| Reference | Title | Download |
Overview |
| Map O.1 | Use of aggregated cellphone records to track mobility week by week during COVID-19 lockdowns in The Gambia, March–May, 2020 | https://bit.do/WDR2021-Map-O_1 |
| Map O.2 | Highly refined data pinpointed areas of Nigeria that needed better sanitation | https://bit.do/WDR2021-Map-O_2 |
| Map O.3 | Combining satellite imagery with household survey data increases the resolution of the poverty map of Tanzania | https://bit.do/WDR2021-Map-O_3 |
| Map O.4 | Data infrastructure is not yet widespread across all parts of the world | https://bit.do/WDR2021-Map-O_4 |
| Figure O.4 | The legal and regulatory framework for data governance remains a work in progress across all country income groupings | https://bit.do/WDR2021-Fig-O_4 |
| Figure O.5 | Since 1990, the global trade in data-driven services has grown exponentially and now constitutes half of trade in services | https://bit.do/WDR2021-Fig-O_5 |
Chapter 1 |
| Figure 1.1 | The share of people counted in a census grew from about 1 in 10 in 1850 to 9 in 10 today | https://bit.do/WDR2021-Fig-1_1 |
| Figure B1.4.1 | Use of repurposed data to study COVID-19: Published articles, by type of private intent data used | https://bit.do/WDR2021-Fig-B1_4_1 |
| Map B1.4.1 | Use of repurposed data to study COVID-19: Published articles, by country | https://bit.do/WDR2021-Map-B1_4_1 |
| Figure S1.2.1 | In six years, the composition of debt has shifted dramatically | https://bit.do/WDR2021-Fig-S1_2_1 |
Chapter 2 |
| Figure 2.2 | Improving access to water: Using real-time sensor data to reduce repair time for broken hand pumps in Kenya | https://bit.do/WDR2021-Fig-2_2 |
| Map 2.1 | Reducing poverty: Mapping pockets of poverty in Croatia allowed better targeting of antipoverty funds | https://bit.do/WDR2021-Map-2_1 |
| Figure 2.3 | Gaps in geospatial datasets are especially large in lower-income countries | https://bit.do/WDR2021-Fig-2_3 |
| Figure B2.3.1 | Proportion of COVID-19 cases reported with sex-disaggregated data by 190 countries | https://bit.do/WDR2021-Fig-B2_3_1 |
| Figure 2.4 | Lower-income countries, especially those affected by fragility and conflict, have less comparable poverty data than other country groups | https://bit.do/WDR2021-Fig-2_4 |
| Figure 2.5 | Lower-income countries are less likely than other countries to adhere to international bestpractice statistical standards and methodologies | https://bit.do/WDR2021-Fig-2_5 |
| Figure 2.7 | Most countries do not fully fund their national statistical plans | https://bit.do/WDR2021-Fig-2_7 |
| Figure 2.8 | The older a country’s statistical laws, the lower is its statistical performance and the less open are its data | https://bit.do/WDR2021-Fig-2_8 |
| Figure 2.9 | Greater NSO independence and freedom of the press are positively correlated with better statistical performance | https://bit.do/WDR2021-Fig-2_9 |
| Figure S2.1.1 | Prevalence of female genital mutilation in women ages 15–49, by country income level, 2010–19 | https://bit.do/WDR2021-Fig-S2_1_1 |
Chapter 3 |
| Figure 3.6.a | Internet traffic in low- and middle-income countries is concentrated in several US-based firms | https://bit.do/WDR2021-Fig-3_6_a |
| Figure S3.1.1 | Private company use of public data is extremely valuable in the United States, suggesting the value of open government data | https://bit.do/WDR2021-Fig-S3_1_1 |
Chapter 4 |
| Map B4.1.1 | Mapping the home location of smartphone users in Jakarta, 2020 | https://bit.do/WDR2021-Map-B4_1_1 |
| Figure B4.1.1 | Smartphone location data reveal the changes in the time users spend at home in Jakarta | https://bit.do/WDR2021-Fig-B4_1_1 |
| Figure B4.2.1 | Use of repurposed data to study COVID-19: Published articles, by type of private intent data used | https://bit.do/WDR2021-Fig-B4_2_1 |
| Map B4.2.1 | Uses of repurposed data to study COVID-19: Published articles, by country | https://bit.do/WDR2021-Map-B4_2_1 |
| Map 4.1 | Private intent data can provide unique and comparable information not collected by national governments such as the number of adults who lack a formal financial account | https://bit.do/WDR2021-Map-4_1 |
| Map 4.2 | Agricultural extension services can be tailored to the slower, older broadband internet accessible to many small-scale farmers | https://bit.do/WDR2021-Map-4_2 |
