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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).

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