />
also needed in tax policy, social protection, and labor<br />
market institutions to facilitate the transition of<br />
workers from old economy jobs to new economy jobs,<br />
and address the distributional consequences of the<br />
digital revolution.<br />
Notes<br />
1. World Bank 2014c.<br />
2. Throughout this chapter, “opportunities” refer to<br />
people’s short- and long-term capacity to generate<br />
income (Bussolo and Calva 2014). In addition, and<br />
taking a wide perspective, it is also used to include<br />
gains to consumers.<br />
3. WDR 2016 team, based on STEP surveys (World<br />
Bank, various years); Central Asia World Bank Skills<br />
surveys (World Bank, various years); Survey-based<br />
Harmonized Indicators Program (SHIP) (World<br />
Bank, various years); Socio-Economic Database<br />
for Latin America and the Caribbean (SEDLAC)<br />
(CEDLAS and the World Bank); South Asia Region<br />
MicroDatabase (SARMD) (World Bank, various<br />
years); Europe and Central Asia Poverty (ECAPOV)<br />
Database (various years); East Asia Pacific Poverty<br />
(EAPPOV) Database (World Bank, various years);<br />
the I2D2 dataset (International Income Distribution<br />
Database) (World Bank, various years); ILO<br />
Laborsta database (ILO, various years); and the<br />
National Bureau of Statistics of China (various<br />
years). Automation probabilities adapted from Frey<br />
and Osborne (2013).<br />
4. WDR 2016 team calculations, based on ILO Key<br />
Indicators of the Labour Market (KILM; various<br />
years), ILO Laborsta database (various years), World<br />
Bank’s International Income Distribution Database<br />
(I2D2; various years), and the National Bureau of<br />
Statistics of China (various years). For more details,<br />
see figure 2.15.<br />
5. WDR 2016 team calculations, based on World Development<br />
Indicators (World Bank, various years).