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20 WORLD DEVELOPMENT REPORT 2016 Box O.5 What Facebook “Likes” reveal—the convenience-privacy trade-off When economists such as George Stigler and Richard Posner wrote about privacy and economics in the early 1980s, they raised many issues debated today, but at that time the “storage and retrieval of information, and its accurate dissemination, [were] often extremely expensive.” a Today, an enormous collection of identifiable information is making service delivery more efficient and more relevant. Service providers can better target or price their products based on known characteristics and preferences. Search engines provide more relevant search results. Health and auto insurers can better price premiums with verifiable information about exercise or driving behavior. And governments can use data systems to reduce the bureaucratic burden for citizens. In Estonia’s e-government system, citizens never have to provide the same information twice. The problem is that few people know how these large amounts of data are collected and used—and who controls them. Users are not always aware of and providers often don’t tell what information is collected. The secret snooping by governments can be for legitimate law enforcement reasons, but sometimes violates laws and rights, as the Edward Snowden revelations about spying by the security agencies of the United States, the United Kingdom, and others have shown. A consequence has been a new “data nationalism,” where countries are demanding that data about their residents be stored within their territory or favoring domestic technology that may be inferior or more expensive, but is trusted more. Data collectors often sell the data to others. One data broker assembled an average of 1,500 pieces of information about more than half a billion consumers worldwide from information people provided voluntarily on various websites. But even easily accessible data such as Facebook “Likes” can predict sensitive characteristics including “sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender.” b And smartphone sensors can infer a user’s “mood, stress levels, personality type, bipolar disorder, demographics (e.g., gender, marital status, job status, age), smoking habits, overall wellbeing, progression of Parkinson’s disease, sleep patterns, happiness, levels of exercise, and types of physical activity or movement.” c The risks? Cybercrime such as identity theft when data stored insecurely fall into the wrong hands. Discrimination when customers are charged a higher premium or interest rate, or denied a job based on erroneous information they can’t easily correct. Persistence of dated information that denies protection from embarrassing but irrelevant information or a second chance, which prompted Europe’s “right to be forgotten” ruling. And perhaps most important, reduced trust and thus suboptimal use of the internet. These concerns vary across societies. Fifty-eight percent of Nigerians and 57 percent of Indians believe private information on the internet is very secure, but only 18 percent of French and 16 percent of German respondents do. d Sources: WDR 2016 team, based on Peppet 2014; Castro 2013; Economist 2014; Kosinski, Stillwell, and Graepel 2013. a. Posner 1981. b. Kosinski, Stillwell, and Graepel 2013. c. See Peppet (2014) for individual references. d. CIGI and Ipsos 2014. allows large firms to provide services and products at low cost or free of charge, and their high profits fuel investments in research and development (R&D). At the same time, it is clear that competition and market structure on the internet are in many ways not so different from the offline world. Policies need to ensure that all innovative companies can enter markets and compete on equal terms. Otherwise, the economic performance between firms of different size and in different countries could diverge further and contribute to similarly divergent performance of national economies. Rising inequality: The race between skills and technology If the internet and related technologies promote growth, how are the gains shared in the labor market? While digital technologies raise productivity and enhance overall welfare, labor market disruptions can be painful and can result in higher inequality. Global trends provide some indication. One is that the share of national income that has gone to labor, especially routine labor, has fallen quite sharply in many developing countries—though Brazil and Ukraine are exceptions (figure O.15). 31 Inequality has
OVERVIEW 21 Figure O.15 Labor shares of national income are falling in many countries, including some developing countries Trends in labor shares in output since 1975 percentage points every 10 years Poland Mexico Hungary Estonia Bahrain Slovenia Lithuania South Africa Norway Luxembourg Micronesia, Fed. Sts. Namibia Latvia New Zealand China Finland Tunisia Argentina Slovak Republic Germany Austria Sweden France Italy Australia Taiwan, China Canada Japan Denmark Switzerland United States Netherlands Belgium Czech Republic Spain Singapore United Kingdom Portugal Bolivia Turkey Armenia Colombia Kenya Thailand Costa Rica Iceland Belarus Moldova Korea, Rep. Ukraine Brazil –15 –10 –5 0 5 10 Change in labor shares in national income Source: Karabarbounis and Neiman 2013. Data at http://bit.do /WDR2016-FigO_15. Figure O.16 Falling labor shares in national income are associated with rising inequality Change in Gini coefficient vs. growth in labor share in national income, 1995–2010 Change in the Gini coefficient in consumption or income (%) 30 25 20 15 10 5 0 –5 –10 –15 CHN LVA BGR EST CRI –20 –30 –25 –20 –15 –10 –5 0 5 10 15 20 25 Change in the share of national output going to labor (%) Source: Eden and Gaggl 2015, for the WDR 2016. Data at http://bit.do/WDR2016-FigO_16. FIN GBR ITA NOR ESP HND GRC ARG PAN TUN increased more where this shift in incomes toward capital and away from labor has been higher (figure O.16). A number of recent studies have linked technological change to this rising inequality (see chapter 2 in the full Report). A related trend is the polarization—or “hollowing out”—of the labor market, not only in advanced economies, but increasingly also in many developing countries. The share of employment in high-skilled occupations is up, as is the share of low-skilled jobs. The share of middle-skilled employment, in contrast, is down in most developing countries for which detailed data are available (figure O.17). And these types of jobs are often near the top of the income distribution in low-income countries, as in Africa. A notable exception to these global trends is China, where growing mechanization in agriculture has led to a (perhaps temporary) increase in routine, midlevel labor. Exceptions also include some countries rich in natural resources and commodity exporters, which include several countries in Central Asia and Latin America. What explains all this? Machines can increasingly perform routine tasks more quickly and cheaply than humans, and much of what is considered nonroutine today—such as translation, insurance underwriting, or even medical diagnostics—computers might do just as well tomorrow. Unlike previous technological transformations such as the mechanization of DNK IRL PRY BLR
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Page 5 and 6: A World Bank Group Flagship Report
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AGRICULTURE 91 Figure F1.1 Introduc
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e-HEALTH 191 Figure F3.1 Sequencing
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SECTORAL POLICIES 233 US$74 million
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