| Figure 4.1 | Gaps in network coverage differ across farm sizes, affecting agricultural extension services | https://bit.do/WDR2021-Fig-4_1 |
| Figure 4.2 | Artificial intelligence specialists gravitate to the US market, no matter where they are educated | https://bit.do/WDR2021-Fig-4_2 |
| Map S4.1.1 | Large gaps remain in global reporting on basic weather data | https://bit.do/WDR2021-Map-S4_1_1 |
Chapter 5 |
| Figure 5.2 | The developing world overwhelmingly accesses data using wireless networks | https://bit.do/WDR2021-Fig-5_2 |
| Figure 5.3 | Gaps in 3G wireless broadband internet coverage have been shrinking, but usage gaps remain stubbornly high | https://bit.do/WDR2021-Fig-5_3 |
| Figure 5.4 | Globally, the coverage of wireless technologies reflects their constant upgrading | https://bit.do/WDR2021-Fig-5_4 |
| Figure 5.5 | In low- and middle-income countries, nearly 70 percent of those who do not use the internet are held back by deficiencies in digital literacy
| https://bit.do/WDR2021-Fig-5_5 |
| Figure 5.6 | Inequities in mobile data consumption across country income groups and regions are huge | https://bit.do/WDR2021-Fig-5_6 |
| Figure 5.7 | The monthly price for 1 gigabyte of data is unaffordable in low-income countries | https://bit.do/WDR2021-Fig-5_7 |
| Figure 5.8 | Data consumption is very sensitive to market prices and service affordability | https://bit.do/WDR2021-Fig-5_8 |
| Map 5.1 | The global fiber-optic cable submarine network reaches all corners of the world, but data infrastructure is unevenly developed | https://bit.do/WDR2021-Map-5_1 |
| Figure 5.10 | Data infrastructure is relatively scarce in low- and middle-income countries | https://bit.do/WDR2021-Fig-5_10 |
| Figure B5.1.1 | Low- and middle-income countries are educating ICT professionals but not retaining them | https://bit.do/WDR2021-Fig-B5_1_1 |
| Figure B5.1.2 | Major wage differentials for ICT professionals create a brain drain, especially in low- and middle-income countries | https://bit.do/WDR2021-Fig-B5_1_2 |
| Figure S5.2.1 | Worldwide greenhouse gas emissions from data consumption have been flat, even though electricity consumption has been growing | https://bit.do/WDR2021-Fig-S5_2_1 |
Chapter 6 |
| Figure 6.3 | Gaps in the regulatory framework for cybersecurity are glaring across country income groups | https://bit.do/WDR2021-Fig-6_3 |
| Figure 6.4 | Progress on personal data protection legislation differs markedly across country income groups | https://bit.do/WDR2021-Fig-6_4 |
| Figure 6.5 | Adoption of e-commerce and related legislation is widespread across country income groups | https://bit.do/WDR2021-Fig-6_5 |
| Figure 6.6 | Regulations enabling access to and reuse of public intent data are unevenly developed across country income groups | https://bit.do/WDR2021-Fig-6_6 |
| Figure 6.7 | Adoption of enablers for sharing private intent data lags those for public intent data across country income groups | https://bit.do/WDR2021-Fig-6_7 |
Chapter 7 |
| Figure 7.2 | In the digital economy, antitrust cases related to passenger transport are more prevalent in middle-income countries than in high-income countries | https://bit.do/WDR2021-Fig-7_2 |
| Figure 7.3 | Among anticompetitive practices, abuse of dominance is more widespread worldwide across multiple sectors of the digital economy | https://bit.do/WDR2021-Fig-7_3 |
| Figure 7.4 | Since 1990, the global trade in data-driven services has grown exponentially and now constitutes half of trade in services | https://bit.do/WDR2021-Fig-7_4 |
| Map 7.1 | Uptake of regulatory models to cross-border data flows | https://bit.do/WDR2021-Map-7_1 |
| Figure 7.6 | East Asian countries are losing a substantial volume of tax revenue by failing to apply current VAT rules to digital services | https://bit.do/WDR2021-Fig-7_6 |
Chapter 8 |
| Figure 8.3 | No low-income and few lower-middle-income countries have a separate data governance entity; most embed them in another government institution | https://bit.do/WDR2021-Fig-8_3 |
| Figure 8.4 | The lower the country income level, the fewer are the countries with data protection authorities | https://bit.do/WDR2021-Fig-8_4 |
| Figure 8.5 | More than half of countries across all income groups have antitrust authorities | https://bit.do/WDR2021-Fig-8_5 |
| Figure 8.6 | Only about one-quarter of low-income countries have cybersecurity agencies | https://bit.do/WDR2021-Fig-8_6 |
